content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
import sys
input = sys.stdin.readline
T = int(input())
fb = [0 for _ in range(50)]
fb[0], fb[1] = 1,1
for i in range(2,50): fb[i] = fb[i-1]+fb[i-2]
for _ in range(T):
N = int(input())
L = []
while N:
for i in range(50):
if fb[i]>N:
L.append(fb[i-1])
N-=fb[i-1]
break
if fb[i]==N:
L.append(fb[i])
N=0
break
for i in sorted(L): print(i, end=' ')
print()
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] | 1.571875 | 320 |
import glob
import os
import platform
import shutil
import sys
import pyxel
if __name__ == "__main__":
run()
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] | 2.878049 | 41 |
import pickle
import numpy as np
from projeto.settings import WEIGHTS_PATH
from loguru import logger
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] | 3.607143 | 28 |
# Copyright 2019-2020 Not Just A Toy Corp.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import typing as ty
from distutils.util import strtobool
from falcon_heavy.utils import force_str, FalconHeavyUnicodeDecodeError
from .base import BaseType, ValidationResult, Messages, Types
from .path import Path
from .exceptions import SchemaError
from .errors import Error
__all__ = (
'StringType',
'Number',
'GenericNumberType',
'NumberType',
'IntegerType',
'BooleanType',
)
class StringType(BaseType[str]):
"""String type
:param min_length: invalidate when value length less than specified
:param max_length: invalidate when value length greater than specified
:param pattern: invalidate when value is not match to specified pattern
"""
MESSAGES: ty.ClassVar[Messages] = {
'type': "Must be a string",
'cast': "Couldn't cast to a string",
'min_length': "Must be no less than {0} characters in length",
'max_length': "Must be no greater than {0} characters in length",
'pattern': "Does not match the pattern"
}
TYPES: ty.ClassVar[Types] = (str, )
__slots__ = (
'min_length',
'max_length',
'pattern'
)
Number = ty.Union[int, float]
T_num = ty.TypeVar('T_num', int, float, Number)
class GenericNumberType(BaseType[T_num]):
"""Generic number type
:param minimum: invalidate when value less than specified minimum
:param maximum: Invalidate when value greater than specified maximum
:param exclusive_minimum: when True, it indicates that the range excludes the minimum value.
When False (or not included), it indicates that the range includes the minimum value
:param exclusive_maximum: when True, it indicates that the range excludes the maximum value.
When false (or not included), it indicates that the range includes the maximum value
:param multiple_of: invalidate when value is not multiple of specified
"""
MESSAGES: ty.ClassVar[Messages] = {
'type': "Must be a number",
'cast': "Couldn't cast to a number",
'minimum': "Is less than the minimum of {0}",
'exclusive_minimum': "Is less than or equal to the minimum of {0}",
'maximum': "Is greater than the maximum of {0}",
'exclusive_maximum': "Is greater than or equal to the maximum of {0}",
'multiple_of': "Is not a multiple of {0}"
}
TYPES: ty.ClassVar[Types] = (int, float)
__slots__ = (
'minimum',
'maximum',
'exclusive_minimum',
'exclusive_maximum',
'multiple_of'
)
class NumberType(GenericNumberType[Number]):
"""Number type"""
class IntegerType(GenericNumberType[int]):
"""Integer type"""
MESSAGES: ty.ClassVar[Messages] = {
'type': "Must be an integer",
'cast': "Couldn't cast to an integer"
}
TYPES: ty.ClassVar[Types] = (int, )
__slots__ = ()
class BooleanType(BaseType[bool]):
"""Boolean type"""
MESSAGES: ty.ClassVar[Messages] = {
'type': "Must be a boolean",
'cast': "Couldn't cast to a boolean"
}
TYPES: ty.ClassVar[Types] = (bool, )
__slots__ = ()
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] | 2.86034 | 1,296 |
import logging
import async_timeout
logger = logging.getLogger(__name__)
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from alembic import op
import sqlalchemy as sa
"""empty message
Revision ID: 1e09e871fb65
Revises: ('0b4e7a8a7e64', '0701782c564d')
Create Date: 2019-03-28 23:08:21.960495
"""
# revision identifiers, used by Alembic.
revision = '1e09e871fb65'
down_revision = ('0b4e7a8a7e64', '0701782c564d')
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import os
import pickle
from asgiref.sync import sync_to_async
@sync_to_async
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import numpy as np
class ParamLine_py(object):
""" A 3D parametric line.
Parameters
----------
P0: ndarray(3,)
A tail point.
P1: ndarray(3,)
A head point.
Attributes
----------
P0
P1
u: ndarray(3,)
Show the direction of the parametric line
Examples
--------
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from owcsimpy.geoutils.draw import draw
>>> from owcsimpy.geoobjects.bases.paramline_py import ParamLine_py as Line
>>> # Generate a line l
>>> l = Line(np.array([0.5,0.5,0.5]),np.ones(3))
>>> # Draw
>>> fig,ax = draw(lines=l,figsize=(5,6))
>>> # Get a point at t = 0.25
>>> P = l.getPoint(0.25)
>>> print("Point at t=0.25 is {}".format(P))
Point at t=0.25 is [0.625 0.625 0.625]
>>> # Draw
>>> x,y,z = P
>>> ax.scatter(x,y,z)
>>> plt.show()
.. plot::
:format: doctest
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from owcsimpy.geoutils.draw import draw
>>> from owcsimpy.geoobjects.bases.paramline_py import ParamLine_py as Line
>>> # Generate a line l
>>> l = Line(np.array([0.5,0.5,0.5]),np.ones(3))
>>> # Draw
>>> fig,ax = draw(lines=l,figsize=(5,6))
>>> # Get a point at t = 0.25
>>> P = l.getPoint(0.25)
>>> print("Point at t=0.25 is {}".format(P))
>>> # Draw
>>> x,y,z = P
>>> ax.scatter(x,y,z)
>>> plt.show()
Notes
-----
A parametric line, :math:`l(t)`, is defined as:
.. math:: l(t) = P_0 + \mathbf{u} t, \mathrm{where}\ \mathbf{u} = P_1-P_0.
"""
def getPoint(self,t0):
""" Get a point at t=t0.
Returns
-------
ndarray(3,)
Return P0+u t0
"""
return self.P0+self.u*t0
def isValid(self,P):
""" Check wheter the point P is in line.
Returns
-------
bool
"""
up = P-self.P0 # u prime
return True if np.allclose(up,np.zeros(3)) else np.allclose(
up/np.linalg.norm(up),self.u/np.linalg.norm(self.u))
def getParam(self,P):
""" Get t of P. Or, find t s.t. P0+u t = P
Returns
-------
float or None
Return None if P is not in the line.
"""
# Check validity of P
if self.isValid(P):
idxnotzero = np.where(self.u!=0)[0]
return (P-self.P0)[idxnotzero]/self.u[idxnotzero]
else:
return None
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
6045,
628,
198
] | 1.963883 | 1,329 |
from __future__ import annotations
from jsonclasses import jsonclass, types
@jsonclass
| [
6738,
11593,
37443,
834,
1330,
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198,
6738,
33918,
37724,
1330,
33918,
4871,
11,
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628,
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31,
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4871,
628
] | 4.285714 | 21 |
from django.contrib.auth.models import User
from django.db.models.signals import pre_save, post_save, post_delete
from django.dispatch import receiver
from .models import Post, Subscriber
from .functions import notify_new_content, notify_new_subscriber
@receiver(post_save, sender=Post)
def send_mail_to_subscriber(sender, **kwargs):
"""
Sending message to subscribers when exist a new post.
When a post is created
"""
if kwargs.get('created', False):
post = kwargs.get("instance")
notify_new_content(post.id)
@receiver(post_save, sender=Subscriber)
def new_subscriber(sender, **kwargs):
"""
Sending message staff when exist a new subscriber.
"""
if kwargs.get('created', False):
subscriber = kwargs.get("instance")
notify_new_subscriber(subscriber.id)
| [
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86,
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220,
220,
220,
32944,
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62,
3605,
62,
7266,
1416,
24735,
7,
7266,
1416,
24735,
13,
312,
8,
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] | 2.75 | 300 |
from django.contrib import admin
from django.contrib.auth.admin import UserAdmin
from .models import User, Agreement
# Register your models here.
admin.site.register(User, UserAdmin)
admin.site.register(Agreement)
| [
6738,
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] | 3.444444 | 63 |
import sys
import os
import json
import traceback
import importlib
from flask import request, make_response, Blueprint, abort
from lifoid.config import settings
from lifoid.logging.mixin import ServiceLogger
from lifoid.constants import E_GET, E_POST
from lifoid.events import process_event
from lifoid.exceptions import (LifoidRequestForbiddenError,
LifoidRequestUnknownError)
sys.path.insert(0, os.getcwd())
logger = ServiceLogger()
try:
app_settings_module = importlib.import_module(
settings.lifoid_settings_module
)
logger.debug('Templates path: {}'.format(
app_settings_module.TEMPLATES_PATH))
webhook = Blueprint('webhook', __name__,
template_folder=app_settings_module.TEMPLATES_PATH)
except ImportError:
logger.error('No templates path configured')
webhook = Blueprint('webhook', __name__)
@webhook.route('/webhook', methods=['GET', 'POST'])
def index():
"""
Universal webhook endpoint for all messenger applications.
"""
logger.debug('Webhook blueprint invoked')
try:
if request.method == 'POST':
e_type = E_POST
data = request.get_data()
if data.startswith(b'payload'):
event = json.loads(request.form['payload'])
else:
event = json.loads(request.get_data())
elif request.method == 'GET':
e_type = E_GET
event = request.args
else:
return make_response('Request method not supported', 404)
logger.debug('{} {}'.format(e_type, event))
asynchronous = settings.pasync == 'yes'
resp, perf = process_event(e_type, event, asynchronous)
logger.info('Request processed in {}'.format(perf))
if resp is not None:
logger.debug('Http Response: {}'.format(resp))
return make_response(resp, 200)
return make_response('OK', 200)
except KeyError:
logger.error(traceback.format_exc())
logger.error('Missing key argument')
return make_response('Missing key argument', 404)
except LifoidRequestForbiddenError:
return abort(403)
except LifoidRequestUnknownError:
return make_response('Unknown request', 404)
except:
logger.error(request.get_data())
logger.error(traceback.format_exc())
return make_response('', 200)
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220,
220,
220,
220,
220,
1441,
787,
62,
26209,
10786,
3256,
939,
8,
198
] | 2.49226 | 969 |
# flake8: noqa
from .version import __version__
from .authorized_service import AuthorizedService, get_email_and_key
| [
2,
781,
539,
23,
25,
645,
20402,
198,
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198
] | 3.371429 | 35 |
from collections import UserDict
from copy import copy
from . import *
class TagContainer(UserDict):
"""
Manages a coherent group of tags
Instances are callable. That syntax is the preferred way to get a tag, since
it will always return a tag object. Accessing an undeclared tag in this way
will return an UnknownTag object instead of raising an error.
This object can also be accessed like a normal dict.
"""
def __init__(self):
"""
Create a new tag container
All tag containers start with a special DescriptionTag element for
consistency.
"""
super().__init__()
self._add_taglike(DescriptionTag)
self.problems = []
def __call__(self, tag_name: str):
"""
Get a tag by its name
Tags which have already been defined will be returned as-is. Undefined
tag names are assigned an UnknownTag object, which is then returned.
Args:
tag_name (str): Name of the tag to fetch
Returns:
A Tag or UnknownTag object
"""
tag = self.data.get(tag_name)
if tag is None:
tag = UnknownTag(tag_name)
self.append(tag)
return tag
def append(self, tag):
"""
Add a tag to this container
The tag will be indexed by its name
Args:
tag (Tag): A tag object
"""
if tag.name in self.data:
return
self.data[tag.name] = tag
def update(self, values: dict):
"""
Update multiple tags using data from the values dict
Args:
values (dict): Dictionary of lists whose keys are tag names
"""
for key, data in values.items():
self(key).update(data)
def all(self):
"""
Iterate over the stored tag objects
Returns:
Iterator for the stored tag objects
"""
return iter(self.data.values())
def names(self):
"""
Get a view of the tag names
Returns:
Dictionary view of the saved tag names
"""
return self.data.keys()
def present(self):
"""
Create a new container with only the tags marked as present
Returns:
TagContainer object with only present tags
"""
new_container = copy(self)
new_container.data = {k: v for k, v in self.data.items() if v.present}
return new_container
def _add_taglike(self, klass, *args, **kwargs):
"""
Internal method to create and append a new tag
Args:
klass (object): Tag class to create
args, kwargs: Other arguments as appropriate for the tag to create
"""
self.append(klass(*args, **kwargs))
def add_tag(self, *args, **kwargs):
"""
Add a new tag
"""
self._add_taglike(Tag, *args, **kwargs)
def add_flag(self, *args, **kwargs):
"""
Add a new flag
"""
self._add_taglike(Flag, *args, **kwargs)
def add_group(self, *args, **kwargs):
"""
Add a new group tag
"""
self._add_taglike(GroupTag, *args, **kwargs)
@property
def valid(self):
"""
bool: Whether this tag is internally valid
This property is only meaningful after calling validate()
"""
return len(self.problems) == 0
def validate(self, strict: bool=False):
"""
Validate all of the tags in this container
Args:
strict (bool): Whether to report non-critical errors and omissions
Returns:
True if this tag has no validation problems, false if not
"""
self.problems = []
for key, tag in self.data.items():
tag.validate(strict=strict)
self.problems.extend(tag.problems)
if key != tag.name:
self.problems.append("Tag '{}' has wrong key: '{}'".format(tag.name, key))
return self.valid
def sanitize(self):
"""
Ask all tags to remove their hidden values
Calls sanitize on each tag
"""
for tag in self.values():
tag.sanitize()
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] | 2.29801 | 1,859 |
#
# apologies for the camelCase - the previous version was in JS
import dateutil.parser
import datetime
from datetime import date, time
from time import sleep
import http.client
import xml.etree.ElementTree as ET
## ''Aquila 6 April 2009 > 5 mag
# 42.3476°N 13.3800°ECoordinates: 42.3476°N 13.3800°E[1]
# http://webservices.ingv.it/fdsnws/event/1/query?starttime=2009-04-01T00:00:00&endtime=2009-04-10T00:00:00
# curl -i "http://webservices.ingv.it/fdsnws/event/1/query?starttime=2010-01-01T00:00:00&endtime=2010-01-01T06:00:00"
# using low-level version to log connection issues
if __name__ == "__main__":
INGV().main()
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'''
Script that tests a trained models on its training dataset. It does the same
testing routine as the one in the overall utilities_models_tf.py script.
It saves
¤ the information about the test for easy later plotting
¤ ROC (per-class and overall using micro and macro average)
¤ PP curve (per-class and overall using micro and macro average)
¤ summary of performance for easy read of the final scores
Steps
1 - get paths and models to test
2 - load testing dataset
3 - get predictions using the test function in the utilities_models_tf.py
4 - plot and save confusion matrix
5 - plot and save ROC curve
6 - save detailed info of the testing and summary
'''
import os
import sys
import cv2
import glob
import json
import time
import pickle
import random
import pathlib
import argparse
import importlib
import numpy as np
from datetime import datetime
from collections import OrderedDict
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
import tensorflow as tf
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import load_model
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # or any {'0', '1', '2'}
# local imports
import utilities
import utilities_models_tf
## 1 - get models information and additional files
parser = argparse.ArgumentParser(description='Script that prints a summary of the model perfomance.')
parser.add_argument('-m','--model_path' ,required=True, help='Specify the folder where the trained model is located')
parser.add_argument('-d','--dataset_path' ,required=False, help='Specify where the dataset is located', default=False)
parser.add_argument('-mv','--model_version' ,required=False, help='Specify if to run the training on the best model (best) or the last (last)', default="best")
args = parser.parse_args()
model_path = args.model_path
dataset_path = args.dataset_path
model_version = args.model_version
# # # DEBUG
# model_path = '/flush/iulta54/Research/P3_OCT_SPLIT_PROPERLY_YOUR_DATA/trained_models/LightOCT_per_image_split_5_folds_rkf_10_lr0.0001_batch64_AIIMS_rls_True'
# dataset_path = "/flush/iulta54/Research/Data/OCT/AIIMS_Dataset/original"
# model_version = "best"
title="Testing script"
print(f'\n{"-"*len(title)}')
print(f'{title}')
print(f'{"-"*len(title)}\n')
# check forlders
if not os.path.isdir(model_path):
raise ValueError(f'Model not found. Given {model_path}')
else:
# check that the configuration file is in place
if not os.path.isfile(os.path.join(model_path,'config.json')):
raise ValueError(f'Configuration file not found for the given model. Check that the model was configured and trained. Given {os.path.join(model_path,"config.json")}')
else:
print("Model and config file found.")
# check that the model_version setting is a correct one (best, last, ensamble-not jet implemented)
if not any([model_version==s for s in ["best", "last", "ensamble"]]):
raise ValueError(f'The given model version for the testing is unknown. Given {model_version}, expected best, last or ensamble')
print(f'Working on model {os.path.basename(model_path)}')
print(f'Model configuration set for testing: {model_version}')
## 2 - load testing dataset
importlib.reload(utilities)
# load configuration file
with open(os.path.join(model_path,'config.json')) as json_file:
config = json.load(json_file)
config['label_description'] = config['unique_labels']
if dataset_path is False:
dataset_path = config['dataset_folder']
# check if dataset folder
if not os.path.isdir(dataset_path):
raise ValueError(f'Dataset path not found. Given {dataset_path}')
else:
print('Dataset path found.')
# take one testing
# make sure that the files point to this system dataset
# fix names based on the given dataset path
if any([config['dataset_type'] == 'retinal', config['dataset_type'] == 'Kermany']):
if config['dataset_split_strategy'] == 'original':
idx = 4
else:
idx = 3
elif config['dataset_type'] == 'AIIMS':
idx = 4
elif config['dataset_type'] == 'Srinivas':
idx = 5
test_img= []
# build file names to point to this given dataset
for f in config['test']:
aus = [pathlib.Path(f).parts[-i] for i in reversed(range(idx))][0:-1]
aus.insert(0, dataset_path)
test_img.append(os.path.join(*aus))
# create generator based on model specifications and dataset
if any([config['dataset_type'] == 'retinal', config['dataset_type'] == 'Kermany']):
data_gen = utilities.Kermany_data_gen
elif config['dataset_type'] == 'AIIMS':
data_gen = utilities.AIIMS_data_gen
elif config['dataset_type'] == 'Srinivas':
data_gen = utilities.Srinivas_data_gen
test_dataset = data_gen(test_img,
unique_labels=config['unique_labels'],
batch_size=16,
training=False,
channels=config['n_channels'],
input_size=config['input_size'],
random_label_experiment=config['random_label_experiment'],
random_label_experiment_seed=291209)
## perform testing for each fold the model was trained on
importlib.reload(utilities_models_tf)
test_fold_summary = {}
folds = glob.glob(os.path.join(model_path,"fold_*"))
# get the right model based on the model_version_specification
if model_version=="best":
model_name_version = "model.tf"
elif model_version=="last":
model_name_version = "last_model.tf"
for idx, f in enumerate(folds):
print(f'Working on fold {idx+1}/{len(folds)}')
# load model
if os.path.exists(os.path.join(f, model_name_version)):
model = tf.keras.models.load_model(os.path.join(f, model_name_version), compile=False)
else:
raise Exception('Model not found')
test_gt, test_prediction, test_time = utilities_models_tf.test_independent(model, config, test_dataset)
test_fold_summary[idx]={
'ground_truth':np.argmax(test_gt.numpy(), axis=-1),
'prediction':test_prediction.numpy(),
'test_time':float(test_time)
}
## save and plot
from collections import OrderedDict
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
'''
Saving overall cross validation test results and images:
- Confisuion matrix
- ROC curve
- Precision-Recall curve
- test summary file with the prediction for every test image (test_summary.txt)
Here add also the information needed to re-plot the ROC and PP curves (fpr,
tpr, roc_auc, precision and recall - micro and macro average)
The test_summary.txt file is a dictionary with the following entries:
- model_name: string
- labels: list of the true values fot the tested images
- fold_test_values: list containing the predictions for every fold (list of lists)
- test_time: string
- test date: string
- accuracy: float
- false_positive_rate: list containing the fpr for every class (list of lists)
- false_positive_rate_micro_avg: list containing the micro average fpr (used for the overall roc plot)
- false_positive_rate_macro_avg: list containing the macro average fpr (used for the overall roc plot)
- true_positive_rate: list containing the tpr for every class (list of lists)
- true_positive_rate_micro_avg: list containing the micro average tpr (used for the overall roc plot)
- true_positive_rate_macro_avg: list containing the macro average tpr (used for the overall roc plot)
- precision: list containing the precision values for every class to plot the PP (list of lists)
- precision_micro_avg: list of overall micro average of precision
- average_precision: average precision value computed using micro average
- recall: list containing the recall value for every class to plot the PP (list of lists)
- recall_micro_avg: list of overall micro average of recall
- F1: list of micro and macro average f1-score
Since the full test_summary file is long to open, the scores are also saved in a separate file for easy access
scores_test_summary.txt
'''
print(f'Saving information...')
# ############# save the information that is already available
test_summary = OrderedDict()
# for backwards compatibility
if config['number_crossvalidation_repetitions']:
n_cv = config['N_FOLDS']*config['number_crossvalidation_repetitions']
else:
n_cv = config['N_FOLDS']
test_summary['model_name'] = config['model_save_name']
test_summary['labels'] = [int(i) for i in test_fold_summary[0]['ground_truth']]
test_summary['folds_test_logits_values'] = [test_fold_summary[cv]['prediction'].tolist() for cv in range(n_cv)]
test_summary['test_time'] = utilities.tictoc_from_time(np.sum([test_fold_summary[cv]['test_time'] for cv in range(n_cv)]))
test_summary['test_model_version'] = model_version
test_summary['test_date'] = time.strftime("%Y%m%d-%H%M%S")
# ############ plot and save confucion matrix
ensemble_pred_argmax = []
ensemble_pred_logits = []
# compute ensemble
# compute the logits mean along the folds
ensemble_pred_logits = np.array(test_summary['folds_test_logits_values']).mean(axis=0)
# compute argmax prediction
ensemble_pred_argmax = np.argmax(ensemble_pred_logits, axis=1)
acc = utilities.plotConfusionMatrix(test_summary['labels'], ensemble_pred_argmax,
classes=config['label_description'],
savePath=model_path,
saveName=f'ConfusionMatrix_{model_version}_model',
draw=False)
# ############ plot and save ROC curve
fpr, tpr, roc_auc = utilities.plotROC(test_summary['labels'], ensemble_pred_logits,
classes=config['label_description'],
savePath=model_path,
saveName=f'Multiclass_ROC_{model_version}_model',
draw=False)
# make elements of the dictionary to be lists for saving
for key, value in fpr.items():
fpr[key]=value.tolist()
for key, value in tpr.items():
tpr[key]=value.tolist()
for key, value in roc_auc.items():
roc_auc[key]=value.tolist()
# ############ plot and save PR curve
precision, recall, average_precision, F1 = utilities.plotPR(test_summary['labels'],
ensemble_pred_logits,
classes=config['label_description'],
savePath=model_path,
saveName=f'Multiclass_PR_{model_version}_model',
draw=False)
# make elements of the dictionary to be lists for saving
for key, value in precision.items():
precision[key]=value.tolist()
for key, value in recall.items():
recall[key]=value.tolist()
# save all the information in the test summary
test_summary['accuracy'] = acc
# test_summary['false_positive_rate'] = [fpr[i].tolist() for i in range(len(class_labels))]
test_summary['false_positive_rate'] = fpr
# test_summary['false_positive_rate_micro_avg'] = fpr['micro'].tolist()
# test_summary['false_positive_rate_macro_avg'] = fpr['macro'].tolist()
test_summary['true_positive_rate'] = tpr
# test_summary['true_positive_rate'] = [tpr[i].tolist() for i in range(len(class_labels))]
# test_summary['true_positive_rate_micro_avg'] = tpr['micro'].tolist()
# test_summary['true_positive_rate_macro_avg'] = tpr['macro'].tolist()
test_summary['roc_auc'] = roc_auc
test_summary['precision'] = precision
# test_summary['precision'] = [precision[i].tolist() for i in range(len(class_labels))]
# test_summary['precision_micro_avg'] = precision['micro'].tolist()
test_summary['recall'] = recall
# test_summary['recall'] = [recall[i].tolist() for i in range(len(class_labels))]
# test_summary['recall_micro_avg'] = recall['micro'].tolist()
test_summary['average_precision'] = average_precision
test_summary['F1'] = F1
# save summary file
with open(os.path.join(model_path,f'{model_version}_model_version_test_summary.txt'), 'w') as fp:
json.dump(test_summary, fp)
## save summary (can be improved, but using the routine from print_model_performance)
from sklearn.metrics import average_precision_score, recall_score, roc_auc_score, f1_score, confusion_matrix, accuracy_score, matthews_corrcoef
def get_metrics(true_logits, pred_logits, average='macro'):
'''
Utility that given confusion matrics, returns a dictionary containing:
tptnfpfn : overall TP, TN, FP, FN values for each of the classes
precision (specificity) : for each of the classes
recall (sensitivity) : for each of the classes
f1-score : for each of the classes
auc : for each of the classes
overall_acc : over all classes
overall_specificity : over all classes
overall_precision : over all classes
overall_f1-score : over all classes
overall_auc : over all classes
'''
# compute confusion matrix
cnf_matrix = confusion_matrix(np.argmax(true_logits,-1), np.argmax(pred_logits, -1))
# compute TP, TN, FP, FN
FP = (cnf_matrix.sum(axis=0) - np.diag(cnf_matrix)).astype(float)
FN = (cnf_matrix.sum(axis=1) - np.diag(cnf_matrix)).astype(float)
TP = (np.diag(cnf_matrix)).astype(float)
TN = (cnf_matrix.sum() - (FP + FN + TP)).astype(float)
# compute per class metrics
summary_dict = {
'precision': TN / (FP+TN),
'recall': TP / (TP+FN),
'accuracy': (TP+TN) / (TP+TN+FP+FN),
'f1-score': TP / (TP + 0.5*(FP+FN)),
'auc': roc_auc_score(true_logits, pred_logits, average=None),
}
# compute overall metrics
# note that these metrics, especially those that depend on a threshold,
# will the average metrics over all the tresholds and will be different
# compared to the single threshold computed above (threshold=0.5)
summary_dict['overall_precision']=average_precision_score(true_logits,
pred_logits,
average=average)
summary_dict['overall_recall']=recall_score(np.argmax(true_logits,-1),
np.argmax(pred_logits,-1),
average=average)
summary_dict['overall_accuracy']=accuracy_score(np.argmax(true_logits,-1),
np.argmax(pred_logits,-1))
summary_dict['overall_f1-score']=f1_score(np.argmax(true_logits,-1),
np.argmax(pred_logits,-1),
average=average)
summary_dict['overall_auc']=roc_auc_score(true_logits,
pred_logits,
multi_class='ovr',
average=average)
summary_dict['matthews_correlation_coef']=matthews_corrcoef(np.argmax(true_logits,-1),
np.argmax(pred_logits,-1),)
return summary_dict
n_folds = len(folds)
print()
labels = np.eye(np.unique(test_summary['labels']).shape[0])[test_summary['labels']]
pred_logits = test_summary['folds_test_logits_values']
# Computupe per fold performance
per_fold_performance = []
for f in range(len(folds)):
per_fold_performance.append(get_metrics(labels, pred_logits[f]))
# compute ensamble performance
# compute the logits mean along the folds
ensemble_pred_logits = np.array(pred_logits).mean(axis=0)
# compute argmax prediction
ensemble_pred_argmax = np.argmax(ensemble_pred_logits, axis=1)
performance_ensamble = get_metrics(labels, ensemble_pred_logits)
# ######################### printing on file
summary = open(os.path.join(model_path,f'{model_version}_model_version_short_test_summary.txt'), 'w')
summary.write(f'\nModel Name: {os.path.basename(model_path)}\n\n')
# add test time overall and per image
average_test_time = np.mean([test_fold_summary[cv]['test_time'] for cv in range(n_cv)])
average_test_time_per_image = np.mean([test_fold_summary[cv]['test_time'] for cv in range(n_cv)])/labels.shape[0]
summary.write(f'Overall model test time (average over folds): {utilities.tictoc_from_time(average_test_time)}\n')
summary.write(f'Average test time per image (average over folds): {utilities.tictoc_from_time(average_test_time_per_image)}\n\n')
# print a summary of the testing per fold, class and ensamble
keys = ['precision','recall', 'accuracy', 'f1-score', 'auc']
max_len_k=max([len(k) for k in keys])
classes = config['label_description']
max_len_c=max(max([len(k) for k in classes]),len('Overall'))
max_len_f=max([len(s) for s in ["Fold", "Average","STD","Ensamble"]])
# build dict that holds the avg of all metrics
avg_dict = {k:[] for k in keys}
# build headers strings
fold_str = f'{"Fold":^{max_len_f}}'
class_str = f'{"Class":^{max_len_c}}'
keys_str = ''.join([f'{k.capitalize():^{max_len_k+2}}' for k in keys])
# start printing
summary.write(f'\n{"¤"*(max_len_f+max_len_c+len(keys_str))}'+'\n')
summary.write(f'{"¤ Per-fold metrics ¤":^{(max_len_f+max_len_c+len(keys_str))}}'+'\n')
summary.write(f'{"¤"*(max_len_f+max_len_c+len(keys_str))}\n')
# print header
summary.write(fold_str+class_str+keys_str+'\n')
# print per fold and class metrics
for idx, fp in enumerate(per_fold_performance):
fold_num_str = f'{str(idx+1):^{max_len_f}}'
# print per class performance
for idc, c in enumerate(classes):
class_metrics = ''.join([f'{str(round(fp[k][idc],3)):^{max_len_k+2}}' for k in keys])
if idc == 0:
summary.write(fold_num_str+f'{c:^{max_len_c}}'+class_metrics+'\n')
else:
summary.write(f'{" ":^{max_len_f}}'+f'{c:^{max_len_c}}'+class_metrics+'\n')
# print overall performance
summary.write(f'{"-"*(max_len_f+max_len_c+len(keys_str))}'+'\n')
overall_metric_str = ''.join([f'{str(round(fp["overall_"+k],3)):^{max_len_k+2}}' for k in keys])
summary.write(fold_num_str+f'{"Overall":^{max_len_c}}'+overall_metric_str+'\n')
summary.write(f'{"-"*(max_len_f+max_len_c+len(keys_str))}'+'\n\n')
# save overall metrics for later
[avg_dict[k].append(fp['overall_'+k]) for k in keys]
# print average overall metrics for the folds
avg_overall_metric_str = ''.join([f'{str(round(np.mean(avg_dict[k]),3)):^{max_len_k+2}}' for k in keys])
std_overall_metric_str = ''.join([f'{str(round(np.std(avg_dict[k]),3)):^{max_len_k+2}}' for k in keys])
summary.write(f'{"="*(max_len_f+max_len_c+len(keys_str))}'+'\n')
summary.write(fold_str+class_str+keys_str+'\n')
summary.write(f'{"Average":^{max_len_f}}'+f'{"":^{len(class_str)}}'+avg_overall_metric_str+'\n')
summary.write(f'{"STD":^{max_len_f}}'+f'{"":^{len(class_str)}}'+std_overall_metric_str+'\n')
# plot ensamble metrics
summary.write(f'\n{"¤"*(max_len_f+max_len_c+len(keys_str))}'+'\n')
summary.write(f'{"¤ Ensamble metrics ¤":^{(max_len_f+max_len_c+len(keys_str))}}'+'\n')
summary.write(f'{"¤"*(max_len_f+max_len_c+len(keys_str))}\n')
# print header
summary.write(f'{"Ensamble":^{max_len_f}}'+class_str+keys_str+'\n')
# print per class performance
fp = performance_ensamble
for idc, c in enumerate(classes):
class_metrics = ''.join([f'{str(round(fp[k][idc],3)):^{max_len_k+2}}' for k in keys])
summary.write(f'{" ":^{max_len_f}}'+f'{c:^{max_len_c}}'+class_metrics+'\n')
# print overall performance
summary.write(f'{"-"*(max_len_f+max_len_c+len(keys_str))}'+'\n')
overall_metric_str = ''.join([f'{str(round(fp["overall_"+k],3)):^{max_len_k+2}}' for k in keys])
summary.write(f'{" ":^{max_len_f}}'+f'{"Overall":^{max_len_c}}'+overall_metric_str+'\n')
summary.close()
## print on terminal
print('Ensemble preformance\n')
max_len = max([len(key) for key in performance_ensamble.keys()])
[print(f'{key:{max_len}s}: {value}') for key, value in performance_ensamble.items()]
## save also the information in a .csv file useful for plotting (and hypothesis testing in case)
'''
The file should allow for easy plotting of the models performance based on
classification type, model, model_version (best, last, ensemble_best, ensemble_last),
metrics, training time inference time.
'''
def get_time_from_string(time_string):
'''
Utility that given a time_string formated as 0d:0h:0m:0s:0ms,
returns the number of hours
'''
splits = time_string.split(':')
# get the values for d, h, m, s, ms
time_values = [ [] for _ in range(len(splits))]
[[time_values[idx].append(c) for c in v if c.isnumeric()] for idx, v in enumerate(splits)]
time_values = [int(''.join(v)) for v in time_values]
hours = sum([tv*hv for tv, hv in zip(time_values, [24, 1, 1/60, 1/60**2, 1/60**3])])
return hours
def get_mean_training_time(model_path, model_version='best', debug=False):
'''
Utility that returns the mean trianing time over the folds
'''
fold_paths = glob.glob(os.path.join(model_path, 'fold_*',''))
per_fold_training_time = []
for idx, fp in enumerate(fold_paths):
# open summary training
with open(os.path.join(fp,'model_summary_json.txt')) as json_file:
training_summary = json.load(json_file)
# get number of hours for the best model
training_time = get_time_from_string(training_summary['TRAINING_TIME'])
# adjust if needed the training time for the last model
if model_version == 'last':
epoch_training_time = training_time/(training_summary['EPOCHS']+1e-6)
training_time = epoch_training_time*250
if debug:
print(f'Fold {idx+1} ({model_version}): {training_time:0.2f}')
per_fold_training_time.append(training_time)
return per_fold_training_time
## work on saving
import csv
summary_file = os.path.join(model_path,f'{model_version}_tabular_test_summary.csv')
csv_file = open(summary_file, "w")
writer = csv.writer(csv_file)
csv_header = ['classification_type',
'nbr_classes',
'model_type',
'model_version',
'fold',
'precision',
'recall',
'accuracy',
'f1-score',
'auc',
'training_time',
'matthews_correlation_coef',
]
writer.writerow(csv_header)
per_fold_training_time = get_mean_training_time(model_path, model_version=model_version)
classification_type = 'per-disease' if config['dataset_type']=='retinal' else 'normal-vs-cancer'
nbr_classes = 4 if config['dataset_type']=='retinal' else 2
# loop through all the folds and save information
rows_to_write = []
for idx, fp in enumerate(per_fold_performance):
rows_to_write.append([classification_type,
nbr_classes,
config['model_configuration'],
model_version,
idx+1,
fp['overall_precision'],
fp['overall_recall'],
fp['overall_accuracy'],
fp['overall_f1-score'],
fp['overall_auc'],
per_fold_training_time[idx],
fp['matthews_correlation_coef'],
])
# add ensamble information
rows_to_write.append([classification_type,
nbr_classes,
config['model_configuration'],
'ensemble',
'ensemble',
performance_ensamble['overall_precision'],
performance_ensamble['overall_recall'],
performance_ensamble['overall_accuracy'],
performance_ensamble['overall_f1-score'],
performance_ensamble['overall_auc'],
per_fold_training_time[idx],
performance_ensamble['matthews_correlation_coef'],
])
writer.writerows(rows_to_write)
csv_file.close()
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33761,
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220,
220,
220,
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299,
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37724,
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220,
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220,
220,
4566,
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19849,
62,
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705,
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220,
220,
2854,
62,
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6,
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220,
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220,
220,
220,
220,
583,
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62,
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62,
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220,
220,
220,
220,
2854,
62,
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62,
10215,
49501,
62,
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6,
4357,
198,
220,
220,
220,
33761,
198,
198,
16002,
13,
16002,
1666,
7,
8516,
62,
1462,
62,
13564,
8,
198,
40664,
62,
7753,
13,
19836,
3419,
628
] | 2.474942 | 9,498 |
from yunionclient.common import base
| [
6738,
331,
24592,
16366,
13,
11321,
1330,
2779,
628,
198
] | 3.9 | 10 |
"""The script retrieves explicit connectives marking temporal, comparison, contingency, and expansion relations
and prints the total number of these connectives per article section."""
import sys
import spacy
from collections import Counter
nlp = spacy.load("en_core_web_sm")
# The script below retrieves the connectives from each article abstract
# and prints their total number.
# To change the section type, simply replace "abstracts.txt" with
# "introductions.txt" -- for introductions,
# "relatedwork.txt" -- for related works,
# "discussions.txt" -- for discussions,
# "conclusions.txt" -- for conclusions.
f = open("abstracts.txt", "r", encoding="utf-8")
for line in f:
if len(line) > 0:
items = line.split(" ")
if len(items) > 1:
file_name = items[0]
file_text = items[1]
low_case = file_text.lower()
doc = nlp(low_case)
temporal = {"after", "afterwards", "before",
"earlier", "later", "meanwhile",
"next", "previously", "simultaneously", "thereafter",
"till", "until", "ultimately"
}
comparison = {"although", "but", "conversely", "however",
"instead", "nevertheless", "nonetheless", "rather",
"though", "whereas", "yet", "regardless", "despite", "though"
}
contingency = {"as", "because", "consequently", "hence", "if",
"thereby", "therefore", "thus", "so", "indeed", "accordingly"}
expansion = {"also", "alternatively", "besides", "else", "except",
"finally", "further", "furthermore", "likewise",
"moreover", "neither", "nor", "or", "otherwise", "overall", "plus",
"separately", "similarly", "specifically",
"especially", "first", "second", "firstly", "secondly"}
temporal_relations = []
comparison_relations = []
contingency_relations = []
expansion_relations = []
for token in doc:
if token.text == token.text in temporal:
temporal_relations.append(token.text)
elif token.text == token.text in comparison:
comparison_relations.append(token.text)
elif token.text == token.text in contingency:
contingency_relations.append(token.text)
elif token.text == token.text in comparison:
expansion_relations.append(token.text)
print(file_name)
print("Temporal connectives found in the section:", temporal_relations)
print("Total number of connectives:", len(temporal_relations))
print("Comparison connectives found in the section:", comparison_relations)
print("Total number of connectives:", len(comparison_relations))
print("Contingency connectives found in the section:", contingency_relations)
print("Total number of connectives:", len(contingency_relations))
print("Expansion connectives found in the section:", expansion_relations)
print("Total number of connectives:", len(expansion_relations)) | [
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366,
2016,
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69,
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366,
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710,
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220,
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366,
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306,
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366,
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453,
1600,
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220,
220,
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220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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306,
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366,
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306,
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201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
21964,
62,
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796,
17635,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7208,
62,
39468,
796,
17635,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
38820,
62,
39468,
796,
17635,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7118,
62,
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796,
17635,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
11241,
287,
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25,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
11241,
13,
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6624,
11241,
13,
5239,
287,
21964,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
21964,
62,
39468,
13,
33295,
7,
30001,
13,
5239,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
11241,
13,
5239,
6624,
11241,
13,
5239,
287,
7208,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7208,
62,
39468,
13,
33295,
7,
30001,
13,
5239,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
11241,
13,
5239,
6624,
11241,
13,
5239,
287,
38820,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
38820,
62,
39468,
13,
33295,
7,
30001,
13,
5239,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
11241,
13,
5239,
6624,
11241,
13,
5239,
287,
7208,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7118,
62,
39468,
13,
33295,
7,
30001,
13,
5239,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
7753,
62,
3672,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
12966,
35738,
2018,
1083,
1043,
287,
262,
2665,
25,
1600,
21964,
62,
39468,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
14957,
1271,
286,
2018,
1083,
25,
1600,
18896,
7,
11498,
35738,
62,
39468,
4008,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
50249,
1653,
2018,
1083,
1043,
287,
262,
2665,
25,
1600,
7208,
62,
39468,
8,
201,
198,
220,
220,
220,
220,
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"""
Basic metadata reports/queries
@author: dan myung ([email protected])
create: 10/19/2009
Notes:
A place where oft reused queries off xformmanager.metadata can be referenced.
This is somewhat redundant with functionality already existent in the apps/reports/util.py file which already does
a basic metadata query.
"""
from hq.models import Domain, Organization, ReporterProfile, BlacklistedUser
from receiver.models import Submission
from reporters.models import Reporter, ReporterGroup
from datetime import datetime, timedelta
from xformmanager.models import Metadata
def build_filtered_metadataquery(intervalstart, intervalend, domain=None, reportergroup=None, reporterprofile=None, formdefs=[]):
"""
Simple report function to prepare the metadata query for the eventual magic you will do to it.
The required arguments are a timespan, and at least one of the following:
Domain
A single reporter group
A single reporter profile
or an array of formdatadef
Returns the filtered Metadata queryset
"""
if domain is None and reportergroup is None and reporterprofile is None and len(formdefs)== 0:
raise Exception("Insufficient arguments to run query")
filtered = Metadata.objects.filter(timeend__gte=intervalstart).filter(timeend__lte=intervalend)
exclude_usernames = []
if domain != None:
#next, get the blacklisted users from this domain and exclude them
blist = BlacklistedUser.objects.filter(domains=domain, active=True)
exclude_usernames = blist.values_list('username',flat=True)
filtered = filtered.filter(formdefmodel__domain=domain).exclude(username__in=exclude_usernames)
if reportergroup != None:
raise Exception ("Reportergroup filtration not implemented yet")
if reporterprofile != None:
#note, consistency on chw_id vs. chw_username still needs to be worked out. for this usage, we'll stick to chw_username
#chw_id should probably used long term
filtered = filtered.filter(username=reporterprofile.chw_username).exclude(username__in=exclude_usernames)
if len(formdefs) > 0:
filtered = filtered.filter(formdefmodel__in=formdefs).exclude(username__in=exclude_usernames)
return filtered
def timeend_by_hour_of_day(intervalstart, intervalend, domain=None, reportergroup=None, reporterprofile=None, formdefs=[]):
"""
Simple report function to get a histogram of the timeend by hour.
The required arguments are a timespan, and at least one of the following:
Domain
A single reporter group
A single reporter profile
or an array of formdatadef
This will return an array of counts, 24 in length for each hour with integers in it [1,4,1,2,4,2,...]
"""
filtered = build_filtered_metadataquery(intervalstart, intervalend, domain=domain, reportergroup=reportergroup, reporterprofile=reporterprofile, formdefs=formdefs)
hourcounts = []
for hour in range(0,24):
hourcounts.append(filtered.extra(where=['hour(timeend)=%d' % hour ]).count())
return hourcounts
def timestart_by_hour_of_day(intervalstart, intervalend, domain=None, reportergroup=None, reporterprofile=None, formdefs=[]):
"""
Simple report function to get a histogram of the timestart by hour.
The required arguments are a timespan, and at least one of the following:
Domain
A single reporter group
A single reporter profile
or an array of formdatadef
This will return an array of counts, 24 in length for each hour with integers in it [1,4,1,2,4,2,...]
"""
filtered = build_filtered_metadataquery(intervalstart, intervalend, domain=domain, reportergroup=reportergroup, reporterprofile=reporterprofile, formdefs=formdefs)
hourcounts = []
for hour in range(0,24):
hourcounts.append(filtered.extra(where=['hour(timestart)=%d' % hour ]).count())
return hourcounts
def timedelta_by_hour_of_day(intervalstart, intervalend, domain=None, reportergroup=None, reporterprofile=None, formdefs=[]):
"""
Simple report function to get a histogram of the average time delta of the timestart and timeeend, and show the results
by hour of the timeend.
The required arguments are a timespan, and at least one of the following:
Domain
A single reporter group
A single reporter profile
or an array of formdatadef
This will return an array of counts, 24 in length for each hour with integers in it [1,4,1,2,4,2,...]
"""
filtered = build_filtered_metadataquery(intervalstart, intervalend, domain=domain, reportergroup=reportergroup, reporterprofile=reporterprofile, formdefs=formdefs)
hourcounts = []
for hour in range(0,24):
filterhour_timeend = filtered.extra(where=['hour(timeend)=%d' % hour ])
totalseconds = 0
for filt in filterhour_timeend:
totalseconds = totalseconds + (filt.timeend - filt.timestart).seconds
if filterhour_timeend.count() > 0:
avg = (totalseconds/filterhour_timeend.count())/60
else:
avg = -1
hourcounts.append(avg)
return hourcounts
def receivetime_by_hour_of_day(intervalstart, intervalend, domain=None, reportergroup=None, reporterprofile=None, formdefs=[]):
"""
Simple report function to get a histogram of the time received by the server
The required arguments are a timespan, and at least one of the following:
Domain
A single reporter group
A single reporter profile
or an array of formdatadef
This will return an array of counts, 24 in length for each hour with integers in it [1,4,1,2,4,2,...]
"""
filtered = build_filtered_metadataquery(intervalstart, intervalend, domain=domain, reportergroup=reportergroup, reporterprofile=reporterprofile, formdefs=formdefs)
#ok, so we got the filtered results, now we need to cross link it with the submissions to get the submit_time
submission_ids = filtered.values_list('attachment__submission__id', flat=True)
submissions = Submission.objects.filter(id__in=submission_ids)
hourcounts = []
for hour in range(0,24):
hourshift = (hour + 17)%24
#hourshift = hour
hourcounts.append(submissions.extra(where=['hour(submit_time)=%d' % hourshift ]).count())
return hourcounts
def metadata_submission_stats(intervalstart, intervalend, domain=None, reportergroup=None, reporterprofile=None, formdefs=[]):
"""
Using the same metadata filtration, establish stats on Metadata
"""
filtered = build_filtered_metadataquery(intervalstart, intervalend, domain=domain, reportergroup=reportergroup, reporterprofile=reporterprofile, formdefs=formdefs)
#total by deviceid
#duplicates
#metadata/users
pass
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] | 2.782626 | 2,544 |
################################################################################
# filename: switch_skill.py
# date: 07. Apr. 2021
# username: winkste
# name: Stephan Wink
# description: This module handles the input signal of a switch.
# In the first implementation it will only support polling of an
# input pin. In future implementations also reacting on interrupts
# shall be possible, therefore a mode variable is defined and
# handed over to the object during construction
#
#
#
################################################################################
################################################################################
# Imports
import time
from src.skills.abs_skill import AbstractSkill
from src.mqtt.user_subs import UserSubs
from src.mqtt.user_pubs import UserPubs
import machine
import src.utils.trace as T
################################################################################
# Variables
_NO_VALUE = 0xff
_SWITCH_OFF_TIME = 1000
SWITCH_SKILL_MODE_POLL = 0
SWITCH_SKILL_MODE_ISR = 1
_SWITCH_STATE_LOW = 0
_SWITCH_STATE_HIGH = 1
_SWITCH_STATE_INIT = 0xff
_SWITCH_STATE_DICT_INV = {
_SWITCH_STATE_LOW: _SWITCH_STATE_HIGH,
_SWITCH_STATE_HIGH: _SWITCH_STATE_LOW,
}
################################################################################
# Functions
################################################################################
# Classes
################################################################################
# @brief This is the switch skill, handling a switch input signal
################################################################################
################################################################################
# Scripts
T.configure(__name__, T.INFO)
if __name__ == "__main__":
# execute only if run as a script
T.trace(__name__, T.WARNING, 'no main script defined ')
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] | 3.92915 | 494 |
from vyper.utils import GAS_IDENTITY, GAS_IDENTITYWORD
from vyper.exceptions import (
InvalidLiteralException,
TypeMismatchException
)
from vyper.parser.lll_node import (
LLLnode
)
from vyper.types import (
BaseType,
ByteArrayType,
ContractType,
NullType,
StructType,
MappingType,
TupleType,
ListType,
)
from vyper.types import (
is_base_type,
are_units_compatible,
get_size_of_type,
ceil32
)
from vyper.utils import (
SizeLimits,
MemoryPositions,
DECIMAL_DIVISOR
)
# Get a decimal number as a fraction with denominator multiple of 10
# Is a number of decimal form (e.g. 65281) or 0x form (e.g. 0xff01) or 0b binary form (e.g. 0b0001)
# Copies byte array
# Copy bytes
# Accepts 4 arguments:
# (i) an LLL node for the start position of the source
# (ii) an LLL node for the start position of the destination
# (iii) an LLL node for the length
# (iv) a constant for the max length
# Takes a <32 byte array as input, and outputs a number.
# Take a value representing a memory or storage location, and descend down to an element or member variable
# Convert from one base type to another
# Unwrap location
| [
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530,
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791,
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4067,
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] | 2.820331 | 423 |
from __future__ import absolute_import
import random
from six.moves import range
| [
6738,
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62,
11748,
198,
11748,
4738,
198,
6738,
2237,
13,
76,
5241,
1330,
2837,
628
] | 4.1 | 20 |
from xbeecoord import *
from time import sleep
import multiprocessing
reset()
sleep(5)
getRssi()
sleep(2)
wakeup()
startwait()
'''
def main():
reset()
sleep(5)
getRssi()
sleep(2)
wakeup()
startwait()
if __name__=='__main__':
p = multiprocessing.Process(target=main,name="routine")
p.start()
sleep(300)
print "terminating routine"
p.terminate()
''' | [
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] | 2.414474 | 152 |
##
# @file rulesanml.py
# @author Ankit Srivastava <[email protected]>
#
# Copyright 2018 Georgia Institute of Technology
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import micronap.sdk as ap
import exceptions
import os
import re
import sys
from regexparser import RegexParser
class RulesAnml(object):
"""
Class for storing ANML-NFAs corresponding to the Snort rules.
"""
def add(self, keyword, sid, patterns):
"""
Add the given patterns, identified by the sid, to the bucket corresponding to the keyword.
"""
# try to add the pattern to a dummy anml object first
# this will throw an error, if there are any issues with patterns
anml = ap.Anml()
network = anml.CreateAutomataNetwork()
self._add_patterns(network, sid, patterns)
# check if the rule satisfies the maximum STEs limit
automaton, emap = anml.CompileAnml()
info = automaton.GetInfo()
if info.ste_count > 49152 / 2:
raise AnmlException, '\nAdding patterns for rule with SID %d failed.\nRequired resources exceeded those in one half-core.\n'%sid
bucket = keyword
if self._maxStes > 0:
if info.ste_count > self._maxStes:
bucket = '%s_%d'%(keyword, sid)
if info.clock_divisor > 1:
bucket = '%s_%d'%(keyword, info.clock_divisor)
#print keyword, sid, info.clock_divisor
# create a new network if it doesn't exist
if bucket not in self._anmlNetworks:
anml = ap.Anml()
network = anml.CreateAutomataNetwork(anmlId = bucket)
self._anmlNetworks[bucket] = (anml, network)
else:
network = self._anmlNetworks[bucket][1]
# now add pattern to the network
self._add_patterns(network, sid, patterns)
def export(self, directory):
"""
Write out all the ANML-NFAs to the given directory.
"""
for bucket, anmlNetwork in self._anmlNetworks.iteritems():
anmlNetwork[1].ExportAnml(os.path.join(directory, bucket + '.anml'))
def compile(self, directory):
"""
Compile all the ANML-NFAs and write the AP-FSMs to the given directory.
"""
for bucket, anmlNetwork in self._anmlNetworks.iteritems():
#if 'general' not in keyword:
#continue
print '\nCompiling %s\n'%bucket
try:
automata, emap = anmlNetwork[0].CompileAnml()
info = automata.GetInfo()
print 'Clock divisor', info.clock_divisor
automata.Save(os.path.join(directory, bucket + '.fsm'))
except ap.ApError, e:
sys.stderr.write('\nCompilation failed with the following error message.\n%s\n'%(str(e)))
sys.stderr.flush()
print '\nDone.\n'
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] | 2.427136 | 1,393 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2003-2009 Edgewall Software
# Copyright (C) 2003-2005 Jonas Borgström <[email protected]>
# Copyright (C) 2005 Christopher Lenz <[email protected]>
# All rights reserved.
#
# This software is licensed as described in the file COPYING, which
# you should have received as part of this distribution. The terms
# are also available at http://trac.edgewall.org/wiki/TracLicense.
#
# This software consists of voluntary contributions made by many
# individuals. For the exact contribution history, see the revision
# history and logs, available at http://trac.edgewall.org/log/.
#
# Author: Jonas Borgström <[email protected]>
# Christopher Lenz <[email protected]>
from __future__ import with_statement
import doctest
import os
import unittest
import sys
try:
from babel import Locale
locale_en = Locale.parse('en_US')
except ImportError:
locale_en = None
from trac.config import Configuration
from trac.core import Component, ComponentManager
from trac.env import Environment
from trac.db.api import _parse_db_str, DatabaseManager
from trac.db.sqlite_backend import SQLiteConnection
from trac.db.util import ConnectionWrapper
import trac.db.postgres_backend
import trac.db.mysql_backend
from trac.ticket.default_workflow import load_workflow_config_snippet
from trac.util import translation
def Mock(bases=(), *initargs, **kw):
"""
Simple factory for dummy classes that can be used as replacement for the
real implementation in tests.
Base classes for the mock can be specified using the first parameter, which
must be either a tuple of class objects or a single class object. If the
bases parameter is omitted, the base class of the mock will be object.
So to create a mock that is derived from the builtin dict type, you can do:
>>> mock = Mock(dict)
>>> mock['foo'] = 'bar'
>>> mock['foo']
'bar'
Attributes of the class are provided by any additional keyword parameters.
>>> mock = Mock(foo='bar')
>>> mock.foo
'bar'
Objects produces by this function have the special feature of not requiring
the 'self' parameter on methods, because you should keep data at the scope
of the test function. So you can just do:
>>> mock = Mock(add=lambda x,y: x+y)
>>> mock.add(1, 1)
2
To access attributes from the mock object from inside a lambda function,
just access the mock itself:
>>> mock = Mock(dict, do=lambda x: 'going to the %s' % mock[x])
>>> mock['foo'] = 'bar'
>>> mock.do('foo')
'going to the bar'
Because assignments or other types of statements don't work in lambda
functions, assigning to a local variable from a mock function requires some
extra work:
>>> myvar = [None]
>>> mock = Mock(set=lambda x: myvar.__setitem__(0, x))
>>> mock.set(1)
>>> myvar[0]
1
"""
if not isinstance(bases, tuple):
bases = (bases,)
cls = type('Mock', bases, {})
mock = cls(*initargs)
for k, v in kw.items():
setattr(mock, k, v)
return mock
class MockPerm(object):
"""Fake permission class. Necessary as Mock can not be used with operator
overloading."""
username = ''
__contains__ = has_permission
assert_permission = require
class TestSetup(unittest.TestSuite):
"""
Test suite decorator that allows a fixture to be setup for a complete
suite of test cases.
"""
def setUp(self):
"""Sets up the fixture, and sets self.fixture if needed"""
pass
def tearDown(self):
"""Tears down the fixture"""
pass
def run(self, result):
"""Setup the fixture (self.setUp), call .setFixture on all the tests,
and tear down the fixture (self.tearDown)."""
self.setUp()
if hasattr(self, 'fixture'):
for test in self._tests:
if hasattr(test, 'setFixture'):
test.setFixture(self.fixture)
unittest.TestSuite.run(self, result)
self.tearDown()
return result
def _wrapped_run(self, *args, **kwargs):
"Python 2.7 / unittest2 compatibility - there must be a better way..."
self.setUp()
if hasattr(self, 'fixture'):
for test in self._tests:
if hasattr(test, 'setFixture'):
test.setFixture(self.fixture)
unittest.TestSuite._wrapped_run(self, *args, **kwargs)
self.tearDown()
# -- Database utilities
# -- Environment stub
class EnvironmentStub(Environment):
"""A stub of the trac.env.Environment object for testing."""
href = abs_href = None
global_databasemanager = None
def __init__(self, default_data=False, enable=None, disable=None,
path=None, destroying=False):
"""Construct a new Environment stub object.
:param default_data: If True, populate the database with some
defaults.
:param enable: A list of component classes or name globs to
activate in the stub environment.
"""
ComponentManager.__init__(self)
Component.__init__(self)
self.systeminfo = []
import trac
self.path = path
if self.path is None:
self.path = os.path.dirname(trac.__file__)
if not os.path.isabs(self.path):
self.path = os.path.join(os.getcwd(), self.path)
# -- configuration
self.config = Configuration(None)
# We have to have a ticket-workflow config for ''lots'' of things to
# work. So insert the basic-workflow config here. There may be a
# better solution than this.
load_workflow_config_snippet(self.config, 'basic-workflow.ini')
self.config.set('logging', 'log_level', 'DEBUG')
self.config.set('logging', 'log_type', 'stderr')
if enable is not None:
self.config.set('components', 'trac.*', 'disabled')
else:
self.config.set('components', 'tracopt.versioncontrol.svn.*',
'enabled')
for name_or_class in enable or ():
config_key = self._component_name(name_or_class)
self.config.set('components', config_key, 'enabled')
for name_or_class in disable or ():
config_key = self._component_name(name_or_class)
self.config.set('components', config_key, 'disabled')
# -- logging
from trac.log import logger_handler_factory
self.log, self._log_handler = logger_handler_factory('test')
# -- database
self.config.set('components', 'trac.db.*', 'enabled')
self.dburi = get_dburi()
init_global = False
if self.global_databasemanager:
self.components[DatabaseManager] = global_databasemanager
else:
self.config.set('trac', 'database', self.dburi)
self.global_databasemanager = DatabaseManager(self)
self.config.set('trac', 'debug_sql', True)
self.config.set('logging', 'log_type', 'stderr')
self.config.set('logging', 'log_level', 'DEBUG')
init_global = not destroying
if default_data or init_global:
self.reset_db(default_data)
from trac.web.href import Href
self.href = Href('/trac.cgi')
self.abs_href = Href('http://example.org/trac.cgi')
self.known_users = []
translation.activate(locale_en)
def reset_db(self, default_data=None):
"""Remove all data from Trac tables, keeping the tables themselves.
:param default_data: after clean-up, initialize with default data
:return: True upon success
"""
from trac import db_default
scheme, db_prop = _parse_db_str(self.dburi)
tables = []
remove_sqlite_db = False
try:
with self.db_transaction as db:
db.rollback() # make sure there's no transaction in progress
# check the database version
database_version = db(
"SELECT value FROM system WHERE name='database_version'")
if database_version:
database_version = int(database_version[0][0])
if database_version == db_default.db_version:
# same version, simply clear the tables (faster)
m = sys.modules[__name__]
reset_fn = 'reset_%s_db' % scheme
if hasattr(m, reset_fn):
tables = getattr(m, reset_fn)(self, db_prop)
else:
# different version or version unknown, drop the tables
remove_sqlite_db = True
self.destroy_db(scheme, db_prop)
except Exception, e:
# "Database not found ...",
# "OperationalError: no such table: system" or the like
pass
db = None # as we might shutdown the pool FIXME no longer needed!
if scheme == 'sqlite' and remove_sqlite_db:
path = db_prop['path']
if path != ':memory:':
if not os.path.isabs(path):
path = os.path.join(self.path, path)
self.global_databasemanager.shutdown()
os.remove(path)
if not tables:
self.global_databasemanager.init_db()
# we need to make sure the next get_db_cnx() will re-create
# a new connection aware of the new data model - see #8518.
if self.dburi != 'sqlite::memory:':
self.global_databasemanager.shutdown()
with self.db_transaction as db:
if default_data:
for table, cols, vals in db_default.get_data(db):
db.executemany("INSERT INTO %s (%s) VALUES (%s)"
% (table, ','.join(cols),
','.join(['%s' for c in cols])),
vals)
else:
db("INSERT INTO system (name, value) VALUES (%s, %s)",
('database_version', str(db_default.db_version)))
# overriden
def locate(fn):
"""Locates a binary on the path.
Returns the fully-qualified path, or None.
"""
exec_suffix = '.exe' if os.name == 'nt' else ''
for p in ["."] + os.environ['PATH'].split(os.pathsep):
f = os.path.join(p, fn + exec_suffix)
if os.path.exists(f):
return f
return None
INCLUDE_FUNCTIONAL_TESTS = True
if __name__ == '__main__':
#FIXME: this is a bit inelegant
if '--skip-functional-tests' in sys.argv:
sys.argv.remove('--skip-functional-tests')
INCLUDE_FUNCTIONAL_TESTS = False
unittest.main(defaultTest='suite')
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] | 2.206842 | 5,086 |
# WAP to accept a folder name from user and create zip file out of contents of it.
import shutil
if __name__ == "__main__":
main() | [
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# -*- coding: utf-8 -*-
"""
pagarmecoreapi
This file was automatically generated by APIMATIC v2.0 ( https://apimatic.io ).
"""
class CreatePriceBracketRequest(object):
"""Implementation of the 'CreatePriceBracketRequest' model.
Request for creating a price bracket
Attributes:
start_quantity (int): Start quantity
price (int): Price
end_quantity (int): End quantity
overage_price (int): Overage price
"""
# Create a mapping from Model property names to API property names
_names = {
"start_quantity":'start_quantity',
"price":'price',
"end_quantity":'end_quantity',
"overage_price":'overage_price'
}
def __init__(self,
start_quantity=None,
price=None,
end_quantity=None,
overage_price=None):
"""Constructor for the CreatePriceBracketRequest class"""
# Initialize members of the class
self.start_quantity = start_quantity
self.price = price
self.end_quantity = end_quantity
self.overage_price = overage_price
@classmethod
def from_dictionary(cls,
dictionary):
"""Creates an instance of this model from a dictionary
Args:
dictionary (dictionary): A dictionary representation of the object as
obtained from the deserialization of the server's response. The keys
MUST match property names in the API description.
Returns:
object: An instance of this structure class.
"""
if dictionary is None:
return None
# Extract variables from the dictionary
start_quantity = dictionary.get('start_quantity')
price = dictionary.get('price')
end_quantity = dictionary.get('end_quantity')
overage_price = dictionary.get('overage_price')
# Return an object of this model
return cls(start_quantity,
price,
end_quantity,
overage_price)
| [
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] | 2.204868 | 986 |
from dataclasses import dataclass, field
from typing import List, AnyStr, Iterator, Any, Dict
import numpy as np
import pandas as pd
from gmpy2 import mpz
@dataclass
class Item:
""" Describes an item of an attribute.
The item is the lowest level representation of the attribute. It is defined by at least one condition and,
for example, in the case of a NominalAttribute it can be given by the condition: x = blue_eyes; and in the
NumericAttribute by: x < 3;
Attributes
----------
bit_array : gmpy2.mpz
Bit representation of the indexes covered by the item's condition.
description : str
Text describing the item.
numper_operators : int
Number of operators necessary to describe the item.
activation_function : object
Partial function applied to DataFrame that returns boolean vector of instances where item is "present".
"""
bitarray: mpz
parent_variable: AnyStr
description: AnyStr
number_operators: int
activation_function: object
@dataclass
class Attribute:
""" Describes an explainable variable.
Contains all information regarding a certain attribute. This is the parent class for NumericAttribute and
NominalAttribute, which add extra specific information to this.
Attributes
----------
name : str
Name of the attribute.
values : np.ndarray
Vector of values associated with attribute.
max_operators : int
Maximum number of operators allowed for this variable.
min_support : int
Minimum support of numer of instances covered by an item or pattern
"""
name: AnyStr
values: np.ndarray
max_operators: int
min_support: Any # it can be an int or a float
cardinality_operator : Dict[int, int] =field(init=False)
items : List[Item] = field(default_factory=list, init=False) | [
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] | 3.147899 | 595 |
'''
Created on Sep 19, 2021
@author: immanueltrummer
'''
import argparse
import collections
import json
import pandas as pd
import sqlite3
def get_db_path(spider_dir, db_id):
""" Return path to SQLite database file.
Args:
spider_dir: path to SPIDER benchmark
db_id: database identifier
Returns:
path to SQLite database file
"""
return f'{spider_dir}/database/{db_id}/{db_id}.sqlite'
def extract(spider_dir, db_json):
""" Extract data from database into .csv files.
Args:
spider_dir: path to SPIDER main directory
db_json: JSON description of database
"""
db_id = db_json['db_id']
db_dir = f'{spider_dir}/database/{db_id}'
db_path = f'{db_dir}/{db_id}.sqlite'
print(f'Path to DB: {db_path}')
with sqlite3.connect(db_path) as con:
#con.text_factory = bytes
con.text_factory = lambda b: b.decode(errors = 'ignore')
for tbl in db_json['table_names_original']:
query = f'select * from {tbl}'
df = pd.read_sql_query(query, con)
out_path = f'{db_dir}/{tbl}.csv'
df.to_csv(out_path, index=False)
def get_result(spider_dir, query_json):
""" Execute query and return result.
Args:
spider_dir: path to SPIDER benchmark
query_json: describes query by JSON
Returns:
query result
"""
db_id = query_json['db_id']
db_path = get_db_path(spider_dir, db_id)
sql = query_json['query']
with sqlite3.connect(db_path) as con:
cur = con.cursor()
cur.execute(sql)
result = cur.fetchall()
print(f'Query: {sql}; Result: {result}')
return result
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('spider', type=str, help='Path to SPIDER benchmark')
args = parser.parse_args()
tables_path = f'{args.spider}/tables.json'
db_to_s = {}
with open(tables_path) as file:
tables = json.load(file)
nr_dbs = len(tables)
for db_idx, db in enumerate(tables):
db_id = db['db_id']
db_to_s[db_id] = db
print(f'Extracting {db_id} ({db_idx+1}/{nr_dbs})')
extract(args.spider, db)
db_path = f'{args.spider}/schemata.json'
with open(db_path, 'w') as file:
json.dump(db_to_s, file)
for in_file in ['train_spider', 'dev']:
db_to_q = collections.defaultdict(lambda:[])
all_results = []
train_path = f'{args.spider}/{in_file}.json'
with open(train_path) as file:
queries = json.load(file)
nr_queries = len(queries)
nr_valid = 0
for q_idx, q_json in enumerate(queries):
query = q_json['query']
question = q_json['question']
db_id = q_json['db_id']
print(f'"{query}" on "{db_id}" ({q_idx+1}/{nr_queries})')
db_to_q[db_id].append(q_json)
try:
result = get_result(args.spider, q_json)
row = {
'db_id':db_id, 'question':question,
'query':query, 'results':result}
all_results.append(row)
nr_valid += 1
except:
print(f'Invalid Query: {query} on {db_id}')
print(f'Processed {nr_valid}/{nr_queries} queries')
results_path = f'{args.spider}/results_{in_file}.json'
with open(results_path, 'w') as file:
json.dump(all_results, file)
q_path = f'{args.spider}/{in_file}_queries.json'
with open(q_path, 'w') as file:
json.dump(db_to_q, file) | [
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8
] | 1.928499 | 1,972 |
import mobula.layers as L
import numpy as np
| [
11748,
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13,
75,
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299,
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] | 3 | 15 |
# Generated by Django 3.1.7 on 2021-06-01 08:44
from django.conf import settings
from django.db import migrations, models
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2,
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] | 3.1 | 40 |
# -*- coding: utf-8 -*-
# vim: set sw=2 ts=2 sts=2 et :
'''Defines a comment
(@@ c.(This is a comment!) x z) -> (x z)
'''
from lambd import *
| [
2,
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] | 2.164179 | 67 |
import setuptools
import os
with open("README.md", "r") as fh:
long_description = fh.read()
version = os.popen('git tag -l --sort -version:refname | head -n 1').read().split('\n', 1)[0]
setuptools.setup(
name="sherlockpipe", # Replace with your own username
version=version,
author="F.J. Pozuelos & M. Dévora",
author_email="[email protected]",
description="Search for Hints of Exoplanets fRom Lightcurves Of spaCe based seeKers",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/franpoz/SHERLOCK",
packages=setuptools.find_packages(),
include_package_data=True,
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires='>=3.6',
install_requires=["numpy",
"cython",
"pandas",
"lightkurve",
"transitleastsquares",
"requests",
"eleanor",
"wotan",
"matplotlib",
"pyyaml",
"allesfitter",
"seaborn",
"bokeh",
"astroplan",
"astroquery",
"sklearn",
"scipy",
"tess-point",
"reproject==0.4",
"reportlab",
"astropy",
"mock > 2.0.0"
]
)
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1875,
362,
13,
15,
13,
15,
1,
198,
220,
220,
220,
2361,
198,
8,
198
] | 1.730372 | 968 |
# -*- coding: utf-8 -*-
# flake8: noqa
from . import api
from . import tasks
from . import web
| [
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] | 2.526316 | 38 |
default_app_config = 'sensor.apps.SmogerConfig'
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import mongoengine
from mongoengine import fields, Document, ImproperlyConfigured
from creators.models import Creator | [
198,
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l = range(8)
print filter(lambda x: x % 2 == 0, l)
# [0, 2, 4, 6]
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import unittest
from pystrings.lineup_students import lineup_students
s1 = "Tadashi Takahiro Takao Takashi Takayuki Takehiko Takeo Takeshi Takeshi"
lst1 = [
"Takehiko",
"Takayuki",
"Takahiro",
"Takeshi",
"Takeshi",
"Takashi",
"Tadashi",
"Takeo",
"Takao",
]
s2 = "Michio Miki Mikio Minori Minoru Mitsuo Mitsuru Nao Naoki Naoko Noboru Nobu Nobuo ,Nobuyuki Nori Norio Osamu Rafu Raiden Ringo Rokuro Ronin Ryo Ryoichi Ryota Ryozo Ryuichi Ryuu Saburo Sadao Samuru Satoru Satoshi Seiichi Seiji Senichi Shichiro Shig Shigekazu Shigeo Shigeru Shima Shin Shinichi Shinji Shiro Shoichi Shoji Shuichi Shuji Shunichi Susumu Tadao Tadashi Takahiro Takao Takashi Takayuki Takehiko Takeo Takeshi Takeshi Takumi Tama Tamotsu Taro Tatsuo Tatsuya Teruo Tetsip Tetsuya Tomi Tomio Toru Toshi Toshiaki Toshihiro Toshio Toshiyuki Toyo Tsuneo Tsutomu Tsuyoshi Uyeda Yasahiro Yasuhiro Yasuo Yasushi Yemon Yogi Yoichi Yori Yoshi Yoshiaki Yoshihiro Yoshikazu Yoshimitsu Yoshinori Yoshio Yoshiro Yoshito Yoshiyuki Yuichi Yuji Yuki"
lst2 = [
"Yoshimitsu",
"Yoshiyuki",
"Yoshinori",
"Yoshikazu",
"Yoshihiro",
"Toshiyuki",
"Toshihiro",
"Shigekazu",
",Nobuyuki",
"Yoshiaki",
"Yasuhiro",
"Yasahiro",
"Tsuyoshi",
"Toshiaki",
"Takehiko",
"Takayuki",
"Takahiro",
"Shunichi",
"Shinichi",
"Shichiro",
"Yoshito",
"Yoshiro",
"Yasushi",
"Tsutomu",
"Tetsuya",
"Tatsuya",
"Tamotsu",
"Takeshi",
"Takeshi",
"Takashi",
"Tadashi",
"Shuichi",
"Shoichi",
"Shigeru",
"Senichi",
"Seiichi",
"Satoshi",
"Ryuichi",
"Ryoichi",
"Mitsuru",
"Yuichi",
"Yoshio",
"Yoichi",
"Tsuneo",
"Toshio",
"Tetsip",
"Tatsuo",
"Takumi",
"Susumu",
"Shinji",
"Shigeo",
"Satoru",
"Samuru",
"Saburo",
"Rokuro",
"Raiden",
"Noboru",
"Mitsuo",
"Minoru",
"Minori",
"Michio",
"Yoshi",
"Yemon",
"Yasuo",
"Uyeda",
"Toshi",
"Tomio",
"Teruo",
"Takeo",
"Takao",
"Tadao",
"Shuji",
"Shoji",
"Shiro",
"Shima",
"Seiji",
"Sadao",
"Ryozo",
"Ryota",
"Ronin",
"Ringo",
"Osamu",
"Norio",
"Nobuo",
"Naoko",
"Naoki",
"Mikio",
"Yuki",
"Yuji",
"Yori",
"Yogi",
"Toyo",
"Toru",
"Tomi",
"Taro",
"Tama",
"Shin",
"Shig",
"Ryuu",
"Rafu",
"Nori",
"Nobu",
"Miki",
"Ryo",
"Nao",
]
s3 = "Yoshiro Yoshiro Tsuyoshi Shoichi Naoko Yori Takayuki Tsutomu Shigeo"
lst3 = [
"Tsuyoshi",
"Takayuki",
"Yoshiro",
"Yoshiro",
"Tsutomu",
"Shoichi",
"Shigeo",
"Naoko",
"Yori",
]
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] | 1.912439 | 1,439 |
#@<OUT> CLI --help
The following objects provide command line operations:
cli_tester
CLI Integration Testing Plugin
cluster
Represents an InnoDB cluster.
dba
InnoDB cluster and replicaset management functions.
rs
Represents an InnoDB ReplicaSet.
shell
Gives access to general purpose functions and properties.
util
Global object that groups miscellaneous tools like upgrade checker and
JSON import.
#@<OUT> CLI plugin --help
The following object provides command line operations at 'cli_tester':
emptyChild
Empty object, exposes no functions but child does.
The following operations are available at 'cli_tester':
test
Testing cmd line args.
#@<OUT> CLI plugin function --help
NAME
test - Testing cmd line args.
SYNTAX
cli_tester test <stritem> <strlist> --namedstr=<str> <options>
WHERE
stritem: String - string parameter.
strlist: Array - string list parameter.
OPTIONS
--namedstr=<str>
String named parameter (for being after a list parameter).
--str=<str>
String option.
--strlist=<str list>
String list option.
#@<OUT> Interactive plugin function --help
ERROR: Invalid operation for cli_tester object: test-interactive
#@<OUT> Interactive plugin function help
NAME
testInteractive - Testing interactive function.
SYNTAX
cli_tester.testInteractive()
#@<OUT> CLI plugin nested child --help
The following object provides command line operations at 'cli_tester emptyChild':
grandChild
Grand child object exposing a function.
#@<OUT> CLI plugin nested grand child --help
The following operations are available at 'cli_tester emptyChild grandChild':
grand-child-function
Testing cmd line args in nested objects.
#@<OUT> CLI plugin nested grand child function --help
The following operations are available at 'cli_tester emptyChild grandChild':
grand-child-function
Testing cmd line args in nested objects.
#@<OUT> Test string list handling
String Parameter: 1,2,3,4,5
String List Parameter Item: 1
String List Parameter Item: 2
String List Parameter Item: 3
String List Parameter Item: 4
String List Parameter Item: 5
String Named Item: 1,2,3,4,5
String Option: 1,2,3,4,5
String List Option Item: 1
String List Option Item: 2
String List Option Item: 3
String List Option Item: 4
String List Option Item: 5
#@<OUT> Test string list quoted args for interpreter
String Parameter: 1,2,3,4,5
String List Parameter Item: 1,2
String List Parameter Item: 3
String List Parameter Item: 4,5
String Named Item: 1,2,3,4,5
String Option: 1,2,3,4,5
String List Option Item: 1,2
String List Option Item: 3
String List Option Item: 4,5
#@<OUT> Escaped comma in list parsing
String Parameter: 1,2,3,4,5
String List Parameter Item: 1,2
String List Parameter Item: 3
String List Parameter Item: 4,5
String Named Item: 1,2,3,4,5
String Option: 1,2,3,4,5
String List Option Item: 1,2
String List Option Item: 3
String List Option Item: 4,5
#@<OUT> Escaped quoting: \", \'
String Parameter: "1",2,3,4,5
String List Parameter Item: 1
String List Parameter Item: 2
String List Parameter Item: 3
String List Parameter Item: 4
String List Parameter Item: 5
String Named Item: 1,2,"3",4,5
String Option: 1,2,"3",4,5
String List Option Item: 1
String List Option Item: 2
String List Option Item: 3
String List Option Item: 4
String List Option Item: 5
String Parameter: '1',2,3,4,5
String List Parameter Item: 1
String List Parameter Item: 2
String List Parameter Item: 3
String List Parameter Item: 4
String List Parameter Item: 5
String Named Item: 1,2,'3',4,5
String Option: 1,2,'3',4,5
String List Option Item: 1
String List Option Item: 2
String List Option Item: 3
String List Option Item: 4
String List Option Item: 5
#@<OUT> Escaped equal: \=
String Parameter: =1,2,3,4,5
String List Parameter Item: 1=2
String List Parameter Item: 3
String List Parameter Item: 4=5
String Named Item: =1,2,3,4=5
String Option: =1,2,3,4=5
String List Option Item: 1=2
String List Option Item: 3
String List Option Item: 4=5
#@<OUT> CLI calling plugin nested grand child function
Unique Parameter: Success!!
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] | 3.075055 | 1,359 |
# encoding: utf8
#
# This file is part of the Neurons project.
# Copyright (c), Arskom Ltd. (arskom.com.tr),
# Burak Arslan <[email protected]>.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the Arskom Ltd., the neurons project nor the names of
# its its contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
from lxml import etree
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198,
2,
198,
198,
6738,
300,
19875,
1330,
2123,
631,
628,
198
] | 3.37451 | 510 |
from param import Param
from numpy import zeros, zeros_like, roll
from gmg.hierarchy import Gmg
import fortran_advection as fa
import fortran_operators as fo
import fortran_diag as fd
from fourier import Fourier
| [
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] | 3.086957 | 69 |
# -*- coding: utf-8 -*-
import unittest
| [
2,
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9,
12,
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25,
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69,
12,
23,
532,
9,
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198,
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395,
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] | 2.1 | 20 |
import pandas as pd
import numpy as np
def gini(x):
"""Calculate the Gini coefficient for series of observed values (x).
where:
* x is the array of observed values (and all x are positive, non-zero values)
Note: x must be a numpy array or a pandas series
See: https://www.statsdirect.com/help/default.htm#nonparametric_methods/gini.htm
"""
# - sanity checks
if not isinstance(x, pd.Series) and not isinstance(x, np.ndarray):
raise TypeError('input series must be a pandas Series or a numpy ndarray')
if x.min() <= 0:
raise ValueError('all input values in series must be positive and non-zero')
# let's work with numpy arrays
if isinstance(x, pd.Series):
x = x.to_numpy()
# sort values in ascending order
x = np.sort(x)
# n is the number of values observed
n = len(x)
# i is the rank of the x-values when sorted in ascending order
i = np.arange(1, n+1)
# - calculate the Gini coefficient
return ((2 * i - n - 1) * x).sum() / (n * x.sum())
# An alternate approach ...
def gini2(x):
""" Calculate Gini from observed values using summation-as-integration """
if not isinstance(x, pd.Series) and not isinstance(x, np.ndarray):
raise TypeError('input series must be a pandas Series or a numpy array')
if x.min() <= 0:
raise ValueError('all input values in series must be positive and non-zero')
# let's work with numpy arrays
if isinstance(x, pd.Series):
x = x.to_numpy()
# calculate the line-of-equality and the lorenz curve
lorenz = np.sort(x).cumsum()
height = lorenz[-1]
n = len(lorenz)
equality = np.arange(1, n+1) * height / n
# calculate Gini
A_area = (equality - lorenz).sum() # area between lorenz and equality
AB_area = n * height / 2 # area of triangle
return A_area / AB_area
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220,
220,
1441,
317,
62,
20337,
1220,
9564,
62,
20337,
198
] | 2.552457 | 753 |
#!/usr/bin/env python
# Copyright (c) 2013 Martin Katrenik, Snowplow Analytics Ltd. All rights reserved.
#
# This program is licensed to you under the Apache License Version 2.0,
# and you may not use this file except in compliance with the Apache
# License Version 2.0.
# You may obtain a copy of the Apache License Version 2.0 at
# http://www.apache.org/licenses/LICENSE-2.0.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the Apache License Version 2.0 is
# distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied.
# See the Apache License Version 2.0 for the specific language
# governing permissions and limitations there under.
# Authors:: Martin Katrenik, Alex Dean (mailto:[email protected])
# Copyright:: Copyright (c) 2013 Martin Katrenik, Snowplow Analytics Ltd
# License:: Apache License Version 2.0
# Syncs common referer-parser resources to the
# language-specific sub projects.
#
# Syncs:
# 1. The referers.yml, plus a generated JSON equivalent
# 2. The referer-tests.json
#
# Finishes by committing the synchronized resources.
import os
import shutil
import json
import yaml
import subprocess
root_path = os.path.dirname(__file__)
# Source paths
REFERER_SOURCE = os.path.join(root_path, 'resources', 'referers.yml')
REFERER_JSON_OUT = 'referers.json'
TEST_SOURCE = os.path.join(root_path, 'resources', 'referer-tests.json')
# Target paths
REFERER_TARGETS = [
os.path.join(root_path, "ruby","data"),
os.path.join(root_path, "java-scala","src","main","resources"),
os.path.join(root_path, "python","referer_parser","data"),
os.path.join(root_path, "nodejs","data"),
os.path.join(root_path, "dotnet","RefererParser","Resources"),
os.path.join(root_path, "php","data"),
os.path.join(root_path, "go", "data")
]
TEST_TARGETS = [
os.path.join(root_path, "java-scala","src","test","resources"),
# Add remainder as paths determined etc
]
# JSON builder
JSON = build_json()
# File ops
# Sync process
for dest in REFERER_TARGETS:
sync_referers_to(dest)
for dest in TEST_TARGETS:
sync_tests_to(dest)
# Commit on current branch
commit = "git commit {0}".format(" ".join(REFERER_TARGETS + TEST_TARGETS))
msg = "\"Updated {0}, {1} and {2} in sub-folder following update(s) to master copy\"".format(REFERER_SOURCE, REFERER_JSON_OUT, TEST_SOURCE)
subprocess.call(commit + ' -m' + msg, shell=True)
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76,
6,
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11,
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28,
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8,
198
] | 2.990326 | 827 |
import numpy as np
import tensorflow as tf
from scipy.stats import multivariate_normal as normal
class Equation(object):
"""Base class for defining PDE related function."""
def w_tf(self, x, u):
"""Running cost in control problems."""
raise NotImplementedError
def Z_tf(self, x):
"""Terminal cost in control problems."""
raise NotImplementedError
def b_np(self, x): #num_sample * 1
"""a function whose level set is the boundary, in numpy"""
return np.sum(x**2, 1, keepdims=True) - (self.R ** 2)
def b_tf(self, x): #num_sample * 1
"""a function whose level set is the boundary, in tensorflow"""
return tf.reduce_sum(x**2, 1, keepdims=True) - (self.R ** 2)
def V_true(self, x):
"""True value function"""
raise NotImplementedError
def u_true(self, x):
"""Optimal control"""
raise NotImplementedError
def sigma(self, x, u, num_sample): #num_sample x dim x dim_w
"""diffusion coefficient"""
raise NotImplementedError
def drift(self, x, u):
"""drift in the SDE"""
raise NotImplementedError
def diffusion(self, x, u, dw, num_sample):
"""diffusion in the SDE"""
raise NotImplementedError
class LQR(Equation):
"""linear quadratic regulator"""
class VDP(Equation):
"""Van Der Pol oscillator"""
class ekn(Equation):
"""Diffusive Eikonal equation"""
class LQR_var(Equation):
"""linear quadratic regulator"""
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] | 2.357466 | 663 |
# -*- coding: utf-8 -*-
'''
File Name: handle_config.py
Author: WillDX
mail: [email protected]
Created Time: 2016年04月19日 星期二 09时36分58秒
'''
import json
from ConfigParser import ConfigParser
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# coding=utf-8
"""
main script for training and testing mask rcnn on MSCOCO/DIVA/MEVA dataset
multi gpu version
"""
import argparse
import cv2
import math
import json
import random
import operator
import time
import os
import pickle
import sys
import threading
# so here won"t have poll allocator info
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
# solve the issue of a bug in while loop, when you import the graph in
# multi-gpu, prefix is not added in while loop op [tf 1.14]
# https://github.com/tensorflow/tensorflow/issues/26526
os.environ["TF_ENABLE_CONTROL_FLOW_V2"] = "1"
# remove all the annoying warnings from tf v1.10 to v1.13
import logging
logging.getLogger("tensorflow").disabled = True
import matplotlib
# avoid the warning "gdk_cursor_new_for_display:
# assertion 'GDK_IS_DISPLAY (display)' failed" with Python 3
matplotlib.use('Agg')
import tensorflow as tf
import numpy as np
import pycocotools.mask as cocomask
from pycocotools.coco import COCO
from tqdm import tqdm
from glob import glob
from models import get_model
from models import pack
from models import initialize
from trainer import Trainer
from tester import Tester
from nn import resizeImage
from nn import fill_full_mask
from utils import evalcoco
from utils import match_detection
from utils import computeAP
from utils import computeAR_2
from utils import grouper
from utils import gather_dt
from utils import gather_gt
from utils import match_dt_gt
from utils import gather_act_singles
from utils import aggregate_eval
from utils import weighted_average
from utils import parse_nvidia_smi
from utils import sec2time
from utils import Dataset
from utils import Summary
from utils import nms_wrapper
from utils import FIFO_ME
# for using a COCO model to finetuning with DIVA data.
from class_ids import targetClass2id
from class_ids import targetAct2id
from class_ids import targetSingleAct2id
from class_ids import targetClass2id_mergeProp
from class_ids import targetClass2id_new
from class_ids import targetClass2id_new_nopo
from class_ids import targetAct2id_bupt
from class_ids import bupt_act_mapping
from class_ids import targetAct2id_meva
from class_ids import meva_act_mapping
from class_ids import coco_obj_class_to_id
from class_ids import coco_obj_id_to_class
from class_ids import coco_obj_to_actev_obj
targetid2class = {targetClass2id[one]:one for one in targetClass2id}
targetactid2class = {targetAct2id[one]:one for one in targetAct2id}
targetsingleactid2class = {
targetSingleAct2id[one]:one for one in targetSingleAct2id}
# coco class to DIVA class
eval_target = {
"Vehicle": ["car", "motorcycle", "bus", "truck", "vehicle"],
"Person": "person",
}
eval_best = "Person" # not used anymore, we use average as the best metric
# load all ground truth into memory
# given the gen_gt_diva
# train on diva dataset
# given a list of images, do the forward, save each image result separately
# for testing, dataset -> {"imgs":[],"ids":[]}, imgs is the image file path,
# test on coco dataset
gpu_util_logs = []
gpu_temp_logs = []
# use nvidia-smi to
if __name__ == "__main__":
config = get_args()
if config.mode == "pack":
config.is_pack_model = True
if config.is_pack_model:
pack(config)
else:
if config.log_time_and_gpu:
gpu_log_interval = 10 # every k seconds
start_time = time.time()
gpu_check_thread = threading.Thread(
target=log_gpu_util,
args=[gpu_log_interval, (config.gpuid_start, config.gpu)])
gpu_check_thread.daemon = True
gpu_check_thread.start()
if config.mode == "train":
train_diva(config)
elif config.mode == "test":
test(config)
elif config.mode == "forward":
forward(config)
else:
raise Exception("mode %s not supported"%(config.mode))
if config.log_time_and_gpu:
end_time = time.time()
print("total run time %s (%s), log gpu utilize every %s seconds and "
"get median %.2f%% and average %.2f%%. GPU temperature median "
"%.2f and average %.2f (C)" % (
sec2time(end_time - start_time),
end_time - start_time,
gpu_log_interval,
np.median(gpu_util_logs)*100,
np.mean(gpu_util_logs)*100,
np.median(gpu_temp_logs),
np.mean(gpu_temp_logs),
))
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] | 2.73561 | 1,581 |
import os
import pandas as pd
from app import app, db
from models.price import Currency
@app.cli.command("import_currency")
def import_currency():
"""
Loads up a provided CSV file of world currencies and extracts the unicode symbol
and text label associated with each currency. Loads these into the Currency table
in the price model.
CSV to use: https://gist.github.com/Chintan7027/fc4708d8b5c8d1639a7c#file-currency-symbols-csv
The Serbia Dinar has a '.' at the end which is removed by this script.
INR value should be updated manually with the _unicode-decimal value 8377
Run with 'flask import_currency'
"""
csv_file = os.path.join(app.root_path, "ingest/currency/currency.csv")
df = pd.read_csv(csv_file)
df = df.dropna()
MAX_SYMBOL_LENGTH = 3
for index, row in df.iterrows():
unicode_decimal = str(row["_unicode-decimal"])
unicode_as_array = unicode_decimal.split(", ")[:MAX_SYMBOL_LENGTH]
symbol = "".join([chr(int(u)) for u in unicode_as_array])
acronym = str(row["_code"])
text = row["__text"]
currency = db.session.query(Currency).filter_by(acronym=acronym).one_or_none()
if not currency:
print("adding currency: ", acronym + " " + text + " " + symbol)
currency = Currency(symbol=symbol, acronym=acronym, text=text)
db.session.add(currency)
else:
currency.symbol = symbol
currency.acronym = acronym
currency.text = text
db.session.commit()
@app.cli.command("delete_currency_table_values")
def delete_currency():
"""
Deletes all Currency entries from the database.
Run with 'flask delete_currency_table_values'
"""
currencies = db.session.query(Currency).all()
for currency in currencies:
db.session.delete(currency)
db.session.commit()
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] | 2.552703 | 740 |
# License: BSD 3 clause
import numpy as np
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from django.core.management.base import BaseCommand, CommandError
from django.contrib.auth.models import User
from match.models import Ranking, Match, Game, ParticipantRole, t
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# -*- coding: utf-8 -*-
# Copyright (C) 2014-2017 Andrey Antukh <[email protected]>
# Copyright (C) 2014-2017 Jesús Espino <[email protected]>
# Copyright (C) 2014-2017 David Barragán <[email protected]>
# Copyright (C) 2014-2017 Alejandro Alonso <[email protected]>
# Copyright (C) 2014-2017 Anler Hernández <[email protected]>
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import uuid
from django.core.urlresolvers import reverse
from taiga.permissions.choices import MEMBERS_PERMISSIONS, ANON_PERMISSIONS
from taiga.base.utils import json
from tests import factories as f
from tests.utils import helper_test_http_method, disconnect_signals, reconnect_signals
from taiga.projects import choices as project_choices
from taiga.projects.votes.services import add_vote
from taiga.projects.notifications.services import add_watcher
from taiga.projects.occ import OCCResourceMixin
from unittest import mock
import pytest
pytestmark = pytest.mark.django_db
@pytest.fixture
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62,
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628,
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198,
31,
9078,
9288,
13,
69,
9602,
628,
628,
198
] | 3.303719 | 484 |
import logging
from rssdldmng.utils.restserver import RESTHttpServer
_LOGGER = logging.getLogger(__name__)
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"""
Django settings for vimeoct project.
Generated by 'django-admin startproject' using Django 3.2.8.
For more information on this file, see
https://docs.djangoproject.com/en/3.2/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.2/ref/settings/
"""
from dotenv import load_dotenv, find_dotenv
import os
from pathlib import Path
# Build paths inside the project like this: BASE_DIR / 'subdir'.
BASE_DIR = Path(__file__).resolve().parent.parent
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'django-insecure-6jcn$qgw^3cv7zp81m04%eni^mv@ter=u54uwppqg34czm45u#'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
#DEBUG = False
ALLOWED_HOSTS = ["online.tmi.mirai.nagoya-u.ac.jp"]
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'social_django',
'auth0login'
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'vimeoct.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'vimeoct.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.2/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': BASE_DIR / 'db.sqlite3',
}
}
# Password validation
# https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/3.2/topics/i18n/
LANGUAGE_CODE = 'ja-jp'
TIME_ZONE = 'Asia/Tokyo'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/3.2/howto/static-files/
STATIC_URL = '/static/'
# Default primary key field type
# https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field
DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
ENV_FILE = find_dotenv()
if ENV_FILE:
load_dotenv(ENV_FILE)
# SOCIAL AUTH AUTH0 BACKEND CONFIG
SOCIAL_AUTH_TRAILING_SLASH = False
SOCIAL_AUTH_AUTH0_KEY = os.environ.get('AUTH0_CLIENT_ID')
SOCIAL_AUTH_AUTH0_SECRET = os.environ.get('AUTH0_CLIENT_SECRET')
SOCIAL_AUTH_AUTH0_SCOPE = [
'openid',
'profile',
'email'
]
SOCIAL_AUTH_AUTH0_DOMAIN = os.environ.get('AUTH0_DOMAIN')
AUDIENCE = None
if os.environ.get('AUTH0_AUDIENCE'):
AUDIENCE = os.environ.get('AUTH0_AUDIENCE')
else:
if SOCIAL_AUTH_AUTH0_DOMAIN:
AUDIENCE = 'https://' + SOCIAL_AUTH_AUTH0_DOMAIN + '/userinfo'
if AUDIENCE:
SOCIAL_AUTH_AUTH0_AUTH_EXTRA_ARGUMENTS = {'audience': AUDIENCE}
AUTHENTICATION_BACKENDS = {
'auth0login.auth0backend.Auth0',
'django.contrib.auth.backends.ModelBackend'
}
LOGIN_URL = '/login/auth0'
LOGIN_REDIRECT_URL = '/dashboard'
# This is for staticfile serving
# You have to set STATIC_ROOT in .env
# If you change static file, please do "python3 manage.py collectstatic"
if os.environ.get('STATIC_ROOT'):
STATIC_ROOT = os.environ.get('STATIC_ROOT')
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] | 2.360042 | 1,897 |
from collections import namedtuple
_VersionInfo = namedtuple("_VersionInfo", "major minor patch")
version_info = _VersionInfo(0, 3, 0)
__version__ = ".".join(str(v) for v in version_info)
__license__ = "MIT"
__author__ = "FalseDev"
__title__ = "discord-ext-wizard"
__copyright__ = "Copyright 2021 {}".format(__author__)
__uri__ = "https://github.com/{}/{}".format(__author__, __title__)
from .checks import *
from .mixins import *
from .prompt import *
from .wizard import *
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] | 2.862275 | 167 |
# Copyright (c) 2021, Aakvatech Limited and contributors
# For license information, please see license.txt
# import frappe
from __future__ import unicode_literals
import frappe
import time
import datetime
from frappe.model.document import Document
from frappe import _
import json
from frappe.utils import nowdate
from trans_ms.utlis.dimension import set_dimension
class TransportationOrder(Document):
'''def set_parent_in_children(self):
"""Updates `parent` and `parenttype` property in all children."""
#If reference doctype is set
if self.get('reference_docname'):
for d in self.get_all_children():
d.parent = self.get('reference_docname')
d.parenttype = self.get('reference_doctype')
if self.get('reference_doctype') == "Import":
d.parentfield = "assign_transport"
else:
for d in self.get_all_children():
d.parent = self.name
d.parenttype = self.doctype'''
# Custom load method for loading child tables data from imports and exports request
def load_from_db(self):
"""Load document and children from database and create properties
from fields"""
if not getattr(self, "_metaclass", False) and self.meta.issingle:
single_doc = frappe.db.get_singles_dict(self.doctype)
if not single_doc:
single_doc = frappe.new_doc(self.doctype).as_dict()
single_doc["name"] = self.doctype
del single_doc["__islocal"]
super(Document, self).__init__(single_doc)
self.init_valid_columns()
self._fix_numeric_types()
else:
d = frappe.db.get_value(self.doctype, self.name, "*", as_dict=1)
if not d:
frappe.throw(
_("{0} {1} not found").format(_(self.doctype), self.name),
frappe.DoesNotExistError,
)
super(Document, self).__init__(d)
if self.name == "DocType" and self.doctype == "DocType":
from frappe.model.meta import doctype_table_fields
table_fields = doctype_table_fields
else:
table_fields = self.meta.get_table_fields()
#
# For table fields load from request origin(if there is) else load normal
# Also add back compatibiilty for when transport assignements were being loaded from import
#
for df in table_fields:
if d.reference_doctype and d.reference_docname:
# Fieldname depending on if it's Export or Import
if d.reference_doctype == "Import" and df.fieldname == "cargo":
fieldname = "cargo_information"
elif (
d.reference_doctype == "Import"
and df.fieldname == "assign_transport"
):
fieldname = "assign_transport"
if df.fieldname == "assign_transport" and self.get("version") == 2:
children = frappe.db.get_values(
df.options,
{
"parent": self.name,
"parenttype": self.doctype,
"parentfield": "assign_transport",
},
"*",
as_dict=True,
order_by="idx asc",
)
else:
children = frappe.db.get_values(
df.options,
{
"parent": d.reference_docname,
"parenttype": d.reference_doctype,
"parentfield": fieldname,
},
"*",
as_dict=True,
order_by="idx asc",
)
if children:
self.set(df.fieldname, children)
else:
self.set(df.fieldname, [])
else:
children = frappe.db.get_values(
df.options,
{
"parent": self.name,
"parenttype": self.doctype,
"parentfield": df.fieldname,
},
"*",
as_dict=True,
order_by="idx asc",
)
if children:
self.set(df.fieldname, children)
else:
self.set(df.fieldname, [])
# sometimes __setup__ can depend on child values, hence calling again at the end
if hasattr(self, "__setup__"):
self.__setup__()
@frappe.whitelist(allow_guest=True)
@frappe.whitelist(allow_guest=True)
@frappe.whitelist(allow_guest=True)
@frappe.whitelist()
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13,
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7,
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17821,
8,
628,
198,
31,
69,
430,
27768,
13,
1929,
270,
46331,
3419,
198
] | 1.875721 | 2,599 |
import argparse
import pathlib
import sys
import torch
import torchaudio
import yaml
import common
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Convert wav to mel spectrogram')
parser.add_argument('wav_dir', type=pathlib.Path, help='path to directory of speaker directories containing wav files')
parser.add_argument('mel_dir', type=pathlib.Path, help='path to directory to save mel spectrograms')
parser.add_argument('embed_dir', type=pathlib.Path, help='path to directory to save embeddings')
parser.add_argument('encoder_path', type=pathlib.Path, help='path to speaker encoder')
parser.add_argument('config_path', type=pathlib.Path, help='path to config')
if 'debugpy' in sys.modules:
args = parser.parse_args([
'autovc/wavs-jvs',
'vc3/mel-jvs',
'vc3/embed-jvs',
'autovc2/dvector.pt',
'vc3/preprocess.yaml',
])
else:
args = parser.parse_args([])
main(**vars(args))
| [
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13,
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220,
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220,
220,
26498,
796,
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13,
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62,
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12962,
628,
220,
220,
220,
1388,
7,
1174,
85,
945,
7,
22046,
4008,
198
] | 2.482014 | 417 |
import pydocweb.docweb.rst as rst
from pydocweb.docweb.utils import *
from pydocweb.docweb.models import *
from views_docstring import EditForm
#------------------------------------------------------------------------------
# Wiki
#------------------------------------------------------------------------------
WIKI_CACHE_AGE = 60
@permission_required('docweb.change_wikipage')
| [
11748,
279,
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62,
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10786,
15390,
12384,
13,
3803,
62,
20763,
541,
496,
11537,
198
] | 4.107527 | 93 |
import gym
import numpy
import random
import logging
from gym import spaces
from gym.utils import seeding
| [
11748,
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299,
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198,
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6738,
11550,
1330,
9029,
198,
6738,
11550,
13,
26791,
1330,
384,
8228,
628
] | 4 | 27 |
from datetime import datetime
from .ChromosomeBot import get_or_create
from ..geneprotein import organism_info
| [
6738,
4818,
8079,
1330,
4818,
8079,
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398,
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462,
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6738,
11485,
5235,
538,
35574,
1330,
26433,
62,
10951,
628,
198
] | 3.53125 | 32 |
from django.http import Http404
from django.shortcuts import render
from play.models import Game, Save
| [
6738,
42625,
14208,
13,
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1330,
367,
29281,
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1330,
8543,
198,
6738,
711,
13,
27530,
1330,
3776,
11,
12793,
628
] | 3.714286 | 28 |
# -*- coding:utf-8 -*-
import shutil
import os
import sys
import subprocess
from platform import system
input_arg = output_arg = None
if __name__ == "__main__":
if system() != 'Linux' and system() != 'Darwin':
print('only linux is supported currently')
else:
main()
| [
2,
532,
9,
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25,
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220,
220,
220,
220,
220,
220,
1388,
3419,
198
] | 2.740741 | 108 |
"""
To do:
- catch KeyboardInterrupt while evaluating
- show all?
- show statistics
- fix up exception handling; decorator?
"""
import cmd
from church.ast import ParseError
from church.environment import (
UndefinedNameError,
)
from church.eval import (
environment,
reduce,
Suspension,
)
from church.expr import (
definition,
expr,
name,
unexpr,
)
from church.token import (
TokenError,
)
INTRO_TEXT = """\
Welcome to the interactive lambda calculus interpreter.
Type 'help' to see supported commands.
"""
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13,
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705,
16794,
6,
284,
766,
4855,
9729,
13,
198,
37811,
628
] | 3.101695 | 177 |
import pytest
from aydin.io.datasets import cropped_newyork
from aydin.it.classic_denoisers.demo.demo_2D_dictionary_fixed import (
demo_dictionary_fixed,
)
from aydin.it.classic_denoisers.demo.demo_2D_dictionary_learned import (
demo_dictionary_learned,
)
from aydin.it.classic_denoisers.dictionary_fixed import denoise_dictionary_fixed
from aydin.it.classic_denoisers.test.util_test_nd import check_nd
@pytest.mark.heavy
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198,
198,
31,
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9288,
13,
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13,
23701,
628
] | 2.573964 | 169 |
import pprint
import json
pp = pprint.PrettyPrinter(indent = 4)
if __name__ == '__main__':
pp.pprint(main())
| [
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] | 2.533333 | 45 |
# coding:utf-8
restaurant = Restaurant('丽水云泉大酒店', 'duck')
print(restaurant.restaurant_name)
print(restaurant.cuisine_type)
restaurant.describe_restaurant()
restaurant.open_restaurant()
| [
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# -*- coding: utf-8 -*-
import os
import h5py
class HDF5(object):
"""HDF5 handler
Offer the hdf5 format file handler
Parameters
---------
fpath : str,
Path of hdf5 file
mode : str,
Open h5 as write and/or read mode
`a` : open as read/write (Default)
`w` : open as write only
`r` : open as read only
Attributes
---------
h5 : hdf5 class
"""
def read(self, ext=None):
"""Read vector or array from h5 file
Parameters
---------
ext : str
File extention including h5 file
Returns
-------
array : array,
Array of hdf5 packed data
"""
if ext is None:
raise ValueError("Please specify an existing extention.")
return self.h5[ext].value
def save(self, data, ext=None):
"""Write vector or array into h5 file
Parameters
---------
data :
Vector or array will be wrote into h5 file
ext: str
File label of saved file
"""
# remove if 'ext' already exist
if ext in self.h5.keys():
del self.h5[ext]
self.h5.create_dataset(ext, data=data)
self.h5.flush()
return
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220,
220,
220,
220,
220,
220,
220,
1619,
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13,
71,
20,
58,
2302,
60,
628,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
71,
20,
13,
17953,
62,
19608,
292,
316,
7,
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11,
1366,
28,
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8,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
71,
20,
13,
25925,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
198
] | 2.148087 | 601 |
# -*- coding: utf-8 -*-
# Generated by Django 1.9.8 on 2017-04-06 09:14
from __future__ import unicode_literals
from django.db import migrations, models
| [
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] | 2.719298 | 57 |
print("Hello World")
########## Edit below this line
# print your name to the console
########## Edit above this line
# Once you've finished save and commit your changes to GitHub | [
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] | 4.155556 | 45 |
##CBWitness-EvaluateCallBack Function
import json
import boto3
import os
import datetime
import pytz
import base64
from botocore.exceptions import ClientError
from boto3.dynamodb.conditions import Key
#===============================
| [
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220,
220,
220,
220,
198,
198,
2,
4770,
25609,
18604,
628,
628
] | 3.375 | 72 |
import sys, os
sys.path.append(os.getcwd())
import ConfigParser
cp = ConfigParser.ConfigParser();
cp.read('../.environ')
os.environ.update({key.upper():value for key, value in cp.items('env')})
from django.core.wsgi import get_wsgi_application
application = get_wsgi_application()
| [
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] | 2.989474 | 95 |
# SPDX-License-Identifier: BSD-3-Clause
# Copyright (c) 2022 Osyris contributors (https://github.com/osyris-project/osyris)
from .tools import make_label
import numpy as np
| [
2,
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] | 2.916667 | 60 |
muster = open("muster.txt", "r")
output = open("output.txt", "r")
mustervals = []
outputvals = []
for line in muster:
a,b,val = line.split(" ")
mustervals.append(int(val))
for line in output:
outputvals.append(int(line))
print len(mustervals)
print len(outputvals)
length = min(len(mustervals), len(outputvals))
for i in range(length):
if mustervals[i] != outputvals[i]:
print i
print "muster: " + str(mustervals[i])
print "output: " + str(outputvals[i])
| [
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712,
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58,
72,
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58,
72,
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201,
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197,
197,
4798,
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197,
197,
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5819,
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366,
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965,
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874,
58,
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366,
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25,
366,
1343,
965,
7,
22915,
12786,
58,
72,
12962,
201,
198
] | 2.38835 | 206 |
"""
Given a string containing just the characters '(' and ')', find the length of the longest valid (well-formed) parentheses substring.
https://leetcode.com/problems/longest-valid-parentheses/
"""
test_cases = ["(()", ")()())", "())()()()", "())((())()", "())((()))", "()(()", "))(()))()"]
results = [2, 4, 6, 6, 6, 2, 4]
if __name__ == "__main__":
app = Solution()
for test_case, correct_result in zip(test_cases, results):
assert (
app.longestValidParentheses(test_case) == correct_result
), f"My result: {app.longestValidParentheses(test_case)}, correct result: {correct_result}\nTest Case: {test_case}"
| [
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62,
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10612,
277,
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3666,
1255,
25,
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1324,
13,
6511,
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47139,
24546,
39815,
7,
9288,
62,
7442,
8,
5512,
3376,
1255,
25,
1391,
30283,
62,
20274,
32239,
77,
14402,
8913,
25,
1391,
9288,
62,
7442,
36786,
198
] | 2.64898 | 245 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from setuptools import setup, find_packages
import shop_subscribe
with open('README.rst') as fd:
README = fd.read()
CLASSIFIERS = [
'Environment :: Web Environment',
'Framework :: Django',
'Intended Audience :: Developers',
'License :: OSI Approved :: BSD License',
'Operating System :: OS Independent',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.5',
'Topic :: Software Development :: Libraries :: Application Frameworks',
'Topic :: Internet :: WWW/HTTP :: Dynamic Content',
]
setup(
author="Richard Case",
author_email="[email protected]",
name="djangoshop-subscribe",
packages=find_packages(exclude=['doc']),
version=shop_subscribe.__version__,
description="An email subscription plugin for Django-SHOP",
long_description=README,
url='https://github.com/racitup/djangoshop-subscribe',
license='BSD License',
platforms=['OS Independent'],
classifiers=CLASSIFIERS,
keywords= ['Django', 'Django-SHOP'],
include_package_data=True,
zip_safe=False,
install_requires=[
'Django>=1.10.0,<1.11',
'django-shop>=0.11',
'django-post-office>3.0.0',
],
)
| [
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220,
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35,
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29,
28,
16,
13,
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13,
15,
11,
27,
16,
13,
1157,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
705,
28241,
14208,
12,
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29,
28,
15,
13,
1157,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
705,
28241,
14208,
12,
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12,
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29,
18,
13,
15,
13,
15,
3256,
198,
220,
220,
220,
16589,
198,
8,
198
] | 2.635438 | 491 |
from ..base import GnuRecipe
| [
6738,
11485,
8692,
1330,
18509,
84,
37523,
628
] | 3.75 | 8 |
# -*- coding: utf-8 -*-
import math
import numbers
import re
import tenacity
from sqlalchemy import exc, util
from sqlalchemy.engine import Engine, reflection
from sqlalchemy.engine.default import DefaultDialect
from sqlalchemy.exc import NoSuchTableError, OperationalError
from sqlalchemy.sql.compiler import (
DDLCompiler,
GenericTypeCompiler,
IdentifierPreparer,
SQLCompiler,
)
from sqlalchemy.sql.sqltypes import (
BIGINT,
BINARY,
BOOLEAN,
DATE,
DECIMAL,
FLOAT,
INTEGER,
NULLTYPE,
STRINGTYPE,
TIMESTAMP,
)
from tenacity import retry_if_exception, stop_after_attempt, wait_exponential
import pyathena
class UniversalSet(object):
"""UniversalSet
https://github.com/dropbox/PyHive/blob/master/pyhive/common.py"""
class AthenaDMLIdentifierPreparer(IdentifierPreparer):
"""PrestoIdentifierPreparer
https://github.com/dropbox/PyHive/blob/master/pyhive/sqlalchemy_presto.py"""
reserved_words = UniversalSet()
class AthenaStatementCompiler(SQLCompiler):
"""PrestoCompiler
https://github.com/dropbox/PyHive/blob/master/pyhive/sqlalchemy_presto.py"""
_TYPE_MAPPINGS = {
"boolean": BOOLEAN,
"real": FLOAT,
"float": FLOAT,
"double": FLOAT,
"tinyint": INTEGER,
"smallint": INTEGER,
"integer": INTEGER,
"bigint": BIGINT,
"decimal": DECIMAL,
"char": STRINGTYPE,
"varchar": STRINGTYPE,
"array": STRINGTYPE,
"row": STRINGTYPE, # StructType
"varbinary": BINARY,
"map": STRINGTYPE,
"date": DATE,
"timestamp": TIMESTAMP,
}
| [
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11,
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366,
808,
1298,
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2751,
25216,
11,
220,
1303,
32112,
6030,
198,
220,
220,
220,
366,
7785,
39491,
1298,
347,
1268,
13153,
11,
198,
220,
220,
220,
366,
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1298,
19269,
2751,
25216,
11,
198,
220,
220,
220,
366,
4475,
1298,
360,
6158,
11,
198,
220,
220,
220,
366,
16514,
27823,
1298,
31742,
6465,
23518,
11,
198,
92,
628
] | 2.454121 | 643 |
import asyncio
from thispersondoesnotexist import get_online_person, save_online_person
loop = asyncio.get_event_loop()
loop.run_until_complete(main()) | [
11748,
30351,
952,
198,
198,
6738,
428,
19276,
623,
3028,
1662,
38476,
1330,
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7,
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28955
] | 3.163265 | 49 |
import tensorflow as tf
import numpy as np
import pytest
from ..lad import lad, lad_polyfit
sess = tf.Session()
true_params = np.array([3, 10])
x = np.linspace(-1, 1, 10000)
y = np.random.laplace(loc=x * true_params[0] + true_params[1], scale=1.)
@pytest.mark.parametrize("yerr", [(None), (np.ones(len(y))), (np.std(y))])
@pytest.mark.parametrize("yerr", [(None), (np.ones(len(y))), (np.std(y))])
@pytest.mark.parametrize("order", [(1), (2), (3)])
@pytest.mark.parametrize("beta_true", [(np.arange(20).reshape(1, -1)),
(np.concatenate([np.zeros(10), np.arange(10)]).reshape(1, -1))])
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] | 2.114094 | 298 |
"""Helpers for running a local webserver to receive authorization code."""
import socket
from contextlib import closing
from pydata_google_auth import exceptions
LOCALHOST = "localhost"
DEFAULT_PORTS_TO_TRY = 100
def is_port_open(port):
"""Check if a port is open on localhost.
Based on StackOverflow answer: https://stackoverflow.com/a/43238489/101923
Parameters
----------
port : int
A port to check on localhost.
Returns
-------
is_open : bool
True if a socket can be opened at the requested port.
"""
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
try:
sock.bind((LOCALHOST, port))
sock.listen(1)
except socket.error:
is_open = False
else:
is_open = True
return is_open
def find_open_port(start=8080, stop=None):
"""Find an open port between ``start`` and ``stop``.
Parameters
----------
start : Optional[int]
Beginning of range of ports to try. Defaults to 8080.
stop : Optional[int]
End of range of ports to try (not including exactly equals ``stop``).
This function tries 100 possible ports if no ``stop`` is specified.
Returns
-------
Optional[int]
``None`` if no open port is found, otherwise an integer indicating an
open port.
"""
if not stop:
stop = start + DEFAULT_PORTS_TO_TRY
for port in range(start, stop):
if is_port_open(port):
return port
# No open ports found.
return None
def run_local_server(app_flow):
"""Run local webserver installed app flow on some open port.
Parameters
----------
app_flow : google_auth_oauthlib.flow.InstalledAppFlow
Installed application flow to fetch user credentials.
Returns
-------
google.auth.credentials.Credentials
User credentials from installed application flow.
Raises
------
pydata_google_auth.exceptions.PyDataConnectionError
If no open port can be found in the range from 8080 to 8089,
inclusive.
"""
port = find_open_port()
if not port:
raise exceptions.PyDataConnectionError("Could not find open port.")
return app_flow.run_local_server(host=LOCALHOST, port=port)
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] | 2.644242 | 877 |
from django.contrib.contenttypes.fields import GenericForeignKey
from django.contrib.contenttypes.models import ContentType
from django.db import models
from django.utils import timezone
from enumfields import Enum # Uses Ethan Furman's "enum34" backport
from enumfields import EnumField
from sorl.thumbnail import ImageField
from content.models import Content
from taxonomy.models import Term
from team.models import Team
# Create your models here.
from web import settings
# Sales Campaign
| [
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] | 3.862595 | 131 |
# This file was automatically generated by SWIG (http://www.swig.org).
# Version 4.0.0
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info as _swig_python_version_info
if _swig_python_version_info < (2, 7, 0):
raise RuntimeError('Python 2.7 or later required')
# Import the low-level C/C++ module
if __package__ or '.' in __name__:
from . import _pycplex_platform
else:
import _pycplex_platform
try:
import builtins as __builtin__
except ImportError:
import __builtin__
def _swig_add_metaclass(metaclass):
"""Class decorator for adding a metaclass to a SWIG wrapped class - a slimmed down version of six.add_metaclass"""
return wrapper
class _SwigNonDynamicMeta(type):
"""Meta class to enforce nondynamic attributes (no new attributes) for a class"""
__setattr__ = _swig_setattr_nondynamic_class_variable(type.__setattr__)
CPX_FEATURES_H = _pycplex_platform.CPX_FEATURES_H
CPX_FEATURE_REMOTE_OBJECT = _pycplex_platform.CPX_FEATURE_REMOTE_OBJECT
CPX_FEATURE_DISTRIBUTED_MIP = _pycplex_platform.CPX_FEATURE_DISTRIBUTED_MIP
CPX_CPXAUTOINTTYPES_H_H = _pycplex_platform.CPX_CPXAUTOINTTYPES_H_H
CPXBYTE_DEFINED = _pycplex_platform.CPXBYTE_DEFINED
CPXINT_DEFINED = _pycplex_platform.CPXINT_DEFINED
CPXLONG_DEFINED = _pycplex_platform.CPXLONG_DEFINED
CPXSHORT_DEFINED = _pycplex_platform.CPXSHORT_DEFINED
CPXULONG_DEFINED = _pycplex_platform.CPXULONG_DEFINED
CPX_STR_PARAM_MAX = _pycplex_platform.CPX_STR_PARAM_MAX
# Register cpxiodevice in _pycplex_platform:
_pycplex_platform.cpxiodevice_swigregister(cpxiodevice)
cvar = _pycplex_platform.cvar
CPX_NULL = cvar.CPX_NULL
ext_name = cvar.ext_name
CPX_VERSION = _pycplex_platform.CPX_VERSION
CPX_VERSION_VERSION = _pycplex_platform.CPX_VERSION_VERSION
CPX_VERSION_RELEASE = _pycplex_platform.CPX_VERSION_RELEASE
CPX_VERSION_MODIFICATION = _pycplex_platform.CPX_VERSION_MODIFICATION
CPX_VERSION_FIX = _pycplex_platform.CPX_VERSION_FIX
CPX_INFBOUND = _pycplex_platform.CPX_INFBOUND
CPX_MINBOUND = _pycplex_platform.CPX_MINBOUND
CPX_PWL_MAXSLOPE = _pycplex_platform.CPX_PWL_MAXSLOPE
CPX_PWL_MINSLOPE = _pycplex_platform.CPX_PWL_MINSLOPE
CPX_PARAMTYPE_NONE = _pycplex_platform.CPX_PARAMTYPE_NONE
CPX_PARAMTYPE_INT = _pycplex_platform.CPX_PARAMTYPE_INT
CPX_PARAMTYPE_DOUBLE = _pycplex_platform.CPX_PARAMTYPE_DOUBLE
CPX_PARAMTYPE_STRING = _pycplex_platform.CPX_PARAMTYPE_STRING
CPX_PARAMTYPE_LONG = _pycplex_platform.CPX_PARAMTYPE_LONG
CPX_NO_SOLN = _pycplex_platform.CPX_NO_SOLN
CPX_AUTO_SOLN = _pycplex_platform.CPX_AUTO_SOLN
CPX_BASIC_SOLN = _pycplex_platform.CPX_BASIC_SOLN
CPX_NONBASIC_SOLN = _pycplex_platform.CPX_NONBASIC_SOLN
CPX_PRIMAL_SOLN = _pycplex_platform.CPX_PRIMAL_SOLN
CPX_PRECOL_LOW = _pycplex_platform.CPX_PRECOL_LOW
CPX_PRECOL_UP = _pycplex_platform.CPX_PRECOL_UP
CPX_PRECOL_FIX = _pycplex_platform.CPX_PRECOL_FIX
CPX_PRECOL_AGG = _pycplex_platform.CPX_PRECOL_AGG
CPX_PRECOL_OTHER = _pycplex_platform.CPX_PRECOL_OTHER
CPX_PREROW_RED = _pycplex_platform.CPX_PREROW_RED
CPX_PREROW_AGG = _pycplex_platform.CPX_PREROW_AGG
CPX_PREROW_OTHER = _pycplex_platform.CPX_PREROW_OTHER
CPX_AUTO = _pycplex_platform.CPX_AUTO
CPX_ON = _pycplex_platform.CPX_ON
CPX_OFF = _pycplex_platform.CPX_OFF
CPX_MAX = _pycplex_platform.CPX_MAX
CPX_MIN = _pycplex_platform.CPX_MIN
CPX_DATACHECK_OFF = _pycplex_platform.CPX_DATACHECK_OFF
CPX_DATACHECK_WARN = _pycplex_platform.CPX_DATACHECK_WARN
CPX_DATACHECK_ASSIST = _pycplex_platform.CPX_DATACHECK_ASSIST
CPX_PPRIIND_PARTIAL = _pycplex_platform.CPX_PPRIIND_PARTIAL
CPX_PPRIIND_AUTO = _pycplex_platform.CPX_PPRIIND_AUTO
CPX_PPRIIND_DEVEX = _pycplex_platform.CPX_PPRIIND_DEVEX
CPX_PPRIIND_STEEP = _pycplex_platform.CPX_PPRIIND_STEEP
CPX_PPRIIND_STEEPQSTART = _pycplex_platform.CPX_PPRIIND_STEEPQSTART
CPX_PPRIIND_FULL = _pycplex_platform.CPX_PPRIIND_FULL
CPX_DPRIIND_AUTO = _pycplex_platform.CPX_DPRIIND_AUTO
CPX_DPRIIND_FULL = _pycplex_platform.CPX_DPRIIND_FULL
CPX_DPRIIND_STEEP = _pycplex_platform.CPX_DPRIIND_STEEP
CPX_DPRIIND_FULLSTEEP = _pycplex_platform.CPX_DPRIIND_FULLSTEEP
CPX_DPRIIND_STEEPQSTART = _pycplex_platform.CPX_DPRIIND_STEEPQSTART
CPX_DPRIIND_DEVEX = _pycplex_platform.CPX_DPRIIND_DEVEX
CPX_PARALLEL_DETERMINISTIC = _pycplex_platform.CPX_PARALLEL_DETERMINISTIC
CPX_PARALLEL_AUTO = _pycplex_platform.CPX_PARALLEL_AUTO
CPX_PARALLEL_OPPORTUNISTIC = _pycplex_platform.CPX_PARALLEL_OPPORTUNISTIC
CPX_WRITELEVEL_AUTO = _pycplex_platform.CPX_WRITELEVEL_AUTO
CPX_WRITELEVEL_ALLVARS = _pycplex_platform.CPX_WRITELEVEL_ALLVARS
CPX_WRITELEVEL_DISCRETEVARS = _pycplex_platform.CPX_WRITELEVEL_DISCRETEVARS
CPX_WRITELEVEL_NONZEROVARS = _pycplex_platform.CPX_WRITELEVEL_NONZEROVARS
CPX_WRITELEVEL_NONZERODISCRETEVARS = _pycplex_platform.CPX_WRITELEVEL_NONZERODISCRETEVARS
CPX_OPTIMALITYTARGET_AUTO = _pycplex_platform.CPX_OPTIMALITYTARGET_AUTO
CPX_OPTIMALITYTARGET_OPTIMALCONVEX = _pycplex_platform.CPX_OPTIMALITYTARGET_OPTIMALCONVEX
CPX_OPTIMALITYTARGET_FIRSTORDER = _pycplex_platform.CPX_OPTIMALITYTARGET_FIRSTORDER
CPX_OPTIMALITYTARGET_OPTIMALGLOBAL = _pycplex_platform.CPX_OPTIMALITYTARGET_OPTIMALGLOBAL
CPX_ALG_NONE = _pycplex_platform.CPX_ALG_NONE
CPX_ALG_AUTOMATIC = _pycplex_platform.CPX_ALG_AUTOMATIC
CPX_ALG_PRIMAL = _pycplex_platform.CPX_ALG_PRIMAL
CPX_ALG_DUAL = _pycplex_platform.CPX_ALG_DUAL
CPX_ALG_NET = _pycplex_platform.CPX_ALG_NET
CPX_ALG_BARRIER = _pycplex_platform.CPX_ALG_BARRIER
CPX_ALG_SIFTING = _pycplex_platform.CPX_ALG_SIFTING
CPX_ALG_CONCURRENT = _pycplex_platform.CPX_ALG_CONCURRENT
CPX_ALG_BAROPT = _pycplex_platform.CPX_ALG_BAROPT
CPX_ALG_PIVOTIN = _pycplex_platform.CPX_ALG_PIVOTIN
CPX_ALG_PIVOTOUT = _pycplex_platform.CPX_ALG_PIVOTOUT
CPX_ALG_PIVOT = _pycplex_platform.CPX_ALG_PIVOT
CPX_ALG_FEASOPT = _pycplex_platform.CPX_ALG_FEASOPT
CPX_ALG_MIP = _pycplex_platform.CPX_ALG_MIP
CPX_ALG_BENDERS = _pycplex_platform.CPX_ALG_BENDERS
CPX_ALG_MULTIOBJ = _pycplex_platform.CPX_ALG_MULTIOBJ
CPX_ALG_ROBUST = _pycplex_platform.CPX_ALG_ROBUST
CPX_AT_LOWER = _pycplex_platform.CPX_AT_LOWER
CPX_BASIC = _pycplex_platform.CPX_BASIC
CPX_AT_UPPER = _pycplex_platform.CPX_AT_UPPER
CPX_FREE_SUPER = _pycplex_platform.CPX_FREE_SUPER
CPX_NO_VARIABLE = _pycplex_platform.CPX_NO_VARIABLE
CPX_CONTINUOUS = _pycplex_platform.CPX_CONTINUOUS
CPX_BINARY = _pycplex_platform.CPX_BINARY
CPX_INTEGER = _pycplex_platform.CPX_INTEGER
CPX_SEMICONT = _pycplex_platform.CPX_SEMICONT
CPX_SEMIINT = _pycplex_platform.CPX_SEMIINT
CPX_PREREDUCE_PRIMALANDDUAL = _pycplex_platform.CPX_PREREDUCE_PRIMALANDDUAL
CPX_PREREDUCE_DUALONLY = _pycplex_platform.CPX_PREREDUCE_DUALONLY
CPX_PREREDUCE_PRIMALONLY = _pycplex_platform.CPX_PREREDUCE_PRIMALONLY
CPX_PREREDUCE_NOPRIMALORDUAL = _pycplex_platform.CPX_PREREDUCE_NOPRIMALORDUAL
CPX_PREREFORM_ALL = _pycplex_platform.CPX_PREREFORM_ALL
CPX_PREREFORM_INTERFERE_CRUSH = _pycplex_platform.CPX_PREREFORM_INTERFERE_CRUSH
CPX_PREREFORM_INTERFERE_UNCRUSH = _pycplex_platform.CPX_PREREFORM_INTERFERE_UNCRUSH
CPX_PREREFORM_NONE = _pycplex_platform.CPX_PREREFORM_NONE
CPX_CONFLICT_EXCLUDED = _pycplex_platform.CPX_CONFLICT_EXCLUDED
CPX_CONFLICT_POSSIBLE_MEMBER = _pycplex_platform.CPX_CONFLICT_POSSIBLE_MEMBER
CPX_CONFLICT_POSSIBLE_LB = _pycplex_platform.CPX_CONFLICT_POSSIBLE_LB
CPX_CONFLICT_POSSIBLE_UB = _pycplex_platform.CPX_CONFLICT_POSSIBLE_UB
CPX_CONFLICT_MEMBER = _pycplex_platform.CPX_CONFLICT_MEMBER
CPX_CONFLICT_LB = _pycplex_platform.CPX_CONFLICT_LB
CPX_CONFLICT_UB = _pycplex_platform.CPX_CONFLICT_UB
CPX_CONFLICTALG_AUTO = _pycplex_platform.CPX_CONFLICTALG_AUTO
CPX_CONFLICTALG_FAST = _pycplex_platform.CPX_CONFLICTALG_FAST
CPX_CONFLICTALG_PROPAGATE = _pycplex_platform.CPX_CONFLICTALG_PROPAGATE
CPX_CONFLICTALG_PRESOLVE = _pycplex_platform.CPX_CONFLICTALG_PRESOLVE
CPX_CONFLICTALG_IIS = _pycplex_platform.CPX_CONFLICTALG_IIS
CPX_CONFLICTALG_LIMITSOLVE = _pycplex_platform.CPX_CONFLICTALG_LIMITSOLVE
CPX_CONFLICTALG_SOLVE = _pycplex_platform.CPX_CONFLICTALG_SOLVE
CPXPROB_LP = _pycplex_platform.CPXPROB_LP
CPXPROB_MILP = _pycplex_platform.CPXPROB_MILP
CPXPROB_FIXEDMILP = _pycplex_platform.CPXPROB_FIXEDMILP
CPXPROB_NODELP = _pycplex_platform.CPXPROB_NODELP
CPXPROB_QP = _pycplex_platform.CPXPROB_QP
CPXPROB_MIQP = _pycplex_platform.CPXPROB_MIQP
CPXPROB_FIXEDMIQP = _pycplex_platform.CPXPROB_FIXEDMIQP
CPXPROB_NODEQP = _pycplex_platform.CPXPROB_NODEQP
CPXPROB_QCP = _pycplex_platform.CPXPROB_QCP
CPXPROB_MIQCP = _pycplex_platform.CPXPROB_MIQCP
CPXPROB_NODEQCP = _pycplex_platform.CPXPROB_NODEQCP
CPX_LPREADER_LEGACY = _pycplex_platform.CPX_LPREADER_LEGACY
CPX_LPREADER_NEW = _pycplex_platform.CPX_LPREADER_NEW
CPX_PARAM_ALL_MIN = _pycplex_platform.CPX_PARAM_ALL_MIN
CPX_PARAM_ALL_MAX = _pycplex_platform.CPX_PARAM_ALL_MAX
CPX_CALLBACK_PRIMAL = _pycplex_platform.CPX_CALLBACK_PRIMAL
CPX_CALLBACK_DUAL = _pycplex_platform.CPX_CALLBACK_DUAL
CPX_CALLBACK_NETWORK = _pycplex_platform.CPX_CALLBACK_NETWORK
CPX_CALLBACK_PRIMAL_CROSSOVER = _pycplex_platform.CPX_CALLBACK_PRIMAL_CROSSOVER
CPX_CALLBACK_DUAL_CROSSOVER = _pycplex_platform.CPX_CALLBACK_DUAL_CROSSOVER
CPX_CALLBACK_BARRIER = _pycplex_platform.CPX_CALLBACK_BARRIER
CPX_CALLBACK_PRESOLVE = _pycplex_platform.CPX_CALLBACK_PRESOLVE
CPX_CALLBACK_QPBARRIER = _pycplex_platform.CPX_CALLBACK_QPBARRIER
CPX_CALLBACK_QPSIMPLEX = _pycplex_platform.CPX_CALLBACK_QPSIMPLEX
CPX_CALLBACK_TUNING = _pycplex_platform.CPX_CALLBACK_TUNING
CPX_CALLBACK_INFO_PRIMAL_OBJ = _pycplex_platform.CPX_CALLBACK_INFO_PRIMAL_OBJ
CPX_CALLBACK_INFO_DUAL_OBJ = _pycplex_platform.CPX_CALLBACK_INFO_DUAL_OBJ
CPX_CALLBACK_INFO_PRIMAL_INFMEAS = _pycplex_platform.CPX_CALLBACK_INFO_PRIMAL_INFMEAS
CPX_CALLBACK_INFO_DUAL_INFMEAS = _pycplex_platform.CPX_CALLBACK_INFO_DUAL_INFMEAS
CPX_CALLBACK_INFO_PRIMAL_FEAS = _pycplex_platform.CPX_CALLBACK_INFO_PRIMAL_FEAS
CPX_CALLBACK_INFO_DUAL_FEAS = _pycplex_platform.CPX_CALLBACK_INFO_DUAL_FEAS
CPX_CALLBACK_INFO_ITCOUNT = _pycplex_platform.CPX_CALLBACK_INFO_ITCOUNT
CPX_CALLBACK_INFO_CROSSOVER_PPUSH = _pycplex_platform.CPX_CALLBACK_INFO_CROSSOVER_PPUSH
CPX_CALLBACK_INFO_CROSSOVER_PEXCH = _pycplex_platform.CPX_CALLBACK_INFO_CROSSOVER_PEXCH
CPX_CALLBACK_INFO_CROSSOVER_DPUSH = _pycplex_platform.CPX_CALLBACK_INFO_CROSSOVER_DPUSH
CPX_CALLBACK_INFO_CROSSOVER_DEXCH = _pycplex_platform.CPX_CALLBACK_INFO_CROSSOVER_DEXCH
CPX_CALLBACK_INFO_CROSSOVER_SBCNT = _pycplex_platform.CPX_CALLBACK_INFO_CROSSOVER_SBCNT
CPX_CALLBACK_INFO_PRESOLVE_ROWSGONE = _pycplex_platform.CPX_CALLBACK_INFO_PRESOLVE_ROWSGONE
CPX_CALLBACK_INFO_PRESOLVE_COLSGONE = _pycplex_platform.CPX_CALLBACK_INFO_PRESOLVE_COLSGONE
CPX_CALLBACK_INFO_PRESOLVE_AGGSUBST = _pycplex_platform.CPX_CALLBACK_INFO_PRESOLVE_AGGSUBST
CPX_CALLBACK_INFO_PRESOLVE_COEFFS = _pycplex_platform.CPX_CALLBACK_INFO_PRESOLVE_COEFFS
CPX_CALLBACK_INFO_USER_PROBLEM = _pycplex_platform.CPX_CALLBACK_INFO_USER_PROBLEM
CPX_CALLBACK_INFO_TUNING_PROGRESS = _pycplex_platform.CPX_CALLBACK_INFO_TUNING_PROGRESS
CPX_CALLBACK_INFO_ENDTIME = _pycplex_platform.CPX_CALLBACK_INFO_ENDTIME
CPX_CALLBACK_INFO_ITCOUNT_LONG = _pycplex_platform.CPX_CALLBACK_INFO_ITCOUNT_LONG
CPX_CALLBACK_INFO_CROSSOVER_PPUSH_LONG = _pycplex_platform.CPX_CALLBACK_INFO_CROSSOVER_PPUSH_LONG
CPX_CALLBACK_INFO_CROSSOVER_PEXCH_LONG = _pycplex_platform.CPX_CALLBACK_INFO_CROSSOVER_PEXCH_LONG
CPX_CALLBACK_INFO_CROSSOVER_DPUSH_LONG = _pycplex_platform.CPX_CALLBACK_INFO_CROSSOVER_DPUSH_LONG
CPX_CALLBACK_INFO_CROSSOVER_DEXCH_LONG = _pycplex_platform.CPX_CALLBACK_INFO_CROSSOVER_DEXCH_LONG
CPX_CALLBACK_INFO_PRESOLVE_AGGSUBST_LONG = _pycplex_platform.CPX_CALLBACK_INFO_PRESOLVE_AGGSUBST_LONG
CPX_CALLBACK_INFO_PRESOLVE_COEFFS_LONG = _pycplex_platform.CPX_CALLBACK_INFO_PRESOLVE_COEFFS_LONG
CPX_CALLBACK_INFO_ENDDETTIME = _pycplex_platform.CPX_CALLBACK_INFO_ENDDETTIME
CPX_CALLBACK_INFO_STARTTIME = _pycplex_platform.CPX_CALLBACK_INFO_STARTTIME
CPX_CALLBACK_INFO_STARTDETTIME = _pycplex_platform.CPX_CALLBACK_INFO_STARTDETTIME
CPX_TUNE_AVERAGE = _pycplex_platform.CPX_TUNE_AVERAGE
CPX_TUNE_MINMAX = _pycplex_platform.CPX_TUNE_MINMAX
CPX_TUNE_ABORT = _pycplex_platform.CPX_TUNE_ABORT
CPX_TUNE_TILIM = _pycplex_platform.CPX_TUNE_TILIM
CPX_TUNE_DETTILIM = _pycplex_platform.CPX_TUNE_DETTILIM
CPX_FEASOPT_MIN_SUM = _pycplex_platform.CPX_FEASOPT_MIN_SUM
CPX_FEASOPT_OPT_SUM = _pycplex_platform.CPX_FEASOPT_OPT_SUM
CPX_FEASOPT_MIN_INF = _pycplex_platform.CPX_FEASOPT_MIN_INF
CPX_FEASOPT_OPT_INF = _pycplex_platform.CPX_FEASOPT_OPT_INF
CPX_FEASOPT_MIN_QUAD = _pycplex_platform.CPX_FEASOPT_MIN_QUAD
CPX_FEASOPT_OPT_QUAD = _pycplex_platform.CPX_FEASOPT_OPT_QUAD
CPX_BENDERSSTRATEGY_OFF = _pycplex_platform.CPX_BENDERSSTRATEGY_OFF
CPX_BENDERSSTRATEGY_AUTO = _pycplex_platform.CPX_BENDERSSTRATEGY_AUTO
CPX_BENDERSSTRATEGY_USER = _pycplex_platform.CPX_BENDERSSTRATEGY_USER
CPX_BENDERSSTRATEGY_WORKERS = _pycplex_platform.CPX_BENDERSSTRATEGY_WORKERS
CPX_BENDERSSTRATEGY_FULL = _pycplex_platform.CPX_BENDERSSTRATEGY_FULL
CPX_ANNOTATIONDATA_LONG = _pycplex_platform.CPX_ANNOTATIONDATA_LONG
CPX_ANNOTATIONDATA_DOUBLE = _pycplex_platform.CPX_ANNOTATIONDATA_DOUBLE
CPX_ANNOTATIONOBJ_OBJ = _pycplex_platform.CPX_ANNOTATIONOBJ_OBJ
CPX_ANNOTATIONOBJ_COL = _pycplex_platform.CPX_ANNOTATIONOBJ_COL
CPX_ANNOTATIONOBJ_ROW = _pycplex_platform.CPX_ANNOTATIONOBJ_ROW
CPX_ANNOTATIONOBJ_SOS = _pycplex_platform.CPX_ANNOTATIONOBJ_SOS
CPX_ANNOTATIONOBJ_IND = _pycplex_platform.CPX_ANNOTATIONOBJ_IND
CPX_ANNOTATIONOBJ_QC = _pycplex_platform.CPX_ANNOTATIONOBJ_QC
CPX_ANNOTATIONOBJ_LAST = _pycplex_platform.CPX_ANNOTATIONOBJ_LAST
CPXIIS_COMPLETE = _pycplex_platform.CPXIIS_COMPLETE
CPXIIS_PARTIAL = _pycplex_platform.CPXIIS_PARTIAL
CPXIIS_AT_LOWER = _pycplex_platform.CPXIIS_AT_LOWER
CPXIIS_FIXED = _pycplex_platform.CPXIIS_FIXED
CPXIIS_AT_UPPER = _pycplex_platform.CPXIIS_AT_UPPER
CPX_BARORDER_AUTO = _pycplex_platform.CPX_BARORDER_AUTO
CPX_BARORDER_AMD = _pycplex_platform.CPX_BARORDER_AMD
CPX_BARORDER_AMF = _pycplex_platform.CPX_BARORDER_AMF
CPX_BARORDER_ND = _pycplex_platform.CPX_BARORDER_ND
CPX_MIPEMPHASIS_BALANCED = _pycplex_platform.CPX_MIPEMPHASIS_BALANCED
CPX_MIPEMPHASIS_FEASIBILITY = _pycplex_platform.CPX_MIPEMPHASIS_FEASIBILITY
CPX_MIPEMPHASIS_OPTIMALITY = _pycplex_platform.CPX_MIPEMPHASIS_OPTIMALITY
CPX_MIPEMPHASIS_BESTBOUND = _pycplex_platform.CPX_MIPEMPHASIS_BESTBOUND
CPX_MIPEMPHASIS_HIDDENFEAS = _pycplex_platform.CPX_MIPEMPHASIS_HIDDENFEAS
CPX_MIPEMPHASIS_HEURISTIC = _pycplex_platform.CPX_MIPEMPHASIS_HEURISTIC
CPX_TYPE_VAR = _pycplex_platform.CPX_TYPE_VAR
CPX_TYPE_SOS1 = _pycplex_platform.CPX_TYPE_SOS1
CPX_TYPE_SOS2 = _pycplex_platform.CPX_TYPE_SOS2
CPX_TYPE_USER = _pycplex_platform.CPX_TYPE_USER
CPX_TYPE_ANY = _pycplex_platform.CPX_TYPE_ANY
CPX_VARSEL_MININFEAS = _pycplex_platform.CPX_VARSEL_MININFEAS
CPX_VARSEL_DEFAULT = _pycplex_platform.CPX_VARSEL_DEFAULT
CPX_VARSEL_MAXINFEAS = _pycplex_platform.CPX_VARSEL_MAXINFEAS
CPX_VARSEL_PSEUDO = _pycplex_platform.CPX_VARSEL_PSEUDO
CPX_VARSEL_STRONG = _pycplex_platform.CPX_VARSEL_STRONG
CPX_VARSEL_PSEUDOREDUCED = _pycplex_platform.CPX_VARSEL_PSEUDOREDUCED
CPX_NODESEL_DFS = _pycplex_platform.CPX_NODESEL_DFS
CPX_NODESEL_BESTBOUND = _pycplex_platform.CPX_NODESEL_BESTBOUND
CPX_NODESEL_BESTEST = _pycplex_platform.CPX_NODESEL_BESTEST
CPX_NODESEL_BESTEST_ALT = _pycplex_platform.CPX_NODESEL_BESTEST_ALT
CPX_MIPORDER_COST = _pycplex_platform.CPX_MIPORDER_COST
CPX_MIPORDER_BOUNDS = _pycplex_platform.CPX_MIPORDER_BOUNDS
CPX_MIPORDER_SCALEDCOST = _pycplex_platform.CPX_MIPORDER_SCALEDCOST
CPX_BRANCH_GLOBAL = _pycplex_platform.CPX_BRANCH_GLOBAL
CPX_BRANCH_DOWN = _pycplex_platform.CPX_BRANCH_DOWN
CPX_BRANCH_UP = _pycplex_platform.CPX_BRANCH_UP
CPX_BRDIR_DOWN = _pycplex_platform.CPX_BRDIR_DOWN
CPX_BRDIR_AUTO = _pycplex_platform.CPX_BRDIR_AUTO
CPX_BRDIR_UP = _pycplex_platform.CPX_BRDIR_UP
CPX_CUT_COVER = _pycplex_platform.CPX_CUT_COVER
CPX_CUT_GUBCOVER = _pycplex_platform.CPX_CUT_GUBCOVER
CPX_CUT_FLOWCOVER = _pycplex_platform.CPX_CUT_FLOWCOVER
CPX_CUT_CLIQUE = _pycplex_platform.CPX_CUT_CLIQUE
CPX_CUT_FRAC = _pycplex_platform.CPX_CUT_FRAC
CPX_CUT_MIR = _pycplex_platform.CPX_CUT_MIR
CPX_CUT_FLOWPATH = _pycplex_platform.CPX_CUT_FLOWPATH
CPX_CUT_DISJ = _pycplex_platform.CPX_CUT_DISJ
CPX_CUT_IMPLBD = _pycplex_platform.CPX_CUT_IMPLBD
CPX_CUT_ZEROHALF = _pycplex_platform.CPX_CUT_ZEROHALF
CPX_CUT_MCF = _pycplex_platform.CPX_CUT_MCF
CPX_CUT_LOCALCOVER = _pycplex_platform.CPX_CUT_LOCALCOVER
CPX_CUT_TIGHTEN = _pycplex_platform.CPX_CUT_TIGHTEN
CPX_CUT_OBJDISJ = _pycplex_platform.CPX_CUT_OBJDISJ
CPX_CUT_LANDP = _pycplex_platform.CPX_CUT_LANDP
CPX_CUT_USER = _pycplex_platform.CPX_CUT_USER
CPX_CUT_TABLE = _pycplex_platform.CPX_CUT_TABLE
CPX_CUT_SOLNPOOL = _pycplex_platform.CPX_CUT_SOLNPOOL
CPX_CUT_LOCALIMPLBD = _pycplex_platform.CPX_CUT_LOCALIMPLBD
CPX_CUT_BQP = _pycplex_platform.CPX_CUT_BQP
CPX_CUT_RLT = _pycplex_platform.CPX_CUT_RLT
CPX_CUT_BENDERS = _pycplex_platform.CPX_CUT_BENDERS
CPX_CUT_NUM_TYPES = _pycplex_platform.CPX_CUT_NUM_TYPES
CPX_MIPSEARCH_AUTO = _pycplex_platform.CPX_MIPSEARCH_AUTO
CPX_MIPSEARCH_TRADITIONAL = _pycplex_platform.CPX_MIPSEARCH_TRADITIONAL
CPX_MIPSEARCH_DYNAMIC = _pycplex_platform.CPX_MIPSEARCH_DYNAMIC
CPX_MIPKAPPA_OFF = _pycplex_platform.CPX_MIPKAPPA_OFF
CPX_MIPKAPPA_AUTO = _pycplex_platform.CPX_MIPKAPPA_AUTO
CPX_MIPKAPPA_SAMPLE = _pycplex_platform.CPX_MIPKAPPA_SAMPLE
CPX_MIPKAPPA_FULL = _pycplex_platform.CPX_MIPKAPPA_FULL
CPX_MIPSTART_AUTO = _pycplex_platform.CPX_MIPSTART_AUTO
CPX_MIPSTART_CHECKFEAS = _pycplex_platform.CPX_MIPSTART_CHECKFEAS
CPX_MIPSTART_SOLVEFIXED = _pycplex_platform.CPX_MIPSTART_SOLVEFIXED
CPX_MIPSTART_SOLVEMIP = _pycplex_platform.CPX_MIPSTART_SOLVEMIP
CPX_MIPSTART_REPAIR = _pycplex_platform.CPX_MIPSTART_REPAIR
CPX_MIPSTART_NOCHECK = _pycplex_platform.CPX_MIPSTART_NOCHECK
CPX_CALLBACK_MIP = _pycplex_platform.CPX_CALLBACK_MIP
CPX_CALLBACK_MIP_BRANCH = _pycplex_platform.CPX_CALLBACK_MIP_BRANCH
CPX_CALLBACK_MIP_NODE = _pycplex_platform.CPX_CALLBACK_MIP_NODE
CPX_CALLBACK_MIP_HEURISTIC = _pycplex_platform.CPX_CALLBACK_MIP_HEURISTIC
CPX_CALLBACK_MIP_SOLVE = _pycplex_platform.CPX_CALLBACK_MIP_SOLVE
CPX_CALLBACK_MIP_CUT_LOOP = _pycplex_platform.CPX_CALLBACK_MIP_CUT_LOOP
CPX_CALLBACK_MIP_PROBE = _pycplex_platform.CPX_CALLBACK_MIP_PROBE
CPX_CALLBACK_MIP_FRACCUT = _pycplex_platform.CPX_CALLBACK_MIP_FRACCUT
CPX_CALLBACK_MIP_DISJCUT = _pycplex_platform.CPX_CALLBACK_MIP_DISJCUT
CPX_CALLBACK_MIP_FLOWMIR = _pycplex_platform.CPX_CALLBACK_MIP_FLOWMIR
CPX_CALLBACK_MIP_INCUMBENT_NODESOLN = _pycplex_platform.CPX_CALLBACK_MIP_INCUMBENT_NODESOLN
CPX_CALLBACK_MIP_DELETENODE = _pycplex_platform.CPX_CALLBACK_MIP_DELETENODE
CPX_CALLBACK_MIP_BRANCH_NOSOLN = _pycplex_platform.CPX_CALLBACK_MIP_BRANCH_NOSOLN
CPX_CALLBACK_MIP_CUT_LAST = _pycplex_platform.CPX_CALLBACK_MIP_CUT_LAST
CPX_CALLBACK_MIP_CUT_FEAS = _pycplex_platform.CPX_CALLBACK_MIP_CUT_FEAS
CPX_CALLBACK_MIP_CUT_UNBD = _pycplex_platform.CPX_CALLBACK_MIP_CUT_UNBD
CPX_CALLBACK_MIP_INCUMBENT_HEURSOLN = _pycplex_platform.CPX_CALLBACK_MIP_INCUMBENT_HEURSOLN
CPX_CALLBACK_MIP_INCUMBENT_USERSOLN = _pycplex_platform.CPX_CALLBACK_MIP_INCUMBENT_USERSOLN
CPX_CALLBACK_MIP_INCUMBENT_MIPSTART = _pycplex_platform.CPX_CALLBACK_MIP_INCUMBENT_MIPSTART
CPX_CALLBACK_INFO_BEST_INTEGER = _pycplex_platform.CPX_CALLBACK_INFO_BEST_INTEGER
CPX_CALLBACK_INFO_BEST_REMAINING = _pycplex_platform.CPX_CALLBACK_INFO_BEST_REMAINING
CPX_CALLBACK_INFO_NODE_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_NODE_COUNT
CPX_CALLBACK_INFO_NODES_LEFT = _pycplex_platform.CPX_CALLBACK_INFO_NODES_LEFT
CPX_CALLBACK_INFO_MIP_ITERATIONS = _pycplex_platform.CPX_CALLBACK_INFO_MIP_ITERATIONS
CPX_CALLBACK_INFO_CUTOFF = _pycplex_platform.CPX_CALLBACK_INFO_CUTOFF
CPX_CALLBACK_INFO_CLIQUE_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_CLIQUE_COUNT
CPX_CALLBACK_INFO_COVER_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_COVER_COUNT
CPX_CALLBACK_INFO_MIP_FEAS = _pycplex_platform.CPX_CALLBACK_INFO_MIP_FEAS
CPX_CALLBACK_INFO_FLOWCOVER_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_FLOWCOVER_COUNT
CPX_CALLBACK_INFO_GUBCOVER_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_GUBCOVER_COUNT
CPX_CALLBACK_INFO_IMPLBD_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_IMPLBD_COUNT
CPX_CALLBACK_INFO_PROBE_PHASE = _pycplex_platform.CPX_CALLBACK_INFO_PROBE_PHASE
CPX_CALLBACK_INFO_PROBE_PROGRESS = _pycplex_platform.CPX_CALLBACK_INFO_PROBE_PROGRESS
CPX_CALLBACK_INFO_FRACCUT_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_FRACCUT_COUNT
CPX_CALLBACK_INFO_FRACCUT_PROGRESS = _pycplex_platform.CPX_CALLBACK_INFO_FRACCUT_PROGRESS
CPX_CALLBACK_INFO_DISJCUT_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_DISJCUT_COUNT
CPX_CALLBACK_INFO_DISJCUT_PROGRESS = _pycplex_platform.CPX_CALLBACK_INFO_DISJCUT_PROGRESS
CPX_CALLBACK_INFO_FLOWPATH_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_FLOWPATH_COUNT
CPX_CALLBACK_INFO_MIRCUT_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_MIRCUT_COUNT
CPX_CALLBACK_INFO_FLOWMIR_PROGRESS = _pycplex_platform.CPX_CALLBACK_INFO_FLOWMIR_PROGRESS
CPX_CALLBACK_INFO_ZEROHALFCUT_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_ZEROHALFCUT_COUNT
CPX_CALLBACK_INFO_MY_THREAD_NUM = _pycplex_platform.CPX_CALLBACK_INFO_MY_THREAD_NUM
CPX_CALLBACK_INFO_USER_THREADS = _pycplex_platform.CPX_CALLBACK_INFO_USER_THREADS
CPX_CALLBACK_INFO_MIP_REL_GAP = _pycplex_platform.CPX_CALLBACK_INFO_MIP_REL_GAP
CPX_CALLBACK_INFO_MCFCUT_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_MCFCUT_COUNT
CPX_CALLBACK_INFO_KAPPA_STABLE = _pycplex_platform.CPX_CALLBACK_INFO_KAPPA_STABLE
CPX_CALLBACK_INFO_KAPPA_SUSPICIOUS = _pycplex_platform.CPX_CALLBACK_INFO_KAPPA_SUSPICIOUS
CPX_CALLBACK_INFO_KAPPA_UNSTABLE = _pycplex_platform.CPX_CALLBACK_INFO_KAPPA_UNSTABLE
CPX_CALLBACK_INFO_KAPPA_ILLPOSED = _pycplex_platform.CPX_CALLBACK_INFO_KAPPA_ILLPOSED
CPX_CALLBACK_INFO_KAPPA_MAX = _pycplex_platform.CPX_CALLBACK_INFO_KAPPA_MAX
CPX_CALLBACK_INFO_KAPPA_ATTENTION = _pycplex_platform.CPX_CALLBACK_INFO_KAPPA_ATTENTION
CPX_CALLBACK_INFO_LANDPCUT_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_LANDPCUT_COUNT
CPX_CALLBACK_INFO_USERCUT_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_USERCUT_COUNT
CPX_CALLBACK_INFO_TABLECUT_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_TABLECUT_COUNT
CPX_CALLBACK_INFO_SOLNPOOLCUT_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_SOLNPOOLCUT_COUNT
CPX_CALLBACK_INFO_BENDERS_COUNT = _pycplex_platform.CPX_CALLBACK_INFO_BENDERS_COUNT
CPX_CALLBACK_INFO_NODE_COUNT_LONG = _pycplex_platform.CPX_CALLBACK_INFO_NODE_COUNT_LONG
CPX_CALLBACK_INFO_NODES_LEFT_LONG = _pycplex_platform.CPX_CALLBACK_INFO_NODES_LEFT_LONG
CPX_CALLBACK_INFO_MIP_ITERATIONS_LONG = _pycplex_platform.CPX_CALLBACK_INFO_MIP_ITERATIONS_LONG
CPX_CALLBACK_INFO_LAZY_SOURCE = _pycplex_platform.CPX_CALLBACK_INFO_LAZY_SOURCE
CPX_CALLBACK_INFO_NODE_SIINF = _pycplex_platform.CPX_CALLBACK_INFO_NODE_SIINF
CPX_CALLBACK_INFO_NODE_NIINF = _pycplex_platform.CPX_CALLBACK_INFO_NODE_NIINF
CPX_CALLBACK_INFO_NODE_ESTIMATE = _pycplex_platform.CPX_CALLBACK_INFO_NODE_ESTIMATE
CPX_CALLBACK_INFO_NODE_DEPTH = _pycplex_platform.CPX_CALLBACK_INFO_NODE_DEPTH
CPX_CALLBACK_INFO_NODE_OBJVAL = _pycplex_platform.CPX_CALLBACK_INFO_NODE_OBJVAL
CPX_CALLBACK_INFO_NODE_TYPE = _pycplex_platform.CPX_CALLBACK_INFO_NODE_TYPE
CPX_CALLBACK_INFO_NODE_VAR = _pycplex_platform.CPX_CALLBACK_INFO_NODE_VAR
CPX_CALLBACK_INFO_NODE_SOS = _pycplex_platform.CPX_CALLBACK_INFO_NODE_SOS
CPX_CALLBACK_INFO_NODE_SEQNUM = _pycplex_platform.CPX_CALLBACK_INFO_NODE_SEQNUM
CPX_CALLBACK_INFO_NODE_USERHANDLE = _pycplex_platform.CPX_CALLBACK_INFO_NODE_USERHANDLE
CPX_CALLBACK_INFO_NODE_NODENUM = _pycplex_platform.CPX_CALLBACK_INFO_NODE_NODENUM
CPX_CALLBACK_INFO_NODE_SEQNUM_LONG = _pycplex_platform.CPX_CALLBACK_INFO_NODE_SEQNUM_LONG
CPX_CALLBACK_INFO_NODE_NODENUM_LONG = _pycplex_platform.CPX_CALLBACK_INFO_NODE_NODENUM_LONG
CPX_CALLBACK_INFO_NODE_DEPTH_LONG = _pycplex_platform.CPX_CALLBACK_INFO_NODE_DEPTH_LONG
CPX_CALLBACK_INFO_SOS_TYPE = _pycplex_platform.CPX_CALLBACK_INFO_SOS_TYPE
CPX_CALLBACK_INFO_SOS_SIZE = _pycplex_platform.CPX_CALLBACK_INFO_SOS_SIZE
CPX_CALLBACK_INFO_SOS_IS_FEASIBLE = _pycplex_platform.CPX_CALLBACK_INFO_SOS_IS_FEASIBLE
CPX_CALLBACK_INFO_SOS_MEMBER_INDEX = _pycplex_platform.CPX_CALLBACK_INFO_SOS_MEMBER_INDEX
CPX_CALLBACK_INFO_SOS_MEMBER_REFVAL = _pycplex_platform.CPX_CALLBACK_INFO_SOS_MEMBER_REFVAL
CPX_CALLBACK_INFO_SOS_NUM = _pycplex_platform.CPX_CALLBACK_INFO_SOS_NUM
CPX_CALLBACK_INFO_IC_NUM = _pycplex_platform.CPX_CALLBACK_INFO_IC_NUM
CPX_CALLBACK_INFO_IC_IMPLYING_VAR = _pycplex_platform.CPX_CALLBACK_INFO_IC_IMPLYING_VAR
CPX_CALLBACK_INFO_IC_IMPLIED_VAR = _pycplex_platform.CPX_CALLBACK_INFO_IC_IMPLIED_VAR
CPX_CALLBACK_INFO_IC_SENSE = _pycplex_platform.CPX_CALLBACK_INFO_IC_SENSE
CPX_CALLBACK_INFO_IC_COMPL = _pycplex_platform.CPX_CALLBACK_INFO_IC_COMPL
CPX_CALLBACK_INFO_IC_RHS = _pycplex_platform.CPX_CALLBACK_INFO_IC_RHS
CPX_CALLBACK_INFO_IC_IS_FEASIBLE = _pycplex_platform.CPX_CALLBACK_INFO_IC_IS_FEASIBLE
CPX_INCUMBENT_ID = _pycplex_platform.CPX_INCUMBENT_ID
CPX_RAMPUP_DISABLED = _pycplex_platform.CPX_RAMPUP_DISABLED
CPX_RAMPUP_AUTO = _pycplex_platform.CPX_RAMPUP_AUTO
CPX_RAMPUP_DYNAMIC = _pycplex_platform.CPX_RAMPUP_DYNAMIC
CPX_RAMPUP_INFINITE = _pycplex_platform.CPX_RAMPUP_INFINITE
CPX_CALLBACK_DEFAULT = _pycplex_platform.CPX_CALLBACK_DEFAULT
CPX_CALLBACK_FAIL = _pycplex_platform.CPX_CALLBACK_FAIL
CPX_CALLBACK_SET = _pycplex_platform.CPX_CALLBACK_SET
CPX_CALLBACK_ABORT_CUT_LOOP = _pycplex_platform.CPX_CALLBACK_ABORT_CUT_LOOP
CPX_USECUT_FORCE = _pycplex_platform.CPX_USECUT_FORCE
CPX_USECUT_PURGE = _pycplex_platform.CPX_USECUT_PURGE
CPX_USECUT_FILTER = _pycplex_platform.CPX_USECUT_FILTER
CPX_INTEGER_FEASIBLE = _pycplex_platform.CPX_INTEGER_FEASIBLE
CPX_INTEGER_INFEASIBLE = _pycplex_platform.CPX_INTEGER_INFEASIBLE
CPX_IMPLIED_INTEGER_FEASIBLE = _pycplex_platform.CPX_IMPLIED_INTEGER_FEASIBLE
CPX_CON_LOWER_BOUND = _pycplex_platform.CPX_CON_LOWER_BOUND
CPX_CON_UPPER_BOUND = _pycplex_platform.CPX_CON_UPPER_BOUND
CPX_CON_LINEAR = _pycplex_platform.CPX_CON_LINEAR
CPX_CON_QUADRATIC = _pycplex_platform.CPX_CON_QUADRATIC
CPX_CON_SOS = _pycplex_platform.CPX_CON_SOS
CPX_CON_INDICATOR = _pycplex_platform.CPX_CON_INDICATOR
CPX_CON_PWL = _pycplex_platform.CPX_CON_PWL
CPX_CON_ABS = _pycplex_platform.CPX_CON_ABS
CPX_CON_MINEXPR = _pycplex_platform.CPX_CON_MINEXPR
CPX_CON_MAXEXPR = _pycplex_platform.CPX_CON_MAXEXPR
CPX_CON_LAST_CONTYPE = _pycplex_platform.CPX_CON_LAST_CONTYPE
CPX_INDICATOR_IF = _pycplex_platform.CPX_INDICATOR_IF
CPX_INDICATOR_ONLYIF = _pycplex_platform.CPX_INDICATOR_ONLYIF
CPX_INDICATOR_IFANDONLYIF = _pycplex_platform.CPX_INDICATOR_IFANDONLYIF
CPXNET_NO_DISPLAY_OBJECTIVE = _pycplex_platform.CPXNET_NO_DISPLAY_OBJECTIVE
CPXNET_TRUE_OBJECTIVE = _pycplex_platform.CPXNET_TRUE_OBJECTIVE
CPXNET_PENALIZED_OBJECTIVE = _pycplex_platform.CPXNET_PENALIZED_OBJECTIVE
CPXNET_PRICE_AUTO = _pycplex_platform.CPXNET_PRICE_AUTO
CPXNET_PRICE_PARTIAL = _pycplex_platform.CPXNET_PRICE_PARTIAL
CPXNET_PRICE_MULT_PART = _pycplex_platform.CPXNET_PRICE_MULT_PART
CPXNET_PRICE_SORT_MULT_PART = _pycplex_platform.CPXNET_PRICE_SORT_MULT_PART
CPX_NETFIND_PURE = _pycplex_platform.CPX_NETFIND_PURE
CPX_NETFIND_REFLECT = _pycplex_platform.CPX_NETFIND_REFLECT
CPX_NETFIND_SCALE = _pycplex_platform.CPX_NETFIND_SCALE
CPX_QCPDUALS_NO = _pycplex_platform.CPX_QCPDUALS_NO
CPX_QCPDUALS_IFPOSSIBLE = _pycplex_platform.CPX_QCPDUALS_IFPOSSIBLE
CPX_QCPDUALS_FORCE = _pycplex_platform.CPX_QCPDUALS_FORCE
CPX_CPXAUTOCONSTANTS_H_H = _pycplex_platform.CPX_CPXAUTOCONSTANTS_H_H
CPX_BENDERS_ANNOTATION = _pycplex_platform.CPX_BENDERS_ANNOTATION
CPX_BENDERS_MASTERVALUE = _pycplex_platform.CPX_BENDERS_MASTERVALUE
CPX_BIGINT = _pycplex_platform.CPX_BIGINT
CPX_BIGLONG = _pycplex_platform.CPX_BIGLONG
CPX_CALLBACKCONTEXT_BRANCHING = _pycplex_platform.CPX_CALLBACKCONTEXT_BRANCHING
CPX_CALLBACKCONTEXT_CANDIDATE = _pycplex_platform.CPX_CALLBACKCONTEXT_CANDIDATE
CPX_CALLBACKCONTEXT_GLOBAL_PROGRESS = _pycplex_platform.CPX_CALLBACKCONTEXT_GLOBAL_PROGRESS
CPX_CALLBACKCONTEXT_LOCAL_PROGRESS = _pycplex_platform.CPX_CALLBACKCONTEXT_LOCAL_PROGRESS
CPX_CALLBACKCONTEXT_RELAXATION = _pycplex_platform.CPX_CALLBACKCONTEXT_RELAXATION
CPX_CALLBACKCONTEXT_THREAD_DOWN = _pycplex_platform.CPX_CALLBACKCONTEXT_THREAD_DOWN
CPX_CALLBACKCONTEXT_THREAD_UP = _pycplex_platform.CPX_CALLBACKCONTEXT_THREAD_UP
CPX_DUAL_OBJ = _pycplex_platform.CPX_DUAL_OBJ
CPX_EXACT_KAPPA = _pycplex_platform.CPX_EXACT_KAPPA
CPX_KAPPA = _pycplex_platform.CPX_KAPPA
CPX_KAPPA_ATTENTION = _pycplex_platform.CPX_KAPPA_ATTENTION
CPX_KAPPA_ILLPOSED = _pycplex_platform.CPX_KAPPA_ILLPOSED
CPX_KAPPA_MAX = _pycplex_platform.CPX_KAPPA_MAX
CPX_KAPPA_STABLE = _pycplex_platform.CPX_KAPPA_STABLE
CPX_KAPPA_SUSPICIOUS = _pycplex_platform.CPX_KAPPA_SUSPICIOUS
CPX_KAPPA_UNSTABLE = _pycplex_platform.CPX_KAPPA_UNSTABLE
CPX_LAZYCONSTRAINTCALLBACK_HEUR = _pycplex_platform.CPX_LAZYCONSTRAINTCALLBACK_HEUR
CPX_LAZYCONSTRAINTCALLBACK_MIPSTART = _pycplex_platform.CPX_LAZYCONSTRAINTCALLBACK_MIPSTART
CPX_LAZYCONSTRAINTCALLBACK_NODE = _pycplex_platform.CPX_LAZYCONSTRAINTCALLBACK_NODE
CPX_LAZYCONSTRAINTCALLBACK_USER = _pycplex_platform.CPX_LAZYCONSTRAINTCALLBACK_USER
CPX_MAX_COMP_SLACK = _pycplex_platform.CPX_MAX_COMP_SLACK
CPX_MAX_DUAL_INFEAS = _pycplex_platform.CPX_MAX_DUAL_INFEAS
CPX_MAX_DUAL_RESIDUAL = _pycplex_platform.CPX_MAX_DUAL_RESIDUAL
CPX_MAX_INDSLACK_INFEAS = _pycplex_platform.CPX_MAX_INDSLACK_INFEAS
CPX_MAX_INT_INFEAS = _pycplex_platform.CPX_MAX_INT_INFEAS
CPX_MAX_PI = _pycplex_platform.CPX_MAX_PI
CPX_MAX_PRIMAL_INFEAS = _pycplex_platform.CPX_MAX_PRIMAL_INFEAS
CPX_MAX_PRIMAL_RESIDUAL = _pycplex_platform.CPX_MAX_PRIMAL_RESIDUAL
CPX_MAX_PWLSLACK_INFEAS = _pycplex_platform.CPX_MAX_PWLSLACK_INFEAS
CPX_MAX_QCPRIMAL_RESIDUAL = _pycplex_platform.CPX_MAX_QCPRIMAL_RESIDUAL
CPX_MAX_QCSLACK = _pycplex_platform.CPX_MAX_QCSLACK
CPX_MAX_QCSLACK_INFEAS = _pycplex_platform.CPX_MAX_QCSLACK_INFEAS
CPX_MAX_RED_COST = _pycplex_platform.CPX_MAX_RED_COST
CPX_MAX_SCALED_DUAL_INFEAS = _pycplex_platform.CPX_MAX_SCALED_DUAL_INFEAS
CPX_MAX_SCALED_DUAL_RESIDUAL = _pycplex_platform.CPX_MAX_SCALED_DUAL_RESIDUAL
CPX_MAX_SCALED_PI = _pycplex_platform.CPX_MAX_SCALED_PI
CPX_MAX_SCALED_PRIMAL_INFEAS = _pycplex_platform.CPX_MAX_SCALED_PRIMAL_INFEAS
CPX_MAX_SCALED_PRIMAL_RESIDUAL = _pycplex_platform.CPX_MAX_SCALED_PRIMAL_RESIDUAL
CPX_MAX_SCALED_RED_COST = _pycplex_platform.CPX_MAX_SCALED_RED_COST
CPX_MAX_SCALED_SLACK = _pycplex_platform.CPX_MAX_SCALED_SLACK
CPX_MAX_SCALED_X = _pycplex_platform.CPX_MAX_SCALED_X
CPX_MAX_SLACK = _pycplex_platform.CPX_MAX_SLACK
CPX_MAX_X = _pycplex_platform.CPX_MAX_X
CPX_MULTIOBJ_BARITCNT = _pycplex_platform.CPX_MULTIOBJ_BARITCNT
CPX_MULTIOBJ_BESTOBJVAL = _pycplex_platform.CPX_MULTIOBJ_BESTOBJVAL
CPX_MULTIOBJ_BLEND = _pycplex_platform.CPX_MULTIOBJ_BLEND
CPX_MULTIOBJ_DEGCNT = _pycplex_platform.CPX_MULTIOBJ_DEGCNT
CPX_MULTIOBJ_DETTIME = _pycplex_platform.CPX_MULTIOBJ_DETTIME
CPX_MULTIOBJ_DEXCH = _pycplex_platform.CPX_MULTIOBJ_DEXCH
CPX_MULTIOBJ_DPUSH = _pycplex_platform.CPX_MULTIOBJ_DPUSH
CPX_MULTIOBJ_ERROR = _pycplex_platform.CPX_MULTIOBJ_ERROR
CPX_MULTIOBJ_ITCNT = _pycplex_platform.CPX_MULTIOBJ_ITCNT
CPX_MULTIOBJ_METHOD = _pycplex_platform.CPX_MULTIOBJ_METHOD
CPX_MULTIOBJ_NODECNT = _pycplex_platform.CPX_MULTIOBJ_NODECNT
CPX_MULTIOBJ_NODELEFTCNT = _pycplex_platform.CPX_MULTIOBJ_NODELEFTCNT
CPX_MULTIOBJ_OBJVAL = _pycplex_platform.CPX_MULTIOBJ_OBJVAL
CPX_MULTIOBJ_PEXCH = _pycplex_platform.CPX_MULTIOBJ_PEXCH
CPX_MULTIOBJ_PHASE1CNT = _pycplex_platform.CPX_MULTIOBJ_PHASE1CNT
CPX_MULTIOBJ_PPUSH = _pycplex_platform.CPX_MULTIOBJ_PPUSH
CPX_MULTIOBJ_PRIORITY = _pycplex_platform.CPX_MULTIOBJ_PRIORITY
CPX_MULTIOBJ_SIFTITCNT = _pycplex_platform.CPX_MULTIOBJ_SIFTITCNT
CPX_MULTIOBJ_SIFTPHASE1CNT = _pycplex_platform.CPX_MULTIOBJ_SIFTPHASE1CNT
CPX_MULTIOBJ_STATUS = _pycplex_platform.CPX_MULTIOBJ_STATUS
CPX_MULTIOBJ_TIME = _pycplex_platform.CPX_MULTIOBJ_TIME
CPX_NO_PRIORITY_CHANGE = _pycplex_platform.CPX_NO_PRIORITY_CHANGE
CPX_OBJ_GAP = _pycplex_platform.CPX_OBJ_GAP
CPX_PRIMAL_OBJ = _pycplex_platform.CPX_PRIMAL_OBJ
CPX_RELAXATION_FLAG_NOSOLVE = _pycplex_platform.CPX_RELAXATION_FLAG_NOSOLVE
CPX_SOLNPOOL_DIV = _pycplex_platform.CPX_SOLNPOOL_DIV
CPX_SOLNPOOL_FIFO = _pycplex_platform.CPX_SOLNPOOL_FIFO
CPX_SOLNPOOL_FILTER_DIVERSITY = _pycplex_platform.CPX_SOLNPOOL_FILTER_DIVERSITY
CPX_SOLNPOOL_FILTER_RANGE = _pycplex_platform.CPX_SOLNPOOL_FILTER_RANGE
CPX_SOLNPOOL_OBJ = _pycplex_platform.CPX_SOLNPOOL_OBJ
CPX_STAT_ABORT_DETTIME_LIM = _pycplex_platform.CPX_STAT_ABORT_DETTIME_LIM
CPX_STAT_ABORT_DUAL_OBJ_LIM = _pycplex_platform.CPX_STAT_ABORT_DUAL_OBJ_LIM
CPX_STAT_ABORT_IT_LIM = _pycplex_platform.CPX_STAT_ABORT_IT_LIM
CPX_STAT_ABORT_OBJ_LIM = _pycplex_platform.CPX_STAT_ABORT_OBJ_LIM
CPX_STAT_ABORT_PRIM_OBJ_LIM = _pycplex_platform.CPX_STAT_ABORT_PRIM_OBJ_LIM
CPX_STAT_ABORT_TIME_LIM = _pycplex_platform.CPX_STAT_ABORT_TIME_LIM
CPX_STAT_ABORT_USER = _pycplex_platform.CPX_STAT_ABORT_USER
CPX_STAT_BENDERS_NUM_BEST = _pycplex_platform.CPX_STAT_BENDERS_NUM_BEST
CPX_STAT_CONFLICT_ABORT_CONTRADICTION = _pycplex_platform.CPX_STAT_CONFLICT_ABORT_CONTRADICTION
CPX_STAT_CONFLICT_ABORT_DETTIME_LIM = _pycplex_platform.CPX_STAT_CONFLICT_ABORT_DETTIME_LIM
CPX_STAT_CONFLICT_ABORT_IT_LIM = _pycplex_platform.CPX_STAT_CONFLICT_ABORT_IT_LIM
CPX_STAT_CONFLICT_ABORT_MEM_LIM = _pycplex_platform.CPX_STAT_CONFLICT_ABORT_MEM_LIM
CPX_STAT_CONFLICT_ABORT_NODE_LIM = _pycplex_platform.CPX_STAT_CONFLICT_ABORT_NODE_LIM
CPX_STAT_CONFLICT_ABORT_OBJ_LIM = _pycplex_platform.CPX_STAT_CONFLICT_ABORT_OBJ_LIM
CPX_STAT_CONFLICT_ABORT_TIME_LIM = _pycplex_platform.CPX_STAT_CONFLICT_ABORT_TIME_LIM
CPX_STAT_CONFLICT_ABORT_USER = _pycplex_platform.CPX_STAT_CONFLICT_ABORT_USER
CPX_STAT_CONFLICT_FEASIBLE = _pycplex_platform.CPX_STAT_CONFLICT_FEASIBLE
CPX_STAT_CONFLICT_MINIMAL = _pycplex_platform.CPX_STAT_CONFLICT_MINIMAL
CPX_STAT_FEASIBLE = _pycplex_platform.CPX_STAT_FEASIBLE
CPX_STAT_FEASIBLE_RELAXED_INF = _pycplex_platform.CPX_STAT_FEASIBLE_RELAXED_INF
CPX_STAT_FEASIBLE_RELAXED_QUAD = _pycplex_platform.CPX_STAT_FEASIBLE_RELAXED_QUAD
CPX_STAT_FEASIBLE_RELAXED_SUM = _pycplex_platform.CPX_STAT_FEASIBLE_RELAXED_SUM
CPX_STAT_FIRSTORDER = _pycplex_platform.CPX_STAT_FIRSTORDER
CPX_STAT_INFEASIBLE = _pycplex_platform.CPX_STAT_INFEASIBLE
CPX_STAT_INForUNBD = _pycplex_platform.CPX_STAT_INForUNBD
CPX_STAT_MULTIOBJ_INFEASIBLE = _pycplex_platform.CPX_STAT_MULTIOBJ_INFEASIBLE
CPX_STAT_MULTIOBJ_INForUNBD = _pycplex_platform.CPX_STAT_MULTIOBJ_INForUNBD
CPX_STAT_MULTIOBJ_NON_OPTIMAL = _pycplex_platform.CPX_STAT_MULTIOBJ_NON_OPTIMAL
CPX_STAT_MULTIOBJ_OPTIMAL = _pycplex_platform.CPX_STAT_MULTIOBJ_OPTIMAL
CPX_STAT_MULTIOBJ_STOPPED = _pycplex_platform.CPX_STAT_MULTIOBJ_STOPPED
CPX_STAT_MULTIOBJ_UNBOUNDED = _pycplex_platform.CPX_STAT_MULTIOBJ_UNBOUNDED
CPX_STAT_NUM_BEST = _pycplex_platform.CPX_STAT_NUM_BEST
CPX_STAT_OPTIMAL = _pycplex_platform.CPX_STAT_OPTIMAL
CPX_STAT_OPTIMAL_FACE_UNBOUNDED = _pycplex_platform.CPX_STAT_OPTIMAL_FACE_UNBOUNDED
CPX_STAT_OPTIMAL_INFEAS = _pycplex_platform.CPX_STAT_OPTIMAL_INFEAS
CPX_STAT_OPTIMAL_RELAXED_INF = _pycplex_platform.CPX_STAT_OPTIMAL_RELAXED_INF
CPX_STAT_OPTIMAL_RELAXED_QUAD = _pycplex_platform.CPX_STAT_OPTIMAL_RELAXED_QUAD
CPX_STAT_OPTIMAL_RELAXED_SUM = _pycplex_platform.CPX_STAT_OPTIMAL_RELAXED_SUM
CPX_STAT_UNBOUNDED = _pycplex_platform.CPX_STAT_UNBOUNDED
CPX_SUM_COMP_SLACK = _pycplex_platform.CPX_SUM_COMP_SLACK
CPX_SUM_DUAL_INFEAS = _pycplex_platform.CPX_SUM_DUAL_INFEAS
CPX_SUM_DUAL_RESIDUAL = _pycplex_platform.CPX_SUM_DUAL_RESIDUAL
CPX_SUM_INDSLACK_INFEAS = _pycplex_platform.CPX_SUM_INDSLACK_INFEAS
CPX_SUM_INT_INFEAS = _pycplex_platform.CPX_SUM_INT_INFEAS
CPX_SUM_PI = _pycplex_platform.CPX_SUM_PI
CPX_SUM_PRIMAL_INFEAS = _pycplex_platform.CPX_SUM_PRIMAL_INFEAS
CPX_SUM_PRIMAL_RESIDUAL = _pycplex_platform.CPX_SUM_PRIMAL_RESIDUAL
CPX_SUM_PWLSLACK_INFEAS = _pycplex_platform.CPX_SUM_PWLSLACK_INFEAS
CPX_SUM_QCPRIMAL_RESIDUAL = _pycplex_platform.CPX_SUM_QCPRIMAL_RESIDUAL
CPX_SUM_QCSLACK = _pycplex_platform.CPX_SUM_QCSLACK
CPX_SUM_QCSLACK_INFEAS = _pycplex_platform.CPX_SUM_QCSLACK_INFEAS
CPX_SUM_RED_COST = _pycplex_platform.CPX_SUM_RED_COST
CPX_SUM_SCALED_DUAL_INFEAS = _pycplex_platform.CPX_SUM_SCALED_DUAL_INFEAS
CPX_SUM_SCALED_DUAL_RESIDUAL = _pycplex_platform.CPX_SUM_SCALED_DUAL_RESIDUAL
CPX_SUM_SCALED_PI = _pycplex_platform.CPX_SUM_SCALED_PI
CPX_SUM_SCALED_PRIMAL_INFEAS = _pycplex_platform.CPX_SUM_SCALED_PRIMAL_INFEAS
CPX_SUM_SCALED_PRIMAL_RESIDUAL = _pycplex_platform.CPX_SUM_SCALED_PRIMAL_RESIDUAL
CPX_SUM_SCALED_RED_COST = _pycplex_platform.CPX_SUM_SCALED_RED_COST
CPX_SUM_SCALED_SLACK = _pycplex_platform.CPX_SUM_SCALED_SLACK
CPX_SUM_SCALED_X = _pycplex_platform.CPX_SUM_SCALED_X
CPX_SUM_SLACK = _pycplex_platform.CPX_SUM_SLACK
CPX_SUM_X = _pycplex_platform.CPX_SUM_X
CPXERR_ABORT_STRONGBRANCH = _pycplex_platform.CPXERR_ABORT_STRONGBRANCH
CPXERR_ADJ_SIGN_QUAD = _pycplex_platform.CPXERR_ADJ_SIGN_QUAD
CPXERR_ADJ_SIGN_SENSE = _pycplex_platform.CPXERR_ADJ_SIGN_SENSE
CPXERR_ADJ_SIGNS = _pycplex_platform.CPXERR_ADJ_SIGNS
CPXERR_ARC_INDEX_RANGE = _pycplex_platform.CPXERR_ARC_INDEX_RANGE
CPXERR_ARRAY_BAD_SOS_TYPE = _pycplex_platform.CPXERR_ARRAY_BAD_SOS_TYPE
CPXERR_ARRAY_NOT_ASCENDING = _pycplex_platform.CPXERR_ARRAY_NOT_ASCENDING
CPXERR_ARRAY_TOO_LONG = _pycplex_platform.CPXERR_ARRAY_TOO_LONG
CPXERR_BAD_ARGUMENT = _pycplex_platform.CPXERR_BAD_ARGUMENT
CPXERR_BAD_BOUND_SENSE = _pycplex_platform.CPXERR_BAD_BOUND_SENSE
CPXERR_BAD_BOUND_TYPE = _pycplex_platform.CPXERR_BAD_BOUND_TYPE
CPXERR_BAD_CHAR = _pycplex_platform.CPXERR_BAD_CHAR
CPXERR_BAD_CTYPE = _pycplex_platform.CPXERR_BAD_CTYPE
CPXERR_BAD_DECOMPOSITION = _pycplex_platform.CPXERR_BAD_DECOMPOSITION
CPXERR_BAD_DIRECTION = _pycplex_platform.CPXERR_BAD_DIRECTION
CPXERR_BAD_EXPO_RANGE = _pycplex_platform.CPXERR_BAD_EXPO_RANGE
CPXERR_BAD_EXPONENT = _pycplex_platform.CPXERR_BAD_EXPONENT
CPXERR_BAD_FILETYPE = _pycplex_platform.CPXERR_BAD_FILETYPE
CPXERR_BAD_ID = _pycplex_platform.CPXERR_BAD_ID
CPXERR_BAD_INDCONSTR = _pycplex_platform.CPXERR_BAD_INDCONSTR
CPXERR_BAD_INDICATOR = _pycplex_platform.CPXERR_BAD_INDICATOR
CPXERR_BAD_INDTYPE = _pycplex_platform.CPXERR_BAD_INDTYPE
CPXERR_BAD_LAZY_UCUT = _pycplex_platform.CPXERR_BAD_LAZY_UCUT
CPXERR_BAD_LUB = _pycplex_platform.CPXERR_BAD_LUB
CPXERR_BAD_METHOD = _pycplex_platform.CPXERR_BAD_METHOD
CPXERR_BAD_MULTIOBJ_ATTR = _pycplex_platform.CPXERR_BAD_MULTIOBJ_ATTR
CPXERR_BAD_NAME = _pycplex_platform.CPXERR_BAD_NAME
CPXERR_BAD_NUMBER = _pycplex_platform.CPXERR_BAD_NUMBER
CPXERR_BAD_OBJ_SENSE = _pycplex_platform.CPXERR_BAD_OBJ_SENSE
CPXERR_BAD_PARAM_NAME = _pycplex_platform.CPXERR_BAD_PARAM_NAME
CPXERR_BAD_PARAM_NUM = _pycplex_platform.CPXERR_BAD_PARAM_NUM
CPXERR_BAD_PIVOT = _pycplex_platform.CPXERR_BAD_PIVOT
CPXERR_BAD_PRIORITY = _pycplex_platform.CPXERR_BAD_PRIORITY
CPXERR_BAD_PROB_TYPE = _pycplex_platform.CPXERR_BAD_PROB_TYPE
CPXERR_BAD_ROW_ID = _pycplex_platform.CPXERR_BAD_ROW_ID
CPXERR_BAD_SECTION_BOUNDS = _pycplex_platform.CPXERR_BAD_SECTION_BOUNDS
CPXERR_BAD_SECTION_ENDATA = _pycplex_platform.CPXERR_BAD_SECTION_ENDATA
CPXERR_BAD_SECTION_QMATRIX = _pycplex_platform.CPXERR_BAD_SECTION_QMATRIX
CPXERR_BAD_SENSE = _pycplex_platform.CPXERR_BAD_SENSE
CPXERR_BAD_SOS_TYPE = _pycplex_platform.CPXERR_BAD_SOS_TYPE
CPXERR_BAD_STATUS = _pycplex_platform.CPXERR_BAD_STATUS
CPXERR_BAS_FILE_SHORT = _pycplex_platform.CPXERR_BAS_FILE_SHORT
CPXERR_BAS_FILE_SIZE = _pycplex_platform.CPXERR_BAS_FILE_SIZE
CPXERR_BENDERS_MASTER_SOLVE = _pycplex_platform.CPXERR_BENDERS_MASTER_SOLVE
CPXERR_CALLBACK = _pycplex_platform.CPXERR_CALLBACK
CPXERR_CALLBACK_INCONSISTENT = _pycplex_platform.CPXERR_CALLBACK_INCONSISTENT
CPXERR_CAND_NOT_POINT = _pycplex_platform.CPXERR_CAND_NOT_POINT
CPXERR_CAND_NOT_RAY = _pycplex_platform.CPXERR_CAND_NOT_RAY
CPXERR_CNTRL_IN_NAME = _pycplex_platform.CPXERR_CNTRL_IN_NAME
CPXERR_COL_INDEX_RANGE = _pycplex_platform.CPXERR_COL_INDEX_RANGE
CPXERR_COL_REPEAT_PRINT = _pycplex_platform.CPXERR_COL_REPEAT_PRINT
CPXERR_COL_REPEATS = _pycplex_platform.CPXERR_COL_REPEATS
CPXERR_COL_ROW_REPEATS = _pycplex_platform.CPXERR_COL_ROW_REPEATS
CPXERR_COL_UNKNOWN = _pycplex_platform.CPXERR_COL_UNKNOWN
CPXERR_CONFLICT_UNSTABLE = _pycplex_platform.CPXERR_CONFLICT_UNSTABLE
CPXERR_COUNT_OVERLAP = _pycplex_platform.CPXERR_COUNT_OVERLAP
CPXERR_COUNT_RANGE = _pycplex_platform.CPXERR_COUNT_RANGE
CPXERR_CPUBINDING_FAILURE = _pycplex_platform.CPXERR_CPUBINDING_FAILURE
CPXERR_DBL_MAX = _pycplex_platform.CPXERR_DBL_MAX
CPXERR_DECOMPRESSION = _pycplex_platform.CPXERR_DECOMPRESSION
CPXERR_DETTILIM_STRONGBRANCH = _pycplex_platform.CPXERR_DETTILIM_STRONGBRANCH
CPXERR_DUP_ENTRY = _pycplex_platform.CPXERR_DUP_ENTRY
CPXERR_DYNFUNC = _pycplex_platform.CPXERR_DYNFUNC
CPXERR_DYNLOAD = _pycplex_platform.CPXERR_DYNLOAD
CPXERR_ENCODING_CONVERSION = _pycplex_platform.CPXERR_ENCODING_CONVERSION
CPXERR_EXTRA_BV_BOUND = _pycplex_platform.CPXERR_EXTRA_BV_BOUND
CPXERR_EXTRA_FR_BOUND = _pycplex_platform.CPXERR_EXTRA_FR_BOUND
CPXERR_EXTRA_FX_BOUND = _pycplex_platform.CPXERR_EXTRA_FX_BOUND
CPXERR_EXTRA_INTEND = _pycplex_platform.CPXERR_EXTRA_INTEND
CPXERR_EXTRA_INTORG = _pycplex_platform.CPXERR_EXTRA_INTORG
CPXERR_EXTRA_SOSEND = _pycplex_platform.CPXERR_EXTRA_SOSEND
CPXERR_EXTRA_SOSORG = _pycplex_platform.CPXERR_EXTRA_SOSORG
CPXERR_FAIL_OPEN_READ = _pycplex_platform.CPXERR_FAIL_OPEN_READ
CPXERR_FAIL_OPEN_WRITE = _pycplex_platform.CPXERR_FAIL_OPEN_WRITE
CPXERR_FILE_ENTRIES = _pycplex_platform.CPXERR_FILE_ENTRIES
CPXERR_FILE_FORMAT = _pycplex_platform.CPXERR_FILE_FORMAT
CPXERR_FILE_IO = _pycplex_platform.CPXERR_FILE_IO
CPXERR_FILTER_VARIABLE_TYPE = _pycplex_platform.CPXERR_FILTER_VARIABLE_TYPE
CPXERR_ILL_DEFINED_PWL = _pycplex_platform.CPXERR_ILL_DEFINED_PWL
CPXERR_IN_INFOCALLBACK = _pycplex_platform.CPXERR_IN_INFOCALLBACK
CPXERR_INDEX_NOT_BASIC = _pycplex_platform.CPXERR_INDEX_NOT_BASIC
CPXERR_INDEX_RANGE = _pycplex_platform.CPXERR_INDEX_RANGE
CPXERR_INDEX_RANGE_HIGH = _pycplex_platform.CPXERR_INDEX_RANGE_HIGH
CPXERR_INDEX_RANGE_LOW = _pycplex_platform.CPXERR_INDEX_RANGE_LOW
CPXERR_INT_TOO_BIG = _pycplex_platform.CPXERR_INT_TOO_BIG
CPXERR_INT_TOO_BIG_INPUT = _pycplex_platform.CPXERR_INT_TOO_BIG_INPUT
CPXERR_INVALID_NUMBER = _pycplex_platform.CPXERR_INVALID_NUMBER
CPXERR_LIMITS_TOO_BIG = _pycplex_platform.CPXERR_LIMITS_TOO_BIG
CPXERR_LINE_TOO_LONG = _pycplex_platform.CPXERR_LINE_TOO_LONG
CPXERR_LO_BOUND_REPEATS = _pycplex_platform.CPXERR_LO_BOUND_REPEATS
CPXERR_LOCK_CREATE = _pycplex_platform.CPXERR_LOCK_CREATE
CPXERR_LP_NOT_IN_ENVIRONMENT = _pycplex_platform.CPXERR_LP_NOT_IN_ENVIRONMENT
CPXERR_LP_PARSE = _pycplex_platform.CPXERR_LP_PARSE
CPXERR_MASTER_SOLVE = _pycplex_platform.CPXERR_MASTER_SOLVE
CPXERR_MIPSEARCH_WITH_CALLBACKS = _pycplex_platform.CPXERR_MIPSEARCH_WITH_CALLBACKS
CPXERR_MISS_SOS_TYPE = _pycplex_platform.CPXERR_MISS_SOS_TYPE
CPXERR_MSG_NO_CHANNEL = _pycplex_platform.CPXERR_MSG_NO_CHANNEL
CPXERR_MSG_NO_FILEPTR = _pycplex_platform.CPXERR_MSG_NO_FILEPTR
CPXERR_MSG_NO_FUNCTION = _pycplex_platform.CPXERR_MSG_NO_FUNCTION
CPXERR_MULTIOBJ_SUBPROB_SOLVE = _pycplex_platform.CPXERR_MULTIOBJ_SUBPROB_SOLVE
CPXERR_MULTIPLE_PROBS_IN_REMOTE_ENVIRONMENT = _pycplex_platform.CPXERR_MULTIPLE_PROBS_IN_REMOTE_ENVIRONMENT
CPXERR_NAME_CREATION = _pycplex_platform.CPXERR_NAME_CREATION
CPXERR_NAME_NOT_FOUND = _pycplex_platform.CPXERR_NAME_NOT_FOUND
CPXERR_NAME_TOO_LONG = _pycplex_platform.CPXERR_NAME_TOO_LONG
CPXERR_NAN = _pycplex_platform.CPXERR_NAN
CPXERR_NEED_OPT_SOLN = _pycplex_platform.CPXERR_NEED_OPT_SOLN
CPXERR_NEGATIVE_SURPLUS = _pycplex_platform.CPXERR_NEGATIVE_SURPLUS
CPXERR_NET_DATA = _pycplex_platform.CPXERR_NET_DATA
CPXERR_NET_FILE_SHORT = _pycplex_platform.CPXERR_NET_FILE_SHORT
CPXERR_NO_BARRIER_SOLN = _pycplex_platform.CPXERR_NO_BARRIER_SOLN
CPXERR_NO_BASIC_SOLN = _pycplex_platform.CPXERR_NO_BASIC_SOLN
CPXERR_NO_BASIS = _pycplex_platform.CPXERR_NO_BASIS
CPXERR_NO_BOUND_SENSE = _pycplex_platform.CPXERR_NO_BOUND_SENSE
CPXERR_NO_BOUND_TYPE = _pycplex_platform.CPXERR_NO_BOUND_TYPE
CPXERR_NO_COLUMNS_SECTION = _pycplex_platform.CPXERR_NO_COLUMNS_SECTION
CPXERR_NO_CONFLICT = _pycplex_platform.CPXERR_NO_CONFLICT
CPXERR_NO_DECOMPOSITION = _pycplex_platform.CPXERR_NO_DECOMPOSITION
CPXERR_NO_DUAL_SOLN = _pycplex_platform.CPXERR_NO_DUAL_SOLN
CPXERR_NO_ENDATA = _pycplex_platform.CPXERR_NO_ENDATA
CPXERR_NO_ENVIRONMENT = _pycplex_platform.CPXERR_NO_ENVIRONMENT
CPXERR_NO_FILENAME = _pycplex_platform.CPXERR_NO_FILENAME
CPXERR_NO_ID = _pycplex_platform.CPXERR_NO_ID
CPXERR_NO_ID_FIRST = _pycplex_platform.CPXERR_NO_ID_FIRST
CPXERR_NO_INT_X = _pycplex_platform.CPXERR_NO_INT_X
CPXERR_NO_KAPPASTATS = _pycplex_platform.CPXERR_NO_KAPPASTATS
CPXERR_NO_LU_FACTOR = _pycplex_platform.CPXERR_NO_LU_FACTOR
CPXERR_NO_MEMORY = _pycplex_platform.CPXERR_NO_MEMORY
CPXERR_NO_MIPSTART = _pycplex_platform.CPXERR_NO_MIPSTART
CPXERR_NO_NAME_SECTION = _pycplex_platform.CPXERR_NO_NAME_SECTION
CPXERR_NO_NAMES = _pycplex_platform.CPXERR_NO_NAMES
CPXERR_NO_NORMS = _pycplex_platform.CPXERR_NO_NORMS
CPXERR_NO_NUMBER = _pycplex_platform.CPXERR_NO_NUMBER
CPXERR_NO_NUMBER_BOUND = _pycplex_platform.CPXERR_NO_NUMBER_BOUND
CPXERR_NO_NUMBER_FIRST = _pycplex_platform.CPXERR_NO_NUMBER_FIRST
CPXERR_NO_OBJ_NAME = _pycplex_platform.CPXERR_NO_OBJ_NAME
CPXERR_NO_OBJ_SENSE = _pycplex_platform.CPXERR_NO_OBJ_SENSE
CPXERR_NO_OBJECTIVE = _pycplex_platform.CPXERR_NO_OBJECTIVE
CPXERR_NO_OP_OR_SENSE = _pycplex_platform.CPXERR_NO_OP_OR_SENSE
CPXERR_NO_OPERATOR = _pycplex_platform.CPXERR_NO_OPERATOR
CPXERR_NO_ORDER = _pycplex_platform.CPXERR_NO_ORDER
CPXERR_NO_PROBLEM = _pycplex_platform.CPXERR_NO_PROBLEM
CPXERR_NO_QP_OPERATOR = _pycplex_platform.CPXERR_NO_QP_OPERATOR
CPXERR_NO_QUAD_EXP = _pycplex_platform.CPXERR_NO_QUAD_EXP
CPXERR_NO_RHS_COEFF = _pycplex_platform.CPXERR_NO_RHS_COEFF
CPXERR_NO_RHS_IN_OBJ = _pycplex_platform.CPXERR_NO_RHS_IN_OBJ
CPXERR_NO_ROW_NAME = _pycplex_platform.CPXERR_NO_ROW_NAME
CPXERR_NO_ROW_SENSE = _pycplex_platform.CPXERR_NO_ROW_SENSE
CPXERR_NO_ROWS_SECTION = _pycplex_platform.CPXERR_NO_ROWS_SECTION
CPXERR_NO_SENSIT = _pycplex_platform.CPXERR_NO_SENSIT
CPXERR_NO_SOLN = _pycplex_platform.CPXERR_NO_SOLN
CPXERR_NO_SOLNPOOL = _pycplex_platform.CPXERR_NO_SOLNPOOL
CPXERR_NO_SOS = _pycplex_platform.CPXERR_NO_SOS
CPXERR_NO_TREE = _pycplex_platform.CPXERR_NO_TREE
CPXERR_NO_VECTOR_SOLN = _pycplex_platform.CPXERR_NO_VECTOR_SOLN
CPXERR_NODE_INDEX_RANGE = _pycplex_platform.CPXERR_NODE_INDEX_RANGE
CPXERR_NODE_ON_DISK = _pycplex_platform.CPXERR_NODE_ON_DISK
CPXERR_NOT_DUAL_UNBOUNDED = _pycplex_platform.CPXERR_NOT_DUAL_UNBOUNDED
CPXERR_NOT_FIXED = _pycplex_platform.CPXERR_NOT_FIXED
CPXERR_NOT_FOR_BENDERS = _pycplex_platform.CPXERR_NOT_FOR_BENDERS
CPXERR_NOT_FOR_DISTMIP = _pycplex_platform.CPXERR_NOT_FOR_DISTMIP
CPXERR_NOT_FOR_MIP = _pycplex_platform.CPXERR_NOT_FOR_MIP
CPXERR_NOT_FOR_MULTIOBJ = _pycplex_platform.CPXERR_NOT_FOR_MULTIOBJ
CPXERR_NOT_FOR_QCP = _pycplex_platform.CPXERR_NOT_FOR_QCP
CPXERR_NOT_FOR_QP = _pycplex_platform.CPXERR_NOT_FOR_QP
CPXERR_NOT_MILPCLASS = _pycplex_platform.CPXERR_NOT_MILPCLASS
CPXERR_NOT_MIN_COST_FLOW = _pycplex_platform.CPXERR_NOT_MIN_COST_FLOW
CPXERR_NOT_MIP = _pycplex_platform.CPXERR_NOT_MIP
CPXERR_NOT_MIQPCLASS = _pycplex_platform.CPXERR_NOT_MIQPCLASS
CPXERR_NOT_ONE_PROBLEM = _pycplex_platform.CPXERR_NOT_ONE_PROBLEM
CPXERR_NOT_QP = _pycplex_platform.CPXERR_NOT_QP
CPXERR_NOT_SAV_FILE = _pycplex_platform.CPXERR_NOT_SAV_FILE
CPXERR_NOT_UNBOUNDED = _pycplex_platform.CPXERR_NOT_UNBOUNDED
CPXERR_NULL_POINTER = _pycplex_platform.CPXERR_NULL_POINTER
CPXERR_ORDER_BAD_DIRECTION = _pycplex_platform.CPXERR_ORDER_BAD_DIRECTION
CPXERR_OVERFLOW = _pycplex_platform.CPXERR_OVERFLOW
CPXERR_PARAM_INCOMPATIBLE = _pycplex_platform.CPXERR_PARAM_INCOMPATIBLE
CPXERR_PARAM_TOO_BIG = _pycplex_platform.CPXERR_PARAM_TOO_BIG
CPXERR_PARAM_TOO_SMALL = _pycplex_platform.CPXERR_PARAM_TOO_SMALL
CPXERR_PRESLV_ABORT = _pycplex_platform.CPXERR_PRESLV_ABORT
CPXERR_PRESLV_BAD_PARAM = _pycplex_platform.CPXERR_PRESLV_BAD_PARAM
CPXERR_PRESLV_BASIS_MEM = _pycplex_platform.CPXERR_PRESLV_BASIS_MEM
CPXERR_PRESLV_COPYORDER = _pycplex_platform.CPXERR_PRESLV_COPYORDER
CPXERR_PRESLV_COPYSOS = _pycplex_platform.CPXERR_PRESLV_COPYSOS
CPXERR_PRESLV_CRUSHFORM = _pycplex_platform.CPXERR_PRESLV_CRUSHFORM
CPXERR_PRESLV_DETTIME_LIM = _pycplex_platform.CPXERR_PRESLV_DETTIME_LIM
CPXERR_PRESLV_DUAL = _pycplex_platform.CPXERR_PRESLV_DUAL
CPXERR_PRESLV_FAIL_BASIS = _pycplex_platform.CPXERR_PRESLV_FAIL_BASIS
CPXERR_PRESLV_INF = _pycplex_platform.CPXERR_PRESLV_INF
CPXERR_PRESLV_INForUNBD = _pycplex_platform.CPXERR_PRESLV_INForUNBD
CPXERR_PRESLV_NO_BASIS = _pycplex_platform.CPXERR_PRESLV_NO_BASIS
CPXERR_PRESLV_NO_PROB = _pycplex_platform.CPXERR_PRESLV_NO_PROB
CPXERR_PRESLV_SOLN_MIP = _pycplex_platform.CPXERR_PRESLV_SOLN_MIP
CPXERR_PRESLV_SOLN_QP = _pycplex_platform.CPXERR_PRESLV_SOLN_QP
CPXERR_PRESLV_START_LP = _pycplex_platform.CPXERR_PRESLV_START_LP
CPXERR_PRESLV_TIME_LIM = _pycplex_platform.CPXERR_PRESLV_TIME_LIM
CPXERR_PRESLV_UNBD = _pycplex_platform.CPXERR_PRESLV_UNBD
CPXERR_PRESLV_UNCRUSHFORM = _pycplex_platform.CPXERR_PRESLV_UNCRUSHFORM
CPXERR_PRIIND = _pycplex_platform.CPXERR_PRIIND
CPXERR_PRM_DATA = _pycplex_platform.CPXERR_PRM_DATA
CPXERR_PROTOCOL = _pycplex_platform.CPXERR_PROTOCOL
CPXERR_Q_DIVISOR = _pycplex_platform.CPXERR_Q_DIVISOR
CPXERR_Q_DUP_ENTRY = _pycplex_platform.CPXERR_Q_DUP_ENTRY
CPXERR_Q_NOT_INDEF = _pycplex_platform.CPXERR_Q_NOT_INDEF
CPXERR_Q_NOT_POS_DEF = _pycplex_platform.CPXERR_Q_NOT_POS_DEF
CPXERR_Q_NOT_SYMMETRIC = _pycplex_platform.CPXERR_Q_NOT_SYMMETRIC
CPXERR_QCP_SENSE = _pycplex_platform.CPXERR_QCP_SENSE
CPXERR_QCP_SENSE_FILE = _pycplex_platform.CPXERR_QCP_SENSE_FILE
CPXERR_QUAD_EXP_NOT_2 = _pycplex_platform.CPXERR_QUAD_EXP_NOT_2
CPXERR_QUAD_IN_ROW = _pycplex_platform.CPXERR_QUAD_IN_ROW
CPXERR_RANGE_SECTION_ORDER = _pycplex_platform.CPXERR_RANGE_SECTION_ORDER
CPXERR_RESTRICTED_VERSION = _pycplex_platform.CPXERR_RESTRICTED_VERSION
CPXERR_RHS_IN_OBJ = _pycplex_platform.CPXERR_RHS_IN_OBJ
CPXERR_RIM_REPEATS = _pycplex_platform.CPXERR_RIM_REPEATS
CPXERR_RIM_ROW_REPEATS = _pycplex_platform.CPXERR_RIM_ROW_REPEATS
CPXERR_RIMNZ_REPEATS = _pycplex_platform.CPXERR_RIMNZ_REPEATS
CPXERR_ROW_INDEX_RANGE = _pycplex_platform.CPXERR_ROW_INDEX_RANGE
CPXERR_ROW_REPEAT_PRINT = _pycplex_platform.CPXERR_ROW_REPEAT_PRINT
CPXERR_ROW_REPEATS = _pycplex_platform.CPXERR_ROW_REPEATS
CPXERR_ROW_UNKNOWN = _pycplex_platform.CPXERR_ROW_UNKNOWN
CPXERR_SAV_FILE_DATA = _pycplex_platform.CPXERR_SAV_FILE_DATA
CPXERR_SAV_FILE_VALUE = _pycplex_platform.CPXERR_SAV_FILE_VALUE
CPXERR_SAV_FILE_WRITE = _pycplex_platform.CPXERR_SAV_FILE_WRITE
CPXERR_SBASE_ILLEGAL = _pycplex_platform.CPXERR_SBASE_ILLEGAL
CPXERR_SBASE_INCOMPAT = _pycplex_platform.CPXERR_SBASE_INCOMPAT
CPXERR_SINGULAR = _pycplex_platform.CPXERR_SINGULAR
CPXERR_STR_PARAM_TOO_LONG = _pycplex_platform.CPXERR_STR_PARAM_TOO_LONG
CPXERR_SUBPROB_SOLVE = _pycplex_platform.CPXERR_SUBPROB_SOLVE
CPXERR_SYNCPRIM_CREATE = _pycplex_platform.CPXERR_SYNCPRIM_CREATE
CPXERR_SYSCALL = _pycplex_platform.CPXERR_SYSCALL
CPXERR_THREAD_FAILED = _pycplex_platform.CPXERR_THREAD_FAILED
CPXERR_TILIM_CONDITION_NO = _pycplex_platform.CPXERR_TILIM_CONDITION_NO
CPXERR_TILIM_STRONGBRANCH = _pycplex_platform.CPXERR_TILIM_STRONGBRANCH
CPXERR_TOO_MANY_COEFFS = _pycplex_platform.CPXERR_TOO_MANY_COEFFS
CPXERR_TOO_MANY_COLS = _pycplex_platform.CPXERR_TOO_MANY_COLS
CPXERR_TOO_MANY_RIMNZ = _pycplex_platform.CPXERR_TOO_MANY_RIMNZ
CPXERR_TOO_MANY_RIMS = _pycplex_platform.CPXERR_TOO_MANY_RIMS
CPXERR_TOO_MANY_ROWS = _pycplex_platform.CPXERR_TOO_MANY_ROWS
CPXERR_TOO_MANY_THREADS = _pycplex_platform.CPXERR_TOO_MANY_THREADS
CPXERR_TREE_MEMORY_LIMIT = _pycplex_platform.CPXERR_TREE_MEMORY_LIMIT
CPXERR_TUNE_MIXED = _pycplex_platform.CPXERR_TUNE_MIXED
CPXERR_UNIQUE_WEIGHTS = _pycplex_platform.CPXERR_UNIQUE_WEIGHTS
CPXERR_UNSUPPORTED_CONSTRAINT_TYPE = _pycplex_platform.CPXERR_UNSUPPORTED_CONSTRAINT_TYPE
CPXERR_UNSUPPORTED_OPERATION = _pycplex_platform.CPXERR_UNSUPPORTED_OPERATION
CPXERR_UP_BOUND_REPEATS = _pycplex_platform.CPXERR_UP_BOUND_REPEATS
CPXERR_WORK_FILE_OPEN = _pycplex_platform.CPXERR_WORK_FILE_OPEN
CPXERR_WORK_FILE_READ = _pycplex_platform.CPXERR_WORK_FILE_READ
CPXERR_WORK_FILE_WRITE = _pycplex_platform.CPXERR_WORK_FILE_WRITE
CPXERR_XMLPARSE = _pycplex_platform.CPXERR_XMLPARSE
CPXMESSAGEBUFSIZE = _pycplex_platform.CPXMESSAGEBUFSIZE
CPXMI_BIGM_COEF = _pycplex_platform.CPXMI_BIGM_COEF
CPXMI_BIGM_TO_IND = _pycplex_platform.CPXMI_BIGM_TO_IND
CPXMI_BIGM_VARBOUND = _pycplex_platform.CPXMI_BIGM_VARBOUND
CPXMI_CANCEL_TOL = _pycplex_platform.CPXMI_CANCEL_TOL
CPXMI_EPGAP_LARGE = _pycplex_platform.CPXMI_EPGAP_LARGE
CPXMI_EPGAP_OBJOFFSET = _pycplex_platform.CPXMI_EPGAP_OBJOFFSET
CPXMI_FEAS_TOL = _pycplex_platform.CPXMI_FEAS_TOL
CPXMI_FRACTION_SCALING = _pycplex_platform.CPXMI_FRACTION_SCALING
CPXMI_IND_NZ_LARGE_NUM = _pycplex_platform.CPXMI_IND_NZ_LARGE_NUM
CPXMI_IND_NZ_SMALL_NUM = _pycplex_platform.CPXMI_IND_NZ_SMALL_NUM
CPXMI_IND_RHS_LARGE_NUM = _pycplex_platform.CPXMI_IND_RHS_LARGE_NUM
CPXMI_IND_RHS_SMALL_NUM = _pycplex_platform.CPXMI_IND_RHS_SMALL_NUM
CPXMI_KAPPA_ILLPOSED = _pycplex_platform.CPXMI_KAPPA_ILLPOSED
CPXMI_KAPPA_SUSPICIOUS = _pycplex_platform.CPXMI_KAPPA_SUSPICIOUS
CPXMI_KAPPA_UNSTABLE = _pycplex_platform.CPXMI_KAPPA_UNSTABLE
CPXMI_LB_LARGE_NUM = _pycplex_platform.CPXMI_LB_LARGE_NUM
CPXMI_LB_SMALL_NUM = _pycplex_platform.CPXMI_LB_SMALL_NUM
CPXMI_LC_NZ_LARGE_NUM = _pycplex_platform.CPXMI_LC_NZ_LARGE_NUM
CPXMI_LC_NZ_SMALL_NUM = _pycplex_platform.CPXMI_LC_NZ_SMALL_NUM
CPXMI_LC_RHS_LARGE_NUM = _pycplex_platform.CPXMI_LC_RHS_LARGE_NUM
CPXMI_LC_RHS_SMALL_NUM = _pycplex_platform.CPXMI_LC_RHS_SMALL_NUM
CPXMI_MULTIOBJ_COEFFS = _pycplex_platform.CPXMI_MULTIOBJ_COEFFS
CPXMI_MULTIOBJ_LARGE_NUM = _pycplex_platform.CPXMI_MULTIOBJ_LARGE_NUM
CPXMI_MULTIOBJ_MIX = _pycplex_platform.CPXMI_MULTIOBJ_MIX
CPXMI_MULTIOBJ_OPT_TOL = _pycplex_platform.CPXMI_MULTIOBJ_OPT_TOL
CPXMI_MULTIOBJ_SMALL_NUM = _pycplex_platform.CPXMI_MULTIOBJ_SMALL_NUM
CPXMI_NZ_LARGE_NUM = _pycplex_platform.CPXMI_NZ_LARGE_NUM
CPXMI_NZ_SMALL_NUM = _pycplex_platform.CPXMI_NZ_SMALL_NUM
CPXMI_OBJ_LARGE_NUM = _pycplex_platform.CPXMI_OBJ_LARGE_NUM
CPXMI_OBJ_SMALL_NUM = _pycplex_platform.CPXMI_OBJ_SMALL_NUM
CPXMI_OPT_TOL = _pycplex_platform.CPXMI_OPT_TOL
CPXMI_PWL_SLOPE_LARGE_NUM = _pycplex_platform.CPXMI_PWL_SLOPE_LARGE_NUM
CPXMI_PWL_SLOPE_SMALL_NUM = _pycplex_platform.CPXMI_PWL_SLOPE_SMALL_NUM
CPXMI_QC_LINNZ_LARGE_NUM = _pycplex_platform.CPXMI_QC_LINNZ_LARGE_NUM
CPXMI_QC_LINNZ_SMALL_NUM = _pycplex_platform.CPXMI_QC_LINNZ_SMALL_NUM
CPXMI_QC_QNZ_LARGE_NUM = _pycplex_platform.CPXMI_QC_QNZ_LARGE_NUM
CPXMI_QC_QNZ_SMALL_NUM = _pycplex_platform.CPXMI_QC_QNZ_SMALL_NUM
CPXMI_QC_RHS_LARGE_NUM = _pycplex_platform.CPXMI_QC_RHS_LARGE_NUM
CPXMI_QC_RHS_SMALL_NUM = _pycplex_platform.CPXMI_QC_RHS_SMALL_NUM
CPXMI_QOBJ_LARGE_NUM = _pycplex_platform.CPXMI_QOBJ_LARGE_NUM
CPXMI_QOBJ_SMALL_NUM = _pycplex_platform.CPXMI_QOBJ_SMALL_NUM
CPXMI_QOPT_TOL = _pycplex_platform.CPXMI_QOPT_TOL
CPXMI_RHS_LARGE_NUM = _pycplex_platform.CPXMI_RHS_LARGE_NUM
CPXMI_RHS_SMALL_NUM = _pycplex_platform.CPXMI_RHS_SMALL_NUM
CPXMI_SAMECOEFF_COL = _pycplex_platform.CPXMI_SAMECOEFF_COL
CPXMI_SAMECOEFF_IND = _pycplex_platform.CPXMI_SAMECOEFF_IND
CPXMI_SAMECOEFF_LAZY = _pycplex_platform.CPXMI_SAMECOEFF_LAZY
CPXMI_SAMECOEFF_MULTIOBJ = _pycplex_platform.CPXMI_SAMECOEFF_MULTIOBJ
CPXMI_SAMECOEFF_OBJ = _pycplex_platform.CPXMI_SAMECOEFF_OBJ
CPXMI_SAMECOEFF_QLIN = _pycplex_platform.CPXMI_SAMECOEFF_QLIN
CPXMI_SAMECOEFF_QUAD = _pycplex_platform.CPXMI_SAMECOEFF_QUAD
CPXMI_SAMECOEFF_RHS = _pycplex_platform.CPXMI_SAMECOEFF_RHS
CPXMI_SAMECOEFF_ROW = _pycplex_platform.CPXMI_SAMECOEFF_ROW
CPXMI_SAMECOEFF_UCUT = _pycplex_platform.CPXMI_SAMECOEFF_UCUT
CPXMI_SINGLE_PRECISION = _pycplex_platform.CPXMI_SINGLE_PRECISION
CPXMI_SYMMETRY_BREAKING_INEQ = _pycplex_platform.CPXMI_SYMMETRY_BREAKING_INEQ
CPXMI_UB_LARGE_NUM = _pycplex_platform.CPXMI_UB_LARGE_NUM
CPXMI_UB_SMALL_NUM = _pycplex_platform.CPXMI_UB_SMALL_NUM
CPXMI_UC_NZ_LARGE_NUM = _pycplex_platform.CPXMI_UC_NZ_LARGE_NUM
CPXMI_UC_NZ_SMALL_NUM = _pycplex_platform.CPXMI_UC_NZ_SMALL_NUM
CPXMI_UC_RHS_LARGE_NUM = _pycplex_platform.CPXMI_UC_RHS_LARGE_NUM
CPXMI_UC_RHS_SMALL_NUM = _pycplex_platform.CPXMI_UC_RHS_SMALL_NUM
CPXMI_WIDE_COEFF_RANGE = _pycplex_platform.CPXMI_WIDE_COEFF_RANGE
CPXMIP_ABORT_FEAS = _pycplex_platform.CPXMIP_ABORT_FEAS
CPXMIP_ABORT_INFEAS = _pycplex_platform.CPXMIP_ABORT_INFEAS
CPXMIP_ABORT_RELAXATION_UNBOUNDED = _pycplex_platform.CPXMIP_ABORT_RELAXATION_UNBOUNDED
CPXMIP_ABORT_RELAXED = _pycplex_platform.CPXMIP_ABORT_RELAXED
CPXMIP_DETTIME_LIM_FEAS = _pycplex_platform.CPXMIP_DETTIME_LIM_FEAS
CPXMIP_DETTIME_LIM_INFEAS = _pycplex_platform.CPXMIP_DETTIME_LIM_INFEAS
CPXMIP_FAIL_FEAS = _pycplex_platform.CPXMIP_FAIL_FEAS
CPXMIP_FAIL_FEAS_NO_TREE = _pycplex_platform.CPXMIP_FAIL_FEAS_NO_TREE
CPXMIP_FAIL_INFEAS = _pycplex_platform.CPXMIP_FAIL_INFEAS
CPXMIP_FAIL_INFEAS_NO_TREE = _pycplex_platform.CPXMIP_FAIL_INFEAS_NO_TREE
CPXMIP_FEASIBLE = _pycplex_platform.CPXMIP_FEASIBLE
CPXMIP_FEASIBLE_RELAXED_INF = _pycplex_platform.CPXMIP_FEASIBLE_RELAXED_INF
CPXMIP_FEASIBLE_RELAXED_QUAD = _pycplex_platform.CPXMIP_FEASIBLE_RELAXED_QUAD
CPXMIP_FEASIBLE_RELAXED_SUM = _pycplex_platform.CPXMIP_FEASIBLE_RELAXED_SUM
CPXMIP_INFEASIBLE = _pycplex_platform.CPXMIP_INFEASIBLE
CPXMIP_INForUNBD = _pycplex_platform.CPXMIP_INForUNBD
CPXMIP_MEM_LIM_FEAS = _pycplex_platform.CPXMIP_MEM_LIM_FEAS
CPXMIP_MEM_LIM_INFEAS = _pycplex_platform.CPXMIP_MEM_LIM_INFEAS
CPXMIP_NODE_LIM_FEAS = _pycplex_platform.CPXMIP_NODE_LIM_FEAS
CPXMIP_NODE_LIM_INFEAS = _pycplex_platform.CPXMIP_NODE_LIM_INFEAS
CPXMIP_OPTIMAL = _pycplex_platform.CPXMIP_OPTIMAL
CPXMIP_OPTIMAL_INFEAS = _pycplex_platform.CPXMIP_OPTIMAL_INFEAS
CPXMIP_OPTIMAL_POPULATED = _pycplex_platform.CPXMIP_OPTIMAL_POPULATED
CPXMIP_OPTIMAL_POPULATED_TOL = _pycplex_platform.CPXMIP_OPTIMAL_POPULATED_TOL
CPXMIP_OPTIMAL_RELAXED_INF = _pycplex_platform.CPXMIP_OPTIMAL_RELAXED_INF
CPXMIP_OPTIMAL_RELAXED_QUAD = _pycplex_platform.CPXMIP_OPTIMAL_RELAXED_QUAD
CPXMIP_OPTIMAL_RELAXED_SUM = _pycplex_platform.CPXMIP_OPTIMAL_RELAXED_SUM
CPXMIP_OPTIMAL_TOL = _pycplex_platform.CPXMIP_OPTIMAL_TOL
CPXMIP_POPULATESOL_LIM = _pycplex_platform.CPXMIP_POPULATESOL_LIM
CPXMIP_SOL_LIM = _pycplex_platform.CPXMIP_SOL_LIM
CPXMIP_TIME_LIM_FEAS = _pycplex_platform.CPXMIP_TIME_LIM_FEAS
CPXMIP_TIME_LIM_INFEAS = _pycplex_platform.CPXMIP_TIME_LIM_INFEAS
CPXMIP_UNBOUNDED = _pycplex_platform.CPXMIP_UNBOUNDED
CPX_CPXAUTOENUMS_H_H = _pycplex_platform.CPX_CPXAUTOENUMS_H_H
CPXCALLBACKINFO_THREADID = _pycplex_platform.CPXCALLBACKINFO_THREADID
CPXCALLBACKINFO_NODECOUNT = _pycplex_platform.CPXCALLBACKINFO_NODECOUNT
CPXCALLBACKINFO_ITCOUNT = _pycplex_platform.CPXCALLBACKINFO_ITCOUNT
CPXCALLBACKINFO_BEST_SOL = _pycplex_platform.CPXCALLBACKINFO_BEST_SOL
CPXCALLBACKINFO_BEST_BND = _pycplex_platform.CPXCALLBACKINFO_BEST_BND
CPXCALLBACKINFO_THREADS = _pycplex_platform.CPXCALLBACKINFO_THREADS
CPXCALLBACKINFO_FEASIBLE = _pycplex_platform.CPXCALLBACKINFO_FEASIBLE
CPXCALLBACKINFO_TIME = _pycplex_platform.CPXCALLBACKINFO_TIME
CPXCALLBACKINFO_DETTIME = _pycplex_platform.CPXCALLBACKINFO_DETTIME
CPXCALLBACKINFO_NODEUID = _pycplex_platform.CPXCALLBACKINFO_NODEUID
CPXCALLBACKINFO_NODEDEPTH = _pycplex_platform.CPXCALLBACKINFO_NODEDEPTH
CPXCALLBACKINFO_CANDIDATE_SOURCE = _pycplex_platform.CPXCALLBACKINFO_CANDIDATE_SOURCE
CPXCALLBACKINFO_RESTARTS = _pycplex_platform.CPXCALLBACKINFO_RESTARTS
CPXCALLBACKINFO_AFTERCUTLOOP = _pycplex_platform.CPXCALLBACKINFO_AFTERCUTLOOP
CPXCALLBACKINFO_NODESLEFT = _pycplex_platform.CPXCALLBACKINFO_NODESLEFT
CPXCALLBACKSOLUTION_NOCHECK = _pycplex_platform.CPXCALLBACKSOLUTION_NOCHECK
CPXCALLBACKSOLUTION_CHECKFEAS = _pycplex_platform.CPXCALLBACKSOLUTION_CHECKFEAS
CPXCALLBACKSOLUTION_PROPAGATE = _pycplex_platform.CPXCALLBACKSOLUTION_PROPAGATE
CPXCALLBACKSOLUTION_SOLVE = _pycplex_platform.CPXCALLBACKSOLUTION_SOLVE
CPXINFO_BYTE = _pycplex_platform.CPXINFO_BYTE
CPXINFO_SHORT = _pycplex_platform.CPXINFO_SHORT
CPXINFO_INT = _pycplex_platform.CPXINFO_INT
CPXINFO_LONG = _pycplex_platform.CPXINFO_LONG
CPXINFO_DOUBLE = _pycplex_platform.CPXINFO_DOUBLE
CPXPUBLICPARAMS_H = _pycplex_platform.CPXPUBLICPARAMS_H
CPX_PARAM_ADVIND = _pycplex_platform.CPX_PARAM_ADVIND
CPX_PARAM_AGGFILL = _pycplex_platform.CPX_PARAM_AGGFILL
CPX_PARAM_AGGIND = _pycplex_platform.CPX_PARAM_AGGIND
CPX_PARAM_CLOCKTYPE = _pycplex_platform.CPX_PARAM_CLOCKTYPE
CPX_PARAM_CRAIND = _pycplex_platform.CPX_PARAM_CRAIND
CPX_PARAM_DEPIND = _pycplex_platform.CPX_PARAM_DEPIND
CPX_PARAM_DPRIIND = _pycplex_platform.CPX_PARAM_DPRIIND
CPX_PARAM_PRICELIM = _pycplex_platform.CPX_PARAM_PRICELIM
CPX_PARAM_EPMRK = _pycplex_platform.CPX_PARAM_EPMRK
CPX_PARAM_EPOPT = _pycplex_platform.CPX_PARAM_EPOPT
CPX_PARAM_EPPER = _pycplex_platform.CPX_PARAM_EPPER
CPX_PARAM_EPRHS = _pycplex_platform.CPX_PARAM_EPRHS
CPX_PARAM_SIMDISPLAY = _pycplex_platform.CPX_PARAM_SIMDISPLAY
CPX_PARAM_ITLIM = _pycplex_platform.CPX_PARAM_ITLIM
CPX_PARAM_ROWREADLIM = _pycplex_platform.CPX_PARAM_ROWREADLIM
CPX_PARAM_NETFIND = _pycplex_platform.CPX_PARAM_NETFIND
CPX_PARAM_COLREADLIM = _pycplex_platform.CPX_PARAM_COLREADLIM
CPX_PARAM_NZREADLIM = _pycplex_platform.CPX_PARAM_NZREADLIM
CPX_PARAM_OBJLLIM = _pycplex_platform.CPX_PARAM_OBJLLIM
CPX_PARAM_OBJULIM = _pycplex_platform.CPX_PARAM_OBJULIM
CPX_PARAM_PERIND = _pycplex_platform.CPX_PARAM_PERIND
CPX_PARAM_PERLIM = _pycplex_platform.CPX_PARAM_PERLIM
CPX_PARAM_PPRIIND = _pycplex_platform.CPX_PARAM_PPRIIND
CPX_PARAM_PREIND = _pycplex_platform.CPX_PARAM_PREIND
CPX_PARAM_REINV = _pycplex_platform.CPX_PARAM_REINV
CPX_PARAM_SCAIND = _pycplex_platform.CPX_PARAM_SCAIND
CPX_PARAM_SCRIND = _pycplex_platform.CPX_PARAM_SCRIND
CPX_PARAM_SINGLIM = _pycplex_platform.CPX_PARAM_SINGLIM
CPX_PARAM_TILIM = _pycplex_platform.CPX_PARAM_TILIM
CPX_PARAM_PREDUAL = _pycplex_platform.CPX_PARAM_PREDUAL
CPX_PARAM_PREPASS = _pycplex_platform.CPX_PARAM_PREPASS
CPX_PARAM_DATACHECK = _pycplex_platform.CPX_PARAM_DATACHECK
CPX_PARAM_REDUCE = _pycplex_platform.CPX_PARAM_REDUCE
CPX_PARAM_PRELINEAR = _pycplex_platform.CPX_PARAM_PRELINEAR
CPX_PARAM_LPMETHOD = _pycplex_platform.CPX_PARAM_LPMETHOD
CPX_PARAM_QPMETHOD = _pycplex_platform.CPX_PARAM_QPMETHOD
CPX_PARAM_WORKDIR = _pycplex_platform.CPX_PARAM_WORKDIR
CPX_PARAM_WORKMEM = _pycplex_platform.CPX_PARAM_WORKMEM
CPX_PARAM_THREADS = _pycplex_platform.CPX_PARAM_THREADS
CPX_PARAM_CONFLICTALG = _pycplex_platform.CPX_PARAM_CONFLICTALG
CPX_PARAM_CONFLICTDISPLAY = _pycplex_platform.CPX_PARAM_CONFLICTDISPLAY
CPX_PARAM_SIFTDISPLAY = _pycplex_platform.CPX_PARAM_SIFTDISPLAY
CPX_PARAM_SIFTALG = _pycplex_platform.CPX_PARAM_SIFTALG
CPX_PARAM_SIFTITLIM = _pycplex_platform.CPX_PARAM_SIFTITLIM
CPX_PARAM_MPSLONGNUM = _pycplex_platform.CPX_PARAM_MPSLONGNUM
CPX_PARAM_MEMORYEMPHASIS = _pycplex_platform.CPX_PARAM_MEMORYEMPHASIS
CPX_PARAM_NUMERICALEMPHASIS = _pycplex_platform.CPX_PARAM_NUMERICALEMPHASIS
CPX_PARAM_FEASOPTMODE = _pycplex_platform.CPX_PARAM_FEASOPTMODE
CPX_PARAM_PARALLELMODE = _pycplex_platform.CPX_PARAM_PARALLELMODE
CPX_PARAM_TUNINGMEASURE = _pycplex_platform.CPX_PARAM_TUNINGMEASURE
CPX_PARAM_TUNINGREPEAT = _pycplex_platform.CPX_PARAM_TUNINGREPEAT
CPX_PARAM_TUNINGTILIM = _pycplex_platform.CPX_PARAM_TUNINGTILIM
CPX_PARAM_TUNINGDISPLAY = _pycplex_platform.CPX_PARAM_TUNINGDISPLAY
CPX_PARAM_WRITELEVEL = _pycplex_platform.CPX_PARAM_WRITELEVEL
CPX_PARAM_RANDOMSEED = _pycplex_platform.CPX_PARAM_RANDOMSEED
CPX_PARAM_DETTILIM = _pycplex_platform.CPX_PARAM_DETTILIM
CPX_PARAM_FILEENCODING = _pycplex_platform.CPX_PARAM_FILEENCODING
CPX_PARAM_APIENCODING = _pycplex_platform.CPX_PARAM_APIENCODING
CPX_PARAM_OPTIMALITYTARGET = _pycplex_platform.CPX_PARAM_OPTIMALITYTARGET
CPX_PARAM_CLONELOG = _pycplex_platform.CPX_PARAM_CLONELOG
CPX_PARAM_TUNINGDETTILIM = _pycplex_platform.CPX_PARAM_TUNINGDETTILIM
CPX_PARAM_CPUMASK = _pycplex_platform.CPX_PARAM_CPUMASK
CPX_PARAM_SOLUTIONTYPE = _pycplex_platform.CPX_PARAM_SOLUTIONTYPE
CPX_PARAM_WARNLIM = _pycplex_platform.CPX_PARAM_WARNLIM
CPX_PARAM_SIFTSIM = _pycplex_platform.CPX_PARAM_SIFTSIM
CPX_PARAM_DYNAMICROWS = _pycplex_platform.CPX_PARAM_DYNAMICROWS
CPX_PARAM_RECORD = _pycplex_platform.CPX_PARAM_RECORD
CPX_PARAM_PARAMDISPLAY = _pycplex_platform.CPX_PARAM_PARAMDISPLAY
CPX_PARAM_FOLDING = _pycplex_platform.CPX_PARAM_FOLDING
CPX_PARAM_PREREFORM = _pycplex_platform.CPX_PARAM_PREREFORM
CPX_PARAM_WORKERALG = _pycplex_platform.CPX_PARAM_WORKERALG
CPX_PARAM_BENDERSSTRATEGY = _pycplex_platform.CPX_PARAM_BENDERSSTRATEGY
CPX_PARAM_BENDERSFEASCUTTOL = _pycplex_platform.CPX_PARAM_BENDERSFEASCUTTOL
CPX_PARAM_BENDERSOPTCUTTOL = _pycplex_platform.CPX_PARAM_BENDERSOPTCUTTOL
CPX_PARAM_MULTIOBJDISPLAY = _pycplex_platform.CPX_PARAM_MULTIOBJDISPLAY
CPX_PARAM_BRDIR = _pycplex_platform.CPX_PARAM_BRDIR
CPX_PARAM_BTTOL = _pycplex_platform.CPX_PARAM_BTTOL
CPX_PARAM_CLIQUES = _pycplex_platform.CPX_PARAM_CLIQUES
CPX_PARAM_COEREDIND = _pycplex_platform.CPX_PARAM_COEREDIND
CPX_PARAM_COVERS = _pycplex_platform.CPX_PARAM_COVERS
CPX_PARAM_CUTLO = _pycplex_platform.CPX_PARAM_CUTLO
CPX_PARAM_CUTUP = _pycplex_platform.CPX_PARAM_CUTUP
CPX_PARAM_EPAGAP = _pycplex_platform.CPX_PARAM_EPAGAP
CPX_PARAM_EPGAP = _pycplex_platform.CPX_PARAM_EPGAP
CPX_PARAM_EPINT = _pycplex_platform.CPX_PARAM_EPINT
CPX_PARAM_MIPDISPLAY = _pycplex_platform.CPX_PARAM_MIPDISPLAY
CPX_PARAM_MIPINTERVAL = _pycplex_platform.CPX_PARAM_MIPINTERVAL
CPX_PARAM_INTSOLLIM = _pycplex_platform.CPX_PARAM_INTSOLLIM
CPX_PARAM_NODEFILEIND = _pycplex_platform.CPX_PARAM_NODEFILEIND
CPX_PARAM_NODELIM = _pycplex_platform.CPX_PARAM_NODELIM
CPX_PARAM_NODESEL = _pycplex_platform.CPX_PARAM_NODESEL
CPX_PARAM_OBJDIF = _pycplex_platform.CPX_PARAM_OBJDIF
CPX_PARAM_MIPORDIND = _pycplex_platform.CPX_PARAM_MIPORDIND
CPX_PARAM_RELOBJDIF = _pycplex_platform.CPX_PARAM_RELOBJDIF
CPX_PARAM_STARTALG = _pycplex_platform.CPX_PARAM_STARTALG
CPX_PARAM_SUBALG = _pycplex_platform.CPX_PARAM_SUBALG
CPX_PARAM_TRELIM = _pycplex_platform.CPX_PARAM_TRELIM
CPX_PARAM_VARSEL = _pycplex_platform.CPX_PARAM_VARSEL
CPX_PARAM_BNDSTRENIND = _pycplex_platform.CPX_PARAM_BNDSTRENIND
CPX_PARAM_HEURFREQ = _pycplex_platform.CPX_PARAM_HEURFREQ
CPX_PARAM_MIPORDTYPE = _pycplex_platform.CPX_PARAM_MIPORDTYPE
CPX_PARAM_CUTSFACTOR = _pycplex_platform.CPX_PARAM_CUTSFACTOR
CPX_PARAM_RELAXPREIND = _pycplex_platform.CPX_PARAM_RELAXPREIND
CPX_PARAM_PRESLVND = _pycplex_platform.CPX_PARAM_PRESLVND
CPX_PARAM_BBINTERVAL = _pycplex_platform.CPX_PARAM_BBINTERVAL
CPX_PARAM_FLOWCOVERS = _pycplex_platform.CPX_PARAM_FLOWCOVERS
CPX_PARAM_IMPLBD = _pycplex_platform.CPX_PARAM_IMPLBD
CPX_PARAM_PROBE = _pycplex_platform.CPX_PARAM_PROBE
CPX_PARAM_GUBCOVERS = _pycplex_platform.CPX_PARAM_GUBCOVERS
CPX_PARAM_STRONGCANDLIM = _pycplex_platform.CPX_PARAM_STRONGCANDLIM
CPX_PARAM_STRONGITLIM = _pycplex_platform.CPX_PARAM_STRONGITLIM
CPX_PARAM_FRACCAND = _pycplex_platform.CPX_PARAM_FRACCAND
CPX_PARAM_FRACCUTS = _pycplex_platform.CPX_PARAM_FRACCUTS
CPX_PARAM_FRACPASS = _pycplex_platform.CPX_PARAM_FRACPASS
CPX_PARAM_FLOWPATHS = _pycplex_platform.CPX_PARAM_FLOWPATHS
CPX_PARAM_MIRCUTS = _pycplex_platform.CPX_PARAM_MIRCUTS
CPX_PARAM_DISJCUTS = _pycplex_platform.CPX_PARAM_DISJCUTS
CPX_PARAM_AGGCUTLIM = _pycplex_platform.CPX_PARAM_AGGCUTLIM
CPX_PARAM_MIPCBREDLP = _pycplex_platform.CPX_PARAM_MIPCBREDLP
CPX_PARAM_CUTPASS = _pycplex_platform.CPX_PARAM_CUTPASS
CPX_PARAM_MIPEMPHASIS = _pycplex_platform.CPX_PARAM_MIPEMPHASIS
CPX_PARAM_SYMMETRY = _pycplex_platform.CPX_PARAM_SYMMETRY
CPX_PARAM_DIVETYPE = _pycplex_platform.CPX_PARAM_DIVETYPE
CPX_PARAM_RINSHEUR = _pycplex_platform.CPX_PARAM_RINSHEUR
CPX_PARAM_LBHEUR = _pycplex_platform.CPX_PARAM_LBHEUR
CPX_PARAM_REPEATPRESOLVE = _pycplex_platform.CPX_PARAM_REPEATPRESOLVE
CPX_PARAM_PROBETIME = _pycplex_platform.CPX_PARAM_PROBETIME
CPX_PARAM_POLISHTIME = _pycplex_platform.CPX_PARAM_POLISHTIME
CPX_PARAM_REPAIRTRIES = _pycplex_platform.CPX_PARAM_REPAIRTRIES
CPX_PARAM_EPLIN = _pycplex_platform.CPX_PARAM_EPLIN
CPX_PARAM_EPRELAX = _pycplex_platform.CPX_PARAM_EPRELAX
CPX_PARAM_FPHEUR = _pycplex_platform.CPX_PARAM_FPHEUR
CPX_PARAM_EACHCUTLIM = _pycplex_platform.CPX_PARAM_EACHCUTLIM
CPX_PARAM_SOLNPOOLCAPACITY = _pycplex_platform.CPX_PARAM_SOLNPOOLCAPACITY
CPX_PARAM_SOLNPOOLREPLACE = _pycplex_platform.CPX_PARAM_SOLNPOOLREPLACE
CPX_PARAM_SOLNPOOLGAP = _pycplex_platform.CPX_PARAM_SOLNPOOLGAP
CPX_PARAM_SOLNPOOLAGAP = _pycplex_platform.CPX_PARAM_SOLNPOOLAGAP
CPX_PARAM_SOLNPOOLINTENSITY = _pycplex_platform.CPX_PARAM_SOLNPOOLINTENSITY
CPX_PARAM_POPULATELIM = _pycplex_platform.CPX_PARAM_POPULATELIM
CPX_PARAM_MIPSEARCH = _pycplex_platform.CPX_PARAM_MIPSEARCH
CPX_PARAM_MIQCPSTRAT = _pycplex_platform.CPX_PARAM_MIQCPSTRAT
CPX_PARAM_ZEROHALFCUTS = _pycplex_platform.CPX_PARAM_ZEROHALFCUTS
CPX_PARAM_HEUREFFORT = _pycplex_platform.CPX_PARAM_HEUREFFORT
CPX_PARAM_POLISHAFTEREPAGAP = _pycplex_platform.CPX_PARAM_POLISHAFTEREPAGAP
CPX_PARAM_POLISHAFTEREPGAP = _pycplex_platform.CPX_PARAM_POLISHAFTEREPGAP
CPX_PARAM_POLISHAFTERNODE = _pycplex_platform.CPX_PARAM_POLISHAFTERNODE
CPX_PARAM_POLISHAFTERINTSOL = _pycplex_platform.CPX_PARAM_POLISHAFTERINTSOL
CPX_PARAM_POLISHAFTERTIME = _pycplex_platform.CPX_PARAM_POLISHAFTERTIME
CPX_PARAM_MCFCUTS = _pycplex_platform.CPX_PARAM_MCFCUTS
CPX_PARAM_MIPKAPPASTATS = _pycplex_platform.CPX_PARAM_MIPKAPPASTATS
CPX_PARAM_AUXROOTTHREADS = _pycplex_platform.CPX_PARAM_AUXROOTTHREADS
CPX_PARAM_INTSOLFILEPREFIX = _pycplex_platform.CPX_PARAM_INTSOLFILEPREFIX
CPX_PARAM_PROBEDETTIME = _pycplex_platform.CPX_PARAM_PROBEDETTIME
CPX_PARAM_POLISHAFTERDETTIME = _pycplex_platform.CPX_PARAM_POLISHAFTERDETTIME
CPX_PARAM_LANDPCUTS = _pycplex_platform.CPX_PARAM_LANDPCUTS
CPX_PARAM_NODECUTS = _pycplex_platform.CPX_PARAM_NODECUTS
CPX_PARAM_RAMPUPDURATION = _pycplex_platform.CPX_PARAM_RAMPUPDURATION
CPX_PARAM_RAMPUPDETTILIM = _pycplex_platform.CPX_PARAM_RAMPUPDETTILIM
CPX_PARAM_RAMPUPTILIM = _pycplex_platform.CPX_PARAM_RAMPUPTILIM
CPX_PARAM_LOCALIMPLBD = _pycplex_platform.CPX_PARAM_LOCALIMPLBD
CPX_PARAM_BQPCUTS = _pycplex_platform.CPX_PARAM_BQPCUTS
CPX_PARAM_RLTCUTS = _pycplex_platform.CPX_PARAM_RLTCUTS
CPX_PARAM_SUBMIPSTARTALG = _pycplex_platform.CPX_PARAM_SUBMIPSTARTALG
CPX_PARAM_SUBMIPSUBALG = _pycplex_platform.CPX_PARAM_SUBMIPSUBALG
CPX_PARAM_SUBMIPSCAIND = _pycplex_platform.CPX_PARAM_SUBMIPSCAIND
CPX_PARAM_SUBMIPNODELIMIT = _pycplex_platform.CPX_PARAM_SUBMIPNODELIMIT
CPX_PARAM_SOS1REFORM = _pycplex_platform.CPX_PARAM_SOS1REFORM
CPX_PARAM_SOS2REFORM = _pycplex_platform.CPX_PARAM_SOS2REFORM
CPX_PARAM_BAREPCOMP = _pycplex_platform.CPX_PARAM_BAREPCOMP
CPX_PARAM_BARGROWTH = _pycplex_platform.CPX_PARAM_BARGROWTH
CPX_PARAM_BAROBJRNG = _pycplex_platform.CPX_PARAM_BAROBJRNG
CPX_PARAM_BARALG = _pycplex_platform.CPX_PARAM_BARALG
CPX_PARAM_BARCOLNZ = _pycplex_platform.CPX_PARAM_BARCOLNZ
CPX_PARAM_BARDISPLAY = _pycplex_platform.CPX_PARAM_BARDISPLAY
CPX_PARAM_BARITLIM = _pycplex_platform.CPX_PARAM_BARITLIM
CPX_PARAM_BARMAXCOR = _pycplex_platform.CPX_PARAM_BARMAXCOR
CPX_PARAM_BARORDER = _pycplex_platform.CPX_PARAM_BARORDER
CPX_PARAM_BARSTARTALG = _pycplex_platform.CPX_PARAM_BARSTARTALG
CPX_PARAM_BARCROSSALG = _pycplex_platform.CPX_PARAM_BARCROSSALG
CPX_PARAM_BARQCPEPCOMP = _pycplex_platform.CPX_PARAM_BARQCPEPCOMP
CPX_PARAM_QPNZREADLIM = _pycplex_platform.CPX_PARAM_QPNZREADLIM
CPX_PARAM_CALCQCPDUALS = _pycplex_platform.CPX_PARAM_CALCQCPDUALS
CPX_PARAM_QPMAKEPSDIND = _pycplex_platform.CPX_PARAM_QPMAKEPSDIND
CPX_PARAM_QTOLININD = _pycplex_platform.CPX_PARAM_QTOLININD
CPX_PARAM_NETITLIM = _pycplex_platform.CPX_PARAM_NETITLIM
CPX_PARAM_NETEPOPT = _pycplex_platform.CPX_PARAM_NETEPOPT
CPX_PARAM_NETEPRHS = _pycplex_platform.CPX_PARAM_NETEPRHS
CPX_PARAM_NETPPRIIND = _pycplex_platform.CPX_PARAM_NETPPRIIND
CPX_PARAM_NETDISPLAY = _pycplex_platform.CPX_PARAM_NETDISPLAY
CPX_CPXAUTOTYPES_H_H = _pycplex_platform.CPX_CPXAUTOTYPES_H_H
CPX_CPXAUTOSTRUCTS_H_H = _pycplex_platform.CPX_CPXAUTOSTRUCTS_H_H
# Register cpxdeserializer in _pycplex_platform:
_pycplex_platform.cpxdeserializer_swigregister(cpxdeserializer)
# Register cpxserializer in _pycplex_platform:
_pycplex_platform.cpxserializer_swigregister(cpxserializer)
CPX_CPLEXX_H = _pycplex_platform.CPX_CPLEXX_H
CPX_APIMODEL_SMALL = _pycplex_platform.CPX_APIMODEL_SMALL
CPX_APIMODEL_LARGE = _pycplex_platform.CPX_APIMODEL_LARGE
CPX_APIMODEL = _pycplex_platform.CPX_APIMODEL
# Register cb_struct in _pycplex_platform:
_pycplex_platform.cb_struct_swigregister(cb_struct)
CPX_CPLEXE_H = _pycplex_platform.CPX_CPLEXE_H
CPXE_H = _pycplex_platform.CPXE_H
MIPE_H = _pycplex_platform.MIPE_H
CPXAUTOE_H = _pycplex_platform.CPXAUTOE_H
CPX_AUTOES_H = _pycplex_platform.CPX_AUTOES_H
CPX_AUTOEL_H = _pycplex_platform.CPX_AUTOEL_H
CPX_AUTOEX_H = _pycplex_platform.CPX_AUTOEX_H
# Register cpxpyiodevice in _pycplex_platform:
_pycplex_platform.cpxpyiodevice_swigregister(cpxpyiodevice)
# Register intPtr in _pycplex_platform:
_pycplex_platform.intPtr_swigregister(intPtr)
# Register cpxlongPtr in _pycplex_platform:
_pycplex_platform.cpxlongPtr_swigregister(cpxlongPtr)
# Register doublePtr in _pycplex_platform:
_pycplex_platform.doublePtr_swigregister(doublePtr)
# Register CPXLPptrPtr in _pycplex_platform:
_pycplex_platform.CPXLPptrPtr_swigregister(CPXLPptrPtr)
# Register CPXENVptrPtr in _pycplex_platform:
_pycplex_platform.CPXENVptrPtr_swigregister(CPXENVptrPtr)
# Register CPXCHANNELptrPtr in _pycplex_platform:
_pycplex_platform.CPXCHANNELptrPtr_swigregister(CPXCHANNELptrPtr)
# Register CPXPARAMSETptrPtr in _pycplex_platform:
_pycplex_platform.CPXPARAMSETptrPtr_swigregister(CPXPARAMSETptrPtr)
# Register intArray in _pycplex_platform:
_pycplex_platform.intArray_swigregister(intArray)
# Register doubleArray in _pycplex_platform:
_pycplex_platform.doubleArray_swigregister(doubleArray)
# Register longArray in _pycplex_platform:
_pycplex_platform.longArray_swigregister(longArray)
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] | 2.007196 | 36,548 |
"""
Generator class for some Process-based mixing algorithms.
Copyright 2020 Thomas Jackson Park & Jeremy Pavier
"""
import os
import random
import inspect
from pydub import AudioSegment
from GenerIter.process import Process
from GenerIter.util import debug, nextPowerOf2
import GenerIter.excepts as robox
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import numpy as np
DT = np.float32
eps = 1e-12
# Globals
components = []
params = []
# Global forward/backward
# Optimization functions
# Values
# Parameters
# Xavier initializer
# Utility function for shape inference with broadcasting
#### Actual components
class Add: # Add with broadcasting
"""
Class name: Add
Class usage: add two matrices x, y with broadcasting supported by numpy "+" operation.
Class function:
forward: calculate x + y with possible broadcasting
backward: calculate derivative w.r.t to x and y, when calculate the derivative w.r.t to y, we sum up all the axis over grad except the last dimension.
"""
class Mul: # Multiply with broadcasting
"""
Class Name: Mul
Class Usage: elementwise multiplication with two matrix
Class Functions:
forward: compute the result x*y
backward: compute the derivative w.r.t x and y
"""
class VDot: # Matrix multiply (fully-connected layer)
"""
Class Name: VDot
Class Usage: matrix multiplication where x, y are matrices
y is expected to be a parameter and there is a convention that parameters come last. Typical usage is x is batch feature vector with shape (batch_size, f_dim), y a parameter with shape (f_dim, f_dim2).
Class Functions:
forward: compute the vector matrix multplication result
backward: compute the derivative w.r.t x and y, where derivative of x and y are both matrices
"""
class Log: # Elementwise Log
"""
Class Name: Log
Class Usage: compute the elementwise log(x) given x.
Class Functions:
forward: compute log(x)
backward: compute the derivative w.r.t input vector x
"""
class Sigmoid:
"""
Class Name: Sigmoid
Class Usage: compute the elementwise sigmoid activation. Input is vector or matrix. In case of vector, [x_{0}, x_{1}, ..., x_{n}], output is vector [y_{0}, y_{1}, ..., y_{n}] where y_{i} = 1/(1 + exp(-x_{i}))
Class Functions:
forward: compute activation y_{i} for all i.
backward: compute the derivative w.r.t input vector/matrix x
"""
class Tanh:
"""
Class Name: Tanh
Class Usage: compute the elementwise Tanh activation. Input is vector or matrix. In case of vector, [x_{0}, x_{1}, ..., x_{n}], output is vector [y_{0}, y_{1}, ..., y_{n}] where y_{i} = (exp(x_{i}) - exp(-x_{i}))/(exp(x_{i}) + exp(-x_{i}))
Class Functions:
forward: compute activation y_{i} for all i.
backward: compute the derivative w.r.t input vector/matrix x
"""
class RELU:
"""
Class Name: RELU
Class Usage: compute the elementwise RELU activation. Input is vector or matrix. In case of vector, [x_{0}, x_{1}, ..., x_{n}], output is vector [y_{0}, y_{1}, ..., y_{n}] where y_{i} = max(0, x_{i})
Class Functions:
forward: compute activation y_{i} for all i.
backward: compute the derivative w.r.t input vector/matrix x
"""
class LeakyRELU:
"""
Class Name: LeakyRELU
Class Usage: compute the elementwise LeakyRELU activation. Input is vector or matrix. In case of vector, [x_{0}, x_{1}, ..., x_{n}], output is vector [y_{0}, y_{1}, ..., y_{n}] where y_{i} = 0.01*x_{i} for x_{i} < 0 and y_{i} = x_{i} for x_{i} > 0
Class Functions:
forward: compute activation y_{i} for all i.
backward: compute the derivative w.r.t input vector/matrix x
"""
class Softplus:
"""
Class Name: Softplus
Class Usage: compute the elementwise Softplus activation.
Class Functions:
forward: compute activation y_{i} for all i.
backward: compute the derivative w.r.t input vector/matrix x
"""
class SoftMax:
"""
Class Name: SoftMax
Class Usage: compute the softmax activation for each element in the matrix, normalization by each all elements in each batch (row). Specificaly, input is matrix [x_{00}, x_{01}, ..., x_{0n}, ..., x_{b0}, x_{b1}, ..., x_{bn}], output is a matrix [p_{00}, p_{01}, ..., p_{0n},...,p_{b0},,,p_{bn} ] where p_{bi} = exp(x_{bi})/(exp(x_{b0}) + ... + exp(x_{bn}))
Class Functions:
forward: compute probability p_{bi} for all b, i.
backward: compute the derivative w.r.t input matrix x
"""
class LogLoss:
"""
Class Name: LogLoss
Class Usage: compute the elementwise -log(x) given matrix x. this is the loss function we use in most case.
Class Functions:
forward: compute -log(x)
backward: compute the derivative w.r.t input matrix x
"""
class Mean:
"""
Class Name: Mean
Class Usage: compute the mean given a vector x.
Class Functions:
forward: compute (x_{0} + ... + x_{n})/n
backward: compute the derivative w.r.t input vector x
"""
class Sum:
"""
Class Name: Sum
Class Usage: compute the sum of a matrix.
"""
class MeanwithMask:
"""
Class Name: MeanwithMask
Class Usage: compute the mean given a vector x with mask.
Class Functions:
forward: compute x = x*mask and then sum over nonzeros in x/#(nozeros in x)
backward: compute the derivative w.r.t input vector matrix
"""
class Aref: # out = x[idx]
"""
Class Name: Aref
Class Usage: get some specific entry in a matrix. x is the matrix with shape (batch_size, N) and idx
is vector contains the entry index and x is differentiable.
Class Functions:
forward: compute x[b, idx(b)]
backward: compute the derivative w.r.t input matrix x
"""
class Accuracy:
"""
Class Name: Accuracy
Class Usage: check the predicted label is correct or not. x is the probability vector where each probability is for each class. idx is ground truth label.
Class Functions:
forward: find the label that has maximum probability and compare it with the ground truth label.
backward: None
"""
class Reshape:
"""
Class name: Reshape
Class usage: Reshape the tensor x to specific shape.
Class function:
forward: Reshape the tensor x to specific shape
backward: calculate derivative w.r.t to x, which is simply reshape the income gradient to x's original shape
"""
# clip the gradient if the norm of gradient is larger than some threshold, this is crucial for RNN.
##################################################### Recurrent Components ##############################################
class Embed:
"""
Class name: Embed
Class usage: Embed layer.
Class function:
forward: given the embeeding matrix w2v and word idx, return its corresponding embedding vector.
backward: calculate the derivative w.r.t to embedding matrix
"""
class ConCat:
"""
Class name: ConCat
Class usage: ConCat layer.
Class function:
forward: concat two matrix along with the axis 1.
backward: calculate the derivative w.r.t to matrix a and y.
"""
class ArgMax:
"""
Class name: ArgMax
Class usage: ArgMax layer.
Class function:
forward: given x, calculate the index which has the maximum value
backward: None
"""
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] | 2.833661 | 2,543 |
import os
import psutil
| [
11748,
28686,
198,
11748,
26692,
22602,
198
] | 3.428571 | 7 |
#
## PyMoira client library
##
## This file contains the utility functions common for multiple modules.
#
import datetime
from .errors import UserError
def convertToMoiraValue(val):
"""Converts data from Python to Moira protocol representation."""
if type(val) == bool:
return '1' if val else '0'
else:
return str(val)
def responseToDict(description, response):
"""Transforms the query response to a dictionary using a description
of format ( (field name, type) ), where types are bool, int, string and
date time."""
if len(description) != len(response):
raise UserError("Error returned the response with invalid number of entries")
result = {}
for value, field in ( (response[i], description[i]) for i in range( 0, len(response) ) ):
name, datatype = field
if datatype == bool:
result[name] = convertMoiraBool(value)
elif datatype == int:
result[name] = convertMoiraInt(value)
elif datatype == datetime.datetime:
result[name] = convertMoiraDateTime(value)
elif datatype == str:
result[name] = value
else:
raise UserError("Unsupported Moira data type specified: %s" % datatype)
return result
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5298,
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25,
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8,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1441,
1255,
198
] | 2.639918 | 486 |
import unittest
import base64
from detect_face import lambda_handler
# 顔があると200が返ってくる
# 顔がないと204が返ってくる
# 画像以外のファイルを拡張子を偽ってアップロードしてもダメ
if __name__ == "__main__":
unittest.main()
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555,
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] | 1.604839 | 124 |
import json
from django.core.exceptions import ObjectDoesNotExist
from tastypie import http
from tastypie.bundle import Bundle
from tastypie.exceptions import ImmediateHttpResponse
from tastypie.resources import ModelResource
import commonware.log
import oauth2
from translations.fields import PurifiedField, TranslatedField
log = commonware.log.getLogger('z.api')
| [
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] | 3.627451 | 102 |
# Generated by Django 3.2.8 on 2021-10-16 07:05
from django.db import migrations, models
import django.db.models.deletion
| [
2,
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] | 2.818182 | 44 |
'''
Created on 27/02/2013
@author: butler
'''
import json
class JParser(object):
'''
Parser for json config file. As this is designed to be '
'''
def __init__(self, file_name):
'''
'''
self.file_name = file_name
"""
self.delta_t_ms
self.sample_rate
self.t_min_s
self.t_max_s
"""
self.parse()
| [
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13,
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] | 1.931034 | 203 |
import datetime
import pytz
from decimal import Decimal
| [
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12972,
22877,
201,
198,
6738,
32465,
1330,
4280,
4402,
201
] | 3.625 | 16 |
x = 1
y = 'Hello'
z = 10.123
d = x + z
print(d)
print(type(x))
print(type(y))
print(y.upper())
print(y.lower())
print(type(z))
a = [1,2,3,4,5,6,6,7,8]
print(len(a))
print(a.count(6))
b = list(range(10,100,10))
print(b)
stud_marks = {"Madhan":90, "Raj":25,"Mani":80}
print(stud_marks.keys())
print(stud_marks.values())
ab = list(range(10,100,10))
ab.append(100)
ab.append(110)
ab.remove(110)
print(ab[0:2]) | [
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8,
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58,
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] | 2 | 209 |
#!/usr/bin/env python
import logging
import socket
import urllib.request
import time
LOGGER = logging.getLogger('mierzyciel.telegram')
if __name__ == '__main__':
fmt = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
logging.basicConfig(level='INFO', format=fmt)
main()
| [
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220,
1388,
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198
] | 2.508621 | 116 |
""" Test utilities. """
import pytest
from aries_staticagent import utils, Message
from aries_staticagent.mtc import (
AUTHCRYPT_AFFIRMED,
AUTHCRYPT_DENIED,
ANONCRYPT_AFFIRMED,
ANONCRYPT_DENIED,
)
@pytest.fixture
def test_preprocess():
"""Test preprocessing decorator."""
@utils.preprocess(preprocessor)
handled = test_handler({})
assert handled["preprocessed"]
@pytest.mark.asyncio
async def test_preprocess_async_handler():
"""Test preprocessing decorator."""
@utils.preprocess(preprocessor)
handled = await test_handler({})
assert handled["preprocessed"]
@pytest.mark.asyncio
async def test_preprocess_async_handler_and_preprocessor():
"""Test preprocessing decorator."""
@utils.preprocess_async(preprocessor)
handled = await test_handler({})
assert handled["preprocessed"]
def test_validate(message):
"""Test validation of message"""
@utils.validate(validator)
validate_test(message)
def test_validate_modify_msg():
"""Test validation can modify the message."""
@utils.validate(validator)
test_handler({})
def test_validate_with_other_decorators():
"""Test validation of message"""
def fake_route():
"""Register route decorator."""
return _fake_route_decorator
@utils.validate(validator)
@fake_route()
@fake_route()
@utils.validate(validator)
handled = validate_test({"@id": "12345"})
assert handled["validated"]
handled = validate_test2({"@id": "12345"})
assert handled["validated"]
def test_mtc_decorator(message):
"""Test the MTC decorator."""
@utils.mtc(AUTHCRYPT_AFFIRMED, AUTHCRYPT_DENIED)
message.mtc[AUTHCRYPT_AFFIRMED] = True
message.mtc[AUTHCRYPT_DENIED] = False
mtc_test(message)
def test_mtc_decorator_not_met(message):
"""Test the MTC decorator."""
@utils.mtc(AUTHCRYPT_AFFIRMED)
message.mtc[AUTHCRYPT_AFFIRMED] = True
message.mtc[AUTHCRYPT_DENIED] = False
with pytest.raises(utils.InsufficientMessageTrust):
mtc_test(message)
def test_authcrypted_decorator(message):
"""Test the authcrypted decorator."""
@utils.authcrypted
message.mtc[AUTHCRYPT_AFFIRMED] = True
message.mtc[AUTHCRYPT_DENIED] = False
mtc_test(message)
def test_authcrypted_decorator_not_met(message):
"""Test the authcrypted decorator."""
@utils.authcrypted
message.mtc[AUTHCRYPT_AFFIRMED] = True
with pytest.raises(utils.InsufficientMessageTrust):
mtc_test(message)
def test_anoncrypted_decorator(message):
"""Test the anoncrypted decorator."""
@utils.anoncrypted
message.mtc[ANONCRYPT_AFFIRMED] = True
message.mtc[ANONCRYPT_DENIED] = False
mtc_test(message)
def test_anoncrypted_decorator_not_met(message):
"""Test the anoncrypted decorator."""
@utils.anoncrypted
message.mtc[ANONCRYPT_AFFIRMED] = True
with pytest.raises(utils.InsufficientMessageTrust):
mtc_test(message)
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] | 2.521044 | 1,188 |
import FWCore.ParameterSet.Config as cms
heavyFlavorValidation = cms.EDAnalyzer("HeavyFlavorValidation",
DQMFolder = cms.untracked.string("HLT/HeavyFlavor"),
TriggerProcessName = cms.untracked.string("HLT"),
TriggerPathName = cms.untracked.string("HLT_Mu5"),
TriggerSummaryRAW = cms.untracked.string("hltTriggerSummaryRAW"),
TriggerSummaryAOD = cms.untracked.string("hltTriggerSummaryAOD"),
TriggerResults = cms.untracked.string("TriggerResults"),
RecoMuons = cms.InputTag("muons"),
GenParticles = cms.InputTag("genParticles"),
# list IDs of muon mothers, -1:don't check, 0:particle gun, 23:Z, 443:J/psi, 553:Upsilon, 531:Bs, 333:Phi
MotherIDs = cms.untracked.vint32(23,443,553,531,333,0),
GenGlobDeltaRMatchingCut = cms.untracked.double(0.1),
GlobL1DeltaRMatchingCut = cms.untracked.double(0.3),
GlobL2DeltaRMatchingCut = cms.untracked.double(0.3),
GlobL3DeltaRMatchingCut = cms.untracked.double(0.1),
DeltaEtaBins = cms.untracked.vdouble(100, -.5, .5),
DeltaPhiBins = cms.untracked.vdouble(100, -.5, .5),
MuonPtBins = cms.untracked.vdouble(1., 3., 5., 9., 15., 32., 64., 128., 256., 512., 1024., 2048.),
MuonEtaBins = cms.untracked.vdouble(16, -2.4, 2.4),
MuonPhiBins = cms.untracked.vdouble(12, -3.15, 3.15),
DimuonPtBins = cms.untracked.vdouble(0., 2., 4., 6., 8., 10., 15., 25., 50., 100.),
DimuonEtaBins = cms.untracked.vdouble(16, -2.4, 2.4),
DimuonDRBins = cms.untracked.vdouble(10, 0., 1.)
)
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] | 2.268702 | 655 |
"""
Authors: Wouter Van Gansbeke, Simon Vandenhende
Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
"""
import os
import yaml
from easydict import EasyDict
from utils.utils import mkdir_if_missing
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from __future__ import print_function
from __future__ import division
from . import _C
import matplotlib.pyplot as plt
import numpy as np
from copy import copy, deepcopy
EXTENDED_PERCENT = 0.1
###################################################################################################################################################
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##########################################################################
# Copyright (c) 2017-2018 Bertrand Néron. All rights reserved. #
# Use of this source code is governed by a BSD-style license that can be #
# found in the LICENSE file. #
##########################################################################
import argparse
class VersionAction(argparse._VersionAction):
"""Class to allow argparse to handel more complex version output"""
def __call__(self, parser, namespace, values, option_string=None):
"""Override the :meth:`argparse._VersionAction.__call__` to use
a RawTextHelpFormatter only for version action whatever the class_formatter
specified for the :class:`argparse.ArgumentParser` object.
"""
version = self.version
if version is None:
version = parser.version
formatter = argparse.RawTextHelpFormatter(parser.prog)
formatter.add_text(version)
parser._print_message(formatter.format_help(), argparse._sys.stdout)
parser.exit()
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] | 2.853165 | 395 |
import utils
from sklearn.model_selection import train_test_split
import lsh_random_projection as LSH
import pickle
build_model()
with open('lsh.pkl', 'rb') as input:
lsh = pickle.load(input)
# lsh.get()
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# Example of applying VGG16 classifier
# Based on Keras book sec 5.4.3.
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input, decode_predictions
from keras.preprocessing import image
#import matplotlib.pyplot as plt
import numpy as np
#import urllib
#from keras.applications import ResNet50
#from keras.applications.resnet50 import preprocess_input, decode_predictions
#model = ResNet50(weights='imagenet')
model = VGG16(weights='imagenet')
model.summary() # see vgg16-summary.txt for details
# Load image from file
img_path = 'figures/cat_dog.jpg' # From https://github.com/ramprs/grad-cam/blob/master/images/cat_dog.jpg
#img_path = 'figures/Elephant_mating_ritual_3.jpg' # https://en.wikipedia.org/wiki/African_elephant#/media/File:Elephant_mating_ritual_3.jpg
#img_path = 'figures/Serengeti_Elefantenherde2.jpg' #https://en.wikipedia.org/wiki/African_elephant#/media/File:Serengeti_Elefantenherde2.jpg
#img_path = 'figures/dog-cat-openimages.jpg'
#img_path = 'figures/dog-ball-openimages.jpg' # https://www.flickr.com/photos/akarmy/5423588107
#img_path = 'figures/dog-car-backpack-openimages.jpg' # https://www.flickr.com/photos/mountrainiernps/14485323038
# Retrieve image from web
#url = "https://en.wikipedia.org/wiki/African_elephant#/media/File:Elephant_mating_ritual_3.jpg"
#urllib.request.urlretrieve(url, "/tmp/img.png")
#img = plt.imread('/tmp/img.png')
# `img` is a PIL image of size 224x224
img = image.load_img(img_path, target_size=(224, 224))
# `x` is a float32 Numpy array of shape (224, 224, 3)
x = image.img_to_array(img)
# We add a dimension to transform our array into a "batch"
# of size (1, 224, 224, 3)
x = np.expand_dims(x, axis=0)
# Finally we preprocess the batch
# (this does channel-wise color normalization)
x = preprocess_input(x)
preds = model.predict(x)
# decode the results into a list of tuples (class, description, probability)
# (one such list for each sample in the batch)
print('Predicted:', decode_predictions(preds, top=10)[0])
'''
Predicted: [
('n02108422', 'bull_mastiff', 0.40943894),
('n02108089', 'boxer', 0.3950904),
('n02109047', 'Great_Dane', 0.039510112),
('n02109525', 'Saint_Bernard', 0.031701218), '
('n02129604', 'tiger', 0.019169593),
('n02093754', 'Border_terrier', 0.018684039),
('n02110958', 'pug', 0.014893572),
('n02123159', 'tiger_cat', 0.014403002),
('n02105162', 'malinois', 0.010533252),
('n03803284', 'muzzle', 0.005662783)]
'''
# For img_path = 'data/Elephant_mating_ritual_3.jpg'
#Predicted: [('n02504458', 'African_elephant', 0.93163019),
#('n01871265', 'tusker', 0.053829707), ('n02504013', 'Indian_elephant', 0.014539864)]
# For img_path = 'data/Serengeti_Elefantenherde2.jpg'
#Predicted: [('n01871265', 'tusker', 0.61881274),
#('n02504458', 'African_elephant', 0.25420085), ('n02504013', 'Indian_elephant', 0.11940476)]
# Animal > Chordate > Vertebrate > Mammal > Tusker
#http://image-net.org/synset?wnid=n01871265
#Any mammal with prominent tusks (especially an elephant or wild boar)
# Animal > Chordate > Vertebrate > Mammal > Placental Mammal ...
# > Proboscidian > Elephant > African Elephant
# http://image-net.org/synset?wnid=n02504458
# An elephant native to Africa having enormous flapping ears and ivory tusks
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4023,
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13,
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14,
28869,
2617,
30,
675,
312,
28,
77,
486,
5774,
1065,
2996,
198,
2,
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46103,
351,
9208,
256,
385,
591,
357,
16480,
281,
20950,
393,
4295,
1489,
283,
8,
198,
198,
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13792,
1875,
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198,
2,
1875,
30873,
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19825,
1875,
42651,
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5510,
42651,
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2638,
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9060,
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2398,
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28869,
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28,
77,
2999,
33580,
29334,
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1052,
20950,
6868,
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5478,
1719,
9812,
781,
5912,
11368,
290,
32630,
256,
385,
591,
628,
628,
198
] | 2.574132 | 1,268 |
from .web_requests import safeGet
| [
6738,
764,
12384,
62,
8897,
3558,
1330,
3338,
3855,
628,
628,
628,
220,
220,
220,
220,
198
] | 2.588235 | 17 |
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