repo_name stringlengths 7 92 | path stringlengths 5 149 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 911 693k | license stringclasses 15
values |
|---|---|---|---|---|---|
sauliusl/seaborn | seaborn/tests/test_palettes.py | 3 | 11509 | import colorsys
import numpy as np
import matplotlib as mpl
import pytest
import nose.tools as nt
import numpy.testing as npt
import matplotlib.pyplot as plt
from .. import palettes, utils, rcmod
from ..external import husl
from ..colors import xkcd_rgb, crayons
from distutils.version import LooseVersion
mpl_ge_150 ... | bsd-3-clause |
Karel-van-de-Plassche/bokeh | bokeh/util/sampledata.py | 2 | 6899 | #-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2017, Anaconda, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.txt, distributed with this software.
#---------------------------------------------------... | bsd-3-clause |
valexandersaulys/prudential_insurance_kaggle | venv/lib/python2.7/site-packages/pandas/tests/test_common.py | 9 | 44081 | # -*- coding: utf-8 -*-
import collections
from datetime import datetime
import re
import nose
from nose.tools import assert_equal, assert_true
import numpy as np
import pandas as pd
from pandas.tslib import iNaT, NaT
from pandas import Series, DataFrame, date_range, DatetimeIndex, Timestamp, Float64Index
from pandas ... | gpl-2.0 |
par2/lamana | lamana/models/fixtures/fixture_model_class.py | 1 | 10351 | #------------------------------------------------------------------------------
'''Class-style Model
This fixture is used to test the importing of models, handled by the
`theories.handshake()` module. As of 0.4.11, models can:
- be located in the `lamana.models` folder
- module and classes can have any pythonic name... | bsd-3-clause |
mxlei01/healthcareai-py | healthcareai/common/model_eval.py | 4 | 13696 | """Model evaluation tools."""
import os
import sklearn
import itertools
import numpy as np
import pandas as pd
import sklearn.metrics as skmetrics
from matplotlib import pyplot as plt
from healthcareai.common.healthcareai_error import HealthcareAIError
DIAGONAL_LINE_COLOR = '#bbbbbb'
DIAGONAL_LINE_STYLE = 'dotted'... | mit |
tmhm/scikit-learn | sklearn/mixture/tests/test_gmm.py | 200 | 17427 | import unittest
import copy
import sys
from nose.tools import assert_true
import numpy as np
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_raises)
from scipy import stats
from sklearn import mixture
from sklearn.datasets.samples_generator import make_spd_ma... | bsd-3-clause |
costypetrisor/scikit-learn | examples/linear_model/plot_ols_ridge_variance.py | 387 | 2060 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Ordinary Least Squares and Ridge Regression Variance
=========================================================
Due to the few points in each dimension and the straight
line that linear regression uses to follow thes... | bsd-3-clause |
mne-tools/mne-tools.github.io | 0.22/_downloads/52a5ebd4d6b8bcb7eccdf9bc2b0fcfcc/plot_cluster_stats_evoked.py | 18 | 3021 | """
=======================================================
Permutation F-test on sensor data with 1D cluster level
=======================================================
One tests if the evoked response is significantly different
between conditions. Multiple comparison problem is addressed
with cluster level permuta... | bsd-3-clause |
kevin-intel/scikit-learn | sklearn/cluster/tests/test_dbscan.py | 14 | 15405 | """
Tests for DBSCAN clustering algorithm
"""
import pickle
import numpy as np
import warnings
from scipy.spatial import distance
from scipy import sparse
import pytest
from sklearn.utils._testing import assert_array_equal
from sklearn.neighbors import NearestNeighbors
from sklearn.cluster import DBSCAN
from skle... | bsd-3-clause |
wazeerzulfikar/scikit-learn | sklearn/svm/tests/test_bounds.py | 6 | 2369 | import numpy as np
from scipy import sparse as sp
from sklearn.svm.bounds import l1_min_c
from sklearn.svm import LinearSVC
from sklearn.linear_model.logistic import LogisticRegression
from sklearn.utils.testing import assert_true, raises
from sklearn.utils.testing import assert_raise_message
dense_X = [[-1, 0], [0... | bsd-3-clause |
ominux/scikit-learn | examples/linear_model/plot_sgd_iris.py | 4 | 2171 | """
========================================
Plot multi-class SGD on the iris dataset
========================================
Plot decision surface of multi-class SGD on iris dataset.
The hyperplanes corresponding to the three one-versus-all (OVA) classifiers
are represented by the dashed lines.
"""
print __doc__
i... | bsd-3-clause |
Vvucinic/Wander | venv_2_7/lib/python2.7/site-packages/traitlets/config/loader.py | 3 | 28215 | # encoding: utf-8
"""A simple configuration system."""
# Copyright (c) IPython Development Team.
# Distributed under the terms of the Modified BSD License.
import argparse
import copy
import logging
import os
import re
import sys
import json
from ast import literal_eval
from ipython_genutils.path import filefind
fro... | artistic-2.0 |
Jigsaw-Code/net-analysis | netanalysis/traffic/data/api_repository.py | 1 | 3175 | #!/usr/bin/python
#
# Copyright 2019 Jigsaw Operations LLC
#
# 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 applicabl... | apache-2.0 |
maheshakya/scikit-learn | sklearn/semi_supervised/label_propagation.py | 2 | 15104 | # coding=utf8
"""
Label propagation in the context of this module refers to a set of
semisupervised classification algorithms. In the high level, these algorithms
work by forming a fully-connected graph between all points given and solving
for the steady-state distribution of labels at each point.
These algorithms per... | bsd-3-clause |
LennonLab/ScalingMicroBiodiversity | ExtraTests/lognormal/Prestons_a.py | 2 | 2188 | from __future__ import division
#from bigfloat import BigFloat, sqrt, exp, log, log2, erf, const_pi
import numpy as np
import math
from numpy import log, log2, exp, sqrt,log10
from scipy.optimize import fsolve
import scipy.optimize as opt
import matplotlib.pyplot as plt
from scipy.special import erf
import sys
pi = ma... | gpl-3.0 |
solarjoe/numpy | numpy/lib/npyio.py | 3 | 75574 | from __future__ import division, absolute_import, print_function
import sys
import os
import re
import itertools
import warnings
import weakref
from operator import itemgetter, index as opindex
import numpy as np
from . import format
from ._datasource import DataSource
from numpy.core.multiarray import packbits, unpa... | bsd-3-clause |
zfrenchee/pandas | pandas/core/api.py | 1 | 3146 |
# pylint: disable=W0614,W0401,W0611
# flake8: noqa
import numpy as np
from pandas.core.algorithms import factorize, unique, value_counts
from pandas.core.dtypes.missing import isna, isnull, notna, notnull
from pandas.core.categorical import Categorical
from pandas.core.groupby import Grouper
from pandas.io.formats.f... | bsd-3-clause |
woodscn/scipy | scipy/special/c_misc/struve_convergence.py | 76 | 3725 | """
Convergence regions of the expansions used in ``struve.c``
Note that for v >> z both functions tend rapidly to 0,
and for v << -z, they tend to infinity.
The floating-point functions over/underflow in the lower left and right
corners of the figure.
Figure legend
=============
Red region
Power series is clo... | bsd-3-clause |
ashhher3/seaborn | seaborn/tests/test_axisgrid.py | 11 | 41072 | import warnings
import numpy as np
import pandas as pd
from scipy import stats
import matplotlib as mpl
import matplotlib.pyplot as plt
from distutils.version import LooseVersion
import nose.tools as nt
import numpy.testing as npt
from numpy.testing.decorators import skipif
import pandas.util.testing as tm
from . im... | bsd-3-clause |
rseubert/scikit-learn | examples/applications/plot_out_of_core_classification.py | 255 | 13919 | """
======================================================
Out-of-core classification of text documents
======================================================
This is an example showing how scikit-learn can be used for classification
using an out-of-core approach: learning from data that doesn't fit into main
memory. ... | bsd-3-clause |
andyr0id/PyGFNN | examples/gfnn/example1F.py | 1 | 1657 | #!/usr/bin/env python
__author__ = 'Andrew J. Lambert, andy@andyroid.co.uk'
"""
example1P
A one layer network with fixed internal connections
"""
from pygfnn.tools.plotting.gfnn import *
import pygfnn.tools.shortcuts as gfnn
import numpy as np
import timeit
import matplotlib.pyplot as plt
import scipy.io as sio
if... | gpl-2.0 |
jereze/scikit-learn | examples/datasets/plot_iris_dataset.py | 283 | 1928 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
The Iris Dataset
=========================================================
This data sets consists of 3 different types of irises'
(Setosa, Versicolour, and Virginica) petal and sepal
length, stored in a 150x4 numpy... | bsd-3-clause |
ashhher3/scikit-learn | sklearn/cluster/tests/test_affinity_propagation.py | 31 | 2633 | """
Testing for Clustering methods
"""
import numpy as np
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.cluster.affinity_propagation_ import AffinityPropagation
from sklearn.cluster.affinity_propagatio... | bsd-3-clause |
earlbellinger/asteroseismology | grid/calibrate.py | 1 | 3590 | #### Calibrate a solar model
#### Author: Earl Bellinger ( bellinger@mps.mpg.de )
#### Stellar Ages & Galactic Evolution Group
#### Max-Planck-Institut fur Sonnensystemforschung
#### Department of Astronomy, Yale University
import numpy as np
import pandas as pd
from scipy import optimize
from os import ... | gpl-2.0 |
shaneknapp/spark | python/pyspark/pandas/indexes/base.py | 2 | 84541 | #
# 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 |
danforthcenter/plantcv | tests/tests.py | 1 | 288502 | #!/usr/bin/env python
import pytest
import os
import shutil
import json
import numpy as np
import cv2
import sys
import pandas as pd
from plotnine import ggplot
from plantcv import plantcv as pcv
import plantcv.learn
import plantcv.parallel
import plantcv.utils
# Import matplotlib and use a null Template to block plot... | mit |
awanke/bokeh | examples/plotting/file/boxplot.py | 43 | 2269 | import numpy as np
import pandas as pd
from bokeh.plotting import figure, show, output_file
# Generate some synthetic time series for six different categories
cats = list("abcdef")
yy = np.random.randn(2000)
g = np.random.choice(cats, 2000)
for i, l in enumerate(cats):
yy[g == l] += i // 2
df = pd.DataFrame(dict(s... | bsd-3-clause |
lin-credible/scikit-learn | sklearn/svm/tests/test_sparse.py | 95 | 12156 | from nose.tools import assert_raises, assert_true, assert_false
import numpy as np
from scipy import sparse
from numpy.testing import (assert_array_almost_equal, assert_array_equal,
assert_equal)
from sklearn import datasets, svm, linear_model, base
from sklearn.datasets import make_classif... | bsd-3-clause |
lhirschfeld/JargonBot | custombot.py | 1 | 3061 | import pickle
import praw
import random
from textblob import TextBlob
from datetime import datetime
from sklearn import linear_model
class RedditBot:
"""A class that performs basic operations, working with Reddit's
PRAW API."""
def __init__(self, botName):
# Setup the bot and primary variables.
... | mit |
RPGOne/scikit-learn | examples/decomposition/plot_pca_vs_fa_model_selection.py | 70 | 4523 | """
===============================================================
Model selection with Probabilistic PCA and Factor Analysis (FA)
===============================================================
Probabilistic PCA and Factor Analysis are probabilistic models.
The consequence is that the likelihood of new data can be u... | bsd-3-clause |
assad2012/ggplot | ggplot/geoms/geom_linerange.py | 12 | 2881 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import sys
from .geom import geom
from ggplot.utils import is_categorical
import numpy as np
class geom_linerange(geom):
"""Plot intervals represented by vertical lines
Parameters
---------
... | bsd-2-clause |
RPGOne/scikit-learn | sklearn/decomposition/tests/test_dict_learning.py | 46 | 9267 | import numpy as np
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils import check_array
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from... | bsd-3-clause |
glemaitre/UnbalancedDataset | examples/over-sampling/plot_smote.py | 2 | 2231 | """
=====
SMOTE
=====
An illustration of the SMOTE method and its variant.
"""
# Authors: Fernando Nogueira
# Christos Aridas
# Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License: MIT
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.decomposition imp... | mit |
rabrahm/ceres | utils/FastRotators/spfr.py | 1 | 18831 | from pylab import *
import pyfits
from PyAstronomy import pyasl
import scipy
from scipy import interpolate
from scipy import ndimage
from scipy import signal
import pickle
from matplotlib.backends.backend_pdf import PdfPages
import os
#from pyevolve import G1DList
#from pyevolve import GSimpleGA
from multiprocessing i... | mit |
xiaoweih/DLV | networks/imageNet.py | 1 | 1666 | import os, struct
from array import array as pyarray
from cvxopt.base import matrix
import numpy as np
import PIL.Image
# FIXME: need actual class names
def LABELS(index):
ls = labels()
if len(ls) > 0:
return ls[index]
else: return range(1000)[index]
def labels():
file = open('networks/ima... | gpl-3.0 |
CranleighAD/isams-tools | settings_example.py | 1 | 2947 | # enable or disable the whole program
ENABLED = True
# if we're in testing mode, output more debug and allow testers to add their own email
DEBUG = True
# used with above, you can check the output of emails that would have been sent
SEND_EMAILS = True
# iSAMS Batch API key
API_KEY = "11D497FF-A7D9-4646-A6B8-D9D1B871... | gpl-3.0 |
aabadie/scikit-learn | examples/manifold/plot_lle_digits.py | 138 | 8594 | """
=============================================================================
Manifold learning on handwritten digits: Locally Linear Embedding, Isomap...
=============================================================================
An illustration of various embeddings on the digits dataset.
The RandomTreesEmbed... | bsd-3-clause |
annashcherbina/FASTQSim | src/msa_from_sam.py | 2 | 15121 | #This file does three things:
#1. calculate the frequency of mutations, insertions, and deletions at each position in an
#NGS read.
#2. find the degree of coverage of the reference genome
#3. find the fraction of each subject read that was aligned to a corresponding sequence
#in the refenece genome.
# @author ... | gpl-3.0 |
ffmmjj/desafio-dados-2016 | data_preparation_pipeline/outliers_separation.py | 1 | 1185 | import luigi
import pandas as pd
from augment_data import AppendFeaturesAggregatedFromTeachersDatasetToSchool
class SplitSchoolOutliersData(luigi.Task):
input_task = AppendFeaturesAggregatedFromTeachersDatasetToSchool()
def requires(self):
return self.input_task
def output(self):
return... | apache-2.0 |
nakul02/systemml | src/main/python/systemml/classloader.py | 4 | 7952 | #-------------------------------------------------------------
#
# 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... | apache-2.0 |
nelango/ViralityAnalysis | model/lib/sklearn/gaussian_process/gaussian_process.py | 78 | 34552 | # -*- coding: utf-8 -*-
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# (mostly translation, see implementation details)
# Licence: BSD 3 clause
from __future__ import print_function
import numpy as np
from scipy import linalg, optimize
from ..base import BaseEstimator, RegressorMixin
from ..metrics... | mit |
daodaoliang/neural-network-animation | matplotlib/tests/test_table.py | 10 | 2083 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.testing.decorators import image_comparison
@image_comparison(baseline_images=['table_zorder'],
extensions=['png... | mit |
phvu/DDF | python/tests/test_ml.py | 3 | 1794 | from __future__ import unicode_literals
import unittest
import pandas as pd
from py4j.java_gateway import Py4JJavaError
import test_base
from ddf import ml
class TestMl(test_base.BaseTest):
"""
Test ML functions
"""
def testKmeans(self):
model = ml.kmeans(self.mtcars, 2, 5, 10)
self... | apache-2.0 |
rbiswas4/SNsims | snsims_previous/snsims/tmp/models.py | 1 | 2804 | #!/usr/bin/env python
import sncosmo.models
import numpy
class SEDFileSource(sncosmo.models.TimeSeriesSource):
"""A TimeSeriesSource stored in a 3-column ASCII file format, for PHASE,
LAMBDA, and F_LAMBDA. The hash symbol # is a comment line.
The spectral flux density of this model is given by
..... | mit |
jereze/scikit-learn | sklearn/utils/fixes.py | 39 | 13318 | """Compatibility fixes for older version of python, numpy and scipy
If you add content to this file, please give the version of the package
at which the fixe is no longer needed.
"""
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# ... | bsd-3-clause |
mattilyra/scikit-learn | benchmarks/bench_isotonic.py | 38 | 3047 | """
Benchmarks of isotonic regression performance.
We generate a synthetic dataset of size 10^n, for n in [min, max], and
examine the time taken to run isotonic regression over the dataset.
The timings are then output to stdout, or visualized on a log-log scale
with matplotlib.
This allows the scaling of the algorit... | bsd-3-clause |
laserson/ibis | docs/sphinxext/ipython_sphinxext/ipython_directive.py | 9 | 37645 | # -*- coding: utf-8 -*-
"""
Sphinx directive to support embedded IPython code.
This directive allows pasting of entire interactive IPython sessions, prompts
and all, and their code will actually get re-executed at doc build time, with
all prompts renumbered sequentially. It also allows you to input code as a pure
pyth... | apache-2.0 |
mjsauvinen/P4UL | pyRaster/tif2NumpyTile.py | 1 | 1956 | #!/usr/bin/env python3
import sys
import argparse
import numpy as np
from mapTools import *
from utilities import filesFromList, writeLog
from plotTools import addImagePlot
import matplotlib.pyplot as plt
'''
Author: Mikko Auvinen
mikko.auvinen@helsinki.fi
University of Helsinki &
Finnish Meteor... | mit |
jwiggins/scikit-image | skimage/future/graph/rag.py | 2 | 14784 | import networkx as nx
import numpy as np
from numpy.lib.stride_tricks import as_strided
from scipy import ndimage as ndi
import math
from ... import draw, measure, segmentation, util, color
try:
from matplotlib import colors
from matplotlib import cm
except ImportError:
pass
def min_weight(graph, src, dst... | bsd-3-clause |
cyliustack/sofa | bin/sofa_analyze.py | 1 | 50661 | import argparse
import matplotlib
matplotlib.use('agg')
import csv
import json
import multiprocessing as mp
import os
import random
import re
import sys
from functools import partial
from operator import attrgetter, itemgetter
import networkx as nx
import numpy as np
import pandas as pd
import time
from sofa_aisi impor... | apache-2.0 |
scls19fr/blaze | blaze/tests/test_interactive.py | 8 | 9834 | from blaze.interactive import Data, compute, concrete_head, expr_repr, to_html
import datetime
from odo import into, append
from odo.backends.csv import CSV
from blaze import discover
from blaze.compute.core import compute
from blaze.compute.python import compute
from blaze.expr import symbol
from datashape import dsh... | bsd-3-clause |
ChanChiChoi/scikit-learn | sklearn/datasets/twenty_newsgroups.py | 72 | 13586 | """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 |
equialgo/scikit-learn | examples/datasets/plot_random_dataset.py | 348 | 2254 | """
==============================================
Plot randomly generated classification dataset
==============================================
Plot several randomly generated 2D classification datasets.
This example illustrates the :func:`datasets.make_classification`
:func:`datasets.make_blobs` and :func:`datasets.... | bsd-3-clause |
joshbohde/scikit-learn | sklearn/svm/tests/test_sparse.py | 2 | 6699 | import numpy as np
import scipy.sparse
from sklearn import datasets, svm, linear_model
from numpy.testing import assert_array_almost_equal, \
assert_array_equal, assert_equal
from nose.tools import assert_raises
from sklearn.datasets.samples_generator import make_classification
from . import test_svm
# test samp... | bsd-3-clause |
Insight-book/data-science-from-scratch | first-edition/code/gradient_descent.py | 53 | 5895 | from __future__ import division
from collections import Counter
from linear_algebra import distance, vector_subtract, scalar_multiply
import math, random
def sum_of_squares(v):
"""computes the sum of squared elements in v"""
return sum(v_i ** 2 for v_i in v)
def difference_quotient(f, x, h):
return (f(x +... | unlicense |
anntzer/scikit-learn | sklearn/utils/_estimator_html_repr.py | 1 | 9497 | from contextlib import closing
from contextlib import suppress
from io import StringIO
from string import Template
import uuid
import html
from sklearn import config_context
class _VisualBlock:
"""HTML Representation of Estimator
Parameters
----------
kind : {'serial', 'parallel', 'single'}
... | bsd-3-clause |
henry0312/LightGBM | python-package/lightgbm/basic.py | 1 | 150238 | # coding: utf-8
"""Wrapper for C API of LightGBM."""
import ctypes
import json
import os
import warnings
from collections import OrderedDict
from copy import deepcopy
from functools import wraps
from logging import Logger
from tempfile import NamedTemporaryFile
from typing import Any, Dict, List, Set, Union
import num... | mit |
Dapid/pywt | demo/dwt_signal_decomposition.py | 1 | 1789 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import numpy as np
import matplotlib.pyplot as plt
import pywt
ecg = np.load(os.path.join('data', 'ecg.npy'))
data1 = np.concatenate((np.arange(1, 400),
np.arange(398, 600),
np.arange(601, 1024)))
x = np.linspa... | mit |
jmmease/pandas | pandas/conftest.py | 7 | 2021 | import pytest
import numpy
import pandas
import pandas.util.testing as tm
def pytest_addoption(parser):
parser.addoption("--skip-slow", action="store_true",
help="skip slow tests")
parser.addoption("--skip-network", action="store_true",
help="skip network tests")
... | bsd-3-clause |
keflavich/APEX_CMZ_H2CO | plot_codes/parameter_comparisons.py | 2 | 20997 | import matplotlib
import paths
matplotlib.rc_file(paths.pcpath('pubfiguresrc'))
import os
import pylab as pl
from astropy import table
from paths import analysispath
import numpy as np
from astropy import coordinates
from astropy import units as u
import heating
pcfittable = table.Table.read(os.path.join(analysispath,... | bsd-3-clause |
ilastikdev/opengm | src/interfaces/python/examples/potts_gui.py | 14 | 1100 | import numpy
import opengm
import vigra
import matplotlib.pyplot as plt
import matplotlib.cm as cm
gradScale = 0.1
energyNotEqual = 0.2
sigma=0.2
resizeFactor=2
img=vigra.impex.readImage('lena.bmp')
shape=img.shape
imgLab=vigra.colors.transform_RGB2Lab(img)
shape=(shape[0]*resizeFactor,shape[1]*resizeFactor)
... | mit |
spbguru/repo1 | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_wxagg.py | 70 | 9051 | from __future__ import division
"""
backend_wxagg.py
A wxPython backend for Agg. This uses the GUI widgets written by
Jeremy O'Donoghue (jeremy@o-donoghue.com) and the Agg backend by John
Hunter (jdhunter@ace.bsd.uchicago.edu)
Copyright (C) 2003-5 Jeremy O'Donoghue, John Hunter, Illinois Institute of
Technolo... | gpl-3.0 |
kinglogxzl/rqalpha | rqalpha/examples/simple_macd.py | 2 | 2164 | # 可以自己import我们平台支持的第三方python模块，比如pandas、numpy等。
import talib
import numpy as np
import math
import pandas
# 在这个方法中编写任何的初始化逻辑。context对象将会在你的算法策略的任何方法之间做传递。
def init(context):
context.s1 = "000001.XSHE"
# 使用MACD需要设置长短均线和macd平均线的参数
context.SHORTPERIOD = 12
context.LONGPERIOD = 26
context.SMOOTHPERIOD... | apache-2.0 |
MartinDelzant/scikit-learn | examples/classification/plot_lda.py | 70 | 2413 | """
====================================================================
Normal and Shrinkage Linear Discriminant Analysis for classification
====================================================================
Shows how shrinkage improves classification.
"""
from __future__ import division
import numpy as np
import... | bsd-3-clause |
jmetzen/scikit-learn | examples/svm/plot_oneclass.py | 80 | 2338 | """
==========================================
One-class SVM with non-linear kernel (RBF)
==========================================
An example using a one-class SVM for novelty detection.
:ref:`One-class SVM <svm_outlier_detection>` is an unsupervised
algorithm that learns a decision function for novelty detection:
... | bsd-3-clause |
pdamodaran/yellowbrick | yellowbrick/text/dispersion.py | 1 | 10916 | # yellowbrick.text.dispersion
# Implementations of lexical dispersions for text visualization.
#
# Author: Larry Gray
# Created: 2018-06-21 10:06
#
# Copyright (C) 2018 District Data Labs
# For license information, see LICENSE.txt
#
# ID: dispersion.py [] lwgray@gmail.com $
"""
Implementation of lexical dispersion ... | apache-2.0 |
robcarver17/systematictradingexamples | plots_for_perhaps/compareoptmethods.py | 1 | 22426 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import plot, show, xticks, xlabel, ylabel, legend, yscale, title, savefig, rcParams, figure, hist, text, bar, subplots
import Image
def file_process(filename):
fig = plt.gcf()
fig.set_size_inches(18.5,10.5)
fig.savefig("/home/rob/%s.... | gpl-2.0 |
cloud-fan/spark | python/pyspark/pandas/internal.py | 1 | 68330 | #
# 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 |
phobson/statsmodels | statsmodels/tools/tests/test_tools.py | 1 | 20793 | """
Test functions for models.tools
"""
from statsmodels.compat.python import lrange, range
import numpy as np
from numpy.random import standard_normal
from numpy.testing import (assert_equal, assert_array_equal,
assert_almost_equal, assert_string_equal, TestCase)
from nose.tools import (asse... | bsd-3-clause |
nhuntwalker/astroML | book_figures/chapter1/fig_moving_objects.py | 3 | 1911 | """
SDSS Moving Object Data
-----------------------
Figure 1.8.
The orbital semimajor axis vs. the orbital inclination angle diagram for the
first 10,000 catalog entries from the SDSS Moving Object Catalog (after
applying several quality cuts). The gaps at approximately 2.5, 2.8, and 3.3 AU
are called the Kirkwood gap... | bsd-2-clause |
waidyanatha/pingsam | visualize.py | 1 | 8668 | import numpy as np
import datetime as dtm
from dateutil import rrule
import pandas as pd
import csv
import matplotlib.pylab as plt
import sys, os
#lets first create the csv file
#
#change this to actual csv file name
pingfile="weeklylogs.csv"
#paramters @plotinterval = 10 minutes
plotinterval = 10
#csv file columns
col... | mit |
pbauman/libmesh | doc/statistics/libmesh_pagehits.py | 1 | 10542 | #!/usr/bin/env python
import matplotlib.pyplot as plt
import numpy as np
# Import stuff for working with dates
from datetime import datetime
from matplotlib.dates import date2num
# Hits/month, pages, and gigabytes served.
# To get the Google analytics data:
# .) Go to analytics.google.com.
# .) There should be (as o... | lgpl-2.1 |
mfjb/scikit-learn | benchmarks/bench_sample_without_replacement.py | 397 | 8008 | """
Benchmarks for sampling without replacement of integer.
"""
from __future__ import division
from __future__ import print_function
import gc
import sys
import optparse
from datetime import datetime
import operator
import matplotlib.pyplot as plt
import numpy as np
import random
from sklearn.externals.six.moves i... | bsd-3-clause |
crichardson17/starburst_atlas | Low_resolution_sims/Dusty_LowRes/Padova_inst/padova_inst_2/fullgrid/Rest.py | 30 | 9192 | import csv
import matplotlib.pyplot as plt
from numpy import *
import scipy.interpolate
import math
from pylab import *
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
import matplotlib.patches as patches
from matplotlib.path import Path
import os
# --------------------------------------------------... | gpl-2.0 |
andrewgiessel/folium | folium/utilities.py | 1 | 19979 | # -*- coding: utf-8 -*-
"""
Utilities
-------
Utility module for Folium helper functions.
"""
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import time
import math
import zlib
import struct
import json
import base64
from jinja2 import Environment, Packa... | mit |
tomlof/scikit-learn | sklearn/decomposition/dict_learning.py | 19 | 46220 | """ Dictionary learning
"""
from __future__ import print_function
# Author: Vlad Niculae, Gael Varoquaux, Alexandre Gramfort
# License: BSD 3 clause
import time
import sys
import itertools
from math import sqrt, ceil
import numpy as np
from scipy import linalg
from numpy.lib.stride_tricks import as_strided
from ..b... | bsd-3-clause |
xiaoxiamii/scikit-learn | examples/applications/plot_model_complexity_influence.py | 323 | 6372 | """
==========================
Model Complexity Influence
==========================
Demonstrate how model complexity influences both prediction accuracy and
computational performance.
The dataset is the Boston Housing dataset (resp. 20 Newsgroups) for
regression (resp. classification).
For each class of models we m... | bsd-3-clause |
costypetrisor/scikit-learn | examples/tree/plot_tree_regression_multioutput.py | 43 | 1791 | """
===================================================================
Multi-output Decision Tree Regression
===================================================================
An example to illustrate multi-output regression with decision tree.
The :ref:`decision trees <tree>`
is used to predict simultaneously the ... | bsd-3-clause |
sjperkins/tensorflow | tensorflow/examples/learn/multiple_gpu.py | 49 | 3078 | # 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 appl... | apache-2.0 |
jkitchin/jasp | jasp/jasp_bandstructure.py | 3 | 3749 | '''Calculate bandstructure diagrams in jasp'''
from jasp import *
import os
import matplotlib.pyplot as plt
from ase.dft import DOS
def get_bandstructure(self,
kpts_path=None,
kpts_nintersections=10):
"""Calculate band structure along :param kpts_path:
:param list... | gpl-2.0 |
haeusser/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 |
CondensedOtters/PHYSIX_Utils | Projects/Moog_2016-2019/CO2/CO2_NN/analysis.py | 1 | 9175 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 14 05:54:11 2020
@author: mathieumoog
"""
import cpmd
import filexyz
import numpy as np
import matplotlib.pyplot as plt
# MSMbuilder ( lacks CK validation )
from msmbuilder.msm import MarkovStateModel
from msmbuilder.msm import BayesianMarkovStateM... | gpl-3.0 |
timestocome/Test-stock-prediction-algorithms | StockMarketLinearRegression/PredictGold.py | 2 | 3318 | # http://github.com/timestocome
# Attempt to predict gold prices and find outliers
# http://web.ipac.caltech.edu/staff/fmasci/home/astro_refs/TestForRandomness_RunsTest.pdf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
####################################################################... | mit |
jseabold/scikit-learn | sklearn/linear_model/tests/test_omp.py | 272 | 7752 | # Author: Vlad Niculae
# Licence: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equa... | bsd-3-clause |
Ziqi-Li/bknqgis | bokeh/bokeh/plotting/helpers.py | 1 | 24268 | from __future__ import absolute_import
from collections import Iterable, OrderedDict, Sequence
import difflib
import itertools
import re
import textwrap
import warnings
import numpy as np
import sys
from six import string_types, reraise
from ..models import (
BoxSelectTool, BoxZoomTool, CategoricalAxis,
TapT... | gpl-2.0 |
FlorisHoogenboom/sklearn-helpers | tests/test_preprocessing.py | 1 | 3095 | import unittest
import numpy as np
import pandas as pd
from sklearn_helpers.preprocessing import \
EnhancedLabelEncoder, MultiColumnLabelEncoder
class EnhancedLabelEncoderTest(unittest.TestCase):
def test_accepts_only_1d(self):
"""It should only accept only a 1d array"""
ehe = EnhancedLabelEnc... | mit |
aerrami/mtools | mtools/test/test_all_import.py | 7 | 1549 | from nose.tools import nottest, make_decorator
from functools import wraps
# tools without any external dependencies
from mtools.mlogfilter.mlogfilter import MLogFilterTool
from mtools.mlogvis.mlogvis import MLogVisTool
from mtools.mloginfo.mloginfo import MLogInfoTool
tools = [MLogFilterTool, MLogVisTool, MLogInfoTo... | apache-2.0 |
hadim/spindle_tracker | spindle_tracker/io/trackmate.py | 1 | 5663 | import itertools
import xml.etree.cElementTree as et
import networkx as nx
import pandas as pd
import numpy as np
def trackmate_peak_import(trackmate_xml_path, get_tracks=False):
"""Import detected peaks with TrackMate Fiji plugin.
Parameters
----------
trackmate_xml_path : str
TrackMate XML... | bsd-3-clause |
nikitasingh981/scikit-learn | examples/linear_model/plot_sparse_recovery.py | 70 | 7486 | """
============================================================
Sparse recovery: feature selection for sparse linear models
============================================================
Given a small number of observations, we want to recover which features
of X are relevant to explain y. For this :ref:`sparse linear ... | bsd-3-clause |
zaxtax/scikit-learn | benchmarks/bench_lasso.py | 297 | 3305 | """
Benchmarks of Lasso vs LassoLars
First, we fix a training set and increase the number of
samples. Then we plot the computation time as function of
the number of samples.
In the second benchmark, we increase the number of dimensions of the
training set. Then we plot the computation time as function of
the number o... | bsd-3-clause |
gskielian/SimpleCV | scripts/install/win/OpenKinect/freenect-examples/demo_mp_async.py | 15 | 1082 | #!/usr/bin/env python
import freenect
import matplotlib.pyplot as mp
import signal
import frame_convert
mp.ion()
image_rgb = None
image_depth = None
keep_running = True
def display_depth(dev, data, timestamp):
global image_depth
data = frame_convert.pretty_depth(data)
mp.gray()
mp.fig... | bsd-3-clause |
YukiKita/fio | tools/hist/fiologparser_hist.py | 3 | 15090 | #!/usr/bin/env python2.7
"""
Utility for converting *_clat_hist* files generated by fio into latency statistics.
Example usage:
$ fiologparser_hist.py *_clat_hist*
end-time, samples, min, avg, median, 90%, 95%, 99%, max
1000, 15, 192, 1678.107, 1788.859, 1856.076, ... | gpl-2.0 |
h2educ/scikit-learn | examples/covariance/plot_outlier_detection.py | 235 | 3891 | """
==========================================
Outlier detection with several methods.
==========================================
When the amount of contamination is known, this example illustrates two
different ways of performing :ref:`outlier_detection`:
- based on a robust estimator of covariance, which is assumin... | bsd-3-clause |
rubikloud/scikit-learn | examples/decomposition/plot_kernel_pca.py | 353 | 2011 | """
==========
Kernel PCA
==========
This example shows that Kernel PCA is able to find a projection of the data
that makes data linearly separable.
"""
print(__doc__)
# Authors: Mathieu Blondel
# Andreas Mueller
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomp... | bsd-3-clause |
deeplook/bokeh | bokeh/charts/builder/line_builder.py | 43 | 5360 | """This is the Bokeh charts interface. It gives you a high level API to build
complex plot is a simple way.
This is the Line class which lets you build your Line charts just
passing the arguments to the Chart class and calling the proper functions.
"""
#-----------------------------------------------------------------... | bsd-3-clause |
molpopgen/pyseq | docs/conf.py | 2 | 9974 | # -*- coding: utf-8 -*-
#
# pylibseq documentation build configuration file, created by
# sphinx-quickstart on Mon Oct 19 19:11:29 2015.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# ... | gpl-2.0 |
sriki18/scipy | scipy/signal/_max_len_seq.py | 41 | 4942 | # Author: Eric Larson
# 2014
"""Tools for MLS generation"""
import numpy as np
from ._max_len_seq_inner import _max_len_seq_inner
__all__ = ['max_len_seq']
# These are definitions of linear shift register taps for use in max_len_seq()
_mls_taps = {2: [1], 3: [2], 4: [3], 5: [3], 6: [5], 7: [6], 8: [7, 6, 1],
... | bsd-3-clause |
OpenSourcePolicyCenter/taxdata | puf_stage1/factors_finalprep.py | 1 | 3867 | """
Transform Stage_I_factors.csv (written by the stage1.py script) and
benefit_growth_rates.csv into growfactors.csv (used by Tax-Calculator).
"""
import numpy as np
import pandas as pd
import os
# pylint: disable=invalid-name
CUR_PATH = os.path.abspath(os.path.dirname(__file__))
first_benefit_year = 2014
inben_file... | mit |
aminert/scikit-learn | sklearn/cross_validation.py | 96 | 58309 | """
The :mod:`sklearn.cross_validation` module includes utilities for cross-
validation and performance evaluation.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>,
# Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
from... | bsd-3-clause |
wazeerzulfikar/scikit-learn | sklearn/linear_model/tests/test_base.py | 33 | 17862 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from scipy import linalg
from itertools import product
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils... | bsd-3-clause |
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