repo_name stringlengths 7 79 | path stringlengths 4 179 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 959 798k | license stringclasses 15
values |
|---|---|---|---|---|---|
ricket1978/db.py | db/db.py | 1 | 63918 | import threading
import glob
import gzip
try:
from StringIO import StringIO # Python 2.7
except:
from io import StringIO # Python 3.3+
import uuid
import json
import base64
import re
import os
import sys
import pandas as pd
from prettytable import PrettyTable
import pybars
from .queries import mysql as mys... | bsd-2-clause |
ndevenish/simplehistogram | simplehist/hists.py | 1 | 6669 | # coding: utf-8
"""
hists.py
Copyright (c) 2014 Nicholas Devenish <n.e.devenish@sussex.ac.uk>
An easy, quick, lightweight histogram class based on ndarray
Initialise with bin indices:
>>> a = Hist([0, 1, 2, 3])
>>> len(a)
3
>>> a.bins
array([0, 1, 2, 3])
Optionally include data:
>>> Hist([0, 1, 2, 3], ... | mit |
rsteed11/GAT | gat/core/sna/pmesii.py | 1 | 5988 | # import packages
import networkx as nx
import numpy as np
import pandas as pd
# import csv or xlsx into pandas
dataFrameList = (pd.read_excel("World Bank Data Iran.xlsx"),
pd.read_excel("CIRI Iran.xlsx"),
pd.read_excel("DPI Iran.xlsx"))
df = pd.concat(dataFrameList)
headerList = df.c... | mit |
MohammedWasim/scikit-learn | examples/applications/plot_stock_market.py | 227 | 8284 | """
=======================================
Visualizing the stock market structure
=======================================
This example employs several unsupervised learning techniques to extract
the stock market structure from variations in historical quotes.
The quantity that we use is the daily variation in quote ... | bsd-3-clause |
wbinventor/openmc | tests/regression_tests/mgxs_library_no_nuclides/test.py | 4 | 2092 | import hashlib
import openmc
import openmc.mgxs
from openmc.examples import pwr_pin_cell
from tests.testing_harness import PyAPITestHarness
class MGXSTestHarness(PyAPITestHarness):
def __init__(self, *args, **kwargs):
# Generate inputs using parent class routine
super().__init__(*args, **kwargs)... | mit |
mortada/tensorflow | tensorflow/contrib/learn/python/learn/dataframe/tensorflow_dataframe.py | 75 | 29377 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
jeffzheng1/tensorflow | tensorflow/contrib/learn/python/learn/tests/dataframe/arithmetic_transform_test.py | 24 | 2349 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
mxjl620/scikit-learn | sklearn/tests/test_grid_search.py | 53 | 28730 | """
Testing for grid search module (sklearn.grid_search)
"""
from collections import Iterable, Sized
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.externals.six.moves import xrange
from itertools import chain, product
import pickle
import sys
import numpy as np
import scipy.sparse as sp
... | bsd-3-clause |
larsmans/scikit-learn | sklearn/metrics/tests/test_pairwise.py | 20 | 21057 | import numpy as np
from numpy import linalg
from scipy.sparse import dok_matrix, csr_matrix, issparse
from scipy.spatial.distance import cosine, cityblock, minkowski
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_al... | bsd-3-clause |
ch3ll0v3k/scikit-learn | sklearn/ensemble/partial_dependence.py | 251 | 15097 | """Partial dependence plots for tree ensembles. """
# Authors: Peter Prettenhofer
# License: BSD 3 clause
from itertools import count
import numbers
import numpy as np
from scipy.stats.mstats import mquantiles
from ..utils.extmath import cartesian
from ..externals.joblib import Parallel, delayed
from ..externals im... | bsd-3-clause |
nlholdem/icodoom | ICO1/deep_feedback_learning_old/vizdoom/backprop_old.py | 1 | 8854 | #!/usr/bin/env python
from __future__ import print_function
from vizdoom import *
import sys
import threading
from time import sleep
from matplotlib import pyplot as plt
import numpy as np
import cv2
sys.path.append('../../deep_feedback_learning')
import deep_feedback_learning
# Create DoomGame instance. It will ru... | gpl-3.0 |
ljwolf/pysal_core | libpysal/weights/tests/test_Distance.py | 2 | 11190 |
from ...common import RTOL, ATOL, pandas
from ...cg.kdtree import KDTree
from ..util import get_points_array
from ... import cg
from ... import weights
from .. import Distance as d, Contiguity as c
from ...io import geotable as pdio
from ...io.FileIO import FileIO as psopen
import numpy as np
from ... import examples ... | bsd-3-clause |
dwaithe/FCS_point_correlator | focuspoint/correlation_gui.py | 1 | 51790 | import struct
import numpy as np
#import scipy.weave as weave
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import sys, csv, os
from PyQt5 import QtGui, QtCore, QtWidgets
#import matplotlib
#matplotlib.use('Agg')
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanva... | gpl-2.0 |
cdegroc/scikit-learn | examples/applications/plot_species_distribution_modeling.py | 7 | 7349 | """
=============================
Species distribution modeling
=============================
Modeling species' geographic distributions is an important
problem in conservation biology. In this example we
model the geographic distribution of two south american
mammals given past observations and 14 environmental
varia... | bsd-3-clause |
gbrammer/unicorn | object_examples.py | 2 | 57686 | import os
import pyfits
import numpy as np
import glob
import shutil
import matplotlib.pyplot as plt
USE_PLOT_GUI=False
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
import threedhst
import threedhst.eazyPy as eazy
import threedhst.catIO as catIO
import unicorn
imp... | mit |
guschmue/tensorflow | tensorflow/python/estimator/canned/dnn_test.py | 31 | 16390 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
supriyantomaftuh/syzygy | third_party/numpy/files/numpy/core/function_base.py | 82 | 5474 | __all__ = ['logspace', 'linspace']
import numeric as _nx
from numeric import array
def linspace(start, stop, num=50, endpoint=True, retstep=False):
"""
Return evenly spaced numbers over a specified interval.
Returns `num` evenly spaced samples, calculated over the
interval [`start`, `stop` ].
Th... | apache-2.0 |
DonBeo/scikit-learn | sklearn/datasets/twenty_newsgroups.py | 26 | 13430 | """Caching loader for the 20 newsgroups text classification dataset
The description of the dataset is available on the official website at:
http://people.csail.mit.edu/jrennie/20Newsgroups/
Quoting the introduction:
The 20 Newsgroups data set is a collection of approximately 20,000
newsgroup documents,... | bsd-3-clause |
rohanp/scikit-learn | sklearn/feature_selection/tests/test_chi2.py | 56 | 2400 | """
Tests for chi2, currently the only feature selection function designed
specifically to work with sparse matrices.
"""
import numpy as np
from scipy.sparse import coo_matrix, csr_matrix
import scipy.stats
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.feature_selection.univariate_selection im... | bsd-3-clause |
spacelis/anatool | anatool/experiment/predcate.py | 1 | 10575 | #!python
# -*- coding: utf-8 -*-
"""File: timeweb.py
Description:
History:
0.1.0 The first version.
"""
__version__ = '0.1.0'
__author__ = 'SpaceLis'
from matplotlib import pyplot as plt
from anatool.analysis.timemodel import TimeModel
from anatool.analysis.textmodel import LanguageModel
from anatool.an... | mit |
dingocuster/scikit-learn | examples/model_selection/plot_roc.py | 96 | 4487 | """
=======================================
Receiver Operating Characteristic (ROC)
=======================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality.
ROC curves typically feature true positive rate on the Y axis, and false
positive rate on the X a... | bsd-3-clause |
barentsen/dave | lpp/newlpp/lppTransform.py | 1 | 8388 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 23 20:32:12 2018
Functions to correctly fold and bin a light curve.
Calculate the lpp metric: transform to lower dimensions, knn
Depends on class from reading in a previously created LPP metric Map
Depends on reading in the light curve to data stru... | mit |
idc9/law-net | vertex_metrics_experiment/code/pipeline_helper_functions.py | 1 | 2134 | import glob
import pandas as pd
import numpy as np
from scipy.sparse import csr_matrix
def load_snapshots(subnet_dir):
"""
Loads the snapshot data frames into a dict indexed by the file names
Parameters
----------
subnet_dir: path to experiment data files
Output
------
python dict
... | mit |
kbrannan/PyHSPF | src/pyhspf/forecasting/extract_timeseries.py | 2 | 7323 | #!/usr/bin/env python
#
# extract_NRCM.py
# David J. Lampert
#
# extracts the grid point for a watershed from the preprocessed NRCM data
import os, shutil, pickle, datetime, numpy
from multiprocessing import Pool, cpu_count
from matplotlib import pyplot, path, patches
from shapefile import Reader
def ins... | bsd-3-clause |
ebilionis/variational-reformulation-of-inverse-problems | unittests/test_optimize_catalysis_prop_noise_diff.py | 1 | 6076 | """
A first test for the ELBO on the catalysis problem.
The target is consisted of an uninformative prior and a Gaussian likelihood.
The approximating mixture has two components.
Author:
Panagiotis Tsilifis
Date:
6/6/2014
"""
import numpy as np
import matplotlib.pyplot as plt
import os
import cPickle as p... | gpl-2.0 |
allenh1/rosdep | test/test_rosdep_dependency_graph.py | 3 | 8384 | # Copyright (c) 2012, Willow Garage, Inc.
# 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 cond... | bsd-3-clause |
lys-coding/4gillian | One and Only Collective/pricelist/run.py | 1 | 1707 | #!/usr/local/bin/python3 -W ignore
import os
import pandas as pd
class PriceList:
def __init__(self):
self.number=[]
self.name=[]
self.price=[]
self.map={}
def from_file(self,filepath):
xl=pd.ExcelFile(filepath)
df=xl.parse(xl.sheet_names[0])
flag=False
... | apache-2.0 |
greentfrapp/adversarialautoencoder | adversarialautoencoder.py | 1 | 22282 | """
Replicates the Adversarial Autoencoder architecture (Figure 1) from
Makhzani, Alireza, et al. "Adversarial autoencoders." arXiv preprint arXiv:1511.05644 (2015).
https://arxiv.org/abs/1511.05644
Refer to Appendix A.1 from paper for implementation details
"""
try:
raw_input
except:
raw_input = input
import argpar... | mit |
actuaryzhang/spark | python/pyspark/sql/utils.py | 5 | 7527 | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | apache-2.0 |
bikong2/scikit-learn | sklearn/utils/estimator_checks.py | 21 | 51976 | from __future__ import print_function
import types
import warnings
import sys
import traceback
import inspect
import pickle
from copy import deepcopy
import numpy as np
from scipy import sparse
import struct
from sklearn.externals.six.moves import zip
from sklearn.externals.joblib import hash, Memory
from sklearn.ut... | bsd-3-clause |
brunston/stellarpyl | text.py | 3 | 12212 | # -*- coding: utf-8 -*-
"""
stellarPYL - python stellar spectra processing software
Copyright (c) 2016 Brunston Poon
@file: text
This program comes with absolutely no warranty.
"""
import time
def welcome():
print("""
Welcome to stellarPYL, Copyright (C) 2015 Brunston Poon, type 'licence' for info
Type 'quit' or ... | gpl-3.0 |
vishnumani2009/OpenSource-Open-Ended-Statistical-toolkit | FRONTEND/anovafront.py | 1 | 9167 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'anova.ui'
#
# Created: Sat Apr 11 09:33:51 2015
# by: PyQt4 UI code generator 4.10.4
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore, QtGui
try:
_fromUtf8 = QtCore.QString.fromUtf8
except Attribut... | gpl-3.0 |
jseabold/scikit-learn | sklearn/datasets/species_distributions.py | 64 | 7917 | """
=============================
Species distribution dataset
=============================
This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/apps/redlist/details/3038/0>`_... | bsd-3-clause |
ssaeger/scikit-learn | doc/tutorial/text_analytics/solutions/exercise_01_language_train_model.py | 25 | 2252 | """Build a language detector model
The goal of this exercise is to train a linear classifier on text features
that represent sequences of up to 3 consecutive characters so as to be
recognize natural languages by using the frequencies of short character
sequences as 'fingerprints'.
"""
# Author: Olivier Grisel <olivie... | bsd-3-clause |
jbosboom/opentuner | stats_app/stats_app/views/charts.py | 6 | 1669 | import datetime
import django
from django.shortcuts import render
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.dates import DateFormatter
from matplotlib.figure import Figure
import random
from opentuner.utils import stats_matplotlib as stats
def display_graph(request):... | mit |
alfonsokim/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/mlab.py | 69 | 104273 | """
Numerical python functions written for compatability with matlab(TM)
commands with the same names.
Matlab(TM) compatible functions
-------------------------------
:func:`cohere`
Coherence (normalized cross spectral density)
:func:`csd`
Cross spectral density uing Welch's average periodogram
:func:`detrend`... | agpl-3.0 |
david-ragazzi/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/pylab.py | 70 | 10245 | """
This is a procedural interface to the matplotlib object-oriented
plotting library.
The following plotting commands are provided; the majority have
Matlab(TM) analogs and similar argument.
_Plotting commands
acorr - plot the autocorrelation function
annotate - annotate something in the figure
arrow ... | gpl-3.0 |
lenovor/scikit-learn | sklearn/tests/test_random_projection.py | 142 | 14033 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from sklearn.metrics import euclidean_distances
from sklearn.random_projection import johnson_lindenstrauss_min_dim
from sklearn.random_projection import gaussian_random_matrix
from sklearn.random_projection import sparse_random_matrix
from... | bsd-3-clause |
wavelets/zipline | tests/risk/answer_key.py | 39 | 11989 | #
# Copyright 2014 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | apache-2.0 |
madcowswe/ODrive | analysis/cogging_torque/cogging_harmonics.py | 2 | 1304 |
import numpy as np
import matplotlib.pyplot as plt
encoder_cpr = 2400
stator_slots = 12
pole_pairs = 7
N = data.size
fft = np.fft.rfft(data)
freq = np.fft.rfftfreq(N, d=1./encoder_cpr)
harmonics = [0]
harmonics += [(i+1)*stator_slots for i in range(pole_pairs)]
harmonics += [pole_pairs]
harmonics += [(i+1)*2*pole_p... | mit |
nikitasingh981/scikit-learn | sklearn/decomposition/tests/test_kernel_pca.py | 74 | 8472 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import (assert_array_almost_equal, assert_less,
assert_equal, assert_not_equal,
assert_raises)
from sklearn.decomposition import PCA, KernelPCA
from sklearn.datasets import mak... | bsd-3-clause |
schae234/gingivere | tests/motor_insert.py | 2 | 1470 | from __future__ import print_function
from collections import defaultdict
import copy
import pandas as pd
import tornado.ioloop
from tornado import gen
import load_raw_data
import motor
from tests import shelve_api
def insert_patient(patient):
count = 0
for data in load_raw_data.walk_training_mats(patient)... | mit |
architecture-building-systems/CEAforArcGIS | cea/technologies/network_layout/steiner_spanning_tree.py | 1 | 18985 | """
This script calculates the minimum spanning tree of a shapefile network
"""
import math
import os
import networkx as nx
import pandas as pd
from geopandas import GeoDataFrame as gdf
from networkx.algorithms.approximation.steinertree import steiner_tree
from shapely.geometry import LineString
from typing import... | mit |
gclenaghan/scikit-learn | sklearn/ensemble/tests/test_forest.py | 3 | 41612 | """
Testing for the forest module (sklearn.ensemble.forest).
"""
# Authors: Gilles Louppe,
# Brian Holt,
# Andreas Mueller,
# Arnaud Joly
# License: BSD 3 clause
import pickle
from collections import defaultdict
from itertools import combinations
from itertools import product
import numpy ... | bsd-3-clause |
decvalts/landlab | landlab/__init__.py | 1 | 1460 | #! /usr/bin/env python
"""
The Landlab
:Package name: TheLandlab
:Version: 0.1.0
:Release date: 2013-03-24
:Authors:
Greg Tucker,
Nicole Gasparini,
Erkan Istanbulluoglu,
Daniel Hobley,
Sai Nudurupati,
Jordan Adams,
Eric Hutton
:URL: http://csdms.colorado.edu/trac/landlab
:License: MIT
"""
from __futur... | mit |
jmschrei/scikit-learn | sklearn/utils/tests/test_extmath.py | 7 | 21916 | # Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis Engemann <d.engemann@fz-juelich.de>
#
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from scipy import linalg
from scipy import stats
from sklearn.utils.testing import assert_eq... | bsd-3-clause |
mattdelhey/rice-scrape | scrape/scrape_eval.py | 2 | 3448 | import dryscrape
import re
import sys
import os
import numpy as np
import pandas as pd
sys.path.append('scrape')
from helpers import login_to_evaluations
UserID = ''
PIN = ''
project_dir = '/Users/mdelhey/rice-scrape/'
YEAR_SCRAPE = '2013'
TERM_SCRAPE = 'Spring'
# Boilerplate
os.chdir(project_dir)
try: __file__... | mit |
igabr/Metis_Projects_Chicago_2017 | 05-project-kojack/prophet_helper.py | 1 | 1164 | import pandas as pd
import numpy as np
from fbprophet import Prophet
from datetime import date
def prophet_forecast(row_of_df):
holidays = []
df = pd.DataFrame(row_of_df)
col_name = df.columns[0]
# print("Creating time series model for {}".format(col_name))
# print("Starting_date passed to prophet is {}.".format(... | mit |
eg-zhang/scikit-learn | examples/classification/plot_lda_qda.py | 78 | 5046 | """
====================================================================
Linear and Quadratic Discriminant Analysis with confidence ellipsoid
====================================================================
Plot the confidence ellipsoids of each class and decision boundary
"""
print(__doc__)
from scipy import lin... | bsd-3-clause |
nicolas998/Op_Interpolated | 06_Codigos/viejos/Figuras_Qsim.py | 2 | 2786 | #!/usr/bin/env python
from wmf import wmf
import numpy as np
import pickle
import pandas as pnd
import pylab as pl
import argparse
import textwrap
import os
from multiprocessing import Pool
#-------------------------------------------------------------------
#FUNBCIONES LOCALES
#------------------------------------... | gpl-3.0 |
DGrady/pandas | pandas/core/computation/ops.py | 15 | 15900 | """Operator classes for eval.
"""
import operator as op
from functools import partial
from datetime import datetime
import numpy as np
from pandas.core.dtypes.common import is_list_like, is_scalar
import pandas as pd
from pandas.compat import PY3, string_types, text_type
import pandas.core.common as com
from pandas.... | bsd-3-clause |
cloudera/ibis | ibis/backends/pandas/execution/maps.py | 1 | 6331 | import collections
import functools
import pandas as pd
import toolz
import ibis.expr.operations as ops
from ..dispatch import execute_node
@execute_node.register(ops.MapLength, pd.Series)
def execute_map_length_series(op, data, **kwargs):
# TODO: investigate whether calling a lambda is faster
return data.... | apache-2.0 |
rflamary/POT | docs/source/auto_examples/plot_otda_d2.py | 2 | 5388 | # -*- coding: utf-8 -*-
"""
===================================================
OT for domain adaptation on empirical distributions
===================================================
This example introduces a domain adaptation in a 2D setting. It explicits
the problem of domain adaptation and introduces some optimal ... | mit |
StefReck/Km3-Autoencoder | scripts/evaluation_dataset.py | 1 | 45607 | # -*- coding: utf-8 -*-
"""
Evaluate model performance after training.
This is for comparison of supervised accuracy on different datasets.
Especially for the plots for the broken data comparison.
The usual setup is that simulations are broken (so that the AE has not to be trained again)
so 3 tests are necessary:
... | mit |
davidgbe/scikit-learn | sklearn/tests/test_metaestimators.py | 226 | 4954 | """Common tests for metaestimators"""
import functools
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.externals.six import iterkeys
from sklearn.datasets import make_classification
from sklearn.utils.testing import assert_true, assert_false, assert_raises
from sklearn.pipeline import Pipeline... | bsd-3-clause |
mbayon/TFG-MachineLearning | vbig/lib/python2.7/site-packages/pandas/tests/io/parser/test_read_fwf.py | 7 | 15261 | # -*- coding: utf-8 -*-
"""
Tests the 'read_fwf' function in parsers.py. This
test suite is independent of the others because the
engine is set to 'python-fwf' internally.
"""
from datetime import datetime
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas import DataF... | mit |
joetidwell/daftHM | examples/classic.py | 7 | 1057 | """
The Quintessential PGM
======================
This is a demonstration of a very common structure found in graphical models.
It has been rendered using Daft's default settings for all the parameters
and it shows off how much beauty is baked in by default.
"""
from matplotlib import rc
rc("font", family="serif", s... | mit |
vortex-ape/scikit-learn | sklearn/feature_selection/rfe.py | 2 | 20029 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Vincent Michel <vincent.michel@inria.fr>
# Gilles Louppe <g.louppe@gmail.com>
#
# License: BSD 3 clause
"""Recursive feature elimination for feature ranking"""
import numpy as np
from ..utils import check_X_y, safe_sqr
from ..utils.metaes... | bsd-3-clause |
allenai/document-qa | docqa/eval/squad_full_document_eval.py | 1 | 8928 | import argparse
from typing import List, Optional
import numpy as np
import pandas as pd
from tqdm import tqdm
from docqa import trainer
from docqa.data_processing.qa_training_data import ContextAndQuestion, Answer, ParagraphAndQuestionDataset
from docqa.data_processing.span_data import TokenSpans
from docqa.data_pro... | apache-2.0 |
massmutual/scikit-learn | sklearn/decomposition/tests/test_truncated_svd.py | 240 | 6055 | """Test truncated SVD transformer."""
import numpy as np
import scipy.sparse as sp
from sklearn.decomposition import TruncatedSVD
from sklearn.utils import check_random_state
from sklearn.utils.testing import (assert_array_almost_equal, assert_equal,
assert_raises, assert_greater,
... | bsd-3-clause |
alurban/mentoring | tidal_disruption/scripts/kepler_potentials.py | 1 | 1465 | # Imports.
import numpy as np
from numpy import pi
import matplotlib.pyplot as plt
import matplotlib.patheffects as PE
from matplotlib import ticker
# Physical constants.
G = 6.67408e-11 # Newton's constant in m^3 / kg / s
MSun = 1.989e30 # Solar mass in kg
M = 1.4 * MSun # Mass of each neutron star in this exampl... | gpl-3.0 |
fabioticconi/scikit-learn | benchmarks/bench_plot_randomized_svd.py | 38 | 17557 | """
Benchmarks on the power iterations phase in randomized SVD.
We test on various synthetic and real datasets the effect of increasing
the number of power iterations in terms of quality of approximation
and running time. A number greater than 0 should help with noisy matrices,
which are characterized by a slow spectr... | bsd-3-clause |
ashhher3/scikit-learn | examples/linear_model/plot_ransac.py | 250 | 1673 | """
===========================================
Robust linear model estimation using RANSAC
===========================================
In this example we see how to robustly fit a linear model to faulty data using
the RANSAC algorithm.
"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn import ... | bsd-3-clause |
DailyActie/Surrogate-Model | 01-codes/scikit-learn-master/sklearn/manifold/tests/test_locally_linear.py | 1 | 5242 | from itertools import product
import numpy as np
from nose.tools import assert_true
from numpy.testing import assert_almost_equal, assert_array_almost_equal
from scipy import linalg
from sklearn import neighbors, manifold
from sklearn.manifold.locally_linear import barycenter_kneighbors_graph
from sklearn.utils.testin... | mit |
wazeerzulfikar/scikit-learn | examples/decomposition/plot_pca_3d.py | 10 | 2432 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Principal components analysis (PCA)
=========================================================
These figures aid in illustrating how a point cloud
can be very flat in one direction--which is where PCA
comes in to ch... | bsd-3-clause |
aywander/pluto-outflows | Tools/pyPLUTO/examples/Sph_disk.py | 3 | 1488 | import os
import sys
from numpy import *
from matplotlib.pyplot import *
import pyPLUTO as pp
plutodir = os.environ['PLUTO_DIR']
wdir = plutodir+'/Test_Problems/MHD/FARGO/Spherical_Disk/'
nlinf = pp.nlast_info(w_dir=wdir,datatype='vtk')
D = pp.pload(nlinf['nlast'],w_dir=wdir,datatype='vtk') # Loading the data into a ... | gpl-2.0 |
benneely/qdact-basic-analysis | notebooks/python_scripts/07_consultloc_tables.py | 1 | 6839 | # -*- coding: utf-8 -*-
"""
Created on Tue Jul 5 14:15:45 2016
@author: nn31
"""
import pandas as pd
import pickle
import re
import numpy as np
from robocomp import Model
from scipy import stats
import scipy
#read in cmmi data
cmmi = pd.read_csv("/Volumes/DCCRP_projects/CMMI/data/QDACT 05-03-2016.csv",
... | gpl-3.0 |
antiface/mne-python | mne/decoding/tests/test_ems.py | 19 | 1969 | # Author: Denis A. Engemann <d.engemann@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
from nose.tools import assert_equal, assert_raises
from mne import io, Epochs, read_events, pick_types
from mne.utils import requires_sklearn
from mne.decoding import compute_ems
data_dir = op.join(op.dirname(__file_... | bsd-3-clause |
lthurlow/Network-Grapher | proj/external/matplotlib-1.2.1/lib/matplotlib/backends/backend_gtk.py | 2 | 44416 | from __future__ import division, print_function
import os, sys, warnings
def fn_name(): return sys._getframe(1).f_code.co_name
if sys.version_info[0] >= 3:
warnings.warn(
"The gtk* backends have not been tested with Python 3.x",
ImportWarning)
try:
import gobject
import gtk; gdk = gtk.gdk... | mit |
CforED/Machine-Learning | examples/feature_selection/plot_rfe_with_cross_validation.py | 161 | 1380 | """
===================================================
Recursive feature elimination with cross-validation
===================================================
A recursive feature elimination example with automatic tuning of the
number of features selected with cross-validation.
"""
print(__doc__)
import matplotlib.p... | bsd-3-clause |
jusjusjus/Motiftoolbox | Fitzhugh_3-cell/webrun.py | 1 | 5211 | #!/usr/bin/env python
import matplotlib #
matplotlib.use('Agg') # This prevents the x server to start.
import mpld3 as m
from mpld3 import plugins
from flask import Flask,request
import pylab as pl
import numpy as np
import system as sys
import info as nf
import network3N as netw
import torus as tor
import traces as... | gpl-2.0 |
louispotok/pandas | pandas/tests/frame/test_replace.py | 3 | 44681 | # -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
from datetime import datetime
import re
from pandas.compat import (zip, range, lrange, StringIO)
from pandas import (DataFrame, Series, Index, date_range, compat,
Timestamp)
import pandas as pd
from numpy import nan
imp... | bsd-3-clause |
katyhuff/cyder | output/contour_plot.py | 1 | 10705 |
"""
Comparison of griddata and tricontour for an unstructured triangular grid.
"""
from __future__ import print_function
import matplotlib.pyplot as plt
from pylab import *
import matplotlib.tri as tri
import numpy as np
from numpy.random import uniform, seed
from matplotlib.mlab import griddata
import pdb
class Co... | bsd-3-clause |
brianlorenz/COSMOS_IMACS_Redshifts | Emission_Fitting/FitEmissionOneSig.py | 1 | 38467 | #Fits an emission ine with a Gaussian and returns the amplitude, standard deviation, and continuum line
#Usage: run FitEmission.py 'a6' 4861 to fit the lines at rest wavelengths 6563 (Ha) for the a6 mask.
#Typing run FitEmission.py 'a6' 'HaNII' will fit all three lines around Ha simulaaneously
import numpy ... | mit |
daodaoliang/bokeh | examples/plotting/server/boxplot.py | 42 | 2372 | # The plot server must be running
# Go to http://localhost:5006/bokeh to view this plot
import numpy as np
import pandas as pd
from bokeh.plotting import figure, show, output_server
# Generate some synthetic time series for six different categories
cats = list("abcdef")
data = np.random.randn(2000)
g = np.random.cho... | bsd-3-clause |
winklerand/pandas | pandas/tests/tseries/offsets/test_offsets.py | 1 | 131710 | import os
from distutils.version import LooseVersion
from datetime import date, datetime, timedelta
import pytest
from pandas.compat import range
from pandas import compat
import numpy as np
from pandas.compat.numpy import np_datetime64_compat
from pandas.core.series import Series
from pandas.tseries.frequencies im... | bsd-3-clause |
kedz/cuttsum | trec2015/cuttsum/summarizers/_oracle.py | 1 | 12599 | import os
from cuttsum.resources import MultiProcessWorker
from cuttsum.pipeline import ArticlesResource
from cuttsum.misc import si2df
import cuttsum.judgements
import numpy as np
import pandas as pd
import sumpy
class RetrospectiveMonotoneSubmodularOracle(MultiProcessWorker):
def __init__(self):
self.d... | apache-2.0 |
mixturemodel-flow/tensorflow | tensorflow/contrib/learn/python/learn/tests/dataframe/dataframe_test.py | 62 | 3753 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
hrjn/scikit-learn | examples/model_selection/plot_validation_curve.py | 141 | 1931 | """
==========================
Plotting Validation Curves
==========================
In this plot you can see the training scores and validation scores of an SVM
for different values of the kernel parameter gamma. For very low values of
gamma, you can see that both the training score and the validation score are
low. ... | bsd-3-clause |
xapharius/HadoopML | Engine/src/examples/ensemble_simulation.py | 2 | 2325 | '''
Created on Mar 23, 2015
@author: xapharius
'''
import numpy as np
from simulation.mr_simulator.ensemble_simulator import EnsembleSimulator
from simulation.sampler.bootstrap_sampler import BootstrapSampler
from sklearn.linear_model import LinearRegression
from datahandler.numerical2.numerical_data_handler import... | mit |
surhudm/scipy | scipy/integrate/_bvp.py | 61 | 39966 | """Boundary value problem solver."""
from __future__ import division, print_function, absolute_import
from warnings import warn
import numpy as np
from numpy.linalg import norm, pinv
from scipy.sparse import coo_matrix, csc_matrix
from scipy.sparse.linalg import splu
from scipy.optimize import OptimizeResult
EPS =... | bsd-3-clause |
joshloyal/pydata-amazon-products | amazon_products/text_plots.py | 1 | 5332 | import numpy as np
import pandas as pd
import seaborn as sns
import wordcloud
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn.manifold import TSNE
from sklearn.preprocessing import Normalizer
from sklearn.pipeline import make_pipeline
def sample... | mit |
HopeFOAM/HopeFOAM | ThirdParty-0.1/ParaView-5.0.1/Applications/ParaView/Testing/Python/TestPythonViewMatplotlibScript.py | 1 | 1798 | # Set up a basic scene for rendering.
from paraview.simple import *
import os
import sys
script = """
import paraview.numpy_support
# Utility to get next color
def getNextColor():
colors = 'bgrcmykw'
for c in colors:
yield c
# This function must be defined. It is where specific data arrays are requested.
def... | gpl-3.0 |
phoebe-project/phoebe2-docs | 2.1/examples/legacy.py | 1 | 5270 | #!/usr/bin/env python
# coding: utf-8
# Comparing PHOEBE 2 vs PHOEBE Legacy
# ============================
#
# **NOTE**: PHOEBE 1.0 legacy is an alternate backend and is not installed with PHOEBE 2.0. In order to run this backend, you'll need to have [PHOEBE 1.0](https://phoebe-project.org/1.0) installed and manuall... | gpl-3.0 |
andyraib/data-storage | python_scripts/env/lib/python3.6/site-packages/mpl_toolkits/axes_grid1/axes_rgb.py | 6 | 7005 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import numpy as np
from .axes_divider import make_axes_locatable, Size, locatable_axes_factory
import sys
from .mpl_axes import Axes
def make_rgb_axes(ax, pad=0.01, axes_class=None, add_all=True):... | apache-2.0 |
DonBeo/statsmodels | statsmodels/duration/tests/test_phreg.py | 6 | 11981 | import os
import numpy as np
from statsmodels.duration.hazard_regression import PHReg
from numpy.testing import (assert_allclose,
assert_equal)
import pandas as pd
# TODO: Include some corner cases: data sets with empty strata, strata
# with no events, entry times after censoring times,... | bsd-3-clause |
animesh-garg/cgt | examples/broken/mnist_torchstyle.py | 22 | 3157 | import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original', data_home='~/cgt/data') # XXX
print(mnist.data.shape)
print(mnist.target.shape)
np.unique(mnist.target)
#plt.imshow(mnist.data[1, :].reshape(28, 28))
#plt.show()
# do some preprocess... | mit |
mbayon/TFG-MachineLearning | venv/lib/python3.6/site-packages/sklearn/feature_selection/tests/test_feature_select.py | 43 | 26651 | """
Todo: cross-check the F-value with stats model
"""
from __future__ import division
import itertools
import warnings
import numpy as np
from scipy import stats, sparse
from numpy.testing import run_module_suite
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from... | mit |
skosukhin/spack | lib/spack/docs/tutorial/examples/PyPackage/0.package.py | 1 | 2422 | ##############################################################################
# Copyright (c) 2013-2017, Lawrence Livermore National Security, LLC.
# Produced at the Lawrence Livermore National Laboratory.
#
# This file is part of Spack.
# Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved.
# LLNL-CODE-64... | lgpl-2.1 |
dieterich-lab/dorina | app/main.py | 1 | 2510 | #!/usr/bin/env python
# -*- coding: utf-8
"""
Created on 16:54 19/03/2018 2018
"""
import pandas as pd
from bokeh.io import curdoc
from bokeh.layouts import layout, widgetbox
from bokeh.models import ColumnDataSource, HoverTool, Div
from bokeh.models.widgets import Slider, Select, TextInput
from bokeh.plotting import... | gpl-3.0 |
marcocaccin/scikit-learn | examples/neighbors/plot_digits_kde_sampling.py | 251 | 2022 | """
=========================
Kernel Density Estimation
=========================
This example shows how kernel density estimation (KDE), a powerful
non-parametric density estimation technique, can be used to learn
a generative model for a dataset. With this generative model in place,
new samples can be drawn. These... | bsd-3-clause |
dabura667/electrum | lib/plot.py | 1 | 1704 | from PyQt5.QtGui import *
from electrum.i18n import _
import datetime
from collections import defaultdict
from electrum.util import format_satoshis
from electrum.bitcoin import COIN
import matplotlib
matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt
import matplotlib.dates as md
from matplotlib.patches impor... | mit |
kgullikson88/HET-Scripts | MakeMassRatioDistribution.py | 1 | 9579 | """
This script goes through the stars observed, and searches for both known and new
companions to each target. Right now, it only does known companions automatically
"""
import sys
import os
from matplotlib import rc
import pySIMBAD as sim
# rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
## fo... | gpl-3.0 |
HopkinsIDD/EpiForecastStatMech | epi_forecast_stat_mech/datasets/nanhandling.py | 1 | 3953 | # Lint as: python3
"""A few methods to handle NaNs.
Library functions that take a numpy array.
Assumes dimensions are (location, time).
"""
import itertools
import matplotlib.pyplot as plt
import numpy as np
import scipy
import scipy.ndimage
def _assume_2d(array):
if len(array.shape) != 2:
raise ValueError(
... | gpl-3.0 |
saguziel/incubator-airflow | scripts/perf/scheduler_ops_metrics.py | 30 | 6536 | # -*- coding: utf-8 -*-
#
# 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, softw... | apache-2.0 |
f3r/scikit-learn | sklearn/metrics/tests/test_regression.py | 272 | 6066 | from __future__ import division, print_function
import numpy as np
from itertools import product
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.... | bsd-3-clause |
davidgbe/scikit-learn | examples/applications/face_recognition.py | 191 | 5513 | """
===================================================
Faces recognition example using eigenfaces and SVMs
===================================================
The dataset used in this example is a preprocessed excerpt of the
"Labeled Faces in the Wild", aka LFW_:
http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (2... | bsd-3-clause |
ishanic/scikit-learn | examples/svm/plot_separating_hyperplane_unbalanced.py | 329 | 1850 | """
=================================================
SVM: Separating hyperplane for unbalanced classes
=================================================
Find the optimal separating hyperplane using an SVC for classes that
are unbalanced.
We first find the separating plane with a plain SVC and then plot
(dashed) the ... | bsd-3-clause |
stevenzhang18/Indeed-Flask | lib/pandas/stats/tests/test_moments.py | 9 | 86813 | import nose
import sys
import functools
import warnings
from datetime import datetime
from numpy.random import randn
from numpy.testing.decorators import slow
import numpy as np
from distutils.version import LooseVersion
from pandas import Series, DataFrame, Panel, bdate_range, isnull, notnull, concat
from pandas.uti... | apache-2.0 |
johndpope/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator.py | 5 | 55283 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
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