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modoboa_public_api/views.py | tonioo/modoboa-public-api | 2 | 155773 | <gh_stars>1-10
"""API views."""
from django.conf import settings
from rest_framework import decorators, mixins, response, status, viewsets
from rest_framework.views import APIView
from .models import ModoboaInstance, ModoboaExtension
from .forms import ClientVersionForm
from . import constants
from . import models
from . import serializers
# Legacy API, to deprecate
class CurrentVersionView(APIView):
"""Get current modoboa version."""
def get(self, request, fmt=None):
if request.GET.get("client_version") == "1.2.0-rc2":
# Temp. fix
request.GET = request.GET.copy()
request.GET["client_version"] = "1.2.0"
form = ClientVersionForm(request.GET)
if not form.is_valid():
return response.Response(
{"error": (
"Client version and/or site is missing or incorrect")},
status=status.HTTP_400_BAD_REQUEST
)
args = {
"ip_address": request.META.get("REMOTE_ADDR"),
"hostname": form.cleaned_data["client_site"]
}
if ModoboaInstance.objects.filter(**args).exists():
mdinst = ModoboaInstance.objects.get(**args)
elif ModoboaInstance.objects.filter(hostname=args["hostname"]).exists():
mdinst = ModoboaInstance.objects.get(hostname=args["hostname"])
mdinst.ip_address = args["ip_address"]
elif ModoboaInstance.objects.filter(ip_address=args["ip_address"]).exists():
mdinst = ModoboaInstance.objects.get(ip_address=args["ip_address"])
if args["hostname"] not in constants.BAD_HOSTNAME_LIST:
mdinst.hostname = args["hostname"]
elif args["hostname"] not in constants.BAD_HOSTNAME_LIST:
mdinst = ModoboaInstance(**args)
else:
mdinst = None
if mdinst is not None:
if mdinst.known_version != form.cleaned_data["client_version"]:
mdinst.known_version = form.cleaned_data["client_version"]
mdinst.save()
data = {"version": settings.MODOBOA_CURRENT_VERSION[0],
"changelog_url": settings.MODOBOA_CURRENT_VERSION[1]}
return response.Response(data)
class ExtensionListViewSet(mixins.ListModelMixin, viewsets.GenericViewSet):
"""List all defined extensions."""
queryset = ModoboaExtension.objects.all()
serializer_class = serializers.ModoboaExtensionSerializer
# New API
class InstanceViewSet(mixins.CreateModelMixin, mixins.UpdateModelMixin,
viewsets.GenericViewSet):
"""Create or update instance."""
queryset = ModoboaInstance.objects.all()
serializer_class = serializers.InstanceSerializer
@decorators.list_route(methods=["get"])
def search(self, request, *args, **kwargs):
"""Search an instance."""
hostname = request.GET.get("hostname")
if not hostname:
return response.Response({
"error": "No hostname provided."
}, status=status.HTTP_400_BAD_REQUEST)
ip_address = request.META.get("REMOTE_ADDR")
instance = models.ModoboaInstance.objects.filter(
ip_address=ip_address, hostname=hostname).first()
if not instance:
return response.Response({
"error": "Instance not found."
}, status=status.HTTP_404_NOT_FOUND)
serializer = self.get_serializer(instance)
return response.Response(serializer.data)
class VersionViewSet(viewsets.ViewSet):
"""List all versions."""
def list(self, request):
data = []
for extension in models.ModoboaExtension.objects.all():
data.append({
"name": extension.name, "version": extension.version,
"url": ""})
data.append({
"name": "modoboa", "version": settings.MODOBOA_CURRENT_VERSION[0],
"url": settings.MODOBOA_CURRENT_VERSION[1]
})
serializer = serializers.VersionSerializer(data, many=True)
return response.Response(serializer.data)
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] |
trifacta-dbt/main.py | trifacta/trifacta-dbt-core-profiler | 0 | 189307 | import sys
import logging
import argparse
from dbt.config import PROFILES_DIR as DBT_PROFILES_DIR
from trifacta.util.tfconfig import TRIFACTA_CONFIG_PATH
import os
from _version import __version__
from task.diagnostics import DiagnosticsTask
from task.profile import ProfileTask
from task.clean import CleanTask
def main(args=None):
if args is not None:
args = sys.argv[1:]
execute(args)
def execute(args):
parsed = parse_args(args)
parsed.cls.pre_init_hook(parsed)
task = parsed.cls.from_args(args=parsed)
task.run()
def _build_base_parser():
base_subparser = argparse.ArgumentParser(add_help=False)
base_subparser.add_argument(
'--dbt-project-dir',
default=".",
type=str,
help='''
Directory that holds the DBT project (dbt_project.yml).
'''
)
base_subparser.add_argument(
'--dbt-profiles-dir',
default=DBT_PROFILES_DIR,
type=str,
help='''
Directory that contains profiles.yml
'''
)
base_subparser.add_argument(
'--dbt-profile',
required=False,
type=str,
help='''
Profile to load. Overrides setting in dbt_project.yml
'''
)
base_subparser.add_argument(
'--dbt-target',
default=None,
type=str,
help='''
Target to load for the given profile
''',
)
base_subparser.add_argument(
'--trifacta-config',
default=TRIFACTA_CONFIG_PATH,
type=str,
help='''
Directory that contains trifacta config
'''
)
base_subparser.add_argument(
'--trifacta-profile',
default='DEFAULT',
type=str,
help='''
Profile in trifacta config to use
''',
)
base_subparser.set_defaults(defer=None, state=None)
return base_subparser
def _build_diagnostics_subparser(subparsers, base_subparser):
sub = subparsers.add_parser(
'diagnostics',
parents=[base_subparser],
help='''
Show diagnostic info
''',
aliases=['diag']
)
sub.set_defaults(cls=DiagnosticsTask)
return sub
def _build_profile_subparser(subparsers, base_subparser):
sub = subparsers.add_parser(
'profile',
parents=[base_subparser],
help='''
Install Profiling flow in Trifacta
'''
)
sub.add_argument(
'--trifacta-prefix',
default="",
type=str,
help='''
Prefix to use for artifacts created in Trifacta
'''
)
sub.add_argument(
'--include-list',
default=None,
type=str,
help='''
Comma separated list of tables to include in profiling
'''
)
sub.set_defaults(cls=ProfileTask)
return sub
def _build_clean_subparser(subparsers, base_subparser):
sub = subparsers.add_parser(
'clean',
parents=[base_subparser],
help='''
Delete Profiling flow in Trifacta
'''
)
sub.add_argument(
'--trifacta-prefix',
default="",
type=str,
help='''
Prefix to use for artifacts created in Trifacta
'''
)
sub.set_defaults(cls=CleanTask)
return sub
def parse_args(args):
p = argparse.ArgumentParser(
prog='trifacta-dbt',
description='''
Trifacta DBT Integration
''',
)
p.add_argument(
'--version',
action='version',
version='%(prog)s {version}'.format(version=__version__),
help='''
Show version
'''
)
subs = p.add_subparsers(title='Available sub-commands')
base_parser = _build_base_parser()
_build_diagnostics_subparser(subs, base_parser)
_build_profile_subparser(subs, base_parser)
#_build_clean_subparser(subs, base_parser)
if len(args) == 0:
p.print_help()
sys.exit(1)
parsed = p.parse_args(args)
if hasattr(parsed, 'dbt_profiles_dir'):
parsed.dbt_profiles_dir = os.path.abspath(parsed.dbt_profiles_dir)
if getattr(parsed, 'dbt_project_dir', None) is not None:
expanded_user = os.path.expanduser(parsed.dbt_project_dir)
parsed.dbt_project_dir = os.path.abspath(expanded_user)
if hasattr(parsed, 'trifacta_config'):
parsed.trifacta_config = os.path.abspath(parsed.trifacta_config)
if hasattr(parsed, 'include_list') and parsed.include_list:
parsed.include_list = parsed.include_list.split(',')
return parsed
if __name__ == "__main__":
main(sys.argv)
| [
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13,
13,
13,
361,
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376,
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1115,
13,
1678,
1667,
29898,
9675,
29889,
19218,
29897,
13,
2
] |
pysmore/test_optimizer.py | RainBoltz/pySmore | 0 | 43832 | <gh_stars>0
from libs.optimizer import get_dotproduct_loss
import numpy as np
a = np.array([0.1,0.2,0.3])
b = np.array([0,0.2,0])
L = get_dotproduct_loss(a, b, 1.0)
print(L) | [
1,
529,
12443,
29918,
303,
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13,
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29871,
29896,
29889,
29900,
29897,
13,
13,
2158,
29898,
29931,
29897,
2
] |
tests/test_decorator.py | beproud/django-newauth | 3 | 1610066 | <reponame>beproud/django-newauth<filename>tests/test_decorator.py<gh_stars>1-10
#:coding=utf-8:
from urllib.parse import urlparse
import pytest
from django.test import TestCase as DjangoTestCase
from django.conf import settings
__all__ = (
'DecoratorTest',
)
@pytest.mark.django_db
class DecoratorTest(DjangoTestCase):
fixtures = ['authutils_testdata.json']
def test_login_required_failed(self):
response = self.client.get("/testapp/login_required/")
self.assertEquals(response.status_code, 302)
self.assertEquals(urlparse(response.get("Location", ""))[2], settings.LOGIN_URL)
def test_login_required_testapp_failed(self):
response = self.client.get("/testapp/testapp_login_required/")
self.assertEquals(response.status_code, 302)
self.assertEquals(urlparse(response.get("Location", ""))[2], settings.LOGIN_URL)
| [
1,
529,
276,
1112,
420,
29958,
915,
558,
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sklearn/linear_model/tests/test_sgd.py | SteveKola/scikit-learn | 2 | 130059 | import pickle
import pytest
import numpy as np
import scipy.sparse as sp
import joblib
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_raises_regexp
from sklearn.utils._testing import assert_warns
from sklearn.utils._testing import ignore_warnings
from sklearn.utils.fixes import parse_version
from sklearn import linear_model, datasets, metrics
from sklearn.base import clone, is_classifier
from sklearn.preprocessing import LabelEncoder, scale, MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.exceptions import ConvergenceWarning
from sklearn.model_selection import StratifiedShuffleSplit, ShuffleSplit
from sklearn.linear_model import _sgd_fast as sgd_fast
from sklearn.model_selection import RandomizedSearchCV
def _update_kwargs(kwargs):
if "random_state" not in kwargs:
kwargs["random_state"] = 42
if "tol" not in kwargs:
kwargs["tol"] = None
if "max_iter" not in kwargs:
kwargs["max_iter"] = 5
class _SparseSGDClassifier(linear_model.SGDClassifier):
def fit(self, X, y, *args, **kw):
X = sp.csr_matrix(X)
return super().fit(X, y, *args, **kw)
def partial_fit(self, X, y, *args, **kw):
X = sp.csr_matrix(X)
return super().partial_fit(X, y, *args, **kw)
def decision_function(self, X):
X = sp.csr_matrix(X)
return super().decision_function(X)
def predict_proba(self, X):
X = sp.csr_matrix(X)
return super().predict_proba(X)
class _SparseSGDRegressor(linear_model.SGDRegressor):
def fit(self, X, y, *args, **kw):
X = sp.csr_matrix(X)
return linear_model.SGDRegressor.fit(self, X, y, *args, **kw)
def partial_fit(self, X, y, *args, **kw):
X = sp.csr_matrix(X)
return linear_model.SGDRegressor.partial_fit(self, X, y, *args, **kw)
def decision_function(self, X, *args, **kw):
# XXX untested as of v0.22
X = sp.csr_matrix(X)
return linear_model.SGDRegressor.decision_function(self, X, *args,
**kw)
def SGDClassifier(**kwargs):
_update_kwargs(kwargs)
return linear_model.SGDClassifier(**kwargs)
def SGDRegressor(**kwargs):
_update_kwargs(kwargs)
return linear_model.SGDRegressor(**kwargs)
def SparseSGDClassifier(**kwargs):
_update_kwargs(kwargs)
return _SparseSGDClassifier(**kwargs)
def SparseSGDRegressor(**kwargs):
_update_kwargs(kwargs)
return _SparseSGDRegressor(**kwargs)
# Test Data
# test sample 1
X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]])
Y = [1, 1, 1, 2, 2, 2]
T = np.array([[-1, -1], [2, 2], [3, 2]])
true_result = [1, 2, 2]
# test sample 2; string class labels
X2 = np.array([[-1, 1], [-0.75, 0.5], [-1.5, 1.5],
[1, 1], [0.75, 0.5], [1.5, 1.5],
[-1, -1], [0, -0.5], [1, -1]])
Y2 = ["one"] * 3 + ["two"] * 3 + ["three"] * 3
T2 = np.array([[-1.5, 0.5], [1, 2], [0, -2]])
true_result2 = ["one", "two", "three"]
# test sample 3
X3 = np.array([[1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 1, 1],
[0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0]])
Y3 = np.array([1, 1, 1, 1, 2, 2, 2, 2])
# test sample 4 - two more or less redundant feature groups
X4 = np.array([[1, 0.9, 0.8, 0, 0, 0], [1, .84, .98, 0, 0, 0],
[1, .96, .88, 0, 0, 0], [1, .91, .99, 0, 0, 0],
[0, 0, 0, .89, .91, 1], [0, 0, 0, .79, .84, 1],
[0, 0, 0, .91, .95, 1], [0, 0, 0, .93, 1, 1]])
Y4 = np.array([1, 1, 1, 1, 2, 2, 2, 2])
iris = datasets.load_iris()
# test sample 5 - test sample 1 as binary classification problem
X5 = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]])
Y5 = [1, 1, 1, 2, 2, 2]
true_result5 = [0, 1, 1]
###############################################################################
# Common Test Case to classification and regression
# a simple implementation of ASGD to use for testing
# uses squared loss to find the gradient
def asgd(klass, X, y, eta, alpha, weight_init=None, intercept_init=0.0):
if weight_init is None:
weights = np.zeros(X.shape[1])
else:
weights = weight_init
average_weights = np.zeros(X.shape[1])
intercept = intercept_init
average_intercept = 0.0
decay = 1.0
# sparse data has a fixed decay of .01
if klass in (SparseSGDClassifier, SparseSGDRegressor):
decay = .01
for i, entry in enumerate(X):
p = np.dot(entry, weights)
p += intercept
gradient = p - y[i]
weights *= 1.0 - (eta * alpha)
weights += -(eta * gradient * entry)
intercept += -(eta * gradient) * decay
average_weights *= i
average_weights += weights
average_weights /= i + 1.0
average_intercept *= i
average_intercept += intercept
average_intercept /= i + 1.0
return average_weights, average_intercept
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier,
SGDRegressor, SparseSGDRegressor])
def test_sgd_bad_alpha(klass):
# Check whether expected ValueError on bad alpha
with pytest.raises(ValueError):
klass(alpha=-.1)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier,
SGDRegressor, SparseSGDRegressor])
def test_sgd_bad_penalty(klass):
# Check whether expected ValueError on bad penalty
with pytest.raises(ValueError):
klass(penalty='foobar', l1_ratio=0.85)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier,
SGDRegressor, SparseSGDRegressor])
def test_sgd_bad_loss(klass):
# Check whether expected ValueError on bad loss
with pytest.raises(ValueError):
klass(loss="foobar")
def _test_warm_start(klass, X, Y, lr):
# Test that explicit warm restart...
clf = klass(alpha=0.01, eta0=0.01, shuffle=False,
learning_rate=lr)
clf.fit(X, Y)
clf2 = klass(alpha=0.001, eta0=0.01, shuffle=False,
learning_rate=lr)
clf2.fit(X, Y,
coef_init=clf.coef_.copy(),
intercept_init=clf.intercept_.copy())
# ... and implicit warm restart are equivalent.
clf3 = klass(alpha=0.01, eta0=0.01, shuffle=False,
warm_start=True, learning_rate=lr)
clf3.fit(X, Y)
assert clf3.t_ == clf.t_
assert_array_almost_equal(clf3.coef_, clf.coef_)
clf3.set_params(alpha=0.001)
clf3.fit(X, Y)
assert clf3.t_ == clf2.t_
assert_array_almost_equal(clf3.coef_, clf2.coef_)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier,
SGDRegressor, SparseSGDRegressor])
@pytest.mark.parametrize('lr',
["constant", "optimal", "invscaling", "adaptive"])
def test_warm_start(klass, lr):
_test_warm_start(klass, X, Y, lr)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier,
SGDRegressor, SparseSGDRegressor])
def test_input_format(klass):
# Input format tests.
clf = klass(alpha=0.01, shuffle=False)
clf.fit(X, Y)
Y_ = np.array(Y)[:, np.newaxis]
Y_ = np.c_[Y_, Y_]
with pytest.raises(ValueError):
clf.fit(X, Y_)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier,
SGDRegressor, SparseSGDRegressor])
def test_clone(klass):
# Test whether clone works ok.
clf = klass(alpha=0.01, penalty='l1')
clf = clone(clf)
clf.set_params(penalty='l2')
clf.fit(X, Y)
clf2 = klass(alpha=0.01, penalty='l2')
clf2.fit(X, Y)
assert_array_equal(clf.coef_, clf2.coef_)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier,
SGDRegressor, SparseSGDRegressor])
def test_plain_has_no_average_attr(klass):
clf = klass(average=True, eta0=.01)
clf.fit(X, Y)
assert hasattr(clf, '_average_coef')
assert hasattr(clf, '_average_intercept')
assert hasattr(clf, '_standard_intercept')
assert hasattr(clf, '_standard_coef')
clf = klass()
clf.fit(X, Y)
assert not hasattr(clf, '_average_coef')
assert not hasattr(clf, '_average_intercept')
assert not hasattr(clf, '_standard_intercept')
assert not hasattr(clf, '_standard_coef')
# TODO: remove in 1.0
@pytest.mark.parametrize('klass', [SGDClassifier, SGDRegressor])
def test_sgd_deprecated_attr(klass):
est = klass(average=True, eta0=.01)
est.fit(X, Y)
msg = "Attribute {} was deprecated"
for att in ['average_coef_', 'average_intercept_',
'standard_coef_', 'standard_intercept_']:
with pytest.warns(FutureWarning, match=msg.format(att)):
getattr(est, att)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier,
SGDRegressor, SparseSGDRegressor])
def test_late_onset_averaging_not_reached(klass):
clf1 = klass(average=600)
clf2 = klass()
for _ in range(100):
if is_classifier(clf1):
clf1.partial_fit(X, Y, classes=np.unique(Y))
clf2.partial_fit(X, Y, classes=np.unique(Y))
else:
clf1.partial_fit(X, Y)
clf2.partial_fit(X, Y)
assert_array_almost_equal(clf1.coef_, clf2.coef_, decimal=16)
assert_almost_equal(clf1.intercept_, clf2.intercept_, decimal=16)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier,
SGDRegressor, SparseSGDRegressor])
def test_late_onset_averaging_reached(klass):
eta0 = .001
alpha = .0001
Y_encode = np.array(Y)
Y_encode[Y_encode == 1] = -1.0
Y_encode[Y_encode == 2] = 1.0
clf1 = klass(average=7, learning_rate="constant",
loss='squared_loss', eta0=eta0,
alpha=alpha, max_iter=2, shuffle=False)
clf2 = klass(average=0, learning_rate="constant",
loss='squared_loss', eta0=eta0,
alpha=alpha, max_iter=1, shuffle=False)
clf1.fit(X, Y_encode)
clf2.fit(X, Y_encode)
average_weights, average_intercept = \
asgd(klass, X, Y_encode, eta0, alpha,
weight_init=clf2.coef_.ravel(),
intercept_init=clf2.intercept_)
assert_array_almost_equal(clf1.coef_.ravel(),
average_weights.ravel(),
decimal=16)
assert_almost_equal(clf1.intercept_, average_intercept, decimal=16)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier,
SGDRegressor, SparseSGDRegressor])
def test_sgd_bad_alpha_for_optimal_learning_rate(klass):
# Check whether expected ValueError on bad alpha, i.e. 0
# since alpha is used to compute the optimal learning rate
with pytest.raises(ValueError):
klass(alpha=0, learning_rate="optimal")
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier,
SGDRegressor, SparseSGDRegressor])
def test_early_stopping(klass):
X = iris.data[iris.target > 0]
Y = iris.target[iris.target > 0]
for early_stopping in [True, False]:
max_iter = 1000
clf = klass(early_stopping=early_stopping, tol=1e-3,
max_iter=max_iter).fit(X, Y)
assert clf.n_iter_ < max_iter
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier,
SGDRegressor, SparseSGDRegressor])
def test_adaptive_longer_than_constant(klass):
clf1 = klass(learning_rate="adaptive", eta0=0.01, tol=1e-3,
max_iter=100)
clf1.fit(iris.data, iris.target)
clf2 = klass(learning_rate="constant", eta0=0.01, tol=1e-3,
max_iter=100)
clf2.fit(iris.data, iris.target)
assert clf1.n_iter_ > clf2.n_iter_
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier,
SGDRegressor, SparseSGDRegressor])
def test_validation_set_not_used_for_training(klass):
X, Y = iris.data, iris.target
validation_fraction = 0.4
seed = 42
shuffle = False
max_iter = 10
clf1 = klass(early_stopping=True,
random_state=np.random.RandomState(seed),
validation_fraction=validation_fraction,
learning_rate='constant', eta0=0.01,
tol=None, max_iter=max_iter, shuffle=shuffle)
clf1.fit(X, Y)
assert clf1.n_iter_ == max_iter
clf2 = klass(early_stopping=False,
random_state=np.random.RandomState(seed),
learning_rate='constant', eta0=0.01,
tol=None, max_iter=max_iter, shuffle=shuffle)
if is_classifier(clf2):
cv = StratifiedShuffleSplit(test_size=validation_fraction,
random_state=seed)
else:
cv = ShuffleSplit(test_size=validation_fraction,
random_state=seed)
idx_train, idx_val = next(cv.split(X, Y))
idx_train = np.sort(idx_train) # remove shuffling
clf2.fit(X[idx_train], Y[idx_train])
assert clf2.n_iter_ == max_iter
assert_array_equal(clf1.coef_, clf2.coef_)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier,
SGDRegressor, SparseSGDRegressor])
def test_n_iter_no_change(klass):
X, Y = iris.data, iris.target
# test that n_iter_ increases monotonically with n_iter_no_change
for early_stopping in [True, False]:
n_iter_list = [klass(early_stopping=early_stopping,
n_iter_no_change=n_iter_no_change,
tol=1e-4, max_iter=1000
).fit(X, Y).n_iter_
for n_iter_no_change in [2, 3, 10]]
assert_array_equal(n_iter_list, sorted(n_iter_list))
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier,
SGDRegressor, SparseSGDRegressor])
def test_not_enough_sample_for_early_stopping(klass):
# test an error is raised if the training or validation set is empty
clf = klass(early_stopping=True, validation_fraction=0.99)
with pytest.raises(ValueError):
clf.fit(X3, Y3)
###############################################################################
# Classification Test Case
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_clf(klass):
# Check that SGD gives any results :-)
for loss in ("hinge", "squared_hinge", "log", "modified_huber"):
clf = klass(penalty='l2', alpha=0.01, fit_intercept=True,
loss=loss, max_iter=10, shuffle=True)
clf.fit(X, Y)
# assert_almost_equal(clf.coef_[0], clf.coef_[1], decimal=7)
assert_array_equal(clf.predict(T), true_result)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_bad_l1_ratio(klass):
# Check whether expected ValueError on bad l1_ratio
with pytest.raises(ValueError):
klass(l1_ratio=1.1)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_bad_learning_rate_schedule(klass):
# Check whether expected ValueError on bad learning_rate
with pytest.raises(ValueError):
klass(learning_rate="<unknown>")
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_bad_eta0(klass):
# Check whether expected ValueError on bad eta0
with pytest.raises(ValueError):
klass(eta0=0, learning_rate="constant")
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_max_iter_param(klass):
# Test parameter validity check
with pytest.raises(ValueError):
klass(max_iter=-10000)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_shuffle_param(klass):
# Test parameter validity check
with pytest.raises(ValueError):
klass(shuffle="false")
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_early_stopping_param(klass):
# Test parameter validity check
with pytest.raises(ValueError):
klass(early_stopping="false")
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_validation_fraction(klass):
# Test parameter validity check
with pytest.raises(ValueError):
klass(validation_fraction=-.1)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_n_iter_no_change(klass):
# Test parameter validity check
with pytest.raises(ValueError):
klass(n_iter_no_change=0)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_argument_coef(klass):
# Checks coef_init not allowed as model argument (only fit)
# Provided coef_ does not match dataset
with pytest.raises(TypeError):
klass(coef_init=np.zeros((3,)))
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_provide_coef(klass):
# Checks coef_init shape for the warm starts
# Provided coef_ does not match dataset.
with pytest.raises(ValueError):
klass().fit(X, Y, coef_init=np.zeros((3,)))
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_set_intercept(klass):
# Checks intercept_ shape for the warm starts
# Provided intercept_ does not match dataset.
with pytest.raises(ValueError):
klass().fit(X, Y, intercept_init=np.zeros((3,)))
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_early_stopping_with_partial_fit(klass):
# Test parameter validity check
with pytest.raises(ValueError):
klass(early_stopping=True).partial_fit(X, Y)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_set_intercept_binary(klass):
# Checks intercept_ shape for the warm starts in binary case
klass().fit(X5, Y5, intercept_init=0)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_average_binary_computed_correctly(klass):
# Checks the SGDClassifier correctly computes the average weights
eta = .1
alpha = 2.
n_samples = 20
n_features = 10
rng = np.random.RandomState(0)
X = rng.normal(size=(n_samples, n_features))
w = rng.normal(size=n_features)
clf = klass(loss='squared_loss',
learning_rate='constant',
eta0=eta, alpha=alpha,
fit_intercept=True,
max_iter=1, average=True, shuffle=False)
# simple linear function without noise
y = np.dot(X, w)
y = np.sign(y)
clf.fit(X, y)
average_weights, average_intercept = asgd(klass, X, y, eta, alpha)
average_weights = average_weights.reshape(1, -1)
assert_array_almost_equal(clf.coef_,
average_weights,
decimal=14)
assert_almost_equal(clf.intercept_, average_intercept, decimal=14)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_set_intercept_to_intercept(klass):
# Checks intercept_ shape consistency for the warm starts
# Inconsistent intercept_ shape.
clf = klass().fit(X5, Y5)
klass().fit(X5, Y5, intercept_init=clf.intercept_)
clf = klass().fit(X, Y)
klass().fit(X, Y, intercept_init=clf.intercept_)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_at_least_two_labels(klass):
# Target must have at least two labels
clf = klass(alpha=0.01, max_iter=20)
with pytest.raises(ValueError):
clf.fit(X2, np.ones(9))
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_partial_fit_weight_class_balanced(klass):
# partial_fit with class_weight='balanced' not supported"""
regex = (r"class_weight 'balanced' is not supported for "
r"partial_fit\. In order to use 'balanced' weights, "
r"use compute_class_weight\('balanced', classes=classes, y=y\). "
r"In place of y you can us a large enough sample "
r"of the full training set target to properly "
r"estimate the class frequency distributions\. "
r"Pass the resulting weights as the class_weight "
r"parameter\.")
assert_raises_regexp(ValueError,
regex,
klass(class_weight='balanced').partial_fit,
X, Y, classes=np.unique(Y))
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_multiclass(klass):
# Multi-class test case
clf = klass(alpha=0.01, max_iter=20).fit(X2, Y2)
assert clf.coef_.shape == (3, 2)
assert clf.intercept_.shape == (3,)
assert clf.decision_function([[0, 0]]).shape == (1, 3)
pred = clf.predict(T2)
assert_array_equal(pred, true_result2)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_multiclass_average(klass):
eta = .001
alpha = .01
# Multi-class average test case
clf = klass(loss='squared_loss',
learning_rate='constant',
eta0=eta, alpha=alpha,
fit_intercept=True,
max_iter=1, average=True, shuffle=False)
np_Y2 = np.array(Y2)
clf.fit(X2, np_Y2)
classes = np.unique(np_Y2)
for i, cl in enumerate(classes):
y_i = np.ones(np_Y2.shape[0])
y_i[np_Y2 != cl] = -1
average_coef, average_intercept = asgd(klass, X2, y_i, eta, alpha)
assert_array_almost_equal(average_coef, clf.coef_[i], decimal=16)
assert_almost_equal(average_intercept,
clf.intercept_[i],
decimal=16)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_multiclass_with_init_coef(klass):
# Multi-class test case
clf = klass(alpha=0.01, max_iter=20)
clf.fit(X2, Y2, coef_init=np.zeros((3, 2)),
intercept_init=np.zeros(3))
assert clf.coef_.shape == (3, 2)
assert clf.intercept_.shape, (3,)
pred = clf.predict(T2)
assert_array_equal(pred, true_result2)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_multiclass_njobs(klass):
# Multi-class test case with multi-core support
clf = klass(alpha=0.01, max_iter=20, n_jobs=2).fit(X2, Y2)
assert clf.coef_.shape == (3, 2)
assert clf.intercept_.shape == (3,)
assert clf.decision_function([[0, 0]]).shape == (1, 3)
pred = clf.predict(T2)
assert_array_equal(pred, true_result2)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_set_coef_multiclass(klass):
# Checks coef_init and intercept_init shape for multi-class
# problems
# Provided coef_ does not match dataset
clf = klass()
with pytest.raises(ValueError):
clf.fit(X2, Y2, coef_init=np.zeros((2, 2)))
# Provided coef_ does match dataset
clf = klass().fit(X2, Y2, coef_init=np.zeros((3, 2)))
# Provided intercept_ does not match dataset
clf = klass()
with pytest.raises(ValueError):
clf.fit(X2, Y2, intercept_init=np.zeros((1,)))
# Provided intercept_ does match dataset.
clf = klass().fit(X2, Y2, intercept_init=np.zeros((3,)))
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_predict_proba_method_access(klass):
# Checks that SGDClassifier predict_proba and predict_log_proba methods
# can either be accessed or raise an appropriate error message
# otherwise. See
# https://github.com/scikit-learn/scikit-learn/issues/10938 for more
# details.
for loss in linear_model.SGDClassifier.loss_functions:
clf = SGDClassifier(loss=loss)
if loss in ('log', 'modified_huber'):
assert hasattr(clf, 'predict_proba')
assert hasattr(clf, 'predict_log_proba')
else:
message = ("probability estimates are not "
"available for loss={!r}".format(loss))
assert not hasattr(clf, 'predict_proba')
assert not hasattr(clf, 'predict_log_proba')
with pytest.raises(AttributeError,
match=message):
clf.predict_proba
with pytest.raises(AttributeError,
match=message):
clf.predict_log_proba
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_proba(klass):
# Check SGD.predict_proba
# Hinge loss does not allow for conditional prob estimate.
# We cannot use the factory here, because it defines predict_proba
# anyway.
clf = SGDClassifier(loss="hinge", alpha=0.01,
max_iter=10, tol=None).fit(X, Y)
assert not hasattr(clf, "predict_proba")
assert not hasattr(clf, "predict_log_proba")
# log and modified_huber losses can output probability estimates
# binary case
for loss in ["log", "modified_huber"]:
clf = klass(loss=loss, alpha=0.01, max_iter=10)
clf.fit(X, Y)
p = clf.predict_proba([[3, 2]])
assert p[0, 1] > 0.5
p = clf.predict_proba([[-1, -1]])
assert p[0, 1] < 0.5
p = clf.predict_log_proba([[3, 2]])
assert p[0, 1] > p[0, 0]
p = clf.predict_log_proba([[-1, -1]])
assert p[0, 1] < p[0, 0]
# log loss multiclass probability estimates
clf = klass(loss="log", alpha=0.01, max_iter=10).fit(X2, Y2)
d = clf.decision_function([[.1, -.1], [.3, .2]])
p = clf.predict_proba([[.1, -.1], [.3, .2]])
assert_array_equal(np.argmax(p, axis=1), np.argmax(d, axis=1))
assert_almost_equal(p[0].sum(), 1)
assert np.all(p[0] >= 0)
p = clf.predict_proba([[-1, -1]])
d = clf.decision_function([[-1, -1]])
assert_array_equal(np.argsort(p[0]), np.argsort(d[0]))
lp = clf.predict_log_proba([[3, 2]])
p = clf.predict_proba([[3, 2]])
assert_array_almost_equal(np.log(p), lp)
lp = clf.predict_log_proba([[-1, -1]])
p = clf.predict_proba([[-1, -1]])
assert_array_almost_equal(np.log(p), lp)
# Modified Huber multiclass probability estimates; requires a separate
# test because the hard zero/one probabilities may destroy the
# ordering present in decision_function output.
clf = klass(loss="modified_huber", alpha=0.01, max_iter=10)
clf.fit(X2, Y2)
d = clf.decision_function([[3, 2]])
p = clf.predict_proba([[3, 2]])
if klass != SparseSGDClassifier:
assert np.argmax(d, axis=1) == np.argmax(p, axis=1)
else: # XXX the sparse test gets a different X2 (?)
assert np.argmin(d, axis=1) == np.argmin(p, axis=1)
# the following sample produces decision_function values < -1,
# which would cause naive normalization to fail (see comment
# in SGDClassifier.predict_proba)
x = X.mean(axis=0)
d = clf.decision_function([x])
if np.all(d < -1): # XXX not true in sparse test case (why?)
p = clf.predict_proba([x])
assert_array_almost_equal(p[0], [1 / 3.] * 3)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sgd_l1(klass):
# Test L1 regularization
n = len(X4)
rng = np.random.RandomState(13)
idx = np.arange(n)
rng.shuffle(idx)
X = X4[idx, :]
Y = Y4[idx]
clf = klass(penalty='l1', alpha=.2, fit_intercept=False,
max_iter=2000, tol=None, shuffle=False)
clf.fit(X, Y)
assert_array_equal(clf.coef_[0, 1:-1], np.zeros((4,)))
pred = clf.predict(X)
assert_array_equal(pred, Y)
# test sparsify with dense inputs
clf.sparsify()
assert sp.issparse(clf.coef_)
pred = clf.predict(X)
assert_array_equal(pred, Y)
# pickle and unpickle with sparse coef_
clf = pickle.loads(pickle.dumps(clf))
assert sp.issparse(clf.coef_)
pred = clf.predict(X)
assert_array_equal(pred, Y)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_class_weights(klass):
# Test class weights.
X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0],
[1.0, 1.0], [1.0, 0.0]])
y = [1, 1, 1, -1, -1]
clf = klass(alpha=0.1, max_iter=1000, fit_intercept=False,
class_weight=None)
clf.fit(X, y)
assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([1]))
# we give a small weights to class 1
clf = klass(alpha=0.1, max_iter=1000, fit_intercept=False,
class_weight={1: 0.001})
clf.fit(X, y)
# now the hyperplane should rotate clock-wise and
# the prediction on this point should shift
assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([-1]))
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_equal_class_weight(klass):
# Test if equal class weights approx. equals no class weights.
X = [[1, 0], [1, 0], [0, 1], [0, 1]]
y = [0, 0, 1, 1]
clf = klass(alpha=0.1, max_iter=1000, class_weight=None)
clf.fit(X, y)
X = [[1, 0], [0, 1]]
y = [0, 1]
clf_weighted = klass(alpha=0.1, max_iter=1000,
class_weight={0: 0.5, 1: 0.5})
clf_weighted.fit(X, y)
# should be similar up to some epsilon due to learning rate schedule
assert_almost_equal(clf.coef_, clf_weighted.coef_, decimal=2)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_wrong_class_weight_label(klass):
# ValueError due to not existing class label.
clf = klass(alpha=0.1, max_iter=1000, class_weight={0: 0.5})
with pytest.raises(ValueError):
clf.fit(X, Y)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_wrong_class_weight_format(klass):
# ValueError due to wrong class_weight argument type.
clf = klass(alpha=0.1, max_iter=1000, class_weight=[0.5])
with pytest.raises(ValueError):
clf.fit(X, Y)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_weights_multiplied(klass):
# Tests that class_weight and sample_weight are multiplicative
class_weights = {1: .6, 2: .3}
rng = np.random.RandomState(0)
sample_weights = rng.random_sample(Y4.shape[0])
multiplied_together = np.copy(sample_weights)
multiplied_together[Y4 == 1] *= class_weights[1]
multiplied_together[Y4 == 2] *= class_weights[2]
clf1 = klass(alpha=0.1, max_iter=20, class_weight=class_weights)
clf2 = klass(alpha=0.1, max_iter=20)
clf1.fit(X4, Y4, sample_weight=sample_weights)
clf2.fit(X4, Y4, sample_weight=multiplied_together)
assert_almost_equal(clf1.coef_, clf2.coef_)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_balanced_weight(klass):
# Test class weights for imbalanced data"""
# compute reference metrics on iris dataset that is quite balanced by
# default
X, y = iris.data, iris.target
X = scale(X)
idx = np.arange(X.shape[0])
rng = np.random.RandomState(6)
rng.shuffle(idx)
X = X[idx]
y = y[idx]
clf = klass(alpha=0.0001, max_iter=1000,
class_weight=None, shuffle=False).fit(X, y)
f1 = metrics.f1_score(y, clf.predict(X), average='weighted')
assert_almost_equal(f1, 0.96, decimal=1)
# make the same prediction using balanced class_weight
clf_balanced = klass(alpha=0.0001, max_iter=1000,
class_weight="balanced",
shuffle=False).fit(X, y)
f1 = metrics.f1_score(y, clf_balanced.predict(X), average='weighted')
assert_almost_equal(f1, 0.96, decimal=1)
# Make sure that in the balanced case it does not change anything
# to use "balanced"
assert_array_almost_equal(clf.coef_, clf_balanced.coef_, 6)
# build an very very imbalanced dataset out of iris data
X_0 = X[y == 0, :]
y_0 = y[y == 0]
X_imbalanced = np.vstack([X] + [X_0] * 10)
y_imbalanced = np.concatenate([y] + [y_0] * 10)
# fit a model on the imbalanced data without class weight info
clf = klass(max_iter=1000, class_weight=None, shuffle=False)
clf.fit(X_imbalanced, y_imbalanced)
y_pred = clf.predict(X)
assert metrics.f1_score(y, y_pred, average='weighted') < 0.96
# fit a model with balanced class_weight enabled
clf = klass(max_iter=1000, class_weight="balanced",
shuffle=False)
clf.fit(X_imbalanced, y_imbalanced)
y_pred = clf.predict(X)
assert metrics.f1_score(y, y_pred, average='weighted') > 0.96
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_sample_weights(klass):
# Test weights on individual samples
X = np.array([[-1.0, -1.0], [-1.0, 0], [-.8, -1.0],
[1.0, 1.0], [1.0, 0.0]])
y = [1, 1, 1, -1, -1]
clf = klass(alpha=0.1, max_iter=1000, fit_intercept=False)
clf.fit(X, y)
assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([1]))
# we give a small weights to class 1
clf.fit(X, y, sample_weight=[0.001] * 3 + [1] * 2)
# now the hyperplane should rotate clock-wise and
# the prediction on this point should shift
assert_array_equal(clf.predict([[0.2, -1.0]]), np.array([-1]))
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_wrong_sample_weights(klass):
# Test if ValueError is raised if sample_weight has wrong shape
clf = klass(alpha=0.1, max_iter=1000, fit_intercept=False)
# provided sample_weight too long
with pytest.raises(ValueError):
clf.fit(X, Y, sample_weight=np.arange(7))
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_partial_fit_exception(klass):
clf = klass(alpha=0.01)
# classes was not specified
with pytest.raises(ValueError):
clf.partial_fit(X3, Y3)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_partial_fit_binary(klass):
third = X.shape[0] // 3
clf = klass(alpha=0.01)
classes = np.unique(Y)
clf.partial_fit(X[:third], Y[:third], classes=classes)
assert clf.coef_.shape == (1, X.shape[1])
assert clf.intercept_.shape == (1,)
assert clf.decision_function([[0, 0]]).shape == (1, )
id1 = id(clf.coef_.data)
clf.partial_fit(X[third:], Y[third:])
id2 = id(clf.coef_.data)
# check that coef_ haven't been re-allocated
assert id1, id2
y_pred = clf.predict(T)
assert_array_equal(y_pred, true_result)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_partial_fit_multiclass(klass):
third = X2.shape[0] // 3
clf = klass(alpha=0.01)
classes = np.unique(Y2)
clf.partial_fit(X2[:third], Y2[:third], classes=classes)
assert clf.coef_.shape == (3, X2.shape[1])
assert clf.intercept_.shape == (3,)
assert clf.decision_function([[0, 0]]).shape == (1, 3)
id1 = id(clf.coef_.data)
clf.partial_fit(X2[third:], Y2[third:])
id2 = id(clf.coef_.data)
# check that coef_ haven't been re-allocated
assert id1, id2
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_partial_fit_multiclass_average(klass):
third = X2.shape[0] // 3
clf = klass(alpha=0.01, average=X2.shape[0])
classes = np.unique(Y2)
clf.partial_fit(X2[:third], Y2[:third], classes=classes)
assert clf.coef_.shape == (3, X2.shape[1])
assert clf.intercept_.shape == (3,)
clf.partial_fit(X2[third:], Y2[third:])
assert clf.coef_.shape == (3, X2.shape[1])
assert clf.intercept_.shape == (3,)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_fit_then_partial_fit(klass):
# Partial_fit should work after initial fit in the multiclass case.
# Non-regression test for #2496; fit would previously produce a
# Fortran-ordered coef_ that subsequent partial_fit couldn't handle.
clf = klass()
clf.fit(X2, Y2)
clf.partial_fit(X2, Y2) # no exception here
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
@pytest.mark.parametrize('lr',
["constant", "optimal", "invscaling", "adaptive"])
def test_partial_fit_equal_fit_classif(klass, lr):
for X_, Y_, T_ in ((X, Y, T), (X2, Y2, T2)):
clf = klass(alpha=0.01, eta0=0.01, max_iter=2,
learning_rate=lr, shuffle=False)
clf.fit(X_, Y_)
y_pred = clf.decision_function(T_)
t = clf.t_
classes = np.unique(Y_)
clf = klass(alpha=0.01, eta0=0.01, learning_rate=lr,
shuffle=False)
for i in range(2):
clf.partial_fit(X_, Y_, classes=classes)
y_pred2 = clf.decision_function(T_)
assert clf.t_ == t
assert_array_almost_equal(y_pred, y_pred2, decimal=2)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_regression_losses(klass):
random_state = np.random.RandomState(1)
clf = klass(alpha=0.01, learning_rate="constant",
eta0=0.1, loss="epsilon_insensitive",
random_state=random_state)
clf.fit(X, Y)
assert 1.0 == np.mean(clf.predict(X) == Y)
clf = klass(alpha=0.01, learning_rate="constant",
eta0=0.1, loss="squared_epsilon_insensitive",
random_state=random_state)
clf.fit(X, Y)
assert 1.0 == np.mean(clf.predict(X) == Y)
clf = klass(alpha=0.01, loss="huber", random_state=random_state)
clf.fit(X, Y)
assert 1.0 == np.mean(clf.predict(X) == Y)
clf = klass(alpha=0.01, learning_rate="constant", eta0=0.01,
loss="squared_loss", random_state=random_state)
clf.fit(X, Y)
assert 1.0 == np.mean(clf.predict(X) == Y)
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_warm_start_multiclass(klass):
_test_warm_start(klass, X2, Y2, "optimal")
@pytest.mark.parametrize('klass', [SGDClassifier, SparseSGDClassifier])
def test_multiple_fit(klass):
# Test multiple calls of fit w/ different shaped inputs.
clf = klass(alpha=0.01, shuffle=False)
clf.fit(X, Y)
assert hasattr(clf, "coef_")
# Non-regression test: try fitting with a different label set.
y = [["ham", "spam"][i] for i in LabelEncoder().fit_transform(Y)]
clf.fit(X[:, :-1], y)
###############################################################################
# Regression Test Case
@pytest.mark.parametrize('klass', [SGDRegressor, SparseSGDRegressor])
def test_sgd_reg(klass):
# Check that SGD gives any results.
clf = klass(alpha=0.1, max_iter=2, fit_intercept=False)
clf.fit([[0, 0], [1, 1], [2, 2]], [0, 1, 2])
assert clf.coef_[0] == clf.coef_[1]
@pytest.mark.parametrize('klass', [SGDRegressor, SparseSGDRegressor])
def test_sgd_averaged_computed_correctly(klass):
# Tests the average regressor matches the naive implementation
eta = .001
alpha = .01
n_samples = 20
n_features = 10
rng = np.random.RandomState(0)
X = rng.normal(size=(n_samples, n_features))
w = rng.normal(size=n_features)
# simple linear function without noise
y = np.dot(X, w)
clf = klass(loss='squared_loss',
learning_rate='constant',
eta0=eta, alpha=alpha,
fit_intercept=True,
max_iter=1, average=True, shuffle=False)
clf.fit(X, y)
average_weights, average_intercept = asgd(klass, X, y, eta, alpha)
assert_array_almost_equal(clf.coef_,
average_weights,
decimal=16)
assert_almost_equal(clf.intercept_, average_intercept, decimal=16)
@pytest.mark.parametrize('klass', [SGDRegressor, SparseSGDRegressor])
def test_sgd_averaged_partial_fit(klass):
# Tests whether the partial fit yields the same average as the fit
eta = .001
alpha = .01
n_samples = 20
n_features = 10
rng = np.random.RandomState(0)
X = rng.normal(size=(n_samples, n_features))
w = rng.normal(size=n_features)
# simple linear function without noise
y = np.dot(X, w)
clf = klass(loss='squared_loss',
learning_rate='constant',
eta0=eta, alpha=alpha,
fit_intercept=True,
max_iter=1, average=True, shuffle=False)
clf.partial_fit(X[:int(n_samples / 2)][:], y[:int(n_samples / 2)])
clf.partial_fit(X[int(n_samples / 2):][:], y[int(n_samples / 2):])
average_weights, average_intercept = asgd(klass, X, y, eta, alpha)
assert_array_almost_equal(clf.coef_,
average_weights,
decimal=16)
assert_almost_equal(clf.intercept_[0], average_intercept, decimal=16)
@pytest.mark.parametrize('klass', [SGDRegressor, SparseSGDRegressor])
def test_average_sparse(klass):
# Checks the average weights on data with 0s
eta = .001
alpha = .01
clf = klass(loss='squared_loss',
learning_rate='constant',
eta0=eta, alpha=alpha,
fit_intercept=True,
max_iter=1, average=True, shuffle=False)
n_samples = Y3.shape[0]
clf.partial_fit(X3[:int(n_samples / 2)][:], Y3[:int(n_samples / 2)])
clf.partial_fit(X3[int(n_samples / 2):][:], Y3[int(n_samples / 2):])
average_weights, average_intercept = asgd(klass, X3, Y3, eta, alpha)
assert_array_almost_equal(clf.coef_,
average_weights,
decimal=16)
assert_almost_equal(clf.intercept_, average_intercept, decimal=16)
@pytest.mark.parametrize('klass', [SGDRegressor, SparseSGDRegressor])
def test_sgd_least_squares_fit(klass):
xmin, xmax = -5, 5
n_samples = 100
rng = np.random.RandomState(0)
X = np.linspace(xmin, xmax, n_samples).reshape(n_samples, 1)
# simple linear function without noise
y = 0.5 * X.ravel()
clf = klass(loss='squared_loss', alpha=0.1, max_iter=20,
fit_intercept=False)
clf.fit(X, y)
score = clf.score(X, y)
assert score > 0.99
# simple linear function with noise
y = 0.5 * X.ravel() + rng.randn(n_samples, 1).ravel()
clf = klass(loss='squared_loss', alpha=0.1, max_iter=20,
fit_intercept=False)
clf.fit(X, y)
score = clf.score(X, y)
assert score > 0.5
@pytest.mark.parametrize('klass', [SGDRegressor, SparseSGDRegressor])
def test_sgd_epsilon_insensitive(klass):
xmin, xmax = -5, 5
n_samples = 100
rng = np.random.RandomState(0)
X = np.linspace(xmin, xmax, n_samples).reshape(n_samples, 1)
# simple linear function without noise
y = 0.5 * X.ravel()
clf = klass(loss='epsilon_insensitive', epsilon=0.01,
alpha=0.1, max_iter=20,
fit_intercept=False)
clf.fit(X, y)
score = clf.score(X, y)
assert score > 0.99
# simple linear function with noise
y = 0.5 * X.ravel() + rng.randn(n_samples, 1).ravel()
clf = klass(loss='epsilon_insensitive', epsilon=0.01,
alpha=0.1, max_iter=20,
fit_intercept=False)
clf.fit(X, y)
score = clf.score(X, y)
assert score > 0.5
@pytest.mark.parametrize('klass', [SGDRegressor, SparseSGDRegressor])
def test_sgd_huber_fit(klass):
xmin, xmax = -5, 5
n_samples = 100
rng = np.random.RandomState(0)
X = np.linspace(xmin, xmax, n_samples).reshape(n_samples, 1)
# simple linear function without noise
y = 0.5 * X.ravel()
clf = klass(loss="huber", epsilon=0.1, alpha=0.1, max_iter=20,
fit_intercept=False)
clf.fit(X, y)
score = clf.score(X, y)
assert score > 0.99
# simple linear function with noise
y = 0.5 * X.ravel() + rng.randn(n_samples, 1).ravel()
clf = klass(loss="huber", epsilon=0.1, alpha=0.1, max_iter=20,
fit_intercept=False)
clf.fit(X, y)
score = clf.score(X, y)
assert score > 0.5
@pytest.mark.parametrize('klass', [SGDRegressor, SparseSGDRegressor])
def test_elasticnet_convergence(klass):
# Check that the SGD output is consistent with coordinate descent
n_samples, n_features = 1000, 5
rng = np.random.RandomState(0)
X = rng.randn(n_samples, n_features)
# ground_truth linear model that generate y from X and to which the
# models should converge if the regularizer would be set to 0.0
ground_truth_coef = rng.randn(n_features)
y = np.dot(X, ground_truth_coef)
# XXX: alpha = 0.1 seems to cause convergence problems
for alpha in [0.01, 0.001]:
for l1_ratio in [0.5, 0.8, 1.0]:
cd = linear_model.ElasticNet(alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=False)
cd.fit(X, y)
sgd = klass(penalty='elasticnet', max_iter=50,
alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=False)
sgd.fit(X, y)
err_msg = ("cd and sgd did not converge to comparable "
"results for alpha=%f and l1_ratio=%f"
% (alpha, l1_ratio))
assert_almost_equal(cd.coef_, sgd.coef_, decimal=2,
err_msg=err_msg)
@ignore_warnings
@pytest.mark.parametrize('klass', [SGDRegressor, SparseSGDRegressor])
def test_partial_fit(klass):
third = X.shape[0] // 3
clf = klass(alpha=0.01)
clf.partial_fit(X[:third], Y[:third])
assert clf.coef_.shape == (X.shape[1], )
assert clf.intercept_.shape == (1,)
assert clf.predict([[0, 0]]).shape == (1, )
id1 = id(clf.coef_.data)
clf.partial_fit(X[third:], Y[third:])
id2 = id(clf.coef_.data)
# check that coef_ haven't been re-allocated
assert id1, id2
@pytest.mark.parametrize('klass', [SGDRegressor, SparseSGDRegressor])
@pytest.mark.parametrize('lr',
["constant", "optimal", "invscaling", "adaptive"])
def test_partial_fit_equal_fit(klass, lr):
clf = klass(alpha=0.01, max_iter=2, eta0=0.01,
learning_rate=lr, shuffle=False)
clf.fit(X, Y)
y_pred = clf.predict(T)
t = clf.t_
clf = klass(alpha=0.01, eta0=0.01,
learning_rate=lr, shuffle=False)
for i in range(2):
clf.partial_fit(X, Y)
y_pred2 = clf.predict(T)
assert clf.t_ == t
assert_array_almost_equal(y_pred, y_pred2, decimal=2)
@pytest.mark.parametrize('klass', [SGDRegressor, SparseSGDRegressor])
def test_loss_function_epsilon(klass):
clf = klass(epsilon=0.9)
clf.set_params(epsilon=0.1)
assert clf.loss_functions['huber'][1] == 0.1
def test_l1_ratio():
# Test if l1 ratio extremes match L1 and L2 penalty settings.
X, y = datasets.make_classification(n_samples=1000,
n_features=100, n_informative=20,
random_state=1234)
# test if elasticnet with l1_ratio near 1 gives same result as pure l1
est_en = SGDClassifier(alpha=0.001, penalty='elasticnet', tol=None,
max_iter=6, l1_ratio=0.9999999999,
random_state=42).fit(X, y)
est_l1 = SGDClassifier(alpha=0.001, penalty='l1', max_iter=6,
random_state=42, tol=None).fit(X, y)
assert_array_almost_equal(est_en.coef_, est_l1.coef_)
# test if elasticnet with l1_ratio near 0 gives same result as pure l2
est_en = SGDClassifier(alpha=0.001, penalty='elasticnet', tol=None,
max_iter=6, l1_ratio=0.0000000001,
random_state=42).fit(X, y)
est_l2 = SGDClassifier(alpha=0.001, penalty='l2', max_iter=6,
random_state=42, tol=None).fit(X, y)
assert_array_almost_equal(est_en.coef_, est_l2.coef_)
def test_underflow_or_overlow():
with np.errstate(all='raise'):
# Generate some weird data with hugely unscaled features
rng = np.random.RandomState(0)
n_samples = 100
n_features = 10
X = rng.normal(size=(n_samples, n_features))
X[:, :2] *= 1e300
assert np.isfinite(X).all()
# Use MinMaxScaler to scale the data without introducing a numerical
# instability (computing the standard deviation naively is not possible
# on this data)
X_scaled = MinMaxScaler().fit_transform(X)
assert np.isfinite(X_scaled).all()
# Define a ground truth on the scaled data
ground_truth = rng.normal(size=n_features)
y = (np.dot(X_scaled, ground_truth) > 0.).astype(np.int32)
assert_array_equal(np.unique(y), [0, 1])
model = SGDClassifier(alpha=0.1, loss='squared_hinge', max_iter=500)
# smoke test: model is stable on scaled data
model.fit(X_scaled, y)
assert np.isfinite(model.coef_).all()
# model is numerically unstable on unscaled data
msg_regxp = (r"Floating-point under-/overflow occurred at epoch #.*"
" Scaling input data with StandardScaler or MinMaxScaler"
" might help.")
assert_raises_regexp(ValueError, msg_regxp, model.fit, X, y)
def test_numerical_stability_large_gradient():
# Non regression test case for numerical stability on scaled problems
# where the gradient can still explode with some losses
model = SGDClassifier(loss='squared_hinge', max_iter=10, shuffle=True,
penalty='elasticnet', l1_ratio=0.3, alpha=0.01,
eta0=0.001, random_state=0, tol=None)
with np.errstate(all='raise'):
model.fit(iris.data, iris.target)
assert np.isfinite(model.coef_).all()
@pytest.mark.parametrize('penalty', ['l2', 'l1', 'elasticnet'])
def test_large_regularization(penalty):
# Non regression tests for numerical stability issues caused by large
# regularization parameters
model = SGDClassifier(alpha=1e5, learning_rate='constant', eta0=0.1,
penalty=penalty, shuffle=False,
tol=None, max_iter=6)
with np.errstate(all='raise'):
model.fit(iris.data, iris.target)
assert_array_almost_equal(model.coef_, np.zeros_like(model.coef_))
def test_tol_parameter():
# Test that the tol parameter behaves as expected
X = StandardScaler().fit_transform(iris.data)
y = iris.target == 1
# With tol is None, the number of iteration should be equal to max_iter
max_iter = 42
model_0 = SGDClassifier(tol=None, random_state=0, max_iter=max_iter)
model_0.fit(X, y)
assert max_iter == model_0.n_iter_
# If tol is not None, the number of iteration should be less than max_iter
max_iter = 2000
model_1 = SGDClassifier(tol=0, random_state=0, max_iter=max_iter)
model_1.fit(X, y)
assert max_iter > model_1.n_iter_
assert model_1.n_iter_ > 5
# A larger tol should yield a smaller number of iteration
model_2 = SGDClassifier(tol=0.1, random_state=0, max_iter=max_iter)
model_2.fit(X, y)
assert model_1.n_iter_ > model_2.n_iter_
assert model_2.n_iter_ > 3
# Strict tolerance and small max_iter should trigger a warning
model_3 = SGDClassifier(max_iter=3, tol=1e-3, random_state=0)
model_3 = assert_warns(ConvergenceWarning, model_3.fit, X, y)
assert model_3.n_iter_ == 3
def _test_loss_common(loss_function, cases):
# Test the different loss functions
# cases is a list of (p, y, expected)
for p, y, expected_loss, expected_dloss in cases:
assert_almost_equal(loss_function.py_loss(p, y), expected_loss)
assert_almost_equal(loss_function.py_dloss(p, y), expected_dloss)
def test_loss_hinge():
# Test Hinge (hinge / perceptron)
# hinge
loss = sgd_fast.Hinge(1.0)
cases = [
# (p, y, expected_loss, expected_dloss)
(1.1, 1.0, 0.0, 0.0), (-2.0, -1.0, 0.0, 0.0),
(1.0, 1.0, 0.0, -1.0), (-1.0, -1.0, 0.0, 1.0), (0.5, 1.0, 0.5, -1.0),
(2.0, -1.0, 3.0, 1.0), (-0.5, -1.0, 0.5, 1.0), (0.0, 1.0, 1, -1.0)
]
_test_loss_common(loss, cases)
# perceptron
loss = sgd_fast.Hinge(0.0)
cases = [
# (p, y, expected_loss, expected_dloss)
(1.0, 1.0, 0.0, 0.0), (-0.1, -1.0, 0.0, 0.0),
(0.0, 1.0, 0.0, -1.0), (0.0, -1.0, 0.0, 1.0), (0.5, -1.0, 0.5, 1.0),
(2.0, -1.0, 2.0, 1.0), (-0.5, 1.0, 0.5, -1.0), (-1.0, 1.0, 1.0, -1.0),
]
_test_loss_common(loss, cases)
def test_gradient_squared_hinge():
# Test SquaredHinge
loss = sgd_fast.SquaredHinge(1.0)
cases = [
# (p, y, expected_loss, expected_dloss)
(1.0, 1.0, 0.0, 0.0), (-2.0, -1.0, 0.0, 0.0), (1.0, -1.0, 4.0, 4.0),
(-1.0, 1.0, 4.0, -4.0), (0.5, 1.0, 0.25, -1.0), (0.5, -1.0, 2.25, 3.0)
]
_test_loss_common(loss, cases)
def test_loss_log():
# Test Log (logistic loss)
loss = sgd_fast.Log()
cases = [
# (p, y, expected_loss, expected_dloss)
(1.0, 1.0, np.log(1.0 + np.exp(-1.0)), -1.0 / (np.exp(1.0) + 1.0)),
(1.0, -1.0, np.log(1.0 + np.exp(1.0)), 1.0 / (np.exp(-1.0) + 1.0)),
(-1.0, -1.0, np.log(1.0 + np.exp(-1.0)), 1.0 / (np.exp(1.0) + 1.0)),
(-1.0, 1.0, np.log(1.0 + np.exp(1.0)), -1.0 / (np.exp(-1.0) + 1.0)),
(0.0, 1.0, np.log(2), -0.5), (0.0, -1.0, np.log(2), 0.5),
(17.9, -1.0, 17.9, 1.0), (-17.9, 1.0, 17.9, -1.0),
]
_test_loss_common(loss, cases)
assert_almost_equal(loss.py_dloss(18.1, 1.0), np.exp(-18.1) * -1.0, 16)
assert_almost_equal(loss.py_loss(18.1, 1.0), np.exp(-18.1), 16)
assert_almost_equal(loss.py_dloss(-18.1, -1.0), np.exp(-18.1) * 1.0, 16)
assert_almost_equal(loss.py_loss(-18.1, 1.0), 18.1, 16)
def test_loss_squared_loss():
# Test SquaredLoss
loss = sgd_fast.SquaredLoss()
cases = [
# (p, y, expected_loss, expected_dloss)
(0.0, 0.0, 0.0, 0.0), (1.0, 1.0, 0.0, 0.0), (1.0, 0.0, 0.5, 1.0),
(0.5, -1.0, 1.125, 1.5), (-2.5, 2.0, 10.125, -4.5)
]
_test_loss_common(loss, cases)
def test_loss_huber():
# Test Huber
loss = sgd_fast.Huber(0.1)
cases = [
# (p, y, expected_loss, expected_dloss)
(0.0, 0.0, 0.0, 0.0), (0.1, 0.0, 0.005, 0.1), (0.0, 0.1, 0.005, -0.1),
(3.95, 4.0, 0.00125, -0.05), (5.0, 2.0, 0.295, 0.1),
(-1.0, 5.0, 0.595, -0.1)
]
_test_loss_common(loss, cases)
def test_loss_modified_huber():
# (p, y, expected_loss, expected_dloss)
loss = sgd_fast.ModifiedHuber()
cases = [
# (p, y, expected_loss, expected_dloss)
(1.0, 1.0, 0.0, 0.0), (-1.0, -1.0, 0.0, 0.0), (2.0, 1.0, 0.0, 0.0),
(0.0, 1.0, 1.0, -2.0), (-1.0, 1.0, 4.0, -4.0), (0.5, -1.0, 2.25, 3.0),
(-2.0, 1.0, 8, -4.0), (-3.0, 1.0, 12, -4.0)
]
_test_loss_common(loss, cases)
def test_loss_epsilon_insensitive():
# Test EpsilonInsensitive
loss = sgd_fast.EpsilonInsensitive(0.1)
cases = [
# (p, y, expected_loss, expected_dloss)
(0.0, 0.0, 0.0, 0.0), (0.1, 0.0, 0.0, 0.0), (-2.05, -2.0, 0.0, 0.0),
(3.05, 3.0, 0.0, 0.0), (2.2, 2.0, 0.1, 1.0), (2.0, -1.0, 2.9, 1.0),
(2.0, 2.2, 0.1, -1.0), (-2.0, 1.0, 2.9, -1.0)
]
_test_loss_common(loss, cases)
def test_loss_squared_epsilon_insensitive():
# Test SquaredEpsilonInsensitive
loss = sgd_fast.SquaredEpsilonInsensitive(0.1)
cases = [
# (p, y, expected_loss, expected_dloss)
(0.0, 0.0, 0.0, 0.0), (0.1, 0.0, 0.0, 0.0), (-2.05, -2.0, 0.0, 0.0),
(3.05, 3.0, 0.0, 0.0), (2.2, 2.0, 0.01, 0.2), (2.0, -1.0, 8.41, 5.8),
(2.0, 2.2, 0.01, -0.2), (-2.0, 1.0, 8.41, -5.8)
]
_test_loss_common(loss, cases)
def test_multi_thread_multi_class_and_early_stopping():
# This is a non-regression test for a bad interaction between
# early stopping internal attribute and thread-based parallelism.
clf = SGDClassifier(alpha=1e-3, tol=1e-3, max_iter=1000,
early_stopping=True, n_iter_no_change=100,
random_state=0, n_jobs=2)
clf.fit(iris.data, iris.target)
assert clf.n_iter_ > clf.n_iter_no_change
assert clf.n_iter_ < clf.n_iter_no_change + 20
assert clf.score(iris.data, iris.target) > 0.8
def test_multi_core_gridsearch_and_early_stopping():
# This is a non-regression test for a bad interaction between
# early stopping internal attribute and process-based multi-core
# parallelism.
param_grid = {
'alpha': np.logspace(-4, 4, 9),
'n_iter_no_change': [5, 10, 50],
}
clf = SGDClassifier(tol=1e-2, max_iter=1000, early_stopping=True,
random_state=0)
search = RandomizedSearchCV(clf, param_grid, n_iter=3, n_jobs=2,
random_state=0)
search.fit(iris.data, iris.target)
assert search.best_score_ > 0.8
@pytest.mark.parametrize("backend",
["loky", "multiprocessing", "threading"])
def test_SGDClassifier_fit_for_all_backends(backend):
# This is a non-regression smoke test. In the multi-class case,
# SGDClassifier.fit fits each class in a one-versus-all fashion using
# joblib.Parallel. However, each OvA step updates the coef_ attribute of
# the estimator in-place. Internally, SGDClassifier calls Parallel using
# require='sharedmem'. This test makes sure SGDClassifier.fit works
# consistently even when the user asks for a backend that does not provide
# sharedmem semantics.
# We further test a case where memmapping would have been used if
# SGDClassifier.fit was called from a loky or multiprocessing backend. In
# this specific case, in-place modification of clf.coef_ would have caused
# a segmentation fault when trying to write in a readonly memory mapped
# buffer.
if (parse_version(joblib.__version__) < parse_version('0.12')
and backend == 'loky'):
pytest.skip('loky backend does not exist in joblib <0.12')
random_state = np.random.RandomState(42)
# Create a classification problem with 50000 features and 20 classes. Using
# loky or multiprocessing this make the clf.coef_ exceed the threshold
# above which memmaping is used in joblib and loky (1MB as of 2018/11/1).
X = sp.random(500, 2000, density=0.02, format='csr',
random_state=random_state)
y = random_state.choice(20, 500)
# Begin by fitting a SGD classifier sequentially
clf_sequential = SGDClassifier(max_iter=1000, n_jobs=1,
random_state=42)
clf_sequential.fit(X, y)
# Fit a SGDClassifier using the specified backend, and make sure the
# coefficients are equal to those obtained using a sequential fit
clf_parallel = SGDClassifier(max_iter=1000, n_jobs=4,
random_state=42)
with joblib.parallel_backend(backend=backend):
clf_parallel.fit(X, y)
assert_array_almost_equal(clf_sequential.coef_, clf_parallel.coef_)
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29892,
343,
29897,
13,
13,
1678,
6588,
29918,
705,
5861,
29892,
6588,
29918,
1639,
1547,
353,
408,
29887,
29881,
29898,
29895,
605,
29892,
1060,
29892,
343,
29892,
634,
29874,
29892,
15595,
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13,
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6588,
29918,
705,
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353,
6588,
29918,
705,
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29889,
690,
14443,
29898,
29896,
29892,
448,
29896,
29897,
13,
1678,
4974,
29918,
2378,
29918,
284,
3242,
29918,
11745,
29898,
695,
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29889,
1111,
1389,
3383,
13,
462,
795,
6588,
29918,
705,
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13,
462,
795,
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13,
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4974,
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284,
3242,
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11745,
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695,
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29889,
1639,
1547,
3383,
6588,
29918,
1639,
1547,
29892,
13677,
29922,
29896,
29946,
29897,
13,
13,
13,
29992,
2272,
1688,
29889,
3502,
29889,
3207,
300,
374,
911,
877,
29895,
605,
742,
518,
26016,
29928,
2385,
3709,
29892,
317,
5510,
26016,
29928,
2385,
3709,
2314,
13,
1753,
1243,
29918,
842,
29918,
1639,
1547,
29918,
517,
29918,
1639,
1547,
29898,
29895,
605,
1125,
13,
1678,
396,
5399,
29879,
23404,
29918,
8267,
5718,
3819,
363,
278,
14294,
8665,
13,
1678,
396,
512,
3200,
9696,
23404,
29918,
8267,
29889,
13,
1678,
1067,
29888,
353,
22902,
2141,
9202,
29898,
29990,
29945,
29892,
612,
29945,
29897,
13,
1678,
22902,
2141,
9202,
29898,
29990,
29945,
29892,
612,
29945,
29892,
23404,
29918,
2344,
29922,
695,
29888,
29889,
1639,
1547,
19925,
13,
1678,
1067,
29888,
353,
22902,
2141,
9202,
29898,
29990,
29892,
612,
29897,
13,
1678,
22902,
2141,
9202,
29898,
29990,
29892,
612,
29892,
23404,
29918,
2344,
29922,
695,
29888,
29889,
1639,
1547,
19925,
13,
13,
13,
29992,
2272,
1688,
29889,
3502,
29889,
3207,
300,
374,
911,
877,
29895,
605,
742,
518,
26016,
29928,
2385,
3709,
29892,
317,
5510,
26016,
29928,
2385,
3709,
2314,
13,
1753,
1243,
29918,
5311,
29881,
29918,
271,
29918,
280,
579,
29918,
10184,
29918,
21134,
29898,
29895,
605,
1125,
13,
1678,
396,
17157,
1818,
505,
472,
3203,
1023,
11073,
13,
1678,
1067,
29888,
353,
22902,
29898,
2312,
29922,
29900,
29889,
29900,
29896,
29892,
4236,
29918,
1524,
29922,
29906,
29900,
29897,
13,
1678,
411,
11451,
1688,
29889,
336,
4637,
29898,
1917,
2392,
1125,
13,
4706,
1067,
29888,
29889,
9202,
29898,
29990,
29906,
29892,
7442,
29889,
2873,
29898,
29929,
876,
13,
13,
13,
29992,
2272,
1688,
29889,
3502,
29889,
3207,
300,
374,
911,
877,
29895,
605,
742,
518,
26016,
29928,
2385,
3709,
29892,
317,
5510,
26016,
29928,
2385,
3709,
2314,
13,
1753,
1243,
29918,
3846,
29918,
9202,
29918,
7915,
29918,
1990,
29918,
5521,
8362,
29898,
29895,
605,
1125,
13,
1678,
396,
7687,
29918,
9202,
411,
770,
29918,
7915,
2433,
5521,
8362,
29915,
451,
6969,
15945,
29908,
13,
1678,
6528,
353,
313,
29878,
29908,
1990,
29918,
7915,
525,
5521,
8362,
29915,
338,
451,
6969,
363,
376,
13,
632,
364,
29908,
3846,
29918,
9202,
23301,
512,
1797,
304,
671,
525,
5521,
8362,
29915,
18177,
29892,
376,
13,
632,
364,
29908,
1509,
10272,
29918,
1990,
29918,
7915,
29905,
877,
5521,
8362,
742,
4413,
29922,
13203,
29892,
343,
29922,
29891,
29800,
376,
13,
632,
364,
29908,
797,
2058,
310,
343,
366,
508,
502,
263,
2919,
3307,
4559,
376,
13,
632,
364,
29908,
974,
278,
2989,
6694,
731,
3646,
304,
6284,
376,
13,
632,
364,
29908,
342,
6490,
278,
770,
10868,
18822,
23301,
376,
13,
632,
364,
29908,
7129,
278,
9819,
18177,
408,
278,
770,
29918,
7915,
376,
13,
632,
364,
29908,
15501,
29905,
23157,
13,
1678,
4974,
29918,
336,
4637,
29918,
13087,
29886,
29898,
1917,
2392,
29892,
13,
462,
308,
6528,
29892,
13,
462,
308,
22902,
29898,
1990,
29918,
7915,
2433,
5521,
8362,
2824,
3846,
29918,
9202,
29892,
13,
462,
308,
1060,
29892,
612,
29892,
4413,
29922,
9302,
29889,
13092,
29898,
29979,
876,
13,
13,
13,
29992,
2272,
1688,
29889,
3502,
29889,
3207,
300,
374,
911,
877,
29895,
605,
742,
518,
26016,
29928,
2385,
3709,
29892,
317,
5510,
26016,
29928,
2385,
3709,
2314,
13,
1753,
1243,
29918,
5311,
29881,
29918,
4713,
293,
605,
29898,
29895,
605,
1125,
13,
1678,
396,
14974,
29899,
1990,
1243,
1206,
13,
1678,
1067,
29888,
353,
22902,
29898,
2312,
29922,
29900,
29889,
29900,
29896,
29892,
4236,
29918,
1524,
29922,
29906,
29900,
467,
9202,
29898,
29990,
29906,
29892,
612,
29906,
29897,
13,
1678,
4974,
1067,
29888,
29889,
1111,
1389,
5396,
12181,
1275,
313,
29941,
29892,
29871,
29906,
29897,
13,
1678,
4974,
1067,
29888,
29889,
1639,
1547,
5396,
12181,
1275,
313,
29941,
29892,
29897,
13,
1678,
4974,
1067,
29888,
29889,
7099,
2459,
29918,
2220,
4197,
29961,
29900,
29892,
29871,
29900,
5262,
467,
12181,
1275,
313,
29896,
29892,
29871,
29941,
29897,
13,
1678,
4450,
353,
1067,
29888,
29889,
27711,
29898,
29911,
29906,
29897,
13,
1678,
4974,
29918,
2378,
29918,
11745,
29898,
11965,
29892,
1565,
29918,
2914,
29906,
29897,
13,
13,
13,
29992,
2272,
1688,
29889,
3502,
29889,
3207,
300,
374,
911,
877,
29895,
605,
742,
518,
26016,
29928,
2385,
3709,
29892,
317,
5510,
26016,
29928,
2385,
3709,
2314,
13,
1753,
1243,
29918,
5311,
29881,
29918,
4713,
293,
605,
29918,
12483,
482,
29898,
29895,
605,
1125,
13,
1678,
634,
29874,
353,
869,
29900,
29900,
29896,
13,
1678,
15595,
353,
869,
29900,
29896,
13,
1678,
396,
14974,
29899,
1990,
6588,
1243,
1206,
13,
1678,
1067,
29888,
353,
22902,
29898,
6758,
2433,
26613,
1965,
29918,
6758,
742,
13,
18884,
6509,
29918,
10492,
2433,
23362,
742,
13,
18884,
634,
29874,
29900,
29922,
1187,
29892,
15595,
29922,
2312,
29892,
13,
18884,
6216,
29918,
1639,
1547,
29922,
5574,
29892,
13,
18884,
4236,
29918,
1524,
29922,
29896,
29892,
6588,
29922,
5574,
29892,
528,
21897,
29922,
8824,
29897,
13,
13,
1678,
7442,
29918,
29979,
29906,
353,
7442,
29889,
2378,
29898,
29979,
29906,
29897,
13,
1678,
1067,
29888,
29889,
9202,
29898,
29990,
29906,
29892,
7442,
29918,
29979,
29906,
29897,
13,
1678,
4413,
353,
7442,
29889,
13092,
29898,
9302,
29918,
29979,
29906,
29897,
13,
13,
1678,
363,
474,
29892,
1067,
297,
26985,
29898,
13203,
1125,
13,
4706,
343,
29918,
29875,
353,
7442,
29889,
2873,
29898,
9302,
29918,
29979,
29906,
29889,
12181,
29961,
29900,
2314,
13,
4706,
343,
29918,
29875,
29961,
9302,
29918,
29979,
29906,
2804,
1067,
29962,
353,
448,
29896,
13,
4706,
6588,
29918,
1111,
1389,
29892,
6588,
29918,
1639,
1547,
353,
408,
29887,
29881,
29898,
29895,
605,
29892,
1060,
29906,
29892,
343,
29918,
29875,
29892,
634,
29874,
29892,
15595,
29897,
13,
4706,
4974,
29918,
2378,
29918,
284,
3242,
29918,
11745,
29898,
12483,
482,
29918,
1111,
1389,
29892,
1067,
29888,
29889,
1111,
1389,
29918,
29961,
29875,
1402,
13677,
29922,
29896,
29953,
29897,
13,
4706,
4974,
29918,
284,
3242,
29918,
11745,
29898,
12483,
482,
29918,
1639,
1547,
29892,
13,
462,
9651,
1067,
29888,
29889,
1639,
1547,
29918,
29961,
29875,
1402,
13,
462,
9651,
13677,
29922,
29896,
29953,
29897,
13,
13,
13,
29992,
2272,
1688,
29889,
3502,
29889,
3207,
300,
374,
911,
877,
29895,
605,
742,
518,
26016,
29928,
2385,
3709,
29892,
317,
5510,
26016,
29928,
2385,
3709,
2314,
13,
1753,
1243,
29918,
5311,
29881,
29918,
4713,
293,
605,
29918,
2541,
29918,
2344,
29918,
1111,
1389,
29898,
29895,
605,
1125,
13,
1678,
396,
14974,
29899,
1990,
1243,
1206,
13,
1678,
1067,
29888,
353,
22902,
29898,
2312,
29922,
29900,
29889,
29900,
29896,
29892,
4236,
29918,
1524,
29922,
29906,
29900,
29897,
13,
1678,
1067,
29888,
29889,
9202,
29898,
29990,
29906,
29892,
612,
29906,
29892,
1302,
1389,
29918,
2344,
29922,
9302,
29889,
3298,
359,
3552,
29941,
29892,
29871,
29906,
8243,
13,
9651,
23404,
29918,
2344,
29922,
9302,
29889,
3298,
359,
29898,
29941,
876,
13,
1678,
4974,
1067,
29888,
29889,
1111,
1389,
5396,
12181,
1275,
313,
29941,
29892,
29871,
29906,
29897,
13,
1678,
4974,
1067,
29888,
29889,
1639,
1547,
5396,
12181,
29892,
313,
29941,
29892,
29897,
13,
1678,
4450,
353,
1067,
29888,
29889,
27711,
29898,
29911,
29906,
29897,
13,
1678,
4974,
29918,
2378,
29918,
11745,
29898,
11965,
29892,
1565,
29918,
2914,
29906,
29897,
13,
13,
13,
29992,
2272,
1688,
29889,
3502,
29889,
3207,
300,
374,
911,
877,
29895,
605,
742,
518,
26016,
29928,
2385,
3709,
29892,
317,
5510,
26016,
29928,
2385,
3709,
2314,
13,
1753,
1243,
29918,
5311,
29881,
29918,
4713,
293,
605,
29918,
29876,
9057,
29879,
29898,
29895,
605,
1125,
13,
1678,
396,
14974,
29899,
1990,
1243,
1206,
411,
2473,
29899,
3221,
2304,
13,
1678,
1067,
29888,
353,
22902,
29898,
2312,
29922,
29900,
29889,
29900,
29896,
29892,
4236,
29918,
1524,
29922,
29906,
29900,
29892,
302,
29918,
9057,
29879,
29922,
29906,
467,
9202,
29898,
29990,
29906,
29892,
612,
29906,
29897,
13,
1678,
4974,
1067,
29888,
29889,
1111,
1389,
5396,
12181,
1275,
313,
29941,
29892,
29871,
29906,
29897,
13,
1678,
4974,
1067,
29888,
29889,
1639,
1547,
5396,
12181,
1275,
313,
29941,
29892,
29897,
13,
1678,
4974,
1067,
29888,
29889,
7099,
2459,
29918,
2220,
4197,
29961,
29900,
29892,
29871,
29900,
5262,
467,
12181,
1275,
313,
29896,
29892,
29871,
29941,
29897,
13,
1678,
4450,
353,
1067,
29888,
29889,
27711,
29898,
29911,
29906,
29897,
13,
1678,
4974,
29918,
2378,
29918,
11745,
29898,
11965,
29892,
1565,
29918,
2914,
29906,
29897,
13,
13,
13,
29992,
2272,
1688,
29889,
3502,
29889,
3207,
300,
374,
911,
877,
29895,
605,
742,
518,
26016,
29928,
2385,
3709,
29892,
317,
5510,
26016,
29928,
2385,
3709,
2314,
13,
1753,
1243,
29918,
842,
29918,
1111,
1389,
29918,
4713,
293,
605,
29898,
29895,
605,
1125,
13,
1678,
396,
5399,
29879,
1302,
1389,
29918,
2344,
322,
23404,
29918,
2344,
8267,
363,
2473,
29899,
1990,
13,
1678,
396,
4828,
13,
1678,
396,
9133,
2618,
1302,
1389,
29918,
947,
451,
1993,
8783,
13,
1678,
1067,
29888,
353,
22902,
580,
13,
1678,
411,
11451,
1688,
29889,
336,
4637,
29898,
1917,
2392,
1125,
13,
4706,
1067,
29888,
29889,
9202,
29898,
29990,
29906,
29892,
612,
29906,
29892,
1302,
1389,
29918,
2344,
29922,
9302,
29889,
3298,
359,
3552,
29906,
29892,
29871,
29906,
4961,
13,
13,
1678,
396,
9133,
2618,
1302,
1389,
29918,
947,
1993,
8783,
13,
1678,
1067,
29888,
353,
22902,
2141,
9202,
29898,
29990,
29906,
29892,
612,
29906,
29892,
1302,
1389,
29918,
2344,
29922,
9302,
29889,
3298,
359,
3552,
29941,
29892,
29871,
29906,
4961,
13,
13,
1678,
396,
9133,
2618,
23404,
29918,
947,
451,
1993,
8783,
13,
1678,
1067,
29888,
353,
22902,
580,
13,
1678,
411,
11451,
1688,
29889,
336,
4637,
29898,
1917,
2392,
1125,
13,
4706,
1067,
29888,
29889,
9202,
29898,
29990,
29906,
29892,
612,
29906,
29892,
23404,
29918,
2344,
29922,
9302,
29889,
3298,
359,
3552,
29896,
29892,
4961,
13,
13,
1678,
396,
9133,
2618,
23404,
29918,
947,
1993,
8783,
29889,
13,
1678,
1067,
29888,
353,
22902,
2141,
9202,
29898,
29990,
29906,
29892,
612,
29906,
29892,
23404,
29918,
2344,
29922,
9302,
29889,
3298,
359,
3552,
29941,
29892,
4961,
13,
13,
13,
29992,
2272,
1688,
29889,
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] |
stars.py | hezsystemscorporation/py-turtle-graphs | 0 | 1602624 | <reponame>hezsystemscorporation/py-turtle-graphs
from turtle import *
from random import random,randint
screen = Screen()
width ,height = 800,600
screen.setup(width,height)
screen.bgcolor("black")
screen.mode("logo")
screen.delay(0)#这里要设为0，否则很卡
t = Turtle(visible = False,shape='circle')
t.pencolor("white")
t.fillcolor("white")
t.penup()
t.setheading(-90)
t.goto(width/2,randint(-height/2,height/2))
stars = []
for i in range(200):
star = t.clone()
s =random() /3
star.shapesize(s,s)
star.speed(int(s*10))
star.setx(width/2 + randint(1,width))
star.sety( randint(-height/2,height/2))
star.showturtle()
stars.append(star)
while True:
for star in stars:
star.setx(star.xcor() - 3 * star.speed())
if star.xcor()<-width/2:
star.hideturtle()
star.setx(width/2 + randint(1,width))
star.sety( randint(-height/2,height/2))
star.showturtle()
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13,
2
] |
docs/assets/update_formats.py | Kronuz/Xapiand | 370 | 127720 | <filename>docs/assets/update_formats.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import os
import sys
import json
import msgpack
from collections import OrderedDict
def main():
if len(sys.argv) != 2:
print("usage: {} <file.json>".format(sys.argv[0]))
sys.exit(64)
name, ext = os.path.splitext(sys.argv[1])
json_content = open(name + '.json')
msgpack_content = open(name + '.msgpack', 'wb')
ndjson_content = open(name + '.ndjson', 'w')
http_content = open(name + '.http', 'w')
content = json.load(json_content, object_pairs_hook=OrderedDict)
for i, a in enumerate(content, 1):
msgpack_content.write(msgpack.dumps(a))
aj = json.dumps(a)
print(aj, file=ndjson_content)
print("""PUT /bank/{} HTTP/1.1
Host: localhost:8880
Content-Type: application/json
Content-Length: {}
{}""".format(i, len(aj), aj), file=http_content)
if __name__ == '__main__':
main()
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] |
kAFL-Fuzzer/fuzzer/technique/interesting_values.py | SafeBreach-Labs/hAFL2 | 102 | 188784 | <reponame>SafeBreach-Labs/hAFL2<filename>kAFL-Fuzzer/fuzzer/technique/interesting_values.py
# Copyright (C) 2017-2019 <NAME>, <NAME>, <NAME>
# Copyright (C) 2019-2020 Intel Corporation
#
# SPDX-License-Identifier: AGPL-3.0-or-later
"""
AFL-style 'interesting values' mutations (deterministic stage).
"""
from fuzzer.technique.helper import *
from binascii import hexlify
def mutate_seq_8_bit_interesting(data, func, skip_null=False, effector_map=None, verbose=False):
label="afl_int_1"
for i in range(0, len(data)):
if effector_map:
if not effector_map[i]:
continue
orig = data[i]
if skip_null and orig == 0:
continue
for j in range(len(interesting_8_Bit)):
value = in_range_8(interesting_8_Bit[j])
if (is_not_bitflip(orig ^ value) and
is_not_arithmetic(orig, value, 1)):
data[i] = value
func(data, label=label)
data[i] = orig
def mutate_seq_16_bit_interesting(data, func, skip_null=False, effector_map=None, arith_max=AFL_ARITH_MAX, verbose=False):
label="afl_int_2"
for i in range(len(data) - 1):
if effector_map:
if not (effector_map[i] or effector_map[i + 1]):
continue
orig = data[i:i+2]
oval = struct.unpack('<H', orig)[0]
if skip_null and oval == 0:
continue
for j in range(len(interesting_16_Bit)):
num1 = in_range_16(interesting_16_Bit[j])
num2 = swap_16(num1)
if (is_not_bitflip(oval ^ num1) and
is_not_arithmetic(oval, num1, 2, arith_max=arith_max) and
is_not_interesting(oval, num1, 2, 0)):
data[i:i+2] = struct.pack("<H", num1)
func(data, label=label)
if (num1 != num2 and \
is_not_bitflip(oval ^ num2) and \
is_not_arithmetic(oval, num2, 2, arith_max=arith_max) and \
is_not_interesting(oval, num2, 2, 1)):
data[i:i+2] = struct.pack(">H", num1)
func(data, label=label)
data[i:i+2] = orig
def mutate_seq_32_bit_interesting(data, func, skip_null=False, effector_map=None, arith_max=AFL_ARITH_MAX, verbose=False):
label="afl_int_4"
for i in range(len(data) - 3):
if effector_map:
if effector_map[i:i+4] == b'\x00\x00\x00\x00':
continue
orig = data[i:i+4]
oval = struct.unpack('<I', orig)[0]
if skip_null and oval == 0:
continue
for j in range(len(interesting_32_Bit)):
num1 = in_range_32(interesting_32_Bit[j])
num2 = swap_32(num1)
if (is_not_bitflip(oval ^ num1) and \
is_not_arithmetic(oval, num1, 4, arith_max=arith_max) and \
is_not_interesting(oval, num1, 4, 0)):
data[i:i+4] = struct.pack("<I", num1)
func(data, label=label)
if (num1 != num2 and is_not_bitflip(oval ^ num2) and
is_not_arithmetic(oval, num2, 4, arith_max=arith_max) and
is_not_interesting(oval, num2, 4, 1)):
data[i:i+4] = struct.pack("<I", num2)
func(data, label=label)
data[i:i+4] = orig
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] |
tests/unit/test_convert_from_notebook.py | peopledoc/ml-versioning-tools | 36 | 1616448 | <filename>tests/unit/test_convert_from_notebook.py
from os.path import realpath, dirname, join, exists
import pytest
from pytest import fixture
from mlvtools.conf.conf import MlVToolConf
from mlvtools.docstring_helpers.parse import parse_docstring
from mlvtools.exception import MlVToolException
from mlvtools.ipynb_to_python import export_to_script, get_param_as_python_method_format, is_no_effect, \
get_data_from_docstring, get_arguments_from_docstring, get_arguments_as_param, get_docstring_data, \
DocstringWrapper, is_trailing_cell, get_formatted_cells, filter_trailing_cells
from tests.helpers.utils import gen_notebook, to_notebook_code_cell
CURRENT_DIR = realpath(dirname(__file__))
@fixture
def conf():
return MlVToolConf(top_directory='./')
def test_should_convert_notebook_to_python_script(conf, work_dir):
"""
Test Notebook is converted to python script
"""
output_path = join(work_dir, 'out.py')
notebook_path = gen_notebook(cells=[('code', 'print(\'poney\')')], tmp_dir=work_dir,
file_name='test.ipynb', docstring=None)
export_to_script(input_notebook_path=notebook_path, output_path=output_path, conf=conf)
assert exists(output_path)
with open(output_path, 'r') as fd:
content = fd.read()
# Check main method is created
assert 'def mlvtools_test():' in content
@pytest.mark.parametrize('header', (None, '#Big Title'))
def test_should_detect_parameters(header, conf, work_dir):
"""
Test Notebook is converted to parameterized python script,
parameter cell in Notebook is detected and well handled.
The parameter cell must be the first code cell. It should be detected
if there is a markdown header or not.
"""
output_path = join(work_dir, 'out.py')
docstring_cell = '''
# Parameters
"""
:param str subset: The kind of subset to generate.
:param int rate: The rate of I don't know what
:param param3:
"""
subset = 'train'
toto = 12
'''
notebook_path = gen_notebook(cells=[('code', 'print(\'poney\')')], tmp_dir=work_dir,
file_name='test.ipynb',
docstring=docstring_cell,
header=header)
export_to_script(input_notebook_path=notebook_path, output_path=output_path, conf=conf)
assert exists(output_path)
with open(output_path, 'r') as fd:
content = fd.read()
# Check main method is created
assert 'def mlvtools_test(subset, rate, param3):' in content
def test_should_raise_if_invalid_docstring(conf, work_dir):
"""
Test an MlVTool exception is raised if docstring is invalid
"""
output_path = join(work_dir, 'out.py')
docstring_cell = '''
# Parameters
"""
:param param3
"""
subset = 'train'
toto = 12
'''
notebook_path = gen_notebook(cells=[('code', 'print(\'poney\')')], tmp_dir=work_dir,
file_name='test.ipynb',
docstring=docstring_cell)
with pytest.raises(MlVToolException):
export_to_script(input_notebook_path=notebook_path, output_path=output_path, conf=conf)
def test_should_raise_if_more_than_one_docstring_in_first_cell(conf, work_dir):
"""
Test multi docstring in the parameter cell is detected
"""
output_path = join(work_dir, 'out.py')
docstring_cell = '''
# Parameters
"""
:param param3: Plop
"""
"""
:param param6: AA
"""
subset = 'train'
toto = 12
'''
notebook_path = gen_notebook(cells=[('code', 'print(\'poney\')')], tmp_dir=work_dir,
file_name='test.ipynb',
docstring=docstring_cell)
with pytest.raises(MlVToolException):
export_to_script(input_notebook_path=notebook_path, output_path=output_path, conf=conf)
def test_should_be_resilient_to_empty_notebook(conf, work_dir):
"""
Test templating is resilient to empty Notebook, no exception.
"""
output_path = join(work_dir, 'out.py')
notebook_path = gen_notebook(cells=[('code', 'print(\'poney\')')], tmp_dir=work_dir,
file_name='test.ipynb', docstring=None)
export_to_script(input_notebook_path=notebook_path, output_path=output_path, conf=conf)
assert exists(output_path)
def test_should_extract_parameters_as_python_params():
"""
Test parameters are extracted from docstring and converted to python format
"""
docstring_str = '''"""
:param str param_one: Param1 description
:param int param2:
:param param3: Param3 description
:param param4:
"""'''
parameters = get_param_as_python_method_format(parse_docstring(docstring_str))
assert parameters == 'param_one, param2, param3, param4'
def test_should_extract_parameters_as_python_command_line_arguments():
"""
Test parameters are extracted from docstring and converted to python command line argument
"""
docstring_str = '''"""
:param str param_one: Param1 description
"""'''
docstring, _ = get_docstring_data(docstring_str)
arguments = get_arguments_from_docstring(docstring)
assert arguments == [
{
'name': 'param-one',
'type': 'str',
'help': 'Param1 description',
'is_list': False
}]
def test_should_extract_arguments_as_parameters():
"""
Test get argument as parameters for python method call
"""
docstring_str = '''"""
:param str param_one: Param1 description
:param int param2:
"""'''
args_as_param = get_arguments_as_param(parse_docstring(docstring_str))
assert args_as_param == 'args.param_one, args.param2'
def test_should_convert_list_param_to_python_arg():
"""
Test convert list param to python arg
"""
repr = ':param list param_one: Param1 description\n'
arguments = get_arguments_from_docstring(parse_docstring(repr))
assert arguments == [{'name': 'param-one',
'type': 'str',
'help': 'Param1 description',
'is_list': True
}]
def test_should_not_raise_if_empty_docstring():
"""
Test do not raise if first code cell with empty docstring
"""
first_code_cell = '''
# Some comments
code = 'some code again'
# And comment
'''
docstring_wrapper = get_data_from_docstring([to_notebook_code_cell(first_code_cell)])
assert docstring_wrapper == DocstringWrapper(docstring='', params='', arguments=[], arg_params='')
def test_should_not_raise_if_no_param_in_docstring():
"""
Test do not raise if no param in docstring
"""
docstring = '''"""
Docstring without param
"""'''
first_code_cell = f'''
# Some comments
{docstring}
code = 'some code again'
# And comment
'''
docstring_wrapper = get_data_from_docstring([to_notebook_code_cell(first_code_cell)])
assert docstring_wrapper == DocstringWrapper(docstring=docstring, params='', arguments=[], arg_params='')
def test_should_extract_docstring_and_params():
"""
Test docstring and parameters are well extracted
"""
docstring = '''"""
:param str param_one: Param1 description
:param int param2:
:param param3: Param3 description
:param param4:
"""'''
docstring_cell = f'''
# Some comments
{docstring}
code = 'some code again'
# And comment
'''
docstring_wrapper = get_data_from_docstring([to_notebook_code_cell(docstring_cell)])
expected_arguments = [
{
'name': 'param-one',
'type': 'str',
'help': 'Param1 description',
'is_list': False
},
{
'name': 'param2',
'type': 'int',
'help': '',
'is_list': False
},
{
'name': 'param3',
'type': None,
'help': 'Param3 description',
'is_list': False
},
{
'name': 'param4',
'type': None,
'help': '',
'is_list': False
}
]
assert docstring_wrapper.params == 'param_one, param2, param3, param4'
assert docstring_wrapper.docstring == docstring.strip('\n')
assert docstring_wrapper.arguments == expected_arguments
assert docstring_wrapper.arg_params == 'args.param_one, args.param2, args.param3, args.param4'
def test_should_detect_no_effect_cell_content():
"""
Test that cell containing #No effect statement are detected
"""
resources = {'ignore_keys': ['# No effect']}
standard_cell = '''
#This is a comment but not a No effect
value = 15
'''
assert not is_no_effect(standard_cell, resources)
no_effect_cell = '''
#This is a comment but not a No effect
# No effect
big_res = big_call()
'''
assert is_no_effect(no_effect_cell, resources)
def test_should_detect_trailing_cell():
"""
Test that trailing cell is detected
trailing cell == no effect or not a code cell
"""
resources = {'ignore_keys': ['# No effect']}
standard_cell = {'cell_type': 'code', 'source': '#A comment but not a No effect\nvalue = 15\n'}
assert not is_trailing_cell(standard_cell, resources)
no_effect_cell = {'cell_type': 'code', 'source': '# No effect\n a = 45\n'}
assert is_trailing_cell(no_effect_cell, resources)
comment = {'cell_type': 'markdown', 'source': '## tile 1\n'}
assert is_trailing_cell(comment, resources)
def test_should_filter_trailing_cell():
"""
Test trailing cells filtering return a new list of filtered cells
"""
standard_cell = {'cell_type': 'code', 'source': '#A comment but not a No effect\nvalue = 15\n'}
no_effect_cell = {'cell_type': 'code', 'source': '# No effect\n a = 45\n'}
comment = {'cell_type': 'markdown', 'source': '## tile 1\n'}
filtered_cells = filter_trailing_cells([standard_cell, no_effect_cell, comment], {'ignore_keys': ['# No effect']})
assert filtered_cells == [standard_cell]
def test_should_get_formatted_cells_removing_no_effect_cells():
"""
Test get formatted code and comment cells
No effect cells must be removed
"""
cells = [
{'cell_type': 'markdown', 'source': '\nA title\n'},
{'cell_type': 'code', 'source': '\nimport os\n'},
{'cell_type': 'code', 'source': '\n# No effect \nimport os\n'}
]
formatted_cells = get_formatted_cells(cells, resource={'ignore_keys': ['# No effect']})
assert formatted_cells == [
['# A title'],
['import os']]
def test_should_get_default_formatted_cells():
"""
Test get default formatted code
"""
cells = []
formatted_cells = get_formatted_cells(cells, resource={'ignore_keys': ['# No effect']})
assert formatted_cells == [['pass']]
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jmilkfansblog/controllers/v1/__init__.py | xiaoyh121/program | 176 | 90605 | <gh_stars>100-1000
from pecan import rest
from wsme import types as wtypes
from jmilkfansblog.api.expose import expose as wsexpose
from jmilkfansblog.controllers.v1 import users
from jmilkfansblog.controllers.v1 import posts
class V1(wtypes.Base):
id = wtypes.text
"""The ID of the version, also acts as the release number"""
@staticmethod
def convert():
v1 = V1()
v1.id = 'v1'
return v1
class Controller(rest.RestController):
"""Version 1 API controller root."""
users = users.UsersController()
posts = posts.PostsController()
@wsexpose(V1)
def get(self):
return V1.convert()
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scripts/data-preparation/ch-sy-datatset.py | wannaphong/attacut | 54 | 153894 | <reponame>wannaphong/attacut
import fire
import numpy as np
from attacut import preprocessing, utils
DATA_PATH = "../slim-cut/data/best"
SYALLABLE_TOKENIZED_DATA = "./data/best-syllabled-tokenized"
CHARACTER_DICT = "./attacut/artifacts/attacut-sc/characters.json"
SYLLABLE_DICT = "./attacut/artifacts/attacut-sc/syllables.json"
def get_actual_filename(path: str) -> str:
return "%s/%s" % (SYALLABLE_TOKENIZED_DATA, path.split("/")[-1])
def prepare_syllable_charater_seq_data(files, ch2ix, sy2ix, sampling=10, output_dir=""):
training, validation = files
if sampling:
training = training[:sampling]
validation = validation[:sampling]
output_dir = "%s/best-syllable-crf-and-character-seq-feature-sampling-%d" % (output_dir, sampling)
print("Saving data to %s" % output_dir)
utils.maybe_create_dir(output_dir)
for name, dataset in zip(("training", "val"), (training, validation)):
print("working on : %s" % name)
fout_txt = open("%s/%s.txt" % (output_dir, name), "w")
try:
for path in dataset:
count = 0
with open(path, "r") as fin, open(path.replace(".txt", ".label"), "r") as flab:
has_space_problem = False
for txt, label in zip(fin, flab):
txt = txt.strip().replace("~~", "~")
if not txt:
continue
label = label.strip()
syllables = txt.split("~")
chars_idx = []
char_labels = []
syllable_idx = []
syllable_indices = list(map(
lambda sy: preprocessing.syllable2ix(sy2ix, sy),
syllables
))
if len(syllables) != len(label):
print(txt, path)
print(len(syllables), len(label))
print(syllables)
print(label)
raise SystemExit("xx")
label = list(label)
for ii, (syllable, six, l) in enumerate(zip(syllables, syllable_indices, label)):
if not syllable:
continue
if syllable == " ":
# next syllable is B, then we should also split this space
if label[ii+1] == "1":
l = "1"
else:
l = "0"
chs = list(
map(
lambda c: preprocessing.character2ix(ch2ix, c),
list(syllable)
)
)
total_chs = len(chs)
syllable_idx.extend([six] * total_chs)
chars_idx.extend(chs)
if l == "1":
char_labels.extend(["1"] + ["0"] * (total_chs-1))
else:
char_labels.extend(["0"] * total_chs)
assert len(char_labels) == len(chars_idx)
# check space problem
if not has_space_problem:
for cix, clb in zip(chars_idx, char_labels):
if cix == 3 and clb == "0":
has_space_problem = True
print(txt)
break
fout_txt.write("%s::%s::%s\n" % (
"".join(char_labels),
" ".join(np.array(chars_idx).astype(str)),
" ".join(np.array(syllable_idx).astype(str)),
))
if has_space_problem:
print("problem with space in %s" % path)
finally:
fout_txt.close()
def main(sampling=10, output_dir="./data"):
with open("%s/training.files" % DATA_PATH, "r") as f:
training_files = []
for l in f:
training_files.append(get_actual_filename(l.strip()))
with open("%s/validation.files" % DATA_PATH, "r") as f:
val_files = []
for l in f:
val_files.append(get_actual_filename(l.strip()))
ch2ix = utils.load_dict(CHARACTER_DICT)
sy2ix = utils.load_dict(SYLLABLE_DICT)
prepare_syllable_charater_seq_data(
(training_files, val_files),
ch2ix,
sy2ix,
sampling=sampling,
output_dir=output_dir
)
if __name__ == "__main__":
fire.Fire(main)
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server/src/tests/samples/generators2.py | mewbak/pyright | 0 | 81381 | # This sample tests various type checking operations relating to
# generator functions that use the "yield from" clause.
from typing import Iterator
class ClassA():
pass
class ClassB():
def shouldContinue(self):
return True
class ClassC():
pass
def generator1() -> Iterator[ClassA]:
yield from generator1()
def generator2() -> Iterator[ClassB]:
# This should generate an error because it yields
# an iterator of the wrong type.
yield from generator1()
# This should also generate an error because it
# yields the wrong type.
yield from 1
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RS_scpFile.py | Ryodor/PythonThiefOfPhotos | 2 | 146975 | <filename>RS_scpFile.py<gh_stars>1-10
import paramiko
#hostname = '10.57.29.175'
hostname = '10.57.29.175'
password = '<PASSWORD>'
username = "coding4"
port = 22
mypath='/Users/alan/Desktop/my_file'
remotepath='/Users/coding4/my_file'
t = paramiko.Transport((hostname, 22))
t.connect(username=username, password=password)
sftp = paramiko.SFTPClient.from_transport(t)
def trasnfer_file(path):
""" Tranfer file in other computer
Args:
path(String): path for tranfer file
"""
sftp.put(path, mypath);
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custom_components/mojio/device_tracker.py | GSzabados/mojio-home-assistant | 1 | 151692 | """Support for Mojio Platform."""
import logging
from mojio_sdk.api import API
import requests
import voluptuous as vol
from homeassistant.components.device_tracker import PLATFORM_SCHEMA
from homeassistant.const import (
CONF_CLIENT_ID,
CONF_CLIENT_SECRET,
CONF_DOMAIN,
CONF_PASSWORD,
CONF_USERNAME,
)
import homeassistant.helpers.config_validation as cv
from homeassistant.helpers.event import track_utc_time_change
_LOGGER = logging.getLogger(__name__)
CONF_INCLUDE = "include"
PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend(
{
vol.Required(CONF_USERNAME): cv.string,
vol.Required(CONF_PASSWORD): cv.string,
vol.Required(CONF_DOMAIN): cv.string,
vol.Required(CONF_CLIENT_ID): cv.string,
vol.Required(CONF_CLIENT_SECRET): cv.string,
vol.Optional(CONF_INCLUDE, default=[]): vol.All(cv.ensure_list, [cv.string]),
}
)
def setup_scanner(hass, config: dict, see, discovery_info=None):
"""Set up the DeviceScanner and check if login is valid."""
scanner = MojioDeviceScanner(config, see)
if not scanner.login(hass):
_LOGGER.error("Mojio authentication failed")
return False
return True
class MojioDeviceScanner:
"""Define a scanner for the Mojio platform."""
def __init__(self, config, see):
"""Initialize MojioDeviceScanner."""
self._include = config.get(CONF_INCLUDE)
self._see = see
self._api = API(
config.get(CONF_DOMAIN),
config.get(CONF_CLIENT_ID),
config.get(CONF_CLIENT_SECRET),
config.get(CONF_USERNAME),
config.get(CONF_PASSWORD),
)
def setup(self, hass):
"""Set up a timer and start gathering devices."""
self._refresh()
track_utc_time_change(hass, lambda now: self._refresh(), minute=range(0, 60, 5))
def login(self, hass):
"""Perform a login on the Mojio API."""
if self._api.login():
self.setup(hass)
return True
return False
def _refresh(self) -> None:
"""Refresh device information from the platform."""
try:
vehicles = self._api.get_vehicles()
for vehicle in vehicles:
self._see(
dev_id=vehicle.licence_plate.replace("-", "_"),
gps=(vehicle.location.latitude, vehicle.location.longitude),
icon="mdi:car",
)
except requests.exceptions.ConnectionError:
_LOGGER.error("ConnectionError: Could not connect to Mojio")
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scripts/pipeline/a06a_submission.py | Iolaum/Phi1337 | 0 | 9833 | import pandas as pd
import numpy as np
import pickle
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from math import sqrt
from sklearn.svm import SVR
from sklearn.svm import LinearSVR
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor
def prune(x):
if x < 1:
return 1
elif x > 3:
return 3
else:
return x
def regression(reg_type, standardize_df, debug=False):
# load model
filename = '../../dataset/model_' + reg_type + '.pickle'
lin_model = None
with open(filename, 'rb') as f:
lin_model = pickle.load(f)
score_df_tst = pd.read_pickle('../../dataset/score_df_final_tst.pickle')
# Fill NaN value
# score_df = score_df.fillna(0.0)
# The last column is the target
X = np.array(score_df_tst)
if standardize_df:
print("Standardizing...")
with open("../../dataset/scaler.pickle", 'rb') as handle:
scaler = pickle.load(handle)
X = scaler.transform(X)
# Debug
if debug:
print("Score DataFrame")
print(score_df)
print("")
print("Training Values")
print(X)
print("")
print("Output Values")
print(Y)
print("")
print("Shapes of X and Y")
print(X.shape)
print(Y.shape)
# Debug
if debug:
print("XTR - XTS")
print(xtr.shape)
print(xts.shape)
print("")
print("YTR - YTS")
print(ytr.shape)
print(yts.shape)
print("")
yts_pred = lin_model.predict(X)
#yts_error = sqrt(mean_squared_error(yts_pred, yts))
print("Prediction by (" + reg_type + ") on Test data have finished")
# create submission file
id_series = pd.read_csv('../../dataset/test.csv')['id']
submission_df = pd.DataFrame(id_series, columns=['id'])
submission_df['relevance'] = yts_pred
submission_df['relevance'] = submission_df['relevance'].map(lambda x: prune(x))
submission_df.to_csv('../../dataset/submission.csv', columns=['id', 'relevance'], index=False)
if __name__ == "__main__":
# Change between:
# svr
# linear
# rfr
regression_type = 'svr'
standardize_df = True
regression(regression_type, standardize_df, debug=False) | [
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features/steps/backup.py | Zierman/tabsave | 0 | 41079 | <gh_stars>0
from io import StringIO
from behave import *
import tabsave
from features.environment import *
@given(u'there was no game save named "{game_save:w}"')
def step_impl(context, game_save):
remove_game_saves(game_save)
assert not game_save_exists(game_save), f'Expected {game_save} not to exist, but it exists.'
use_step_matcher("re")
@when(r'Alex tries to run `tabsave (\w+) ?([^`]*)`')
def step_impl(context, game_save: str, options_str: str):
options = get_argument_list(options_str)
context.add_result(Runnable(tabsave.main, (game_save, *options)))
@when(u'Alex runs `tabsave (\w+) ([^`]+)`')
def step_impl(context, game_save: str, options_str: str):
options = get_argument_list(options_str)
result = context.add_result(Runnable(tabsave.main, (game_save, *options)))
if result.exception is not None:
raise result.exception
@when(u'Alex runs `tabsave (\w+)`')
def step_impl(context, game_save: str):
result = context.add_result(Runnable(tabsave.main, (game_save,)))
if result.exception is not None:
raise result.exception
use_step_matcher("parse")
@then(u'Alex should get an error')
def step_impl(context):
assert get_next_run_result(context).exception is not None, f'Expected an error that was not found.'
@given(u'there was a game save named "{game_save:w}"')
def step_impl(context, game_save: str):
create_game_save(game_save)
assert game_save_exists(game_save), f'{game_save} was missing expected files.'
@then(u'game save "{game_save:w}" will have 1 backup which is a copy of the current save')
def step_impl(context, game_save: str):
backup_dir = TEST_GAME_SAVE_DIR / 'backups' / game_save
count = 0
for path_shallow in backup_dir.iterdir():
count += 1
for path in path_shallow.iterdir():
with open(path, 'r') as f:
contents_in_backup = f.readlines()
with open(TEST_GAME_SAVE_DIR / path.name) as f:
contents_in_active_save = f.readlines()
assert contents_in_backup == contents_in_active_save, f'{path} does not match contents of ' \
f'{TEST_GAME_SAVE_DIR / path.name}.'
assert count == 1, f'Expected 1 backup but found {count}.'
| [
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lambda/lambda_read_pdf_s3_trigger.py | srcecde/aws-tutorial-code | 105 | 1609386 | <filename>lambda/lambda_read_pdf_s3_trigger.py
# -*- coding: utf-8 -*-
__author__ = "<NAME> (Srce Cde)"
__license__ = "MIT"
__email__ = "<EMAIL>"
__maintainer__ = "<NAME> (Srce Cde)"
import json
import boto3
import fitz
def lambda_handler(event, context):
"""Read file from s3 on trigger."""
# boto3 client
s3 = boto3.client("s3")
if event:
file_obj = event["Records"][0]
# fetching bucket name from event
bucketname = str(file_obj["s3"]["bucket"]["name"])
# fetching file name from event
filename = str(file_obj["s3"]["object"]["key"])
# retrieving object from S3
fileObj = s3.get_object(Bucket=bucketname, Key=filename)
# reading botocore stream
file_content = fileObj["Body"].read()
# loading pdf from memory/stream
with fitz.open(stream=file_content, filetype="pdf") as doc:
text = ""
# iterating through pdf file pages
for page in doc:
# fetching & appending text to text variable of each page
text += page.getText()
print(text)
return {"statusCode": 200, "body": json.dumps("Thanks from Srce Cde!")}
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catalog/bindings/gmd/linear_ring.py | NIVANorge/s-enda-playground | 0 | 104072 | <gh_stars>0
from dataclasses import dataclass
from bindings.gmd.linear_ring_type import LinearRingType
__NAMESPACE__ = "http://www.opengis.net/gml"
@dataclass
class LinearRing(LinearRingType):
"""A LinearRing is defined by four or more coordinate tuples, with linear
interpolation between them; the first and last coordinates shall be
coincident.
The number of direct positions in the list shall be at least four.
"""
class Meta:
namespace = "http://www.opengis.net/gml"
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plumcot_prodigy/entity.py | julietteBergoend/plumcot-prodigy | 1 | 28637 | <filename>plumcot_prodigy/entity.py<gh_stars>1-10
import prodigy
from prodigy.components.preprocess import add_tokens, split_sentences
from plumcot_prodigy.custom_loaders import *
import requests
from plumcot_prodigy.video import mkv_to_base64
from typing import Dict, List, Text, Union
from typing_extensions import Literal
import spacy
from pathlib import Path
""" Annotate entity linking (3rd person & names)
Displays sentences with tokens to annotate
If the token is an entity linking, write the name of the corresponding character in the text input of Prodigy
Arguments : ep : episode to annotate (e.g TheWalkingDead.Season01.Episode01),
Start prodigy : prodigy entity_linking entity_data <episode_name> <path_to_corpora> -F plumcot_prodigy/entity.py
"""
def remove_video_before_db(examples: List[Dict]) -> List[Dict]:
"""Remove (heavy) "video" and "pictures" key from examples before saving to Prodigy database
Parameters
----------
examples : list of dict
Examples.
Returns
-------
examples : list of dict
Examples with 'video' or 'pictures' key removed.
"""
for eg in examples:
if "video" in eg:
del eg["video"]
if 'field_suggestions' in eg:
del eg['field_suggestions']
return examples
def entity_linking(episode, user_path):
DATA_PLUMCOT = user_path
show = episode.split('.')[0]
season = episode.split('.')[1]
ep= episode.split('.')[2]
# load episode
episodes_list = [episode]
for episode in episodes_list:
print("\nCurrent Episode :", episode)
# process serie or film
if len(episode.split('.')) == 3:
series, _, _ = episode.split('.')
elif len(episode.split('.')) == 2:
series, _ = episode.split('.')
# load mkv, aligned file & aligned sentences
mkv, aligned, sentences = load_files(series, episode, DATA_PLUMCOT)
# criteria : nouns or pronouns to display automaticaly
pronouns = ["he", "she", "his", "her", "himself", "herself", "him"]
nouns = ["mother", "son", "daughter", "wife", "husband", "father", "uncle", "cousin", "aunt", "brother", "sister"]
# store sentence id with criteria position(s) (PROPN or criteria list)
store_ids = {}
# labels to feed in Prodigy
labels = []
# for each sentence with criteria, store idx and positions
for idx, sentence in enumerate(sentences) :
# store word position with criteria
store_position = []
for token in sentence:
# check if it is a PRON or DET
if str(token).lower() in pronouns :
# check if entity_linking field is empty
if token._.entity_linking == "_" or token._.entity_linking == "?":
store_position.append(str(sentence).split().index(str(token)))
# check if it is a name
if token.pos_ == "PROPN":
if str(token) != "ve" and str(token) != "m" and str(token)[0].isupper():
# check if entity_linking field is empty
if token._.entity_linking == "_" or token._.entity_linking == "?":
store_position.append(str(sentence).split().index(str(token)))
# check if it is in nouns
if str(token).lower() in nouns :
# check if entity_linking field is empty
if token._.entity_linking == "_" or token._.entity_linking == "?":
store_position.append(str(sentence).split().index(str(token)))
# keep sentence id, tokens positions, sentence id
store_ids[idx] = (store_position, sentence)
# process sentences for prodigy
for i,j in store_ids.items():
to_return = {"text":''}
# tokens displayed in Prodigy
tokens = []
# spans for EL
spans = []
# character counter in the text
start = 0
# word id
ids = 0
#if previous != None and nexts != None:
if j[0]:
#print(sentences[i])
to_return['text'] += str(sentences[i])
print(i, to_return['text'])
# get tokens
for word in str(sentences[i]).split(' '):
# do no add +1 to start counter for last word
if "\n" in word :
tokens.append({"text": str(word), "start": start , "end": len(str(word))+start-1, "id": ids, "ws": True})
else :
tokens.append({"text": str(word), "start": start , "end": len(str(word))+start, "id": ids, "ws": True})
start+= len(str(word))+1
# change start token for next word
ids+=1
to_return["tokens"] = tokens
# get spans
for position in j[0]:
for dic in tokens:
if position == dic["id"]:
#print("FIND TOKEN",dic["text"])
#print(dic["start"], dic["end"])
corresponding_token = to_return["text"][dic["start"]:dic["end"]].lower()
spans.append({"start" : dic["start"], "end" : dic["end"], "token_start": position, "token_end":position, "label": "EL1"})
to_return["spans"] = spans
# get characters of the show
with open(f"{DATA_PLUMCOT}/{series}/characters.txt", "r") as f:
speakers = [line.split(",")[0] for line in f.readlines()]
# get video (add context)
try :
if i not in range(0,2):
start_left = sentences[i-2]
end_left = sentences[i]
# beug : left index = last sentence index in the list when current sentence is 0
else:
start_left = None
end_left = sentences[i]
except IndexError:
start_left = None
end_left = None
# video
if start_left != None and end_left != None:
start_time = start_left._.start_time
end_time= end_left._.end_time +0.1
else:
start_time = j[1]._.start_time
end_time = j[1]._.end_time +0.1
# corresponding video excerpt
video_excerpt = mkv_to_base64(mkv, start_time, end_time+0.01)
to_return["field_id"] = "entity",
to_return["field_placeholder"] = "firstname_lastname",
to_return["field_rows"] = 1,
to_return["field_suggestions"] = speakers
to_return["sentence"] = str(j[1])
to_return["start_time"] = j[1]._.start_time
to_return["end_time"] = j[1]._.end_time
to_return["meta"] = {"episode": episode, "sentence_id": f"{i}", "processing":f"{i}/{len(sentences)}"}
to_return["video"] = video_excerpt
yield to_return
@prodigy.recipe("entity_linking",
dataset=("The dataset to save to", "positional", None, str),
episode=("Episode to annotate (e.g : TheWalkingDead.Season01.Episode01", "positional", None, str),
user_path=("Path to Plumcot corpora", "positional", None, str),
)
def addresse(dataset: Text, episode: Text, user_path: Text) -> Dict:
blocks = [
{"view_id": "audio"}, {"view_id": "relations"},{"view_id": "text_input", "field_id": "input_1", "field_placeholder":"Type here for EL1..."},{"view_id": "text_input", "field_id": "input_2", "field_placeholder":"Type here for EL2..."}, {"view_id": "text_input", "field_id": "input_3", "field_placeholder":"Type here for EL3..."},
]
stream = entity_linking(episode, user_path)
return {
"dataset": dataset,
"view_id": "blocks",
"stream": stream,
"before_db": remove_video_before_db,
"config": {
"blocks": blocks,
"wrap_relations" : True, # apply line breaks
"hide_newlines": True,
"audio_loop": False,
"audio_autoplay": True,
"show_audio_minimap": False,
"show_audio_timeline": False,
"show_audio_cursor": False,
"relations_span_labels" : ["EL1", "EL2", "EL3"],
"field_autofocus" : False,
},
}
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] |
streams/binary.py | kevdes/videoparser | 0 | 83012 | """ Class to read binary information using various predefined types. """
#
# Copyright (c) 2007 <NAME> <<EMAIL>>
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# 2. The name of the author may not be used to endorse or promote products
# derived from this software without specific prior written permission
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
# OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
# IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
# NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
# THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# Only implement required information to retrieve video and audio information
import datetime
import struct
import cStringIO
from videoparser.streams import endian
class BinaryStream(object):
def __init__(self, fileobj, filesize, endianess=endian.little):
self._endianess = endianess
self._fileobj = fileobj
self._filesize = filesize
def __del__(self):
self.close()
def read(self, length):
if not length:
return ''
return self._fileobj.read(length)
def tell(self):
return self._fileobj.tell()
def seek(self, position):
return self._fileobj.seek(position)
def close(self):
return self._fileobj.close()
def bytes_left(self):
return self._fileobj.tell() < self._filesize
def set_endianess(self, endianess):
self._endianess = endianess
def get_endianess(self):
return self._endianess
def unpack(self, type, length):
""" Shorthand for unpack which uses the endianess defined with
set_endianess(), used internally."""
data = self.read(length)
assert len(data) == length, "Unexpected end of stream"
try:
if self._endianess == endian.big:
return struct.unpack('>' + type, data)[0]
else:
return struct.unpack('<' + type, data)[0]
except struct.error:
print len(data)
print "Unable to unpack '%r'" % data
raise
def read_float(self):
""" Read a 32bit float."""
return self.unpack('f', 4)
def read_qtfloat_32(self):
""" Read a 32bits quicktime float."""
# This comes from hachoir
return self.read_int16() + float(self.read_uint16()) /65535
def read_qt_ufloat32(self):
""" Read a 32bits quicktime float."""
# This comes from hachoir
return self.read_uint16() + float(self.read_uint16()) /65535
def read_uint64(self):
""" Read an unsigned 64bit integer."""
return self.unpack('Q', 8)
def read_int64(self):
""" Read an signed 64bit integer."""
return self.unpack('q', 4)
def read_uint32(self):
""" Read an unsigned 32bit integer."""
return self.unpack('I', 4)
def read_int32(self):
""" Read an signed 32bit integer."""
return self.unpack('i', 4)
def read_uint16(self):
""" Read an unsigned 16bit integer."""
return self.unpack('H', 2)
def read_int16(self):
""" Read an signed 16bit integer."""
return self.unpack('h', 2)
def read_uint8(self):
""" Read an unsigned 8bit integer."""
return ord(self.read(1))
def read_int8(self):
""" Read a signed 8bit integer."""
return struct.unpack('b', self.read(1))[0]
def read_dword(self):
return self.read(4)
def read_word(self):
return self.read(2)
def read_qword(self):
return self.read(8)
def read_byte(self):
return self.read(1)
def read_fourcc(self):
return self.read(4)
def read_timestamp_mac(self):
""" Read a timestamp in mac format (seconds sinds 1904) """
timestamp_base = datetime.datetime(1904, 1, 1, 0, 0)
timestamp_value = datetime.timedelta(seconds=self.read_uint32())
return timestamp_base + timestamp_value
def read_timestamp_win(self):
timestamp_base = datetime.datetime(1601, 1, 1, 0, 0, 0)
timestamp_value = datetime.timedelta(
microseconds=self.read_uint64()/10)
return timestamp_base + timestamp_value
# TODO: FIXME
def read_wchars(self, len, null_terminated=False):
data = self.read(len * 2)
# String is null terminated, remove the null char
if null_terminated:
data = data[:-2]
if self._endianess == endian.big:
return unicode(data, "UTF-16-BE")
else:
return unicode(data, "UTF-16-LE")
def read_subsegment(self, length):
data = self.read(length)
return BinaryStream(cStringIO.StringIO(data), len(data),
self._endianess)
def convert_uintvar(self, data, endianess=None):
""" Convert a string of variable length to an integer """
# using struct.unpack is twice as fast as this function, however
# it's not flexible enough
if endianess is None:
endianess = self._endianess
if endianess == endian.big:
data = data[::-1]
mask = 0
value = ord(data[0])
for octet in data[1:]:
mask += 8
value += (ord(octet) << mask)
return value
# ASF Specification requires the guid type, which is 128 bits aka 16 bytes
def read_guid(self):
# See http://www.ietf.org/rfc/rfc4122.txt for specification
# The version number is in the most significant 4 bits of the time
# stamp (bits 4 through 7 of the time_hi_and_version field).
# Python 2.5 includes a built-in guid module, which should be used
# retrieve version
position = self.tell()
self.seek(position + 6)
version = self.read_uint16() >> 12
self.seek(position)
#print repr([hex(ord(x)) for x in self.read(16)])
self.seek(position)
time_low = self.read_uint32()
time_mid = self.read_uint16()
time_hi = self.read_uint16()
clock_seq_hi = self.read_uint8()
clock_seq_low = self.read_uint8()
node = self.read(6)
#print "uuid version = %d - %X" % (version, time_low)
if version == 1:
node = self.convert_uintvar(node, endian.big)
else:
node = self.convert_uintvar(node, endian.big)
return "%08X-%04X-%04X-%X%X-%012X" % (time_low,
time_mid,
time_hi,
clock_seq_hi,
clock_seq_low,
node)
def read_waveformatex(self):
obj = self.WAVEFORMATEX()
obj.codec_id = self.read_uint16()
obj.channels = self.read_uint16()
obj.sample_rate = self.read_uint32()
obj.bit_rate = self.read_uint32()
obj.block_alignment = self.read_uint16()
obj.bits_per_sample = self.read_uint16()
obj.codec_size = self.read_uint16()
obj.codec_data = self.read_subsegment(obj.codec_size)
return obj
def read_bitmapinfoheader(self):
obj = self.BITMAPINFOHEADER()
obj.format_data_size = self.read_uint32()
obj.image_width = self.read_uint32()
obj.image_height = self.read_uint32()
obj.reserved = self.read_uint16()
obj.bpp = self.read_uint16()
obj.compression_id = self.read(4)
obj.image_size = self.read_uint32()
obj.h_pixels_meter = self.read_uint32()
obj.v_pixels_meter = self.read_uint32()
obj.colors = self.read_uint32()
obj.important_colors = self.read_uint32()
obj.codec_data = self.read_subsegment(obj.format_data_size -
40)
return obj
class BITMAPINFOHEADER(object):
def __repr__(self):
buffer = "BITMAPINFOHEADER structure: \n"
buffer += " %-35s : %s\n" % ("Format Data Size", self.format_data_size)
buffer += " %-35s : %s\n" % ("Image Width", self.image_width)
buffer += " %-35s : %s\n" % ("Image Height", self.image_height)
buffer += " %-35s : %s\n" % ("Reserved", self.reserved)
buffer += " %-35s : %s\n" % ("Bits Per Pixel Count", self.bpp)
buffer += " %-35s : %s\n" % ("Compression ID", self.compression_id)
buffer += " %-35s : %s\n" % ("Image Size", self.image_size)
buffer += " %-35s : %s\n" % ("Horizontal Pixels Per Meter", self.h_pixels_meter)
buffer += " %-35s : %s\n" % ("Vertical Pixels Per Meter", self.v_pixels_meter)
buffer += " %-35s : %s\n" % ("Colors Used Count", self.colors)
buffer += " %-35s : %s\n" % ("Important Colors Count", self.important_colors)
buffer += " %-35s : %s\n" % ("Codec Specific Data", self.codec_data)
return buffer
# Used in ASF and AVI parser, contains audio information
class WAVEFORMATEX(object):
codec_ids = {
0x2004: "A_REAL/COOK",
0x2003: "A_REAL/28_8",
0x2002: "A_REAL/14_4",
0x0130: "A_REAL/SIPR",
0x0270: "A_REAL/ATRC",
0x2001: "A_DTS",
0x2000: "A_AC3",
0x162: "WMAP",
0x161: "WMA2",
0x160: "WMA2",
0x50: "MP2",
0x55: "MP3",
0x1: "A_PCM/INT/LIT",
'unknown': "???",
}
def __repr__(self):
buffer = "WAVEFORMATEX structure: \n"
buffer += " %-35s : %s\n" % ("Codec ID / Format Tag", self.codec_id)
buffer += " %-35s : %s\n" % ("Number of Channels", self.channels)
buffer += " %-35s : %s\n" % ("Samples Per Second", self.sample_rate)
buffer += " %-35s : %s\n" % ("Average Number of Bytes Per Second", self.bit_rate)
buffer += " %-35s : %s\n" % ("Block Alignment", self.block_alignment)
buffer += " %-35s : %s\n" % ("Bits Per Sample", self.bits_per_sample)
buffer += " %-35s : %s\n" % ("Codec Specific Data Size",self.codec_size)
buffer += " %-35s : %s\n" % ("Codec Specific Data", repr(self.codec_data))
return buffer
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] |
netbox_tunnels/views.py | sourcery-ai-bot/netbox-tunnels-plugin | 21 | 96756 | """Django views for network tunnels.
(c) 2020 <NAME>
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from django.contrib.auth.mixins import PermissionRequiredMixin
from utilities.views import BulkDeleteView, BulkImportView, ObjectEditView, ObjectListView
from .filters import TunnelFilter
from .forms import TunnelCreationForm, TunnelFilterForm, TunnelCreationCSVForm
from .models import Tunnel
from .tables import TunnelTable, TunnelBulkTable
class ListTunnelView(PermissionRequiredMixin, ObjectListView):
"""View for listing all Tunnels."""
permission_required = "netbox_tunnels.view_tunnels"
model = Tunnel
queryset = Tunnel.objects.all().order_by("tunnel_id")
filterset = TunnelFilter
filterset_form = TunnelFilterForm
table = TunnelTable
template_name = "netbox_tunnels/tunnels_list.html"
class CreateTunnelView(PermissionRequiredMixin, ObjectEditView):
"""View for creating a new Tunnels."""
permission_required = "netbox_tunnels.add_tunnels"
model = Tunnel
queryset = Tunnel.objects.all()
model_form = TunnelCreationForm
template_name = "netbox_tunnels/tunnel_edit.html"
default_return_url = "plugins:netbox_tunnels:tunnels_list"
class BulkDeleteTunnelView(PermissionRequiredMixin, BulkDeleteView):
"""View for deleting one or more Tunnels."""
permission_required = "netbox_tunnels.delete_tunnels"
queryset = Tunnel.objects.filter()
table = TunnelTable
default_return_url = "plugins:netbox_tunnels:tunnels_list"
class BulkImportTunnelView(PermissionRequiredMixin, BulkImportView):
"""View for bulk-importing a CSV file to create Tunnels."""
permission_required = "netbox_tunnels.add_tunnels"
model_form = TunnelCreationCSVForm
tunnel = TunnelBulkTable
default_return_url = "plugins:netbox_tunnels:tunnels_list"
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] |
jifen/fuhe.py | Tigeraus/MA | 0 | 1607570 | <filename>jifen/fuhe.py
#coding: utf-8
import math
def int_NC(xlrange,ylist):
sum = xlrange[1]-xlrange[0]
n = len(ylist)
if n == 2:
xishulist=[0.5,0.5]
elif n == 3:
xishulist=[1.0/6.0,2.0/3.0,1.0/6.0]
elif n == 4:
xishulist=[1.0/8.0,3.0/8.0,3.0/8.0,1.0/8.0]
elif n == 5:
xishulist=[7.0/90, 16.0/45, 2.0/15, 16.0/45, 7.0/90]
elif n == 6:
xishulist=[19.0/288, 25.0/96, 25.0/144, 25.0/144, 25.0/96, 19.0/288]
elif n == 7:
xishulist=[41.0/840, 9.0/35, 9.0/280, 34.0/105, 9.0/280, 9.0/35, 41.0/840]
elif n == 8:
pass
elif n == 9:
pass
else:
print 'err'
return 0
for i in range(len(xishulist)):
sum = sum + xishulist[i]*ylist[i]
return sum
def main():
xr = [0,1]
ylist = [1,2,4,5,7,-3]
print int_NC(xr,ylist)
pass
if __name__ == '__main__':
main() | [
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2
] |
tests/test_mr_homologs.py | rigdenlab/MrParse | 0 | 69679 | <filename>tests/test_mr_homologs.py
#!/usr/bin/env ccp4-python
import set_mrparse_path
import pytest
import logging
from mrparse.mr_sequence import Sequence
from mrparse.mr_hkl import HklInfo
from mrparse.mr_homolog import homologs_from_hits, calculate_ellg, ellg_data_from_phaser_log
logging.basicConfig(level=logging.DEBUG)
def test_2uvoA_homologs(test_data, get_2uvo_test_hits):
hits = get_2uvo_test_hits
homologs = homologs_from_hits(hits, pdb_dir=test_data.pdb_dir)
h = homologs['1iqb_B_1']
assert h.length == 77
assert abs(h.molecular_weight - 8602) < 0.001
assert h.pdb_id == '1iqb'
assert h.resolution == 1.90
chain_id = 'B'
assert h.chain_id == chain_id
d = h.static_dict
assert d['chain_id'] == chain_id
@pytest.mark.skip(reason="Tests using phaser are currently too slow to run")
def test_2uvoA_homologs_ellg(test_data, get_2uvo_test_hits):
hits = get_2uvo_test_hits
homologs = homologs_from_hits(hits, pdb_dir=test_data.pdb_dir)
h = homologs['1iqb_B_1']
assert h.length == 77
assert abs(h.molecular_weight - 8602) < 0.001
seq_info = Sequence(test_data.x2uvoA_fasta)
hkl_info = HklInfo(test_data.x2uvo_mtz, seq_info=seq_info)
calculate_ellg(homologs, hkl_info)
def test_phaser_log_ellg(test_data, get_2uvo_test_hits):
"""Test the output from phaser without running it every time"""
hits = get_2uvo_test_hits
homologs = homologs_from_hits(hits, pdb_dir=test_data.pdb_dir)
ellg_data = ellg_data_from_phaser_log(test_data.phaser_log, homologs)
assert ellg_data['2x3t_C_1'].ellg == 1378.4
assert ellg_data['2x3t_C_1'].length == 168
assert ellg_data['2x3t_C_1'].molecular_weight == pytest.approx(16483)
if __name__ == '__main__':
import sys
pytest.main([__file__] + sys.argv[1:])
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src/crawler.py | GamjaPower/berlinstartupjobs | 0 | 97147 | # -*- coding: utf-8 -*-
'''
Created on 2018. 9. 17.
@author: jason96
'''
from selenium import webdriver
from pyutil.fileutil import write_file
import time
def crawl():
target_urls = []
try:
for page in range(1, 18):
base_url = 'http://berlinstartupjobs.com/engineering/'
if page != 1:
base_url = base_url + '/page/' + str(page)
driver = webdriver.Chrome('../util/chromedriver')
driver.get(base_url)
xpath = '//div[@class="product-listing-item"]'
for elem in driver.find_elements_by_xpath(xpath):
href = elem.find_element_by_class_name('product-listing-h2')\
.find_element_by_tag_name('a')\
.get_attribute('href')
target_urls.append(href)
driver.close()
time.sleep(1)
finally:
write_file('urls.txt', '\n'.join(target_urls))
if __name__ == '__main__':
crawl()
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580,
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] |
cmds/clear_spam.py | Asperger/PCRD-DiscordBot | 2 | 49835 | <reponame>Asperger/PCRD-DiscordBot
from utils.log import FileLogger
from utils.spammer import clear_spammer, get_spammer
from utils.cmds_registry import register
register(cmd="clear_spam", alias="clear_spam")
register(cmd="clear_spam", alias="cs")
class clear_spam:
def __init__(self):
self.usage = '!clear_spam <指令> [編號]\n指定編號將移除該反應\n不指定將移除該指令及所有反應'
self.auth_warning = '只有公會的管理員或作者才能使用這個功能'
self.request = ""
self.order = -1
def check_param(self, param):
if len(param) > 0:
self.request = param[0]
if len(param) == 2:
if param[1].isdigit():
self.order = int(param[1])
return True
else:
return False
else:
return len(param) == 1
def check_auth(self, auth):
author = get_spammer(self.request, self.order)[1]
return auth['user_admin'] or auth['user_id'] == author
def run(self, user_auth, param):
result = clear_spammer(self.request, self.order)
if result:
return f'{self.request} 指令清除成功'
else:
return f'{self.request} 指令清除失敗'
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src/importer/excel/excelImporter.py | HenrikPilz/BMEcatConverter | 1 | 189207 | '''
Created on 11.05.2017
@author: henrik.pilz
'''
import logging
from openpyxl import load_workbook
import regex
from datamodel import Feature
from datamodel import FeatureSet
from datamodel import Mime
from datamodel import Price
from datamodel import PriceDetails
from datamodel import Product
from error import FormulaFoundException
from error import NumberFormatException
from transformer import SeparatorTransformer
class ExcelImporter(object):
'''
classdocs
'''
# Namen der mögliche Tabellen mit Artikeln
__allowedTablenames = [ 'Artikel', 'Tabelle1', 'Mapping-Master' ]
# Grunddaten eines Artikels. Mindestens die Artikelnummer
__basefieldMapping = {
# Produktrumpf
"supplierArticleId" : "productId"
}
# Artikeldetails
__productDetailMapping = {
# Produktdetails
"descriptionShort" : "title",
"descriptionLong" : "description",
"ean" : "ean",
"supplierAltId" : "supplierAltId",
"buyerId" : "buyerId",
"manufacturerArticleId" : "manufacturerArticleId",
"manufacturerName" : "manufacturerName",
"deliveryTime" : "deliveryTime",
"delivery_time" : "deliveryTime"
}
# Bestelldetails
__orderDetailMapping = {
# Order Details
"orderUnit" : "orderUnit",
"contentUnit" : "contentUnit",
"ContentUnit" : "contentUnit",
"packingQuantity" : "packingQuantity",
"priceQuantity" : "priceQuantity",
"quantityMin" : "quantityMin",
"quantityInterval" : "quantityInterval"
}
# technische Daten
__featureMapping = {
"attribute_name" : "name",
"attributeName" : "name",
"attribute_value" : "values",
"attributeValue" : "values",
"attribute_unit" : "unit",
"attributeUnit" : "unit"
}
# Preisdaten
__priceMapping = {
"priceType" : "priceType",
"price_type" : "priceType",
"priceAmount" : "amount",
"price_amount" : "amount",
"tax" : "tax",
"lowerBound" : "lowerBound",
"lower_bound" : "lowerBound",
"factor" : "factor",
"territory" : "territory",
"currency" : "currency"
}
# Bilddaten
__mimeMapping = {
"mimeType" : "mimeType",
"mime_type" : "mimeType",
"mimeSource" : "source",
"mime_source" : "source",
"mimeDescription" : "description",
"mime_description" : "description",
"mimeAlt" : "altenativeContent",
"mime_alt" : "altenativeContent",
"mimePurpose" : "purpose",
"mime_purpose" : "purpose",
"mimeOrder" : "order",
"mime_order" : "order"
}
__fieldsToTransform = [ "priceAmount", "price_amount", "amount", "tax", "factor", "delivery_time", "deliveryTime" ]
def __init__(self, separatorTransformer=SeparatorTransformer("detect")):
'''
Constructor
'''
self.__indexForProduct = {}
self.__indexForProductDetails = {}
self.__indexForOrderDetails = {}
self.__indexPairsForFeatures = {}
self.__indexTuplesForPrices = {}
self.__indexTuplesForMimes = {}
self.__currentRowIndex = 1
self.__currentSheet = None
self._separatorTransformer = separatorTransformer
self.articles = []
def readWorkbook(self, filename):
wb = load_workbook(filename)
countPossibleCandidates = 0
tablename = None
for allowedSheetname in self.__allowedTablenames:
if allowedSheetname in wb.sheetnames:
countPossibleCandidates += 1
tablename = allowedSheetname
if tablename is None:
raise Exception("Das Tabellenblatt mit den Artikelnamen sollte einen der folgenden Namen tragen: '{0}'".format(", ".join(self.__allowedTablenames)))
if countPossibleCandidates > 1:
raise Exception("Es darf nur ein Tabellenblatt mit den folgenden Artikelnamen existieren: '{0}'".format(", ".join(self.__allowedTablenames)))
self.__currentSheet = wb[tablename]
self.__determineIndexMappings()
self.__readArticles()
def __determineIndexMappings(self):
# gehe durch alle Spalten in Zeile 1 (Headerzeile)
for colIndex in range(1, self.__currentSheet.max_column + 1):
# hole den aktuellen Feldnamen
currentFieldname = self.__currentSheet.cell(column=colIndex, row=1).value
# wenn der Feldnam nicht leer ist
if currentFieldname is not None and len(currentFieldname.strip()) > 0:
self.__detectEntities(currentFieldname, colIndex)
def __detectEntities(self, currentFieldname, colIndex):
# gib ihn aus
logging.debug(currentFieldname)
# ist er in den basefieldKeys (Grunddatenfelder des Artikels)
added = self.__addIndexForMappingIfFieldInMapping(currentFieldname, colIndex, self.__basefieldMapping, self.__indexForProduct)
# ist er product detail relevant
added = added or self.__addIndexForMappingIfFieldInMapping(currentFieldname, colIndex, self.__productDetailMapping, self.__indexForProductDetails)
# bestelldetails
added = added or self.__addIndexForMappingIfFieldInMapping(currentFieldname, colIndex, self.__orderDetailMapping, self.__indexForOrderDetails)
# andere Daten?
if not added:
self.__detectMultiColumnEntities(currentFieldname, colIndex)
def __addIndexForMappingIfFieldInMapping(self, currentFieldname, colIndex, mappingDictionary, indexDictionary):
# ist er in den basefieldKeys (Grunddatenfelder des Artikels)
if currentFieldname in mappingDictionary.keys():
indexDictionary[mappingDictionary[currentFieldname]] = colIndex
return True
return False
def __detectMultiColumnEntities(self, currentFieldname, colIndex):
# dann muessen wir splitten, da der Zahlenanteil
# Sowohl die Zusammengehörigkeit der Daten anzeigt,
# als auch die Reihenfolge bestimmt
fieldName = regex.match("[a-zA-Z_]*", currentFieldname).group(0)
fieldName = fieldName.strip("_")
fieldCount = currentFieldname.replace(fieldName, "").strip("_")
logging.debug("'{0}' : '{1}'".format(fieldName, fieldCount))
# sind es Preise ?
added = self.__addIndexForFieldCountMappingIfFieldInMapping(fieldName, colIndex, fieldCount,
self.__priceMapping, self.__indexTuplesForPrices)
# sind es Bilder ?
added = added or self.__addIndexForFieldCountMappingIfFieldInMapping(fieldName, colIndex, fieldCount,
self.__mimeMapping, self.__indexTuplesForMimes)
# sind es Attribute
added = added or self.__addIndexForFieldCountMappingIfFieldInMapping(fieldName, colIndex, fieldCount,
self.__featureMapping, self.__indexPairsForFeatures)
if not added:
logging.debug("'{0}' : '{1}'".format(fieldName, fieldCount))
def __addIndexForFieldCountMappingIfFieldInMapping(self, currentFieldname, colIndex, fieldCount, mappingDictionary, indexDictionary):
if currentFieldname in mappingDictionary.keys():
self.__setColumnIndexForMapping(indexDictionary, mappingDictionary[currentFieldname], fieldCount, colIndex)
return True
return False
def __setColumnIndexForMapping(self, indexForClassFieldName, classFieldName, fieldCount, colIndex):
if classFieldName not in indexForClassFieldName.keys():
indexForClassFieldName[classFieldName] = { fieldCount : colIndex }
else:
indexForClassFieldName[classFieldName][fieldCount] = colIndex
def __readArticles(self):
for rowIndex in range(2, self.__currentSheet.max_row + 1):
self.__currentRowIndex = rowIndex
self.articles.append(self.__createProduct())
def __createProduct(self):
currentProduct = Product()
self.__transferInformationForMapping(self.__indexForProduct, currentProduct)
currentProduct.addDetails()
try:
self.__transferInformationForMapping(self.__indexForProductDetails, currentProduct.details)
currentProduct.addOrderDetails()
self.__transferInformationForMapping(self.__indexForOrderDetails, currentProduct.orderDetails)
self.__addMultipleOrderedObjects(self.__indexTuplesForMimes, currentProduct, Mime)
priceDetails = PriceDetails()
self.__addMultipleOrderedObjects(self.__indexTuplesForPrices, priceDetails, Price)
currentProduct.addPriceDetails(priceDetails, raiseException=False)
featureSet = FeatureSet()
self.__addMultipleOrderedObjects(self.__indexPairsForFeatures, featureSet, Feature)
currentProduct.addFeatureSet(featureSet)
currentProduct.validate(raiseException=False)
except FormulaFoundException as ffe:
raise FormulaFoundException("{0}: {1}".format(currentProduct.productId, str(ffe)))
return currentProduct
def __transferInformationForMapping(self, mapping, objectForValue):
"""
Überträgt die Informationen vom Objekt über das angegebene Mapping für das Produkt in der Zeile.
"""
for fieldname, colIndex in mapping.items():
try:
self.__determineAndAddValue(colIndex, objectForValue, fieldname)
except NumberFormatException as e:
raise NumberFormatException("Zeile: {0}/Spalte {1}; '{2}' Fehler: {3}".format(self.__currentRowIndex, colIndex,
fieldname, str(e)))
def __addMultipleOrderedObjects(self, mapping, objectContainer, typeOfMultiples):
"""
Fügt mehrere Objekte zum Objektcontainer hinzu
"""
itemsToAddByOrder = {}
for fieldname in mapping.keys():
self.__setValueForFieldNameAndTransformDecimalsIfNeeded(mapping, typeOfMultiples, itemsToAddByOrder, fieldname)
self.__sortAndAddMultipleOrderedObjects(objectContainer, typeOfMultiples, itemsToAddByOrder)
def __setValueForFieldNameAndTransformDecimalsIfNeeded(self, mapping, typeOfMultiples, itemsToAddByOrder, fieldname):
for order, colIndex in mapping[fieldname].items():
try:
if order not in itemsToAddByOrder.keys():
itemsToAddByOrder[order] = typeOfMultiples()
self.__determineAndAddValue(colIndex, itemsToAddByOrder[order], fieldname)
except NumberFormatException as nfe:
raise NumberFormatException("Zeile: {0}/Spalte {1}; '{2}{4}' Fehler: {3}".format(self.__currentRowIndex, colIndex,
fieldname, str(nfe), order))
except Exception as e:
raise Exception("Zeile: {0}/Spalte {1}; '{2}{4}' Fehler: {3}".format(self.__currentRowIndex, colIndex,
fieldname, str(e), order))
def __determineAndAddValue(self, colIndex, objectForValue, fieldname):
value = self.__currentSheet.cell(column=colIndex, row=self.__currentRowIndex).value
value = self.__transformValueIfSupposedTo(fieldname, value)
if value is not None and len(str(value)) > 0:
objectForValue.add(fieldname, value)
def __transformValueIfSupposedTo(self, fieldname, value):
if fieldname in self.__fieldsToTransform:
return self._separatorTransformer.transform(value)
return value
def __sortAndAddMultipleOrderedObjects(self, objectContainer, typeOfMultiples, itemsToAddByOrder):
for key in sorted(itemsToAddByOrder.keys()):
item = itemsToAddByOrder[key]
self.__setOrderIfNotSet(item, key)
self.__exectueAddMethod(objectContainer, "add" + str(typeOfMultiples.__name__), item)
def __setOrderIfNotSet(self, objectWithOrder, order):
try:
if getattr(objectWithOrder, "order") is None:
setattr(objectWithOrder, "order", int(order))
except AttributeError:
logging.debug("Order Attribute could not be set for '{0}'.".format(objectWithOrder.__class__.__name__))
def __exectueAddMethod(self, objectWithAddMethod , addMethodName, arg):
try:
elementHandler = getattr(objectWithAddMethod, addMethodName)
elementHandler(arg)
except AttributeError:
raise NotImplementedError("Class [" + objectWithAddMethod.__class__.__name__ + "] does not implement [" + addMethodName + "]")
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] |
pdil/core/findNode.py | patcorwin/fossil | 41 | 97321 | <gh_stars>10-100
'''
These function find different nodes, but generally will NOT make any if they do not exist,
the `getNodes` does that. This lets all the lib modules find if needed, and only
the portions that actually need to make objects can do so.
'''
from __future__ import print_function, absolute_import
import json
from pymel.core import cmds, PyNode, warning, objExists, ls, joint
from ..add import *
from .. import nodeApi
def getRoot(nodes=None, make=None):
'''
Returns the root bone, trying to account for case and namespaces or None if
not found. `make` should be either 'root' or 'weaponRoot', specifying which
to make (always top level) if a root is not found.
Can be given a list of nodes to search for the root,
'''
names = [ 'b_root', 'b_Root', 'Rig:b_root', 'Rig:b_Root', 'b_weaponRoot', 'Rig:b_weaponRoot' ]
if not nodes:
# Check if any exist top level first
top = ls(assemblies=True)
for name in names:
if name in top:
return PyNode('|' + name)
# See if there is a top level obj in a namespace named b_root of any casing.
searchNodes = nodes if nodes else ls( assemblies=True )
nodes = [obj for obj in searchNodes if simpleName( obj ).lower() == 'b_root' or simpleName(obj).lower() == 'b_weaponroot']
if len(nodes) == 1:
return nodes[0]
# Then check if any exist at all (which will fail if several exist in separate groups).
for name in names:
if objExists( name ):
return PyNode( name )
if make:
return joint(None, n='b_' + make)
else:
return None
def controllers(main=None):
'''
Returns all the animation controllers in the scene.
.. todo:: Add the shapes that have ik/fk switching on them
'''
''' Timing experiments: Results, cmds + sets is WAY faster!
s = time.time()
controls = [c for c in listRelatives(main, ad=True, type='transform') if c.hasAttr('fossilCtrlType')]
print( time.time() - s, 'ls(*.fossilCtrlType) ORIGINAL' )
s = time.time()
controls = [PyNode(c) for c in cmds.listRelatives(main.name(), ad=True, type='transform', f=1) if cmds.attributeQuery('fossilCtrlType', node=c, ex=True)]
print( time.time() - s, 'cmds.listRelatives() list comp' )
s = time.time()
#controls = [c for c in listRelatives(main, ad=True, type='transform') if c.hasAttr('fossilCtrlType')]
allControls = set(cmds.ls('*.fossilCtrlType', type='transform', r=True, o=True))
allControls.intersection_update( cmds.listRelatives(main.name(), ad=True, type='transform') )
controls = [PyNode(o) for o in allControls]
print( time.time() - s, 'cmds and set filtering' )
Swift Cat as main:
0.611000061035 ls(*.fossilCtrlType) ORIGINAL
0.123000144958 cmds.listRelatives() list comp
0.0439999103546 cmds and set filtering
Swift as Main:
2.1210000515 ls(*.fossilCtrlType) ORIGINAL
0.398999929428 cmds.listRelatives() list comp
0.0769999027252 cmds and set filtering
'''
allControls = set( cmds.ls( '*.fossilCtrlType', o=True, r=True, l=True ) )
if main:
mainTransforms = cmds.listRelatives(main.name(), ad=True, type='transform', f=True)
if mainTransforms:
allControls.intersection_update( mainTransforms )
controls = [PyNode(o) for o in allControls]
controls.append(main)
root = rootMotion(main=main)
if root:
controls.append(root)
return controls
else:
warning("No sub controls found for {0}".format(main.name()))
else:
controls = [PyNode(o) for o in allControls]
main = mainGroup()
if main:
controls.append(main)
root = rootMotion(main=main)
if root:
controls.append(root)
return controls
return []
MAIN_CONTROL_TAG = 'fossilMainControl'
def mainGroup(nodePool=None):
'''
Returns the main group containing the rig, named "main", or an object tagged as the main.
todo: This logic should mainly go into `mainControls`, and this just returns the first.
'''
if nodePool:
for n in nodePool:
if simpleName(n) == 'main' or n.hasAttr(MAIN_CONTROL_TAG):
return n
if objExists('|main'):
return PyNode('|main')
else:
# A pymel bug is sometimes returning duplicate nodes
main = list(set([ obj for obj in ls( 'main', r=True) if not obj.getParent() ]))
if len(main) == 1:
return main[0]
# No "main" ojbect was found, looking for tags
plugs = ls('*.' + MAIN_CONTROL_TAG)
if plugs:
return plugs[0].node()
return None
def mainControls():
'''
Returns all the main controls in the scene, ake the main of each character.
todo: see mainGroup, take most of the logic.
'''
return ls( '*.' + MAIN_CONTROL_TAG, o=True, r=True )
def tagAsMain(obj):
obj.addAttr(MAIN_CONTROL_TAG, at='message')
def leadController(obj):
'''
Given a controller, return the main RigController (possibly itself) or
None if not found.
.. todo::
This needs to be merged with the same one in skeletonTool.rig
'''
# &&& Replace this with actual node class references when nodeApi is moved
if isinstance(obj, nodeApi.RigController):
return obj
else:
objs = [ o for o in obj.message.listConnections() if isinstance(o, nodeApi.RigController) ]
if objs:
return objs[0]
return None
def rootMotion(main=None):
'''
Returns the root motion, optionally making one if it doesn't exist.
If main is specified, search its descendents.
'''
''' Timing test with Swift as main, re and split are about equal and 200x faster!
re and split were almost identical, even on the whole scene.
s = time.time()
#oldGetRootMotion(main)
for child in listRelatives(main, ad=True, type='transform'):
#for child in ls(type='transform'):
if lib.dagObj.simpleName( child ) == 'rootMotion':
break
#return child
print( time.time() - s, 'orig')
s = time.time()
for child in cmds.listRelatives(main.name(), ad=True, type='transform'):
#for child in cmds.ls(type='transform'):
if child.rsplit('|',1)[-1].rsplit(':', 1)[-1] == 'rootMotion':
#print( child)
break
print( time.time() - s, 'split')
s = time.time()
simpleName = re.compile( '\w+$' )
for child in cmds.listRelatives(main.name(), ad=True, type='transform'):
#for child in cmds.ls(type='transform'):
if simpleName.search(child).group(0) == 'rootMotion':
#print( child)
break
print( time.time() - s, 're')
'''
if main:
children = cmds.listRelatives(main.name(), ad=True, type='transform', f=True)
if children: # cmds returns None instead of emtpy list
for child in children:
if child.rsplit('|', 1)[-1].rsplit(':', 1)[-1] == 'rootMotion':
return PyNode(child)
return None
if objExists( 'rootMotion' ):
return PyNode( 'rootMotion' )
else:
rms = ls( 'rootMotion', r=True )
if len(rms) == 1:
return rms[0]
return None
def allCards(main=None):
'''
Return all the cards, optionally taking a specific skeletonBlueprint.
'''
# Use cmds for speed (swift takes 0.20 with pymel but 0.04 with cmds)
targetCards = set(
cmds.ls( '*.skeletonInfo', o=True, r=True, l=True ) +
cmds.ls( '*.fossilRigData', o=True, r=True, l=True )
)
if main:
targetCards.intersection_update( cmds.listRelatives(main.name(), ad=True, type='transform', f=True) )
def order(obj):
try:
return cmds.getAttr(obj + '.buildOrder')
except Exception:
pass
try:
return json.loads(cmds.getAttr(obj + '.fossilRigData')).get('buildOrder', 10)
except Exception:
pass
return 10
return [PyNode(c) for c in sorted(targetCards, key=order)]
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Qasc.py | atranel/resqdb | 0 | 183943 | <gh_stars>0
from resqdb.Connection import Connection
from resqdb.functions import save_file
from datetime import datetime
import pandas as pd
import logging
import os
from pptx import Presentation
from pptx.util import Cm, Pt
from pptx.enum.text import PP_ALIGN, MSO_ANCHOR, MSO_AUTO_SIZE
from pptx.chart.data import ChartData
from pptx.enum.chart import XL_CHART_TYPE, XL_LEGEND_POSITION
from pptx.dml.color import RGBColor
from pptx.oxml.xmlchemy import OxmlElement
class Qasc():
def __init__(self):
# Set logging
debug = f'debug_{datetime.now().strftime("%d-%m-%Y")}.log'
log_file = os.path.join(os.getcwd(), debug)
logging.basicConfig(
filename=log_file,
filemode='a',
format='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',
datefmt='%H:%M:%S',
level=logging.DEBUG
)
logging.info('Start to generate QASC reports.')
# Connect to database and get QASC data
con = Connection(data='qasc')
self.preprocessed_data = con.preprocessed_data.copy()
# Get also data with studies
self.study_df = con.study_df
# Get list of Site IDs from the preprocessed data
site_ids = set(self.preprocessed_data['SITE_ID'].tolist())
self.__site_ids = [x for x in site_ids if not x.endswith('_AUD')]
del site_ids
self.table_font_size = Pt(11) # Set default font size for tables
@property
def site_ids(self):
return self.__site_ids
@property
def stats(self):
return self.__stats
@stats.setter
def stats(self, df):
self.__stats = df
@property
def site_id(self):
return self.__site_id
@site_id.setter
def site_id(self, site_id):
self.__site_id = site_id
@property
def pre_df(self):
return self.__pre_df
@pre_df.setter
def pre_df(self, df):
self.__pre_df = df
@property
def post_df(self):
return self.__post_df
@post_df.setter
def post_df(self, df):
self.__post_df = df
@property
def site_df(self):
return self.__site_df
@site_df.setter
def site_df(self, df):
self.__site_df = df
@property
def table_font_size(self):
return self.__table_font_size
@table_font_size.setter
def table_font_size(self, value):
self.__table_font_size = value
def generate_reports(self, site_id):
''' Generate reports for the site ID.
:param site_id: the site ID of the hospital for which the report should be generated
:type site_id: str
'''
self.site_id = site_id.upper() # Make provided site ID uppercased
# check if provided site ID is in preprocessed data
if self.site_id is not None and self.site_id in self.site_ids:
# Filter dataframe for provided site ID
self.site_df = self._filter_by_site(self.site_id)
if self.site_df.empty:
print(f"There are no data for this {site_id} hospital. The available sites are: {self.site_ids}.")
else:
# Obtain data for pre/post period
self._pre_post_data()
# Calculate pre stats and generate baselina reports that are made from pre dataset
self.pre_stats = self.calculate_statistics(df=self.pre_df)
self.generate_baseline_report(df=self.pre_stats)
# If post dataset is not empty, generate also comparison reports otherwise stop the program
if not self.post_df.empty:
self.post_stats = self.calculate_statistics(df=self.post_df)
self.generate_pre_post_report()
else:
print(f"There are no data for this site.")
def _filter_by_site(self, site_id):
''' Return the filtered dataframe for site id filter on column SITE_ID.
:param site_id: site ID for which the report should be generated
:type site_id: str
:returns: filtered data
:rtype: DataFrame
'''
return self.preprocessed_data.loc[self.preprocessed_data['SITE_ID'] == site_id].copy()
def _filter_data(self, first_date, second_date):
''' Return the filtered dataframe where DATE_CREATED is between first and second date.
:param first_date: first date included in the filtration
:type first_date: datetime
:param second_date: second date included in the filtration
:type second_date: datetime
:returns: the filtered dataframe
:rtype: DataFrame
'''
if self.site_df.empty or self.site_df is None:
return self.preprocessed_data.loc[
self.preprocessed_data['DATE_CREATED'].between(first_date, second_date)
].copy()
else:
return self.site_df.loc[
self.preprocessed_data['DATE_CREATED'].between(first_date, second_date)
].copy()
def _pre_post_data(self):
''' Set pre/post dataframe data. '''
# NOTE: for now the pre dataset is set to year 2019 and post dataset to 2020
pre_date1 = datetime(2019, 1, 1)
pre_date2 = datetime(2019, 12, 31)
self.pre_df = self._filter_data(pre_date1, pre_date2)
if (self.pre_df.empty):
print(f"There are no data for pre phase for this {self.site_id} hospital.")
exit()
else:
post_date1 = datetime(2020, 1, 1)
post_date2 = datetime(2020, 12, 31)
self.post_df = self._filter_data(post_date1, post_date2)
if (self.post_df.empty):
if self.pre_df.emtpy:
print(f"There are no data for post phase for this {self.site_id} hospital.")
exit()
def _get_percentage_column_name(self, column_name):
''' Return value where # is replaced by %.
:params column_name: column name to be replaced
:type column_name: string
:returns: the modified column name where # was replaced by %
:rtype: str
'''
return column_name.replace('#', '%', 1)
def _get_patients(self, stats, column_name, groups, out_of):
''' Return # and % of the group.
:param column_name: name of column to be creatd
:type column_name: string
:param groups: grouped dataframe
:type groups: DataFrame
:param out_of: the name of column to be used as denominator
:type out_of: string
:returns: the calculated stats extends with the calculated column
:rtype: DataFrame
'''
stats = stats.merge(groups[[self.main_col, column_name]], how='outer')
# get percentages out of # n
stats[self._get_percentage_column_name(column_name)] = stats.apply(
lambda x: round(((x[column_name]/x[out_of]) * 100), 2) if x[out_of] > 0 else 0, axis=1)
stats.fillna(0, inplace=True)
return stats
def _rename_column(self, df, prev_name, curr_name):
''' Rename the column in dataframe. If column not exists, create column.
:param df: the grouped dataframe
:type df: dataframe
:param prev_name: the name of column to be changed
:type prev_name: str/int
:param curr_name: new name of the column
:type curr_name: str
:returns: modified dataframe
:rtype: DataFrame
'''
if not prev_name in df.columns:
df[prev_name] = 0
return df.rename(columns={prev_name: curr_name})
def calculate_statistics(self, df=None):
''' Calculate the statistics for the temperature, blood glucose and swallow screening.
:param df: the preprocessed data to be calculated, if not provided preprocessed data are used (default is None)
:type df: DataFrame
:returns: the calculated stats
:rtype: DataFrame
'''
# Defina main column to be grouped by
self.main_col = 'SITE_ID'
if df is None:
df = self.preprocessed_data.copy()
# 1. Patients records entered
stats = df.groupby([self.main_col]).size().to_frame('n').reset_index()
# Calculate # of patients per stroke type
groups = df.groupby([self.main_col, 'STROKE_TYPE']).size().unstack().reset_index().fillna(0)
column_name = '# acute stroke'
column_names = {
1.0: '# subarrachnoid hemorrhage',
2.0: '# ischemic stroke',
3.0: '# intracerrebral hemorrhage'
}
# Check if all columns are in the grouped dataframe
for key in column_names.keys():
if key not in groups.columns:
groups[key] = 0
groups.rename(columns=column_names, inplace=True)
groups[column_name] = groups[column_names.values()].sum(axis=1)
stats = self._get_patients(stats=stats, column_name=column_name, groups=groups, out_of='n')
'''
2. Temperature monitring and treatment
column_name = TEMP_MEASUREMENT
question = Was temperature measured?
type = checkbox
answers: 1. at least four times on day one of admission,
2. at least four times on day two of admission,
3. at least four times on day three of admission,
4. none of the above
calculation: 1-3 are selected
'''
groups = df.groupby([self.main_col, 'TEMP_MEASUREMENT']).size().unstack().reset_index().fillna(0)
# remove column with default values from the groups
default = '1,2,3,4'
if default in groups.columns:
groups.drop([default], axis=1, inplace=True)
column_names = groups.columns
include_columns = [x for x in column_names if '1' in x or '2' in x or '3' in x and '4' not in x]
column_name = '# Temperature monitoring and treatment'
groups[column_name] = groups[include_columns].sum(axis=1)
stats = self._get_patients(stats=stats, column_name=column_name, groups=groups, out_of='n')
include_columns = [x for x in column_names if '1' in x and '4' not in x]
column_name = '# Temperature monitored at least four times per day - Day of admission'
groups[column_name] = groups[include_columns].sum(axis=1)
stats = self._get_patients(stats=stats, column_name=column_name, groups=groups, out_of='n')
include_columns = [x for x in column_names if '2' in x and '4' not in x]
column_name = '# Temperature monitored at least four times per day - Day two of admission'
groups[column_name] = groups[include_columns].sum(axis=1)
stats = self._get_patients(stats=stats, column_name=column_name, groups=groups, out_of='n')
include_columns = [x for x in column_names if '3' in x and '4' not in x]
column_name = '# Temperature monitored at least four times per day - Day three of admission'
groups[column_name] = groups[include_columns].sum(axis=1)
stats = self._get_patients(stats=stats, column_name=column_name, groups=groups, out_of='n')
'''
3. Temperature > 37.5°c recorded within 72 hours of admission
column_name = FEVER
question = In the first 72hrs following admission did the patient develop a fever ≥ 37.5 °C?
type = select
answers: 1. Yes,
2. No,
3. Unknown
calculation: # of 1 selected
'''
groups = df.groupby([self.main_col, 'FEVER']).size().unstack().reset_index().fillna(0)
column_name = '# Temperature > 37.5°c recorded within 72 hours of admission'
groups = self._rename_column(df=groups, prev_name=1.0, curr_name=column_name)
stats = self._get_patients(stats=stats, column_name=column_name, groups=groups, out_of='n')
'''
3a. Paracetamol (or other anti-pyretic) given for first temperature > 37.5°c
column_name = PARACETAMOL
question = Was paracetamol (or other antipyretic) for the first elevated temperature (>37.5 °C) administered?
type = select
answers: 1. Yes,
2. No,
3. Unknown
calculation: # of 1 selected
condition: this question is show if question 3 is answered "Yes"
'''
fever_df = df.loc[df['FEVER'] == 1].copy()
groups = fever_df.groupby([self.main_col, 'PARACETAMOL']).size().unstack().reset_index().fillna(0)
column_name = '# Paracetamol (or other anti-pyretic) given for first temperature > 37.5°C'
groups = self._rename_column(df=groups, prev_name=1.0, curr_name=column_name)
stats = self._get_patients(
stats=stats,
column_name=column_name,
groups=groups,
out_of='# Temperature > 37.5°c recorded within 72 hours of admission'
)
'''
3b. Paracetamol (or other anti-pyretic) given with one hour from first temperature > 37.5°c #
column_name = PARACETAMOL_1H
question = Was paracetamol (or other antipyretic) for the first elevated temperature (>37.5 °C) administered within 1 hour?
type = select
answers: 1. Yes,
2. No,
3. Unknown
calculation: # of 1 selected
condition: this question is show if question 3a is answered "Yes"
'''
first_temperature_df = df.loc[df['PARACETAMOL'] == 1].copy()
groups = first_temperature_df.groupby([self.main_col, 'PARACETAMOL_1H']).size().unstack().reset_index().fillna(0)
column_name = '# Paracetamol (or other anti-pyretic) given with one hour from first temperature > 37.5°C'
groups = self._rename_column(df=groups, prev_name=1.0, curr_name=column_name)
stats = self._get_patients(
stats=stats,
column_name=column_name,
groups=groups,
out_of='# Paracetamol (or other anti-pyretic) given for first temperature > 37.5°C'
)
'''
4. Blood glucose monitoring and treatment
column_name = GLUCOSE_LAB
question = Was a venous blood glucose level sample collected and sent to laboratory?
type = select
answers: 1. Yes,
2. No,
3. Unknown
calculation: # of 1 selected
'''
groups = df.groupby([self.main_col, 'GLUCOSE_LAB']).size().unstack().reset_index().fillna(0)
column_name = '# Blood glucose monitoring and treatment'
groups = self._rename_column(df=groups, prev_name=1.0, curr_name=column_name)
stats = self._get_patients(
stats=stats,
column_name=column_name,
groups=groups,
out_of='n'
)
'''
5. Blood Glucose Level (BGL) monitored > four times per day
column_name = GLUCOSE_MONITOR
question = Was a finger-prick blood glucose level recorded
type = checkbox
answers: 1. at least four times on day one of admission,
2. at least four times on day two of admission,
3. at least four times on day three of admission,
4. none of the above
calculation: 1-3 is selected
'''
monitoring_fever_hyperglycemia_df = df.loc[(df['TEMP_MEASUREMENT'].str.contains('1|2|3') & ~df['TEMP_MEASUREMENT'].str.contains('4')) | (df['GLUCOSE_MONITOR'].str.contains('1|2|3') & ~df['GLUCOSE_MONITOR'].str.contains('4'))]
monitoring_fever_hyperglycemia_stats = monitoring_fever_hyperglycemia_df.groupby([self.main_col]).size().to_frame('# monitored for fever and/or hyperglycaemia four times a day').reset_index()
stats = stats.merge(monitoring_fever_hyperglycemia_stats, how='outer')
del monitoring_fever_hyperglycemia_df, monitoring_fever_hyperglycemia_stats
groups = df.groupby([self.main_col, 'GLUCOSE_MONITOR']).size().unstack().reset_index().fillna(0)
# remove column with default values from the groups
default = '1,2,3,4'
if default in groups.columns:
groups.drop([default], axis=1, inplace=True)
column_names = groups.columns
include_columns = [x for x in column_names if '1' in x and '4' not in x]
column_name = '# Blood Glucose Level (BGL) monitored > four times per day - Day of admission'
groups[column_name] = groups[include_columns].sum(axis=1)
stats = self._get_patients(stats=stats, column_name=column_name, groups=groups, out_of='n')
include_columns = [x for x in column_names if '2' in x and '4' not in x]
column_name = '# Blood Glucose Level (BGL) monitored > four times per day - Day two of admission'
groups[column_name] = groups[include_columns].sum(axis=1)
stats = self._get_patients(stats=stats, column_name=column_name, groups=groups, out_of='n')
include_columns = [x for x in column_names if '3' in x and '4' not in x]
column_name = '# Blood Glucose Level (BGL) monitored > four times per day - Day three of admission'
groups[column_name] = groups[include_columns].sum(axis=1)
stats = self._get_patients(stats=stats, column_name=column_name, groups=groups, out_of='n')
'''
6. BGL ≥ 10mmol/L within 48 hours of admission
column_name = GLUCOSE_LEVEL
question = In the first 48 hours following ward admission did the patient develop a finger-prick glucose level of greater or equal to 10mmols/L?
type = select
answers: 1. Yes,
2. No,
3. Unknown
calculation: # of 1 selected
'''
groups = df.groupby([self.main_col, 'GLUCOSE_LEVEL']).size().unstack().reset_index().fillna(0)
column_name = '# BGL ≥ 10mmol/L within 48 hours of admission'
groups = self._rename_column(df=groups, prev_name=1.0, curr_name=column_name)
stats = self._get_patients(
stats=stats,
column_name=column_name,
groups=groups,
out_of='n'
)
'''
6a. Insulin given for first BGL ≥ 10mmol/L
column_name = INSULIN_ADMINISTRATION
question = Was insulin for first elevated finger prick glucose (>=10mmol/L) administered?
type = select
answers: 1. Yes,
2. No,
3. Unknown
calculation: # of 1 selected
'''
bgl_followed_df = df.loc[df['GLUCOSE_LEVEL'] == 1].copy()
groups = bgl_followed_df.groupby([self.main_col, 'INSULIN_ADMINISTRATION']).size().unstack().reset_index().fillna(0)
column_name = '# Insulin given for first BGL ≥ 10mmol/L'
groups = self._rename_column(df=groups, prev_name=1.0, curr_name=column_name)
stats = self._get_patients(
stats=stats,
column_name=column_name,
groups=groups,
out_of='# BGL ≥ 10mmol/L within 48 hours of admission'
)
'''
6b. Insulin given within one hour from first BGL ≥ 10mmol/L #
column_name = INSULIN_ADMINISTRATION_1H
question = Was insulin for first elevated finger prick glucose (>=10mmol/L) administered within 1 hour?
type = select
answers: 1. Yes,
2. No,
3. Unknown
calculation: # of 1 selected
'''
insulin_administration_df = df.loc[df['INSULIN_ADMINISTRATION'] == 1].copy()
groups = insulin_administration_df.groupby([self.main_col, 'INSULIN_ADMINISTRATION_1H']).size().unstack().reset_index().fillna(0)
column_name = '# Insulin given within one hour from first BGL ≥ 10mmol/L'
groups = self._rename_column(df=groups, prev_name=1.0, curr_name=column_name)
stats = self._get_patients(
stats=stats,
column_name=column_name,
groups=groups,
out_of='# Insulin given for first BGL ≥ 10mmol/L'
)
'''
7. Swallow screening
column_name = DYSPHAGIA
question = Was a formal swallowing screen performed (i.e. not a test of gag reflex)?
type = select
answers: 1. Performed,
2. Not performed,
3. Not applicable
calculation: # of 1 selected
'''
'''
8. Formal swallow screen performed
column_name = DYSPHAGIA
question = Was a formal swallowing screen performed (i.e. not a test of gag reflex)?
type = select
answers: 1. Performed,
2. Not performed,
3. Not applicable
calculation: # of 1 selected
'''
groups = df.groupby([self.main_col, 'DYSPHAGIA']).size().unstack().reset_index().fillna(0)
column_name = '# Formal swallow screen performed'
groups = self._rename_column(df=groups, prev_name=1.0, curr_name=column_name)
stats = self._get_patients(
stats=stats,
column_name=column_name,
groups=groups,
out_of='n'
)
'''
8a. Swallow screen performed within 24 hours #
column_name = DYSPHAGIA_24H
question = Did the patient receive a swallowing screen within 24 hours of admission to hospital?
type = select
answers: 1. Yes,
2. No,
3. Unknown
calculation: # of 1 selected
'''
dysphagia_performed_df = df.loc[df['DYSPHAGIA'] == 1].copy()
groups = insulin_administration_df.groupby(
[self.main_col, 'DYSPHAGIA_24H']).size().unstack().reset_index().fillna(0)
column_name = '# Swallow screen performed within 24 hours'
groups = self._rename_column(df=groups, prev_name=1.0, curr_name=column_name)
stats = self._get_patients(
stats=stats,
column_name=column_name,
groups=groups,
out_of='# Formal swallow screen performed'
)
'''
9. Swallow screen or swallow assessment performed before being given oral medications #
column_name = DYSPH_BEFORE_MED
question = Was the swallow screen or swallow assessment performed before the patient was given oral medications?
type = select
answers: 1. Yes,
2. No,
3. Unknown
calculation: # of 1 selected
'''
groups = df.groupby([self.main_col, 'DYSPH_BEFORE_MED']).size().unstack().reset_index().fillna(0)
column_name = '# Swallow screen or swallow assessment performed before being given oral medications'
groups = self._rename_column(df=groups, prev_name=1.0, curr_name=column_name)
stats = self._get_patients(
stats=stats,
column_name=column_name,
groups=groups,
out_of='n'
)
'''
10. Swallow screen or swallow assessment performed before being given oral food or fluids #
column_name = DYSPH_BEFORE_FOOD
question = Was the swallow screen or swallow assessment performed before the patient was given oral food or fluids?
type = select
answers: 1. Yes,
2. No,
3. Unknown
calculation: # of 1 selected
'''
groups = df.groupby([self.main_col, 'DYSPH_BEFORE_FOOD']).size().unstack().reset_index().fillna(0)
column_name = '# Swallow screen or swallow assessment performed before being given oral food or fluids'
groups = self._rename_column(df=groups, prev_name=1.0, curr_name=column_name)
stats = self._get_patients(
stats=stats,
column_name=column_name,
groups=groups,
out_of='n'
)
# save calculated stats into csv file
save_file(
name=f'qasc_stats_{datetime.now().strftime("%Y-%m-%d")}.csv',
data=stats,
)
return stats
def _merge_cells(self, table, first_x, first_y, second_x, second_y):
''' Merge cells based on index.
:param table: the table to be modified
:type table: table shape
:param first_x: the 1st coordinate
:type first_x: int
:param first_y: the 2nd coordinate
:type first_y: int
:param second_x: 3rd coordinate
:type second_x: int
:param second_y: 4th coordinate
:type second_y: int
:returns: the merge cell
:rtype: cell
'''
cell = table.cell(first_x, first_y)
other_cell = table.cell(second_x, second_y)
cell.merge(other_cell)
cell.vertical_anchor = MSO_ANCHOR.MIDDLE
return cell
def _insert_values(self, table, row, values, bold=False):
''' Insert values into 'n' and '%' columns.
:param table: the table
:type table: table shape
:param row: the number of row to add values
:type row: int
:param values: the list of values
:type values: list
:param bold: text should be bold
:type bold: boolean
'''
for i in range(0, len(values)):
cell = table.cell(row, i + 2) # Get cell in the 2nd row
cell.text = values[i]
cell.vertical_anchor = MSO_ANCHOR.MIDDLE
for paragraphs in cell.text_frame.paragraphs:
paragraphs.alignment = PP_ALIGN.CENTER
for run in paragraphs.runs:
run.font.size = self.table_font_size
run.font.bold = bold
def _insert_subrows(self, table, row, col, values):
''' Insert values into 'n' and '%' columns.
:param table: the table
:type table: table shape
:param row: the number of row to add values
:type row: int
:param col: the number of column to add values
:type col: int
:param values: the list of values
:type values: list
:param bold: text should be bold
:type bold: boolean
'''
for i in range(0, len(values)):
cell = table.cell(row, col) # Get cell in the 2nd row
cell.text = values[i]
cell.vertical_anchor = MSO_ANCHOR.MIDDLE
for paragraphs in cell.text_frame.paragraphs:
paragraphs.alignment = PP_ALIGN.LEFT
for run in paragraphs.runs:
run.font.size = self.table_font_size
row += 1
def _add_column_name(self, cell, text, alignment='left', bold=False, italic=False, level=False):
''' Insert text into row as column name.
:param cell: cell that should be modified
:type cell: cell shape
:param text: the text
:type text: str
:param bold: text should be bold
:type bold: boolean
:param italic: text should be italic
:type italic: boolean
:param aligment: how the text should be aligned (Default: left)
:type alignment: str
'''
# Set alignment
if alignment == 'left':
alignment = PP_ALIGN.LEFT
elif alignment == 'right':
alignment = PP_ALIGN.RIGHT
elif alignment == 'center':
alignment = PP_ALIGN.CENTER
cell.text = text
cell.vertical_anchor = MSO_ANCHOR.MIDDLE
for paragraph in cell.text_frame.paragraphs:
paragraph.alignment = alignment
# This will make text indented
if level:
paragraph.level = 1
for run in paragraph.runs:
run.font.size = self.table_font_size
run.font.bold = bold
run.font.italic = italic
def _add_textbox(self, specs, slide, text, bold=False, italic=False, underline=False):
''' Add textbox with explanation text into the presentation.
:param specs: the position of the textbox
:type specs: dict
:param slide: slide to which textbox should be add
:type slide: slide shape
:param text: textbox's text
:type text: str
:param bold: make text bold
:type bold: boolean (default: False)
:param italic: make text italic
:type italic: boolean (default: False)
:param underline: make text underlined
:type underline: boolean (default: False)
'''
txBox = slide.shapes.add_textbox(specs['left'], specs['top'], specs['width'], specs['height'])
txBox.text_frame.clear()
txBox.text_frame.word_wrap = True
p = txBox.text_frame.paragraphs[0]
p.alignment = PP_ALIGN.JUSTIFY
run = p.add_run()
run.font.size = Pt(11)
run.font.underline = underline
run.font.bold = bold
run.font.italic = italic
run.text = text
def _set_transparency(self, transparency, elm):
""" The function set the transparency of the row.
:param transparency: the transparency in %
:type transparency: int
:param elm: the element which transparency should be changed
:type elm: format.line.color._xFill
"""
a = str(100 - transparency) + '196'
alpha = OxmlElement('a:alpha')
alpha.set('val', a)
elm.srgbClr.append(alpha)
def _add_run(self, txtBox, text, bold=False, italic=False):
''' Add paragraph to the textbox. Each paragraph can have multiple runs.
:param txtBox: textbox to which add teh paragraph
:type txtBox: textBox shape
:param text: text to be insert into txtBox
:type text: str
:param bold: true if text should be boolean
:type bold: boolean
'''
p = txtBox.text_frame.paragraphs[0]
run = p.add_run()
run.text = text
run.font.size = Pt(11)
run.font.bold = bold
run.font.italic = italic
def _get_column_values(self, column_name, baseline):
''' Return list of columns to be geenrated in the table based on the report type.
:param column_name: name of column to be included in the results
:type column_name: str
:param baseline: True if baseline report is generate
:type baseline: boolean
:returns: list of column name based on the report type
:rtype: list
'''
if baseline:
columns = [
str(self.pre_stats[column_name].iloc[0]),
str(self.pre_stats[self._get_percentage_column_name(column_name)].iloc[0])]
else:
columns = [
str(self.pre_stats[self._get_percentage_column_name(column_name)].iloc[0]),
str(self.post_stats[self._get_percentage_column_name(column_name)].iloc[0])
]
return columns
def _create_table(self, slide, table_specs, title, trow, tcol, baseline=False):
''' Generate table in the report.
:param slide: the slide to which the table should be added
:type slide: slide
:param table_specs: specify a position of thetable
:type table_specs: dict
:param title: the name of the table
:type title: str
:param trow: number of rows of the table
:type trow: int
:param tcol: number of columns of the table
:type tcol: int
:param baseline: True if baseline report should be generated
:type baseline: boolean
'''
# Add table to the slide, we need table with 21 rows and 3 columns
shape = slide.shapes.add_table(
trow, #rows
tcol, # cols
table_specs['left'],
table_specs['top'],
table_specs['width'],
table_specs['height'])
# Set look of the table
# Change table style (https://github.com/scanny/python-pptx/issues/27)
# This is a bit complicated how to change table style, I always do it that I select table style in powerpoint, save powerpoint and open xml file. There you can find style ID.
style_id = '{5940675A-B579-460E-94D1-54222C63F5DA}'
tbl = shape._element.graphic.graphicData.tbl
tbl[0][-1].text = style_id
table = shape.table
# Set column width based on the report type
if baseline:
widths = [
int(table_specs['width'] * 0.5),
int(table_specs['width'] * 0.2),
int(table_specs['width'] * 0.15),
int(table_specs['width'] * 0.15)
]
else:
widths = [
int(table_specs['width'] * 0.58),
int(table_specs['width'] * 0.2),
int(table_specs['width'] * 0.11),
int(table_specs['width'] * 0.11)
]
for i in range(0, len(table.columns)):
table.columns[i].width = widths[i]
# Merge header row
cell = self._merge_cells(table, 0, 0, 0, len(table.columns) - 1)
self._add_column_name(cell, title, alignment='center', bold=True)
# 2nd row
nrow = 1
cell = self._merge_cells(table, nrow, 0, nrow, 1)
self._add_column_name(cell, "")
# Set the header of table based on report type
columns = ['n', '%'] if baseline else ['Pre n (%)', 'Post n (%)']
self._insert_values(table=table, row=nrow, values=columns, bold=True)
# Patient records entered
nrow += 1
cell = self._merge_cells(table, nrow, 0, nrow, 1)
text = 'Patient records entered'
self._add_column_name(cell, text)
columns = [
str(self.pre_stats['n'].iloc[0]),
'100'
] if baseline else [
str(self.pre_stats['n'].iloc[0]),
str(self.post_stats['n'].iloc[0])
]
self._insert_values(table=table, row=nrow, values=columns)
# Temperature monitoring and treatment
nrow += 1
cell = self._merge_cells(table, nrow, 0,nrow, 1)
text = 'Temperature monitoring and treatment'
self._add_column_name(cell, text, bold=True, italic=True)
name = '# Temperature monitoring and treatment'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
nrow += 1
cell = self._merge_cells(table, nrow, 0, nrow + 2, 0)
text = 'Temperature monitored at least four times \nper day #'
self._add_column_name(cell, text, bold=True, italic=True)
columns = [
'Day of admission',
'Day two of admission',
'Day three of admission',
]
self._insert_subrows(table=table, row=nrow, col=1, values=columns)
name = '# Temperature monitored at least four times per day - Day of admission'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
nrow += 1
name = '# Temperature monitored at least four times per day - Day two of admission'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
nrow += 1
name = '# Temperature monitored at least four times per day - Day three of admission'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
# Temperature > 37.5°C recorded within 72 hours of admission
nrow += 1
cell = self._merge_cells(table, nrow, 0, nrow, 1)
text = 'Temperature > 37.5°C recorded within 72 hours of admission'
self._add_column_name(cell, text, alignment='left')
name = '# Temperature > 37.5°c recorded within 72 hours of admission'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
# Paracetamol (or other anti-pyretic) given for first temperature > 37.5°C
nrow += 1
cell = self._merge_cells(table, nrow, 0, nrow, 1)
text = 'Paracetamol (or other anti-pyretic) given for first temperature > 37.5°C'
self._add_column_name(cell, text, alignment='left', level=True)
name = '# Paracetamol (or other anti-pyretic) given for first temperature > 37.5°C'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
# Paracetamol (or other anti-pyretic) given with one hour from first temperature > 37.5°C #
nrow += 1
cell = self._merge_cells(table, nrow, 0, nrow, 1)
text = 'Paracetamol (or other anti-pyretic) given with one hour from first temperature > 37.5°C #'
self._add_column_name(cell, text, alignment='left', bold=True, level=True)
name = '# Paracetamol (or other anti-pyretic) given with one hour from first temperature > 37.5°C'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
# Blood glucose monitoring and treatment
nrow += 1
cell = self._merge_cells(table, nrow, 0, nrow, 1)
text = 'Blood glucose monitoring and treatment'
self._add_column_name(cell, text, alignment='left', bold=True, italic=True)
name = '# Blood glucose monitoring and treatment'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
# Blood Glucose Level (BGL) monitored > four times per day
nrow += 1
cell = self._merge_cells(table, nrow, 0, nrow + 1, 0)
text = 'Blood Glucose Level (BGL) monitored > four times per day'
self._add_column_name(cell, text, alignment='left', bold=True)
columns = [
'Day of admission',
'Day two of admission',
]
self._insert_subrows(table=table, row=nrow, col=1, values=columns)
name = '# Blood Glucose Level (BGL) monitored > four times per day - Day of admission'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
nrow += 1
name = '# Blood Glucose Level (BGL) monitored > four times per day - Day two of admission'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
nrow += 1
cell = self._merge_cells(table, nrow, 0, nrow, 1)
text = 'BGL ≥ 10mmol/L within 48 hours of admission'
self._add_column_name(cell, text, alignment='left')
name = '# BGL ≥ 10mmol/L within 48 hours of admission'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
nrow += 1
cell = self._merge_cells(table, nrow, 0, nrow, 1)
text = 'Insulin given for first BGL ≥ 10mmol/L'
self._add_column_name(cell, text, alignment='left', bold=True, level=True)
name = '# Insulin given for first BGL ≥ 10mmol/L'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
nrow += 1
cell = self._merge_cells(table, nrow, 0, nrow, 1)
text = 'Insulin given within one hour from first BGL ≥ 10mmol/L #'
self._add_column_name(cell, text, alignment='left', bold=True, level=True)
name = '# Insulin given within one hour from first BGL ≥ 10mmol/L'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
nrow += 1
cell = self._merge_cells(table, nrow, 0, nrow, 1)
text = 'Swallow screening'
self._add_column_name(cell, text, alignment='left', bold=True, italic=True)
nrow += 1
cell = self._merge_cells(table, nrow, 0, nrow, 1)
text = 'Formal swallow screen performed'
self._add_column_name(cell, text, alignment='left')
name = '# Formal swallow screen performed'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
nrow += 1
cell = self._merge_cells(table, nrow, 0, nrow, 1)
text = 'Swallow screen performed within 24 hours #'
self._add_column_name(cell, text, alignment='left', bold=True, level=True)
name = '# Swallow screen performed within 24 hours'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
nrow += 1
cell = self._merge_cells(table, nrow, 0, nrow, 1)
text = 'Swallow screen or swallow assessment performed before being given oral medications #'
self._add_column_name(cell, text, alignment='left', bold=True)
name = '# Swallow screen or swallow assessment performed before being given oral medications'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
nrow += 1
cell = self._merge_cells(table, nrow, 0, nrow, 1)
text = 'Swallow screen or swallow assessment performed before being given oral food or fluids #'
self._add_column_name(cell, text, alignment='left', bold=True)
name = '# Swallow screen or swallow assessment performed before being given oral food or fluids'
columns = self._get_column_values(name, baseline)
self._insert_values(table=table, row=nrow, values=columns)
# Create iterator through cells in the table
def iter_cells(table):
for row in table.rows:
for cell in row.cells:
yield cell
# Set font size of all cells in the table based on the report type
for cell in iter_cells(table):
cell.text_frame.autosize = MSO_AUTO_SIZE.SHAPE_TO_FIT_TEXT
for paragraph in cell.text_frame.paragraphs:
paragraph.font.size = self.table_font_size
def generate_baseline_report(self, df=None):
''' Generate baseline data summary feedback.
:param df: dataframe to be used for values (default: None)
:type df: dataframe
'''
self.table_font_size = Pt(11) # Set default font size of the table in baseline report
# Define template for the presentation
master = os.path.normpath(os.path.join(os.path.dirname(__file__), 'backgrounds', 'qasc_baseline.pptx'))
# Filter dataframe for site
hospital_name = self.study_df.loc[self.study_df['unique_identifier'] == self.site_id, 'facility_name'].iloc[0]
# Create output filename containing qasc, current date and site ID
output_file = f'qasc_{self.site_id}_{datetime.now().strftime("%Y-%m-%d")}.pptx'
# set main text to be added into reports
main_texts = [
'Congratulations on completing your baseline audit. We have summarized the results for you in the table below. Please share these results with your team. These data can assist you when discussing the barriers and enablers to implementation of the FeSS clinical protocols at your hospital.',
'It is important to please let us know if there are problems with the data that can be explained further (eg. was there a question the people entering data may not have understood properly?)',
'Please don’t hesitate to contact the NRI if you require clarification on any of the items above.'
]
# Create new Presentaiton object
prs = Presentation(master)
# Get first slide
first_slide = prs.slides[0]
# Set specification of the table
table_specs = {
'height': Cm(18),
'width': Cm(19),
'left': Cm(1),
'top': Cm(5)
}
# Create table
self._create_table(
slide=first_slide,
table_specs=table_specs,
title=f'Table 1: FeSS Management for {hospital_name}',
trow=21,
tcol=4,
baseline=True)
# Add the rest of explaining texts
specs = {
0: {
'height': Cm(2),
'width': Cm(19),
'left': Cm(1),
'top': Cm(3)
},
1: {
'height': Cm(1),
'width': Cm(19),
'left': Cm(1),
'top': Cm(25.5)
},
2: {
'height': Cm(1),
'width': Cm(19),
'left': Cm(1),
'top': Cm(27)
},
}
for i in range(0, len(main_texts)):
self._add_textbox(specs[i], first_slide, main_texts[i])
# Create graph on the second slide
second_slide = prs.slides.add_slide(prs.slide_layouts[0])
graph_df = df[[
'% Temperature monitored at least four times per day - Day of admission',
'% Paracetamol (or other anti-pyretic) given with one hour from first temperature > 37.5°C',
'% Blood Glucose Level (BGL) monitored > four times per day - Day of admission',
'% Insulin given within one hour from first BGL ≥ 10mmol/L',
'% Swallow screen performed within 24 hours',
]].copy()
new_column_names = ["Temp (Day 1)", "Paracetamol (1hr)", "BGL's (Day 1)", "Insulin (1hr)", "Swallow screen (24hrs)"]
graph_df.rename(columns=dict(zip(graph_df.columns, new_column_names)),inplace=True)
column_name = 'Baseline audit'
graph_df = graph_df.T.rename(columns={0: column_name})
# Create chart data
chart_data = ChartData()
chart_data.categories = new_column_names
chart_data.add_series(column_name, graph_df[column_name].tolist())
# Add chart on slide
specs = {
'height': Cm(10),
'width': Cm(19),
'left': Cm(1),
'top': Cm(3)
}
chart = second_slide.shapes.add_chart(
XL_CHART_TYPE.COLUMN_CLUSTERED, specs['left'],specs['top'], specs['width'],specs['height'], chart_data).chart
plot = chart.plots[0]
# All bars with the same color
plot.vary_by_categories = False
# Set maximum to 100
value_axis = chart.value_axis
value_axis.maximum_scale = 100
value_axis.major_gridlines.format.line.width = Pt(0.5)
value_axis.major_gridlines.format.line.color.rgb = RGBColor(206, 206, 206) # Set color to gray (A6A6A6)
value_axis.format.line.color.rgb = RGBColor(0, 0, 0)
solidFill = value_axis.format.line.color._xFill
self._set_transparency(100, solidFill)
# change font size of values
tick_labels = value_axis.tick_labels
tick_labels.font.size = Pt(11)
# Value for y-axis (change font size, name, and other things)
category_axis = chart.category_axis
# Set 100% transparency to category axis
category_axis.format.line.color.rgb = RGBColor(206, 206, 206)
solidFill = category_axis.format.line.color._xFill
self._set_transparency(100, solidFill)
# Change font size of category labels
category_labels = category_axis.tick_labels
category_labels.font.size = Pt(11)
# Set graph of title
graph_title = f'Figure 1: FeSS Management {hospital_name} Hospital'
chart_text = chart.chart_title.text_frame
chart_text.text = graph_title
chart_text.paragraphs[0].font.size = Pt(12)
chart_text.paragraphs[0].font.color.rgb = RGBColor(89, 89, 89)
# Save presentation
path = os.path.join(os.getcwd(), output_file)
save_file(output_file)
prs.save(path)
def generate_pre_post_report(self):
''' Generate report with pre/post comparison. '''
# Set smaller font size of the table
self.table_font_size = Pt(9.5)
# Define template
master = os.path.normpath(os.path.join(os.path.dirname(__file__), 'backgrounds', 'qasc_comparison.pptx'))
# Get hospital name based on study ID
hospital_name = self.study_df.loc[self.study_df['unique_identifier'] == self.site_id, 'facility_name'].iloc[0]
# Create output filename containing qasc, current date and site ID
output_file = f'qasc_comp_{self.site_id}_{datetime.now().strftime("%Y-%m-%d")}.pptx'
# Create Presentation object
prs = Presentation(master)
# Get first slide
first_slide = prs.slides[0]
# Add title
title_text = f'QASC Europe Project: Post-Intervention audit summary {hospital_name} Hospital'
specs = {
'height': Cm(1),
'width': Cm(18),
'left': Cm(0.6),
'top': Cm(1.5),
}
self._add_textbox(specs, first_slide, title_text, bold=True, underline=True)
# Add first paragraph with explanation and congratulations
# A bit longer code because only some letters was made bold, so I had to created more runs in paragraph.
specs = {
'height': Cm(2),
'width': Cm(19.5),
'left': Cm(0.6),
'top': Cm(2),
}
txBox = first_slide.shapes.add_textbox(specs['left'], specs['top'], specs['width'], specs['height'])
txBox.text_frame.clear()
txBox.text_frame.word_wrap = True
self._add_run(
txBox,
'Congratulations on completing the QASC Europe project audits on the use of the FeSS (',
)
self._add_run(txBox, 'F', bold=True)
self._add_run(txBox, 'ever, ')
self._add_run(txBox, 'S', bold=True)
self._add_run(txBox, 'ugar, and ')
self._add_run(txBox, 'S', bold=True)
self._add_run(txBox, 'wallowing) protocols for stroke patients. The summaries below reflect ')
self._add_run(txBox, f"your hospital’s performance for the {self.pre_stats['n'].iloc[0]} stroke patients you reviewed for the baseline audit XX/XX/XXXX and the {self.post_stats['n'].iloc[0]} patients you reviewed during the post intervention period XX/XX/XXXX. ", bold=True)
self._add_run(txBox, ' We present the number of patients audited (n) and the proportion of patients who met criteria (%).')
# Specification of the table position
table_specs = {
'height': Cm(16),
'width': Cm(19),
'left': Cm(0.8),
'top': Cm(4.0)
}
# Create table
self._create_table(
slide=first_slide,
table_specs=table_specs,
title=f'FeSS Management for {hospital_name} Hospital',
trow=21,
tcol=4,
baseline=False)
# Add explanation text below table
text = "# Variables shown in bold above are the key recommendations to be followed in the QASC Europe project"
specs = {
'height': Cm(0.5),
'width': Cm(18),
'left': Cm(0.6),
'top': Cm(20),
}
self._add_textbox(specs, first_slide, text)
# Second slide
second_slide = prs.slides[1]
# Add temperature block
column_name = '# Temperature monitoring and treatment'
pre_temp = self.pre_stats[self._get_percentage_column_name(column_name)].iloc[0]
post_temp = self.post_stats[self._get_percentage_column_name(column_name)].iloc[0]
difference = "no difference" if post_temp <= pre_temp else "an increase"
column_name = '# Temperature monitored at least four times per day - Day of admission'
pre_temp_first = self.pre_stats[self._get_percentage_column_name(column_name)].iloc[0]
post_temp_first = self.post_stats[self._get_percentage_column_name(column_name)].iloc[0]
column_name = '# Temperature monitored at least four times per day - Day two of admission'
pre_temp_second = self.pre_stats[self._get_percentage_column_name(column_name)].iloc[0]
post_temp_second = self.post_stats[self._get_percentage_column_name(column_name)].iloc[0]
column_name = '# Temperature monitored at least four times per day - Day three of admission'
pre_temp_third = self.pre_stats[self._get_percentage_column_name(column_name)].iloc[0]
post_temp_third = self.post_stats[self._get_percentage_column_name(column_name)].iloc[0]
column_name = '# Temperature > 37.5°c recorded within 72 hours of admission'
post_temp_rec_total = self.post_stats[column_name].iloc[0]
column_name = '# Paracetamol (or other anti-pyretic) given with one hour from first temperature > 37.5°C'
post_paracetamol = self.post_stats[column_name].iloc[0]
post_paracetamol_perc = self.post_stats[self._get_percentage_column_name(column_name)].iloc[0]
text = "1. Temperature"
specs = {
'height': Cm(0.7),
'width': Cm(28.5),
'left': Cm(0.6),
'top': Cm(1.5),
}
self._add_textbox(specs, second_slide, text, bold=True)
proportion_text = f"i) From pre to post implementation there was {difference} in the proportion of patients who: were monitored four times a day for fever on Day 1 ({pre_temp_first}% vs {post_temp_first}% respectively), Day 2 ({pre_temp_second}% vs {post_temp_second}%) and Day 3 ({pre_temp_third}% vs {post_temp_third}%)."
specs = {
'height': Cm(1.2),
'width': Cm(27.3),
'left': Cm(1.8),
'top': Cm(2),
}
self._add_textbox(specs, second_slide, proportion_text)
paracetamol_text = f"ii) {post_paracetamol} out of {post_temp_rec_total} ({post_paracetamol_perc}%) patients with a temperature > 37.5°C received Paracetamol (or alternatively antipyretic) within one hour."
specs = {
'height': Cm(0.7),
'width': Cm(27.3),
'left': Cm(1.8),
'top': Cm(3),
}
self._add_textbox(specs, second_slide, paracetamol_text)
# Add blood glucose monitoring
column_name = '# Blood glucose monitoring and treatment'
pre_blood = self.pre_stats[self._get_percentage_column_name(column_name)].iloc[0]
post_blood = self.post_stats[self._get_percentage_column_name(column_name)].iloc[0]
difference = "no difference" if post_blood <= pre_blood else "an increase"
column_name = '# Blood Glucose Level (BGL) monitored > four times per day - Day of admission'
pre_blood_first = self.pre_stats[self._get_percentage_column_name(column_name)].iloc[0]
post_blood_first = self.post_stats[self._get_percentage_column_name(column_name)].iloc[0]
column_name = '# Blood Glucose Level (BGL) monitored > four times per day - Day two of admission'
pre_blood_second = self.pre_stats[self._get_percentage_column_name(column_name)].iloc[0]
post_blood_second = self.post_stats[self._get_percentage_column_name(column_name)].iloc[0]
column_name = '# Insulin given for first BGL ≥ 10mmol/L'
post_insulin_total = self.post_stats[column_name].iloc[0]
column_name = '# Insulin given within one hour from first BGL ≥ 10mmol/L'
post_insulin = self.post_stats[column_name].iloc[0]
post_insulin_perc = self.post_stats[self._get_percentage_column_name(column_name)].iloc[0]
text = "2. Blood glucose monitoring"
specs = {
'height': Cm(0.7),
'width': Cm(28.5),
'left': Cm(0.6),
'top': Cm(4.0),
}
self._add_textbox(specs, second_slide, text, bold=True)
proportion_text = f"iii) {difference.capitalize()} in the proportion of patients observed four times daily for hyperglycaemia on Day 1 ({pre_blood_first}% vs {post_blood_first}% respectively) and Day 2 ({pre_blood_second}% vs {post_blood_second}%)."
specs = {
'height': Cm(1.2),
'width': Cm(27.3),
'left': Cm(1.8),
'top': Cm(4.5),
}
self._add_textbox(specs, second_slide, proportion_text)
paracetamol_text = f"iv) {post_insulin} out of {post_insulin_total} ({post_insulin_perc}%) patients with BGL's > 10mmol/L were treated with insulin."
specs = {
'height': Cm(0.7),
'width': Cm(27.3),
'left': Cm(1.8),
'top': Cm(5.0),
}
self._add_textbox(specs, second_slide, paracetamol_text)
# Add swallow screening
column_name = '# Swallow screen performed within 24 hours'
pre_swallow = self.pre_stats[self._get_percentage_column_name(column_name)].iloc[0]
post_swallow = self.post_stats[self._get_percentage_column_name(column_name)].iloc[0]
difference = "no difference" if post_swallow <= pre_swallow else "an increase"
column_name = '# Swallow screen or swallow assessment performed before being given oral medications'
post_oral_med = self.post_stats[column_name].iloc[0]
post_oral_med_perc = self.post_stats[self._get_percentage_column_name(column_name)].iloc[0]
column_name = '# Swallow screen or swallow assessment performed before being given oral food or fluids'
post_food = self.post_stats[column_name].iloc[0]
post_food_perc = self.post_stats[self._get_percentage_column_name(column_name)].iloc[0]
text = "3. Swallow screening"
specs = {
'height': Cm(0.7),
'width': Cm(28.5),
'left': Cm(0.6),
'top': Cm(6.0),
}
self._add_textbox(specs, second_slide, text, bold=True)
swallow_text = f"v) {difference.capitalize()} in the proportion of patients who received a swallow screen within 24hrs ({pre_swallow}% vs {post_swallow}%)."
specs = {
'height': Cm(0.7),
'width': Cm(27.3),
'left': Cm(1.8),
'top': Cm(6.5),
}
self._add_textbox(specs, second_slide, swallow_text)
medication_text = f"vi) {post_oral_med} ({post_oral_med_perc}%) patients received oral medications before having their swallow screened and {post_food} ({post_food_perc}%) patients received oral food or fluid before having their swallow screened."
specs = {
'height': Cm(1.2),
'width': Cm(27.3),
'left': Cm(1.8),
'top': Cm(7),
}
self._add_textbox(specs, second_slide, medication_text)
# Summary box
specs = {
'height': Cm(2),
'width': Cm(27.3),
'left': Cm(0.6),
'top': Cm(8.5),
}
txBox = second_slide.shapes.add_textbox(specs['left'], specs['top'], specs['width'], specs['height'])
txBox.text_frame.clear()
txBox.text_frame.word_wrap = True
self._add_run(
txBox,
'SUMMARY: ',
bold=True
)
temp_monitoring = self.post_stats['# monitored for fever and/or hyperglycaemia four times a day'].iloc[0]
acute_stroke_pts = self.post_stats['# acute stroke'].iloc[0]
self._add_run(txBox, f"Monitoring for fever and/or hyperglycaemia four times a day for the first 48 - 72 hours of admission was recorded for {temp_monitoring} patient of the {acute_stroke_pts} acute stroke patients audited. A swallow screen or assessment was performed for {post_swallow} patient. Most of those patients who required treatment for fever and hyperglycaemia had Paracetamol (or other anti-pyretic) or Insulin administered within the recommended one-hour time period.", italic=True)
# Add comparison graph
data = {
'Criterium': ["Temp (Day 1)", "BGL's (Day 1)", "Swallow screen first (24hrs)"],
'Pre': [pre_temp_first, pre_blood_first, pre_swallow],
'Post': [post_temp_first, post_blood_first, post_swallow]}
graph_data = pd.DataFrame(data=data)
column_names = ["Pre", "Post"]
chart_data = ChartData()
chart_data.categories = graph_data['Criterium'].tolist()
for val in column_names:
chart_data.add_series(val, graph_data[val].tolist())
# Add chart on slide
specs = {
'height': Cm(9.5),
'width': Cm(15),
'left': Cm(0.6),
'top': Cm(10)
}
chart = second_slide.shapes.add_chart(
XL_CHART_TYPE.COLUMN_CLUSTERED, specs['left'],specs['top'], specs['width'],specs['height'], chart_data).chart
plot = chart.plots[0]
# All bars with the same color
plot.vary_by_categories = False
# Show data labels and set font
plot.has_data_labels = True
# Set layout of labels
data_labels = plot.data_labels
data_labels.font.size = Pt(9)
# Set colors of each serie
colors = [
RGBColor(192, 80, 77),
RGBColor(79, 129, 189)
]
# Iterate over column names and set color of the series and add labels
for i in range(0, len(column_names)):
series = chart.series[i]
fill = series.format.fill
# Set colors of series
fill.solid()
fill.fore_color.rgb = colors[i]
# Set maximum to 100
value_axis = chart.value_axis
value_axis.maximum_scale = 100
# change font size of values
tick_labels = value_axis.tick_labels
tick_labels.font.size = Pt(10)
# Show major gridlines
value_axis.major_gridlines.format.line.width = Pt(0.5)
value_axis.major_gridlines.format.line.color.rgb = RGBColor(206, 206, 206) # Set color to gray (A6A6A6)
value_axis.format.line.color.rgb = RGBColor(0, 0, 0)
solidFill = value_axis.format.line.color._xFill
self._set_transparency(100, solidFill)
# Value for y-axis (change font size, name, and other things)
category_axis = chart.category_axis
# Set 100% transparency to category axis
category_axis.format.line.color.rgb = RGBColor(206, 206, 206)
solidFill = category_axis.format.line.color._xFill
self._set_transparency(100, solidFill)
# change font size of category axis
category_labels = category_axis.tick_labels
category_labels.font.size = Pt(10)
# Set graph of title
graph_title = f'Figure 1: Hospital {hospital_name} Pre/Post FeSS intervention'
chart_text = chart.chart_title.text_frame
chart_text.text = graph_title
chart_text.paragraphs[0].font.size = Pt(12)
chart_text.paragraphs[0].font.color.rgb = RGBColor(89, 89, 89)
# Set legend
chart.has_legend = True
chart.legend.position = XL_LEGEND_POSITION.TOP
chart.legend.include_in_layout = False
chart.legend.font.size = Pt(9)
# Save presentation
path = os.path.join(os.getcwd(), output_file)
save_file(output_file)
prs.save(path) | [
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13,
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7122,
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9970,
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1962,
29918,
1445,
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13,
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4905,
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29897,
13,
4706,
544,
29879,
29889,
7620,
29898,
2084,
29897,
2
] |
day23-2.py | AnthonyFloyd/2017-AdventOfCode-Python | 0 | 181430 | <reponame>AnthonyFloyd/2017-AdventOfCode-Python
counter = 0
b = 106700
c = 123700
step = 17
for target in range(b, c + step, step):
flag = False
d = 2
while d != target:
e = target // d
if e < d:
break
if target % d == 0:
flag = True
#print("d: {0:d} e: {1:d} b: {2:d}".format(d, e, target))
break
d += 1
if flag:
counter += 1
else:
print("Prime: {0:d}".format(target))
print("{0:d}".format(counter))
#905 is correct
| [
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338,
1959,
13,
2
] |
leds/jars/model.py | FlamingLotusGirls/Serenity | 2 | 102698 | <filename>leds/jars/model.py<gh_stars>1-10
import json
import numpy as np
class Model(object):
"""
Minimal base class for a model describing the layout of the LEDs in physical space.
"""
def __init__(self, num_leds):
self.numLEDs = num_leds
class JarModel(Model):
"""
The model has the following members, derived from the 'points.json' file
(or whatever file you decide to load):
- rawPoints. Array of raw positions, unscaled.
- nodes. Array of scaled positions - coordinates have been scaled to [0,1]
"""
def __init__(self, jar_id, points_filename='jars.json'):
# pull raw positions from JSON
json_data = json.load(open(points_filename))
jar_data = json_data[str(jar_id)]
self.rawPoints = self._nodesFromJSON(jar_data)
# Scaled Nodes: It's easier to work with coordinates in the range
# [0, 1], so find the bounding box and scale the coordinates
# accordingly
# The minimum and maximum are 3-vectors in the same coordinate space as self.nodes.
self.minAABB = [ min(v[i] for v in self.rawPoints) for i in range(3) ]
self.maxAABB = [ max(v[i] for v in self.rawPoints) for i in range(3) ]
self.nodes = np.array(
[
[
(v[i] - self.minAABB[i]) / (self.maxAABB[i] - self.minAABB[i])
for i in range(3)
]
for v in self.rawPoints
]
)
# Init base model
Model.__init__(self, len(self.rawPoints))
def _nodesFromJSON(self, jar_data):
points = []
for val in jar_data:
points.append(val['point'])
return np.array(points)
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2
] |
controllers/task_handler.py | MuhammadWaleedUsman/PythonTornadoDockerTraefikMySql | 0 | 35235 | from .db_handler import cursor
from tornado import concurrent
import tornado.web
executor = concurrent.futures.ThreadPoolExecutor(8)
def start_task(arg):
print("The Task has started") # Async task
return True
def stop_task(arg):
print("The Task has stopped") # Async task
return True
class HandlerStart(tornado.web.RequestHandler):
def post(self, username):
cursor.execute("SELECT * FROM users")
users = cursor.fetchall()
user_names = []
if users:
keys = ("id", "name", "email")
list_of_users = [dict(zip(keys, values)) for values in users]
for user in list_of_users:
user_names.append(user['name'])
if username in set(user_names):
flag = True
else:
flag = False
else:
flag = False
if flag:
executor.submit(start_task, username)
response = "started"
else:
response = "User Doesn't exist"
self.write('request accepted |' + str(username) + ' | ' + str(response))
class HandlerStop(tornado.web.RequestHandler):
def post(self, username):
cursor.execute("SELECT * FROM users")
users = cursor.fetchall()
user_names = []
if users:
keys = ("id", "name", "email")
list_of_users = [dict(zip(keys, values)) for values in users]
for user in list_of_users:
user_names.append(user['name'])
if username in set(user_names):
flag = True
else:
flag = False
else:
flag = False
if flag:
executor.submit(stop_task, username)
response = "stopped"
else:
response = "User Doesn't exist"
self.write('request accepted |' + str(username) + ' | ' + str(response))
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525,
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525,
718,
851,
29898,
5327,
876,
13,
2
] |
feed.py | rlexmann/CatFeeder | 0 | 42210 | import os
import sys
import fcntl
fh=0
def run_once():
global fh
fh=open(os.path.realpath(__file__),'r')
try:
fcntl.flock(fh,fcntl.LOCK_EX|fcntl.LOCK_NB)
except:
os._exit(0)
run_once()
| [
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] |
aurorae/cnab240/v10_7/types.py | vintasoftware/aurorae | 9 | 126975 | <filename>aurorae/cnab240/v10_7/types.py<gh_stars>1-10
# pylint: disable=unsubscriptable-object
from datetime import datetime
from decimal import Decimal
from enum import Enum, IntEnum
from typing import ClassVar, Union
from pydantic import BaseModel, conint, constr, validator
STR_FILL_VALUE = " "
INT_FILL_VALUE = "0"
REGISTRATION_TYPE_MAP = {
"ISENTO/NAO INFORMADO": 0,
"CPF": 1,
"CGC/CNPJ": 2,
"PIS/PASEP": 3,
"OUTROS": 9,
}
# fmt: off
VALID_CHARACTERS = (
"abcdefghijklmnopqrstuvxywz"
"ABCDEFGHIJKLMNOPQRSTUVXYWZ"
"0123456789"
". "
)
# fmt: on
class CNABComposedField(BaseModel):
def get_field_names(self):
return self.__fields__.keys()
def get_fields(self):
fields = []
for field_name in self.get_field_names():
field = getattr(self, field_name)
fields.append(field)
return fields
def as_fixed_width(self):
formatted_value = ""
for field in self.get_fields():
formatted_value = f"{formatted_value}{field.as_fixed_width()}"
assert len(formatted_value) == self._max_str_length
return formatted_value
class CNABString(BaseModel):
@property
def value(self):
value = self.__root__
if isinstance(value, (CNABString, CNABComposedField)):
value = value.as_fixed_width()
return value
@validator("__root__", pre=True, check_fields=False, allow_reuse=True)
def validate_string(cls, value): # noqa
if not isinstance(value, (CNABString, CNABComposedField)):
assert all(c in VALID_CHARACTERS for c in value), "Invalid characters"
return value
def as_fixed_width(self):
return self.value.ljust(self._max_str_length, STR_FILL_VALUE).upper()
class CNABEnum(BaseModel):
@property
def value(self):
return self.__root__.value
def as_fixed_width(self):
return str(self.value)
class CNABEnumFillInt(BaseModel):
def as_fixed_width(self):
return str(self.__root__.value).rjust(self._max_str_length, INT_FILL_VALUE)
class CNABEnumAlphaFill(BaseModel):
def as_fixed_width(self):
return str(self.__root__.value).ljust(self._max_str_length, STR_FILL_VALUE)
class PositiveInt(BaseModel):
@property
def value(self):
return self.__root__
@validator("__root__", pre=True, check_fields=False, allow_reuse=True)
def validate_int(cls, value): # noqa
"""Prevent coersion from float/decimal to int, allow coersion from string"""
if isinstance(value, (Decimal, float)):
raise ValueError(
"value cannot be a float or Decimal, should be an integer instead"
)
return value
class CNABPositiveInt(PositiveInt):
def as_fixed_width(self):
return str(self.__root__).rjust(self._max_str_length, INT_FILL_VALUE)
class CNABAlphaPositiveInt(PositiveInt):
def as_fixed_width(self):
return str(self.__root__).ljust(self._max_str_length, STR_FILL_VALUE)
class CNABDate(BaseModel):
_min_str_length: ClassVar[int] = 8
_max_str_length: ClassVar[int] = 8
__root__: constr(min_length=_min_str_length, max_length=_max_str_length)
@validator("__root__", allow_reuse=True)
def validate_date(cls, value): # noqa
try:
datetime.strptime(value, "%d%m%Y")
except ValueError:
raise ValueError("{value} is not valid")
else:
return value
def as_fixed_width(self):
return self.__root__
class CNABTime(BaseModel):
_min_str_length: ClassVar[int] = 6
_max_str_length: ClassVar[int] = 6
__root__: constr(min_length=_min_str_length, max_length=_max_str_length)
@validator("__root__", allow_reuse=True)
def validate_time(cls, value): # noqa
try:
datetime.strptime(value, "%H%M%S")
except ValueError:
raise ValueError("{value} is not valid")
else:
return value
def as_fixed_width(self):
return self.__root__
class BankCode(CNABPositiveInt):
_max_str_length: ClassVar[int] = 3
_min_int: ClassVar[int] = 1
_max_int: ClassVar[int] = 999
__root__: conint(ge=_min_int, le=_max_int)
class ServiceBatchEnum(str, Enum):
file_header = "0000"
file_trailer = "9999"
class ServiceBatch(CNABEnum):
__root__: ServiceBatchEnum
class SequentialServiceBatch(CNABPositiveInt):
_max_str_length: ClassVar[int] = 4
_min_int: ClassVar[int] = 1
_max_int: ClassVar[int] = 9998
__root__: conint(ge=_min_int, le=_max_int)
class EntryTypeEnum(IntEnum):
file_header = 0
batch_header = 1
batch_initial_records = 2
details = 3
batch_final_records = 4
batch_trailer = 5
file_trailer = 9
class EntryType(CNABEnum):
__root__: EntryTypeEnum
class RegistrationTypeEnum(Enum):
exempt = "ISENTO/NAO INFORMADO"
cpf = "CPF"
cnpj = "CGC/CNPJ"
pis = "PIS/PASEP"
others = "OUTROS"
class CNABRegistrationTypeEnum(IntEnum):
exempt = 0
cpf = 1
cnpj = 2
pis = 3
others = 9
class RegistrationType(CNABEnum):
__root__: RegistrationTypeEnum
class CNABRegistrationType(CNABEnum):
__root__: CNABRegistrationTypeEnum
@validator("__root__", pre=True, allow_reuse=True)
def validate_registration_type(cls, root_value): # noqa
if isinstance(root_value, RegistrationTypeEnum):
return REGISTRATION_TYPE_MAP[root_value.value]
return root_value
class RegistrationNumber(CNABPositiveInt):
_max_str_length: ClassVar[int] = 14
_min_int: ClassVar[int] = 1
_max_int: ClassVar[int] = 99999999999999
__root__: conint(ge=_min_int, le=_max_int)
class BankConventionCode(CNABAlphaPositiveInt):
_max_str_length: ClassVar[int] = 20
_min_int: ClassVar[int] = 1
_max_int: ClassVar[int] = 99999999999999999999
__root__: conint(ge=_min_int, le=_max_int)
class BankAgencyNumber(CNABPositiveInt):
_max_str_length: ClassVar[int] = 5
_min_int: ClassVar[int] = 1
_max_int: ClassVar[int] = 99999
__root__: conint(ge=_min_int, le=_max_int)
class BankAccountNumber(CNABPositiveInt):
_max_str_length: ClassVar[int] = 12
_min_int: ClassVar[int] = 1
_max_int: ClassVar[int] = 999999999999
__root__: conint(ge=_min_int, le=_max_int)
class BankDigitCheck(CNABAlphaPositiveInt):
_max_str_length: ClassVar[int] = 1
_min_int: ClassVar[int] = 0
_max_int: ClassVar[int] = 9
__root__: constr(max_length=_max_str_length)
@validator("__root__")
def valid_int(cls, v): # noqa
try:
v_as_int = int(v)
except ValueError:
return ""
if v_as_int > cls._max_int:
raise ValueError(f"{v_as_int} is greater than the max value {cls._max_int}")
if v_as_int < cls._min_int:
raise ValueError(f"{v_as_int} is smaller than the min value {cls._min_int}")
return v
class CompanyName(CNABString):
_max_str_length: ClassVar[int] = 30
__root__: constr(max_length=_max_str_length)
class BankName(CNABString):
_max_str_length: ClassVar[int] = 30
__root__: constr(max_length=_max_str_length)
class RecipientName(CNABString):
_max_str_length: ClassVar[int] = 30
__root__: constr(max_length=_max_str_length)
class RemmitanceReturnCodeEnum(IntEnum):
shipping = 1
returning = 2
class RemmitanceReturnCode(CNABEnum):
__root__: RemmitanceReturnCodeEnum
class FileSequentialNumber(CNABPositiveInt):
_max_str_length: ClassVar[int] = 6
_min_int: ClassVar[int] = 1
_max_int: ClassVar[int] = 999999
__root__: conint(ge=_min_int, le=_max_int)
class FileRecordDensityEnum(str, Enum):
d1600 = "1600"
d6250 = "6250"
class FileRecordDensity(CNABEnumFillInt):
_max_str_length: ClassVar[int] = 5
__root__: FileRecordDensityEnum
class BankReservedField(CNABString):
_max_str_length: ClassVar[int] = 20
__root__: constr(max_length=_max_str_length)
class CompanyReservedField(CNABString):
_max_str_length: ClassVar[int] = 20
__root__: constr(max_length=_max_str_length)
class FEBRABAN1(CNABString):
_max_str_length = 1
__root__: constr(max_length=_max_str_length)
class FEBRABAN6(CNABString):
_max_str_length = 6
__root__: constr(max_length=_max_str_length)
class FEBRABAN3(CNABString):
_max_str_length: ClassVar[int] = 3
__root__: constr(max_length=_max_str_length)
class FEBRABAN9(CNABString):
_max_str_length: ClassVar[int] = 9
__root__: constr(max_length=_max_str_length)
class FEBRABAN10(CNABString):
_max_str_length: ClassVar[int] = 10
__root__: constr(max_length=_max_str_length)
class FEBRABAN165(CNABString):
_max_str_length = 165
__root__: constr(max_length=_max_str_length)
class FEBRABAN205(CNABString):
_max_str_length = 205
__root__: constr(max_length=_max_str_length)
class FEBRABAN29(CNABString):
_max_str_length: ClassVar[int] = 29
__root__: constr(max_length=_max_str_length)
class SmallAddressCityName(CNABString):
_max_str_length = 15
__root__: constr(max_length=_max_str_length)
class AddressDistrict(CNABString):
_max_str_length = 15
__root__: constr(max_length=_max_str_length)
class RebateAmount(CNABPositiveInt):
_max_str_length: ClassVar[int] = 15
_min_int: ClassVar[int] = 0
_max_int: ClassVar[int] = 999999999999999
__root__: conint(ge=_min_int, le=_max_int)
class DiscountAmount(CNABPositiveInt):
_max_str_length: ClassVar[int] = 15
_min_int: ClassVar[int] = 0
_max_int: ClassVar[int] = 999999999999999
__root__: conint(ge=_min_int, le=_max_int)
class ArrearsAmount(CNABPositiveInt):
_max_str_length: ClassVar[int] = 15
_min_int: ClassVar[int] = 0
_max_int: ClassVar[int] = 999999999999999
__root__: conint(ge=_min_int, le=_max_int)
class FineAmount(CNABPositiveInt):
_max_str_length: ClassVar[int] = 15
_min_int: ClassVar[int] = 0
_max_int: ClassVar[int] = 999999999999999
__root__: conint(ge=_min_int, le=_max_int)
class InformationRegistrationType(CNABString):
_max_str_length: ClassVar[int] = 15
__root__: constr(max_length=_max_str_length)
class OperationTypeEnum(str, Enum):
credit_entry = "C"
debit_entry = "D"
conciliation_statement = "E"
cash_management_statement = "G"
information_securities_secured_from_bank = "I"
shipping_file = "R"
return_file = "T"
class OperationType(CNABEnum):
__root__: OperationTypeEnum
class ServiceTypeEnum(str, Enum):
charge = "01"
electronic_payment_slip = "03"
bank_reconciliation = "04"
debts = "05"
check_custody = "06"
cash_management = "07"
query_margin_nformation = "08"
registration_of_consignment_rRetention = "09"
dividend_payment = "10"
consignment_maintenance = "11"
consignment_of_installments = "12"
consignment_disallowance_inss = "13"
inquiry_taxes_payable = "14"
supplier_payment = "20"
payment_of_bills_taxes = "22"
interoperability_payment_institution_accounts = "23"
buy = "25"
buy_rotary = "26"
payer_claim = "29"
salary_payment = "30"
payment_fees = "32"
payment_stipend = "33"
payment_prebends = "34"
vendor = "40"
forward_seller = "41"
payment_of_insured = "50"
payment_of_transit_traveler_expenses = "60"
authorized_payment = "70"
accredited_payment = "75"
remuneration_payment = "77"
payment_of_authorized_representatives = "80"
payment_benefits = "90"
miscellaneous_payments = "98"
class ServiceType(CNABEnum):
__root__: ServiceTypeEnum
class ReleaseMethodEnum(str, Enum):
credit_in_checking_account = "01"
administrative_payment_check = "02"
doc_ted = "03"
card_salary = "04"
credit_in_account_savings = "05"
op_disposition = "10"
payment_of_accounts_and_tributos_with_bar_code = "11"
tax_DARF_normal = "16"
tax_GPS = "17"
tax_DARF_simple = "18"
tax_IPTU_city = "19"
payment_with_authentication = "20"
tax_DARJ = "21"
tax_GARE_SP_ICMS = "22"
tax_GARE_SP_DR = "23"
tax_GARE_SP_ITCMD = "24"
tax_IPVA = "25"
tax_licenciamento = "26"
tax_DPVAT = "27"
settlement_of_securities_of_the_bank = "30"
payment_of_securities_from_other_banks = "31"
current_account_statement = "40"
TED_other_entitlement = "41"
TED_same_ownership = "43"
TED_transfer_from_investment_account = "44"
PIX = "45"
PIX_qr_code = "47"
debit_checking_account = "50"
statement_for_cash_management = "70"
deposit_judicial_in_account_current = "71"
deposit_judicial_in_savings = "72"
statement_of_investment_account = "73"
payment_of_municipal_taxes_ISS_own_bank = "80"
payment_of_municipal_taxes_ISS_other_bank = "81"
class ReleaseMethod(CNABEnum):
__root__: ReleaseMethodEnum
class BatchLayoutVersionNumberEnum(str, Enum):
v0_46 = "046"
class BatchLayoutVersionNumber(CNABEnum):
__root__: BatchLayoutVersionNumberEnum
class FileLayoutVersionNumberEnum(str, Enum):
v10_3 = "103"
class FileLayoutVersionNumber(CNABEnum):
__root__: FileLayoutVersionNumberEnum
class Message(CNABString):
_max_str_length = 40
__root__: constr(max_length=_max_str_length)
class AddressName(CNABString):
_max_str_length = 30
__root__: constr(max_length=_max_str_length)
class AddressNumber(CNABPositiveInt):
_max_str_length = 5
_min_int: ClassVar[int] = 1
_max_int: ClassVar[int] = 99999
__root__: conint(ge=_min_int, le=_max_int)
class DetailRecordSegmentTypeEnum(str, Enum):
segment_a = "A"
segment_b = "B"
segment_c = "C"
class DetailRecordSegmentType(CNABEnum):
__root__: DetailRecordSegmentTypeEnum
class PersonName(CNABString):
_max_str_length: ClassVar[int] = 30
__root__: constr(max_length=_max_str_length)
class RecordSequentialNumber(CNABPositiveInt):
_max_str_length: ClassVar[int] = 5
_min_int: ClassVar[int] = 1
_max_int: ClassVar[int] = 99999
__root__: conint(ge=_min_int, le=_max_int)
class InitiationFormEnum(str, Enum):
pix_phone = "01"
pix_email = "02"
pix_cpf_cnpj = "03"
random_key = "04"
bank_info = "05"
class InitiationForm(CNABEnumAlphaFill):
_max_str_length: ClassVar[int] = 3
__root__: InitiationFormEnum
class SIAPE6(CNABPositiveInt):
_max_str_length: ClassVar[int] = 6
_min_int: ClassVar[int] = 0
_max_int: ClassVar[int] = 999999
__root__: conint(ge=_min_int, le=_max_int)
class ISPBCode(CNABPositiveInt):
_max_str_length: ClassVar[int] = 8
_min_int: ClassVar[int] = 0
_max_int: ClassVar[int] = 99999999
__root__: conint(ge=_min_int, le=_max_int)
class AddressDetails(CNABString):
_max_str_length = 15
__root__: constr(max_length=_max_str_length)
class AddressCityName(CNABString):
_max_str_length = 20
__root__: constr(max_length=_max_str_length)
class AddressCEP(CNABPositiveInt):
_max_str_length = 5
_min_int: ClassVar[int] = 0
_max_int: ClassVar[int] = 99999
__root__: conint(ge=_min_int, le=_max_int)
class AddressCEPComplement(CNABPositiveInt):
_max_str_length = 3
_min_int: ClassVar[int] = 0
_max_int: ClassVar[int] = 999
__root__: conint(ge=_min_int, le=_max_int)
class AddressState(CNABString):
_max_str_length = 2
__root__: constr(max_length=_max_str_length)
class PaymentMethodEnum(str, Enum):
direct_debit = "01"
loan_financing_debit = "02"
credit_card_debit = "03"
class PaymentMethod(CNABEnum):
__root__: PaymentMethodEnum
class MovimentationTypeEnum(IntEnum):
inclusion = 0
consultation = 1
suspension = 2
chargeback = 3
reactivation = 4
update = 5
liquidation = 7
exclusion = 9
class MovimentationType(CNABEnum):
__root__: MovimentationTypeEnum
class MovimentationInstructionCodeEnum(str, Enum):
inclusion_of_cleared_detail_record = "00"
inclusion_of_blocked_detail_record = "09"
change_payment_from_cleared_to_blocked = "10"
change_blocked_from_payment_to_cleared = "11"
change_in_security_value = "17"
payment_date_change = "19"
direct_payment_to_supplier = "23"
wallet_maintenance_do_not_pay = "25"
wallet_withdrawal_do_not_pay = "27"
reversal_by_return_from_central_clearing_house = "33"
payer_claim = "40"
delete_previously_added_detail_record = "99"
class MovimentationInstructionCode(CNABEnum):
__root__: MovimentationInstructionCodeEnum
class CentralClearingHouseCodeEnum(str, Enum):
TED = "018"
DOC = "700"
# Used when it's necessary to send TED using the ISPB code
# of the Recipient Financial Institution. In this case, it's mandatory to fill
# the field "ISPB Code" - field 26.3B,
# of the Payment Segment, as described in Note P015.
TED_ISPB = "988"
PIX = "009"
class CentralClearingHouseCode(CNABEnum):
__root__: CentralClearingHouseCodeEnum
class CurrencyEnum(str, Enum):
national_treasury_bonus_tr = "BTN"
real = "BRL"
us_dollar = "USD"
portuguese_shield = "PTE"
french_franc = "FRF"
swiss_franc = "CHF"
japanese_yen = "JPY"
general_price_index = "IGP"
general_market_price_index = "IGM"
pound_sterling = "GBP"
italian_lira = "ITL"
german_mark = "DEM"
daily_referential_rate = "TRD"
standard_capital_unit = "UPC"
standard_financing_unit = "UPF"
tax_reference_unit = "UFR"
european_currenc = "XEU"
class Currency(CNABEnum):
__root__: CurrencyEnum
class CurrencyAmount(CNABPositiveInt):
_max_str_length = 15
_min_int: ClassVar[int] = 0
_max_int: ClassVar[int] = 999999999999999
__root__: conint(ge=_min_int, le=_max_int)
class PaymentAmount(CNABPositiveInt):
_max_str_length: ClassVar[int] = 15
_min_int: ClassVar[int] = 0
_max_int: ClassVar[int] = 999999999999999
__root__: conint(ge=_min_int, le=_max_int)
class BankOrCompanyIssuedDocNumber(CNABString):
_max_str_length: ClassVar[int] = 20
__root__: constr(max_length=_max_str_length)
class PaymentEffectiveAmount(CNABPositiveInt):
_max_str_length: ClassVar[int] = 15
_min_int: ClassVar[int] = 0
_max_int: ClassVar[int] = 999999999999999
__root__: conint(ge=_min_int, le=_max_int)
class ServiceTypeComplementEnum(str, Enum):
account_credit = "01"
payment_of_rent_condominium = "02"
payment_of_duplicate_securities = "03"
payment_of_dividends = "04"
payment_of_school_tuition = "05"
salary_payment = "06"
payment_to_suppliers = "07"
transactions_of_foreign_exchange_funds_stock_exchange = "08"
transfer_of_collection_payment_of_taxes = "09"
international_transfer_in_real = "10"
doc_for_savings = "11"
doc_for_judicial_deposit = "12"
others = "13"
stipend_payment = "16"
remuneration_to_the_member = "17"
pagamento_of_fees = "18"
payment_of_prebend = "19"
class ServiceTypeComplement(CNABEnum):
__root__: ServiceTypeComplementEnum
class TEDFinalityCode(CNABAlphaPositiveInt):
_max_str_length: ClassVar[int] = 5
__root__: constr(max_length=_max_str_length)
class PaymentFinalityComplement(CNABString):
_max_str_length: ClassVar[int] = 2
__root__: constr(max_length=_max_str_length)
class NotifyRecipientEnum(str, Enum):
no_notification = 0
notify_only_sender = 2
notify_only_recipient = 5
notify_sender_and_recipient = 6
notify_recipient_and_2_copies_to_sender = 7
class NotifyRecipient(CNABEnum):
__root__: NotifyRecipientEnum
class ReturnOccurrenceCodes(CNABString):
_max_str_length = 10
__root__: constr(max_length=_max_str_length)
class RecordsNumber(CNABPositiveInt):
_max_str_length = 6
_min_int = 0
_max_int = 999999
__root__: conint(ge=_min_int, le=_max_int)
class ValuesSum(CNABPositiveInt):
_max_str_length: ClassVar[int] = 18
_min_int: ClassVar[int] = 0
_max_int: ClassVar[int] = 999999999999999999
__root__: conint(ge=_min_int, le=_max_int)
class CurrencyAmountSum(CNABPositiveInt):
_max_str_length: ClassVar[int] = 18
_min_int: ClassVar[int] = 0
_max_int: ClassVar[int] = 999999999999999999
__root__: conint(ge=_min_int, le=_max_int)
class DebitNotificationNumber(CNABPositiveInt):
_max_str_length: ClassVar[int] = 6
_min_int: ClassVar[int] = 0
_max_int: ClassVar[int] = 999999
__root__: conint(ge=_min_int, le=_max_int)
class ComposedAddressInformation(CNABComposedField):
_max_str_length: ClassVar[int] = 60
address_number: AddressNumber
address_complement: AddressDetails
address_district: AddressDistrict
address_city: SmallAddressCityName
address_cep: AddressCEP
address_cep_complement: AddressCEPComplement
address_state: AddressState
class ComposedPaymentInformation(CNABComposedField):
_max_str_length: ClassVar[int] = 99
payment_date: CNABDate
payment_amount: PaymentAmount
rebate_amount: RebateAmount
discount_amount: DiscountAmount
arrears_amount: ArrearsAmount
fine_amount: FineAmount
registration_type: InformationRegistrationType
notify_recipient: NotifyRecipient
class PIXTXID(CNABString):
_max_str_length: ClassVar[int] = 30
__root__: constr(max_length=_max_str_length)
class PIXKey(CNABString):
_max_str_length: ClassVar[int] = 60
__root__: constr(max_length=_max_str_length)
class Information10(CNABString):
_max_str_length: ClassVar[int] = 35
__root__: Union[AddressName, PIXTXID]
class Information11(CNABString):
_max_str_length: ClassVar[int] = 60
__root__: Union[PIXKey, Message, ComposedAddressInformation]
class Information12(CNABString):
_max_str_length: ClassVar[int] = 99
__root__: Union[PIXKey, ComposedPaymentInformation]
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1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
6692,
23145,
16854,
29898,
710,
29892,
1174,
398,
1125,
13,
1678,
934,
29918,
6672,
353,
376,
29900,
29900,
29900,
29900,
29908,
13,
1678,
934,
29918,
3018,
3955,
353,
376,
29929,
29929,
29929,
29929,
29908,
13,
13,
13,
1990,
6692,
23145,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
6692,
23145,
16854,
13,
13,
13,
1990,
922,
339,
2556,
3170,
23145,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29946,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29947,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
28236,
1542,
16854,
29898,
2928,
16854,
1125,
13,
1678,
934,
29918,
6672,
353,
29871,
29900,
13,
1678,
9853,
29918,
6672,
353,
29871,
29896,
13,
1678,
9853,
29918,
11228,
29918,
3757,
4339,
353,
29871,
29906,
13,
1678,
4902,
353,
29871,
29941,
13,
1678,
9853,
29918,
8394,
29918,
3757,
4339,
353,
29871,
29946,
13,
1678,
9853,
29918,
3018,
3955,
353,
29871,
29945,
13,
1678,
934,
29918,
3018,
3955,
353,
29871,
29929,
13,
13,
13,
1990,
28236,
1542,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
28236,
1542,
16854,
13,
13,
13,
1990,
2169,
8306,
1542,
16854,
29898,
16854,
1125,
13,
1678,
429,
3456,
353,
376,
3235,
3919,
29949,
29914,
3521,
29949,
2672,
22051,
1529,
3970,
29908,
13,
1678,
274,
7810,
353,
376,
6271,
29943,
29908,
13,
1678,
274,
9302,
29926,
353,
376,
29907,
8766,
29914,
13778,
29925,
29967,
29908,
13,
1678,
20066,
353,
376,
2227,
29903,
29914,
7228,
1660,
29925,
29908,
13,
1678,
4045,
353,
376,
12015,
1672,
29903,
29908,
13,
13,
13,
1990,
315,
29940,
2882,
4597,
8306,
1542,
16854,
29898,
2928,
16854,
1125,
13,
1678,
429,
3456,
353,
29871,
29900,
13,
1678,
274,
7810,
353,
29871,
29896,
13,
1678,
274,
9302,
29926,
353,
29871,
29906,
13,
1678,
20066,
353,
29871,
29941,
13,
1678,
4045,
353,
29871,
29929,
13,
13,
13,
1990,
2169,
8306,
1542,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
2169,
8306,
1542,
16854,
13,
13,
13,
1990,
315,
29940,
2882,
4597,
8306,
1542,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
315,
29940,
2882,
4597,
8306,
1542,
16854,
13,
13,
1678,
732,
3084,
1061,
703,
1649,
4632,
1649,
613,
758,
29922,
5574,
29892,
2758,
29918,
276,
1509,
29922,
5574,
29897,
13,
1678,
822,
12725,
29918,
1727,
8306,
29918,
1853,
29898,
25932,
29892,
3876,
29918,
1767,
1125,
29871,
396,
694,
25621,
13,
4706,
565,
338,
8758,
29898,
4632,
29918,
1767,
29892,
2169,
8306,
1542,
16854,
1125,
13,
9651,
736,
5195,
29954,
9047,
29934,
8098,
29918,
11116,
29918,
23827,
29961,
4632,
29918,
1767,
29889,
1767,
29962,
13,
4706,
736,
3876,
29918,
1767,
13,
13,
13,
1990,
2169,
8306,
4557,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
29946,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
10253,
1168,
7316,
3399,
29898,
13778,
2882,
28630,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29906,
29900,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
10253,
29909,
14703,
4557,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29945,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
10253,
10601,
4557,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
29906,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
10253,
14991,
277,
5596,
29898,
13778,
2882,
28630,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29900,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
1678,
732,
3084,
1061,
703,
1649,
4632,
1649,
1159,
13,
1678,
822,
2854,
29918,
524,
29898,
25932,
29892,
325,
1125,
29871,
396,
694,
25621,
13,
4706,
1018,
29901,
13,
9651,
325,
29918,
294,
29918,
524,
353,
938,
29898,
29894,
29897,
13,
4706,
5174,
7865,
2392,
29901,
13,
9651,
736,
5124,
13,
13,
4706,
565,
325,
29918,
294,
29918,
524,
1405,
1067,
29879,
3032,
3317,
29918,
524,
29901,
13,
9651,
12020,
7865,
2392,
29898,
29888,
29908,
29912,
29894,
29918,
294,
29918,
524,
29913,
338,
7621,
1135,
278,
4236,
995,
426,
25932,
3032,
3317,
29918,
524,
27195,
13,
13,
4706,
565,
325,
29918,
294,
29918,
524,
529,
1067,
29879,
3032,
1195,
29918,
524,
29901,
13,
9651,
12020,
7865,
2392,
29898,
29888,
29908,
29912,
29894,
29918,
294,
29918,
524,
29913,
338,
7968,
1135,
278,
1375,
995,
426,
25932,
3032,
1195,
29918,
524,
27195,
13,
13,
4706,
736,
325,
13,
13,
13,
1990,
6938,
1170,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29941,
29900,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
10253,
1170,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29941,
29900,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
830,
7334,
993,
1170,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29941,
29900,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
5240,
2415,
749,
11609,
3399,
16854,
29898,
2928,
16854,
1125,
13,
1678,
528,
17347,
353,
29871,
29896,
13,
1678,
7863,
353,
29871,
29906,
13,
13,
13,
1990,
5240,
2415,
749,
11609,
3399,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
5240,
2415,
749,
11609,
3399,
16854,
13,
13,
13,
1990,
3497,
16941,
2556,
4557,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29953,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
3497,
9182,
29928,
575,
537,
16854,
29898,
710,
29892,
1174,
398,
1125,
13,
1678,
270,
29896,
29953,
29900,
29900,
353,
376,
29896,
29953,
29900,
29900,
29908,
13,
1678,
270,
29953,
29906,
29945,
29900,
353,
376,
29953,
29906,
29945,
29900,
29908,
13,
13,
13,
1990,
3497,
9182,
29928,
575,
537,
29898,
13778,
2882,
16854,
20876,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29945,
13,
1678,
4770,
4632,
1649,
29901,
3497,
9182,
29928,
575,
537,
16854,
13,
13,
13,
1990,
10253,
1666,
9841,
3073,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29906,
29900,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
6938,
1666,
9841,
3073,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29906,
29900,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
383,
29923,
15176,
2882,
2190,
29896,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29896,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
383,
29923,
15176,
2882,
2190,
29953,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29953,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
383,
29923,
15176,
2882,
2190,
29941,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29941,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
383,
29923,
15176,
2882,
2190,
29929,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
383,
29923,
15176,
2882,
2190,
29896,
29900,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
29900,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
383,
29923,
15176,
2882,
2190,
29896,
29953,
29945,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29896,
29953,
29945,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
383,
29923,
15176,
2882,
2190,
29906,
29900,
29945,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29906,
29900,
29945,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
383,
29923,
15176,
2882,
2190,
29906,
29929,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29906,
29929,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
18285,
7061,
16885,
1170,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29896,
29945,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
16428,
29928,
6801,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29896,
29945,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
12936,
403,
18087,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
29945,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29900,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
3295,
2798,
18087,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
29945,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29900,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
826,
276,
1503,
18087,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
29945,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29900,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
28896,
18087,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
29945,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29900,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
10343,
4597,
8306,
1542,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
29945,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
20462,
1542,
16854,
29898,
710,
29892,
1174,
398,
1125,
13,
1678,
16200,
29918,
8269,
353,
376,
29907,
29908,
13,
1678,
2553,
277,
29918,
8269,
353,
376,
29928,
29908,
13,
1678,
3022,
2638,
362,
29918,
20788,
353,
376,
29923,
29908,
13,
1678,
274,
1161,
29918,
21895,
29918,
20788,
353,
376,
29954,
29908,
13,
1678,
2472,
29918,
344,
2764,
1907,
29918,
3471,
2955,
29918,
3166,
29918,
9157,
353,
376,
29902,
29908,
13,
1678,
528,
17347,
29918,
1445,
353,
376,
29934,
29908,
13,
1678,
736,
29918,
1445,
353,
376,
29911,
29908,
13,
13,
13,
1990,
20462,
1542,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
20462,
1542,
16854,
13,
13,
13,
1990,
6692,
1542,
16854,
29898,
710,
29892,
1174,
398,
1125,
13,
1678,
8323,
353,
376,
29900,
29896,
29908,
13,
1678,
27758,
29918,
27825,
29918,
29879,
3466,
353,
376,
29900,
29941,
29908,
13,
1678,
9124,
29918,
276,
535,
29883,
2638,
362,
353,
376,
29900,
29946,
29908,
13,
1678,
2553,
1372,
353,
376,
29900,
29945,
29908,
13,
1678,
1423,
29918,
29883,
504,
1486,
353,
376,
29900,
29953,
29908,
13,
1678,
274,
1161,
29918,
21895,
353,
376,
29900,
29955,
29908,
13,
1678,
2346,
29918,
9264,
29918,
29876,
5404,
353,
376,
29900,
29947,
29908,
13,
1678,
22583,
29918,
974,
29918,
3200,
10194,
29918,
29878,
8015,
2509,
353,
376,
29900,
29929,
29908,
13,
1678,
25227,
355,
29918,
27825,
353,
376,
29896,
29900,
29908,
13,
1678,
1136,
10194,
29918,
3396,
841,
749,
353,
376,
29896,
29896,
29908,
13,
1678,
1136,
10194,
29918,
974,
29918,
6252,
1860,
353,
376,
29896,
29906,
29908,
13,
1678,
1136,
10194,
29918,
2218,
9536,
749,
29918,
262,
893,
353,
376,
29896,
29941,
29908,
13,
1678,
25501,
16129,
29918,
941,
9100,
29918,
10472,
519,
353,
376,
29896,
29946,
29908,
13,
1678,
1462,
4926,
29918,
27825,
353,
376,
29906,
29900,
29908,
13,
1678,
19179,
29918,
974,
29918,
29890,
6090,
29918,
941,
9100,
353,
376,
29906,
29906,
29908,
13,
1678,
1006,
3372,
3097,
29918,
27825,
29918,
2611,
5008,
29918,
10149,
29879,
353,
376,
29906,
29941,
29908,
13,
1678,
15649,
353,
376,
29906,
29945,
29908,
13,
1678,
15649,
29918,
5450,
653,
353,
376,
29906,
29953,
29908,
13,
1678,
282,
2747,
29918,
29883,
8342,
353,
376,
29906,
29929,
29908,
13,
1678,
4497,
653,
29918,
27825,
353,
376,
29941,
29900,
29908,
13,
1678,
19179,
29918,
1725,
267,
353,
376,
29941,
29906,
29908,
13,
1678,
19179,
29918,
303,
666,
355,
353,
376,
29941,
29941,
29908,
13,
1678,
19179,
29918,
1457,
29890,
1975,
353,
376,
29941,
29946,
29908,
13,
1678,
27042,
353,
376,
29946,
29900,
29908,
13,
1678,
6375,
29918,
29879,
4539,
353,
376,
29946,
29896,
29908,
13,
1678,
19179,
29918,
974,
29918,
1144,
2955,
353,
376,
29945,
29900,
29908,
13,
1678,
19179,
29918,
974,
29918,
3286,
277,
29918,
3018,
955,
261,
29918,
4548,
11259,
353,
376,
29953,
29900,
29908,
13,
1678,
4148,
1891,
29918,
27825,
353,
376,
29955,
29900,
29908,
13,
1678,
1035,
1127,
1573,
29918,
27825,
353,
376,
29955,
29945,
29908,
13,
1678,
1083,
348,
261,
362,
29918,
27825,
353,
376,
29955,
29955,
29908,
13,
1678,
19179,
29918,
974,
29918,
8921,
1891,
29918,
276,
6338,
5056,
353,
376,
29947,
29900,
29908,
13,
1678,
19179,
29918,
1785,
1389,
1169,
353,
376,
29929,
29900,
29908,
13,
1678,
3984,
3729,
23584,
29918,
10472,
1860,
353,
376,
29929,
29947,
29908,
13,
13,
13,
1990,
6692,
1542,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
6692,
1542,
16854,
13,
13,
13,
1990,
23708,
4062,
16854,
29898,
710,
29892,
1174,
398,
1125,
13,
1678,
16200,
29918,
262,
29918,
3198,
292,
29918,
10149,
353,
376,
29900,
29896,
29908,
13,
1678,
19185,
29918,
27825,
29918,
3198,
353,
376,
29900,
29906,
29908,
13,
1678,
1574,
29918,
9446,
353,
376,
29900,
29941,
29908,
13,
1678,
5881,
29918,
19585,
653,
353,
376,
29900,
29946,
29908,
13,
1678,
16200,
29918,
262,
29918,
10149,
29918,
29879,
485,
886,
353,
376,
29900,
29945,
29908,
13,
1678,
1015,
29918,
2218,
3283,
353,
376,
29896,
29900,
29908,
13,
1678,
19179,
29918,
974,
29918,
10149,
29879,
29918,
392,
29918,
29873,
1091,
20864,
29918,
2541,
29918,
1646,
29918,
401,
353,
376,
29896,
29896,
29908,
13,
1678,
8818,
29918,
29928,
1718,
29943,
29918,
8945,
353,
376,
29896,
29953,
29908,
13,
1678,
8818,
29918,
29954,
7024,
353,
376,
29896,
29955,
29908,
13,
1678,
8818,
29918,
29928,
1718,
29943,
29918,
12857,
353,
376,
29896,
29947,
29908,
13,
1678,
8818,
29918,
5690,
29911,
29965,
29918,
12690,
353,
376,
29896,
29929,
29908,
13,
1678,
19179,
29918,
2541,
29918,
23055,
353,
376,
29906,
29900,
29908,
13,
1678,
8818,
29918,
29928,
1718,
29967,
353,
376,
29906,
29896,
29908,
13,
1678,
8818,
29918,
12739,
1525,
29918,
5550,
29918,
2965,
4345,
353,
376,
29906,
29906,
29908,
13,
1678,
8818,
29918,
12739,
1525,
29918,
5550,
29918,
8353,
353,
376,
29906,
29941,
29908,
13,
1678,
8818,
29918,
12739,
1525,
29918,
5550,
29918,
1806,
29907,
5773,
353,
376,
29906,
29946,
29908,
13,
1678,
8818,
29918,
5690,
20449,
353,
376,
29906,
29945,
29908,
13,
1678,
8818,
29918,
506,
13640,
4487,
353,
376,
29906,
29953,
29908,
13,
1678,
8818,
29918,
11191,
29963,
1299,
353,
376,
29906,
29955,
29908,
13,
1678,
16493,
29918,
974,
29918,
344,
2764,
1907,
29918,
974,
29918,
1552,
29918,
9157,
353,
376,
29941,
29900,
29908,
13,
1678,
19179,
29918,
974,
29918,
344,
2764,
1907,
29918,
3166,
29918,
1228,
29918,
29890,
1331,
353,
376,
29941,
29896,
29908,
13,
1678,
1857,
29918,
10149,
29918,
20788,
353,
376,
29946,
29900,
29908,
13,
1678,
323,
3352,
29918,
1228,
29918,
296,
277,
944,
353,
376,
29946,
29896,
29908,
13,
1678,
323,
3352,
29918,
17642,
29918,
776,
10475,
353,
376,
29946,
29941,
29908,
13,
1678,
323,
3352,
29918,
3286,
571,
29918,
3166,
29918,
262,
10147,
358,
29918,
10149,
353,
376,
29946,
29946,
29908,
13,
1678,
349,
6415,
353,
376,
29946,
29945,
29908,
13,
1678,
349,
6415,
29918,
29939,
29878,
29918,
401,
353,
376,
29946,
29955,
29908,
13,
1678,
2553,
277,
29918,
3198,
292,
29918,
10149,
353,
376,
29945,
29900,
29908,
13,
1678,
3229,
29918,
1454,
29918,
29883,
1161,
29918,
21895,
353,
376,
29955,
29900,
29908,
13,
1678,
19754,
277,
29918,
17675,
5611,
29918,
262,
29918,
10149,
29918,
3784,
353,
376,
29955,
29896,
29908,
13,
1678,
19754,
277,
29918,
17675,
5611,
29918,
262,
29918,
29879,
485,
886,
353,
376,
29955,
29906,
29908,
13,
1678,
3229,
29918,
974,
29918,
262,
10147,
358,
29918,
10149,
353,
376,
29955,
29941,
29908,
13,
1678,
19179,
29918,
974,
29918,
29885,
4376,
284,
29918,
941,
9100,
29918,
29902,
1799,
29918,
776,
29918,
9157,
353,
376,
29947,
29900,
29908,
13,
1678,
19179,
29918,
974,
29918,
29885,
4376,
284,
29918,
941,
9100,
29918,
29902,
1799,
29918,
1228,
29918,
9157,
353,
376,
29947,
29896,
29908,
13,
13,
13,
1990,
23708,
4062,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
23708,
4062,
16854,
13,
13,
13,
1990,
350,
905,
3453,
6594,
4557,
16854,
29898,
710,
29892,
1174,
398,
1125,
13,
1678,
325,
29900,
29918,
29946,
29953,
353,
376,
29900,
29946,
29953,
29908,
13,
13,
13,
1990,
350,
905,
3453,
6594,
4557,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
350,
905,
3453,
6594,
4557,
16854,
13,
13,
13,
1990,
3497,
3453,
6594,
4557,
16854,
29898,
710,
29892,
1174,
398,
1125,
13,
1678,
325,
29896,
29900,
29918,
29941,
353,
376,
29896,
29900,
29941,
29908,
13,
13,
13,
1990,
3497,
3453,
6594,
4557,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
3497,
3453,
6594,
4557,
16854,
13,
13,
13,
1990,
7777,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29946,
29900,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
16428,
1170,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29941,
29900,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
16428,
4557,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29945,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
5953,
737,
9182,
17669,
358,
1542,
16854,
29898,
710,
29892,
1174,
398,
1125,
13,
1678,
10768,
29918,
29874,
353,
376,
29909,
29908,
13,
1678,
10768,
29918,
29890,
353,
376,
29933,
29908,
13,
1678,
10768,
29918,
29883,
353,
376,
29907,
29908,
13,
13,
13,
1990,
5953,
737,
9182,
17669,
358,
1542,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
5953,
737,
9182,
17669,
358,
1542,
16854,
13,
13,
13,
1990,
5196,
1170,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29941,
29900,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
14164,
16941,
2556,
4557,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29945,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
512,
4812,
362,
2500,
16854,
29898,
710,
29892,
1174,
398,
1125,
13,
1678,
9277,
29918,
6710,
353,
376,
29900,
29896,
29908,
13,
1678,
9277,
29918,
5269,
353,
376,
29900,
29906,
29908,
13,
1678,
9277,
29918,
6814,
29888,
29918,
29883,
9302,
29926,
353,
376,
29900,
29941,
29908,
13,
1678,
4036,
29918,
1989,
353,
376,
29900,
29946,
29908,
13,
1678,
9124,
29918,
3888,
353,
376,
29900,
29945,
29908,
13,
13,
13,
1990,
512,
4812,
362,
2500,
29898,
13778,
2882,
16854,
28630,
20876,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29941,
13,
1678,
4770,
4632,
1649,
29901,
512,
4812,
362,
2500,
16854,
13,
13,
13,
1990,
22717,
3301,
29923,
29953,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29953,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29900,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
306,
5550,
29933,
3399,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29947,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29900,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
16428,
10602,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29896,
29945,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
16428,
16885,
1170,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29906,
29900,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
16428,
4741,
29925,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29945,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29900,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
16428,
4741,
29925,
1523,
2037,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29941,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29900,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
16428,
2792,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29906,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
14617,
358,
4062,
16854,
29898,
710,
29892,
1174,
398,
1125,
13,
1678,
1513,
29918,
311,
2966,
353,
376,
29900,
29896,
29908,
13,
1678,
24806,
29918,
4951,
19985,
29918,
311,
2966,
353,
376,
29900,
29906,
29908,
13,
1678,
16200,
29918,
7543,
29918,
311,
2966,
353,
376,
29900,
29941,
29908,
13,
13,
13,
1990,
14617,
358,
4062,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
14617,
358,
4062,
16854,
13,
13,
13,
1990,
14104,
2073,
362,
1542,
16854,
29898,
2928,
16854,
1125,
13,
1678,
28694,
353,
29871,
29900,
13,
1678,
8799,
362,
353,
29871,
29896,
13,
1678,
8872,
2673,
353,
29871,
29906,
13,
1678,
8323,
1627,
353,
29871,
29941,
13,
1678,
7657,
440,
362,
353,
29871,
29946,
13,
1678,
2767,
353,
29871,
29945,
13,
1678,
23904,
362,
353,
29871,
29955,
13,
1678,
429,
10085,
353,
29871,
29929,
13,
13,
13,
1990,
14104,
2073,
362,
1542,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
14104,
2073,
362,
1542,
16854,
13,
13,
13,
1990,
14104,
2073,
362,
3379,
4080,
3399,
16854,
29898,
710,
29892,
1174,
398,
1125,
13,
1678,
28694,
29918,
974,
29918,
2841,
1965,
29918,
16432,
29918,
11651,
353,
376,
29900,
29900,
29908,
13,
1678,
28694,
29918,
974,
29918,
1271,
287,
29918,
16432,
29918,
11651,
353,
376,
29900,
29929,
29908,
13,
1678,
1735,
29918,
27825,
29918,
3166,
29918,
2841,
1965,
29918,
517,
29918,
1271,
287,
353,
376,
29896,
29900,
29908,
13,
1678,
1735,
29918,
1271,
287,
29918,
3166,
29918,
27825,
29918,
517,
29918,
2841,
1965,
353,
376,
29896,
29896,
29908,
13,
1678,
1735,
29918,
262,
29918,
8926,
29918,
1767,
353,
376,
29896,
29955,
29908,
13,
1678,
19179,
29918,
1256,
29918,
3167,
353,
376,
29896,
29929,
29908,
13,
1678,
1513,
29918,
27825,
29918,
517,
29918,
19303,
4926,
353,
376,
29906,
29941,
29908,
13,
1678,
17042,
1026,
29918,
3396,
841,
749,
29918,
1867,
29918,
1333,
29918,
10472,
353,
376,
29906,
29945,
29908,
13,
1678,
17042,
1026,
29918,
2541,
4012,
284,
29918,
1867,
29918,
1333,
29918,
10472,
353,
376,
29906,
29955,
29908,
13,
1678,
18764,
284,
29918,
1609,
29918,
2457,
29918,
3166,
29918,
25171,
29918,
8551,
292,
29918,
8697,
353,
376,
29941,
29941,
29908,
13,
1678,
282,
2747,
29918,
29883,
8342,
353,
376,
29946,
29900,
29908,
13,
1678,
5217,
29918,
1457,
16604,
29918,
23959,
29918,
16432,
29918,
11651,
353,
376,
29929,
29929,
29908,
13,
13,
13,
1990,
14104,
2073,
362,
3379,
4080,
3399,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
14104,
2073,
362,
3379,
4080,
3399,
16854,
13,
13,
13,
1990,
8068,
18759,
292,
29950,
1709,
3399,
16854,
29898,
710,
29892,
1174,
398,
1125,
13,
1678,
323,
3352,
353,
376,
29900,
29896,
29947,
29908,
13,
1678,
11662,
29907,
353,
376,
29955,
29900,
29900,
29908,
13,
1678,
396,
501,
8485,
746,
372,
29915,
29879,
5181,
304,
3638,
323,
3352,
773,
278,
306,
5550,
29933,
775,
13,
1678,
396,
310,
278,
830,
7334,
993,
4231,
273,
1455,
28218,
29889,
512,
445,
1206,
29892,
372,
29915,
29879,
9619,
7606,
304,
5445,
13,
1678,
396,
278,
1746,
376,
3235,
29925,
29933,
5920,
29908,
448,
1746,
29871,
29906,
29953,
29889,
29941,
29933,
29892,
13,
1678,
396,
310,
278,
14617,
358,
6667,
358,
29892,
408,
5439,
297,
3940,
349,
29900,
29896,
29945,
29889,
13,
1678,
323,
3352,
29918,
3235,
29925,
29933,
353,
376,
29929,
29947,
29947,
29908,
13,
1678,
349,
6415,
353,
376,
29900,
29900,
29929,
29908,
13,
13,
13,
1990,
8068,
18759,
292,
29950,
1709,
3399,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
8068,
18759,
292,
29950,
1709,
3399,
16854,
13,
13,
13,
1990,
315,
10880,
16854,
29898,
710,
29892,
1174,
398,
1125,
13,
1678,
4797,
29918,
2484,
294,
2857,
29918,
6718,
375,
29918,
509,
353,
376,
29933,
29911,
29940,
29908,
13,
1678,
1855,
353,
376,
29933,
2241,
29908,
13,
1678,
502,
29918,
29881,
26810,
353,
376,
3308,
29928,
29908,
13,
1678,
15056,
23053,
29918,
845,
969,
353,
376,
29925,
4330,
29908,
13,
1678,
285,
4615,
29918,
16799,
29883,
353,
376,
15860,
29943,
29908,
13,
1678,
2381,
790,
29918,
16799,
29883,
353,
376,
3210,
29943,
29908,
13,
1678,
10215,
273,
968,
29918,
11771,
353,
376,
29967,
20055,
29908,
13,
1678,
2498,
29918,
9175,
29918,
2248,
353,
376,
6259,
29925,
29908,
13,
1678,
2498,
29918,
28549,
29918,
9175,
29918,
2248,
353,
376,
6259,
29924,
29908,
13,
1678,
282,
618,
29918,
2475,
1847,
353,
376,
7210,
29925,
29908,
13,
1678,
4698,
713,
29918,
492,
336,
353,
376,
1806,
29931,
29908,
13,
1678,
330,
3504,
29918,
3502,
353,
376,
2287,
29924,
29908,
13,
1678,
14218,
29918,
20275,
2556,
29918,
10492,
353,
376,
5659,
29928,
29908,
13,
1678,
3918,
29918,
5030,
2410,
29918,
5441,
353,
376,
4897,
29907,
29908,
13,
1678,
3918,
29918,
4951,
19985,
29918,
5441,
353,
376,
4897,
29943,
29908,
13,
1678,
8818,
29918,
5679,
29918,
5441,
353,
376,
29965,
15860,
29908,
13,
1678,
14721,
273,
29918,
21962,
3977,
353,
376,
29990,
29923,
29965,
29908,
13,
13,
13,
1990,
315,
10880,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
315,
10880,
16854,
13,
13,
13,
1990,
315,
10880,
18087,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29896,
29945,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29900,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
14617,
358,
18087,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
29945,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29900,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
10253,
2816,
21410,
29902,
893,
6742,
14526,
4557,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29906,
29900,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
14617,
358,
13971,
573,
18087,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
29945,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29900,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
6692,
1542,
1523,
2037,
16854,
29898,
710,
29892,
1174,
398,
1125,
13,
1678,
3633,
29918,
11944,
277,
353,
376,
29900,
29896,
29908,
13,
1678,
19179,
29918,
974,
29918,
7771,
29918,
1116,
5817,
1974,
353,
376,
29900,
29906,
29908,
13,
1678,
19179,
29918,
974,
29918,
20908,
5926,
29918,
344,
2764,
1907,
353,
376,
29900,
29941,
29908,
13,
1678,
19179,
29918,
974,
29918,
29881,
3640,
1975,
353,
376,
29900,
29946,
29908,
13,
1678,
19179,
29918,
974,
29918,
27041,
29918,
9161,
654,
353,
376,
29900,
29945,
29908,
13,
1678,
4497,
653,
29918,
27825,
353,
376,
29900,
29953,
29908,
13,
1678,
19179,
29918,
517,
29918,
19303,
27801,
353,
376,
29900,
29955,
29908,
13,
1678,
22160,
29918,
974,
29918,
1079,
647,
29918,
6543,
29918,
27159,
29879,
29918,
17712,
29918,
6543,
353,
376,
29900,
29947,
29908,
13,
1678,
6782,
29918,
974,
29918,
10855,
29918,
27825,
29918,
974,
29918,
941,
9100,
353,
376,
29900,
29929,
29908,
13,
1678,
6121,
29918,
3286,
571,
29918,
262,
29918,
6370,
353,
376,
29896,
29900,
29908,
13,
1678,
1574,
29918,
1454,
29918,
29879,
485,
886,
353,
376,
29896,
29896,
29908,
13,
1678,
1574,
29918,
1454,
29918,
17675,
5611,
29918,
311,
1066,
277,
353,
376,
29896,
29906,
29908,
13,
1678,
4045,
353,
376,
29896,
29941,
29908,
13,
1678,
380,
666,
355,
29918,
27825,
353,
376,
29896,
29953,
29908,
13,
1678,
1083,
348,
261,
362,
29918,
517,
29918,
1552,
29918,
14242,
353,
376,
29896,
29955,
29908,
13,
1678,
10203,
4487,
29918,
974,
29918,
1725,
267,
353,
376,
29896,
29947,
29908,
13,
1678,
19179,
29918,
974,
29918,
1457,
29890,
355,
353,
376,
29896,
29929,
29908,
13,
13,
13,
1990,
6692,
1542,
1523,
2037,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
6692,
1542,
1523,
2037,
16854,
13,
13,
13,
1990,
323,
3352,
15790,
537,
3399,
29898,
13778,
2882,
28630,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29945,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
14617,
358,
15790,
537,
1523,
2037,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29906,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
2216,
1598,
1123,
7334,
993,
16854,
29898,
710,
29892,
1174,
398,
1125,
13,
1678,
694,
29918,
24671,
353,
29871,
29900,
13,
1678,
26051,
29918,
6194,
29918,
15452,
353,
29871,
29906,
13,
1678,
26051,
29918,
6194,
29918,
4361,
29886,
993,
353,
29871,
29945,
13,
1678,
26051,
29918,
15452,
29918,
392,
29918,
4361,
29886,
993,
353,
29871,
29953,
13,
1678,
26051,
29918,
4361,
29886,
993,
29918,
392,
29918,
29906,
29918,
9708,
583,
29918,
517,
29918,
15452,
353,
29871,
29955,
13,
13,
13,
1990,
2216,
1598,
1123,
7334,
993,
29898,
13778,
2882,
16854,
1125,
13,
1678,
4770,
4632,
1649,
29901,
2216,
1598,
1123,
7334,
993,
16854,
13,
13,
13,
1990,
7106,
22034,
26841,
29907,
2631,
29898,
13778,
2882,
1231,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29896,
29900,
13,
1678,
4770,
4632,
1649,
29901,
378,
710,
29898,
3317,
29918,
2848,
29922,
29918,
3317,
29918,
710,
29918,
2848,
29897,
13,
13,
13,
1990,
7983,
4557,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
353,
29871,
29953,
13,
1678,
903,
1195,
29918,
524,
353,
29871,
29900,
13,
1678,
903,
3317,
29918,
524,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
2630,
1041,
11139,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
29947,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29900,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
315,
10880,
18087,
11139,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29896,
29947,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29900,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
29929,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
7089,
277,
12958,
4557,
29898,
13778,
2882,
9135,
3321,
2928,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29953,
13,
1678,
903,
1195,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29900,
13,
1678,
903,
3317,
29918,
524,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29929,
29929,
29929,
29929,
29929,
29929,
13,
13,
1678,
4770,
4632,
1649,
29901,
378,
524,
29898,
479,
29922,
29918,
1195,
29918,
524,
29892,
454,
29922,
29918,
3317,
29918,
524,
29897,
13,
13,
13,
1990,
422,
4752,
7061,
20350,
29898,
13778,
2882,
1523,
4752,
3073,
1125,
13,
1678,
903,
3317,
29918,
710,
29918,
2848,
29901,
4134,
9037,
29961,
524,
29962,
353,
29871,
29953,
29900,
13,
13,
1678,
3211,
29918,
4537,
29901,
16428,
4557,
13,
1678,
3211,
29918,
510,
2037,
29901,
16428,
10602,
13,
1678,
3211,
29918,
29881,
6801,
29901,
16428,
29928,
6801,
13,
1678,
3211,
29918,
12690,
29901,
18285,
7061,
16885,
1170,
13,
1678,
3211,
29918,
13300,
29901,
16428,
4741,
29925,
13,
1678,
3211,
29918,
13300,
29918,
510,
2037,
29901,
16428,
4741,
29925,
1523,
2037,
13,
1678,
3211,
29918,
3859,
29901,
16428,
2792,
13,
13,
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my_wallet/apiv1/__init__.py | ibolorino/wallet-backend | 0 | 77630 | default_app_config = "my_wallet.apiv1.apps.ApiV1Config"
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src/wavestate/iirrational/annotate/markdown.py | wavestate/wavestate-iirrational | 0 | 192456 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: © 2021 Massachusetts Institute of Technology.
# SPDX-FileCopyrightText: © 2021 <NAME> <<EMAIL>>
# NOTICE: authors should document their contributions in concisely in NOTICE
# with details inline in source files, comments, and docstrings.
"""
"""
import os
import re
import errno
import os.path as path
from ..utilities.mpl import asavefig
from ..utilities.strings import padding_remove
def header_line(level=0, section_history=(), heading=""):
if not heading:
heading = ""
section_link = ".".join([str(n) for n in section_history])
if section_link:
section_link = " `{0}` ".format(section_link)
else:
section_link = " "
return ("#" * (level + 1)) + section_link + heading
def docdb_md_section(
F_md,
doc_db,
section_history,
heading_level,
md_verbosity=None,
plot_verbosity=None,
plot_fname_func=None,
do_plot=True,
regex_secname="",
use_ext=True,
plot_format="png",
):
if md_verbosity is not None:
if doc_db.verbosity:
if doc_db.verbosity > md_verbosity:
return
header = header_line(
heading_level,
section_history,
doc_db.name,
)
lines = [
header,
"",
]
if doc_db.method:
lines.append("> `{0}`".format(padding_remove(doc_db.method)))
lines.append("")
if doc_db.about:
lines.append(padding_remove(doc_db.about))
lines.append("")
if not do_plot:
if re.findall(regex_secname, doc_db.name):
do_plot = True
will_plot = True
if plot_verbosity is not None:
if doc_db.verbosity:
if doc_db.verbosity > plot_verbosity:
will_plot = False
if do_plot and will_plot and doc_db.plotter and doc_db.fitter:
title = doc_db.plot_title
if not title:
title = ""
plot_fname = plot_fname_func(
section_history,
doc_db,
with_folder=False,
)
if use_ext:
ext = "." + plot_format
else:
ext = ""
lines.append("".format(plot_fname + ext, title=title))
lines.append("")
F_md.writelines((l + "\n" for l in lines))
if doc_db.sequence:
for idx, seq_name in enumerate(doc_db.sequence):
docdb_md_section(
F_md,
doc_db[seq_name],
section_history=section_history + [idx + 1],
heading_level=heading_level + 1,
md_verbosity=md_verbosity,
plot_verbosity=plot_verbosity,
plot_fname_func=plot_fname_func,
do_plot=do_plot,
regex_secname=regex_secname,
plot_format=plot_format,
use_ext=use_ext,
)
return
def docdb_md_plots(
doc_db,
section_history,
plot_verbosity=None,
plot_fname_func=None,
do_plot=True,
regex_secname="",
clear_plots=False,
plot_format="png",
dpi=100,
):
if plot_verbosity is not None:
if doc_db.verbosity:
if doc_db.verbosity > plot_verbosity:
return
if not do_plot:
if re.findall(regex_secname, doc_db.name):
do_plot = True
if do_plot and doc_db.plotter and doc_db.fitter:
plot_fname = plot_fname_func(
section_history,
doc_db,
with_folder=True,
)
print("PLOTTING: ", plot_fname)
Fb = doc_db.plotter(doc_db.fitter)
Fb.formats[plot_format].use = True
Fb.formats[plot_format].facecolorize = True
Fb.formats[plot_format].dpi = dpi
Fb.save_show = False
Fb.save(plot_fname, fixname=False)
elif clear_plots:
plot_fname = plot_fname_func(
section_history,
doc_db,
with_folder=True,
)
print("REMOVING: ", plot_fname)
try:
os.remove(plot_fname + "." + plot_format)
except OSError as e:
if e.errno != errno.ENOENT:
print(e)
if doc_db.sequence:
for idx, seq_name in enumerate(doc_db.sequence):
docdb_md_plots(
doc_db[seq_name],
section_history=section_history + [idx + 1],
plot_verbosity=plot_verbosity,
plot_fname_func=plot_fname_func,
do_plot=do_plot,
regex_secname=regex_secname,
clear_plots=clear_plots,
plot_format=plot_format,
dpi=dpi,
)
return
def docdb2markdown(
folder_name,
doc_db_root,
md_name="digest.md",
include_plots=True,
include_md=True,
plot_verbosity=None,
md_verbosity=None,
clear_plots=False,
regex_plotsections=None,
ipy_display=False,
use_ext=True,
plot_format="png",
dpi=100,
exporter=None,
MP_workers=1,
):
try:
os.makedirs(folder_name)
except OSError as e:
if e.errno != errno.EEXIST:
raise
def plot_fname_func(
section_history,
doc_db,
with_folder=True,
):
section_link = "-".join([str(n) for n in section_history])
if section_link:
fname = "plot-" + section_link
else:
fname = "plot-main"
fname = fname.replace("_", "-")
if with_folder:
return path.join(folder_name, fname)
else:
return fname
if regex_plotsections is not None:
do_plot = False
else:
do_plot = True
if include_md:
with open(path.join(folder_name, md_name), "w") as F_md:
docdb_md_section(
F_md,
doc_db_root,
section_history=[],
heading_level=0,
md_verbosity=None,
plot_fname_func=plot_fname_func,
plot_verbosity=plot_verbosity,
regex_secname=regex_plotsections,
do_plot=do_plot,
plot_format=plot_format,
use_ext=use_ext,
)
if include_plots:
with asavefig.pool(workers=MP_workers):
docdb_md_plots(
doc_db_root,
section_history=[],
plot_verbosity=plot_verbosity,
plot_fname_func=plot_fname_func,
regex_secname=regex_plotsections,
do_plot=do_plot,
clear_plots=clear_plots,
plot_format=plot_format,
dpi=dpi,
)
if ipy_display:
with open(path.join(folder_name, md_name), "r") as F_md:
md_data = F_md.read()
md_data = re.sub(r"\((.*)\.png\)", r"({0}/\1.png)".format(folder_name), md_data)
try:
from IPython.display import display_markdown
except ImportError:
print("Can't Import IPython.display")
else:
display_markdown(md_data, raw=True)
if exporter is not None:
exporter(
md_name=md_name,
folder=folder_name,
)
return
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] |
data_models/aws_cloudtrail_data_model.py | panther-labs/panther-cli | 4 | 131659 | <reponame>panther-labs/panther-cli<gh_stars>1-10
import ipaddress
import panther_event_type_helpers as event_type
from panther_base_helpers import deep_get
def get_event_type(event):
# currently, only tracking a few event types
if (
event.get("eventName") == "ConsoleLogin"
and deep_get(event, "userIdentity", "type") == "IAMUser"
):
if deep_get(event, "responseElements", "ConsoleLogin") == "Failure":
return event_type.FAILED_LOGIN
if deep_get(event, "responseElements", "ConsoleLogin") == "Success":
return event_type.SUCCESSFUL_LOGIN
if event.get("eventName") == "CreateUser":
return event_type.USER_ACCOUNT_CREATED
if event.get("eventName") == "CreateAccountResult":
return event_type.ACCOUNT_CREATED
return None
def load_ip_address(event):
"""
CloudTrail occassionally sets non-IPs in the sourceIPAddress field.
This method ensures that either an IPv4 or IPv6 address is always returned.
"""
source_ip = event.get("sourceIPAddress")
if not source_ip:
return None
try:
ipaddress.IPv4Address(source_ip)
except ipaddress.AddressValueError:
try:
ipaddress.IPv6Address(source_ip)
except ipaddress.AddressValueError:
return None
return source_ip
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xquant/engine/commission.py | wbpyer/wbpyer | 2 | 88250 | <reponame>wbpyer/wbpyer<filename>xquant/engine/commission.py
# -*- coding: utf-8 -*-
"""
Commission 佣金+费税模型
New in V0.3.4
@author:
@version: 0.4
"""
from abc import ABCMeta, abstractmethod
import math
class Commission(object):
"""
佣金和费税
"""
__metaclass__ = ABCMeta
@abstractmethod
def get_commission(self):
raise NotImplementedError("Should implement get_commission()!")
class ZeroCommission(Commission):
"""
无费率
"""
def get_commission(self):
return 0.0
def __repr__(self):
return "{class_name}".format(class_name=self.__class__.__name__)
class PerShareCommission(Commission):
"""
基于交易份额（或称数量）计算手续费，一般是上交所的过户费，每1000股收费1元
"""
def __init__(self, rate=0.001, min_comm=0.0):
self.rate_per_share = rate
self.min_comm = min_comm
def get_commission(self, quantity):
return max(math.ceil(quantity * self.rate_per_share), self.min_comm)
def __repr__(self):
return "{class_name}(rate_per_share={rate}, min_commission={min_comm})".format(
class_name=self.__class__.__name__, rate=self.rate_per_share, min_comm=self.min_comm)
class PerMoneyCommission(Commission):
"""
基于交易花费（trade cost）计算手续费，可以是期货点数或者人民币，允许设置最小手续费
"""
def __init__(self, rate=1.5e-4, min_comm=0.0):
"""
例如买焦合约共计10000点（单份合约*数量），默认费率万分1.5，则交易成本是1.5个点
参数：
rate: 单边费率
min_comm: 最低手续费，默认为股票5元
"""
self.rate_per_money = float(rate)
self.min_comm = float(min_comm)
def get_commission(self, full_cost):
return max(full_cost * self.rate_per_money, self.min_comm)
def __repr__(self):
return "{class_name}(rate_per_money={rate}, min_commission={min_comm})".format(
class_name=self.__class__.__name__, rate=self.rate_per_money, min_comm=self.min_comm)
class PerTradeCommission(Commission):
"""
基于交易次数计算手续费
"""
pass
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] |
tests/test_import.py | buzzjmd/keld-python-cli | 1 | 111134 | # -*- coding: utf-8 -*-
import keldcli
def test_import():
assert keldcli
assert keldcli.__title__
assert keldcli.__version__
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Python/zzz_training_challenge/UdemyPythonPro/Chapter5_Functions/Functions/command_line_arguments2.py | Kreijeck/learning | 0 | 125381 | <filename>Python/zzz_training_challenge/UdemyPythonPro/Chapter5_Functions/Functions/command_line_arguments2.py
import argparse
def main():
# erstellt parser
parser = argparse.ArgumentParser()
# Argumente hinzufügen:
# Name: default mit '--' <- Konvention
# help: Optional als Hilfe
# type: Optional: Typ
# required: Wird das argument pflichtend benötigt
#
parser.add_argument("--age", help="Enter your age (int)", type=int, required=True)
parser.add_argument("--name", help="Enter your name (str)", type=str, required=True)
parser.add_argument("--admin", help="Are your an admin? (bool)", type=bool, required=False)
# Einlesen und Zugreifen der Parameter
args = parser.parse_args()
age = args.age
name = args.name
is_admin = args.admin
print(age, type(age))
print(name, type(name))
# Achtung Argumente werden als Strings abgespeichert!! auch wenn Type = bool
# Das heißt auch wenn im Argument "False" angegeben
# wird ist dies kein leerer String und damit "True"
print(is_admin, type(is_admin))
if __name__ == "__main__":
main()
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run_es_repo_mgr.py | wjimenez5271/curator | 0 | 41600 | <reponame>wjimenez5271/curator
#!/usr/bin/env python
"""Wrapper for running es_repo_mgr from source."""
from curator.es_repo_mgr import main
if __name__ == '__main__':
main()
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tests/test_coverage_map.py | LSSTDESC/healsparse | 8 | 123501 | from __future__ import division, absolute_import, print_function
import unittest
import numpy.testing as testing
import numpy as np
import healpy as hp
import healsparse
class CoverageMapTestCase(unittest.TestCase):
def test_coverage_map_float(self):
"""
Test coverage_map functionality for floats
"""
nside_coverage = 16
nside_map = 512
# Number of non-masked pixels in the coverage map resolution
non_masked_px = 10.5
nfine = (nside_map//nside_coverage)**2
full_map = np.zeros(hp.nside2npix(nside_map)) + hp.UNSEEN
full_map[0: int(non_masked_px*nfine)] = 1 + np.random.random(size=int(non_masked_px*nfine))
# Generate sparse map
sparse_map = healsparse.HealSparseMap(healpix_map=full_map, nside_coverage=nside_coverage)
# Build the "original" coverage map
cov_map_orig = self.compute_cov_map(nside_coverage, non_masked_px, nfine,
sparse_map._cov_map.bit_shift)
# Get the built coverage map
cov_map = sparse_map.coverage_map
# Test the coverage map generation and lookup
testing.assert_array_almost_equal(cov_map_orig, cov_map)
def test_coverage_map_int(self):
"""
Test coverage_map functionality for ints
"""
nside_coverage = 16
nside_map = 512
# Number of non-masked pixels in the coverage map resolution
non_masked_px = 10.5
nfine = (nside_map//nside_coverage)**2
sentinel = healsparse.utils.check_sentinel(np.int32, None)
full_map = np.zeros(hp.nside2npix(nside_map), dtype=np.int32) + sentinel
full_map[0: int(non_masked_px*nfine)] = 1
sparse_map = healsparse.HealSparseMap(healpix_map=full_map,
nside_coverage=nside_coverage,
sentinel=sentinel)
cov_map_orig = self.compute_cov_map(nside_coverage, non_masked_px, nfine,
sparse_map._cov_map.bit_shift)
cov_map = sparse_map.coverage_map
testing.assert_array_almost_equal(cov_map_orig, cov_map)
def test_coverage_map_recarray(self):
"""
Test coverage_map functionality for a recarray
"""
nside_coverage = 16
nside_map = 512
# Number of non-masked pixels in the coverage map resolution
non_masked_px = 10.5
nfine = (nside_map//nside_coverage)**2
dtype = [('a', np.float64),
('b', np.int32)]
sparse_map = healsparse.HealSparseMap.make_empty(nside_coverage, nside_map,
dtype, primary='a')
sparse_map.update_values_pix(np.arange(int(non_masked_px*nfine)),
np.ones(1, dtype=dtype))
cov_map_orig = self.compute_cov_map(nside_coverage, non_masked_px, nfine,
sparse_map._cov_map.bit_shift)
cov_map = sparse_map.coverage_map
testing.assert_array_almost_equal(cov_map_orig, cov_map)
def test_coverage_map_widemask(self):
"""
Test coverage_map functionality for wide masks
"""
nside_coverage = 16
nside_map = 512
# Number of non-masked pixels in the coverage map resolution
non_masked_px = 10.5
nfine = (nside_map//nside_coverage)**2
# Do a 1-byte wide
sparse_map = healsparse.HealSparseMap.make_empty(nside_coverage, nside_map,
healsparse.WIDE_MASK,
wide_mask_maxbits=2)
# Set bits in different columns
sparse_map.set_bits_pix(np.arange(int(non_masked_px*nfine)), [1])
cov_map_orig = self.compute_cov_map(nside_coverage, non_masked_px, nfine,
sparse_map._cov_map.bit_shift)
cov_map = sparse_map.coverage_map
testing.assert_array_almost_equal(cov_map_orig, cov_map)
# Do a 3-byte wide
sparse_map = healsparse.HealSparseMap.make_empty(nside_coverage, nside_map,
healsparse.WIDE_MASK,
wide_mask_maxbits=24)
# Set bits in different columns
sparse_map.set_bits_pix(np.arange(int(2*nfine)), [2])
sparse_map.set_bits_pix(np.arange(int(non_masked_px*nfine)), [20])
cov_map_orig = self.compute_cov_map(nside_coverage, non_masked_px, nfine,
sparse_map._cov_map.bit_shift)
cov_map = sparse_map.coverage_map
testing.assert_array_almost_equal(cov_map_orig, cov_map)
def compute_cov_map(self, nside_coverage, non_masked_px, nfine, bit_shift):
cov_map_orig = np.zeros(hp.nside2npix(nside_coverage), dtype=np.float64)
idx_cov = np.right_shift(np.arange(int(non_masked_px*nfine)), bit_shift)
unique_idx_cov = np.unique(idx_cov)
idx_counts = np.bincount(idx_cov, minlength=hp.nside2npix(nside_coverage)).astype(np.float64)
cov_map_orig[unique_idx_cov] = idx_counts[unique_idx_cov]/nfine
return cov_map_orig
def test_large_coverage_map_warning(self):
"""
Test coverage_map raises warning for large
values of nside_coverage
"""
nside_coverage = 256
nside_map = 512
# Generate sparse map and check that it rasises a warning
testing.assert_warns(ResourceWarning, healsparse.HealSparseMap.make_empty, nside_sparse=nside_map,
nside_coverage=nside_coverage, dtype=np.float32)
if __name__ == '__main__':
unittest.main()
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PyFSM/pyfsm/elements/state_unit_tests.py | wafec/wafec-py-fsm | 0 | 24773 | <gh_stars>0
import unittest
from pyfsm.elements import *
class StateUnitSimpleFsmTests(unittest.TestCase):
def setUp(self):
self.fsm = StateUnit()
self.state1 = StateUnit()
self.state2 = StateUnit()
self.link1 = LinkElement()
self.fsm.starters = [self.state1]
self.state1.parent = self.fsm
self.state2.parent = self.fsm
self.state1.levelers = [self.state1]
self.state2.levelers = [self.state2]
self.state1.links = [self.link1]
self.link1.sources = [self.state1]
self.link1.destinations = [self.state2]
self.state3 = StateUnit()
self.link2 = LinkElement()
self.link2.sources = [self.state2]
self.link2.destinations = [self.state3]
self.state2.links = [self.link2]
self.state3.parent = self.fsm
self.state3.levelers = [self.state1]
self.fsm.children = [self.state1, self.state2, self.state3]
self.fsm.name = 'fsm'
self.state1.name = 'state1'
self.state2.name = 'state2'
self.state3.name = 'state3'
def test_fsm_with_enter(self):
event = TransportEvent()
event.id = 'test'
self.fsm.initialize(event)
self.assertTrue(self.state1.active)
self.assertTrue(self.fsm.active)
self.assertFalse(self.state2.active)
def test_fsm_with_first_transition(self):
event = TransportEvent()
event.id = 'test'
self.fsm.initialize(event)
self.fsm.receive(event)
self.assertTrue(self.fsm.active)
self.assertTrue(self.state2.active)
self.assertFalse(self.state1.active)
def test_fsm_with_second_transition(self):
event = TransportEvent()
event.id = 'test'
self.fsm.initialize(event)
self.fsm.receive(event)
self.fsm.receive(event)
self.assertTrue(self.state3.active)
self.assertFalse(self.state2.active)
self.assertFalse(self.state1.active)
self.assertTrue(self.fsm.active)
def test_fsm_with_entry_action(self):
count = 0
def increment_count(args):
nonlocal count
count += 1
event = TransportEvent()
event.id = 'test'
self.state1.entry_actions = [increment_count]
self.fsm.initialize(event)
self.fsm.receive(event)
self.assertEqual(1, count)
def test_fsm_with_entry_action_and_view(self):
def change_view(args):
args.event.view['test'] = 'value'
event = TransportEvent()
event.id = 'test'
self.state1.entry_actions = [change_view]
self.fsm.initialize(event)
self.fsm.receive(event)
self.assertEqual('value', event.view['test'])
def test_fsm_with_entry_in_all_units(self):
count = 0
def increment_count(args):
nonlocal count
count += 1
event = TransportEvent()
event.id = 'test'
self.fsm.entry_actions = [increment_count]
self.state1.entry_actions = [increment_count]
self.state2.entry_actions = [increment_count]
self.state3.entry_actions = [increment_count]
self.fsm.initialize(event)
self.fsm.receive(event)
self.fsm.receive(event)
self.assertEqual(4, count)
def test_fsm_with_exit(self):
count = 0
def increment_count(args):
nonlocal count
count += 1
self.state1.exit_actions = [increment_count]
event = TransportEvent()
event.id = 'test'
self.fsm.initialize(event)
self.fsm.receive(event)
self.assertEqual(1, count)
self.fsm.receive(event)
self.assertEqual(1, count)
def test_fsm_with_exit_and_entry(self):
count = 0
def increment_count(args):
nonlocal count
count += 1
def decrement_count(args):
nonlocal count
count -= 1
self.fsm.entry_actions = [increment_count]
self.fsm.exit_actions = [decrement_count]
self.state1.entry_actions = [increment_count]
self.state1.exit_actions = [decrement_count]
self.state2.entry_actions = [increment_count]
self.state2.exit_actions = [decrement_count]
self.state3.entry_actions = [increment_count]
self.state3.exit_actions = [increment_count]
event = TransportEvent()
event.id = 'test'
self.fsm.initialize(event)
self.assertEqual(2, count)
self.fsm.receive(event)
self.assertEqual(2, count)
self.fsm.receive(event)
self.assertEqual(2, count)
class StateUnitComplexFsmTests(unittest.TestCase):
def setUp(self):
self.fsm = StateUnit()
self.state10 = StateUnit()
self.state20 = StateUnit()
self.state11 = StateUnit()
self.state12 = StateUnit()
self.state21 = StateUnit()
self.state22 = StateUnit()
self.state23 = StateUnit()
self.fsm.starters = [self.state10, self.state20]
self.fsm.children = [self.state10, self.state11, self.state12, self.state20, self.state21, self.state22,
self.state23]
self.state10.parent = self.fsm
self.state11.parent = self.fsm
self.state12.parent = self.fsm
self.state20.parent = self.fsm
self.state21.parent = self.fsm
self.state22.parent = self.fsm
self.state23.parent = self.fsm
self.state10.levelers = [self.state10]
self.state11.levelers = [self.state10]
self.state12.levelers = [self.state10]
self.state20.levelers = [self.state20]
self.state21.levelers = [self.state20]
self.state22.levelers = [self.state20]
self.state23.levelers = [self.state20]
self.link11 = LinkElement()
self.link12 = LinkElement()
self.link21 = LinkElement()
self.link22 = LinkElement()
self.link23 = LinkElement()
self.link11.sources = [self.state10]
self.link11.destinations = [self.state11]
self.link12.sources = [self.state11]
self.link12.destinations = [self.state12]
self.link21 = LinkElement()
self.link21.sources = [self.state20]
self.link21.destinations = [self.state21]
self.link22 = LinkElement()
self.link22.sources = [self.state21]
self.link22.destinations = [self.state22]
self.link23 = LinkElement()
self.link23.sources = [self.state22]
self.link23.destinations = [self.state23]
self.state10.links = [self.link11]
self.state11.links = [self.link12]
self.state20.links = [self.link21]
self.state21.links = [self.link22]
self.state22.links = [self.link23]
self.link11.accepts = ['event11']
self.link12.accepts = ['event11', 'event12']
self.link21.accepts = ['event21']
self.link22.accepts = ['event21', 'event22']
self.link23.accepts = ['event21', 'event22', 'event23']
self.event11 = TransportEvent()
self.event12 = TransportEvent()
self.event21 = TransportEvent()
self.event22 = TransportEvent()
self.event23 = TransportEvent()
self.event0 = TransportEvent()
self.event0.id = 'event0'
self.event11.id = 'event11'
self.event12.id = 'event12'
self.event21.id = 'event21'
self.event22.id = 'event22'
self.event23.id = 'event23'
def test_fsm_with_event(self):
self.fsm.initialize(self.event0)
self.assertTrue(self.fsm.active)
self.assertTrue(self.state10.active)
self.assertTrue(self.state20.active)
self.assertFalse(self.state11.active)
self.assertFalse(self.state21.active)
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] |
project_postgresql/tests/test_db.py | righ/djangomodel2alchemymap | 17 | 1608822 | <reponame>righ/djangomodel2alchemymap
import pytest
from sqlalchemy import (
select,
insert,
delete,
update,
func,
)
@pytest.fixture(scope='function')
def author_model():
from models_sqla import Author
return Author
@pytest.fixture(scope='function')
def author_table(author_model):
return author_model.__table__
@pytest.fixture(scope='function')
def author_a():
from books.models import Author
return Author.objects.get_or_create(name='a', age=20)[0]
@pytest.fixture(scope='function')
def author_b():
from books.models import Author
return Author.objects.get_or_create(name='b', age=15)[0]
@pytest.fixture()
def authors():
from books.models import Author
return Author.objects.all().order_by('id')
@pytest.mark.django_db
class Test_query_expression:
def _callFUT(self, stmt):
from d2a.db import query_expression
return query_expression(stmt)
def test_query_expression(self, author_table, author_a, author_b):
stmt = select([
author_table.c.id,
author_table.c.name,
]).select_from(author_table).order_by(author_table.c.age)
actual = self._callFUT(stmt)
expected = [
{'id': author_b.id, 'name': author_b.name},
{'id': author_a.id, 'name': author_a.name},
]
assert actual == expected
@pytest.mark.django_db
class Test_execute_expression:
def _callFUT(self, stmt):
from d2a.db import execute_expression
return execute_expression(stmt)
def test_insert_expression(self, author_table, authors):
expected = [
{'name': 'a', 'age': 10},
{'name': 'b', 'age': 20},
{'name': 'c', 'age': 30},
]
stmt = insert(author_table).values(expected)
assert self._callFUT(stmt) == 3
actual = list(authors.values('name', 'age'))
assert actual == expected
def test_update_expression(self, author_table, author_a, author_b, authors):
stmt = update(author_table).where(author_table.c.id == author_a.id).values(
name=func.UPPER(author_table.c.name),
age=author_table.c.age + 1,
)
assert self._callFUT(stmt) == 1
actual = list(authors.values('name', 'age'))
expected = [
{'name': 'A', 'age': 21},
{'name': 'b', 'age': 15},
]
assert actual == expected
def test_delete_expression(self, author_table, author_a, author_b, authors):
stmt = delete(author_table).where(author_table.c.id == author_a.id)
assert self._callFUT(stmt) == 1
actual = list(authors.values('name', 'age'))
expected = [
{'name': 'b', 'age': 15},
]
assert actual == expected
@pytest.mark.skip
class Test_make_session:
def _callFUT(self, **kwargs):
from d2a.db import make_session
return make_session(**kwargs)
def test_make_session(self, author_model):
with self._callFUT(autocommit=True, autoflush=True) as session:
author = author_model()
author.name = 'c'
author.age = 30
session.add(author)
actual = [
{'name': a.name, 'age': a.age}
for a in session.query(author_model).all()
]
expected = [
{'name': 'c', 'age': 30},
]
assert actual == expected
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] |
qtui.py | kwdgwl/mzlnmsl | 1 | 136466 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'mzlnmsl.ui'
#
# Created by: PyQt5 UI code generator 5.15.4
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again. Do not edit this file unless you know what you are doing.
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_MainWindow(object):
def setupUi(self, MainWindow):
MainWindow.setObjectName("MainWindow")
MainWindow.resize(1600, 900)
MainWindow.setMinimumSize(QtCore.QSize(1600, 900))
MainWindow.setMaximumSize(QtCore.QSize(1600, 900))
self.centralwidget = QtWidgets.QWidget(MainWindow)
self.centralwidget.setObjectName("centralwidget")
self.Title = QtWidgets.QLabel(self.centralwidget)
self.Title.setGeometry(QtCore.QRect(10, 10, 431, 61))
font = QtGui.QFont()
font.setFamily("宋体")
font.setPointSize(26)
font.setBold(False)
font.setWeight(50)
self.Title.setFont(font)
self.Title.setText("")
self.Title.setAlignment(QtCore.Qt.AlignCenter)
self.Title.setObjectName("Title")
self.groupBox = QtWidgets.QGroupBox(self.centralwidget)
self.groupBox.setGeometry(QtCore.QRect(450, 10, 1141, 751))
self.groupBox.setObjectName("groupBox")
self.browser = QtWebEngineWidgets.QWebEngineView(self.groupBox)
self.browser.setGeometry(QtCore.QRect(10, 19, 1121, 721))
self.browser.setObjectName("browser")
self.groupBox_A = QtWidgets.QGroupBox(self.centralwidget)
self.groupBox_A.setGeometry(QtCore.QRect(10, 80, 431, 101))
self.groupBox_A.setObjectName("groupBox_A")
self.label_A_uuid = QtWidgets.QLabel(self.groupBox_A)
self.label_A_uuid.setGeometry(QtCore.QRect(10, 25, 54, 12))
self.label_A_uuid.setAlignment(QtCore.Qt.AlignCenter)
self.label_A_uuid.setObjectName("label_A_uuid")
self.label_A_model = QtWidgets.QLabel(self.groupBox_A)
self.label_A_model.setGeometry(QtCore.QRect(10, 50, 54, 12))
self.label_A_model.setAlignment(QtCore.Qt.AlignCenter)
self.label_A_model.setObjectName("label_A_model")
self.radioButton_A_ios = QtWidgets.QRadioButton(self.groupBox_A)
self.radioButton_A_ios.setGeometry(QtCore.QRect(70, 75, 41, 16))
self.radioButton_A_ios.setChecked(True)
self.radioButton_A_ios.setObjectName("radioButton_A_ios")
self.radioButton_A_android = QtWidgets.QRadioButton(self.groupBox_A)
self.radioButton_A_android.setGeometry(QtCore.QRect(115, 75, 71, 16))
self.radioButton_A_android.setObjectName("radioButton_A_android")
self.label_A_type = QtWidgets.QLabel(self.groupBox_A)
self.label_A_type.setGeometry(QtCore.QRect(10, 77, 54, 12))
self.label_A_type.setAlignment(QtCore.Qt.AlignCenter)
self.label_A_type.setObjectName("label_A_type")
self.lineEdit_A_uuid = QtWidgets.QLineEdit(self.groupBox_A)
self.lineEdit_A_uuid.setGeometry(QtCore.QRect(70, 20, 351, 21))
self.lineEdit_A_uuid.setText("")
self.lineEdit_A_uuid.setObjectName("lineEdit_A_uuid")
self.lineEdit_A_model = QtWidgets.QLineEdit(self.groupBox_A)
self.lineEdit_A_model.setGeometry(QtCore.QRect(70, 45, 121, 21))
self.lineEdit_A_model.setText("")
self.lineEdit_A_model.setObjectName("lineEdit_A_model")
self.pushButton_A_random = QtWidgets.QPushButton(self.groupBox_A)
self.pushButton_A_random.setGeometry(QtCore.QRect(196, 46, 81, 21))
self.pushButton_A_random.setObjectName("pushButton_A_random")
self.pushButton_Z_start = QtWidgets.QPushButton(self.groupBox_A)
self.pushButton_Z_start.setGeometry(QtCore.QRect(300, 60, 121, 31))
self.pushButton_Z_start.setObjectName("pushButton_Z_start")
self.groupBox_B = QtWidgets.QGroupBox(self.centralwidget)
self.groupBox_B.setGeometry(QtCore.QRect(10, 190, 431, 181))
self.groupBox_B.setFlat(False)
self.groupBox_B.setCheckable(False)
self.groupBox_B.setObjectName("groupBox_B")
self.label_B_username = QtWidgets.QLabel(self.groupBox_B)
self.label_B_username.setGeometry(QtCore.QRect(10, 25, 54, 12))
self.label_B_username.setAlignment(QtCore.Qt.AlignCenter)
self.label_B_username.setObjectName("label_B_username")
self.label_B_password = QtWidgets.QLabel(self.groupBox_B)
self.label_B_password.setGeometry(QtCore.QRect(10, 50, 54, 12))
self.label_B_password.setAlignment(QtCore.Qt.AlignCenter)
self.label_B_password.setObjectName("label_B_password")
self.label_B_utoken = QtWidgets.QLabel(self.groupBox_B)
self.label_B_utoken.setGeometry(QtCore.QRect(10, 80, 54, 12))
self.label_B_utoken.setAlignment(QtCore.Qt.AlignCenter)
self.label_B_utoken.setObjectName("label_B_utoken")
self.label_B_userid = QtWidgets.QLabel(self.groupBox_B)
self.label_B_userid.setGeometry(QtCore.QRect(10, 105, 54, 12))
self.label_B_userid.setAlignment(QtCore.Qt.AlignCenter)
self.label_B_userid.setObjectName("label_B_userid")
self.pushButton_B_login = QtWidgets.QPushButton(self.groupBox_B)
self.pushButton_B_login.setGeometry(QtCore.QRect(180, 105, 241, 31))
self.pushButton_B_login.setObjectName("pushButton_B_login")
self.pushButton_B_seslogin = QtWidgets.QPushButton(self.groupBox_B)
self.pushButton_B_seslogin.setGeometry(QtCore.QRect(180, 140, 241, 31))
self.pushButton_B_seslogin.setObjectName("pushButton_B_seslogin")
self.checkBox_B_saveaccount = QtWidgets.QCheckBox(self.groupBox_B)
self.checkBox_B_saveaccount.setGeometry(QtCore.QRect(260, 23, 81, 16))
self.checkBox_B_saveaccount.setChecked(True)
self.checkBox_B_saveaccount.setObjectName("checkBox_B_saveaccount")
self.checkBox_B_savepassword = QtWidgets.QCheckBox(self.groupBox_B)
self.checkBox_B_savepassword.setGeometry(QtCore.QRect(260, 48, 71, 16))
self.checkBox_B_savepassword.setObjectName("checkBox_B_savepassword")
self.lineEdit_B_username = QtWidgets.QLineEdit(self.groupBox_B)
self.lineEdit_B_username.setGeometry(QtCore.QRect(70, 20, 181, 21))
self.lineEdit_B_username.setText("")
self.lineEdit_B_username.setObjectName("lineEdit_B_username")
self.lineEdit_B_password = QtWidgets.QLineEdit(self.groupBox_B)
self.lineEdit_B_password.setGeometry(QtCore.QRect(70, 45, 181, 21))
self.lineEdit_B_password.setText("")
self.lineEdit_B_password.setEchoMode(QtWidgets.QLineEdit.PasswordEchoOnEdit)
self.lineEdit_B_password.setObjectName("lineEdit_B_password")
self.lineEdit_B_utoken = QtWidgets.QLineEdit(self.groupBox_B)
self.lineEdit_B_utoken.setGeometry(QtCore.QRect(70, 75, 351, 21))
self.lineEdit_B_utoken.setText("")
self.lineEdit_B_utoken.setObjectName("lineEdit_B_utoken")
self.lineEdit_B_userid = QtWidgets.QLineEdit(self.groupBox_B)
self.lineEdit_B_userid.setGeometry(QtCore.QRect(70, 100, 101, 21))
self.lineEdit_B_userid.setText("")
self.lineEdit_B_userid.setObjectName("lineEdit_B_userid")
self.groupBox_D = QtWidgets.QGroupBox(self.centralwidget)
self.groupBox_D.setGeometry(QtCore.QRect(10, 450, 431, 51))
self.groupBox_D.setObjectName("groupBox_D")
self.label_D_initloc = QtWidgets.QLabel(self.groupBox_D)
self.label_D_initloc.setGeometry(QtCore.QRect(10, 25, 54, 12))
self.label_D_initloc.setAlignment(QtCore.Qt.AlignCenter)
self.label_D_initloc.setObjectName("label_D_initloc")
self.pushButton_D_send = QtWidgets.QPushButton(self.groupBox_D)
self.pushButton_D_send.setGeometry(QtCore.QRect(360, 20, 61, 21))
self.pushButton_D_send.setObjectName("pushButton_D_send")
self.pushButton_D_random = QtWidgets.QPushButton(self.groupBox_D)
self.pushButton_D_random.setGeometry(QtCore.QRect(290, 20, 61, 21))
self.pushButton_D_random.setObjectName("pushButton_D_random")
self.lineEdit_D_initloc = QtWidgets.QLineEdit(self.groupBox_D)
self.lineEdit_D_initloc.setGeometry(QtCore.QRect(70, 20, 211, 21))
self.lineEdit_D_initloc.setText("")
self.lineEdit_D_initloc.setObjectName("lineEdit_D_initloc")
self.groupBox_E = QtWidgets.QGroupBox(self.centralwidget)
self.groupBox_E.setGeometry(QtCore.QRect(10, 510, 431, 91))
self.groupBox_E.setObjectName("groupBox_E")
self.label_E_reqtime = QtWidgets.QLabel(self.groupBox_E)
self.label_E_reqtime.setGeometry(QtCore.QRect(90, 25, 200, 12))
self.label_E_reqtime.setText("")
self.label_E_reqtime.setObjectName("label_E_reqtime")
self.clabel_E_reqtime = QtWidgets.QLabel(self.groupBox_E)
self.clabel_E_reqtime.setGeometry(QtCore.QRect(30, 25, 54, 12))
self.clabel_E_reqtime.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_E_reqtime.setObjectName("clabel_E_reqtime")
self.clabel_E_initloc = QtWidgets.QLabel(self.groupBox_E)
self.clabel_E_initloc.setGeometry(QtCore.QRect(30, 45, 54, 12))
self.clabel_E_initloc.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_E_initloc.setObjectName("clabel_E_initloc")
self.label_E_initloc = QtWidgets.QLabel(self.groupBox_E)
self.label_E_initloc.setGeometry(QtCore.QRect(90, 45, 200, 12))
self.label_E_initloc.setText("")
self.label_E_initloc.setObjectName("label_E_initloc")
self.label_E_runid = QtWidgets.QLabel(self.groupBox_E)
self.label_E_runid.setGeometry(QtCore.QRect(90, 65, 75, 12))
self.label_E_runid.setText("")
self.label_E_runid.setObjectName("label_E_runid")
self.clabel_E_runid = QtWidgets.QLabel(self.groupBox_E)
self.clabel_E_runid.setGeometry(QtCore.QRect(30, 65, 54, 12))
self.clabel_E_runid.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_E_runid.setObjectName("clabel_E_runid")
self.pushButton_E_trash = QtWidgets.QPushButton(self.groupBox_E)
self.pushButton_E_trash.setGeometry(QtCore.QRect(320, 50, 101, 31))
self.pushButton_E_trash.setObjectName("pushButton_E_trash")
self.clabel_E_resume = QtWidgets.QLabel(self.groupBox_E)
self.clabel_E_resume.setGeometry(QtCore.QRect(310, 20, 111, 21))
self.clabel_E_resume.setObjectName("clabel_E_resume")
self.clabel_E_dist = QtWidgets.QLabel(self.groupBox_E)
self.clabel_E_dist.setGeometry(QtCore.QRect(170, 65, 54, 12))
self.clabel_E_dist.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_E_dist.setObjectName("clabel_E_dist")
self.label_E_dist = QtWidgets.QLabel(self.groupBox_E)
self.label_E_dist.setGeometry(QtCore.QRect(230, 65, 75, 12))
self.label_E_dist.setText("")
self.label_E_dist.setObjectName("label_E_dist")
self.groupBox_C = QtWidgets.QGroupBox(self.centralwidget)
self.groupBox_C.setGeometry(QtCore.QRect(10, 380, 431, 61))
self.groupBox_C.setObjectName("groupBox_C")
self.clabel_C_name = QtWidgets.QLabel(self.groupBox_C)
self.clabel_C_name.setGeometry(QtCore.QRect(10, 20, 54, 12))
self.clabel_C_name.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_C_name.setObjectName("clabel_C_name")
self.clabel_C_school = QtWidgets.QLabel(self.groupBox_C)
self.clabel_C_school.setGeometry(QtCore.QRect(10, 40, 54, 12))
self.clabel_C_school.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_C_school.setObjectName("clabel_C_school")
self.clabel_C_completed = QtWidgets.QLabel(self.groupBox_C)
self.clabel_C_completed.setGeometry(QtCore.QRect(280, 20, 54, 12))
self.clabel_C_completed.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_C_completed.setObjectName("clabel_C_completed")
self.clabel_C_target = QtWidgets.QLabel(self.groupBox_C)
self.clabel_C_target.setGeometry(QtCore.QRect(280, 40, 54, 12))
self.clabel_C_target.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_C_target.setObjectName("clabel_C_target")
self.label_C_target = QtWidgets.QLabel(self.groupBox_C)
self.label_C_target.setGeometry(QtCore.QRect(340, 40, 54, 12))
self.label_C_target.setText("")
self.label_C_target.setObjectName("label_C_target")
self.label_C_completed = QtWidgets.QLabel(self.groupBox_C)
self.label_C_completed.setGeometry(QtCore.QRect(340, 20, 54, 12))
self.label_C_completed.setText("")
self.label_C_completed.setObjectName("label_C_completed")
self.label_C_name = QtWidgets.QLabel(self.groupBox_C)
self.label_C_name.setGeometry(QtCore.QRect(70, 20, 200, 12))
self.label_C_name.setText("")
self.label_C_name.setObjectName("label_C_name")
self.label_C_school = QtWidgets.QLabel(self.groupBox_C)
self.label_C_school.setGeometry(QtCore.QRect(70, 40, 200, 12))
self.label_C_school.setText("")
self.label_C_school.setObjectName("label_C_school")
self.groupBox_Z = QtWidgets.QGroupBox(self.centralwidget)
self.groupBox_Z.setGeometry(QtCore.QRect(450, 770, 1141, 121))
self.groupBox_Z.setObjectName("groupBox_Z")
self.label_Z_status = QtWidgets.QLabel(self.groupBox_Z)
self.label_Z_status.setGeometry(QtCore.QRect(20, 20, 1111, 61))
font = QtGui.QFont()
font.setFamily("宋体")
font.setPointSize(26)
font.setBold(False)
font.setWeight(50)
self.label_Z_status.setFont(font)
self.label_Z_status.setObjectName("label_Z_status")
self.pushButton_Z_logout = QtWidgets.QPushButton(self.groupBox_Z)
self.pushButton_Z_logout.setGeometry(QtCore.QRect(10, 90, 61, 23))
self.pushButton_Z_logout.setObjectName("pushButton_Z_logout")
self.checkBox_Z_debug = QtWidgets.QCheckBox(self.groupBox_Z)
self.checkBox_Z_debug.setGeometry(QtCore.QRect(90, 90, 91, 21))
self.checkBox_Z_debug.setObjectName("checkBox_Z_debug")
self.groupBox_F = QtWidgets.QGroupBox(self.centralwidget)
self.groupBox_F.setGeometry(QtCore.QRect(10, 610, 431, 131))
self.groupBox_F.setObjectName("groupBox_F")
self.clabel_F_route = QtWidgets.QLabel(self.groupBox_F)
self.clabel_F_route.setGeometry(QtCore.QRect(13, 25, 34, 12))
self.clabel_F_route.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_F_route.setObjectName("clabel_F_route")
self.pushButton_F_confirm = QtWidgets.QPushButton(self.groupBox_F)
self.pushButton_F_confirm.setGeometry(QtCore.QRect(350, 90, 71, 31))
self.pushButton_F_confirm.setObjectName("pushButton_F_confirm")
self.lineEdit_F_route = QtWidgets.QLineEdit(self.groupBox_F)
self.lineEdit_F_route.setGeometry(QtCore.QRect(50, 20, 371, 21))
self.lineEdit_F_route.setText("")
self.lineEdit_F_route.setObjectName("lineEdit_F_route")
self.pushButton_F_undo = QtWidgets.QPushButton(self.groupBox_F)
self.pushButton_F_undo.setGeometry(QtCore.QRect(270, 90, 71, 31))
self.pushButton_F_undo.setObjectName("pushButton_F_undo")
self.label_F_dist = QtWidgets.QLabel(self.groupBox_F)
self.label_F_dist.setGeometry(QtCore.QRect(130, 90, 131, 31))
font = QtGui.QFont()
font.setFamily("宋体")
font.setPointSize(12)
self.label_F_dist.setFont(font)
self.label_F_dist.setText("")
self.label_F_dist.setObjectName("label_F_dist")
self.clabel_F_dist = QtWidgets.QLabel(self.groupBox_F)
self.clabel_F_dist.setGeometry(QtCore.QRect(10, 90, 111, 31))
font = QtGui.QFont()
font.setFamily("宋体")
font.setPointSize(12)
self.clabel_F_dist.setFont(font)
self.clabel_F_dist.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_F_dist.setObjectName("clabel_F_dist")
self.radioButton_F_map = QtWidgets.QRadioButton(self.groupBox_F)
self.radioButton_F_map.setGeometry(QtCore.QRect(140, 50, 121, 20))
self.radioButton_F_map.setChecked(True)
self.radioButton_F_map.setObjectName("radioButton_F_map")
self.radioButton_F_direct = QtWidgets.QRadioButton(self.groupBox_F)
self.radioButton_F_direct.setGeometry(QtCore.QRect(270, 50, 91, 20))
self.radioButton_F_direct.setChecked(False)
self.radioButton_F_direct.setObjectName("radioButton_F_direct")
self.clabel_F_routemethod = QtWidgets.QLabel(self.groupBox_F)
self.clabel_F_routemethod.setGeometry(QtCore.QRect(20, 51, 111, 16))
self.clabel_F_routemethod.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_F_routemethod.setObjectName("clabel_F_routemethod")
self.checkBox_F_hidepoints = QtWidgets.QCheckBox(self.groupBox_F)
self.checkBox_F_hidepoints.setGeometry(QtCore.QRect(30, 75, 71, 16))
self.checkBox_F_hidepoints.setObjectName("checkBox_F_hidepoints")
self.groupBox_G = QtWidgets.QGroupBox(self.centralwidget)
self.groupBox_G.setGeometry(QtCore.QRect(10, 750, 431, 141))
self.groupBox_G.setObjectName("groupBox_G")
self.lineEdit_G_distance = QtWidgets.QLineEdit(self.groupBox_G)
self.lineEdit_G_distance.setGeometry(QtCore.QRect(70, 50, 121, 21))
self.lineEdit_G_distance.setText("")
self.lineEdit_G_distance.setObjectName("lineEdit_G_distance")
self.clabel_G_speed = QtWidgets.QLabel(self.groupBox_G)
self.clabel_G_speed.setGeometry(QtCore.QRect(10, 30, 54, 12))
self.clabel_G_speed.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_G_speed.setObjectName("clabel_G_speed")
self.lineEdit_G_speed = QtWidgets.QLineEdit(self.groupBox_G)
self.lineEdit_G_speed.setGeometry(QtCore.QRect(70, 25, 121, 21))
self.lineEdit_G_speed.setText("")
self.lineEdit_G_speed.setObjectName("lineEdit_G_speed")
self.clabel_G_distance = QtWidgets.QLabel(self.groupBox_G)
self.clabel_G_distance.setGeometry(QtCore.QRect(10, 55, 54, 12))
self.clabel_G_distance.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_G_distance.setObjectName("clabel_G_distance")
self.lineEdit_G_bupin = QtWidgets.QLineEdit(self.groupBox_G)
self.lineEdit_G_bupin.setGeometry(QtCore.QRect(70, 75, 121, 21))
self.lineEdit_G_bupin.setText("")
self.lineEdit_G_bupin.setObjectName("lineEdit_G_bupin")
self.clabel_G_bushu = QtWidgets.QLabel(self.groupBox_G)
self.clabel_G_bushu.setGeometry(QtCore.QRect(10, 105, 54, 12))
self.clabel_G_bushu.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_G_bushu.setObjectName("clabel_G_bushu")
self.clabel_G_bupin = QtWidgets.QLabel(self.groupBox_G)
self.clabel_G_bupin.setGeometry(QtCore.QRect(10, 80, 54, 12))
self.clabel_G_bupin.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_G_bupin.setObjectName("clabel_G_bupin")
self.lineEdit_G_bushu = QtWidgets.QLineEdit(self.groupBox_G)
self.lineEdit_G_bushu.setGeometry(QtCore.QRect(70, 100, 121, 21))
self.lineEdit_G_bushu.setText("")
self.lineEdit_G_bushu.setObjectName("lineEdit_G_bushu")
self.clabel_G_time = QtWidgets.QLabel(self.groupBox_G)
self.clabel_G_time.setGeometry(QtCore.QRect(210, 30, 54, 12))
self.clabel_G_time.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_G_time.setObjectName("clabel_G_time")
self.lineEdit_G_time = QtWidgets.QLineEdit(self.groupBox_G)
self.lineEdit_G_time.setGeometry(QtCore.QRect(270, 25, 121, 21))
self.lineEdit_G_time.setText("")
self.lineEdit_G_time.setObjectName("lineEdit_G_time")
self.pushButton_G_finalconfirm = QtWidgets.QPushButton(self.groupBox_G)
self.pushButton_G_finalconfirm.setGeometry(QtCore.QRect(320, 100, 101, 31))
self.pushButton_G_finalconfirm.setObjectName("pushButton_G_finalconfirm")
self.clabel_G_starttime = QtWidgets.QLabel(self.groupBox_G)
self.clabel_G_starttime.setGeometry(QtCore.QRect(210, 55, 54, 12))
self.clabel_G_starttime.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_G_starttime.setObjectName("clabel_G_starttime")
self.clabel_G_endtime = QtWidgets.QLabel(self.groupBox_G)
self.clabel_G_endtime.setGeometry(QtCore.QRect(210, 75, 54, 12))
self.clabel_G_endtime.setAlignment(QtCore.Qt.AlignCenter)
self.clabel_G_endtime.setObjectName("clabel_G_endtime")
self.label_G_starttime = QtWidgets.QLabel(self.groupBox_G)
self.label_G_starttime.setGeometry(QtCore.QRect(270, 55, 150, 12))
self.label_G_starttime.setText("")
self.label_G_starttime.setObjectName("label_G_starttime")
self.label_G_endtime = QtWidgets.QLabel(self.groupBox_G)
self.label_G_endtime.setGeometry(QtCore.QRect(270, 75, 150, 12))
self.label_G_endtime.setText("")
self.label_G_endtime.setObjectName("label_G_endtime")
MainWindow.setCentralWidget(self.centralwidget)
self.retranslateUi(MainWindow)
QtCore.QMetaObject.connectSlotsByName(MainWindow)
MainWindow.setTabOrder(self.lineEdit_A_uuid, self.lineEdit_A_model)
MainWindow.setTabOrder(self.lineEdit_A_model, self.pushButton_A_random)
MainWindow.setTabOrder(self.pushButton_A_random, self.radioButton_A_ios)
MainWindow.setTabOrder(self.radioButton_A_ios, self.radioButton_A_android)
MainWindow.setTabOrder(self.radioButton_A_android, self.pushButton_Z_start)
MainWindow.setTabOrder(self.pushButton_Z_start, self.lineEdit_B_username)
MainWindow.setTabOrder(self.lineEdit_B_username, self.checkBox_B_saveaccount)
MainWindow.setTabOrder(self.checkBox_B_saveaccount, self.lineEdit_B_password)
MainWindow.setTabOrder(self.lineEdit_B_password, self.checkBox_B_savepassword)
MainWindow.setTabOrder(self.checkBox_B_savepassword, self.lineEdit_B_utoken)
MainWindow.setTabOrder(self.lineEdit_B_utoken, self.lineEdit_B_userid)
MainWindow.setTabOrder(self.lineEdit_B_userid, self.pushButton_B_login)
MainWindow.setTabOrder(self.pushButton_B_login, self.pushButton_B_seslogin)
MainWindow.setTabOrder(self.pushButton_B_seslogin, self.lineEdit_D_initloc)
MainWindow.setTabOrder(self.lineEdit_D_initloc, self.pushButton_D_random)
MainWindow.setTabOrder(self.pushButton_D_random, self.pushButton_D_send)
MainWindow.setTabOrder(self.pushButton_D_send, self.pushButton_E_trash)
MainWindow.setTabOrder(self.pushButton_E_trash, self.lineEdit_F_route)
MainWindow.setTabOrder(self.lineEdit_F_route, self.radioButton_F_map)
MainWindow.setTabOrder(self.radioButton_F_map, self.radioButton_F_direct)
MainWindow.setTabOrder(self.radioButton_F_direct, self.checkBox_F_hidepoints)
MainWindow.setTabOrder(self.checkBox_F_hidepoints, self.pushButton_F_undo)
MainWindow.setTabOrder(self.pushButton_F_undo, self.pushButton_F_confirm)
MainWindow.setTabOrder(self.pushButton_F_confirm, self.lineEdit_G_speed)
MainWindow.setTabOrder(self.lineEdit_G_speed, self.lineEdit_G_distance)
MainWindow.setTabOrder(self.lineEdit_G_distance, self.lineEdit_G_bupin)
MainWindow.setTabOrder(self.lineEdit_G_bupin, self.lineEdit_G_bushu)
MainWindow.setTabOrder(self.lineEdit_G_bushu, self.lineEdit_G_time)
MainWindow.setTabOrder(self.lineEdit_G_time, self.pushButton_G_finalconfirm)
MainWindow.setTabOrder(self.pushButton_G_finalconfirm, self.pushButton_Z_logout)
MainWindow.setTabOrder(self.pushButton_Z_logout, self.checkBox_Z_debug)
def retranslateUi(self, MainWindow):
_translate = QtCore.QCoreApplication.translate
MainWindow.setWindowTitle(_translate("MainWindow", "mzlnmsl"))
self.groupBox.setTitle(_translate("MainWindow", "地图"))
self.groupBox_A.setTitle(_translate("MainWindow", "模拟的设备信息"))
self.label_A_uuid.setText(_translate("MainWindow", "设备uuid"))
self.label_A_model.setText(_translate("MainWindow", "设备名称"))
self.radioButton_A_ios.setText(_translate("MainWindow", "iOS"))
self.radioButton_A_android.setText(_translate("MainWindow", "Android"))
self.label_A_type.setText(_translate("MainWindow", "设备种类"))
self.pushButton_A_random.setText(_translate("MainWindow", "随机生成"))
self.pushButton_Z_start.setText(_translate("MainWindow", "完成设置,开始程序"))
self.groupBox_B.setTitle(_translate("MainWindow", "登录"))
self.label_B_username.setText(_translate("MainWindow", "用户名"))
self.label_B_password.setText(_translate("MainWindow", "密码"))
self.label_B_utoken.setText(_translate("MainWindow", "utoken"))
self.label_B_userid.setText(_translate("MainWindow", "userid"))
self.pushButton_B_login.setText(_translate("MainWindow", "使用账号密码获取新的utoken进行登录"))
self.pushButton_B_seslogin.setText(_translate("MainWindow", "使用已有的utoken, userid直接登录"))
self.checkBox_B_saveaccount.setText(_translate("MainWindow", "保存用户名"))
self.checkBox_B_savepassword.setText(_translate("MainWindow", "保存密码"))
self.groupBox_D.setTitle(_translate("MainWindow", "开始跑步请求"))
self.label_D_initloc.setText(_translate("MainWindow", "起点坐标"))
self.pushButton_D_send.setText(_translate("MainWindow", "发送请求"))
self.pushButton_D_random.setText(_translate("MainWindow", "瞎几把选"))
self.groupBox_E.setTitle(_translate("MainWindow", "当前数据"))
self.clabel_E_reqtime.setText(_translate("MainWindow", "请求时间"))
self.clabel_E_initloc.setText(_translate("MainWindow", "起点坐标"))
self.clabel_E_runid.setText(_translate("MainWindow", "runid"))
self.pushButton_E_trash.setText(_translate("MainWindow", "舍弃当前数据"))
self.clabel_E_resume.setText(_translate("MainWindow", "*已加载的缓存数据"))
self.clabel_E_dist.setText(_translate("MainWindow", "目标距离"))
self.groupBox_C.setTitle(_translate("MainWindow", "用户信息"))
self.clabel_C_name.setText(_translate("MainWindow", "姓名"))
self.clabel_C_school.setText(_translate("MainWindow", "学校"))
self.clabel_C_completed.setText(_translate("MainWindow", "完成"))
self.clabel_C_target.setText(_translate("MainWindow", "目标"))
self.groupBox_Z.setTitle(_translate("MainWindow", "状态"))
self.label_Z_status.setText(_translate("MainWindow", "初始化..."))
self.pushButton_Z_logout.setText(_translate("MainWindow", "登出"))
self.checkBox_Z_debug.setText(_translate("MainWindow", "debug输出"))
self.groupBox_F.setTitle(_translate("MainWindow", "路径规划"))
self.clabel_F_route.setText(_translate("MainWindow", "路径"))
self.pushButton_F_confirm.setText(_translate("MainWindow", "确认路径"))
self.pushButton_F_undo.setText(_translate("MainWindow", "撤销一次"))
self.clabel_F_dist.setText(_translate("MainWindow", "当前路径长度"))
self.radioButton_F_map.setText(_translate("MainWindow", "通过地图规划路径"))
self.radioButton_F_direct.setText(_translate("MainWindow", "直接连接两点"))
self.clabel_F_routemethod.setText(_translate("MainWindow", "添加下一路径点时:"))
self.checkBox_F_hidepoints.setText(_translate("MainWindow", "隐藏点标"))
self.groupBox_G.setTitle(_translate("MainWindow", "提交信息确认"))
self.clabel_G_speed.setText(_translate("MainWindow", "配速 s/km"))
self.clabel_G_distance.setText(_translate("MainWindow", "距离 km"))
self.clabel_G_bushu.setText(_translate("MainWindow", "步数"))
self.clabel_G_bupin.setText(_translate("MainWindow", "步频"))
self.clabel_G_time.setText(_translate("MainWindow", "总时间 s"))
self.pushButton_G_finalconfirm.setText(_translate("MainWindow", "确认提交"))
self.clabel_G_starttime.setText(_translate("MainWindow", "开始时间"))
self.clabel_G_endtime.setText(_translate("MainWindow", "结束时间"))
from PyQt5 import QtWebEngineWidgets
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] |
grammar.py | prtx/SLR-Parser-Implementation | 0 | 143129 | <reponame>prtx/SLR-Parser-Implementation
from context_free_grammar import Context_Free_Grammar
def Grammar():
grammar = Context_Free_Grammar()
grammar.add_terminal('+', '*', '(', ')', 'id')
grammar.add_non_terminal('E', 'T', 'F')
grammar.set_start_symbol('E')
grammar.add_production_rule('E', ['E', '+', 'T'], ['T'])
grammar.add_production_rule('T', ['T', '*', 'F'], ['F'])
grammar.add_production_rule('F', ['(', 'E', ')'], ['id'])
return grammar
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comments/models.py | chench53/jquery-comments-server | 2 | 44163 | from django.db import models
from django.utils import timezone
# Create your models here.
class Creator(models.Model):
fullname = models.CharField(max_length=30)
profile_picture_url = models.URLField()
def __str__(self):
return self.fullname
class Customer(models.Model):
pass
class File(models.Model):
file_url = models.URLField()
file_mime_type = models.CharField(max_length=20)
class Comment(models.Model):
creator = models.ForeignKey(Creator, models.SET_NULL, null=True)
created = models.DateTimeField(blank=True, null=True)
modified = models.DateTimeField(blank=True, null=True)
content = models.TextField()
parent = models.ForeignKey('self', models.CASCADE, null=True, blank=True)
upvote_count = models.IntegerField(default=0)
def __str__(self):
return '{creator}: {content}...'.format(creator=self.creator, content=self.content[:40])
class Upvote(models.Model):
creator = models.ForeignKey(Creator, models.CASCADE, null=True)
comment = models.ForeignKey(Comment, models.CASCADE, null=True)
class Meta:
unique_together = (("creator", "comment"),)
def __str__(self):
return '{creator} upvote {comment}'.format(creator=creator, comment=comment)
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accounts/tests/mixins_tests.py | ygrass/handsome | 0 | 112407 | # -*- coding: utf-8 -*-
from django import forms
from django.test.testcases import TestCase
from accounts.mixins import PhoneFormMixin
class PhoneFormMixinTests(TestCase):
"""
Tests for PhoneFormMixin
"""
def test_clean_username(self):
"""
Check username as phone number
"""
mixin = PhoneFormMixin()
# invalid number
mixin.data = {'username': '123'}
self.assertRaises(forms.ValidationError,
lambda: mixin.clean_username())
# valid number
mixin.data = {'username': '12345678900'}
self.assertTrue(mixin.clean_username() == '12345678900')
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survae/data/loaders/image/celeba.py | robert-giaquinto/survae_flows | 2 | 190332 | from survae.data.datasets.image import SuperResolutionCelebA32Dataset, UnsupervisedCelebA32Dataset
from survae.data.datasets.image import SuperResolutionCelebA64Dataset, UnsupervisedCelebA64Dataset
from survae.data.datasets.image import SuperResolutionCelebA128Dataset, UnsupervisedCelebA128Dataset
from torchvision.transforms import Compose, ToTensor, Resize
from survae.data.transforms import Quantize
from survae.data import TrainValidTestLoader, DATA_PATH
class CelebA(TrainValidTestLoader):
'''
The CelebA dataset of
(Liu et al., 2015): https://arxiv.org/abs/1411.7766
preprocessed to 64x64 as in
(Larsen et al. 2016): https://arxiv.org/abs/1512.09300
(Dinh et al., 2017): https://arxiv.org/abs/1605.08803
'''
def __init__(self, input_size, root=DATA_PATH, num_bits=8, pil_transforms=[], conditional=False, super_resolution=False, sr_scale_factor=4, resize_hw=None, bicubic=False):
super(CelebA, self).__init__()
assert len(input_size) == 3
self.root = root
self.input_size = input_size
self.y_classes = 40
trans_train = pil_transforms + [ToTensor(), Quantize(num_bits)]
trans_test = [ToTensor(), Quantize(num_bits)]
if resize_hw is not None:
trans_train.insert(0, Resize((resize_hw, resize_hw)))
trans_test.insert(0, Resize((resize_hw, resize_hw)))
if conditional:
raise ValueError(f"Conditional CelebA dataset not available yet.")
# Load data
if super_resolution:
if input_size[-1] == 32:
Dataset = SuperResolutionCelebA32Dataset
elif input_size[-1] == 64:
Dataset = SuperResolutionCelebA64Dataset
elif input_size[-1] == 128:
Dataset = SuperResolutionCelebA128Dataset
else:
raise ValueError(f"Invalid input size {input_size}")
self.train = Dataset(root, split='train', transform=Compose(trans_train), sr_scale_factor=sr_scale_factor, bicubic=bicubic)
self.valid = Dataset(root, split='valid', transform=Compose(trans_test), sr_scale_factor=sr_scale_factor, bicubic=bicubic)
self.test = Dataset(root, split='test', transform=Compose(trans_test), sr_scale_factor=sr_scale_factor, bicubic=bicubic)
else:
if input_size[-1] == 32:
Dataset = UnsupervisedCelebA32Dataset
elif input_size[-1] == 64:
Dataset = UnsupervisedCelebA64Dataset
elif input_size[-1] == 128:
Dataset = UnsupervisedCelebA128Dataset
else:
raise ValueError(f"Invalid input size {input_size}")
self.train = Dataset(root, split='train', transform=Compose(trans_train))
self.valid = Dataset(root, split='valid', transform=Compose(trans_test))
self.test = Dataset(root, split='test', transform=Compose(trans_test))
class CelebA32(CelebA):
def __init__(self, root=DATA_PATH, num_bits=8, pil_transforms=[], conditional=False, super_resolution=False, sr_scale_factor=4, resize_hw=None, bicubic=False):
super(CelebA32, self).__init__(input_size=[3,32,32],
root=root,
num_bits=num_bits,
pil_transforms=pil_transforms,
conditional=conditional,
super_resolution=super_resolution, sr_scale_factor=sr_scale_factor, bicubic=bicubic,
resize_hw=resize_hw)
class CelebA64(CelebA):
def __init__(self, root=DATA_PATH, num_bits=8, pil_transforms=[], conditional=False, super_resolution=False, sr_scale_factor=4, resize_hw=None, bicubic=False):
super(CelebA64, self).__init__(input_size=[3,64,64],
root=root,
num_bits=num_bits,
pil_transforms=pil_transforms,
conditional=conditional,
super_resolution=super_resolution, sr_scale_factor=sr_scale_factor,bicubic=bicubic,
resize_hw=resize_hw)
class CelebA128(CelebA):
def __init__(self, root=DATA_PATH, num_bits=8, pil_transforms=[], conditional=False, super_resolution=False, sr_scale_factor=4, resize_hw=None, bicubic=False):
super(CelebA128, self).__init__(input_size=[3,128,128],
root=root,
num_bits=num_bits,
pil_transforms=pil_transforms,
conditional=conditional,
super_resolution=super_resolution, sr_scale_factor=sr_scale_factor, bicubic=bicubic,
resize_hw=resize_hw)
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] |
transphire_transform/dump_load/tests/test_util.py | MPI-Dortmund/transphire_transform | 2 | 111083 | <gh_stars>1-10
"""
MIT License
Copyright (c) 2018 <NAME> Institute of Molecular Physiology
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import numpy as np
import pandas as pd
import pytest
from .. import util
OUTPUT_TEST_FOLDER = 'OUTPUT_TESTS_LOAD_DUMP_UTIL'
class TestCreateHeader:
def test_create_header_list(self):
"""
"""
header = ['a', 'b', 'c', 'd']
header_out = ['_a', '_b', '_c', '_d']
assert util.create_header(names=header, index=False, prefix='') == header_out
def test_create_header_index_list(self):
"""
"""
header = ['a', 'b', 'c', 'd']
header_out = ['_a #1', '_b #2', '_c #3', '_d #4']
assert util.create_header(names=header, index=True, prefix='') == header_out
def test_create_header_array(self):
"""
"""
header = np.array(['a', 'b', 'c', 'd'], dtype=str)
header_out = ['_a', '_b', '_c', '_d']
assert util.create_header(names=header, index=False, prefix='') == header_out
def test_create_header_index_array(self):
"""
"""
header = np.array(['a', 'b', 'c', 'd'], dtype=str)
header_out = ['_a #1', '_b #2', '_c #3', '_d #4']
assert util.create_header(names=header, index=True, prefix='') == header_out
def test_create_header_array_prefix(self):
"""
"""
header = np.array(['a', 'b', 'c', 'd'], dtype=str)
header_out = ['_testa', '_testb', '_testc', '_testd']
assert util.create_header(names=header, index=False, prefix='test') == header_out
def test_create_header_index_array_prefix(self):
"""
"""
header = np.array(['a', 'b', 'c', 'd'], dtype=str)
header_out = ['_testa #1', '_testb #2', '_testc #3', '_testd #4']
assert util.create_header(names=header, index=True, prefix='test') == header_out
def test_create_header_array_empty(self):
"""
"""
header = np.array([], dtype=str)
with pytest.raises(IOError):
util.create_header(names=header, index=True, prefix='')
def test_create_header_list_empty(self):
"""
"""
header = []
with pytest.raises(IOError):
util.create_header(names=header, index=True, prefix='')
class TestDumpFile:
def test_dump_file_empty(self, tmpdir):
"""
"""
data = pd.DataFrame({
})
output_file: str = tmpdir.mkdir(OUTPUT_TEST_FOLDER).join('test_dump_file_empty')
with pytest.raises(IOError):
util.dump_file(
file_name=output_file,
data=data,
header=None,
vertical=True,
)
def test_dump_file_four(self, tmpdir):
"""
"""
data_1 = np.arange(4)
data_2 = ['a', 'b', 'c', 'd']
data_3 = np.array(np.arange(4), dtype=float)
data_4 = [1]*4
data = pd.DataFrame({
'_rlnTest1': data_1,
'_rlnTest2': data_2,
'_rlnTest3': data_3,
'_pipeTest4': data_4,
})
output_file: str = tmpdir.mkdir(OUTPUT_TEST_FOLDER).join('test_dump_file_four')
util.dump_file(
file_name=output_file,
data=data,
header=None,
vertical=True,
)
load_data = util.load_file(file_name=output_file)
assert np.array_equal(load_data.values, data.values)
def test_dump_file_single(self, tmpdir):
"""
"""
data_1 = np.arange(4)
data = pd.DataFrame({
'_rlnTest1': data_1,
})
output_file: str = tmpdir.mkdir(OUTPUT_TEST_FOLDER).join('test_dump_file_single')
util.dump_file(
file_name=output_file,
data=data,
header=None,
vertical=True,
)
load_data = util.load_file(file_name=output_file)
assert np.array_equal(load_data.values, data.values)
def test_dump_file_four_hor(self, tmpdir):
"""
"""
data_1 = np.arange(4)
data_2 = ['a', 'b', 'c', 'd']
data_3 = np.array(np.arange(4), dtype=float)
data_4 = [1]*4
data = pd.DataFrame({
'_rlnTest1': data_1,
'_rlnTest2': data_2,
'_rlnTest3': data_3,
'_pipeTest4': data_4,
})
output_file: str = tmpdir.mkdir(OUTPUT_TEST_FOLDER).join('test_dump_file_four_hor')
util.dump_file(
file_name=output_file,
data=data,
header=None,
vertical=False,
)
load_data = util.load_file(file_name=output_file)
assert np.array_equal(load_data.values, data.values)
def test_dump_file_single_hor(self, tmpdir):
"""
"""
data_1 = np.arange(4)
data = pd.DataFrame({
'_rlnTest1': data_1,
})
output_file: str = tmpdir.mkdir(OUTPUT_TEST_FOLDER).join('test_dump_file_single_hor')
util.dump_file(
file_name=output_file,
data=data,
header=None,
vertical=False,
)
load_data = util.load_file(file_name=output_file)
assert np.array_equal(load_data.values, data.values)
def test_dump_file_four_header(self, tmpdir):
"""
"""
data_1 = np.arange(4)
data_2 = ['a', 'b', 'c', 'd']
data_3 = np.array(np.arange(4), dtype=float)
data_4 = [1]*4
data = pd.DataFrame({
'_rlnTest1': data_1,
'_rlnTest2': data_2,
'_rlnTest3': data_3,
'_pipeTest4': data_4,
})
output_file: str = tmpdir.mkdir(OUTPUT_TEST_FOLDER).join('test_dump_file_four')
util.dump_file(
file_name=output_file,
data=data,
header=data.keys(),
vertical=True,
)
assert util.load_file(file_name=output_file, names=data.keys(), skiprows=4).equals(data)
def test_dump_file_single_header(self, tmpdir):
"""
"""
data_1 = np.arange(4)
data = pd.DataFrame({
'_rlnTest1': data_1,
})
output_file: str = tmpdir.mkdir(OUTPUT_TEST_FOLDER).join('test_dump_file_single')
util.dump_file(
file_name=output_file,
data=data,
header=data.keys(),
vertical=True,
)
assert util.load_file(file_name=output_file, names=data.keys(), skiprows=1).equals(data)
def test_dump_file_four_hor_header(self, tmpdir):
"""
"""
data_1 = np.arange(4)
data_2 = ['a', 'b', 'c', 'd']
data_3 = np.array(np.arange(4), dtype=float)
data_4 = [1]*4
data = pd.DataFrame({
'_rlnTest1': data_1,
'_rlnTest2': data_2,
'_rlnTest3': data_3,
'_pipeTest4': data_4,
})
output_file: str = tmpdir.mkdir(OUTPUT_TEST_FOLDER).join('test_dump_file_four_hor')
util.dump_file(
file_name=output_file,
data=data,
header=None,
vertical=False,
)
util.dump_file(
file_name=output_file,
data=data,
header=data.keys(),
vertical=False,
)
assert util.load_file(file_name=output_file, names=data.keys(), skiprows=1).equals(data)
def test_dump_file_single_hor_header(self, tmpdir):
"""
"""
data_1 = np.arange(4)
data = pd.DataFrame({
'_rlnTest1': data_1,
})
output_file: str = tmpdir.mkdir(OUTPUT_TEST_FOLDER).join('test_dump_file_single_hor')
util.dump_file(
file_name=output_file,
data=data,
header=None,
vertical=False,
)
util.dump_file(
file_name=output_file,
data=data,
header=data.keys(),
vertical=False,
)
assert util.load_file(file_name=output_file, names=data.keys(), skiprows=1).equals(data)
class TestImportKeys:
def test_import_keys_filled_file_should_work(self, tmpdir):
"""
"""
output_file: str = tmpdir.mkdir(OUTPUT_TEST_FOLDER).join('test_import_keys_filled_file_should_work')
keys = ('test_a', 'test_b', 'test_c')
suffix = ('test', 'test2', 'test3')
with open(output_file, 'w') as w:
for key, suf in zip(keys, suffix):
w.write(f'{key} # {suf}\n')
imported_keys = util.import_keys(output_file)
assert keys == imported_keys
def test_import_keys_filled_file_multi_hastag_should_work(self, tmpdir):
"""
"""
output_file: str = tmpdir.mkdir(OUTPUT_TEST_FOLDER).join('test_import_keys_filled_file_multi_hastag_should_work')
keys = ('test_a', 'test_b', 'test_c')
suffix = ('test', 'test2', 'test3')
with open(output_file, 'w') as w:
for key, suf in zip(keys, suffix):
w.write(f'{key} # TEST1 # {suf}\n')
imported_keys = util.import_keys(output_file)
assert keys == imported_keys
def test_import_keys_empty_file_should_work(self, tmpdir):
"""
"""
output_file: str = tmpdir.mkdir(OUTPUT_TEST_FOLDER).join('test_import_keys_empty_file_should_work')
keys = ()
suffix = ()
with open(output_file, 'w') as w:
pass
imported_keys = util.import_keys(output_file)
assert keys == imported_keys
def test_import_keys_contains_whitespace_should_raise_AssertError(self, tmpdir):
"""
"""
output_file: str = tmpdir.mkdir(OUTPUT_TEST_FOLDER).join('test_import_keys_contains_whitespace_should_raise_AssertError')
keys = ()
suffix = ()
keys = ('test_a', 'test_b', 'test_c')
suffix = ('test', 'test2', 'test3')
with open(output_file, 'w') as w:
for key, suf in zip(keys, suffix):
w.write(f'{key} TEST1 # {suf}\n')
with pytest.raises(AssertionError):
util.import_keys(output_file)
class TestParseKeysToDict:
def test_input_unspecified_to_output(self):
data = ('test',)
data_output = {'test': 'test'}
data_return = util.parse_keys_to_dict(data)
assert data_output == data_return
def test_input_unspecified_export_to_output(self):
data = ('test',)
data_output = {'test': 'test'}
data_return = util.parse_keys_to_dict(data, True)
assert data_output == data_return
def test_input_specified_to_output(self):
data = ('test:test2',)
data_output = {'test': 'test2'}
data_return = util.parse_keys_to_dict(data)
assert data_output == data_return
def test_input_specified_export_to_output(self):
data = ('test:test2',)
data_output = {'test2': 'test'}
data_return = util.parse_keys_to_dict(data, True)
assert data_output == data_return
class TestAddToDict():
def test_new_key_should_return_filled_dict(self):
data_dict = {}
key = 'a'
value = 'b'
util.add_to_dict(data_dict, key, value)
assert data_dict == {'a': 'b'}
def test_new_key_int_should_return_filled_dict(self):
data_dict = {}
key = 'a'
value = 1
util.add_to_dict(data_dict, key, value)
assert data_dict == {'a': 1}
def test_double_key_should_attributeerror(self):
data_dict = {'a': 'c'}
key = 'a'
value = 'b'
with pytest.raises(AttributeError):
util.add_to_dict(data_dict, key, value)
class TestExtractFunctionFromFunctionDict:
def dummy_function_1(self): # pragma: no cover
pass
def dummy_function_2(self): # pragma: no cover
pass
def dummy_function_3(self): # pragma: no cover
pass
def test_empty_version_ordered_should_return_dummy_function_3(self):
func_dict = {
'0.0.4': self.dummy_function_1,
'1.1.2': self.dummy_function_2,
'2.2.5': self.dummy_function_3,
}
assert self.dummy_function_3 == util.extract_function_from_function_dict(func_dict)
def test_empty_version_unordered_should_return_dummy_function_3(self):
func_dict = {
'1.1.2': self.dummy_function_2,
'2.2.5': self.dummy_function_3,
'0.0.4': self.dummy_function_1,
}
assert self.dummy_function_3 == util.extract_function_from_function_dict(func_dict)
def test_version_225_ordered_should_return_dummy_function_3(self):
func_dict = {
'0.0.4': self.dummy_function_1,
'1.1.2': self.dummy_function_2,
'2.2.5': self.dummy_function_3,
}
assert self.dummy_function_3 == util.extract_function_from_function_dict(func_dict, '2.2.5')
def test_version_326_ordered_should_return_dummy_function_3(self):
func_dict = {
'0.0.4': self.dummy_function_1,
'1.1.2': self.dummy_function_2,
'2.2.5': self.dummy_function_3,
}
assert self.dummy_function_3 == util.extract_function_from_function_dict(func_dict, '3.2.6')
def test_version_112_ordered_should_return_dummy_function_2(self):
func_dict = {
'0.0.4': self.dummy_function_1,
'1.1.2': self.dummy_function_2,
'2.2.5': self.dummy_function_3,
}
assert self.dummy_function_2 == util.extract_function_from_function_dict(func_dict, '1.1.2')
def test_version_122_ordered_should_return_dummy_function_2(self):
func_dict = {
'0.0.4': self.dummy_function_1,
'1.1.2': self.dummy_function_2,
'2.2.5': self.dummy_function_3,
}
assert self.dummy_function_2 == util.extract_function_from_function_dict(func_dict, '1.2.2')
def test_version_004_ordered_should_return_dummy_function_1(self):
func_dict = {
'0.0.4': self.dummy_function_1,
'1.1.2': self.dummy_function_2,
'2.2.5': self.dummy_function_3,
}
assert self.dummy_function_1 == util.extract_function_from_function_dict(func_dict, '0.0.4')
def test_version_005_ordered_should_return_dummy_function_1(self):
func_dict = {
'0.0.4': self.dummy_function_1,
'1.1.2': self.dummy_function_2,
'2.2.5': self.dummy_function_3,
}
assert self.dummy_function_1 == util.extract_function_from_function_dict(func_dict, '0.0.5')
def test_version_225_unordered_should_return_dummy_function_3(self):
func_dict = {
'0.0.4': self.dummy_function_1,
'2.2.5': self.dummy_function_3,
'1.1.2': self.dummy_function_2,
}
assert self.dummy_function_3 == util.extract_function_from_function_dict(func_dict, '2.2.5')
def test_version_326_unordered_should_return_dummy_function_3(self):
func_dict = {
'0.0.4': self.dummy_function_1,
'2.2.5': self.dummy_function_3,
'1.1.2': self.dummy_function_2,
}
assert self.dummy_function_3 == util.extract_function_from_function_dict(func_dict, '3.2.6')
def test_version_112_unordered_should_return_dummy_function_2(self):
func_dict = {
'0.0.4': self.dummy_function_1,
'2.2.5': self.dummy_function_3,
'1.1.2': self.dummy_function_2,
}
assert self.dummy_function_2 == util.extract_function_from_function_dict(func_dict, '1.1.2')
def test_version_122_unordered_should_return_dummy_function_2(self):
func_dict = {
'0.0.4': self.dummy_function_1,
'2.2.5': self.dummy_function_3,
'1.1.2': self.dummy_function_2,
}
assert self.dummy_function_2 == util.extract_function_from_function_dict(func_dict, '1.2.2')
def test_version_004_unordered_should_return_dummy_function_1(self):
func_dict = {
'0.0.4': self.dummy_function_1,
'2.2.5': self.dummy_function_3,
'1.1.2': self.dummy_function_2,
}
assert self.dummy_function_1 == util.extract_function_from_function_dict(func_dict, '0.0.4')
def test_version_005_unordered_should_return_dummy_function_1(self):
func_dict = {
'0.0.4': self.dummy_function_1,
'2.2.5': self.dummy_function_3,
'1.1.2': self.dummy_function_2,
}
assert self.dummy_function_1 == util.extract_function_from_function_dict(func_dict, '0.0.5')
def test_version_003_unordered_should_raise_AssertionError(self):
func_dict = {
'0.0.4': self.dummy_function_1,
'2.2.5': self.dummy_function_3,
'1.1.2': self.dummy_function_2,
}
with pytest.raises(AssertionError):
util.extract_function_from_function_dict(func_dict, '0.0.3')
def test_version_unequal_format_raises_AssertionError(self):
func_dict = {
'0.0.4': self.dummy_function_1,
'2.2.5': self.dummy_function_3,
'1.1.2': self.dummy_function_2,
}
with pytest.raises(AssertionError):
util.extract_function_from_function_dict(func_dict, '1.0')
def test_version_unequal_format_in_keys_raises_AssertionError(self):
func_dict = {
'0.0': self.dummy_function_1,
'2.2.5': self.dummy_function_3,
'1.1.2': self.dummy_function_2,
}
with pytest.raises(AssertionError):
util.extract_function_from_function_dict(func_dict, '1.0.1')
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1990,
4321,
17518,
15506,
29901,
13,
13,
1678,
822,
1243,
29918,
5215,
29918,
8149,
29918,
26940,
29918,
1445,
29918,
9344,
29918,
1287,
29898,
1311,
29892,
13128,
3972,
1125,
13,
4706,
9995,
13,
4706,
9995,
13,
4706,
1962,
29918,
1445,
29901,
851,
353,
13128,
3972,
29889,
11256,
3972,
29898,
12015,
12336,
29918,
18267,
29918,
29943,
5607,
8032,
467,
7122,
877,
1688,
29918,
5215,
29918,
8149,
29918,
26940,
29918,
1445,
29918,
9344,
29918,
1287,
1495,
13,
4706,
6611,
353,
6702,
1688,
29918,
29874,
742,
525,
1688,
29918,
29890,
742,
525,
1688,
29918,
29883,
1495,
13,
4706,
25557,
353,
6702,
1688,
742,
525,
1688,
29906,
742,
525,
1688,
29941,
1495,
13,
4706,
411,
1722,
29898,
4905,
29918,
1445,
29892,
525,
29893,
1495,
408,
281,
29901,
13,
9651,
363,
1820,
29892,
7332,
297,
14319,
29898,
8149,
29892,
25557,
1125,
13,
18884,
281,
29889,
3539,
29898,
29888,
29915,
29912,
1989,
29913,
396,
426,
29879,
1137,
1012,
29876,
1495,
13,
13,
4706,
19673,
29918,
8149,
353,
3667,
29889,
5215,
29918,
8149,
29898,
4905,
29918,
1445,
29897,
13,
4706,
4974,
6611,
1275,
19673,
29918,
8149,
13,
13,
1678,
822,
1243,
29918,
5215,
29918,
8149,
29918,
26940,
29918,
1445,
29918,
9910,
29918,
29882,
579,
351,
29918,
9344,
29918,
1287,
29898,
1311,
29892,
13128,
3972,
1125,
13,
4706,
9995,
13,
4706,
9995,
13,
4706,
1962,
29918,
1445,
29901,
851,
353,
13128,
3972,
29889,
11256,
3972,
29898,
12015,
12336,
29918,
18267,
29918,
29943,
5607,
8032,
467,
7122,
877,
1688,
29918,
5215,
29918,
8149,
29918,
26940,
29918,
1445,
29918,
9910,
29918,
29882,
579,
351,
29918,
9344,
29918,
1287,
1495,
13,
4706,
6611,
353,
6702,
1688,
29918,
29874,
742,
525,
1688,
29918,
29890,
742,
525,
1688,
29918,
29883,
1495,
13,
4706,
25557,
353,
6702,
1688,
742,
525,
1688,
29906,
742,
525,
1688,
29941,
1495,
13,
4706,
411,
1722,
29898,
4905,
29918,
1445,
29892,
525,
29893,
1495,
408,
281,
29901,
13,
9651,
363,
1820,
29892,
7332,
297,
14319,
29898,
8149,
29892,
25557,
1125,
13,
18884,
281,
29889,
3539,
29898,
29888,
29915,
29912,
1989,
29913,
396,
17067,
1254,
29896,
396,
426,
29879,
1137,
1012,
29876,
1495,
13,
13,
4706,
19673,
29918,
8149,
353,
3667,
29889,
5215,
29918,
8149,
29898,
4905,
29918,
1445,
29897,
13,
4706,
4974,
6611,
1275,
19673,
29918,
8149,
13,
13,
1678,
822,
1243,
29918,
5215,
29918,
8149,
29918,
6310,
29918,
1445,
29918,
9344,
29918,
1287,
29898,
1311,
29892,
13128,
3972,
1125,
13,
4706,
9995,
13,
4706,
9995,
13,
4706,
1962,
29918,
1445,
29901,
851,
353,
13128,
3972,
29889,
11256,
3972,
29898,
12015,
12336,
29918,
18267,
29918,
29943,
5607,
8032,
467,
7122,
877,
1688,
29918,
5215,
29918,
8149,
29918,
6310,
29918,
1445,
29918,
9344,
29918,
1287,
1495,
13,
4706,
6611,
353,
3861,
13,
4706,
25557,
353,
3861,
13,
4706,
411,
1722,
29898,
4905,
29918,
1445,
29892,
525,
29893,
1495,
408,
281,
29901,
13,
9651,
1209,
13,
13,
4706,
19673,
29918,
8149,
353,
3667,
29889,
5215,
29918,
8149,
29898,
4905,
29918,
1445,
29897,
13,
4706,
4974,
6611,
1275,
19673,
29918,
8149,
13,
13,
1678,
822,
1243,
29918,
5215,
29918,
8149,
29918,
11516,
29918,
1332,
3246,
3535,
29918,
9344,
29918,
22692,
29918,
14697,
2392,
29898,
1311,
29892,
13128,
3972,
1125,
13,
4706,
9995,
13,
4706,
9995,
13,
4706,
1962,
29918,
1445,
29901,
851,
353,
13128,
3972,
29889,
11256,
3972,
29898,
12015,
12336,
29918,
18267,
29918,
29943,
5607,
8032,
467,
7122,
877,
1688,
29918,
5215,
29918,
8149,
29918,
11516,
29918,
1332,
3246,
3535,
29918,
9344,
29918,
22692,
29918,
14697,
2392,
1495,
13,
4706,
6611,
353,
3861,
13,
4706,
25557,
353,
3861,
13,
4706,
6611,
353,
6702,
1688,
29918,
29874,
742,
525,
1688,
29918,
29890,
742,
525,
1688,
29918,
29883,
1495,
13,
4706,
25557,
353,
6702,
1688,
742,
525,
1688,
29906,
742,
525,
1688,
29941,
1495,
13,
4706,
411,
1722,
29898,
4905,
29918,
1445,
29892,
525,
29893,
1495,
408,
281,
29901,
13,
9651,
363,
1820,
29892,
7332,
297,
14319,
29898,
8149,
29892,
25557,
1125,
13,
18884,
281,
29889,
3539,
29898,
29888,
29915,
29912,
1989,
29913,
17067,
1254,
29896,
396,
426,
29879,
1137,
1012,
29876,
1495,
13,
13,
4706,
411,
11451,
1688,
29889,
336,
4637,
29898,
14697,
291,
2392,
1125,
13,
9651,
3667,
29889,
5215,
29918,
8149,
29898,
4905,
29918,
1445,
29897,
13,
13,
13,
1990,
4321,
12914,
15506,
1762,
21533,
29901,
13,
13,
1678,
822,
1243,
29918,
2080,
29918,
348,
6550,
2164,
29918,
517,
29918,
4905,
29898,
1311,
1125,
13,
4706,
848,
353,
6702,
1688,
742,
29897,
13,
4706,
848,
29918,
4905,
353,
11117,
1688,
2396,
525,
1688,
10827,
13,
4706,
848,
29918,
2457,
353,
3667,
29889,
5510,
29918,
8149,
29918,
517,
29918,
8977,
29898,
1272,
29897,
13,
4706,
4974,
848,
29918,
4905,
1275,
848,
29918,
2457,
13,
13,
1678,
822,
1243,
29918,
2080,
29918,
348,
6550,
2164,
29918,
15843,
29918,
517,
29918,
4905,
29898,
1311,
1125,
13,
4706,
848,
353,
6702,
1688,
742,
29897,
13,
4706,
848,
29918,
4905,
353,
11117,
1688,
2396,
525,
1688,
10827,
13,
4706,
848,
29918,
2457,
353,
3667,
29889,
5510,
29918,
8149,
29918,
517,
29918,
8977,
29898,
1272,
29892,
5852,
29897,
13,
4706,
4974,
848,
29918,
4905,
1275,
848,
29918,
2457,
13,
13,
1678,
822,
1243,
29918,
2080,
29918,
6550,
2164,
29918,
517,
29918,
4905,
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1311,
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13,
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353,
6702,
1688,
29901,
1688,
29906,
742,
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13,
4706,
848,
29918,
4905,
353,
11117,
1688,
2396,
525,
1688,
29906,
10827,
13,
4706,
848,
29918,
2457,
353,
3667,
29889,
5510,
29918,
8149,
29918,
517,
29918,
8977,
29898,
1272,
29897,
13,
4706,
4974,
848,
29918,
4905,
1275,
848,
29918,
2457,
13,
13,
1678,
822,
1243,
29918,
2080,
29918,
6550,
2164,
29918,
15843,
29918,
517,
29918,
4905,
29898,
1311,
1125,
13,
4706,
848,
353,
6702,
1688,
29901,
1688,
29906,
742,
29897,
13,
4706,
848,
29918,
4905,
353,
11117,
1688,
29906,
2396,
525,
1688,
10827,
13,
4706,
848,
29918,
2457,
353,
3667,
29889,
5510,
29918,
8149,
29918,
517,
29918,
8977,
29898,
1272,
29892,
5852,
29897,
13,
4706,
4974,
848,
29918,
4905,
1275,
848,
29918,
2457,
13,
13,
13,
1990,
4321,
2528,
1762,
21533,
7295,
13,
13,
1678,
822,
1243,
29918,
1482,
29918,
1989,
29918,
9344,
29918,
2457,
29918,
26940,
29918,
8977,
29898,
1311,
1125,
13,
4706,
848,
29918,
8977,
353,
6571,
13,
4706,
1820,
353,
525,
29874,
29915,
13,
4706,
995,
353,
525,
29890,
29915,
13,
4706,
3667,
29889,
1202,
29918,
517,
29918,
8977,
29898,
1272,
29918,
8977,
29892,
1820,
29892,
995,
29897,
13,
4706,
4974,
848,
29918,
8977,
1275,
11117,
29874,
2396,
525,
29890,
10827,
13,
13,
1678,
822,
1243,
29918,
1482,
29918,
1989,
29918,
524,
29918,
9344,
29918,
2457,
29918,
26940,
29918,
8977,
29898,
1311,
1125,
13,
4706,
848,
29918,
8977,
353,
6571,
13,
4706,
1820,
353,
525,
29874,
29915,
13,
4706,
995,
353,
29871,
29896,
13,
4706,
3667,
29889,
1202,
29918,
517,
29918,
8977,
29898,
1272,
29918,
8977,
29892,
1820,
29892,
995,
29897,
13,
4706,
4974,
848,
29918,
8977,
1275,
11117,
29874,
2396,
29871,
29896,
29913,
13,
13,
1678,
822,
1243,
29918,
8896,
29918,
1989,
29918,
9344,
29918,
12715,
2704,
29898,
1311,
1125,
13,
4706,
848,
29918,
8977,
353,
11117,
29874,
2396,
525,
29883,
10827,
13,
4706,
1820,
353,
525,
29874,
29915,
13,
4706,
995,
353,
525,
29890,
29915,
13,
4706,
411,
11451,
1688,
29889,
336,
4637,
29898,
6708,
2392,
1125,
13,
9651,
3667,
29889,
1202,
29918,
517,
29918,
8977,
29898,
1272,
29918,
8977,
29892,
1820,
29892,
995,
29897,
13,
13,
13,
1990,
4321,
5647,
1461,
6678,
4591,
6678,
21533,
29901,
13,
13,
1678,
822,
20254,
29918,
2220,
29918,
29896,
29898,
1311,
1125,
396,
282,
23929,
29901,
694,
4612,
13,
4706,
1209,
13,
13,
1678,
822,
20254,
29918,
2220,
29918,
29906,
29898,
1311,
1125,
396,
282,
23929,
29901,
694,
4612,
13,
4706,
1209,
13,
13,
1678,
822,
20254,
29918,
2220,
29918,
29941,
29898,
1311,
1125,
396,
282,
23929,
29901,
694,
4612,
13,
4706,
1209,
13,
13,
1678,
822,
1243,
29918,
6310,
29918,
3259,
29918,
21693,
29918,
9344,
29918,
2457,
29918,
29881,
11770,
29918,
2220,
29918,
29941,
29898,
1311,
1125,
13,
4706,
3653,
29918,
8977,
353,
426,
13,
9651,
525,
29900,
29889,
29900,
29889,
29946,
2396,
1583,
29889,
29881,
11770,
29918,
2220,
29918,
29896,
29892,
13,
9651,
525,
29896,
29889,
29896,
29889,
29906,
2396,
1583,
29889,
29881,
11770,
29918,
2220,
29918,
29906,
29892,
13,
9651,
525,
29906,
29889,
29906,
29889,
29945,
2396,
1583,
29889,
29881,
11770,
29918,
2220,
29918,
29941,
29892,
13,
9651,
500,
13,
4706,
4974,
1583,
29889,
29881,
11770,
29918,
2220,
29918,
29941,
1275,
3667,
29889,
21111,
29918,
2220,
29918,
3166,
29918,
2220,
29918,
8977,
29898,
9891,
29918,
8977,
29897,
13,
13,
1678,
822,
1243,
29918,
6310,
29918,
3259,
29918,
348,
21693,
29918,
9344,
29918,
2457,
29918,
29881,
11770,
29918,
2220,
29918,
29941,
29898,
1311,
1125,
13,
4706,
3653,
29918,
8977,
353,
426,
13,
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525,
29896,
29889,
29896,
29889,
29906,
2396,
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29906,
29889,
29945,
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29889,
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11770,
29918,
2220,
29918,
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29892,
13,
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525,
29900,
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29900,
29889,
29946,
2396,
1583,
29889,
29881,
11770,
29918,
2220,
29918,
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29892,
13,
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500,
13,
4706,
4974,
1583,
29889,
29881,
11770,
29918,
2220,
29918,
29941,
1275,
3667,
29889,
21111,
29918,
2220,
29918,
3166,
29918,
2220,
29918,
8977,
29898,
9891,
29918,
8977,
29897,
13,
13,
1678,
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1243,
29918,
3259,
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29906,
29906,
29945,
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21693,
29918,
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29918,
2457,
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2220,
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3653,
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426,
13,
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525,
29900,
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29900,
29889,
29946,
2396,
1583,
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29881,
11770,
29918,
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13,
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29889,
29896,
29889,
29906,
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11770,
29918,
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29918,
29906,
29892,
13,
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29906,
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29906,
29889,
29945,
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1583,
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29941,
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21111,
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29918,
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13,
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21693,
29918,
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vectorizing.py | ramankh/multi-digit-recognition | 0 | 93453 | import cv2
import numpy as np
import pickle
import h5py
number_of_images = 13068 #33402 #
#dic_pick = pickle.load( open( "train_metadata.pickle", "rb" ) )
dic_pick = pickle.load( open( "test_metadata.pickle", "rb" ) )
length = [len(x) for x in dic_pick["label"]]
inpt= np.ndarray((number_of_images,784))
labels = np.ndarray((number_of_images, 7))
labels_one_hot = np.ndarray((number_of_images, 5, 10)) #(0-9 and blank)
length_one_hot = np.ndarray((number_of_images,7)) # (0-5 and more than 5)
labels_one_hot.fill(0)
labels.fill(-1)
i=0
while i<number_of_images:
#img = cv2.imread("cropped/{}.png".format(i+1), cv2.CV_LOAD_IMAGE_GRAYSCALE)
img = cv2.imread("cropped_test/{}.png".format(i+1), cv2.CV_LOAD_IMAGE_GRAYSCALE)
inpt[i] = np.reshape(img, -1)
# Iterate over all image labels and
# creat label classes array for each image
# creat one hot class representation which last item
# is the blank digit
# creat one hot class for numbers length which
# in this experiment is limited to 5 digits
for index,item in enumerate(dic_pick["label"][i]):
print "i={}--index={}".format(i,index)
if index<5:
labels[i][index] = item
if item == 10:
labels_one_hot[i][index][0] = 1
else:
labels_one_hot[i][index][item] = 1
digit_numbers = len(dic_pick["label"][i])
# find length of labels and save theme one-hot coded
if digit_numbers<=5:
length_one_hot[i][digit_numbers] = 1
else:
length_one_hot[i][6] = 1
i+=1
#print labels_one_hot.shape
char_one_lbl = np.array([labels_one_hot[i][0][:] for i in range(number_of_images-1)])
char_two_lbl = np.array([labels_one_hot[i][1][:] for i in range(number_of_images-1)])
char_three_lbl = np.array([labels_one_hot[i][2][:] for i in range(number_of_images-1)])
char_four_lbl = np.array([labels_one_hot[i][3][:] for i in range(number_of_images-1)])
char_five_lbl = np.array([labels_one_hot[i][4][:] for i in range(number_of_images-1)])
data_dict = {
'data': inpt,
'labels': labels,
'length': length,
'length_lbl': length_one_hot,
'ch_one_lbl' : char_one_lbl,
'ch_two_lbl' : char_two_lbl,
'ch_three_lbl' : char_three_lbl,
'ch_four_lbl' : char_four_lbl,
'ch_five_lbl' : char_five_lbl
}
#pickle_out = open("data.pickle", "wb")
pickle_out = open("test_data.pickle", "wb")
pickle.dump(data_dict, pickle_out, protocol=2)
pickle_out.close()
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app/waterQual/legacy/extractForcingBuffer.py | fkwai/geolearn | 0 | 1603036 | <filename>app/waterQual/legacy/extractForcingBuffer.py
import ee
ee.Initialize()
import os
import time
import numpy as np
import pandas as pd
import datetime as dt
from hydroDL.data import usgs, gee
workDir = r'C:\Users\geofk\work\waterQuality'
# siteNo = '04086120'
siteNo = '053416972'
# find out site
dfSite = usgs.readUsgsText(os.path.join(workDir, 'inventory_NWIS_streamflow'))
rowSite = dfSite.loc[dfSite['site_no'] == siteNo]
lat = rowSite['dec_lat_va'].values[0]
lon = rowSite['dec_long_va'].values[0]
# not right somehow
# sd = dt.datetime.strptime(rowSite['qw_begin_date'].values[0], '%Y-%m-%d')
# ed = dt.datetime.strptime(rowSite['qw_end_date'].values[0], '%Y-%m-%d')
dfDaily = usgs.readUsgsText(os.path.join(workDir, 'data', 'dailyTS', siteNo),
dataType='dailyTS')
sd = pd.to_datetime(dfDaily['datetime'].values[0])
ed = pd.to_datetime(dfDaily['datetime'].values[-1])
# (lon, lat) = (-92.4363056, 45.12002778)
# (lon, lat) = (-88.4008333, 43.62444444)
# (lon, lat) = (-88.4063056, 43.12002778)
point = ee.Geometry.Point(lon, lat)
region = point.buffer(100)
datasetName = 'OREGONSTATE/PRISM/AN81d'
fieldLst = ['ppt', 'tmean', 'tdmean']
# datasetName = 'NASA/NLDAS/FORA0125_H002'
# fieldLst=['total_precipitation','temperature']
# test of gee server
imageCol = ee.ImageCollection(datasetName).filterDate(
gee.utils.t2ee(sd), gee.utils.t2ee(ed)).filterBounds(point).select(
fieldLst).sort('system:time_start')
imageLst = imageCol.toList(5)
image = ee.Image(imageLst.get(0))
temp = image.reduceRegion(ee.Reducer.median(), region).getInfo()
print(temp)
nd = 10
t1 = sd
if (ed - sd).days > nd:
t2 = t1 + dt.timedelta(days=nd)
else:
t2 = ed
dfLst = list()
while t1 < t2 <= ed:
t0 = time.time()
imageCol = ee.ImageCollection("OREGONSTATE/PRISM/AN81d").filterDate(
gee.utils.t2ee(t1), gee.utils.t2ee(t2)).filterBounds(point).select(
fieldLst).sort('system:time_start')
df = gee.getRegion(imageCol, fieldLst, region)
dfLst.append(df)
t1 = t2
t2 = t1 + dt.timedelta(days=nd)
if t2 > ed:
t2 = ed
print('downloaded {}/{} time {}'.format((t1 - sd).days, (ed - sd).days,
time.time() - t0))
out = pd.concat(dfLst)
out = out.sort_values(by=['datetime'])
savePath = os.path.join(
os.path.join(workDir, 'data', 'forcing', siteNo + 'temp'))
out.to_csv(savePath, index=False)
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] |
slam/slamBase.py | TimeEscaper/rl_data_association | 3 | 136625 | """
<NAME>
<EMAIL>
24-Apr-2018
<NAME>
<EMAIL>
26-Nov-2018
"""
import numpy as np
from abc import ABC, abstractmethod
class SlamBase(ABC):
def __init__(self, slam_type, data_association, update_type, Q):
"""
:param slam_type: Which SLAM algorithm to run: ONLINE SLAM (ekf) or smoothing the full trajcetoryby using Factor graphs (sam).
:param data_association: The type of data association to perform during the update step.
Valid string choices include: {'known', 'nn', 'nndg', 'jcbb'}.
:param update_type: The type of update to perform in the SLAM algorithm.
Valid string choices include: {'batch', 'sequential'}.
:param Q: The observation noise covariance matrix: numpy.ndarray of size 2x2 for range and bearing measurements.
"""
assert isinstance(slam_type, str)
assert isinstance(data_association, str)
assert isinstance(update_type, str)
assert isinstance(Q, np.ndarray)
assert slam_type in {'ekf', 'sam'}
assert update_type in {'batch', 'sequential'}
assert data_association in {'known', 'ml', 'jcbb'}
self.slam_type = slam_type
self.da_type = data_association
self.update_type = update_type
self.t = 0
self.state_dim = 3 # The number of state variables: x, y, theta (initially).
self.obs_dim = 2 # The number of variables per observation: range, bearing.
self.lm_dim = 2 # The number of variables per landmark: x, y.
@abstractmethod
def predict(self, u, dt=None):
"""
Updates mu_bar and Sigma_bar after taking a single prediction step after incorporating the control.
:param u: The control for prediction (format: np.ndarray([drot1, dtran, drot2])).
:param dt: The time difference between the previous state and the current state being predicted.
"""
raise NotImplementedError()
@abstractmethod
def update(self, z):
"""
Performs data association to figure out previously seen landmarks vs. new landmarks
in the observations list and updates mu and Sigma after incorporating them.
:param z: Observation measurements (format: numpy.ndarray of size Kx3
observations where each row is [range, bearing, landmark_id]).
"""
raise NotImplementedError()
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protoflow/applications/__init__.py | theblackfly/protoflow | 3 | 103531 | from .glvq import GLVQ
from .deeplvq import DeepLVQ
from .gmlvq import GMLVQ
# from .knn import KNN
# from .lvq1 import LVQ1
from .lvqmln import LVQMLN
from .network import Network
__all__ = [
'GLVQ',
'DeepLVQ',
'GMLVQ',
# 'KNN',
# 'LVQ1',
'LVQMLN',
'Network',
]
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node.py | niki4etoo/WSN | 1 | 98596 | <gh_stars>1-10
class Node:
starting = True
ending = False
name = ""
weight = [1, 20]
active = True
edge_length = 0
nodes = []
def __init__(self, name, grid, posX, posY, active):
self.name = name # Name of the node
self.grid = grid # Which grid the node will be placed
self.posX = posX # Position X on the grid
self.posY = posY # Position Y on the grid
self.active = active # Is active
pass
def add(self, newNode):
#To DO
self.nodes.append(newNode)
print("Added new node")
pass
def remove(self, node):
#To DO
self.nodes = []
print("The node has been removed")
pass
def update(self, index, node):
self.nodes.pop(index)
self.nodes.insert(index, node)
print("The node has been updated")
pass
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PythonAndroid/youtube-dl/lib/python3.5/youtube_dl/extractor/zippcast.py | jianglei12138/python-3.5.1 | 10 | 1604964 | from __future__ import unicode_literals
import re
from .common import InfoExtractor
from ..utils import (
determine_ext,
str_to_int,
)
class ZippCastIE(InfoExtractor):
_VALID_URL = r'https?://(?:www\.)?zippcast\.com/(?:video/|videoview\.php\?.*\bvplay=)(?P<id>[0-9a-zA-Z]+)'
_TESTS = [{
# m3u8, hq direct link
'url': 'http://www.zippcast.com/video/c9cfd5c7e44dbc29c81',
'md5': '5ea0263b5606866c4d6cda0fc5e8c6b6',
'info_dict': {
'id': 'c9cfd5c7e44dbc29c81',
'ext': 'mp4',
'title': '[Vinesauce] Vinny - Digital Space Traveler',
'description': 'Muted on youtube, but now uploaded in it\'s original form.',
'thumbnail': 're:^https?://.*\.jpg$',
'uploader': 'vinesauce',
'view_count': int,
'categories': ['Entertainment'],
'tags': list,
},
}, {
# f4m, lq ipod direct link
'url': 'http://www.zippcast.com/video/b79c0a233e9c6581775',
'only_matching': True,
}, {
'url': 'http://www.zippcast.com/videoview.php?vplay=c9cfd5c7e44dbc29c81&auto=no',
'only_matching': True,
}]
def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(
'http://www.zippcast.com/video/%s' % video_id, video_id)
formats = []
video_url = self._search_regex(
r'<source[^>]+src=(["\'])(?P<url>.+?)\1', webpage,
'video url', default=None, group='url')
if video_url:
formats.append({
'url': video_url,
'format_id': 'http',
'preference': 0, # direct link is almost always of worse quality
})
src_url = self._search_regex(
r'src\s*:\s*(?:escape\()?(["\'])(?P<url>http://.+?)\1',
webpage, 'src', default=None, group='url')
ext = determine_ext(src_url)
if ext == 'm3u8':
formats.extend(self._extract_m3u8_formats(
src_url, video_id, 'mp4', entry_protocol='m3u8_native',
m3u8_id='hls', fatal=False))
elif ext == 'f4m':
formats.extend(self._extract_f4m_formats(
src_url, video_id, f4m_id='hds', fatal=False))
self._sort_formats(formats)
title = self._og_search_title(webpage)
description = self._og_search_description(webpage) or self._html_search_meta(
'description', webpage)
uploader = self._search_regex(
r'<a[^>]+href="https?://[^/]+/profile/[^>]+>([^<]+)</a>',
webpage, 'uploader', fatal=False)
thumbnail = self._og_search_thumbnail(webpage)
view_count = str_to_int(self._search_regex(
r'>([\d,.]+) views!', webpage, 'view count', fatal=False))
categories = re.findall(
r'<a[^>]+href="https?://[^/]+/categories/[^"]+">([^<]+),?<',
webpage)
tags = re.findall(
r'<a[^>]+href="https?://[^/]+/search/tags/[^"]+">([^<]+),?<',
webpage)
return {
'id': video_id,
'title': title,
'description': description,
'thumbnail': thumbnail,
'uploader': uploader,
'view_count': view_count,
'categories': categories,
'tags': tags,
'formats': formats,
}
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pytest_statsd/plugin.py | jlane9/pytest-statsd | 4 | 171579 | <reponame>jlane9/pytest-statsd<filename>pytest_statsd/plugin.py
"""pytest_statsd.plugin
.. codeauthor:: <NAME> <<EMAIL>>
"""
from __future__ import absolute_import
import time
import statsd
def pytest_addoption(parser):
"""
:param parser:
:return:
"""
group = parser.getgroup('terminal reporting')
group.addoption('--stats-d', action='store_true',
help='send test results to graphite')
group.addoption('--stats-host', action='store', dest='stats_host',
metavar='host', default='localhost',
help='statsd host. default is \'localhost\'')
group.addoption('--stats-port', action='store', dest='stats_port',
metavar='port', default=8125,
help='statsd port. default is 8125')
group.addoption('--stats-prefix', action='store', dest='stats_prefix',
metavar='prefix', default=None,
help='prefix to give all stats')
def pytest_configure(config):
"""
:param config:
:return:
"""
stats_d = config.getoption('stats_d')
# prevent opening statsd on slave nodes (xdist)
if stats_d and not hasattr(config, 'slaveinput'):
config._graphite = GraphiteReport(config)
config.pluginmanager.register(config._graphite)
def pytest_unconfigure(config):
"""
:param config:
:return:
"""
graphite = getattr(config, '_graphite', None)
if graphite:
del config._graphite
config.pluginmanager.unregister(graphite)
class GraphiteReport(object):
"""Graphite report implementation
"""
def __init__(self, config):
self.errors = self.failed = 0
self.passed = self.skipped = 0
self.xfailed = self.xpassed = 0
self.total = 0
self.config = config
self.host = config.getoption('stats_host')
self.port = config.getoption('stats_port')
self.prefix = config.getoption('stats_prefix')
self.suite_start_time = 0
def pytest_runtest_logreport(self, report):
"""Add report metrics
:param pytest.Report report: Test case report
:return:
"""
if report.passed:
self.append_passed(report)
elif report.failed:
self.append_failed(report)
elif report.skipped:
self.append_skipped(report)
def append_passed(self, report):
"""Add passed test metric
:param pytest.Report report: Test case report
:return:
"""
if report.when == 'call':
if hasattr(report, "wasxfail"):
self.xpassed += 1
else:
self.passed += 1
def append_failed(self, report):
"""Add failed test metric
:param pytest.Report report: Test case report
:return:
"""
if report.when == "call":
if hasattr(report, "wasxfail"):
self.xpassed += 1
else:
self.failed += 1
else:
self.errors += 1
def append_skipped(self, report):
"""Add skipped test metric
:param pytest.Report report: Test case report
:return:
"""
if hasattr(report, "wasxfail"):
self.xfailed += 1
else:
self.skipped += 1
def pytest_sessionstart(self, session):
"""before test run begins
:param pytest.Session session:
:return:
"""
self.suite_start_time = time.time()
def pytest_sessionfinish(self, session):
"""whole test run finishes
:param pytest.Session session:
:return:
"""
stats = statsd.StatsClient(self.host, self.port, prefix=self.prefix)
stats.gauge('passed', self.passed)
stats.gauge('skipped', self.skipped)
stats.gauge('failed', self.failed)
stats.gauge('errors', self.errors)
stats.gauge('xfailed', self.xfailed)
stats.gauge('xpassed', self.xpassed)
stats.gauge('total', sum([self.passed, self.skipped, self.failed,
self.errors, self.xfailed, self.xpassed]))
stats.gauge('aggregate_runs', 1, delta=True)
if sum([self.errors, self.failed]) == 0:
stats.gauge('aggregate_passing', 1, delta=True)
else:
stats.gauge('aggregate_failing', 1, delta=True)
duration = int((time.time() - self.suite_start_time) * 1000)
stats.timing('duration', duration)
def pytest_terminal_summary(self, terminalreporter):
"""add additional section in terminal summary reporting
:param terminalreporter:
:return:
"""
terminalreporter.write_sep('-', 'sent results to http://{}:{}'.format(self.host, self.port))
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] |
tools/yaml_config.py | brahim17/bud | 0 | 113244 | <gh_stars>0
import confuse as cf
def configure():
config = cf.Configuration('bud', __name__)
config.set_file('./config.yaml')
return config
def get_path():
cfg = configure()
return str(cfg["directory_path"])
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analysis/Hayward/Scripts/.ipynb_checkpoints/project_functions-checkpoint.py | data301-2020-winter2/course-project-group_1000 | 0 | 162875 | import numpy as np
import pandas as pd
import seaborn as sns
import pandas_profiling as pdp
import matplotlib.pyplot as plt
def load_and_process(csv):
# Method Chain 1 (load data, rename columns, round values to 2 d.p. and sort 'Charges' descending)
df1 = (
pd.read_csv('Medical_Cost.csv')
.rename(columns = {'age': 'Age', 'sex': 'Gender', 'bmi': 'BMI', 'children': 'Children', 'smoker': 'Smoker', 'region': 'Region', 'charges': 'Charges'})
.round({'Charges': 2, 'BMI': 2})
.sort_values('Charges', ascending = False)
)
# Method Chain 2 (change yes/no to Y/N and regions to abbreviations)
df2 = (
df1
.replace({'yes': 'Y', 'no': 'N'})
.replace({'southwest': 'SW', 'southeast': 'SE', 'northeast': 'NE', 'northwest': 'NW'})
)
# return values
return df2
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tests/python/pants_test/backend/jvm/tasks/jvm_compile/java/test_cache_compile_integration.py | qma/pants | 0 | 156424 | <gh_stars>0
# coding=utf-8
# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (absolute_import, division, generators, nested_scopes, print_function,
unicode_literals, with_statement)
import os
from textwrap import dedent
from pants.backend.jvm.tasks.jvm_compile.zinc.zinc_compile import ZincCompile
from pants.base.build_environment import get_buildroot
from pants.util.contextutil import temporary_dir
from pants.util.dirutil import safe_open
from pants_test.backend.jvm.tasks.jvm_compile.base_compile_integration_test import BaseCompileIT
class CacheCompileIntegrationTest(BaseCompileIT):
def run_compile(self, target_spec, config, workdir):
args = ['compile', target_spec]
pants_run = self.run_pants_with_workdir(args, workdir, config)
self.assert_success(pants_run)
def create_file(self, path, value):
with safe_open(path, 'w') as f:
f.write(value)
def test_stale_artifacts_rmd_when_cache_used_with_zinc(self):
with temporary_dir() as cache_dir, \
self.temporary_workdir() as workdir, \
temporary_dir(root_dir=get_buildroot()) as src_dir:
config = {
'cache.compile.zinc': {'write_to': [cache_dir], 'read_from': [cache_dir]},
'compile.zinc': {'incremental_caching': True },
}
srcfile = os.path.join(src_dir, 'org', 'pantsbuild', 'cachetest', 'A.java')
buildfile = os.path.join(src_dir, 'org', 'pantsbuild', 'cachetest', 'BUILD')
self.create_file(srcfile,
dedent("""package org.pantsbuild.cachetest;
class A {}
class Main {}"""))
self.create_file(buildfile,
dedent("""java_library(name='cachetest',
sources=['A.java']
)"""))
cachetest_spec = os.path.join(os.path.basename(src_dir), 'org', 'pantsbuild',
'cachetest:cachetest')
# Caches values A.class, Main.class
self.run_compile(cachetest_spec, config, workdir)
self.create_file(srcfile,
dedent("""package org.pantsbuild.cachetest;
class A {}
class NotMain {}"""))
# Caches values A.class, NotMain.class and leaves them on the filesystem
self.run_compile(cachetest_spec, config, workdir)
self.create_file(srcfile,
dedent("""package org.pantsbuild.cachetest;
class A {}
class Main {}"""))
# Should cause NotMain.class to be removed
self.run_compile(cachetest_spec, config, workdir)
root = os.path.join(workdir, 'compile', 'jvm', 'zinc')
# One target.
self.assertEqual(len(os.listdir(root)), 1)
target_workdir_root = os.path.join(root, os.listdir(root)[0])
target_workdirs = os.listdir(target_workdir_root)
# Two workdirs.
self.assertEqual(len(target_workdirs), 2)
def classfiles(d):
cd = os.path.join(target_workdir_root, d, 'classes', 'org', 'pantsbuild', 'cachetest')
return sorted(os.listdir(cd))
# One workdir should contain NotMain, and the other should contain Main.
self.assertEquals(sorted(classfiles(w) for w in target_workdirs),
sorted([['A.class', 'Main.class'], ['A.class', 'NotMain.class']]))
def test_incremental_caching(self):
"""Tests that with --no-incremental-caching, we don't write incremental artifacts."""
with temporary_dir() as cache_dir, \
self.temporary_workdir() as workdir, \
temporary_dir(root_dir=get_buildroot()) as src_dir:
def config(incremental_caching):
return {
'cache.compile.zinc': {'write_to': [cache_dir], 'read_from': [cache_dir]},
'compile.zinc': {'incremental_caching': incremental_caching},
}
srcfile = os.path.join(src_dir, 'A.java')
buildfile = os.path.join(src_dir, 'BUILD')
spec = os.path.join(src_dir, ':cachetest')
artifact_dir = os.path.join(cache_dir,
ZincCompile.stable_name(),
'{}.cachetest'.format(os.path.basename(src_dir)))
self.create_file(srcfile, """class A {}""")
self.create_file(buildfile, """java_library(name='cachetest', sources=['A.java'])""")
# Confirm that the result is one cached artifact.
self.run_compile(spec, config(False), workdir)
clean_artifacts = os.listdir(artifact_dir)
self.assertEquals(1, len(clean_artifacts))
# Modify the file, and confirm that artifacts haven't changed.
self.create_file(srcfile, """final class A {}""")
self.run_compile(spec, config(False), workdir)
self.assertEquals(clean_artifacts, os.listdir(artifact_dir))
# Modify again, this time with incremental and confirm that we have a second artifact.
self.create_file(srcfile, """public final class A {}""")
self.run_compile(spec, config(True), workdir)
self.assertEquals(2, len(os.listdir(artifact_dir)))
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cumulusci/robotframework/locators_53.py | davisagli/CumulusCI | 0 | 188320 | <gh_stars>0
import copy
from cumulusci.robotframework import locators_52
lex_locators = copy.deepcopy(locators_52.lex_locators)
lex_locators["object_list"] = {
# Note: this matches the <td> with the checkbutton, not the inner checkbutton
# This is because clicking the actual checkbutton will throw an error that
# another element will receive the click.
"checkbutton": '//tbody/tr[.//*[text()="{}"]]//td[.//input[@type="checkbox"]]',
"status_info": "//force-list-view-manager-status-info",
}
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backend/admin/views/__init__.py | gsw945/flask-bigger | 29 | 41709 | # -*- coding: utf-8 -*-
'''admin视图''' | [
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2
] |
tests/test_pinblock.py | adelosa/cardutil | 0 | 29684 | <gh_stars>0
import unittest
from cardutil import pinblock
class PinblockTestCase(unittest.TestCase):
def test_pin_block_Iso0(self):
self.assertEqual(
b'\x04\x12\x26\xcb\xa9\x87\x6f\xed',
pinblock.Iso0PinBlock(pin='1234', card_number='4441234567890123').to_bytes())
def test_visa_pvv(self):
test_key = '5CA64B3C22BEC347CA7E6609904BAAED'
self.assertEqual(
'3856', pinblock.calculate_pvv(pin='2205', card_number='4564320000980369', pvv_key=test_key, key_index=1))
def test_visa_pvv_more_values_required(self):
"""
check when additional digits for pvv required
"""
test_key = '5CA64B3C22BEC347CA7E6609904BAAED'
self.assertEqual(
'0885', pinblock.calculate_pvv(pin='0654', card_number='4564320000980369', pvv_key=test_key, key_index=1))
def test_visa_pvv_mixin(self):
# use pin block without card_number property
MyPinBlock = type('MyPinBlock', (pinblock.Iso4PinBlock, pinblock.VisaPVVPinBlockMixin), {})
pb = MyPinBlock(pin='6666')
with self.assertRaises(ValueError):
pb.to_pvv(pvv_key='00' * 8)
self.assertEqual(pb.to_pvv(pvv_key='00' * 8, card_number='1111222233334444'), '1703')
def test_pin_block_Iso0TDESPinBlockWithVisaPVV(self):
pb1 = pinblock.Iso0TDESPinBlockWithVisaPVV(pin='1234', card_number='1111222233334444')
self.assertEqual(pb1.pin, '1234')
self.assertEqual(pb1.card_number, '1111222233334444')
self.assertEqual(pb1.to_bytes(), b'\x04\x12&\xdd\xdc\xcc\xcb\xbb')
self.assertEqual(pb1.to_enc_bytes(key='00' * 16), b'L\t\x06\xd1\x03\x08\x87\x1a')
self.assertEqual(pb1.to_pvv(pvv_key='00' * 16), '6264')
pb2 = pinblock.Iso0TDESPinBlockWithVisaPVV.from_bytes(
pin_block=pb1.to_bytes(), card_number='1111222233334444')
self.assertEqual(pb2.pin, '1234')
pb3 = pinblock.Iso0TDESPinBlockWithVisaPVV.from_enc_bytes(
enc_pin_block=pb1.to_enc_bytes('00' * 16), card_number='1111222233334444', key='00' * 16)
self.assertEqual(pb3.pin, '1234')
def test_pin_block_Iso4AESPinBlockWithVisaPVV(self):
pb1 = pinblock.Iso4AESPinBlockWithVisaPVV(pin='1234', random_value=14932500169729639426)
self.assertEqual(pb1.pin, '1234')
self.assertEqual(pb1.to_bytes()[0:8], b'\x44\x12\x34\xaa\xaa\xaa\xaa\xaa')
self.assertEqual(pb1.to_enc_bytes(key='00' * 16), b',4yaY\xbf\x10j\xf6\xf5\xd2;Y\xfd\xe2\xfe')
self.assertEqual(pb1.to_pvv(pvv_key='00' * 16, card_number='1111222233334444'), '6264')
pb2 = pinblock.Iso4AESPinBlockWithVisaPVV.from_bytes(pin_block=pb1.to_bytes())
self.assertEqual(pb2.pin, '1234')
pb3 = pinblock.Iso4AESPinBlockWithVisaPVV.from_enc_bytes(
enc_pin_block=pb1.to_enc_bytes('00' * 16), key='00' * 16)
self.assertEqual(pb3.pin, '1234')
def test_pinblock_operations(self):
tdes_key = '2222111122221111'
aes_key = tdes_key * 2
key = aes_key
pin_blocks = [
type(
'TDESPinBlock',
(pinblock.Iso0PinBlock, pinblock.TdesEncryptedPinBlockMixin, pinblock.VisaPVVPinBlockMixin), {}),
type(
'AESPinBlock',
(pinblock.Iso4PinBlock, pinblock.AESEncryptedPinBlockMixin, pinblock.VisaPVVPinBlockMixin), {}),
pinblock.Iso0TDESPinBlockWithVisaPVV,
pinblock.Iso4AESPinBlockWithVisaPVV
]
for pb in pin_blocks:
print(pb)
pb = pb(pin='1234', card_number='1111222233334444')
self.assertEqual(pb.pin, '1234')
self.assertEqual(pb.to_pvv(pvv_key='1111222211112222', key_index=1, card_number='1111222233334444'), '9595')
pb2 = pb.from_bytes(pb.to_bytes(), card_number='1111222233334444')
self.assertEqual(pb2.pin, '1234')
pb2 = pb.from_enc_bytes(pb.to_enc_bytes(key=key), key=key, card_number='1111222233334444')
self.assertEqual(pb2.pin, '1234')
if __name__ == '__main__':
unittest.main()
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29953,
29929,
742,
282,
29894,
29894,
29918,
1989,
29922,
1688,
29918,
1989,
29892,
1820,
29918,
2248,
29922,
29896,
876,
13,
13,
1678,
822,
1243,
29918,
1730,
29874,
29918,
29886,
29894,
29894,
29918,
5514,
29918,
5975,
29918,
12403,
29898,
1311,
1125,
13,
4706,
9995,
13,
4706,
1423,
746,
5684,
13340,
363,
282,
29894,
29894,
3734,
13,
4706,
9995,
13,
4706,
1243,
29918,
1989,
353,
525,
29945,
5454,
29953,
29946,
29933,
29941,
29907,
29906,
29906,
29933,
11206,
29941,
29946,
29955,
5454,
29955,
29923,
29953,
29953,
29900,
29929,
29929,
29900,
29946,
5688,
29909,
3352,
29915,
13,
4706,
1583,
29889,
9294,
9843,
29898,
13,
9651,
525,
29900,
29947,
29947,
29945,
742,
12534,
1271,
29889,
15807,
403,
29918,
29886,
29894,
29894,
29898,
12687,
2433,
29900,
29953,
29945,
29946,
742,
5881,
29918,
4537,
2433,
29946,
29945,
29953,
29946,
29941,
29906,
29900,
29900,
29900,
29900,
29929,
29947,
29900,
29941,
29953,
29929,
742,
282,
29894,
29894,
29918,
1989,
29922,
1688,
29918,
1989,
29892,
1820,
29918,
2248,
29922,
29896,
876,
13,
13,
1678,
822,
1243,
29918,
1730,
29874,
29918,
29886,
29894,
29894,
29918,
28084,
262,
29898,
1311,
1125,
13,
4706,
396,
671,
12534,
2908,
1728,
5881,
29918,
4537,
2875,
13,
4706,
1619,
29925,
262,
7445,
353,
1134,
877,
3421,
29925,
262,
7445,
742,
313,
12687,
1271,
29889,
29902,
578,
29946,
29925,
262,
7445,
29892,
12534,
1271,
29889,
6116,
29874,
29925,
29963,
18510,
262,
7445,
29924,
861,
262,
511,
426,
1800,
13,
4706,
282,
29890,
353,
1619,
29925,
262,
7445,
29898,
12687,
2433,
29953,
29953,
29953,
29953,
1495,
13,
4706,
411,
1583,
29889,
9294,
29934,
1759,
267,
29898,
1917,
2392,
1125,
13,
9651,
282,
29890,
29889,
517,
29918,
29886,
29894,
29894,
29898,
29886,
29894,
29894,
29918,
1989,
2433,
29900,
29900,
29915,
334,
29871,
29947,
29897,
13,
4706,
1583,
29889,
9294,
9843,
29898,
24381,
29889,
517,
29918,
29886,
29894,
29894,
29898,
29886,
29894,
29894,
29918,
1989,
2433,
29900,
29900,
29915,
334,
29871,
29947,
29892,
5881,
29918,
4537,
2433,
29896,
29896,
29896,
29896,
29906,
29906,
29906,
29906,
29941,
29941,
29941,
29941,
29946,
29946,
29946,
29946,
5477,
525,
29896,
29955,
29900,
29941,
1495,
13,
13,
1678,
822,
1243,
29918,
12687,
29918,
1271,
29918,
29902,
578,
29900,
29911,
2287,
5550,
262,
7445,
3047,
6116,
29874,
29925,
29963,
29963,
29898,
1311,
1125,
13,
4706,
282,
29890,
29896,
353,
12534,
1271,
29889,
29902,
578,
29900,
29911,
2287,
5550,
262,
7445,
3047,
6116,
29874,
29925,
29963,
29963,
29898,
12687,
2433,
29896,
29906,
29941,
29946,
742,
5881,
29918,
4537,
2433,
29896,
29896,
29896,
29896,
29906,
29906,
29906,
29906,
29941,
29941,
29941,
29941,
29946,
29946,
29946,
29946,
1495,
13,
4706,
1583,
29889,
9294,
9843,
29898,
24381,
29896,
29889,
12687,
29892,
525,
29896,
29906,
29941,
29946,
1495,
13,
4706,
1583,
29889,
9294,
9843,
29898,
24381,
29896,
29889,
7543,
29918,
4537,
29892,
525,
29896,
29896,
29896,
29896,
29906,
29906,
29906,
29906,
29941,
29941,
29941,
29941,
29946,
29946,
29946,
29946,
1495,
13,
4706,
1583,
29889,
9294,
9843,
29898,
24381,
29896,
29889,
517,
29918,
13193,
3285,
289,
12764,
29916,
29900,
29946,
29905,
29916,
29896,
29906,
11035,
29916,
1289,
29905,
29916,
13891,
29905,
29916,
617,
29905,
29916,
10702,
29905,
29916,
1327,
1495,
13,
4706,
1583,
29889,
9294,
9843,
29898,
24381,
29896,
29889,
517,
29918,
3977,
29918,
13193,
29898,
1989,
2433,
29900,
29900,
29915,
334,
29871,
29896,
29953,
511,
289,
29915,
29931,
29905,
29873,
29905,
29916,
29900,
29953,
29905,
29916,
29881,
29896,
29905,
29916,
29900,
29941,
29905,
29916,
29900,
29947,
29905,
29916,
29947,
29955,
29905,
29916,
29896,
29874,
1495,
13,
4706,
1583,
29889,
9294,
9843,
29898,
24381,
29896,
29889,
517,
29918,
29886,
29894,
29894,
29898,
29886,
29894,
29894,
29918,
1989,
2433,
29900,
29900,
29915,
334,
29871,
29896,
29953,
511,
525,
29953,
29906,
29953,
29946,
1495,
13,
13,
4706,
282,
29890,
29906,
353,
12534,
1271,
29889,
29902,
578,
29900,
29911,
2287,
5550,
262,
7445,
3047,
6116,
29874,
29925,
29963,
29963,
29889,
3166,
29918,
13193,
29898,
13,
9651,
12534,
29918,
1271,
29922,
24381,
29896,
29889,
517,
29918,
13193,
3285,
5881,
29918,
4537,
2433,
29896,
29896,
29896,
29896,
29906,
29906,
29906,
29906,
29941,
29941,
29941,
29941,
29946,
29946,
29946,
29946,
1495,
13,
4706,
1583,
29889,
9294,
9843,
29898,
24381,
29906,
29889,
12687,
29892,
525,
29896,
29906,
29941,
29946,
1495,
13,
13,
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282,
29890,
29941,
353,
12534,
1271,
29889,
29902,
578,
29900,
29911,
2287,
5550,
262,
7445,
3047,
6116,
29874,
29925,
29963,
29963,
29889,
3166,
29918,
3977,
29918,
13193,
29898,
13,
9651,
2094,
29918,
12687,
29918,
1271,
29922,
24381,
29896,
29889,
517,
29918,
3977,
29918,
13193,
877,
29900,
29900,
29915,
334,
29871,
29896,
29953,
511,
5881,
29918,
4537,
2433,
29896,
29896,
29896,
29896,
29906,
29906,
29906,
29906,
29941,
29941,
29941,
29941,
29946,
29946,
29946,
29946,
742,
1820,
2433,
29900,
29900,
29915,
334,
29871,
29896,
29953,
29897,
13,
4706,
1583,
29889,
9294,
9843,
29898,
24381,
29941,
29889,
12687,
29892,
525,
29896,
29906,
29941,
29946,
1495,
13,
13,
1678,
822,
1243,
29918,
12687,
29918,
1271,
29918,
29902,
578,
29946,
29909,
2890,
29925,
262,
7445,
3047,
6116,
29874,
29925,
29963,
29963,
29898,
1311,
1125,
13,
4706,
282,
29890,
29896,
353,
12534,
1271,
29889,
29902,
578,
29946,
29909,
2890,
29925,
262,
7445,
3047,
6116,
29874,
29925,
29963,
29963,
29898,
12687,
2433,
29896,
29906,
29941,
29946,
742,
4036,
29918,
1767,
29922,
29896,
29946,
29929,
29941,
29906,
29945,
29900,
29900,
29896,
29953,
29929,
29955,
29906,
29929,
29953,
29941,
29929,
29946,
29906,
29953,
29897,
13,
4706,
1583,
29889,
9294,
9843,
29898,
24381,
29896,
29889,
12687,
29892,
525,
29896,
29906,
29941,
29946,
1495,
13,
4706,
1583,
29889,
9294,
9843,
29898,
24381,
29896,
29889,
517,
29918,
13193,
580,
29961,
29900,
29901,
29947,
1402,
289,
12764,
29916,
29946,
29946,
29905,
29916,
29896,
29906,
29905,
29916,
29941,
29946,
29905,
29916,
7340,
29905,
29916,
7340,
29905,
29916,
7340,
29905,
29916,
7340,
29905,
29916,
7340,
1495,
13,
4706,
1583,
29889,
9294,
9843,
29898,
24381,
29896,
29889,
517,
29918,
3977,
29918,
13193,
29898,
1989,
2433,
29900,
29900,
29915,
334,
29871,
29896,
29953,
511,
289,
742,
29946,
3761,
29979,
29905,
29916,
1635,
29905,
29916,
29896,
29900,
29926,
29905,
24660,
29953,
29905,
24660,
29945,
29905,
29916,
29881,
29906,
29936,
29979,
29905,
29916,
11512,
29905,
17115,
29906,
29905,
29916,
1725,
1495,
13,
4706,
1583,
29889,
9294,
9843,
29898,
24381,
29896,
29889,
517,
29918,
29886,
29894,
29894,
29898,
29886,
29894,
29894,
29918,
1989,
2433,
29900,
29900,
29915,
334,
29871,
29896,
29953,
29892,
5881,
29918,
4537,
2433,
29896,
29896,
29896,
29896,
29906,
29906,
29906,
29906,
29941,
29941,
29941,
29941,
29946,
29946,
29946,
29946,
5477,
525,
29953,
29906,
29953,
29946,
1495,
13,
13,
4706,
282,
29890,
29906,
353,
12534,
1271,
29889,
29902,
578,
29946,
29909,
2890,
29925,
262,
7445,
3047,
6116,
29874,
29925,
29963,
29963,
29889,
3166,
29918,
13193,
29898,
12687,
29918,
1271,
29922,
24381,
29896,
29889,
517,
29918,
13193,
3101,
13,
4706,
1583,
29889,
9294,
9843,
29898,
24381,
29906,
29889,
12687,
29892,
525,
29896,
29906,
29941,
29946,
1495,
13,
13,
4706,
282,
29890,
29941,
353,
12534,
1271,
29889,
29902,
578,
29946,
29909,
2890,
29925,
262,
7445,
3047,
6116,
29874,
29925,
29963,
29963,
29889,
3166,
29918,
3977,
29918,
13193,
29898,
13,
9651,
2094,
29918,
12687,
29918,
1271,
29922,
24381,
29896,
29889,
517,
29918,
3977,
29918,
13193,
877,
29900,
29900,
29915,
334,
29871,
29896,
29953,
511,
1820,
2433,
29900,
29900,
29915,
334,
29871,
29896,
29953,
29897,
13,
4706,
1583,
29889,
9294,
9843,
29898,
24381,
29941,
29889,
12687,
29892,
525,
29896,
29906,
29941,
29946,
1495,
13,
13,
1678,
822,
1243,
29918,
12687,
1271,
29918,
3372,
800,
29898,
1311,
1125,
13,
4706,
260,
2783,
29918,
1989,
353,
525,
29906,
29906,
29906,
29906,
29896,
29896,
29896,
29896,
29906,
29906,
29906,
29906,
29896,
29896,
29896,
29896,
29915,
13,
4706,
263,
267,
29918,
1989,
353,
260,
2783,
29918,
1989,
334,
29871,
29906,
13,
4706,
1820,
353,
263,
267,
29918,
1989,
13,
13,
4706,
12534,
29918,
1271,
29879,
353,
518,
13,
9651,
1134,
29898,
13,
18884,
525,
29911,
2287,
5550,
262,
7445,
742,
13,
18884,
313,
12687,
1271,
29889,
29902,
578,
29900,
29925,
262,
7445,
29892,
12534,
1271,
29889,
29911,
2783,
8566,
14740,
29925,
262,
7445,
29924,
861,
262,
29892,
12534,
1271,
29889,
6116,
29874,
29925,
29963,
18510,
262,
7445,
29924,
861,
262,
511,
6571,
511,
13,
9651,
1134,
29898,
13,
18884,
525,
29909,
2890,
29925,
262,
7445,
742,
13,
18884,
313,
12687,
1271,
29889,
29902,
578,
29946,
29925,
262,
7445,
29892,
12534,
1271,
29889,
16036,
1660,
17608,
14740,
29925,
262,
7445,
29924,
861,
262,
29892,
12534,
1271,
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6116,
29874,
29925,
29963,
18510,
262,
7445,
29924,
861,
262,
511,
6571,
511,
13,
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12534,
1271,
29889,
29902,
578,
29900,
29911,
2287,
5550,
262,
7445,
3047,
6116,
29874,
29925,
29963,
29963,
29892,
13,
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12534,
1271,
29889,
29902,
578,
29946,
29909,
2890,
29925,
262,
7445,
3047,
6116,
29874,
29925,
29963,
29963,
13,
4706,
4514,
13,
4706,
363,
282,
29890,
297,
12534,
29918,
1271,
29879,
29901,
13,
9651,
1596,
29898,
24381,
29897,
13,
9651,
282,
29890,
353,
282,
29890,
29898,
12687,
2433,
29896,
29906,
29941,
29946,
742,
5881,
29918,
4537,
2433,
29896,
29896,
29896,
29896,
29906,
29906,
29906,
29906,
29941,
29941,
29941,
29941,
29946,
29946,
29946,
29946,
1495,
13,
9651,
1583,
29889,
9294,
9843,
29898,
24381,
29889,
12687,
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525,
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29906,
29941,
29946,
1495,
13,
9651,
1583,
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29898,
24381,
29889,
517,
29918,
29886,
29894,
29894,
29898,
29886,
29894,
29894,
29918,
1989,
2433,
29896,
29896,
29896,
29896,
29906,
29906,
29906,
29906,
29896,
29896,
29896,
29896,
29906,
29906,
29906,
29906,
742,
1820,
29918,
2248,
29922,
29896,
29892,
5881,
29918,
4537,
2433,
29896,
29896,
29896,
29896,
29906,
29906,
29906,
29906,
29941,
29941,
29941,
29941,
29946,
29946,
29946,
29946,
5477,
525,
29929,
29945,
29929,
29945,
1495,
13,
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282,
29890,
29906,
353,
282,
29890,
29889,
3166,
29918,
13193,
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29889,
517,
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13193,
3285,
5881,
29918,
4537,
2433,
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29896,
29896,
29896,
29906,
29906,
29906,
29906,
29941,
29941,
29941,
29941,
29946,
29946,
29946,
29946,
1495,
13,
9651,
1583,
29889,
9294,
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24381,
29906,
29889,
12687,
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525,
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29906,
29941,
29946,
1495,
13,
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282,
29890,
29906,
353,
282,
29890,
29889,
3166,
29918,
3977,
29918,
13193,
29898,
24381,
29889,
517,
29918,
3977,
29918,
13193,
29898,
1989,
29922,
1989,
511,
1820,
29922,
1989,
29892,
5881,
29918,
4537,
2433,
29896,
29896,
29896,
29896,
29906,
29906,
29906,
29906,
29941,
29941,
29941,
29941,
29946,
29946,
29946,
29946,
1495,
13,
9651,
1583,
29889,
9294,
9843,
29898,
24381,
29906,
29889,
12687,
29892,
525,
29896,
29906,
29941,
29946,
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13,
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13,
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1649,
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2396,
13,
1678,
443,
27958,
29889,
3396,
580,
13,
2
] |
terrarium/adapters/accumulator.py | dredington/terrarium | 0 | 58394 | #!/usr/bin/env python3
from sentinel import DataAdapter
class Accumulator(DataAdapter):
def __init__(self):
self.data = []
def publish(self, data):
self.data.append(data) | [
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osme/migrations/0002_auto_20210316_1627.py | infolabs/django-osme | 0 | 162972 | # -*- coding: utf-8 -*-
# Generated by Django 1.11.28 on 2021-03-16 13:27
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('osme', '0001_initial'),
]
operations = [
migrations.RemoveField(
model_name='region',
name='query_string',
),
migrations.AddField(
model_name='region',
name='osm_id',
field=models.CharField(db_index=True, default=1, max_length=255, verbose_name='Osm id'),
preserve_default=False,
),
]
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apps/requestlogger/urls.py | commtrack/commtrack-old-to-del | 1 | 199531 | <reponame>commtrack/commtrack-old-to-del
from django.conf.urls.defaults import *
urlpatterns = patterns('',
(r'^requestlog/?$', 'requestlogger.views.list'),
(r'^requestlog/demo/?$', 'requestlogger.views.demo'),
)
| [
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betterLogger/formatter.py | GreenJon902/BetterLogger | 0 | 47677 | <reponame>GreenJon902/BetterLogger<filename>betterLogger/formatter.py
import copy
from logging import Formatter as _Formatter
from betterLogger import colors
from betterLogger import config
from betterLogger.format_funcs import colored_format
class Formatter(_Formatter):
def __init__(self, use_color=False):
_Formatter.__init__(self)
self.use_color = use_color
def format(self, record):
record = copy.deepcopy(record)
"""if record.name in constants.logging.custom_name_per_log_array.keys():
new_name, new_msg = record.msg.split(": ", maxsplit=1)
record.name = str(constants.logging.custom_name_per_log_array[record.name]) % new_name
record.msg = new_msg""" # fix this
class_name = record.name
for log_name, shortened in config.log_names_to_shorten.items():
class_name = class_name.replace(log_name, shortened)
record.msg = colored_format(config.log_format, self.use_color,
custom_tags={"message": record.msg, "logger": self, "level": record.levelname,
"class_name": class_name},
custom_colors={"LEVEL_COLOR": colors.level_to_code[record.levelname]})
return _Formatter.format(self, record)
__all__ = ["Formatter"]
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setuper desktop app/gui/dialogs/weblogindialog.py | dragondjf/CloudSetuper | 22 | 126497 | #!/usr/bin/python
# -*- coding: utf-8 -*-
import os
from PyQt5 import QtGui
from PyQt5 import QtCore
from PyQt5 import QtWidgets
import requests
from dataBase import signal_DB
import threading
from Crypto.Hash import MD5
if __name__ == '__main__':
from basedialog import BaseDialog
else:
from .basedialog import BaseDialog
class AuthLoginDialog(BaseDialog):
def __init__(self, styleoptions, parent=None):
super(AuthLoginDialog, self).__init__(styleoptions, parent)
self.login_nameLabel = QtWidgets.QLabel(u'用户名')
self.login_name = QtWidgets.QLineEdit(self)
self.login_name.setPlaceholderText(u'用户名')
self.login_passwordLabel = QtWidgets.QLabel(u'密码')
self.login_password = QtWidgets.QLineEdit(self)
self.login_password.setEchoMode(QtWidgets.QLineEdit.Password)
self.login_password.setPlaceholderText(u'密码')
self.ipLabel = QtWidgets.QLabel(u'输入主机ip:')
self.ipLineEdit = QtWidgets.QLineEdit(u'127.0.0.1')
self.ipLineEdit.setInputMask('000.000.000.000')
self.portLabel = QtWidgets.QLabel(u'输入主机port:')
self.portLineEdit = QtWidgets.QLineEdit(u'8888')
self.pbLogin = QtWidgets.QPushButton(u'登录', self)
self.pbLogin.setCheckable(True)
self.pbCancel = QtWidgets.QPushButton(u'取消', self)
enterwidget_mainlayout = QtWidgets.QGridLayout()
enterwidget_mainlayout.addWidget(self.pbLogin, 0, 0)
enterwidget_mainlayout.addWidget(self.pbCancel, 0, 1)
self.statusLabel = QtWidgets.QLabel("")
self.statusLabel.setFixedHeight(30)
mainlayout = QtWidgets.QGridLayout()
mainlayout.addWidget(self.login_nameLabel, 0, 0)
mainlayout.addWidget(self.login_name, 0, 1)
mainlayout.addWidget(self.login_passwordLabel, 1, 0)
mainlayout.addWidget(self.login_password, 1, 1)
mainlayout.addWidget(self.ipLabel, 2, 0)
mainlayout.addWidget(self.ipLineEdit, 2, 1)
mainlayout.addWidget(self.portLabel, 3, 0)
mainlayout.addWidget(self.portLineEdit, 3, 1)
mainlayout.addWidget(self.statusLabel, 4, 1, 1, 2)
mainlayout.addLayout(enterwidget_mainlayout, 5, 0, 1, 2)
self.pbLogin.clicked.connect(self.clickEnter)
self.pbCancel.clicked.connect(self.reject)
self.pbLogin.setFixedHeight(30)
self.pbCancel.setFixedHeight(30)
self.layout().addLayout(mainlayout)
self.resize(self.width(), self.height())
self.layout().setContentsMargins(10, 10, 10, 20)
signal_DB.login_sin.connect(self.authLogin)
def clickEnter(self):
name = self.login_name.text()
password = self.login_password.text()
ip = self.ipLineEdit.text()
port = self.portLineEdit.text()
self.statusLabel.setText("正在登录中...")
self.pas = None
self.address = (ip, port)
self.loginThread = AuthLoginThread(name, password, self.address)
self.loginThread.start()
self.pbLogin.setChecked(True)
def cancleConenct(self):
self.pbLogin.setChecked(False)
self.loginThread.flag = False
@QtCore.pyqtSlot(dict)
def authLogin(self, info):
if info['status']:
self.pas = info['result']
self.accept()
else:
self.pbLogin.setChecked(False)
self.statusLabel.setText(info['result'])
class AuthLoginThread(threading.Thread, QtCore.QObject):
"""docstring for AuthLoginThread"""
def __init__(self, user, password, address):
super(AuthLoginThread, self).__init__()
self.user = user
self.password = password
self.address = address
def run(self):
status = None
result = None
try:
userinfo = {"account": self.user, "password": MD5.new(('%s' % self.password).encode('utf-8')).hexdigest()}
result = requests.post('http://%s:%s/login' % self.address, data=userinfo)
loginflag = int(result.json())
if loginflag == 1:
signal_DB.login_sin.emit({'status': True, 'result': "登陆成功"})
elif loginflag == 2:
signal_DB.login_sin.emit({'status': False, 'result': "请输入正确的用户名"})
elif loginflag == 3:
signal_DB.login_sin.emit({'status': False, 'result': "请输入正确的密码"})
except requests.ConnectionError as e:
signal_DB.login_sin.emit({'status': False, 'result':"请输入正确的ip地址或端口号"})
except Exception as e:
print(e)
signal_DB.login_sin.emit({'status': False, 'result':"请输入正确的参数"})
def weblogin(loginoptions):
"""返回True或False"""
dialog = AuthLoginDialog(loginoptions)
if dialog.exec_():
return True, dialog.address
else:
return False, (u'', u'')
if __name__ == '__main__':
import sys
app = QtGui.QApplication(sys.argv)
styleoptions = {
'title': u'登录',
'windowicon': "../skin/images/ov-orange-green.png",
'minsize': (400, 300),
'size': (400, 300),
'logo_title': u'智能光纤云终端管理平台',
'logo_img_url': "../skin/images/ov-orange-green.png",
}
print(login(styleoptions))
sys.exit(app.exec_())
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PDaS/TreeNode.py | Seenivasanseeni/PyDas | 0 | 90781 | """
The TreeNode represents node in a Tree.
This module is perfect for Binary Tree but their implementation needs restructuring of the TreeNode
for other Tree types.
"""
class TreeNode(object):
"""
Node in a Tree has arguments
data,link[0]-leftlink,link[0]-right link
emptiness of a TreeNode is indicated by self.data==None
"""
### The Default Initializer
def __init__(self,data=None,link0=None,link1=None):
self.data=data
self.link=[]
self.link.append(link0)
self.link.append(link1)
### Data in the Tree Node is to be returned
def getData(self):
return self.data
### Compare Method needs to compare only the data.
### Since comparing the subTree Nodes depends on the Tree implementation
### B-Tree , B+ Tree , AVL, Binary Search Tree needs modifications in their compare method
### This TreeNode just need to care about the data present in it
###
### Compare Two Nodes
### -1 if self.data is less than that of arguments
### 0 if both are equal
### 1 if self.data is greater than that of argument
def compare(self,Node2):
if(self.data==None and Node2.data==None):
return True
if((self.data!=None and Node2.data==None) or (self.data!=None and Node2.data==None) ):
return False
if(self.data==Node2.data):
return True
else:
return False
NullNode=TreeNode() | [
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Models_training/PCA_reduction.py | Nagisa-sys/WriterIdentification | 2 | 164345 | import pickle
from sklearn.decomposition import PCA
import numpy as np
class PCA_reduction:
def __init__(self, pca_path):
self.pca_reload = pickle.load(open(pca_path,'rb'))
def reduce_size(self, vector):
return self.pca_reload.transform([vector])[0]
@staticmethod
def create_new_pca_model(vectors, path_to_save, percentage_variance):
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
data_rescaled = scaler.fit_transform(vectors)
pca = PCA(n_components=percentage_variance)
result = pca.fit(data_rescaled)
pickle.dump(pca, open(path_to_save,"wb"))
@staticmethod
def plot_variance_nbComponents(vectors, percentage_variance, figsize=(15, 5)):
import matplotlib.pyplot as plt
pca = PCA().fit(vectors)
fig = plt.figure(figsize=figsize)
plt.plot(np.cumsum(pca.explained_variance_ratio_), marker='o')
plt.axhline(y=percentage_variance, color="red")
plt.xlabel('No. of principal components')
plt.ylabel('cumulative % variance retained')
plt.grid(True)
plt.title('Cumulative explained variance across the number of components ') | [
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index.py | StarSky1/microsoft-python-study | 0 | 3797 | name=input('input your name:');
print('hello');
print(name.capitalize()); | [
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members/forms.py | deadbeatfour/reg-form | 0 | 158682 | from django import forms
from .models import member
class MemberForm(forms.ModelForm):
class Meta:
model = member
exclude = ['timestamp']
labels = {
'name':"<NAME>",
'course':"COURSE",
'year':"YEAR",
'phone_number':"PHONE NUMBER",
'email':"EMAIL",
'message':"MESSAGE",
}
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app/mlp/test_mlp_evaluate-race.py | jingjieli95/UnarySim | 17 | 150922 | # %%
%load_ext autoreload
%autoreload 2
# %%
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torchsummaryX import summary
import matplotlib.pyplot as plt
import time
import os
from UnarySim.kernel.utils import *
from UnarySim.kernel.linear import FSULinear
from UnarySim.kernel.relu import FSUReLU
from UnarySim.sw.stream.gen import RNG, SourceGen, BSGen
from UnarySim.sw.metric.metric import ProgError
# %%
cwd = os.getcwd()
print(cwd)
# %%
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
# %%
# MNIST data loader
transform=transforms.Compose([transforms.Resize((32, 32)), transforms.ToTensor()])
trainset = torchvision.datasets.MNIST(root=cwd+'/data/mnist', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=4)
testset = torchvision.datasets.MNIST(root=cwd+'/data/mnist', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=1, num_workers=4)
# %%
"""
# test binary model clamp
"""
# %%
model_path = cwd+"\saved_model_state_dict"+"_8_clamp"
model_clamp = MLP3_clamp_eval()
model_clamp.to(device)
model_clamp.load_state_dict(torch.load(model_path))
model_clamp.eval()
model_clamp.to(device)
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data[0].to(device), data[1].to(device)
outputs = model_clamp(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size()[0]
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %f %%' % (
100 * correct / total))
# %%
"""
# test unary model nonscaled addition - clamp binary
"""
# %%
correct_binary = 0
correct_unary = 0
bitwidth = 8
total = 0
# binary MLP3_clamp weight init
rng = "Sobol"
encode = "RC"
rng_dim = 1
relu_buf_dep = 4
mode = "bipolar"
scaled = False
bias = True
sample_cnt = 50000
start_cnt = 0
current_index = 0
cycle_correct = torch.zeros(2**(bitwidth)).to(device)
start_time = time.time()
with torch.no_grad():
for data in testloader:
if current_index < start_cnt:
current_index = current_index + 1
continue
current_index = current_index + 1
total += labels.size()[0]
images, labels = data[0].to(device), data[1].to(device)
# reference binary mlp
outputs_binary = model_clamp(images)
_, predicted_binary = torch.max(outputs_binary.data, 1)
correct_binary += (predicted_binary == labels).sum().item()
# print(model_clamp.fc1_out.min().item(), model_clamp.fc1_out.max().item())
# print(model_clamp.fc2_out.min().item(), model_clamp.fc2_out.max().item())
# print(model_clamp.fc3_out.min().item(), model_clamp.fc3_out.max().item())
# unary part
# input image check
image = images.view(-1, 32*32)
image_SRC = SourceGen(image, bitwidth=bitwidth, mode=mode)().to(device)
image_RNG = RNG(bitwidth, rng_dim, rng)().to(device)
image_BSG = BSGen(image_SRC, image_RNG).to(device)
image_ERR = ProgError(image, mode=mode).to(device)
# unary mlp is decomposed into separate layers
fc1_unary = FSULinear(32*32, 512, model_clamp.fc1.weight.data, model_clamp.fc1.bias.data,
mode=mode, scaled=scaled, bias=bias).to(device)
fc1_ERR = ProgError(model_clamp.fc1_out, mode=mode).to(device)
fc2_unary = FSULinear(512, 512, model_clamp.fc2.weight.data, model_clamp.fc2.bias.data,
mode=mode, scaled=scaled, bias=bias).to(device)
fc2_ERR = ProgError(model_clamp.fc2_out, mode=mode).to(device)
fc3_unary = FSULinear(512, 10, model_clamp.fc3.weight.data, model_clamp.fc3.bias.data,
mode=mode, scaled=scaled, bias=bias).to(device)
fc3_ERR = ProgError(model_clamp.fc3_out, mode=mode).to(device)
relu1_unary = FSUReLU(depth=relu_buf_dep, bitwidth=bitwidth, encode=encode).to(device)
relu1_ERR = ProgError(model_clamp.relu1_out, mode=mode).to(device)
relu2_unary = FSUReLU(depth=relu_buf_dep, bitwidth=bitwidth, encode=encode).to(device)
relu2_ERR = ProgError(model_clamp.relu2_out, mode=mode).to(device)
if total%100 == 0:
print("--- %s seconds ---" % (time.time() - start_time))
print(total, "images are done!!!")
# print(current_index, "-th image with label", labels.item(), ", total image count", total)
for i in range(2**(bitwidth)):
idx = torch.zeros(image_SRC.size()).type(torch.long).to(device)
image_bs = image_BSG(idx + i)
image_ERR.Monitor(image_bs)
# print(image_bs.shape)
# fc1
fc1_unary_out = fc1_unary(image_bs)
# fc1_ERR.Monitor(fc1_unary_out)
# print(fc1_unary_out.shape)
# relu1
relu1_unary_out = relu1_unary(fc1_unary_out)
# relu1_ERR.Monitor(relu1_unary_out)
# print(relu1_unary_out.shape)
# fc2
fc2_unary_out = fc2_unary(relu1_unary_out)
# fc2_ERR.Monitor(fc2_unary_out)
# print(fc2_unary_out.shape)
# relu2
relu2_unary_out = relu2_unary(fc2_unary_out)
# relu2_ERR.Monitor(relu2_unary_out)
# print(relu2_unary_out.shape)
# fc3
fc3_unary_out = fc3_unary(relu2_unary_out)
fc3_ERR.Monitor(fc3_unary_out)
# print(fc3_unary_out.shape)
_, predicted_unary = torch.max(fc3_ERR()[0], 1)
if predicted_unary == labels:
# print(current_index, "-th image succeeds.")
# print(current_index, "-th image with label", labels.item(), ", total image count", total)
# print("before", predicted_unary.item(), cycle_correct[predicted_unary.item()].item())
cycle_correct[i].add_(1)
# print("after", predicted_unary.item(), cycle_correct[predicted_unary.item()].item())
# to_print = 1
# print("image: ",
# image_ERR()[to_print].min().item(),
# image_ERR()[to_print].max().item(),
# image_ERR()[to_print].mul(image_ERR()[to_print]).mean().sqrt().item())
# print("fc1: ",
# fc1_ERR()[to_print].min().item(),
# fc1_ERR()[to_print].max().item(),
# fc1_ERR()[to_print].mul(fc1_ERR()[to_print]).mean().sqrt().item())
# print("relu1: ",
# relu1_ERR()[to_print].min().item(),
# relu1_ERR()[to_print].max().item(),
# relu1_ERR()[to_print].mul(relu1_ERR()[to_print]).mean().sqrt().item())
# print("fc2: ",
# fc2_ERR()[to_print].min().item(),
# fc2_ERR()[to_print].max().item(),
# fc2_ERR()[to_print].mul(fc2_ERR()[to_print]).mean().sqrt().item())
# print("relu1: ",
# relu2_ERR()[to_print].min().item(),
# relu2_ERR()[to_print].max().item(),
# relu1_ERR()[to_print].mul(relu1_ERR()[to_print]).mean().sqrt().item())
# print("fc3: ",
# fc3_ERR()[to_print].min().item(),
# fc3_ERR()[to_print].max().item(),
# fc3_ERR()[to_print].mul(fc3_ERR()[to_print]).mean().sqrt().item())
_, predicted_unary = torch.max(fc3_ERR()[0], 1)
correct_unary += (predicted_unary == labels).sum().item()
if total == sample_cnt:
break
print('Accuracy of the network on %d test images: %f %%' % (total,
100 * correct_binary / total))
print('Accuracy of the network on %d test images: %f %%' % (total,
100 * correct_unary / total))
result = cycle_correct.cpu().numpy()/total
fig = plt.plot([i for i in range(2**bitwidth)], result) # arguments are passed to np.histogram
plt.title("Cycle level accuracy")
plt.show()
with open("cycle_accuracy_mlp_nonscaled_clamp.csv", "w+") as f:
for i in result:
f.write(str(i)+", \n")
# %%
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dotsandboxes/pit-dotsandboxes.py | user01/alpha-zero-general | 2,836 | 178784 | <reponame>user01/alpha-zero-general<gh_stars>1000+
import os
import numpy as np
import Arena
from MCTS import MCTS
from utils import dotdict
from dotsandboxes.DotsAndBoxesGame import DotsAndBoxesGame
from dotsandboxes.DotsAndBoxesPlayers import HumanDotsAndBoxesPlayer, RandomPlayer, GreedyRandomPlayer
from dotsandboxes.keras.NNet import NNetWrapper
g = DotsAndBoxesGame(n=3)
hp1 = HumanDotsAndBoxesPlayer(g).play
hp2 = HumanDotsAndBoxesPlayer(g).play
rp1 = RandomPlayer(g).play
rp2 = RandomPlayer(g).play
grp1 = GreedyRandomPlayer(g).play
grp2 = GreedyRandomPlayer(g).play
numMCTSSims = 50
n1 = NNetWrapper(g)
n1.load_checkpoint(os.path.join('../', 'pretrained_models', 'dotsandboxes', 'keras', '3x3'), 'best.pth.tar')
args1 = dotdict({'numMCTSSims': numMCTSSims, 'cpuct': 1.0})
mcts1 = MCTS(g, n1, args1)
n1p = lambda x: np.argmax(mcts1.getActionProb(x, temp=0))
n2 = NNetWrapper(g)
n2.load_checkpoint(os.path.join('../', 'pretrained_models', 'dotsandboxes', 'keras', '3x3'), 'best.pth.tar')
args2 = dotdict({'numMCTSSims': numMCTSSims, 'cpuct': 1.0})
mcts2 = MCTS(g, n2, args2)
n2p = lambda x: np.argmax(mcts2.getActionProb(x, temp=0))
# Play AlphaZero versus Human
p1 = n1p
p2 = hp1
arena = Arena.Arena(p1, p2, g, display=DotsAndBoxesGame.display)
oneWon, twoWon, draws = arena.playGames(2, verbose=True)
print("oneWon: {}, twoWon: {}, draws: {}".format(oneWon, twoWon, draws))
# # Play Greedy vs Greedy
# p1 = grp1
# p2 = grp2
# arena = Arena.Arena(p1, p2, g, display=DotsAndBoxesGame.display)
# oneWon, twoWon, draws = arena.playGames(100, verbose=False)
# print("oneWon: {}, twoWon: {}, draws: {}".format(oneWon, twoWon, draws))
# # Play AlphaZero vs Greedy
# p1 = n1p
# p2 = grp2
# arena = Arena.Arena(p1, p2, g, display=DotsAndBoxesGame.display)
# oneWon, twoWon, draws = arena.playGames(2, verbose=False)
# print("oneWon: {}, twoWon: {}, draws: {}".format(oneWon, twoWon, draws)) | [
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] |
Part 3/batch_VS_stochastic.py | m9psy/neural_network_habr_guide | 20 | 10679 | import numpy as np
import matplotlib.pyplot as plt
TOTAL = 200
STEP = 0.25
EPS = 0.1
INITIAL_THETA = [9, 14]
def func(x):
return 0.2 * x + 3
def generate_sample(total=TOTAL):
x = 0
while x < total * STEP:
yield func(x) + np.random.uniform(-1, 1) * np.random.uniform(2, 8)
x += STEP
def cost_function(A, Y, theta):
return (Y - A@theta).T@(Y - A@theta)
def batch_descent(A, Y, speed=0.001):
theta = np.array(INITIAL_THETA.copy(), dtype=np.float32)
theta.reshape((len(theta), 1))
previous_cost = 10 ** 6
current_cost = cost_function(A, Y, theta)
while np.abs(previous_cost - current_cost) > EPS:
previous_cost = current_cost
derivatives = [0] * len(theta)
# ---------------------------------------------
for j in range(len(theta)):
summ = 0
for i in range(len(Y)):
summ += (Y[i] - A[i]@theta) * A[i][j]
derivatives[j] = summ
# Выполнение требования одновремменности
theta[0] += speed * derivatives[0]
theta[1] += speed * derivatives[1]
# ---------------------------------------------
current_cost = cost_function(A, Y, theta)
print("Batch cost:", current_cost)
plt.plot(theta[0], theta[1], 'ro')
return theta
def stochastic_descent(A, Y, speed=0.1):
theta = np.array(INITIAL_THETA.copy(), dtype=np.float32)
previous_cost = 10 ** 6
current_cost = cost_function(A, Y, theta)
while np.abs(previous_cost - current_cost) > EPS:
previous_cost = current_cost
# --------------------------------------
# for i in range(len(Y)):
i = np.random.randint(0, len(Y))
derivatives = [0] * len(theta)
for j in range(len(theta)):
derivatives[j] = (Y[i] - A[i]@theta) * A[i][j]
theta[0] += speed * derivatives[0]
theta[1] += speed * derivatives[1]
# --------------------------------------
current_cost = cost_function(A, Y, theta)
print("Stochastic cost:", current_cost)
plt.plot(theta[0], theta[1], 'ro')
return theta
X = np.arange(0, TOTAL * STEP, STEP)
Y = np.array([y for y in generate_sample(TOTAL)])
# Нормализацию вкрячил, чтобы парабалоид красивый был
X = (X - X.min()) / (X.max() - X.min())
A = np.empty((TOTAL, 2))
A[:, 0] = 1
A[:, 1] = X
theta = np.linalg.pinv(A).dot(Y)
print(theta, cost_function(A, Y, theta))
import time
start = time.clock()
theta_stochastic = stochastic_descent(A, Y, 0.1)
print("St:", time.clock() - start, theta_stochastic)
start = time.clock()
theta_batch = batch_descent(A, Y, 0.001)
print("Btch:", time.clock() - start, theta_batch)
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] |
tests/mlmax/test_local_deployments.py | yinsong1986/mlmax | 56 | 40705 | <gh_stars>10-100
import datatest as dt
import pytest
from sagemaker.processing import ProcessingInput, ProcessingOutput
from sagemaker.sklearn.estimator import SKLearn
from sagemaker.sklearn.processing import SKLearnProcessor
# These tests are skipped unless pytest is run with the `--smlocal` flag.
# These tests will be run in Python inspect mode if pytest is run with the
# `--smlocal --inspectlocal` flags.
# For local training a dummy role will be sufficient
role = "arn:aws:iam::111111111111:role/service-role/AmazonSageMaker-ExecutionRole-20200101T000001"
@pytest.fixture()
def processor(inspectlocal):
processor = SKLearnProcessor(
framework_version="0.20.0",
instance_count=1,
instance_type="local",
role=role,
max_runtime_in_seconds=1200,
env={"PYTHONINSPECT": "1"} if inspectlocal else None,
)
return processor
@dt.working_directory(__file__)
@pytest.mark.smlocal
def test_preprocessing_script_in_local_container(processor):
code_path = "../../src/mlmax/preprocessing.py"
execution_mode = "tr ain" # Configure to either 'train', or 'infer'
input_data_path = "input/census-income-sample.csv"
local_data_path = "opt/ml/processing/input"
processor.run(
code=code_path,
inputs=[
ProcessingInput(
source=local_data_path, destination="/opt/ml/processing/input"
)
],
outputs=[
ProcessingOutput(
source="/opt/ml/processing/train", output_name="train_data",
),
ProcessingOutput(
source="/opt/ml/processing/test", output_name="test_data",
),
ProcessingOutput(
source="/opt/ml/processing/model", output_name="proc_model",
),
],
arguments=[
"--train-test-split-ratio",
"0.2",
"--mode",
execution_mode,
"--data-input",
input_data_path,
],
wait=False,
)
@dt.working_directory(__file__)
@pytest.mark.smlocal
def test_training_script_in_local_container(inspectlocal):
code_path = "../../src/mlmax/train.py"
train_data_path = "opt/ml/processing/train/"
test_data_path = "opt/ml/processing/test/"
sklearn = SKLearn(
entry_point=code_path,
role=role,
py_version="py3",
framework_version="0.20.0",
instance_type="local",
hyperparameters={"inspect": True if inspectlocal else None},
)
sklearn.fit(
{"train": "file://" + train_data_path, "test": "file://" + test_data_path},
wait=True,
)
@dt.working_directory(__file__)
@pytest.mark.smlocal
def test_inference_script_in_local_container(processor):
code_path = "../../src/mlmax/inference.py"
local_data_path = "opt/ml/processing/input"
model_path = "opt/ml/processing/model"
processor.run(
code=code_path,
inputs=[
ProcessingInput(
source=local_data_path,
destination="/opt/ml/processing/input",
input_name="input",
),
ProcessingInput(
source=model_path,
destination="/opt/ml/processing/model",
input_name="model",
),
],
outputs=[
ProcessingOutput(source="/opt/ml/processing/test", output_name="test_data",)
],
wait=False,
)
@dt.working_directory(__file__)
@pytest.mark.smlocal
def test_evaluation_script_in_local_container(processor):
code_path = "../../src/mlmax/evaluation.py"
local_data_path = "opt/ml/processing/test"
model_path = "opt/ml/processing/model"
processor.run(
code=code_path,
inputs=[
ProcessingInput(
source=local_data_path,
destination="/opt/ml/processing/test",
input_name="input",
),
ProcessingInput(
source=model_path,
destination="/opt/ml/processing/model",
input_name="model",
),
],
outputs=[
ProcessingOutput(
source="/opt/ml/processing/evaluation", output_name="evaluation",
)
],
wait=False,
)
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] |
venv/lib/python3.7/site-packages/datalad/interface/utils.py | emmetaobrien/dats-validator | 0 | 196611 | # emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 noet:
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the datalad package for the
# copyright and license terms.
#
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
"""Interface utility functions
"""
__docformat__ = 'restructuredtext'
import inspect
import logging
import wrapt
import sys
import re
import shlex
from os import curdir
from os import pardir
from os import listdir
from os.path import join as opj
from os.path import isdir
from os.path import relpath
from os.path import sep
from os.path import split as psplit
from itertools import chain
from six import PY2
import json
# avoid import from API to not get into circular imports
from datalad.utils import with_pathsep as _with_sep # TODO: RF whenever merge conflict is not upon us
from datalad.utils import path_startswith
from datalad.utils import path_is_subpath
from datalad.utils import assure_unicode
from datalad.support.gitrepo import GitRepo
from datalad.support.exceptions import IncompleteResultsError
from datalad import cfg as dlcfg
from datalad.dochelpers import exc_str
from datalad.support.constraints import Constraint
from datalad.ui import ui
import datalad.support.ansi_colors as ac
from datalad.interface.base import Interface
from datalad.interface.base import default_logchannels
from datalad.interface.base import get_allargs_as_kwargs
from datalad.interface.common_opts import eval_params
from datalad.interface.common_opts import eval_defaults
from .results import known_result_xfms
lgr = logging.getLogger('datalad.interface.utils')
def cls2cmdlinename(cls):
"Return the cmdline command name from an Interface class"
r = re.compile(r'([a-z0-9])([A-Z])')
return r.sub('\\1-\\2', cls.__name__).lower()
# TODO remove
# only `drop` and `uninstall` are still using this
def handle_dirty_dataset(ds, mode, msg=None):
"""Detect and treat unsaved changes as instructed by `mode`
Parameters
----------
ds : Dataset or None
Dataset to be inspected. Does nothing if `None`.
mode : {'fail', 'ignore', 'save-before'}
How to act upon discovering unsaved changes.
msg : str or None
Custom message to use for a potential commit.
Returns
-------
None
"""
if ds is None:
# nothing to be handled
return
if msg is None:
msg = '[DATALAD] auto-saved changes'
# make sure that all pending changes (batched annex operations, etc.)
# are actually reflected in Git
if ds.repo:
ds.repo.precommit()
if mode == 'ignore':
return
elif mode == 'fail':
if not ds.repo or ds.repo.is_dirty(index=True,
untracked_files=True,
submodules=True):
raise RuntimeError('dataset {} has unsaved changes'.format(ds))
elif mode == 'save-before':
if not ds.is_installed():
raise RuntimeError('dataset {} is not yet installed'.format(ds))
from datalad.interface.save import Save
Save.__call__(dataset=ds, message=msg)
else:
raise ValueError("unknown if-dirty mode '{}'".format(mode))
def get_tree_roots(paths):
"""Return common root paths for a set of paths
This function determines the smallest set of common root
paths and sorts all given paths under the respective
root.
Returns
-------
dict
paths by root
"""
paths_ws = [_with_sep(p) for p in paths]
# sort all paths under their potential roots
roots = {}
# start from the top to get all paths down the line
# and collate them into as few roots as possible
for s in sorted(paths_ws):
if any([s.startswith(r) for r in roots]):
# this path is already covered by a known root
continue
# find all sub paths
subs = [p for p in paths if p.startswith(s)]
roots[s.rstrip(sep)] = subs
return roots
# TODO remove
# only `remove` and `uninstall` use this, the uses path `path_is_subpath`
def path_is_under(values, path=None):
"""Whether a given path is a subdirectory of any of the given test values
Parameters
----------
values : sequence or dict
Paths to be tested against. This can be a dictionary in which case
all values from all keys will be tested against.
path : path or None
Test path. If None is given, the process' working directory is
used.
Returns
-------
bool
"""
if path is None:
from datalad.utils import getpwd
path = getpwd()
if isinstance(values, dict):
values = chain(*values.values())
for p in values:
rpath = relpath(p, start=path)
if rpath == curdir \
or rpath == pardir \
or set(psplit(rpath)) == {pardir}:
# first match is enough
return True
return False
def discover_dataset_trace_to_targets(basepath, targetpaths, current_trace,
spec, includeds=None):
"""Discover the edges and nodes in a dataset tree to given target paths
Parameters
----------
basepath : path
Path to a start or top-level dataset. Really has to be a path to a
dataset!
targetpaths : list(path)
Any non-zero number of paths that are termination points for the
search algorithm. Can be paths to datasets, directories, or files
(and any combination thereof).
current_trace : list
For a top-level call this should probably always be `[]`
spec : dict
`content_by_ds`-style dictionary that will receive information about the
discovered datasets. Specifically, for each discovered dataset there
will be in item with its path under the key (path) of the respective
superdataset.
includeds : sequence, optional
Any paths given are treated as existing subdatasets, regardless of
whether they can be found in the filesystem. Such subdatasets will appear
under the key of the closest existing dataset in the `spec`.
Returns
-------
None
Function calls itself recursively and populates `spec` dict in-place.
Keys are dataset paths, values are sets of subdataset paths
"""
# convert to set for faster lookup
includeds = includeds if isinstance(includeds, set) else \
set() if includeds is None else set(includeds)
# this beast walks the directory tree from a given `basepath` until
# it discovers any of the given `targetpaths`
# if it finds one, it commits any accummulated trace of visited
# datasets on this edge to the spec
valid_repo = GitRepo.is_valid_repo(basepath)
if valid_repo:
# we are passing into a new dataset, extend the dataset trace
current_trace = current_trace + [basepath]
# this edge is not done, we need to try to reach any downstream
# dataset
undiscovered_ds = set(t for t in targetpaths) # if t != basepath)
# whether anything in this directory matched a targetpath
filematch = False
if isdir(basepath):
for p in listdir(basepath):
p = assure_unicode(opj(basepath, p))
if not isdir(p):
if p in targetpaths:
filematch = True
# we cannot have anything below this one
continue
# OPT listdir might be large and we could have only few items
# in `targetpaths` -- so traverse only those in spec which have
# leading dir basepath
# filter targets matching this downward path
downward_targets = set(
t for t in targetpaths if path_startswith(t, p))
if not downward_targets:
continue
# remove the matching ones from the "todo" list
undiscovered_ds.difference_update(downward_targets)
# go one deeper
discover_dataset_trace_to_targets(
p, downward_targets, current_trace, spec,
includeds=includeds if not includeds else includeds.intersection(
downward_targets))
undiscovered_ds = [t for t in undiscovered_ds
if includeds and
path_is_subpath(t, current_trace[-1]) and
t in includeds]
if filematch or basepath in targetpaths or undiscovered_ds:
for i, p in enumerate(current_trace[:-1]):
# TODO RF prepare proper annotated path dicts
subds = spec.get(p, set())
subds.add(current_trace[i + 1])
spec[p] = subds
if undiscovered_ds:
spec[current_trace[-1]] = spec.get(current_trace[-1], set()).union(
undiscovered_ds)
def eval_results(func):
"""Decorator for return value evaluation of datalad commands.
Note, this decorator is only compatible with commands that return
status dict sequences!
Two basic modes of operation are supported: 1) "generator mode" that
`yields` individual results, and 2) "list mode" that returns a sequence of
results. The behavior can be selected via the kwarg `return_type`.
Default is "list mode".
This decorator implements common functionality for result rendering/output,
error detection/handling, and logging.
Result rendering/output can be triggered via the
`datalad.api.result-renderer` configuration variable, or the
`result_renderer` keyword argument of each decorated command. Supported
modes are: 'default' (one line per result with action, status, path,
and an optional message); 'json' (one object per result, like git-annex),
'json_pp' (like 'json', but pretty-printed spanning multiple lines),
'tailored' custom output formatting provided by each command
class (if any).
Error detection works by inspecting the `status` item of all result
dictionaries. Any occurrence of a status other than 'ok' or 'notneeded'
will cause an IncompleteResultsError exception to be raised that carries
the failed actions' status dictionaries in its `failed` attribute.
Status messages will be logged automatically, by default the following
association of result status and log channel will be used: 'ok' (debug),
'notneeded' (debug), 'impossible' (warning), 'error' (error). Logger
instances included in the results are used to capture the origin of a
status report.
Parameters
----------
func: function
__call__ method of a subclass of Interface,
i.e. a datalad command definition
"""
@wrapt.decorator
def eval_func(wrapped, instance, args, kwargs):
# for result filters and pre/post procedures
# we need to produce a dict with argname/argvalue pairs for all args
# incl. defaults and args given as positionals
allkwargs = get_allargs_as_kwargs(wrapped, args, kwargs)
# determine class, the __call__ method of which we are decorating:
# Ben: Note, that this is a bit dirty in PY2 and imposes restrictions on
# when and how to use eval_results as well as on how to name a command's
# module and class. As of now, we are inline with these requirements as
# far as I'm aware.
mod = sys.modules[wrapped.__module__]
if PY2:
# we rely on:
# - decorated function is method of a subclass of Interface
# - the name of the class matches the last part of the module's name
# if converted to lower
# for example:
# ..../where/ever/mycommand.py:
# class MyCommand(Interface):
# @eval_results
# def __call__(..)
command_class_names = \
[i for i in mod.__dict__
if type(mod.__dict__[i]) == type and
issubclass(mod.__dict__[i], Interface) and
i.lower().startswith(wrapped.__module__.split('.')[-1].replace('datalad_', '').replace('_', ''))]
assert len(command_class_names) == 1, (command_class_names, mod.__name__)
command_class_name = command_class_names[0]
else:
command_class_name = wrapped.__qualname__.split('.')[-2]
_func_class = mod.__dict__[command_class_name]
lgr.debug("Determined class of decorated function: %s", _func_class)
# retrieve common options from kwargs, and fall back on the command
# class attributes, or general defaults if needed
kwargs = kwargs.copy() # we will pop, which might cause side-effect
common_params = {
p_name: kwargs.pop(
p_name,
getattr(_func_class, p_name, eval_defaults[p_name]))
for p_name in eval_params}
# short cuts and configured setup for common options
on_failure = common_params['on_failure']
return_type = common_params['return_type']
# resolve string labels for transformers too
result_xfm = common_params['result_xfm']
if result_xfm in known_result_xfms:
result_xfm = known_result_xfms[result_xfm]
result_renderer = common_params['result_renderer']
# TODO remove this conditional branch entirely, done outside
if not result_renderer:
result_renderer = dlcfg.get('datalad.api.result-renderer', None)
# wrap the filter into a helper to be able to pass additional arguments
# if the filter supports it, but at the same time keep the required interface
# as minimal as possible. Also do this here, in order to avoid this test
# to be performed for each return value
result_filter = common_params['result_filter']
_result_filter = result_filter
if result_filter:
if isinstance(result_filter, Constraint):
_result_filter = result_filter.__call__
if (PY2 and inspect.getargspec(_result_filter).keywords) or \
(not PY2 and inspect.getfullargspec(_result_filter).varkw):
def _result_filter(res):
return result_filter(res, **allkwargs)
def _get_procedure_specs(param_key=None, cfg_key=None, ds=None):
spec = common_params.get(param_key, None)
if spec is not None:
# this is already a list of lists
return spec
from datalad.distribution.dataset import Dataset
ds = ds if isinstance(ds, Dataset) else Dataset(ds) if ds else None
spec = (ds.config if ds and ds.is_installed()
else dlcfg).get(cfg_key, None)
if spec is None:
return
elif not isinstance(spec, tuple):
spec = [spec]
return [shlex.split(s) for s in spec]
# query cfg for defaults
cmdline_name = cls2cmdlinename(_func_class)
dataset_arg = allkwargs.get('dataset', None)
proc_pre = _get_procedure_specs(
'proc_pre',
'datalad.{}.proc-pre'.format(cmdline_name),
ds=dataset_arg)
proc_post = _get_procedure_specs(
'proc_post',
'datalad.{}.proc-post'.format(cmdline_name),
ds=dataset_arg)
# this internal helper function actually drives the command
# generator-style, it may generate an exception if desired,
# on incomplete results
def generator_func(*_args, **_kwargs):
# flag whether to raise an exception
incomplete_results = []
# track what actions were performed how many times
action_summary = {}
if proc_pre and cmdline_name != 'run-procedure':
from datalad.interface.run_procedure import RunProcedure
for procspec in proc_pre:
lgr.debug('Running configured pre-procedure %s', procspec)
for r in _process_results(
RunProcedure.__call__(
procspec,
dataset=dataset_arg,
return_type='generator'),
_func_class, action_summary,
on_failure, incomplete_results,
result_renderer, result_xfm, result_filter,
**_kwargs):
yield r
# if a custom summary is to be provided, collect the results
# of the command execution
results = []
do_custom_result_summary = result_renderer == 'tailored' \
and hasattr(_func_class, 'custom_result_summary_renderer')
# process main results
for r in _process_results(
wrapped(*_args, **_kwargs),
_func_class, action_summary,
on_failure, incomplete_results,
result_renderer, result_xfm, _result_filter, **_kwargs):
yield r
# collect if summary is desired
if do_custom_result_summary:
results.append(r)
if proc_post and cmdline_name != 'run-procedure':
from datalad.interface.run_procedure import RunProcedure
for procspec in proc_post:
lgr.debug('Running configured post-procedure %s', procspec)
for r in _process_results(
RunProcedure.__call__(
procspec,
dataset=dataset_arg,
return_type='generator'),
_func_class, action_summary,
on_failure, incomplete_results,
result_renderer, result_xfm, result_filter,
**_kwargs):
yield r
# result summary before a potential exception
# custom first
if do_custom_result_summary:
_func_class.custom_result_summary_renderer(results)
elif result_renderer == 'default' and action_summary and \
sum(sum(s.values()) for s in action_summary.values()) > 1:
# give a summary in default mode, when there was more than one
# action performed
ui.message("action summary:\n {}".format(
'\n '.join('{} ({})'.format(
act,
', '.join('{}: {}'.format(status, action_summary[act][status])
for status in sorted(action_summary[act])))
for act in sorted(action_summary))))
if incomplete_results:
raise IncompleteResultsError(
failed=incomplete_results,
msg="Command did not complete successfully")
if return_type == 'generator':
# hand over the generator
return generator_func(*args, **kwargs)
else:
@wrapt.decorator
def return_func(wrapped_, instance_, args_, kwargs_):
results = wrapped_(*args_, **kwargs_)
if inspect.isgenerator(results):
# unwind generator if there is one, this actually runs
# any processing
results = list(results)
# render summaries
if not result_xfm and result_renderer == 'tailored':
# cannot render transformed results
if hasattr(_func_class, 'custom_result_summary_renderer'):
_func_class.custom_result_summary_renderer(results)
if return_type == 'item-or-list' and \
len(results) < 2:
return results[0] if results else None
else:
return results
return return_func(generator_func)(*args, **kwargs)
return eval_func(func)
def default_result_renderer(res):
if res.get('status', None) != 'notneeded':
ui.message('{action}({status}): {path}{type}{msg}'.format(
action=ac.color_word(res['action'], ac.BOLD),
status=ac.color_status(res['status']),
path=relpath(res['path'],
res['refds']) if res.get('refds', None) else res[
'path'],
type=' ({})'.format(
ac.color_word(res['type'], ac.MAGENTA)
) if 'type' in res else '',
msg=' [{}]'.format(
res['message'][0] % res['message'][1:]
if isinstance(res['message'], tuple) else res[
'message'])
if 'message' in res else ''))
def _process_results(
results, cmd_class,
action_summary, on_failure, incomplete_results,
result_renderer, result_xfm, result_filter, **kwargs):
# private helper pf @eval_results
# loop over results generated from some source and handle each
# of them according to the requested behavior (logging, rendering, ...)
for res in results:
if not res or 'action' not in res:
# XXX Yarik has to no clue on how to track the origin of the
# record to figure out WTF, so he just skips it
continue
if PY2:
for k, v in res.items():
if isinstance(v, unicode):
res[k] = v.encode('utf-8')
actsum = action_summary.get(res['action'], {})
if res['status']:
actsum[res['status']] = actsum.get(res['status'], 0) + 1
action_summary[res['action']] = actsum
## log message, if a logger was given
# remove logger instance from results, as it is no longer useful
# after logging was done, it isn't serializable, and generally
# pollutes the output
res_lgr = res.pop('logger', None)
if isinstance(res_lgr, logging.Logger):
# didn't get a particular log function, go with default
res_lgr = getattr(res_lgr, default_logchannels[res['status']])
if res_lgr and 'message' in res:
msg = res['message']
msgargs = None
if isinstance(msg, tuple):
msgargs = msg[1:]
msg = msg[0]
if 'path' in res:
msg = '{} [{}({})]'.format(
msg, res['action'], res['path'])
if msgargs:
# support string expansion of logging to avoid runtime cost
res_lgr(msg, *msgargs)
else:
res_lgr(msg)
## error handling
# looks for error status, and report at the end via
# an exception
if on_failure in ('continue', 'stop') \
and res['status'] in ('impossible', 'error'):
incomplete_results.append(res)
if on_failure == 'stop':
# first fail -> that's it
# raise will happen after the loop
break
if result_filter:
try:
if not result_filter(res):
raise ValueError('excluded by filter')
except ValueError as e:
lgr.debug('not reporting result (%s)', exc_str(e))
continue
## output rendering
# TODO RF this in a simple callable that gets passed into this function
if result_renderer is None or result_renderer == 'disabled':
pass
elif result_renderer == 'default':
default_result_renderer(res)
elif result_renderer in ('json', 'json_pp'):
ui.message(json.dumps(
{k: v for k, v in res.items()
if k not in ('message', 'logger')},
sort_keys=True,
indent=2 if result_renderer.endswith('_pp') else None,
default=lambda x: str(x)))
elif result_renderer == 'tailored':
if hasattr(cmd_class, 'custom_result_renderer'):
cmd_class.custom_result_renderer(res, **kwargs)
elif hasattr(result_renderer, '__call__'):
try:
result_renderer(res, **kwargs)
except Exception as e:
lgr.warn('Result rendering failed for: %s [%s]',
res, exc_str(e))
else:
raise ValueError('unknown result renderer "{}"'.format(result_renderer))
if PY2:
for k, v in res.items():
if isinstance(v, str):
res[k] = v.decode('utf-8')
if result_xfm:
res = result_xfm(res)
if res is None:
continue
yield res
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326,
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13,
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278,
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310,
263,
13,
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13,
13,
1678,
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13,
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448,
1378,
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13,
1678,
3653,
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740,
13,
418,
4770,
4804,
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1158,
310,
263,
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310,
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13,
418,
474,
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29872,
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263,
1418,
284,
328,
1899,
5023,
13,
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9995,
13,
13,
1678,
732,
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336,
415,
29889,
19557,
1061,
13,
1678,
822,
19745,
29918,
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336,
2986,
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2777,
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6389,
29892,
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5085,
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13,
4706,
396,
363,
1121,
18094,
322,
758,
29914,
2490,
28648,
13,
4706,
396,
591,
817,
304,
7738,
263,
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411,
1852,
978,
29914,
1191,
1767,
11000,
363,
599,
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13,
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396,
1343,
29889,
21274,
322,
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2183,
408,
2602,
1338,
13,
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599,
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353,
679,
29918,
497,
5085,
29918,
294,
29918,
19290,
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336,
2986,
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6389,
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5085,
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13,
4706,
396,
8161,
770,
29892,
278,
4770,
4804,
1649,
1158,
310,
607,
591,
526,
10200,
1218,
29901,
13,
4706,
396,
4111,
29901,
3940,
29892,
393,
445,
338,
263,
2586,
26616,
297,
349,
29979,
29906,
322,
7275,
267,
25091,
373,
13,
4706,
396,
746,
322,
920,
304,
671,
19745,
29918,
9902,
408,
1532,
408,
373,
920,
304,
1024,
263,
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13,
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396,
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322,
770,
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310,
1286,
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591,
526,
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411,
1438,
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408,
13,
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396,
2215,
408,
306,
29915,
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9543,
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13,
4706,
878,
353,
10876,
29889,
7576,
29961,
29893,
336,
2986,
17255,
5453,
1649,
29962,
13,
4706,
565,
349,
29979,
29906,
29901,
13,
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396,
591,
19104,
373,
29901,
13,
9651,
396,
448,
10200,
630,
740,
338,
1158,
310,
263,
19481,
310,
25796,
13,
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396,
448,
278,
1024,
310,
278,
770,
7087,
278,
1833,
760,
310,
278,
3883,
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13,
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396,
259,
565,
11543,
304,
5224,
13,
9651,
396,
363,
1342,
29901,
13,
9651,
396,
13035,
29914,
3062,
29914,
1310,
29914,
1357,
6519,
29889,
2272,
29901,
13,
9651,
396,
770,
1619,
6255,
29898,
10448,
1125,
13,
9651,
396,
268,
732,
14513,
29918,
9902,
13,
9651,
396,
268,
822,
4770,
4804,
12035,
636,
29897,
13,
9651,
1899,
29918,
1990,
29918,
7039,
353,
320,
13,
18884,
518,
29875,
363,
474,
297,
878,
17255,
8977,
1649,
13,
462,
565,
1134,
29898,
1545,
17255,
8977,
1649,
29961,
29875,
2314,
1275,
1134,
322,
13,
462,
338,
1491,
1990,
29898,
1545,
17255,
8977,
1649,
29961,
29875,
1402,
25796,
29897,
322,
13,
462,
474,
29889,
13609,
2141,
27382,
2541,
29898,
29893,
336,
2986,
17255,
5453,
26914,
5451,
12839,
1495,
14352,
29896,
1822,
6506,
877,
29881,
2075,
328,
29918,
742,
525,
2824,
6506,
877,
29918,
742,
6629,
28166,
13,
9651,
4974,
7431,
29898,
6519,
29918,
1990,
29918,
7039,
29897,
1275,
29871,
29896,
29892,
313,
6519,
29918,
1990,
29918,
7039,
29892,
878,
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978,
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13,
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1899,
29918,
1990,
29918,
978,
353,
1899,
29918,
1990,
29918,
7039,
29961,
29900,
29962,
13,
4706,
1683,
29901,
13,
9651,
1899,
29918,
1990,
29918,
978,
353,
21021,
17255,
15380,
978,
26914,
5451,
12839,
1495,
14352,
29906,
29962,
13,
4706,
903,
9891,
29918,
1990,
353,
878,
17255,
8977,
1649,
29961,
6519,
29918,
1990,
29918,
978,
29962,
13,
4706,
301,
629,
29889,
8382,
703,
6362,
837,
1312,
770,
310,
10200,
630,
740,
29901,
1273,
29879,
613,
903,
9891,
29918,
1990,
29897,
13,
13,
4706,
396,
10563,
3619,
3987,
515,
9049,
5085,
29892,
322,
6416,
1250,
373,
278,
1899,
13,
4706,
396,
770,
8393,
29892,
470,
2498,
21274,
565,
4312,
13,
4706,
9049,
5085,
353,
9049,
5085,
29889,
8552,
580,
29871,
396,
591,
674,
1835,
29892,
607,
1795,
4556,
2625,
29899,
15987,
13,
4706,
3619,
29918,
7529,
353,
426,
13,
9651,
282,
29918,
978,
29901,
9049,
5085,
29889,
7323,
29898,
13,
18884,
282,
29918,
978,
29892,
13,
18884,
679,
5552,
7373,
9891,
29918,
1990,
29892,
282,
29918,
978,
29892,
19745,
29918,
4381,
29879,
29961,
29886,
29918,
978,
12622,
13,
9651,
363,
282,
29918,
978,
297,
19745,
29918,
7529,
29913,
13,
4706,
396,
3273,
5700,
29879,
322,
13252,
6230,
363,
3619,
3987,
13,
4706,
373,
29918,
14057,
545,
353,
3619,
29918,
7529,
1839,
265,
29918,
14057,
545,
2033,
13,
4706,
736,
29918,
1853,
353,
3619,
29918,
7529,
1839,
2457,
29918,
1853,
2033,
13,
4706,
396,
8814,
1347,
11073,
363,
4327,
414,
2086,
13,
4706,
1121,
29918,
24660,
29885,
353,
3619,
29918,
7529,
1839,
2914,
29918,
24660,
29885,
2033,
13,
4706,
565,
1121,
29918,
24660,
29885,
297,
2998,
29918,
2914,
29918,
24660,
1516,
29901,
13,
9651,
1121,
29918,
24660,
29885,
353,
2998,
29918,
2914,
29918,
24660,
1516,
29961,
2914,
29918,
24660,
29885,
29962,
13,
4706,
1121,
29918,
9482,
261,
353,
3619,
29918,
7529,
1839,
2914,
29918,
9482,
261,
2033,
13,
4706,
396,
14402,
3349,
445,
15047,
5443,
9186,
29892,
2309,
5377,
13,
4706,
565,
451,
1121,
29918,
9482,
261,
29901,
13,
9651,
1121,
29918,
9482,
261,
353,
270,
29880,
16859,
29889,
657,
877,
29881,
2075,
328,
29889,
2754,
29889,
2914,
29899,
9482,
261,
742,
6213,
29897,
13,
4706,
396,
12244,
278,
4175,
964,
263,
16876,
304,
367,
2221,
304,
1209,
5684,
6273,
13,
4706,
396,
565,
278,
4175,
11286,
372,
29892,
541,
472,
278,
1021,
931,
3013,
278,
3734,
5067,
13,
4706,
396,
408,
13114,
408,
1950,
29889,
3115,
437,
445,
1244,
29892,
297,
1797,
304,
4772,
445,
1243,
13,
4706,
396,
304,
367,
8560,
363,
1269,
736,
995,
13,
4706,
1121,
29918,
4572,
353,
3619,
29918,
7529,
1839,
2914,
29918,
4572,
2033,
13,
4706,
903,
2914,
29918,
4572,
353,
1121,
29918,
4572,
13,
4706,
565,
1121,
29918,
4572,
29901,
13,
9651,
565,
338,
8758,
29898,
2914,
29918,
4572,
29892,
1281,
4151,
524,
1125,
13,
18884,
903,
2914,
29918,
4572,
353,
1121,
29918,
4572,
17255,
4804,
1649,
13,
9651,
565,
313,
20055,
29906,
322,
16096,
29889,
657,
5085,
3135,
7373,
2914,
29918,
4572,
467,
1989,
9303,
29897,
470,
320,
13,
462,
1678,
313,
1333,
349,
29979,
29906,
322,
16096,
29889,
657,
8159,
5085,
3135,
7373,
2914,
29918,
4572,
467,
29894,
935,
29893,
1125,
13,
13,
18884,
822,
903,
2914,
29918,
4572,
29898,
690,
1125,
13,
462,
1678,
736,
1121,
29918,
4572,
29898,
690,
29892,
3579,
497,
19290,
29897,
13,
13,
4706,
822,
903,
657,
29918,
771,
26600,
29918,
5965,
2395,
29898,
3207,
29918,
1989,
29922,
8516,
29892,
274,
16434,
29918,
1989,
29922,
8516,
29892,
18031,
29922,
8516,
1125,
13,
9651,
1580,
353,
3619,
29918,
7529,
29889,
657,
29898,
3207,
29918,
1989,
29892,
6213,
29897,
13,
9651,
565,
1580,
338,
451,
6213,
29901,
13,
18884,
396,
445,
338,
2307,
263,
1051,
310,
8857,
13,
18884,
736,
1580,
13,
13,
9651,
515,
1418,
284,
328,
29889,
27691,
29889,
24713,
1053,
13373,
24541,
13,
9651,
18031,
353,
18031,
565,
338,
8758,
29898,
6289,
29892,
13373,
24541,
29897,
1683,
13373,
24541,
29898,
6289,
29897,
565,
18031,
1683,
6213,
13,
9651,
1580,
353,
313,
6289,
29889,
2917,
565,
18031,
322,
18031,
29889,
275,
29918,
25537,
580,
13,
462,
1678,
1683,
270,
29880,
16859,
467,
657,
29898,
16859,
29918,
1989,
29892,
6213,
29897,
13,
9651,
565,
1580,
338,
6213,
29901,
13,
18884,
736,
13,
9651,
25342,
451,
338,
8758,
29898,
6550,
29892,
18761,
1125,
13,
18884,
1580,
353,
518,
6550,
29962,
13,
9651,
736,
518,
845,
2506,
29889,
5451,
29898,
29879,
29897,
363,
269,
297,
1580,
29962,
13,
13,
4706,
396,
2346,
274,
16434,
363,
21274,
13,
4706,
9920,
1220,
29918,
978,
353,
1067,
29879,
29906,
9006,
1915,
3871,
7373,
9891,
29918,
1990,
29897,
13,
4706,
8783,
29918,
1191,
353,
599,
19290,
29889,
657,
877,
24713,
742,
6213,
29897,
13,
4706,
9580,
29918,
1457,
353,
903,
657,
29918,
771,
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] |
genes/templatetags/gene_grid_tags.py | SACGF/variantgrid | 5 | 84898 | <gh_stars>1-10
from django.conf import settings
from django.db.models.query_utils import Q
from django.template import Library
from genes.forms import GeneListCategoryAutocompleteForm, NamedCustomGeneListForm, GeneSymbolForm, \
GeneAnnotationReleaseForm, panel_app_server_autocomplete_form_factory
from genes.models import GeneInfo, GeneListCategory, PanelAppServer, GeneAnnotationRelease
from ontology.forms import HPOForm, OMIMForm
from pathtests.forms import ActivePathologyTestForm, SelectPathologyTestVersionForm
from pathtests.models import PathologyTest
from seqauto.forms import EnrichmentKitForm
from seqauto.models import EnrichmentKit
from snpdb.forms import LabSelectForm
from snpdb.models import Company
register = Library()
@register.inclusion_tag("genes/tags/gene_grid_tag.html", takes_context=True)
def gene_grid(context, columns_from_url=None,
init_callback: str = None,
update_url_callback: str = None,
save_gene_list_callback: str = None,
close_button_callback: str = None,
data_columns_whitelist: set = None,
gene_list_categories_whitelist: set = None,
default_enrichment_kits=None,
show_gene_annotation_release: bool = True,
show_custom_gene_form: bool = True,
show_help: bool = True):
user = context["user"]
# Load system defaults
if default_enrichment_kits is None:
default_enrichment_kits = settings.GENE_GRID_DEFAULT_ENRICHMENT_KITS
enrichment_kits = EnrichmentKit.get_enrichment_kits(default_enrichment_kits)
initial_columns = [f"enrichment-kit-{ek.pk}" for ek in enrichment_kits]
if columns_from_url:
initial_columns.extend(columns_from_url.split("/"))
if show_gene_annotation_release:
gene_annotation_releases = GeneAnnotationRelease.get_for_latest_annotation_versions_for_builds()
initial_columns.extend([f"gene-annotation-release-{release.pk}" for release in gene_annotation_releases])
data_columns = []
enrichment_kit_data = {"name": "Enrichment Kit",
"description": "Enrichment Kit",
"icon_css_class": "enrichment-kit-icon",
"form": EnrichmentKitForm()}
data_columns.append(enrichment_kit_data)
enrichment_kit_data = {"name": "Gene Annotation Release",
"description": "Gene Annotation Release (match genes in VEP annotation)",
#"icon_css_class": "",
"form": GeneAnnotationReleaseForm()}
data_columns.append(enrichment_kit_data)
categories = []
gene_list_category_filter = None
company = Company.get_our_company()
if company:
if PathologyTest.objects.exists(): # add test categories
try:
company_name = str(company).replace("_", " ")
category = company.genelistcategory
css_classes = ' '.join([category.icon_css_class, "pathology-test"])
test_data = {"name": category.name,
"icon_css_class": css_classes,
"description": f"{company_name} current test",
"form": ActivePathologyTestForm(),
"form_css_class": "pathology-test-form"}
categories.append(test_data)
historical_test_data = {"name": f"historical-{category.name}",
"icon_css_class": css_classes,
"description": f"{company_name} historical test",
"form": SelectPathologyTestVersionForm(),
"form_css_class": "pathology-test-version-form"}
categories.append(historical_test_data)
except:
pass
gene_list_category_filter = ~Q(company=company) # Skip as already added
for category in GeneListCategory.get_gene_list_categories(gene_list_category_filter):
initial = {"category": category.get("instance")}
category_id = category.get("pk")
prefix = f"category-{category_id}"
category["form"] = GeneListCategoryAutocompleteForm(initial=initial, prefix=prefix)
category["description"] = category.get("name")
category["form_css_class"] = "category-gene-list-form"
categories.append(category)
panel_app_servers = list(PanelAppServer.objects.all().order_by("pk"))
panel_app_form_ids = []
for server in panel_app_servers:
description = f"{server.name} Panel"
prefix = f"server-{server.pk}"
panel_app_form = panel_app_server_autocomplete_form_factory(server=server, prefix=prefix,
description=f"{description}...")
panel_app_form_ids.append(f"id_{prefix}-panel_app_panel") # id_server-1-panel_app_panel
panel_app_data = {"name": "Panel App Panel",
"description": description,
"icon_css_class": server.icon_css_class,
"form": panel_app_form}
categories.append(panel_app_data)
hpo_category_data = {"name": "Lab Gene Classification Counts",
"description": "Lab Gene Classification Counts",
"form": LabSelectForm()}
categories.append(hpo_category_data)
hpo_category_data = {"name": "HPO",
"icon_css_class": "hpo-icon",
"description": "Human Phenotype Ontology",
"form": HPOForm(),
"form_css_class": "hpo-gene-list-form"}
categories.append(hpo_category_data)
omim_category_data = {"name": "OMIM",
"icon_css_class": "omim-icon",
"description": "OMIM",
"form": OMIMForm(),
"form_css_class": "omim-gene-list-form"}
categories.append(omim_category_data)
# Optionally filter based on whitelist given to tag
if data_columns_whitelist is not None:
def in_whitelist(c):
return c["name"] in data_columns_whitelist
data_columns = filter(in_whitelist, data_columns)
if gene_list_categories_whitelist is not None:
def in_whitelist(c):
return c["name"] in gene_list_categories_whitelist
categories = filter(in_whitelist, categories)
has_gene_info = GeneInfo.objects.filter(gene_list__genelistgenesymbol__isnull=False).exists()
if show_custom_gene_form:
named_custom_gene_list_form = NamedCustomGeneListForm(username=user)
else:
named_custom_gene_list_form = None
gene_symbol_form = GeneSymbolForm()
gene_symbol_form.fields["gene_symbol"].widget.attrs['data-placeholder'] = "Add Gene Symbol"
return {
"ENRICHMENT_KIT_COLUMNS": settings.GENE_GRID_ENRICHMENT_KIT_COLUMNS,
"ENRICHMENT_KIT_COLUMN_TOOL_TIPS": settings.GENE_GRID_ENRICHMENT_KIT_COLUMN_TOOL_TIPS,
"ENRICHMENT_KIT_COLUMN_LABELS": settings.GENE_GRID_ENRICHMENT_KIT_COLUMN_LABELS,
"ENRICHMENT_KIT_COLUMN_LABEL_TOOL_TIPS": settings.GENE_GRID_ENRICHMENT_KIT_COLUMN_LABEL_TOOL_TIPS,
"initial_columns": initial_columns,
"init_callback": init_callback,
"update_url_callback": update_url_callback,
"save_gene_list_callback": save_gene_list_callback,
"close_button_callback": close_button_callback,
"data_columns": data_columns,
"categories": categories,
"panel_app_servers": panel_app_servers,
"panel_app_form_ids": panel_app_form_ids,
"has_gene_info": has_gene_info,
"gene_symbol_form": gene_symbol_form,
"named_custom_gene_list_form": named_custom_gene_list_form,
"user": user,
"show_help": show_help,
"form_helper": context.get('form_helper')
}
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src/tutorial/scripts/publisher.py | swapnilsurdi/race-on-ros | 0 | 116257 | #!/usr/bin/env python
import rospy
from std_msgs.msg import Float32
import math
RATE = 10 # Publishing rate of new data per second
FREQ = 1 # Frequency of the sinusoidal signal
# Execute this when run as a script
if __name__ == '__main__':
rospy.init_node('publisher')
pub = rospy.Publisher('data', Float32, queue_size=1)
rate = rospy.Rate(RATE)
step = 0
while not rospy.is_shutdown():
# Generate new data point
value = math.sin(2*math.pi*step/RATE)
# Log and publish data
rospy.loginfo("Publishing {:.3f}".format(value))
pub.publish(value)
# Advance the sequence
step = step + 1
# Wait to match the rate
rate.sleep()
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Lab4/sigmoidGradient.py | A2Zntu/ML_HW | 0 | 116627 | from sigmoid import sigmoid
import numpy as np
def sigmoidGradient(z):
#SIGMOIDGRADIENT returns the gradient of the sigmoid function
#evaluated at z
# g = SIGMOIDGRADIENT(z) computes the gradient of the sigmoid function
# evaluated at z. This should work regardless if z is a matrix or a
# vector. In particular, if z is a vector or matrix, you should return
# the gradient for each element.
# The value g should be correctly computed by your code below.
g = 0
# ====================== YOUR CODE HERE ======================
# Instructions: Compute the gradient of the sigmoid function evaluated at
# each value of z (z can be a matrix, vector or scalar).
try:
if len(z.shape) == 2:
g = np.multiply(sigmoid(z), np.transpose(np.ones((z.shape[0], z.shape[1]))-sigmoid(z)))
elif len(z.shape) == 1:
g = np.multiply(sigmoid(z), np.transpose(np.ones((z.shape[0]))-sigmoid(z)))
except AttributeError:
g = sigmoid(z) * (1 - sigmoid(z))
# =============================================================
return g | [
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evap/evaluation/templatetags/navbar_templatetags.py | felixrindt/EvaP | 29 | 152725 | from django.template import Library
from evap.evaluation.models import Semester
from evap.settings import DEBUG, LANGUAGES
register = Library()
@register.inclusion_tag("navbar.html")
def include_navbar(user, language):
return {
"user": user,
"current_language": language,
"languages": LANGUAGES,
"published_result_semesters": Semester.get_all_with_published_unarchived_results(),
"result_semesters": Semester.get_all_with_unarchived_results(),
"grade_document_semesters": Semester.objects.filter(grade_documents_are_deleted=False),
"debug": DEBUG,
}
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plotext/plot.py | asartori86/plotext | 0 | 163528 | # /usr/bin/env python3
# -*- coding: utf-8 -*-
##############################################
############## Importing ###############
##############################################
import utility as _utility
import docstrings as _docstrings
import subprocess as _subprocess
##############################################
########### Initialization #############
##############################################
_platform = _utility.platform()
#_platform = "windows"
if _platform == "windows":
_subprocess.call('', shell = True)
_utility.marker.pop('small')
_utility.marker_sequence.remove('small')
_shell = _utility.shell()
##############################################
########## Basic Containers ############
##############################################
class _figure():
def __init__(self):
self.width = None
self.height = None
self.rows = 1
self.cols = 1
self.set_subplots()
self.row = 0
self.col = 0
self.set_subplot()
self.canvas = ""
def set_subplots(self):
self.subplots = [[_subplot(r, c) for c in range(self.cols)] for r in range(self.rows)]
def get_subplot(self, row = 0 , col = 0):
return self.subplots[row - 1][col - 1]
def set_subplot(self):
self.subplot = self.subplots[self.row][self.col]
class _subplot():
def __init__(self, row, col):
self.row = row
self.col = col
self.yaxis = []
self.label = []
self.label_show = []
self.point_marker = []
self.line_marker = []
self.point_color = []
self.line_color = []
self.x = []
self.y = []
self.signals = 0
self.fillx = []
self.filly = []
self.width = None
self.height = None
self.width_set = None
self.height_set = None
self.title = ""
self.xlabel = ""
self.ylabel = ["", ""]
self.xaxes = [True, True]
self.yaxes = [True, True]
self.grid = [False, False]
self.axes_color = "white"
self.ticks_color = "black"
self.canvas_color = "white"
self.xlim_plot = [None, None]
self.ylim_plot_left = [None, None]
self.ylim_plot_right = [None, None]
self.xticks, self.xlabels = [], []
self.yticks_left, self.ylabels_left = [], []
self.yticks_right, self.ylabels_right = [], []
self.ticks = [5, 7]
self.xscale = "linear"
self.yscale = ["linear", "linear"]
_fig = _figure()
#figure = _fig
#utility = _utility
##############################################
######### Subplots Function ############
##############################################
def subplots(rows = None, cols = None, ):
rows, cols = _utility.set_first_to_both(rows, cols)
_set_rows(rows)
_set_cols(cols)
_fig.set_subplots()
subplot(1, 1)
subplots.__doc__ = _docstrings.subplots_doc
def _set_cols(cols = None):
cols = _utility.set_if_none(cols, 1)
_fig.cols = cols
def _set_rows(rows = None):
rows = _utility.set_if_none(rows, 1)
_fig.rows = rows
def subplot(row = 1, col = 1):
_set_row(row)
_set_col(col)
_fig.set_subplot()
subplot.__doc__ = _docstrings.subplot_doc
def _set_col(col = None):
col = _utility.set_if_none(col, 1)
_fig.col = col - 1
def _set_row(row = None):
row = _utility.set_if_none(row, 1)
_fig.row = row - 1
subplots(1, 1)
subplot(1, 1)
##############################################
####### Draw Related Functions #########
##############################################
def _draw(*args, **kwargs):
_yaxis(kwargs.get("yaxis"))
_label(kwargs.get("label"))
_point_marker(kwargs.get("point_marker"))
_line_marker(kwargs.get("line_marker"))
_point_color(kwargs.get("point_color"))
_line_color(kwargs.get("line_color"))
_data(*args)
_fillx(kwargs.get("fillx"))
_filly(kwargs.get("filly"))
def _yaxis(axis = None):
axis_none = "left"
axis = _utility.set_if_none(axis, axis_none)
axis = "left" if axis != "left" and axis != "right" else axis
_fig.subplot.yaxis.append(axis)
def _label(label = None):
label_none = ""
label = _utility.set_if_none(label, label_none)
label_show = True
_fig.subplot.label.append(label)
_fig.subplot.label_show.append(label_show)
#To-do: data with same label
def _point_marker(marker = None):
index = len(set(_fig.subplot.point_marker)) % len(_utility.marker_sequence)
marker_none = _utility.marker_sequence[index]
marker = "" if marker == "" else marker
marker = _utility.set_if_none(marker, marker_none)
small_test = marker == "small" and _platform == "linux"
marker = _utility.marker[marker] if marker in _utility.marker and not small_test else marker
marker = "small" if small_test else (marker[0] if len(marker) > 0 else marker)
_fig.subplot.point_marker.append(marker)
def _line_marker(marker = None):
index = len(set(_fig.subplot.line_marker)) % len(_utility.marker_sequence)
marker_none = _utility.marker_sequence[index]
marker = "" if marker == "" else marker
marker = _utility.set_if_none(marker, marker_none)
small_test = marker == "small" and _platform == "linux"
marker = _utility.marker[marker] if marker in _utility.marker and not small_test else marker
marker = "small" if small_test else (marker[0] if len(marker) > 0 else marker)
_fig.subplot.line_marker.append(marker)
def _point_color(color = None):
color = None if color not in _utility.color_sequence else color
index = len(set(_fig.subplot.point_color)) % len(_utility.color_sequence)
color_none = _utility.color_sequence[index]
color = _utility.set_if_none(color, color_none)
_fig.subplot.point_color.append(color)
def _line_color(color = None):
color = None if color not in _utility.color_sequence else color
index = len(set(_fig.subplot.line_color)) % len(_utility.color_sequence)
color_none = _utility.color_sequence[index]
color = _utility.set_if_none(color, color_none)
_fig.subplot.line_color.append(color)
def _data(*args):
x, y = _utility.get_data(*args)
_fig.subplot.x.append(x)
_fig.subplot.y.append(y)
_fig.subplot.signals += 1
def _fillx(fill = None):
fill = _utility.set_if_none(fill, False)
fill = bool(fill)
_fig.subplot.fillx.append(fill)
def _filly(fill = None):
fill = _utility.set_if_none(fill, False)
fill = bool(fill)
_fig.subplot.filly.append(fill)
##############################################
########### Clear Functions ############
##############################################
def clear_terminal():
_utility.write('\033c')
_utility._terminal_printed_lines_cnt = 0
clear_terminal.__doc__ = _docstrings.clear_terminal_doc
clt = clear_terminal
def clear_terminal_printed_lines():
# clear the lines that plotext had printed
# (plus 1 because the last line would not has an \n at the end)
n = _utility._terminal_printed_lines_cnt + 1
for i in range(n):
_utility.write("\033[2K")
if i < n - 1:
_utility.write("\033[A")
_utility.write("\033[2K")
_utility._terminal_printed_lines_cnt = 0
def clear_figure():
_fig.__init__()
clear_figure.__doc__ = _docstrings.clear_figure_doc
clf = clear_figure
def clear_plot():
_fig.subplot.__init__(_fig.row, _fig.col)
clear_plot.__doc__ = _docstrings.clear_plot_doc
clp = clear_plot
def clear_data():
_fig.subplot.x = []
_fig.subplot.y = []
_fig.subplot.signals = 0
clear_data.__doc__ = _docstrings.clear_data_doc
cld = clear_data
##############################################
############ Set Functions #############
##############################################
def plotsize(width = None, height = None):
width, height = _utility.set_first_to_both(width, height)
width, height = _utility.set_list_to_both(width, height)
_fig.subplot.width_set = width
_fig.subplot.height_set = height
plotsize.__doc__ = _docstrings.plotsize_doc
plot_size = plotsize
def title(label = None):
label = _utility.set_if_none(label, _fig.subplot.title)
label = None if label == "" else label
_fig.subplot.title = label
title.__doc__ = _docstrings.title_doc
def xlabel(label = ""):
label = _utility.set_if_none(label, _fig.subplot.xlabel)
_fig.subplot.xlabel = label
xlabel.__doc__ = _docstrings.xlabel_doc
def ylabel(label_left = "", label_right = ""):
label_left = _utility.set_if_none(label_left, _fig.subplot.ylabel[0])
label_right = _utility.set_if_none(label_right, _fig.subplot.ylabel[1])
_fig.subplot.ylabel = [label_left, label_right]
ylabel.__doc__ = _docstrings.ylabel_doc
def xaxes(x = None, y = None):
x, y = _utility.set_first_to_both(x, y)
y = bool(y)
x, y = _utility.set_list_if_none([x, y], _fig.subplot.xaxes)
x = bool(x)
x, y = _utility.set_list_to_both(x, y)
_fig.subplot.xaxes = [x, y]
xaxes.__doc__ = _docstrings.xaxes_doc
def yaxes(x = None, y = None):
x, y = _utility.set_first_to_both(x, y)
y = bool(y)
x, y = _utility.set_list_if_none([x, y], _fig.subplot.yaxes)
x = bool(x)
x, y = _utility.set_list_to_both(x, y)
_fig.subplot.yaxes = [x, y]
yaxes.__doc__ = _docstrings.yaxes_doc
def grid(x = None, y = None):
x, y = _utility.set_first_to_both(x, y)
y = bool(y)
x, y = _utility.set_list_if_none([x, y], _fig.subplot.grid)
x = bool(x)
x, y = _utility.set_list_to_both(x, y)
_fig.subplot.grid = [x, y]
grid.__doc__ = _docstrings.grid_doc
def axes_color(color = "white"):
color = _utility.set_if_none(color, _fig.subplot.axes_color)
color = "white" if color not in list(_utility.background_color.keys()) else color
_fig.subplot.axes_color = color
axes_color.__doc__ = _docstrings.axes_color_doc
def ticks_color(color = "black"):
color = _utility.set_if_none(color, _fig.subplot.ticks_color)
color = "black" if color not in list(_utility.fullground_color.keys()) else color
_fig.subplot.ticks_color = color
ticks_color.__doc__ = _docstrings.ticks_color_doc
def canvas_color(color = "white"):
color = _utility.set_if_none(color, _fig.subplot.canvas_color)
color = "white" if color not in list(_utility.background_color.keys()) else color
_fig.subplot.canvas_color = color
canvas_color.__doc__ = _docstrings.canvas_color_doc
def _colorless_subplot(subplot):
subplot.point_color = ["none"] * len(subplot.point_color)
subplot.line_color = ["none"] * len(subplot.line_color)
subplot.axes_color = "none"
subplot.ticks_color = "none"
subplot.canvas_color = "none"
def colorless():
_colorless_subplot(_fig.subplot)
colorless.__doc__ = _docstrings.colorless_doc
cls = colorless
def xlim(left = None, right = None):
left, right = _utility.set_list_to_both(left, right)
left, right = min(left, right), max(left, right)
_fig.subplot.xlim_plot = [left, right]
xlim.__doc__ = _docstrings.xlim_doc
def ylim(lower = None, upper = None, yaxis = "left"):
lower, upper = _utility.set_list_to_both(lower, upper)
lower, upper = min(lower, upper), max(lower, upper)
if yaxis == "left":
_fig.subplot.ylim_plot_left = [lower, upper]
elif yaxis == "right":
_fig.subplot.ylim_plot_right = [lower, upper]
ylim.__doc__ = _docstrings.ylim_doc
def ticks(x = None, y = None):
x, y = _utility.set_first_to_both(x, y)
x, y = _utility.set_list_to_both(x, y)
x_none, y_none = 5, 7
x = _utility.set_if_none(x, x_none)
y = _utility.set_if_none(y, y_none)
_fig.subplot.ticks = [x, y]
ticks.__doc__ = _docstrings.ticks_doc
def xticks(ticks = [], labels = None):
ticks, labels = _utility.set_first_to_both(list(ticks), labels)
labels = list(map(str, list(labels)))
ticks, labels = _utility.sort_data(ticks, labels)
_fig.subplot.xticks, _fig.subplot.xlabels = ticks, labels
_fig.subplot.ticks[0] = len(ticks)
xticks.__doc__ = _docstrings.xticks_doc
def yticks(ticks = [], labels = None, yaxis = "left"):
ticks, labels = _utility.set_first_to_both(list(ticks), labels)
labels = list(map(str, list(labels)))
ticks, labels = _utility.sort_data(ticks, labels)
if yaxis == "left":
_fig.subplot.yticks_left, _fig.subplot.ylabels_left = ticks, labels
elif yaxis == "right":
_fig.subplot.yticks_right, _fig.subplot.ylabels_right = ticks, labels
_fig.subplot.ticks[1] = len(ticks)
yticks.__doc__ = _docstrings.yticks_doc
def xscale(scale = None):
scale = _utility.set_if_none(scale, _fig.subplot.xscale)
scale = "linear" if not (scale in ["linear", "log"]) else scale
_fig.subplot.xscale = scale
xscale.__doc__ = _docstrings.xscale_doc
def yscale(scale = None, yaxis = "left"):
scale = _utility.set_if_none(scale, _fig.subplot.xscale)
scale = "linear" if not (scale in ["linear", "log"]) else scale
if yaxis == "right":
_fig.subplot.yscale[1] = scale
else:
_fig.subplot.yscale[0] = scale
yscale.__doc__ = _docstrings.yscale_doc
##############################################
########### Show Functions #############
##############################################
def show(hide = False):
_figure_size_max()
_figure_size()
#_plots_size()
_coherent_sizes()
for r in range(_fig.rows):
for c in range(_fig.cols):
subplot = _fig.subplots[r][c]
_previous_size(subplot)
_sort_data(subplot)
_height(subplot)
_ylim_data(subplot)
_ylim_plot(subplot)
_yticks(subplot)
_width(subplot)
_xlim_data(subplot)
_xlim_plot(subplot)
_xticks(subplot)
_matrix(subplot)
_add_xgrid(subplot)
_add_ygrid(subplot)
_add_data(subplot)
_add_legend(subplot)
_add_yaxis(subplot)
_add_xaxis(subplot)
_add_title(subplot)
_add_labels(subplot)
_join_matrices()
_fig.canvas = _utility.get_canvas(_fig.matrix)
if hide:
return
_utility.write(_fig.canvas)
show.__doc__ = _docstrings.show_doc
def _figure_size_max():
_fig.width_max, _fig.height_max = terminal_size()
_fig.height_max -= 3
_fig.width_max -= (_fig.cols - 1)
_fig.height_max -= (_fig.rows - 1)
def _figure_size():
# width = _utility.set_if_none(_fig.width, _fig.width_max)
# height = _utility.set_if_none(_fig.height, _fig.height_max)
# width = abs(int(width))
# height = abs(int(height))
# width = _fig.width_max if width > _fig.width_max else width
# height = _fig.height_max if height > _fig.height_max else height
_fig.width = _fig.width_max
_fig.height = _fig.height_max
def _coherent_sizes():
width = []
for c in range(_fig.cols):
w = [_fig.subplots[r][c].width for r in range(_fig.rows)]
w = [el for el in w if el is not None]
w = max(w, default = None)
width.append(w)
height = []
for r in range(_fig.rows):
h = [_fig.subplots[r][c].height for c in range(_fig.cols)]
h = [el for el in h if el is not None]
h = max(h, default = None)
height.append(h)
for c in range(_fig.cols):
for r in range(_fig.rows):
subplot = _fig.subplots[r][c]
subplot.width = width[c]
subplot.height = height[r]
def _previous_size(subplot):
row, col = subplot.row, subplot.col
width, height = 0, 0
for r in range(row):
height += _fig.subplots[r][0].height
for c in range(col):
width += _fig.subplots[0][c].width
_fig.previous_width = width
_fig.previous_height = height
def _sort_data(subplot):
subplot.x_left = [subplot.x[i] for i in range(len(subplot.x)) if subplot.yaxis[i] == "left"]
subplot.y_left = [subplot.y[i] for i in range(len(subplot.x)) if subplot.yaxis[i] == "left"]
subplot.signals_left = len(subplot.y_left)
subplot.x_right = [subplot.x[i] for i in range(len(subplot.x)) if subplot.yaxis[i] == "right"]
subplot.y_right = [subplot.y[i] for i in range(len(subplot.x)) if subplot.yaxis[i] == "right"]
subplot.signals_right = len(subplot.y_right)
subplot.data_left = subplot.x_left != [] and subplot.y_left != []
subplot.data_right = subplot.x_right != [] and subplot.y_right != []
subplot.data = subplot.data_left or subplot.data_right
def _height(subplot):
subplot.height_max = _fig.height - _fig.previous_height
height_none = subplot.height_max // (_fig.rows - subplot.row)
height = _utility.set_if_none(subplot.height_set, height_none)
height = abs(int(height))
height = subplot.height_max if height > subplot.height_max else height
subplot.height = height
subplot.xaxes[0] = 0 if height < 2 else subplot.xaxes[0]
subplot.xaxes[1] = 0 if height < 3 else subplot.xaxes[1]
subplot.ticks[0] = 0 if height < 4 else subplot.ticks[0]
subplot.title = "" if height < 5 else subplot.title
subplot.xlabel = "" if height < 6 else subplot.xlabel
subplot.ylabel = ["", ""] if height < 6 else subplot.ylabel
xaxis_low = int(subplot.xaxes[0] and subplot.data)
xaxis_up = int(subplot.xaxes[1] and subplot.data)
xticks = bool(subplot.ticks[0] and subplot.data)
title = int(subplot.title != "")
labels = int(subplot.xlabel != "" or subplot.ylabel != ["", ""])
tot = title + xaxis_low + xaxis_up + xticks + labels
subplot.height_canvas = subplot.height - tot
def _ylim_data(subplot):
y_left = [_utility.log(subplot.y_left[s]) if subplot.yscale[0] == "log" else subplot.y_left[s] for s in range(subplot.signals_left)]
y_right = [_utility.log(subplot.y_right[s]) if subplot.yscale[1] == "log" else subplot.y_right[s] for s in range(subplot.signals_right)]
subplot.ylim_data_left = _utility.get_lim_data(y_left)
subplot.ylim_data_right = _utility.get_lim_data(y_right)
def _ylim_plot(subplot):
subplot.ylim_plot_left = _utility.set_list_if_none(subplot.ylim_plot_left, subplot.ylim_data_left)
subplot.ylim_plot_right = _utility.set_list_if_none(subplot.ylim_plot_right, subplot.ylim_data_right)
#subplot.dy = (subplot.ylim_plot[1] - subplot.ylim_plot[0]) / subplot.height_canvas
def _yticks(subplot):
if subplot.yticks_left == [] and subplot.ticks[1] and subplot.data_left:
if subplot.yscale[0] == "linear":
subplot.yticks_left = _utility.get_ticks(subplot.ylim_plot_left, subplot.ticks[1])
subplot.ylabels_left = _utility.get_labels(subplot.yticks_left)
if subplot.yscale[0] == "log":
subplot.yticks_left, subplot.ylabels_left = _utility.get_log_ticks(subplot.ylim_plot_left, subplot.ticks[1])
subplot.yticks_rows_left = _utility.get_matrix_data(subplot.yticks_left, subplot.ylim_plot_left, subplot.height_canvas)
if subplot.yticks_right == [] and subplot.ticks[1] and subplot.data_right:
if subplot.yscale[1] == "linear":
subplot.yticks_right = _utility.get_ticks(subplot.ylim_plot_right, subplot.ticks[1])
subplot.ylabels_right = _utility.get_labels(subplot.yticks_right)
if subplot.yscale[1] == "log":
subplot.yticks_right, subplot.ylabels_right = _utility.get_log_ticks(subplot.ylim_plot_right, subplot.ticks[1])
subplot.yticks_rows_right = _utility.get_matrix_data(subplot.yticks_right, subplot.ylim_plot_right, subplot.height_canvas)
def _width(subplot):
subplot.width_max = _fig.width - _fig.previous_width
width_none = subplot.width_max // (_fig.cols - subplot.col)
width = _utility.set_if_none(subplot.width_set, width_none)
width = abs(int(width))
width = subplot.width_max if width > subplot.width_max else width
subplot.width = width
subplot.yaxes[0] = 0 if width < 2 else subplot.yaxes[0]
subplot.yaxes[1] = 0 if width < 3 else subplot.yaxes[1]
ylabels_width_left = max(map(len, subplot.ylabels_left), default = 0) * bool(subplot.ticks[1] and subplot.data_left)
subplot.ticks[1] = 0 if width < 3 + ylabels_width_left else subplot.ticks[1]
ylabels_width_left = 0 if width < 3 + ylabels_width_left else ylabels_width_left
ylabels_width_right = max(map(len, subplot.ylabels_right), default = 0) * bool(subplot.ticks[1] and subplot.data_right)
ylabels_width_right = 0 if width < ylabels_width_right + ylabels_width_left + 3 else ylabels_width_right
yaxis_left = int(subplot.yaxes[0] and subplot.data)
yaxis_right = int(subplot.yaxes[1] and subplot.data)
tot = ylabels_width_left + yaxis_left + yaxis_right + ylabels_width_right
subplot.width_canvas = subplot.width - tot
subplot.ylabels_width_left = ylabels_width_left
subplot.ylabels_width_right = ylabels_width_right
ylabel_length = len(subplot.ylabel[0]) + 3
if subplot.width < ylabel_length:
subplot.height_canvas += 1
def _xlim_data(subplot):
x = [_utility.log(subplot.x[s]) if subplot.xscale == "log" else subplot.x[s] for s in range(subplot.signals)]
subplot.xlim_data = _utility.get_lim_data(x)
def _xlim_plot(subplot):
subplot.xlim_plot = _utility.set_list_if_none(subplot.xlim_plot, subplot.xlim_data)
#subplot.dy = (subplot.ylim_plot[1] - subplot.ylim_plot[0]) / subplot.height_canvas
def _xticks(subplot):
if subplot.xticks == [] and subplot.ticks[0]:
if subplot.xscale == "linear":
subplot.xticks = _utility.get_ticks(subplot.xlim_plot, subplot.ticks[0])
subplot.xlabels = _utility.get_labels(subplot.xticks)
if subplot.xscale == "log":
subplot.xticks, subplot.xlabels = _utility.get_log_ticks(subplot.xlim_plot, subplot.ticks[0])
subplot.xticks_cols = _utility.get_matrix_data(subplot.xticks, subplot.xlim_plot, subplot.width_canvas)
def _matrix(subplot):
marker = [" ", "none", subplot.canvas_color]
subplot.matrix = [[marker[:] for c in range(subplot.width_canvas)] for r in range(subplot.height_canvas)]
def _add_xgrid(subplot):
if not subplot.grid[0]:
return
grid_color = subplot.ticks_color
for c in subplot.xticks_cols:
x, y = _utility.get_line([c, c], [0, subplot.height_canvas])
marker = "│"
subplot.matrix = _utility.update_matrix(subplot.matrix, x, y, marker, grid_color)
def _add_ygrid(subplot):
if not subplot.grid[1]:
return
grid_color = subplot.ticks_color
for r in subplot.yticks_rows_left + subplot.yticks_rows_right:
x, y = _utility.get_line([0, subplot.width_canvas], [r, r])
marker = "─"
subplot.matrix = _utility.update_matrix(subplot.matrix, x, y, marker, grid_color)
if subplot.grid[0]:
x = subplot.xticks_cols
y = [r] * len(x)
marker = "┼"
subplot.matrix = _utility.update_matrix(subplot.matrix, x, y, marker, grid_color)
def _add_data(subplot):
for s in range(len(subplot.x)):
point_marker, point_color = subplot.point_marker[s], subplot.point_color[s]
line_marker, line_color = subplot.line_marker[s], subplot.line_color[s]
x, y = subplot.x[s], subplot.y[s]
x_test = subplot.xscale == "log"
x = _utility.log(x) if x_test else x
y_test = (subplot.yscale[0] == "log" and subplot.yaxis[s] == "left") or (subplot.yscale[1] == "log" and subplot.yaxis[s] == "right")
y = _utility.log(y) if y_test else y
mf = 2 if point_marker == "small" or line_marker == "small" else 1 # small marker factor
ylim_plot = subplot.ylim_plot_left if subplot.yaxis[s] == "left" else subplot.ylim_plot_right
x_point = _utility.get_matrix_data(x, subplot.xlim_plot, mf * subplot.width_canvas)
y_point = _utility.get_matrix_data(y, ylim_plot, mf * subplot.height_canvas)
x_line, y_line = [], []
if line_marker != "":
x_line, y_line = _utility.get_line(x_point, y_point)
if subplot.fillx[s]:
height0 = _utility.get_matrix_data([0], ylim_plot, mf * subplot.height_canvas)[0]
x_point, y_point = _utility.fill_data(x_point, y_point, height0)
x_line, y_line = _utility.fill_data(x_line, y_line, height0)
if subplot.filly[s]:
width0 = _utility.get_matrix_data([0], subplot.xlim_plot, mf * subplot.width_canvas)[0]
y_point, x_point = _utility.fill_data(y_point, x_point, width0)
y_line, x_line = _utility.fill_data(y_line, x_line, width0)
x_line = [el / mf for el in x_line]
y_line = [el / mf for el in y_line]
if line_marker != "":
subplot.matrix = _utility.update_matrix(subplot.matrix, x_line, y_line, line_marker, line_color)
x_point = [el / mf for el in x_point]
y_point = [el / mf for el in y_point]
if point_marker != "":
subplot.matrix = _utility.update_matrix(subplot.matrix, x_point, y_point, point_marker, point_color)
def _add_legend(subplot):
label = subplot.label
show = any([el != "" for el in label])
side_test = subplot.data_left and subplot.data_right
if not (show or side_test):
return
l = len(label)
label = ["signal " + str(i + 1) if label[i] == "" and show else label[i] for i in range(l)]
side_label = ["[" + subplot.yaxis[i] + "] " if side_test else "" for i in range(l)]
label = [side_label[i] + label[i] for i in range(l)]
label = [" " + el + " " for el in label]
label = [list(el) for el in label]
w = max(map(len, label))
label = [el + [" "] * (w - len(el)) for el in label]
legend = [[] for i in range(l)]
legend_color = [[] for i in range(l)]
for i in range(l):
point_marker, line_marker = subplot.point_marker[i], subplot.line_marker[i]
point_color, line_color = subplot.point_color[i], subplot.line_color[i]
marker = point_marker if point_marker != "" else line_marker
marker = "▄" if marker == "small" else marker
color = point_color if point_marker != "" else line_color
legend[i] += [marker] * 3
legend[i] += label[i]
legend_color[i] += [color] * 3
legend_color[i] += [subplot.ticks_color] * w
legend = [legend[i] for i in range(len(legend)) if subplot.label_show[i]]
legend_color = [legend_color[i] for i in range(len(legend_color)) if subplot.label_show[i]]
legend = _utility.frame_matrix(legend)
legend_color = _utility.frame_matrix(legend_color, subplot.ticks_color)
legend = [[ [legend[i][j], legend_color[i][j], subplot.canvas_color] for j in range(len(legend[0]))] for i in range(len(legend))]
subplot.matrix = _utility.insert(legend, subplot.matrix) if show or side_test else subplot.matrix
# To do: Legend frame interferes with grid lines
def _add_yaxis(subplot):
if subplot.x == []:
return
labels_left = [" " * subplot.ylabels_width_left for r in range(subplot.height_canvas)]
for i in range(len(subplot.yticks_rows_left)):
r = subplot.yticks_rows_left[i]
if r in range(subplot.height_canvas):
labels_left[r] = str(subplot.ylabels_left[i])[ : subplot.ylabels_width_left]
labels_left = [" " * (subplot.ylabels_width_left - len(el)) + el for el in labels_left]
labels_left = [list(el) for el in labels_left]
labels_left = [[[sub_el, subplot.ticks_color, subplot.axes_color] for sub_el in el] for el in labels_left]
labels_left = labels_left[::-1]
ytick = "┼" if subplot.grid[1] else "┤"
ytick = "│" if subplot.ylabels_width_right == 0 and subplot.grid[1] == False else ytick
ytick = ("┼" if subplot.ylabels_width_left != 0 else "├") if subplot.grid[1] else ("┤" if subplot.ylabels_width_left != 0 else "│")
axis_left = [(ytick if r in subplot.yticks_rows_right + subplot.yticks_rows_left else "│") for r in range(subplot.height_canvas)]
axis_left = [list(el) for el in axis_left]
axis_left = [[[sub_el, subplot.ticks_color, subplot.axes_color] for sub_el in el] for el in axis_left]
axis_left = axis_left[::-1]
labels_right = [" " * subplot.ylabels_width_right for r in range(subplot.height_canvas)]
for i in range(len(subplot.yticks_rows_right)):
r = subplot.yticks_rows_right[i]
if r in range(subplot.height_canvas):
labels_right[r] = str(subplot.ylabels_right[i])[ : subplot.ylabels_width_right]
labels_right = [el + " " * (subplot.ylabels_width_right - len(el)) for el in labels_right]
labels_right = [list(el) for el in labels_right]
labels_right = [[[sub_el, subplot.ticks_color, subplot.axes_color] for sub_el in el] for el in labels_right]
labels_right = labels_right[::-1]
ytick = ("┼" if subplot.ylabels_width_right != 0 else "┤") if subplot.grid[1] else ("├" if subplot.ylabels_width_right != 0 else "│")
axis_right = [(ytick if r in subplot.yticks_rows_right + subplot.yticks_rows_left else "│") for r in range(subplot.height_canvas)]
axis_right = [list(el) for el in axis_right]
axis_right = [[[sub_el, subplot.ticks_color, subplot.axes_color] for sub_el in el] for el in axis_right]
axis_right = axis_right[::-1]
if subplot.yaxes[0]:
for r in range(subplot.height_canvas):
subplot.matrix[r] = axis_left[r] + subplot.matrix[r]
if subplot.yaxes[1]:
for r in range(subplot.height_canvas):
subplot.matrix[r] = subplot.matrix[r] + axis_right[r]
if subplot.ticks[1]:
for r in range(subplot.height_canvas):
subplot.matrix[r] = labels_left[r] + subplot.matrix[r] + labels_right[r]
def _add_xaxis(subplot):
if subplot.x == []:
return
axis_lower = [" "] * subplot.ylabels_width_left + ["└"] * subplot.yaxes[0]
axis_lower += ["─" for r in range(subplot.width_canvas)]
axis_lower += ["┘"] * subplot.yaxes[1] + [" "] * subplot.ylabels_width_right
axis_up = [" "] * subplot.ylabels_width_left + ["┌"] * subplot.yaxes[0]
axis_up += ["─" for r in range(subplot.width_canvas)]
axis_up += ["┐"] * subplot.yaxes[1] + [" "] * subplot.ylabels_width_right
labels_lower = [" "] * subplot.ylabels_width_left + [" "] * subplot.yaxes[0]
iniz_length = len(labels_lower)
labels_lower += [" " for r in range(subplot.width_canvas)]
labels_lower += [" "] * subplot.yaxes[0] + [" "] * subplot.ylabels_width_right
xtick_lower = "┼" if subplot.grid[0] else "┬"
xtick_up = "┬" if subplot.grid[0] else "─"
l = len(subplot.xticks_cols)
for i in range(l):
col = subplot.xticks_cols[i] + iniz_length
#if col >= subplot.width:
# continue
label = str(subplot.xlabels[i])
label_length = len(label)
label_col = list(range(max(col - label_length, 0), min(col + label_length + 1, subplot.width)))
label_col = [c for c in label_col if c + label_length <= subplot.width]
if label_col == []:
continue
label_col = min(label_col, key = lambda x : abs(x - (col - (label_length - 2) / 2)))
if label_col + label_length > subplot.width:
continue
label_prev = labels_lower[label_col - 1: label_col + label_length + 1]
label_prev = list(set(label_prev))
if label_prev == [" "] or label_prev == []:
labels_lower[label_col: label_col + label_length] = list(label)
axis_lower[col] = xtick_lower
axis_up[col] = xtick_up
elif axis_lower[col] == "─":
axis_lower[col] = "┴" if subplot.grid[0] else "─"
axis_up[col] = "┬" if subplot.grid[0] else "─"
axis_up = [[el, subplot.ticks_color, subplot.axes_color] for el in axis_up]
axis_lower = [[el, subplot.ticks_color, subplot.axes_color] for el in axis_lower]
labels_lower = [[el, subplot.ticks_color, subplot.axes_color] for el in labels_lower]
if subplot.xaxes[0]:
subplot.matrix += [axis_lower]
if subplot.xaxes[1]:
subplot.matrix = [axis_up] + subplot.matrix
if subplot.ticks[0]:
subplot.matrix += [labels_lower]
def _add_title(subplot):
if subplot.title == "":
return
width_left = subplot.ylabels_width_left + int(subplot.yaxes[0])
title = subplot.title[ : subplot.width_canvas]
space1 = " " * (width_left + int((subplot.width_canvas - len(title)) / 2))
space2 = " " * (subplot.width - len(title + space1))
title = space1 + title + space2
title = list(title)
title = [[el, subplot.ticks_color, subplot.axes_color] for el in title]
subplot.matrix = [title] + subplot.matrix
def _add_labels(subplot):
if subplot.xlabel == "" and subplot.ylabel == ["", ""]:
return
width_max = subplot.width - 4 * 3
ylabel_left = "[y] " + subplot.ylabel[0]
width_left = subplot.ylabels_width_left + int(subplot.yaxes[0])
ylabel_left = ylabel_left + " " * (width_left - len(ylabel_left))
ylabel_left = ylabel_left[ : width_max // 3]
ylabel_right = ""
if subplot.ylabel[1] != "":
ylabel_right = subplot.ylabel[1] + " [y]"
xlabel = " " + subplot.xlabel + " [x] "
l_left = len(ylabel_left)
l_tot = len(ylabel_left + xlabel + ylabel_right)
if l_tot > subplot.width:
xlabel = ""
l_tot = len(ylabel_left + xlabel + ylabel_right)
if l_tot > subplot.width:
ylabel_right = ""
l_tot = len(ylabel_left + xlabel + ylabel_right)
if l_tot > subplot.width:
ylabel_left = ""
space1 = " " * (width_left + int((subplot.width_canvas - len(xlabel)) / 2) - l_left)
if space1 == '':
return
space1 = " " * subplot.width if space1 == '' else space1
space2 = " " * (subplot.width - l_tot - len(space1))
label = ylabel_left + space1 + xlabel + space2 + ylabel_right
label = list(label)
label = [[el, subplot.ticks_color, subplot.axes_color] for el in label]
subplot.matrix += [label]
#ToDo: a bit messy, need reordering
def _join_matrices():
sep = " "
sep = [sep, "none", "none"]
matrix = []
for c in range(_fig.cols):
matrix_c = []
for r in range(_fig.rows):
matrix_c = _utility.join(matrix_c, _fig.subplots[r][c].matrix, sep, "vertical")
matrix = _utility.join(matrix, matrix_c, sep, "horizontal")
_fig.matrix = matrix
size = [0, 0] if matrix == [] else [len(matrix[0]), len(matrix)]
_fig.width, _fig.height = size
##############################################
######### Plotting Functions ###########
##############################################
def scatter(*args,
yaxis = "left",
label = "",
marker = None,
color = None,
fillx = None,
filly = None):
_draw(
*args,
yaxis = yaxis,
label = label,
point_marker = marker,
line_marker = "",
point_color = color,
line_color = "none",
fillx = fillx,
filly = filly)
scatter.__doc__ = _docstrings.scatter_doc
def plot(*args,
yaxis = "left",
label = "",
marker = None,
color = None,
fillx = None,
filly = None):
_draw(
*args,
yaxis = yaxis,
label = label,
point_marker = "",
line_marker = marker,
point_color = "none",
line_color = color,
fillx = fillx,
filly = filly)
plot.__doc__ = _docstrings.plot_doc
def bar(*args,
yaxis = "left",
label = "",
marker = "small",
color = None,
fill = True,
width = 4 / 5,
orientation = 'vertical'):
x, y = _utility.get_data(*args)
x, x_labels = _utility.bar_xdata(x)
xbar, ybar = _utility.bars(x, y, width)
if orientation in ['vertical', 'v']:
fillx, filly = fill, False
x_ticks = _fig.subplot.xticks + x
x_labels = _fig.subplot.xlabels + x_labels
xticks(x_ticks, x_labels)
elif orientation in ['horizontal', 'h']:
xbar, ybar = ybar, xbar
fillx, filly = False, fill
y_ticks = x + (_fig.subplot.yticks_left if yaxis == "left" else _fig.subplot.yticks_right)
y_labels = x_labels + (_fig.subplot.ylabels_left if yaxis == "left" else _fig.subplot.ylabels_right)
yticks(y_ticks, y_labels, yaxis = yaxis)
for b in range(len(x)):
xb, yb = xbar[b][1:3] + xbar[b][3:5], ybar[b][1:3] + ybar[b][3:5]
if not fill:
xb, yb = xbar[b], ybar[b]
fillx, filly = False, False
if list(set(yb)) != [0]:
plot(xb, yb, yaxis = yaxis, label = label, marker = marker, color = color, fillx = fillx, filly = filly)
if b != 0:
_fig.subplot.point_color[-1] = _fig.subplot.point_color[-2]
_fig.subplot.line_color[-1] = _fig.subplot.line_color[-2]
_fig.subplot.label_show[-1] = False
# _sort_data(_fig.subplot)
# y_plot = _fig.subplot.y_left if yaxis == "left" else _fig.subplot.y_right
# if orientation in ['horizontal', 'h']:
# y_plot = _fig.subplot.x_left if yaxis == "left" else _fig.subplot.x_right
# m, M = _utility.get_lim_data(y_plot)
# if m * M > 0:
# m = 0
# if orientation in ['vertical', 'v']:
# #ylim(m, M, yaxis = yaxis)
# pass
# else:
# xlim(m, M)
bar.__doc__ = _docstrings.bar_doc
def hist(data,
bins = 10,
yaxis = "left",
label = "",
marker = "small",
color = None,
fill = True,
width = 4 / 5,
orientation = 'vertical'):
x, y = _utility.hist_data(data, bins)
bar(x, y, yaxis = yaxis, label = label, marker = marker, color = color, fill = fill, width = width, orientation= orientation)
hist.__doc__ = _docstrings.plot_doc
##############################################
########## Other Functions #############
##############################################
string_to_time = _utility.string_to_time
string_to_time.__doc__ = _docstrings.string_to_time_doc
def get_canvas():
return _fig.canvas
get_canvas.__doc__ = _docstrings.get_canvas_doc
sleep = _utility.sleep
sleep.__doc__ = _docstrings.sleep_doc
def savefig(path = None):
path = _utility.check_path(path)
with open(path , "w+", encoding = "utf-8") as file:
file.write(_utility.remove_color(_fig.canvas))
print("plot saved as " + path)
savefig.__doc__ = _docstrings.savefig_doc
save_fig = savefig
terminal_size = _utility.terminal_size
terminal_size.__doc__ = _docstrings.terminal_size_doc
version = _utility.version
version.__doc__ = _docstrings.version_doc
docstrings = _utility.docstrings
docstrings.__doc__ = _docstrings.docstrings_doc
colors = _utility.colors
colors.__doc__ = _docstrings.colors_doc
markers = _utility.markers
markers.__doc__ = _docstrings.markers_doc
sin = _utility.sin
sin.__doc__ = _docstrings.sin_doc
if __name__ == "__main__":
#test()
import plotext as plt
plt.test()
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apps/users/migrations/0017_auto_20200807_1727.py | lucasjaroszewski/incremental-game | 0 | 175008 | <reponame>lucasjaroszewski/incremental-game
# Generated by Django 3.0.6 on 2020-08-07 20:27
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('users', '0016_auto_20200807_1719'),
]
operations = [
migrations.RenameField(
model_name='fish',
old_name='man_eating_swamp',
new_name='man_eating_marsh',
),
migrations.RenameField(
model_name='fish',
old_name='rinde_port',
new_name='rinde',
),
]
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utils/functions.py | Roozbeh-Bazargani/CPSC-533R-project | 0 | 6382 | <filename>utils/functions.py
import torch
from torch import nn
import math
#0 left hip
#1 left knee
#2 left foot
#3 right hip
#4 right knee
#5 right foot
#6 middle hip
#7 neck
#8 nose
#9 head
#10 left shoulder
#11 left elbow
#12 left wrist
#13 right shoulder
#14 right elbow
#15 right wrist
def random_rotation(J3d):
J = J3d # need copy????
batch_size = J.shape[0]
theta = torch.rand(batch_size).cuda() * 2*torch.tensor(math.pi).cuda() # random theta
root = J[:,:,8] # joint 8 = nose is root
J3d_R = rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(), False)
return J3d_R, theta, root # need these values in the code
def rotation(J, theta, root, is_reversed): # rotation over y axis by theta
D = root[:,2].cuda() # absolute depth of the root joint
batch_size = root.shape[0]
v_t = torch.zeros((batch_size, 3, 1)).cuda()
v_t[:, 2, :] = D.cuda() # translation vector
if is_reversed:
root, v_t = v_t, root # swap
theta = -theta
# R = torch.tensor([[torch.cos(theta), -torch.sin(theta), 0], [torch.sin(theta), torch.cos(theta), 0], [0, 0, 1]]) # rotation matrix over z by theta degrees
R = torch.zeros((batch_size, 3, 3)).cuda() # rotation matrix over y by theta degrees
R[:, 0, 0] = torch.cos(theta)
R[:, 0, 2] = torch.sin(theta)
R[:, 1, 1] = torch.ones(batch_size)
R[:, 2, 0] = -torch.sin(theta)
R[:, 2, 2] = torch.cos(theta)
# R = torch.tensor([[torch.cos(theta), 0, torch.sin(theta)], [0, 1, 0], [-torch.sin(theta), 0, torch.cos(theta)]]) # rotation matrix over y by theta degrees
# R = torch.tensor([[1, 0, 0], [0, torch.cos(theta), -torch.sin(theta)], [0, torch.sin(theta), torch.cos(theta)]]) # rotation matrix over x by theta degrees
J_R = torch.matmul(R, J - root) + v_t # rotation
return J_R
def reverse_rotation(J3d_R, theta, root):
J = J3d_R # need copy????
return rotation(J.cuda(), theta.cuda(), root.unsqueeze(-1).cuda(), True)
def temporal_loss(J, K, J_R, K_R): # J is J3d at time t and K is J3d at time t+k. J_R means the reversed rotation of J
#print(torch.norm(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3, 16) - J_R.reshape(J.shape[0], 3, 16) + K_R.reshape(J.shape[0], 3, 16), dim=1).shape)
#stop
mse_fn = nn.MSELoss()
return mse_fn(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3, 16) - J_R.reshape(J.shape[0], 3, 16) + K_R.reshape(J.shape[0], 3, 16), torch.zeros(J.shape[0], 3, 16).cuda())
#return torch.norm(J.reshape(J.shape[0], 3, 16) - K.reshape(J.shape[0], 3, 16) - J_R.reshape(J.shape[0], 3, 16) + K_R.reshape(J.shape[0], 3, 16), dim=1)**2
'''
def temporal_loss(J, K, J_R, K_R): # J is J3d at time t and K is J3d at time t+k. J_R means the reversed rotation of J
return torch.norm(J - K - J_R + K_R, dim=1)**2
'''
'''
def random_rotation(J3d):
# J = torch.transpose(J3d, 1, 2)
J = J3d
root = torch.zeros(J.shape[0:2])
for i in range(J.shape[0]):
theta = torch.rand(1).cuda() * 2*torch.tensor(math.pi).cuda() # random theta
root[i] = J[i,:,8] # joint 8 = nose is root
temp = rotation(J[i,:,:], theta, root[i].unsqueeze(1), False)
# print(temp.shape)
J[i,:,:] = temp
return J, theta, root # need these values in the code
def rotation(J, theta, root, is_reversed): # rotation over y axis by theta
D = root[2] # absolute depth of the root joint
v_t = torch.tensor([[0], [0], [D]]).cuda() # translation vector
if is_reversed:
root, v_t = v_t, root # swap
theta = -theta
# R = torch.tensor([[torch.cos(theta), -torch.sin(theta), 0], [torch.sin(theta), torch.cos(theta), 0], [0, 0, 1]]) # rotation matrix over z by theta degrees
R = torch.tensor([[torch.cos(theta), 0, torch.sin(theta)], [0, 1, 0], [-torch.sin(theta), 0, torch.cos(theta)]]).cuda() # rotation matrix over y by theta degrees
# R = torch.tensor([[1, 0, 0], [0, torch.cos(theta), -torch.sin(theta)], [0, torch.sin(theta), torch.cos(theta)]]) # rotation matrix over x by theta degrees
J_R = torch.matmul(R, J.cuda() - root.cuda()) + v_t # rotation
return J_R
def reverse_rotation(J3d_R, theta, root):
# J = torch.transpose(J3d_R, 1, 2)
J = J3d_R
for i in range(J.shape[0]):
J[i,:,:] = rotation(J[i,:,:].cuda(), theta.cuda(), root[i].unsqueeze(1).cuda(), True)
return J
''' | [
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main.py | mrinalghosh/dijkstra-heap-compare | 0 | 170453 | <reponame>mrinalghosh/dijkstra-heap-compare
import sys
sys.path.append(".")
import time
import networkx as nx
import matplotlib.pyplot as plt
import csv
from hwcounter import count, count_end
from statistics import mean
from test_graphs.graph_generator import *
from heaps.fibonacci import FibHeap
from heaps.violation import ViolationHeap
from heaps.quake import QuakeHeap
from heaps.minheap import MinHeap
# from heaps.rankpairing import RankPairingHeap
HEAPS = ["Quake", "Fibonacci", "Violation", "MinHeap"]
# HEAPS = ["Quake", "Fibonacci", "Violation", "MinHeap", "RankPairing"]
def get_heap(heap_choice):
"""Get specified heap instance."""
if heap_choice == "Fibonacci":
heap = FibHeap()
elif heap_choice == "Violation":
heap = ViolationHeap()
elif heap_choice == "MinHeap":
heap = MinHeap()
elif heap_choice == "Quake":
heap = QuakeHeap()
# elif heap_choice == "RankPairing":
# heap = RankPairingHeap()
else:
raise NameError(f"{heap_choice} does not exist")
return heap
def dijkstra_path_heaps(G, source, target, heap):
"""Dijkstra's Algorithm Implementation"""
G.nodes[source]["key"] = 0
for n in G.nodes:
G.nodes[n]["node"] = heap.insert(G.nodes[n], n)
while heap:
n = heap.extract_min()
for edge in G.edges(n.name):
u, v = edge
dist = G.nodes[u]["key"] + G[u][v]["weight"]
if G.nodes[v]["key"] > dist:
G.nodes[v]["key"] = dist
G.nodes[v]["pred"] = u
heap.decrease_key(G.nodes[v]["node"], dist)
path = []
path.append(target)
pred = G.nodes[target]["pred"]
while pred is not None:
path.append(pred)
pred = G.nodes[pred]["pred"]
path.reverse()
return path
def visualize_graph(G, path, heap_choice, show=True, labels=True, layout_type="spring"):
"""Visualize a Networkx graph object."""
layout = getattr(nx, f"{layout_type}_layout")(G)
nx.draw(G, with_labels=labels, pos=layout)
path_edges = zip(path, path[1:])
edge_labels = nx.get_edge_attributes(G, "weight")
nx.draw_networkx_edge_labels(G, pos=layout, edge_labels=edge_labels)
nx.draw_networkx_nodes(G, layout, nodelist=path, node_color="r")
nx.draw_networkx_edges(
G, layout, edgelist=list(path_edges), edge_color="r", width=3
)
plt.title(heap_choice)
plt.savefig(f"{heap_choice}.png")
if show:
plt.show()
def sanity_check(heap_choice="Fibonacci", show=False):
"""Quick sanity check for dijkstra implementation"""
G = nx.Graph()
G.add_node("a")
G.add_node("b")
G.add_node("c")
G.add_node("d")
G.add_node("e")
G.add_node("z")
nx.set_node_attributes(G, float("inf"), "key")
nx.set_node_attributes(G, None, "pred")
nx.set_node_attributes(G, None, "node")
G.add_edge("a", "b", weight=4)
G.add_edge("a", "c", weight=2)
G.add_edge("b", "c", weight=1)
G.add_edge("b", "d", weight=5)
G.add_edge("c", "d", weight=8)
G.add_edge("c", "e", weight=10)
G.add_edge("d", "e", weight=2)
G.add_edge("d", "z", weight=6)
G.add_edge("e", "z", weight=5)
heap = get_heap(heap_choice)
path = dijkstra_path_heaps(G=G, source="a", target="z", heap=heap)
if not path:
raise Exception(f"{heap_choice} did not find a path!!")
if show:
visualize_graph(G, path, heap_choice)
def smoke_tests(show=False):
"""Run sanity check for each heap."""
for heap in HEAPS:
try:
sanity_check(heap, show)
except:
print(f"{heap} heap failed running the sanity check!")
def dijkstra_time(G, source, target, heap):
"""Compute Dijkstra's shortest path time."""
repeat = 3
runs = 10
times = []
cycles = []
# Add Graph attrs
nx.set_node_attributes(G, float("inf"), "key")
nx.set_node_attributes(G, None, "pred")
nx.set_node_attributes(G, None, "node")
dijkstra_results = {}
# Run Dijkstra's
for _ in range(repeat):
t0 = time.time()
c0 = count()
for _ in range(runs):
dijkstra_results[G] = dijkstra_path_heaps(G, source, target, heap)
c1 = count_end()
t1 = time.time()
times.append(t1 - t0)
cycles.append(c1 - c0)
# Get the results and pass them back
results = {
"runs": runs,
"repeat": repeat,
"avg_time": mean(times),
"avg_cycles": round(mean(cycles)),
"algo_res": dijkstra_results,
}
return results
def get_search_nodes(G):
"""Get the source and target by getting the most distanced node pair."""
source = None
target = None
max_len = -1
length = dict(nx.all_pairs_dijkstra_path_length(G))
# Search through to see which pair has greatest distance
for node_a in length:
for node_b in length[node_a]:
if length[node_a][node_b] > max_len:
max_len = length[node_a][node_b]
source = node_a
target = node_b
return source, target
def performance_test():
"""Benchmarking Dijkstra with all heaps and graphs."""
# Get the graphs
test_graphs = get_graphs_from_file()
results = {}
for heap_name in HEAPS:
print(f"Running: {heap_name}")
run_list = []
graph_count = 0
for G in test_graphs:
run_info = {}
source, target = get_search_nodes(G)
# Run Dijktra
run_info["time_info"] = dijkstra_time(
G, source, target, get_heap(heap_name)
)
# Record graph info
run_info["graph_info"] = {
"graph_obj": G,
"graph_num": graph_count,
"num_nodes": G.number_of_nodes(),
"num_edges": G.number_of_edges(),
}
print(
"graph: {}, num_nodes: {}, num_edges: {}, avg_time: {}, avg_cycles: {}, path_found = {}".format(
graph_count,
run_info["graph_info"]["num_nodes"],
run_info["graph_info"]["num_edges"],
run_info["time_info"]["avg_time"],
run_info["time_info"]["avg_cycles"],
run_info["time_info"]["algo_res"][G],
)
)
# Add the run info into list
run_list.append(run_info)
graph_count += 1
print(f"Done running for each graph: {heap_name}")
# Add the results
results[heap_name] = run_list
return results
def report(results):
fields = ["num_nodes", "num_edges", "avg_time", "avg_cycles"]
rows = []
paths = []
for heap in results:
rows.append([heap])
rows.append(fields)
heap_data = results[heap]
paths.append([heap])
for run in heap_data:
graph_info = run["graph_info"]
num_nodes = graph_info["num_nodes"]
num_edges = graph_info["num_edges"]
avg_time = run["time_info"]["avg_time"]
avg_cycles = run["time_info"]["avg_cycles"]
rows.append([num_nodes, num_edges, avg_time, avg_cycles])
path = run["time_info"]["algo_res"].values()
for item in path:
paths.append(item)
rows.append([])
paths.append([])
with open("report.csv", "w") as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerows(rows)
with open("paths.csv", "w") as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerows(paths)
if __name__ == "__main__":
# Run a sanity check on single heap
# sanity_check("Fibonacci", show=True)
# Run sanity checks for all heaps
# smoke_tests(show=False)
# Run performance tests
results = performance_test()
print("Finished!")
# Record results into a csv and plot it
report(results)
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] |
tests/endpoints/v1/test_accounts.py | jamesonl/sdk-py | 1 | 133402 | <reponame>jamesonl/sdk-py
import unittest
from predicthq.endpoints.v1.accounts.schemas import Account
from tests import with_mock_client, with_mock_responses, with_client
class AccountsTest(unittest.TestCase):
@with_mock_client()
def test_self_params(self, client):
client.accounts.self()
client.request.assert_called_once_with('get', '/v1/accounts/self/')
@with_client()
@with_mock_responses()
def test_self(self, client, responses):
account = client.accounts.self()
assert isinstance(account, Account)
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29898,
10149,
29892,
16535,
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2
] |
omicron/dal/__init__.py | evimacs/omicron | 4 | 61180 | from omicron.dal.cache import cache
from omicron.dal.postgres import db, init
__all__ = ["init", "db", "cache"]
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tests/dgds_functions_test.py | openearth/hydro-engine-service | 4 | 11796 | import logging
import pytest
from . import auth
from hydroengine_service import dgds_functions
logger = logging.getLogger(__name__)
class TestDGDSFunctions:
@pytest.mark.parametrize('source, start_date, end_date, limit',
[
('projects/dgds-gee/bathymetry/gebco/2019', None, None, 10),
('projects/dgds-gee/glossis/currents', None, None, None),
('projects/dgds-gee/glossis/waterlevel', '2020-11-01', '2020-12-01', None),
('projects/dgds-gee/glossis/wind', '2020-11-01', '2020-11-10', 10),
('projects/dgds-gee/glossis/waveheight', None, None, None),
('projects/dgds-gee/gloffis/weather', None, None, 5),
('projects/dgds-gee/gloffis/hydro', None, None, 5),
('projects/dgds-gee/metocean/waves/percentiles', None, None, 5),
('projects/dgds-gee/chasm/waves', None, None, None),
('projects/dgds-gee/chasm/wind', None, None, None),
('projects/dgds-gee/crucial/evaporation_deficit', None, None, None),
('projects/dgds-gee/crucial/groundwater_declining_trend', None, None, None),
('projects/dgds-gee/msfd/chlorophyll', None, None, None)
])
def test_get_image_collection_info(self, source, start_date, end_date, limit):
image_date_list = dgds_functions.get_image_collection_info(source, start_date, end_date, limit)
assert len(image_date_list) >= 1
assert "imageId" in image_date_list[0]
assert "date" in image_date_list[0]
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openGaussBase/testcase/AI/AI_INDEXADV/Opengauss_Function_Ai_Indexadv_Case0015.py | opengauss-mirror/Yat | 0 | 110585 | <filename>openGaussBase/testcase/AI/AI_INDEXADV/Opengauss_Function_Ai_Indexadv_Case0015.py
"""
Copyright (c) 2022 Huawei Technologies Co.,Ltd.
openGauss is licensed under Mulan PSL v2.
You can use this software according to the terms and conditions of the Mulan PSL v2.
You may obtain a copy of Mulan PSL v2 at:
http://license.coscl.org.cn/MulanPSL2
THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
See the Mulan PSL v2 for more details.
"""
"""
Case Type : 智能运维ai_indexadv模块
Case Name : 结合不同的sql语句进行查询索引推荐
Description :
1.建表1
2.建表2
3.建存储过程1
4.建存储过程2
5.向表1中插入100000条数据,向表2中插入数据并统计数据的数量
6.结合union进行查询索引推荐
7.结合insert语句进行查询索引推荐
8.结合updata语句进行查询索引推荐
9.结合create语句进行查询索引推荐
10.结合视图进行查询索引推荐
11.结合子查询进行查询索引推荐
12.结合空条件进行查询索引推荐
13.结合多条创建索引语句进行查询索引推荐
14.清理环境
Expect :
1.建表1成功
2.建表2成功
3.建存储过程1成功
4.建存储过程2成功
5.向表1中插入100000条数据,向表2中插入数据成功,返回表1,表2数据的数量
6.返回推荐的索引列
7.报错提示无法为查询提供建议，因为找不到查询语句
8.报错提示无法为查询提供建议，因为找不到查询语句
9.报错提示无法为查询提供建议，因为找不到查询语句
10.报错提示无法为查询提供建议，因为无法识别涉及的表
11.返回推荐的索引列
12.报错提示必须输入查询条件
13.报错提示无法为多个查询提供建议
14.清理环境成功
History :
研发代码变更,优化用例适配代码
"""
import unittest
from yat.test import Node
from testcase.utils.Constant import Constant
from testcase.utils.CommonSH import CommonSH
from testcase.utils.Logger import Logger
class SQL(unittest.TestCase):
def setUp(self):
self.logger = Logger()
self.dbuserNode = Node('PrimaryDbUser')
self.primary_sh = CommonSH('PrimaryDbUser')
self.Con = Constant()
self.table1 = 't_table_ai_indexadv_0015'
self.table2 = 't_table_ai_indexadv1_0015'
self.table3 = 't_table_ai_indexadv_test_0015'
self.procedure1 = 'p_procedure_insert_0015'
self.procedure2 = 'p_procedure_insert1_0015'
self.view = 'v_view_ai_index_0015'
self.index_advise = 'gs_index_advise'
def test_ai_virtual_index(self):
self.logger.info('Opengauss_Function_Ai_Indexadv_Case0015 start')
step = 'step1:建表1 expect:建表1成功'
self.logger.info(step)
create_table1 = self.primary_sh.execut_db_sql(f'''
drop table if exists {self.table1};
create table {self.table1}(col_int int,col_numeric numeric,
col_float float,col_char char(10),col_text text,col_time time
);''')
self.logger.info(create_table1)
self.assertIn(self.Con.TABLE_CREATE_SUCCESS, create_table1,
"建表1失败" + step)
step = 'step2:建表2 expect:建表2成功'
self.logger.info(step)
create_table2 = self.primary_sh.execut_db_sql(f'''
drop table if exists {self.table2};
create table {self.table2}(col_int int,col_dec dec,
col_money money,col_boolean boolean,col_char char(10),
col_clob clob);''')
self.logger.info(create_table2)
self.assertIn(self.Con.TABLE_CREATE_SUCCESS, create_table2,
"建表2失败" + step)
step = 'step3:建存储过程1 expect:建存储过程1成功'
self.logger.info(step)
create_procedure1 = self.primary_sh.execut_db_sql(f'''
create or replace procedure {self.procedure1}(a int) is
V_int int;
V_numeric numeric;
V_float float;
V_char char(10);
V_text text;
V_time time;
begin
for i in 1..a
loop
V_int :=i;
V_numeric :=i+1.11;
V_float :=i*5.55;
V_char :='x_'|| i;
V_text :='V_text_'|| i;
V_time :='19:41:20';
execute immediate 'insert into {self.table1} values
(:p1,:p2,:p3,:p4,:p5,:p6)
'using V_int,V_numeric,V_float,V_char,V_text,V_time;
end loop;
end;''')
self.logger.info(create_procedure1)
self.assertIn(self.Con.CREATE_PROCEDURE_SUCCESS_MSG,
create_procedure1, "建存储过程1失败" + step)
step = 'step4:建存储过程2 expect:建存储过程2成功'
self.logger.info(step)
create_procedure2 = self.primary_sh.execut_db_sql(f'''
create or replace procedure {self.procedure2}() is
V_int int;
V_dec dec;
V_money money;
V_boolean boolean;
V_char char(10);
V_clob clob;
begin
for i in 2000..100000
loop
V_int :=i;
V_dec :=i*8.88;
V_money :=i*2;
V_boolean :='t';
V_char :=i ||'_x';
V_clob :='V_clob_'|| i;
execute immediate 'insert into {self.table2} values
(:p1,:p2,:p3,:p4,:p5,:p6)
'using V_int,V_dec,V_money,V_boolean,V_char,V_clob;
end loop;
end;''')
self.logger.info(create_procedure2)
self.assertIn(self.Con.CREATE_PROCEDURE_SUCCESS_MSG,
create_procedure2, "建存储过程2失败" + step)
step = 'step5:向表1中插入100000条数据,向表2中插入数据并统计数据的数量' \
'expect:向表1中插入100000条数据,向表2中插入数据成功,返回' \
'表1,表2数据的数量'
self.logger.info(step)
insert_data = self.primary_sh.execut_db_sql(f'''
call {self.procedure1}(100000);
select count(*) from {self.table1} ;
call {self.procedure2}();
select count(*) from {self.table2} ;''')
self.logger.info(insert_data)
text = '向表1中插入100000条数据,向表2中插入数据并统计数据的数量失败'
self.assertIn('p_procedure_insert_0015', insert_data,
text + step)
self.assertIn('p_procedure_insert1_0015', insert_data,
text + step)
self.assertIn('100000', insert_data, text + step)
self.assertIn('98001', insert_data, text + step)
step = 'step6:结合union进行查询索引推荐 expect:返回推荐的索引列'
self.logger.info(step)
index_advise = self.primary_sh.execut_db_sql(f'''
select * from {self.index_advise}('select col_int,col_char from
{self.table1} union select col_int,col_char from {self.table2}
limit 20;');''')
self.logger.info(index_advise)
str1 = 'public | t_table_ai_indexadv_0015 |'
str2 = 'public | t_table_ai_indexadv1_0015 |'
self.assertIn(str1, index_advise, "执行失败" + step)
self.assertIn(str2, index_advise, "执行失败" + step)
step = 'step7:结合insert语句进行查询索引推荐' \
'expect:报错提示无法为查询提供建议，因为找不到查询语句'
self.logger.info(step)
index_advise = self.primary_sh.execut_db_sql(f'''
select * from {self.index_advise}('insert into {self.table2}
(col_int) values(8)');''')
self.logger.info(index_advise)
str1 = 'ERROR: can not advise for the query'
str2 = 'because not found a select statement.'
self.assertIn(str1, index_advise, "执行失败" + step)
self.assertIn(str2, index_advise, "执行失败" + step)
step = 'step8:结合update语句进行查询索引推荐' \
'expect:报错提示无法为查询提供建议,因为找不到查询语句'
self.logger.info(step)
index_advise = self.primary_sh.execut_db_sql(f'''
select * from {self.index_advise}('update {self.table2}
set col_money =''$8,000.00'' where col_int=2000;');''')
self.logger.info(index_advise)
str1 = 'ERROR: can not advise for the query'
str2 = 'because not found a select statement.'
self.assertIn(str1, index_advise, "执行失败" + step)
self.assertIn(str2, index_advise, "执行失败" + step)
step = 'step9:结合create语句进行查询索引推荐' \
'expect:报错提示无法为查询提供建议，因为找不到查询语句'
self.logger.info(step)
index_advise = self.primary_sh.execut_db_sql(f'''
select * from {self.index_advise}('create global temp table
{self.table3}(id int, val numeric) on commit preserve rows;');''')
self.logger.info(index_advise)
str1 = 'ERROR: can not advise for the query'
str2 = 'because not found a select statement.'
self.assertIn(str1, index_advise, "执行失败" + step)
self.assertIn(str2, index_advise, "执行失败" + step)
step = 'step10:结合视图进行查询索引推荐' \
'expect:报错提示无法为查询提供建议,因为无法识别涉及的表'
self.logger.info(step)
index_advise = self.primary_sh.execut_db_sql(f'''
create view {self.view} as select * from {self.table1}
where {self.table1}.col_int<1000;
select * from {self.index_advise}('select * from {self.view};');
''')
self.logger.info(index_advise)
str1 = 'ERROR: can not advise for the query'
str2 = 'because can not recognize involved tables.'
self.assertIn(str1, index_advise, "执行失败" + step)
self.assertIn(str2, index_advise, "执行失败" + step)
step = 'step11:结合子查询进行查询索引推荐 expect:返回推荐的索引列'
self.logger.info(step)
index_advise = self.primary_sh.execut_db_sql(f'''
select * from {self.index_advise}('select col_int,col_char from
(select col_int,col_char from {self.table1} union select col_int,
col_char from {self.table2} limit 20) where col_char like
''x_74243%'' group by col_int,col_char order by col_int,col_char;');
''')
self.logger.info(index_advise)
str1 = 'public | t_table_ai_indexadv_0015 |'
str2 = 'public | t_table_ai_indexadv1_0015 |'
self.assertIn(str1, index_advise, "执行失败" + step)
self.assertIn(str2, index_advise, "执行失败" + step)
step = 'step12:结合空条件进行查询索引推荐' \
'expect:报错提示必须输入查询条件'
self.logger.info(step)
index_advise = self.primary_sh.execut_db_sql(f'''
select * from {self.index_advise}('');''')
self.logger.info(index_advise)
str1 = 'ERROR: you must enter a query statement.'
self.assertIn(str1, index_advise, "执行失败" + step)
step = 'step13:多条创建索引语句进行查询索引推荐' \
'expect:报错提示无法为多个查询提供建议'
self.logger.info(step)
index_advise = self.primary_sh.execut_db_sql(f'''
select * from {self.index_advise}('select {self.table1}.col_int,
{self.table1}.col_numeric,{self.table2}.col_money from
{self.table1} inner join {self.table2} on {self.table1}.col_int =
{self.table2}.col_int where {self.table1}.col_time=''19:41:20''
order by {self.table1}.col_int desc limit 20;
select * from {self.table1};');''')
self.logger.info(index_advise)
str1 = 'ERROR: can not advise for multiple queries.'
self.assertIn(str1, index_advise, "执行失败" + step)
def tearDown(self):
step = 'step14:清理环境 expect:清理环境成功'
self.logger.info(step)
clean_enviroment = self.primary_sh.execut_db_sql(f'''
drop view {self.view};
drop table {self.table1};
drop table {self.table2};
drop procedure {self.procedure1};
drop procedure {self.procedure2};''')
self.logger.info(clean_enviroment)
self.assertEqual(clean_enviroment.count(self.Con.TABLE_DROP_SUCCESS),
2, "执行失败" + step)
self.assertIn(self.Con.DROP_VIEW_SUCCESS_MSG, clean_enviroment,
"执行失败" + step)
self.assertIn(self.Con.DROP_PROCEDURE_SUCCESS_MSG, clean_enviroment,
"执行失败" + step)
self.assertEqual(clean_enviroment.count('DROP PROCEDURE'), 2,
"执行失败" + step)
self.logger.info('Opengauss_Function_Ai_Indexadv_Case0015 finish')
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demo_pulse.py | gexpander/demo-py-examples | 0 | 118111 | #!/bin/env python3
import gex
import time
# generating a pulse on gpio, test of the unit
with gex.Client(gex.TrxRawUSB()) as client:
out = gex.DOut(client, 'out')
out.pulse_us([0], 20)
out.pulse_us([3], 10) | [
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] |
models/temporal_link/temporal_weights.py | zankner/DNC | 3 | 155152 | import tensorflow as tf
def temporal_weights(temporal_links, read_weights):
forward_weights = tf.matmul(read_weights,
temporal_links, transpose_b = True)
backward_weights = tf.matmul(read_weights, temporal_links)
return forward_weights, backward_weights
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dag/ecs-hybrid-ec2.py | 094459/blogpost-airflow-hybrid | 1 | 1611066 | from airflow import DAG
from datetime import datetime, timedelta
from airflow.providers.amazon.aws.operators.ecs import ECSOperator
default_args = {
'owner': 'ubuntu',
'start_date': datetime(2019, 8, 14),
'retry_delay': timedelta(seconds=60*60)
}
with DAG('hybrid_airflow_ec2_dag', catchup=False, default_args=default_args, schedule_interval=None) as dag:
cloudquery = ECSOperator(
task_id="cloudquery",
dag=dag,
cluster="hybrid-airflow-cluster",
task_definition="apache-airflow",
overrides={ },
launch_type="EC2",
awslogs_group="/ecs/hybrid-airflow",
awslogs_stream_prefix="ecs/Hybrid-ELT-TaskDef"
)
cloudquery
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] |
custom_components/tahoma/config_flow.py | MatthewFlamm/ha-tahoma | 0 | 162973 | <filename>custom_components/tahoma/config_flow.py
"""Config flow for TaHoma integration."""
import logging
from aiohttp import ClientError
from homeassistant import config_entries
from homeassistant.const import CONF_PASSWORD, CONF_USERNAME
from homeassistant.core import callback
from homeassistant.helpers import config_validation as cv
from pyhoma.client import TahomaClient
from pyhoma.exceptions import (
BadCredentialsException,
MaintenanceException,
TooManyRequestsException,
)
import voluptuous as vol
from .const import (
CONF_HUB,
CONF_UPDATE_INTERVAL,
DEFAULT_HUB,
DEFAULT_UPDATE_INTERVAL,
MIN_UPDATE_INTERVAL,
SUPPORTED_ENDPOINTS,
)
from .const import DOMAIN # pylint: disable=unused-import
_LOGGER = logging.getLogger(__name__)
DATA_SCHEMA = vol.Schema(
{
vol.Required(CONF_USERNAME): str,
vol.Required(CONF_PASSWORD): str,
vol.Required(CONF_HUB, default=DEFAULT_HUB): vol.In(SUPPORTED_ENDPOINTS.keys()),
}
)
class ConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
"""Handle a config flow for Somfy TaHoma."""
VERSION = 1
CONNECTION_CLASS = config_entries.CONN_CLASS_CLOUD_POLL
@staticmethod
@callback
def async_get_options_flow(config_entry):
"""Handle the flow."""
return OptionsFlowHandler(config_entry)
async def async_validate_input(self, user_input):
"""Validate user credentials."""
username = user_input.get(CONF_USERNAME)
password = user_input.get(CONF_PASSWORD)
hub = user_input.get(CONF_HUB, DEFAULT_HUB)
endpoint = SUPPORTED_ENDPOINTS[hub]
async with TahomaClient(username, password, api_url=endpoint) as client:
await client.login()
return self.async_create_entry(
title=username,
data=user_input,
)
async def async_step_user(self, user_input=None):
"""Handle the initial step via config flow."""
errors = {}
if user_input:
await self.async_set_unique_id(user_input.get(CONF_USERNAME))
self._abort_if_unique_id_configured()
try:
return await self.async_validate_input(user_input)
except TooManyRequestsException:
errors["base"] = "too_many_requests"
except BadCredentialsException:
errors["base"] = "invalid_auth"
except (TimeoutError, ClientError):
errors["base"] = "cannot_connect"
except MaintenanceException:
errors["base"] = "server_in_maintenance"
except Exception as exception: # pylint: disable=broad-except
errors["base"] = "unknown"
_LOGGER.exception(exception)
return self.async_show_form(
step_id="user", data_schema=DATA_SCHEMA, errors=errors
)
async def async_step_import(self, import_config: dict):
"""Handle the initial step via YAML configuration."""
if not import_config:
return
try:
return await self.async_validate_input(import_config)
except TooManyRequestsException:
_LOGGER.error("too_many_requests")
return self.async_abort(reason="too_many_requests")
except BadCredentialsException:
_LOGGER.error("invalid_auth")
return self.async_abort(reason="invalid_auth")
except (TimeoutError, ClientError):
_LOGGER.error("cannot_connect")
return self.async_abort(reason="cannot_connect")
except MaintenanceException:
_LOGGER.error("server_in_maintenance")
return self.async_abort(reason="server_in_maintenance")
except Exception as exception: # pylint: disable=broad-except
_LOGGER.exception(exception)
return self.async_abort(reason="unknown")
class OptionsFlowHandler(config_entries.OptionsFlow):
"""Handle a option flow for Somfy TaHoma."""
def __init__(self, config_entry):
"""Initialize options flow."""
self.config_entry = config_entry
async def async_step_init(self, user_input=None):
"""Manage the Somfy TaHoma options."""
return await self.async_step_update_interval()
async def async_step_update_interval(self, user_input=None):
"""Manage the options regarding interval updates."""
if user_input is not None:
return self.async_create_entry(title="", data=user_input)
return self.async_show_form(
step_id="update_interval",
data_schema=vol.Schema(
{
vol.Required(
CONF_UPDATE_INTERVAL,
default=self.config_entry.options.get(
CONF_UPDATE_INTERVAL, DEFAULT_UPDATE_INTERVAL
),
): vol.All(cv.positive_int, vol.Clamp(min=MIN_UPDATE_INTERVAL))
}
),
)
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] |
examples/i2b2/i2b2.py | sean-dingxu/sciwing | 0 | 1615958 | from sciwing.modules.lstm2seqencoder import Lstm2SeqEncoder
from sciwing.models.rnn_seq_crf_tagger import RnnSeqCrfTagger
from sciwing.modules.embedders.trainable_word_embedder import TrainableWordEmbedder
from sciwing.modules.embedders.bow_elmo_embedder import BowElmoEmbedder
from sciwing.modules.embedders.concat_embedders import ConcatEmbedders
from sciwing.datasets.seq_labeling.conll_dataset import CoNLLDatasetManager
from sciwing.metrics.token_cls_accuracy import TokenClassificationAccuracy
import sciwing.constants as constants
import torch
import torch.optim as optim
from sciwing.engine.engine import Engine
import argparse
import wasabi
import pathlib
FILES = constants.FILES
PATHS = constants.PATHS
CORA_FILE = FILES["CORA_FILE"]
OUTPUT_DIR = PATHS["OUTPUT_DIR"]
CONFIGS_DIR = PATHS["CONFIGS_DIR"]
DATA_DIR = PATHS["DATA_DIR"]
if __name__ == "__main__":
# read the hyperparams from config file
parser = argparse.ArgumentParser(
description="LSTM CRF Parscit tagger for reference string parsing"
)
parser.add_argument("--exp_name", help="Specify an experiment name", type=str)
parser.add_argument("--model_save_dir", help="Model save directory", type=str)
parser.add_argument("--bs", help="batch size", type=int)
parser.add_argument("--lr", help="learning rate", type=float)
parser.add_argument("--epochs", help="number of epochs", type=int)
parser.add_argument(
"--save_every", help="Save the model every few epochs", type=int
)
parser.add_argument(
"--log_train_metrics_every",
help="Log training metrics every few iterations",
type=int,
)
parser.add_argument(
"--char_emb_dim", help="character embedding dimension", type=int
)
parser.add_argument(
"--emb_type",
help="The type of glove embedding you want. The allowed types are glove_6B_50, glove_6B_100, "
"glove_6B_200, glove_6B_300, parscit",
)
parser.add_argument(
"--hidden_dim", help="Hidden dimension of the lstm encoder", type=int
)
parser.add_argument(
"--bidirectional",
help="Specify Whether the lstm is bidirectional or uni-directional",
action="store_true",
)
parser.add_argument(
"--char_encoder_hidden_dim",
help="Character encoder hidden dimension.",
type=int,
)
parser.add_argument(
"--combine_strategy",
help="How do you want to combine the hidden dimensions of the two "
"combinations",
)
parser.add_argument("--device", help="Device on which the model is run", type=str)
parser.add_argument(
"--sample_proportion",
help="Sampling proportion of dataset for debugging",
type=float,
)
args = parser.parse_args()
msg_printer = wasabi.Printer()
data_dir = pathlib.Path(DATA_DIR)
train_filename = data_dir.joinpath("i2b2.train")
dev_filename = data_dir.joinpath("i2b2.train")
test_filename = data_dir.joinpath("i2b2.train")
data_manager = CoNLLDatasetManager(
train_filename=train_filename,
dev_filename=dev_filename,
test_filename=test_filename,
column_names=["NER", "NER", "NER"],
train_only="ner",
)
word_embedder = TrainableWordEmbedder(
embedding_type=args.emb_type, device=args.device, datasets_manager=data_manager
)
elmo_embedder = BowElmoEmbedder(
datasets_manager=data_manager, layer_aggregation="sum", device=args.device
)
embedder = ConcatEmbedders([word_embedder, elmo_embedder])
lstm2seqencoder = Lstm2SeqEncoder(
embedder=embedder,
hidden_dim=args.hidden_dim,
bidirectional=args.bidirectional,
combine_strategy=args.combine_strategy,
rnn_bias=True,
device=torch.device(args.device),
dropout_value=0.5,
add_projection_layer=False,
)
model = RnnSeqCrfTagger(
rnn2seqencoder=lstm2seqencoder,
encoding_dim=2 * args.hidden_dim
if args.bidirectional and args.combine_strategy == "concat"
else args.hidden_dim,
device=torch.device(args.device),
datasets_manager=data_manager,
)
optimizer = optim.Adam(params=model.parameters(), lr=args.lr)
train_metric = TokenClassificationAccuracy(datasets_manager=data_manager)
dev_metric = TokenClassificationAccuracy(datasets_manager=data_manager)
test_metric = TokenClassificationAccuracy(datasets_manager=data_manager)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer=optimizer, mode="max", factor=0.1, patience=2
)
engine = Engine(
model=model,
datasets_manager=data_manager,
optimizer=optimizer,
batch_size=args.bs,
save_dir=args.model_save_dir,
num_epochs=args.epochs,
train_metric=train_metric,
validation_metric=dev_metric,
test_metric=test_metric,
save_every=args.save_every,
log_train_metrics_every=args.log_train_metrics_every,
device=torch.device(args.device),
track_for_best="macro_fscore",
lr_scheduler=scheduler,
sample_proportion=args.sample_proportion,
use_wandb=True,
experiment_hyperparams=vars(args),
)
engine.run()
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minecraft_launcher_lib/fabric.py | bopchik/Simple-minecraft-mod-launcher | 1 | 7627 | <filename>minecraft_launcher_lib/fabric.py
from .helper import download_file, get_user_agent
from .install import install_minecraft_version
from typing import List, Dict, Union
from xml.dom import minidom
import subprocess
import requests
import tempfile
import random
import os
def get_all_minecraft_versions() -> List[Dict[str,Union[str,bool]]]:
"""
Returns all available Minecraft Versions for fabric
"""
FABRIC_MINECARFT_VERSIONS_URL = "https://meta.fabricmc.net/v2/versions/game"
return requests.get(FABRIC_MINECARFT_VERSIONS_URL,headers={"user-agent": get_user_agent()}).json()
def get_stable_minecraft_versions() -> List[str]:
"""
Returns a list which only contains the stable Minecraft versions that supports fabric
"""
minecraft_versions = get_all_minecraft_versions()
stable_versions = []
for i in minecraft_versions:
if i["stable"] == True:
stable_versions.append(i["version"])
return stable_versions
def get_latest_minecraft_version() -> str:
"""
Returns the latest unstable Minecraft versions that supports fabric. This could be a snapshot.
"""
minecraft_versions = get_all_minecraft_versions()
return minecraft_versions[0]["version"]
def get_latest_stable_minecraft_version() -> str:
"""
Returns the latest stable Minecraft version that supports fabric
"""
stable_versions = get_stable_minecraft_versions()
return stable_versions[0]
def is_minecraft_version_supported(version: str) -> bool:
"""
Checks if a Minecraft version supported by fabric
"""
minecraft_versions = get_all_minecraft_versions()
for i in minecraft_versions:
if i["version"] == version:
return True
return False
def get_all_loader_versions() -> List[Dict[str,Union[str,bool,int]]]:
"""
Returns all loader versions
"""
FABRIC_LOADER_VERSIONS_URL = "https://meta.fabricmc.net/v2/versions/loader"
return requests.get(FABRIC_LOADER_VERSIONS_URL,headers={"user-agent": get_user_agent()}).json()
def get_latest_loader_version() -> str:
"""
Get the latest loader version
"""
loader_versions = get_all_loader_versions()
return loader_versions[0]["version"]
def get_latest_installer_version() -> str:
"""
Returns the latest installer version
"""
FABRIC_INSTALLER_MAVEN_URL = "https://maven.fabricmc.net/net/fabricmc/fabric-installer/maven-metadata.xml"
r = requests.get(FABRIC_INSTALLER_MAVEN_URL,headers={"user-agent": get_user_agent()})
xml_data = minidom.parseString(r.text)
release = xml_data.getElementsByTagName("release")
return release.item(0).lastChild.data
def install_fabric(path: str, minecraft_version: str,loader_version: str=None):
"""
Install a fabric version
"""
#Get latest loader version if not given
if not loader_version:
loader_version = get_latest_loader_version()
#Make sure the Minecraft version is installed
install_minecraft_version(path,minecraft_version)
#Get installer version
installer_version = get_latest_installer_version()
installer_download_url = f"https://maven.fabricmc.net/net/fabricmc/fabric-installer/{installer_version}/fabric-installer-{installer_version}.jar"
#Generate a temporary path for downloading the installer
installer_path = os.path.join(tempfile.gettempdir(),f"fabric-installer-{random.randrange(100,10000)}.tmp")
#Download the installer
download_file(installer_download_url,installer_path)
#Run the installer see https://fabricmc.net/wiki/install#cli_installation
subprocess.run(["java","-jar",installer_path,"client","-dir",path,"-mcversion",minecraft_version,"-loader",loader_version,"-noprofile","-snapshot"])
#Delete the installer we don't need them anymore
os.remove(installer_path)
#Install all libs of fabric
fabric_minecraft_version = f"fabric-loader-{loader_version}-{minecraft_version}"
install_minecraft_version(path,fabric_minecraft_version)
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src/tasks/aws_audit_vpc_flow_logs_task.py | jezd-axyl/platsec-aws-scanner | 0 | 173754 | from dataclasses import dataclass
from typing import Any, Dict
from src.clients.composite.aws_vpc_client import AwsVpcClient
from src.data.aws_organizations_types import Account
from src.tasks.aws_vpc_task import AwsVpcTask
@dataclass
class AwsAuditVPCFlowLogsTask(AwsVpcTask):
def __init__(self, account: Account, enforce: bool, with_subscription_filter: bool) -> None:
super().__init__("audit VPC flow logs compliance", account, enforce)
self.with_subscription_filter = with_subscription_filter
def _run_task(self, client: AwsVpcClient) -> Dict[Any, Any]:
vpcs = client.list_vpcs()
actions = client.enforcement_actions(vpcs, self.with_subscription_filter)
if self.enforce:
apply = [a.apply() for a in actions]
return {"vpcs": vpcs, "enforcement_actions": apply}
else:
plans = [a.plan() for a in actions]
return {"vpcs": vpcs, "enforcement_actions": plans}
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nipy/utils/__init__.py | neurospin/nipy | 1 | 1605596 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
Package containing both generic configuration and testing stuff as well as
general purpose functions that are useful to a broader community and not
restricted to the neuroimaging community. This package may contain
third-party software included here for convenience.
"""
from nibabel.data import make_datasource, DataError, datasource_or_bomber
# Module level datasource instances for convenience
from ..info import DATA_PKGS
templates = datasource_or_bomber(DATA_PKGS['nipy-templates'])
example_data = datasource_or_bomber(DATA_PKGS['nipy-data'])
from nipy.testing import Tester
test = Tester().test
bench = Tester().bench
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lib/concurrence/_threading/__init__.py | concurrence/concurrence | 27 | 116696 | # Copyright (C) 2009, Hyves (Startphone Ltd.)
#
# This module is part of the Concurrence Framework and is released under
# the New BSD License: http://www.opensource.org/licenses/bsd-license.php
def disable_threading():
import sys
import thread
sys.modules['thread'] = thread
import threading
sys.modules['threading'] = threading
import dummy_thread
sys.modules['dummy_thread'] = dummy_thread
import dummy_threading
sys.modules['dummy_threading'] = dummy_threading
import logging
logging.logThreads = 0
#for name, _ in sys.modules.items():
# print name, _
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calculate_fCOS/calc_fCOS_C4pct_run_driver.py | Timothy-W-Hilton/COS_Spatial_Analyses | 0 | 141243 | <reponame>Timothy-W-Hilton/COS_Spatial_Analyses<filename>calculate_fCOS/calc_fCOS_C4pct_run_driver.py<gh_stars>0
#!/usr/bin/env python
# the above shebang uses a virtualenv if one is running, otherwise the
# system python
"""Short example of using python to perform the environment variable
management, etc. needed to use the I/O API fortran library.
Using python has the advantage of providing access to
stem_pytools.ecampbell300_data_paths, where the location of all the
various STEM runs' data files is listed.
<NAME>, UC Merced, 13 January 2015
"""
import os
import os.path
import sys
import subprocess
import re
import netCDF4
import matplotlib.pyplot as plt
import numpy as np
import brewer2mpl
import argparse
import datetime
from stem_pytools import ecampbell300_data_paths as edp
from stem_pytools import na_map
from stem_pytools import STEM_parsers as sp
from timutils import midpt_norm
# ============================================================
def fCOS_from_C4pct_diagnostics():
draw_LRU_map()
runs = edp.get_C3C4runs()
for k in runs.keys():
fcos = sp.parse_STEM_var(nc_fname=runs[k].fcos_path,
t0=datetime.datetime(2008, 7, 1),
t1=datetime.datetime(2008, 8, 31, 23, 59, 59),
varname='cos')
print('{}: min: {:0.2e} max: {:0.2e} mean: {:0.2e}'.format(
k,
fcos['data'].min(),
fcos['data'].max(),
fcos['data'].mean()))
fig, ax = plt.subplots()
cm = ax.pcolor(fcos['data'].squeeze().mean(axis=0),
cmap=plt.get_cmap('Blues'))
plt.colorbar(cm)
plt.title(k)
fig.savefig('/tmp/fcos_{}.png'.format(k))
# ============================================================
def draw_LRU_map():
nc = netCDF4.Dataset(os.environ['LRU_FILE'])
LRU = nc.variables['LRU'][...].squeeze()
nc.close()
stem_lon, stem_lat, topo = sp.parse_STEM_coordinates(
os.path.join(os.getenv('SARIKA_INPUT'), 'TOPO-124x124.nc'))
fcos_norm = midpt_norm.MidpointNormalize(midpoint=1.61)
lru_map = na_map.NAMapFigure(cb_axis=True, t_str='LRU from C4 veg pct')
cm = lru_map.map.pcolor(stem_lon, stem_lat, LRU,
cmap=plt.get_cmap('PuOr'),
latlon=True,
norm=fcos_norm)
ticks = np.concatenate([np.linspace(1.12, 1.84, 5), np.array([1.61])])
cbar = lru_map.fig.colorbar(cm, cax=lru_map.ax_cmap, ticks=ticks)
cbar.ax.set_title('LRU')
lru_map.fig.savefig('LRU_from_c4pct.pdf')
# parse arguments
parser = argparse.ArgumentParser(description=(
("script to calculate fCOS for all GPP products defined"
" in stem_pytools.ecampbell300_data_paths.")))
parser.add_argument('--diagnostics',
dest='run_diagnostics',
action='store_true',
help=('if set, some diagnostic plots are created'
'after the calculations.'))
args = parser.parse_args()
# compile the fortran code
subprocess.call('make -f calc_fCOS_C4pct.mk clobber', shell=True)
subprocess.call('make -f calc_fCOS_C4pct.mk', shell=True)
fname_C4pct = os.path.join('/home', 'thilton', 'projects', 'COS (ecampbell3)',
'C4_percentage', 'ISLSCP_C4_1DEG_932_regridded',
'C4_pct_124x124.nc')
# get the GPP I/O API file for the CASA m15 file
runs = edp.get_C3C4runs()
for this_run in runs.values():
fname_GPP = this_run.gpp_path
sys.stdout.write('\n\nGPP file: {}\n'.format(fname_GPP))
sys.stdout.write('C4 file: {}\n\n'.format(fname_C4pct))
sys.stdout.flush()
if os.path.exists(fname_GPP) and os.path.exists(fname_C4pct):
os.environ['GRIDDESC'] = os.path.join(os.environ['HOME'], 'Data',
'STEM', 'input', 'GRIDDESC.txt')
os.environ['GPP_INPUT'] = fname_GPP
os.environ['C4pct_INPUT'] = fname_C4pct
os.environ['RATIO_FILE'] = './COS_CO2_ratio_const_1.1.nc'
os.environ['LRU_FILE'] = './LRU_from_C4pct.nc'
os.environ['fCOS_FILE'] = (
'./fCOS_{}_2008_124x124_LRUfromC4pct.nc'.format(
re.sub('[\ \-]', '', this_run.model)))
# run the fortran part
if this_run.model.lower().find('casa') >= 0:
t_step = 30000 # 3 hours expressed as HHMMSS
else:
t_step = 7320000 # 30.5 days expressed as HHMMSS
subprocess.call(
'./calc_fCOS_C4pct.x {} {}'.format(
re.sub(' ', '\ ', this_run.model),
t_step),
shell=True)
# move the new fCOS file to its permanent location
newname = os.path.join(os.path.dirname(this_run.fcos_path),
os.path.basename(os.environ['fCOS_FILE']))
sys.stdout.write('\n{} --> {}\n\n'.format(os.environ['fCOS_FILE'],
newname))
sys.stdout.flush()
os.rename(os.environ['fCOS_FILE'], newname)
else:
print('GPP file not found')
if args.run_diagnostics:
print 'creating diagnostic plots'
draw_LRU_map()
# fCOS_from_C4pct_diagnostics()
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networkapi/api_network/tests/v3/unit/networkipv6/async/test_delete.py | vinicius-marinho/GloboNetworkAPI | 73 | 115803 | # -*- coding: utf-8 -*-
import logging
from django.test.client import Client
from mock import patch
from networkapi.api_network.tasks import delete_networkv6
from networkapi.api_network.tasks import undeploy_networkv6
from networkapi.ip.models import NetworkIPv6
from networkapi.test.test_case import NetworkApiTestCase
from networkapi.usuario.models import Usuario
log = logging.getLogger(__name__)
class NetworkIPv6AsyncDeleteSuccessTestCase(NetworkApiTestCase):
def setUp(self):
self.client = Client()
def tearDown(self):
pass
@patch('networkapi.api_network.facade.v3.delete_networkipv6')
@patch('networkapi.api_network.facade.v3.get_networkipv6_by_id')
@patch('networkapi.api_network.tasks.delete_networkv6.update_state')
def test_task_id_create_in_delete_one_netipv6_success(self, *args):
"""Test success of id task generate for netipv6 delete success."""
mock_get_netv6 = args[1]
mock_delete_netv6 = args[2]
net = NetworkIPv6(id=1)
user = Usuario(id='1', nome='test')
mock_delete_netv6.return_value = net
mock_get_netv6.return_value = net
delete_networkv6(1, user.id)
mock_delete_netv6.assert_called_with(1)
class NetworkIPv6AsyncDeleteErrorTestCase(NetworkApiTestCase):
def setUp(self):
self.client = Client()
def tearDown(self):
pass
def test_task_id_create_in_delete_one_netipv6_error(self):
"""Test success of id task generate for netipv6 delete error."""
pass
class NetworkIPv6AsyncDeleteDeploySuccessTestCase(NetworkApiTestCase):
def setUp(self):
self.client = Client()
def tearDown(self):
pass
@patch('networkapi.api_network.facade.v3.undeploy_networkipv6')
@patch('networkapi.api_network.facade.v3.get_networkipv6_by_id')
@patch('networkapi.usuario.models.Usuario.objects.get')
@patch('networkapi.api_network.tasks.undeploy_networkv6.update_state')
def test_task_id_create_in_delete_deploy_one_netipv6_success(self, *args):
"""Test success of id task generate for netipv6 delete deploy success."""
mock_get_user = args[1]
mock_get_netv6 = args[2]
mock_undeploy_netv6 = args[3]
net = NetworkIPv6(id=1)
user = Usuario(id='1', nome='test')
mock_undeploy_netv6.return_value = net
mock_get_netv6.return_value = net
mock_get_user.return_value = user
undeploy_networkv6(net.id, user.id)
mock_undeploy_netv6.assert_called_with(net.id, user)
class NetworkIPv6AsyncDeleteDeployErrorTestCase(NetworkApiTestCase):
def setUp(self):
self.client = Client()
def tearDown(self):
pass
def test_task_id_create_in_delete_deploy_one_netipv6_error(self):
"""Test success of id task generate for netipv6 delete deploy error."""
pass
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] |
tradingview_fxcross.py | project-k-0-1/project-k | 1 | 128644 | """ Tradingview FXcross """
from sa_func import get_broker_affiliate_link
def get_tradingview_fxcross(width, height):
""" Get Tradingview FXcross """
return_data = ''
#theme = get_sa_theme()
url = get_broker_affiliate_link('Tradingview', 'baseurl')
if str(width) == '0':
width = '"100%"'
if str(height) == '0':
height = '"100%"'
return_data = '' +\
'<div class="tradingview-widget-container">'+\
' <div class="tradingview-widget-container__widget"></div>'+\
' <script type="text/javascript" '+\
'src="https://s3.tradingview.com/external-embedding/embed-widget-forex-cross-rates.js" async>'+\
' {'+\
' "width": '+ str(width) +','+\
' "height": '+ str(height) +','+\
' "currencies": ['+\
' "EUR",'+\
' "USD",'+\
' "JPY",'+\
' "GBP",'+\
' "CHF",'+\
' "AUD",'+\
' "CAD",'+\
' "NZD",'+\
' "CNY",'+\
' "SGD",'+\
' "RUB"'+\
' ],'+\
' "locale": "en",'+\
' "largeChartUrl": "'+ str(url) +'"'+\
'}'+\
' </script>'+\
'</div>'
return return_data
| [
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9382,
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383,
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1272,
13,
2
] |
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