repo_name stringlengths 7 90 | path stringlengths 5 191 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 976 581k | license stringclasses 15
values |
|---|---|---|---|---|---|
tzolov/incubator-zeppelin | spark/interpreter/src/main/resources/python/zeppelin_pyspark.py | 9 | 12880 | #
# 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 |
HBPNeurorobotics/nest-simulator | pynest/examples/intrinsic_currents_subthreshold.py | 4 | 7182 | # -*- coding: utf-8 -*-
#
# intrinsic_currents_subthreshold.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version ... | gpl-2.0 |
mdeff/ntds_2016 | project/reports/fake_news/acquisition.py | 2 | 4872 | import configparser
import dateutil
import datetime
import requests
import pandas as pd
class Feature(object):
"""
A feature represent a Facebook graph API field in addition to a
customized name and a formatting function used to clean the collected data
"""
def __init__(self, fbquery, formatter=... | mit |
lyrixderaven/AdventOfCode | 6.advent.py | 1 | 3295 | from inputs import SIXTH
instructions = SIXTH.instructions
from pandas import DataFrame
class the_grid:
grid = None
def __init__(self):
self.grid = DataFrame(index=range(0,1000),columns=range(0,1000))
self.grid = self.grid.fillna(False)
def parse_inst(self, inst):
if 'turn on'... | gpl-2.0 |
bsipocz/statsmodels | statsmodels/datasets/committee/data.py | 25 | 2583 | """First 100 days of the US House of Representatives 1995"""
__docformat__ = 'restructuredtext'
COPYRIGHT = """Used with express permission from the original author,
who retains all rights."""
TITLE = __doc__
SOURCE = """
Jeff Gill's `Generalized Linear Models: A Unifited Approach`
http://jgill.wustl.ed... | bsd-3-clause |
jhsenjaliya/incubator-airflow | airflow/www/views.py | 1 | 97277 | # -*- 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, software
... | apache-2.0 |
joshloyal/scikit-learn | sklearn/manifold/tests/test_locally_linear.py | 85 | 5600 | from itertools import product
import numpy as np
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.testing import assert_less
from sklearn.... | bsd-3-clause |
kapteyn-astro/kapteyn | doc/source/EXAMPLES/mu_skypolygons_zenith.py | 1 | 5310 | from kapteyn import maputils
from matplotlib import pyplot as plt
import numpy
# This script shows that you can plot shapes that cross the pole.
# A shape is plotted with respect to its center and the border points
# are derived in a way that distance and angle are correct for a sphere.
# This makes it impossible to h... | bsd-3-clause |
alexandrebarachant/mne-python | mne/viz/utils.py | 1 | 58985 | """Utility functions for plotting M/EEG data
"""
from __future__ import print_function
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# ... | bsd-3-clause |
TomAugspurger/pandas | pandas/tests/series/indexing/test_delitem.py | 5 | 1404 | import pytest
from pandas import Index, Series
import pandas._testing as tm
class TestSeriesDelItem:
def test_delitem(self):
# GH#5542
# should delete the item inplace
s = Series(range(5))
del s[0]
expected = Series(range(1, 5), index=range(1, 5))
tm.assert_series... | bsd-3-clause |
KellyChan/Python | python/crawlers/crawler/catalogs/lowes/lowes_catalogs_fullurls.py | 3 | 2037 | # -*- coding: utf-8 -*-
"""
Project: eCatalog - Lowes
- link: http://www.lowes.com/
Author: Kelly Chan
Date: Sept 4 2014
Version: v1.0.0
"""
import os
import sys
reload(sys)
sys.setdefaultencoding( "utf-8" )
import re
import time
import urllib
import urllib2
from bs4 import BeautifulSoup
import pandas
def getH... | mit |
RomainBrault/scikit-learn | examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py | 56 | 4172 | """
=====================================================
Multiclass sparse logisitic regression on newgroups20
=====================================================
Comparison of multinomial logistic L1 vs one-versus-rest L1 logistic regression
to classify documents from the newgroups20 dataset. Multinomial logistic
... | bsd-3-clause |
lbdreyer/cartopy | lib/cartopy/mpl/feature_artist.py | 1 | 4889 | # (C) British Crown Copyright 2011 - 2014, Met Office
#
# This file is part of cartopy.
#
# cartopy is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation, either version 3 of the License, or
# (at your option)... | lgpl-3.0 |
hammerlab/mhcflurry | mhcflurry/predict_scan_command.py | 1 | 11652 | '''
Scan protein sequences using the MHCflurry presentation predictor.
By default, sub-sequences (peptides) with affinity percentile ranks less than
2.0 are returned. You can also specify --results-all to return predictions for
all peptides, or --results-best to return the top peptide for each sequence.
Examples:
Sc... | apache-2.0 |
ClinicalGraphics/scikit-image | skimage/feature/tests/test_util.py | 35 | 2818 | import numpy as np
try:
import matplotlib.pyplot as plt
except ImportError:
plt = None
from numpy.testing import assert_equal, assert_raises
from skimage.feature.util import (FeatureDetector, DescriptorExtractor,
_prepare_grayscale_input_2D,
_mask... | bsd-3-clause |
antworteffekt/EDeN | eden/iterated_semisupervised_feature_selection.py | 1 | 7729 | import random
import logging
import numpy as np
from sklearn.semi_supervised import LabelSpreading
from sklearn.feature_selection import RFECV
from sklearn.linear_model import SGDClassifier
from sklearn.preprocessing import LabelEncoder
logger = logging.getLogger(__name__)
def semisupervised_target(target=None,
... | mit |
altairpearl/scikit-learn | examples/linear_model/plot_multi_task_lasso_support.py | 102 | 2319 | #!/usr/bin/env python
"""
=============================================
Joint feature selection with multi-task Lasso
=============================================
The multi-task lasso allows to fit multiple regression problems
jointly enforcing the selected features to be the same across
tasks. This example simulates... | bsd-3-clause |
jerjorg/BZI | BZI/read_and_write.py | 1 | 149764 | import os
import matplotlib
# matplotlib.use("Agg")
import subprocess
import numpy as np
from numpy.linalg import inv, det
import pandas as pd
import pickle
import itertools
import xarray as xd
from BZI.symmetry import make_rptvecs
#######
####### ======================================================================... | gpl-3.0 |
bavardage/statsmodels | statsmodels/discrete/discrete_margins.py | 3 | 25405 | #Splitting out maringal effects to see if they can be generalized
import numpy as np
from scipy.stats import norm
from statsmodels.tools.decorators import cache_readonly, resettable_cache
#### margeff helper functions ####
#NOTE: todo marginal effects for group 2
# group 2 oprobit, ologit, gologit, mlogit, biprobit
... | bsd-3-clause |
kinect110/RPSOM | src/RPSOM_GUI.py | 1 | 27773 | # -*- coding: utf-8 -*-
import wx
import wx.lib.scrolledpanel as scrolled
from RPSOM import Model
import os
import sys
import gc
try:
from RPSOM.transition_graph import output_graph
import RPSOM.RPSOMforOpenGL
except ImportError:
pass
import pandas
import json
MIN_COLUMNS = 0
MID_COLUMNS = 2
MAX_COLUMNS = 3
cl... | mit |
mhdella/scikit-learn | examples/mixture/plot_gmm_classifier.py | 250 | 3918 | """
==================
GMM classification
==================
Demonstration of Gaussian mixture models for classification.
See :ref:`gmm` for more information on the estimator.
Plots predicted labels on both training and held out test data using a
variety of GMM classifiers on the iris dataset.
Compares GMMs with sp... | bsd-3-clause |
BiaDarkia/scikit-learn | sklearn/preprocessing/tests/test_function_transformer.py | 17 | 5314 | import numpy as np
from scipy import sparse
from sklearn.preprocessing import FunctionTransformer
from sklearn.utils.testing import (assert_equal, assert_array_equal,
assert_allclose_dense_sparse)
from sklearn.utils.testing import assert_warns_message, assert_no_warnings
def _make_... | bsd-3-clause |
silky/sms-tools | lectures/07-Sinusoidal-plus-residual-model/plots-code/stochasticModelFrame.py | 22 | 3298 | import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, hanning, triang, blackmanharris, resample
import math
import sys, os, time
from scipy.fftpack import fft, ifft
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import utilFunction... | agpl-3.0 |
SiLab-Bonn/monopix_daq | monopix_daq/analysis/analyze_hits.py | 1 | 12804 | import os, sys, time
import numpy as np
import tables as tb
import yaml
import logging
COL_SIZE = 36
ROW_SIZE = 129
class AnalyzeHits():
def __init__(self,fhit,fraw):
self.fhit=fhit
self.fraw=fraw
self.res={}
def run(self,n=10000000):
with tb.open_file(self.fhit,"a") as f... | gpl-2.0 |
mayblue9/scikit-learn | sklearn/preprocessing/__init__.py | 268 | 1319 | """
The :mod:`sklearn.preprocessing` module includes scaling, centering,
normalization, binarization and imputation methods.
"""
from ._function_transformer import FunctionTransformer
from .data import Binarizer
from .data import KernelCenterer
from .data import MinMaxScaler
from .data import MaxAbsScaler
from .data ... | bsd-3-clause |
SECOORA/GUTILS | gutils/__init__.py | 1 | 7296 | #!python
# coding=utf-8
from __future__ import division # always return floats when dividing
import os
import math
import errno
import warnings
import subprocess
from collections import namedtuple
import numpy as np
import pandas as pd
from six import StringIO
from scipy.signal import boxcar, convolve
import loggin... | mit |
eryueniaobp/contest | Tianchi_power/power.py | 1 | 8372 | from __future__ import print_function
import torch
import torch.nn as nn
from torch import autograd
from torch.autograd import Variable
import torch.optim as optim
import numpy as np
import datetime,time
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pandas as pd
LAG = 1
INPUT_SIZE = 1
... | apache-2.0 |
rajat1994/scikit-learn | sklearn/manifold/tests/test_isomap.py | 226 | 3941 | from itertools import product
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_almost_equal
from sklearn import datasets
from sklearn import manifold
from sklearn import neighbors
from sklearn import pipeline
from sklearn import preprocessing
from sklearn.utils.testing import assert_less
... | bsd-3-clause |
jeremyclover/airflow | setup.py | 3 | 2598 | from setuptools import setup, find_packages
import sys
# Kept manually in sync with airflow.__version__
version = '1.5.1'
celery = [
'celery>=3.1.17',
'flower>=0.7.3'
]
crypto = ['cryptography>=0.9.3']
doc = [
'sphinx>=1.2.3',
'sphinx-argparse>=0.1.13',
'sphinx-rtd-theme>=0.1.6',
'Sphinx-PyPI... | apache-2.0 |
Jordan-Zhu/EdgeSegmentFitting | findendsjunction.py | 1 | 2081 | # -------------------------------------------------------------------------------
# Name: findendsjunction
# Purpose: Finds junctions and endings in a line/edge image.
# -------------------------------------------------------------------------------
import cv2
import numpy as np
from matplotlib import pyplo... | agpl-3.0 |
RPGroup-PBoC/gist_pboc_2017 | code/inclass/plotting_probabilities_repressor_in_class.py | 1 | 1076 | # Import our favorite modules
import numpy as np
import matplotlib.pyplot as plt
# import seaborn
# Define our parameters
Nns = 5E6 # in units of number of binding sites
R = np.logspace(0, 3, 500)
de_r = -15 # in kT
# Calculate the fold_change
fold_change = 1 / (1 + (R / Nns) * np.exp(-de_r))
plt.figure()
plt.plot(... | mit |
cmorgan/trading-with-python | lib/qtpandas.py | 2 | 6058 | '''
Easy integration of DataFrame into pyqt framework
Copyright: Jev Kuznetsov
Licence: BSD
'''
from PyQt4.QtCore import (QAbstractTableModel,Qt,QVariant,QModelIndex,SIGNAL)
from PyQt4.QtGui import (QApplication,QDialog,QVBoxLayout, QTableView,
QWidget,QTableWidget, QHeaderView, QFo... | bsd-3-clause |
frank-tancf/scikit-learn | sklearn/linear_model/tests/test_sag.py | 33 | 28228 | # Authors: Danny Sullivan <dbsullivan23@gmail.com>
# Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>
#
# Licence: BSD 3 clause
import math
import numpy as np
import scipy.sparse as sp
from sklearn.linear_model.sag import get_auto_step_size
from sklearn.linear_model.sag_fast import _multinomial_grad_loss_all_sa... | bsd-3-clause |
homologus/Pandoras-Toolbox-for-Bioinformatics | src/SPAdes/src/debruijn/path_extend/utils/find_single_threshold.py | 2 | 3327 | import sys
import numpy as np
import matplotlib
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
if (len(sys.argv) != 2):
print ("<all_weights>")
exit(1)
fin = open(sys.argv[1]);
Pattern_good = "good "
lst_good = []
Pattern_bad = "bad "
lst_bad = []
good_size = 1
bad_size = 1
for line in fin:
... | gpl-3.0 |
haojunyu/numpy | numpy/core/tests/test_multiarray.py | 16 | 222642 | from __future__ import division, absolute_import, print_function
import collections
import tempfile
import sys
import shutil
import warnings
import operator
import io
import itertools
import ctypes
if sys.version_info[0] >= 3:
import builtins
else:
import __builtin__ as builtins
from decimal import Decimal
i... | bsd-3-clause |
rasbt/biopandas | biopandas/pdb/engines.py | 1 | 8457 | # BioPandas
# Author: Sebastian Raschka <mail@sebastianraschka.com>
# License: BSD 3 clause
# Project Website: http://rasbt.github.io/biopandas/
# Code Repository: https://github.com/rasbt/biopandas
import pandas as pd
amino3to1dict = {'ASH': 'A',
'ALA': 'A',
'CYX': 'C',
... | bsd-3-clause |
alvarofierroclavero/scikit-learn | sklearn/tests/test_multiclass.py | 72 | 24581 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing ... | bsd-3-clause |
mmottahedi/neuralnilm_prototype | scripts/e236.py | 2 | 4858 | from __future__ import print_function, division
import matplotlib
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
from neuralnilm import Net, RealApplianceSource, BLSTMLayer, DimshuffleLayer
from lasagne.nonlinearities import sigmoid, rectify
from lasagne.objectives import crossentropy, mse... | mit |
fedebarabas/ringfinder | ringfinder/testdata_maker.py | 1 | 6194 | # -*- coding: utf-8 -*-
"""
Created on Sat Nov 26 14:37:26 2016
@author: Federico Barabas
"""
import os
import numpy as np
import matplotlib.pyplot as plt
import tifffile as tiff
import ringfinder.utils as utils
import ringfinder.tools as tools
def loadData(folder, ax, subimgPxSize, technique, mag=None):
"""
... | gpl-3.0 |
zuoshifan/instimager | imager/fourier.py | 1 | 23635 | import abc
import numpy as np
import healpy as hp
import h5py
from cora.util import coord
from caput import config
from caput import mpiutil
import telescope
import cylinder
import exotic_cylinder
import visibility
import rotate as rot
import fouriertransform as ft
import deconv
class FourierTransformTelescope(te... | gpl-2.0 |
patrick-nicholson/spark | python/pyspark/sql/dataframe.py | 7 | 69591 | #
# 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 |
pravsripad/mne-python | mne/preprocessing/ica.py | 3 | 119114 | # -*- coding: utf-8 -*-
#
# Authors: Denis A. Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Juergen Dammers <j.dammers@fz-juelich.de>
#
# License: BSD (3-clause)
from inspect import isfunction
from collections import namedtuple
from copy import deepcopy
from... | bsd-3-clause |
trungnt13/scikit-learn | examples/svm/plot_svm_scale_c.py | 223 | 5375 | """
==============================================
Scaling the regularization parameter for SVCs
==============================================
The following example illustrates the effect of scaling the
regularization parameter when using :ref:`svm` for
:ref:`classification <svm_classification>`.
For SVC classificati... | bsd-3-clause |
trankmichael/scikit-learn | examples/neighbors/plot_species_kde.py | 282 | 4059 | """
================================================
Kernel Density Estimate of Species Distributions
================================================
This shows an example of a neighbors-based query (in particular a kernel
density estimate) on geospatial data, using a Ball Tree built upon the
Haversine distance metric... | bsd-3-clause |
Reagankm/KnockKnock | venv/lib/python3.4/site-packages/mpl_toolkits/axes_grid1/axes_grid.py | 7 | 31905 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import matplotlib.cbook as cbook
import matplotlib.pyplot as plt
import matplotlib.axes as maxes
#import matplotlib.colorbar as mcolorbar
from . import colorbar as mcolorbar
import matplotlib as mp... | gpl-2.0 |
jseabold/scikit-learn | examples/applications/topics_extraction_with_nmf_lda.py | 18 | 3891 | """
=======================================================================================
Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation
=======================================================================================
This is an example of applying Non-negative Matrix ... | bsd-3-clause |
plazas/wfirst-detectors-vnl | code/plot_fig3_beta_gamma_delta_mag20.py | 1 | 25308 | #!/usr/bin/python
import numpy as np
import os
import sys
import math
import matplotlib
matplotlib.use('Pdf')
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.font_manager as fm
## 6-1-15
## Simple code to e... | mit |
BorisJeremic/Real-ESSI-Examples | dynamic_test/HHT/alpha0.00/post.py | 9 | 2369 | #!/usr/bin/python
#Standard python libs
import sys
import os
# import datetime
import numpy as np
import h5py
import matplotlib.pyplot as plt
from math import *
#Libs related to scipy and matplotlib
from scipy import *
from scipy.fftpack import fft
from scipy.fftpack.helper import fftfreq
sys.path.append("./" )
# tim... | cc0-1.0 |
ryfeus/lambda-packs | Tensorflow_LightGBM_Scipy_nightly/source/scipy/integrate/quadrature.py | 20 | 28269 | from __future__ import division, print_function, absolute_import
import numpy as np
import math
import warnings
# trapz is a public function for scipy.integrate,
# even though it's actually a numpy function.
from numpy import trapz
from scipy.special import roots_legendre
from scipy.special import gammaln
from scipy.... | mit |
cbertinato/pandas | pandas/tests/resample/test_timedelta.py | 3 | 4505 | from datetime import timedelta
import numpy as np
import pandas as pd
from pandas import DataFrame, Series
from pandas.core.indexes.timedeltas import timedelta_range
import pandas.util.testing as tm
from pandas.util.testing import assert_frame_equal, assert_series_equal
def test_asfreq_bug():
df = DataFrame(dat... | bsd-3-clause |
IndraVikas/scikit-learn | examples/neighbors/plot_species_kde.py | 282 | 4059 | """
================================================
Kernel Density Estimate of Species Distributions
================================================
This shows an example of a neighbors-based query (in particular a kernel
density estimate) on geospatial data, using a Ball Tree built upon the
Haversine distance metric... | bsd-3-clause |
wildux/CDI | 06_P_ScalarQ(2).py | 2 | 1490 | # -*- coding: utf-8 -*-
from scipy import misc
import numpy as np
import matplotlib.pyplot as plt
"""
Joan Rodas
"""
#np.set_printoptions(threshold=np.inf)
imagen = misc.ascent() #Leo la imagen
(n,m)=imagen.shape #filas y columnas de la imagen
"""
Mostrar la imagen habiendo cuantizado los valores de los píxeles en... | gpl-3.0 |
endolith/scipy | scipy/spatial/_plotutils.py | 12 | 7057 | import numpy as np
from scipy._lib.decorator import decorator as _decorator
__all__ = ['delaunay_plot_2d', 'convex_hull_plot_2d', 'voronoi_plot_2d']
@_decorator
def _held_figure(func, obj, ax=None, **kw):
import matplotlib.pyplot as plt # type: ignore[import]
if ax is None:
fig = plt.figure()
... | bsd-3-clause |
seg/2016-ml-contest | ar4/ar4_submission001.py | 2 | 2318 | # Alan Richardson (Ausar Geophysical)
# 2017/01/09
# Simple first attempt using Ridge regression to predict missing PE values, and SVC for the facies
import numpy as np
import pandas as pd
from sklearn import preprocessing, cross_validation, grid_search, linear_model, svm, metrics
# Load + preprocessing
train_data =... | apache-2.0 |
sniemi/SamPy | sandbox/src1/examples/font_table_ttf.py | 1 | 1524 | #!/usr/bin/env python
"""
matplotlib has support for freetype fonts. Here's a little example
using the 'table' command to build a font table that shows the glyphs
by character code.
Usage python font_table_ttf.py somefile.ttf
"""
import sys, os
from matplotlib.ft2font import FT2Font
from pylab import figure, table, s... | bsd-2-clause |
djgagne/scikit-learn | sklearn/utils/multiclass.py | 83 | 12343 |
# Author: Arnaud Joly, Joel Nothman, Hamzeh Alsalhi
#
# License: BSD 3 clause
"""
Multi-class / multi-label utility function
==========================================
"""
from __future__ import division
from collections import Sequence
from itertools import chain
from scipy.sparse import issparse
from scipy.sparse.... | bsd-3-clause |
microelly2/reconstruction | reconstruction/CV_opening.py | 1 | 7039 | # -*- coding: utf-8 -*-
#-------------------------------------------------
#-- reconstruction workbench
#--
#-- microelly 2016 v 0.1
#--
#-- GNU Lesser General Public License (LGPL)
#-------------------------------------------------
__vers__="13.03.2016 0.0"
__dir__='/home/thomas/.FreeCAD/Mod/reconstruction'
import... | lgpl-3.0 |
voxlol/scikit-learn | examples/model_selection/plot_underfitting_overfitting.py | 230 | 2649 | """
============================
Underfitting vs. Overfitting
============================
This example demonstrates the problems of underfitting and overfitting and
how we can use linear regression with polynomial features to approximate
nonlinear functions. The plot shows the function that we want to approximate,
wh... | bsd-3-clause |
cython-testbed/pandas | pandas/tests/frame/test_indexing.py | 3 | 130466 | # -*- coding: utf-8 -*-
from __future__ import print_function
from warnings import catch_warnings, simplefilter
from datetime import datetime, date, timedelta, time
from pandas.compat import map, zip, range, lrange, lzip, long
from pandas import compat
from numpy import nan
from numpy.random import randn
import py... | bsd-3-clause |
LemonATsu/LemonATsu.github.io | markdown_generator/talks.py | 199 | 4000 |
# coding: utf-8
# # Talks markdown generator for academicpages
#
# Takes a TSV of talks with metadata and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook ([see more info here](http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_i... | mit |
saiwing-yeung/scikit-learn | examples/linear_model/plot_ridge_coeffs.py | 157 | 2785 | """
==============================================================
Plot Ridge coefficients as a function of the L2 regularization
==============================================================
.. currentmodule:: sklearn.linear_model
:class:`Ridge` Regression is the estimator used in this example.
Each color in the le... | bsd-3-clause |
JosmanPS/scikit-learn | examples/model_selection/grid_search_text_feature_extraction.py | 253 | 4158 | """
==========================================================
Sample pipeline for text feature extraction and evaluation
==========================================================
The dataset used in this example is the 20 newsgroups dataset which will be
automatically downloaded and then cached and reused for the do... | bsd-3-clause |
samzhang111/scikit-learn | sklearn/metrics/tests/test_score_objects.py | 138 | 14048 | import pickle
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raises_regexp
from sklearn.utils.testing import assert_true
from sklearn.utils.testing im... | bsd-3-clause |
allenwoods/parasys | SumoEnv/simulation.py | 1 | 11039 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
@file environ.py
@author Allen Woods
@date 2016-07-29
@version 16-7-29 下午2:51 ???
SUMO simulation environment
"""
import os
import sys
import time
import socket
from subprocess import Popen, PIPE
import pandas as pd
import numpy as np
from time impor... | mit |
frank-tancf/scikit-learn | examples/model_selection/plot_roc_crossval.py | 37 | 3474 | """
=============================================================
Receiver Operating Characteristic (ROC) with cross validation
=============================================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality using cross-validation.
ROC curv... | bsd-3-clause |
vortex-ape/scikit-learn | examples/manifold/plot_mds.py | 18 | 2730 | """
=========================
Multi-dimensional scaling
=========================
An illustration of the metric and non-metric MDS on generated noisy data.
The reconstructed points using the metric MDS and non metric MDS are slightly
shifted to avoid overlapping.
"""
# Author: Nelle Varoquaux <nelle.varoquaux@gmail.... | bsd-3-clause |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_fdr_stats_evoked.py | 24 | 2752 | """
=======================================
FDR correction on T-test on sensor data
=======================================
One tests if the evoked response significantly deviates from 0.
Multiple comparison problem is addressed with
False Discovery Rate (FDR) correction.
"""
# Authors: Alexandre Gramfort <alexandre.... | bsd-3-clause |
Jaspereclipse/kaggle-transfer-learning-on-stack-exchange-tags | src/data_factory.py | 1 | 8857 | #!/usr/bin/python
# -*- coding: utf-8 -*-
import pandas as pd
import os.path as osp
import numpy as np
import re
from bs4 import BeautifulSoup as bs
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk import FreqDist
import cPickle as pkl
def combine(train_dict, test_dict):
"""Combine a list of csv fi... | apache-2.0 |
gbrammer/sgas-lens | sgas/reprocess_wfc3.py | 1 | 23705 | """
Scripts to reprocess WFC3 exposures with time-variable backgrounds
or satellite trails.
"""
import os
import glob
import shutil
import numpy as np
import numpy.ma
import matplotlib.pyplot as plt
try:
import astropy.io.fits as pyfits
except:
import pyfits
import logging
logger = logging.getLogger('re... | mit |
pyql/PyQL | query.py | 1 | 19203 | from __future__ import print_function
import pandas as pd
import ply
import lexer
import yaccer
import aggregators
import itertools
import regex
import collections
import unittest
# modules for use inside of the query loop
import math
import re
import random
import string
class CacheDict(collections.OrderedDict):
... | gpl-3.0 |
stefanopalmieri/CAMPN | breeder.py | 1 | 4412 | import matplotlib
matplotlib.use('TkAgg')
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import matplotlib.pyplot as plt
import Tkinter as Tk
import networkx as nx
import copy
import numpy as np
from genome import Genome
# This Source Code Form is subject to the terms of the Mozilla Public
# License... | mpl-2.0 |
Saxafras/Spacetime | tests/test_CAs.py | 1 | 7131 | from __future__ import division
from unittest import TestCase
import numpy as np
from matplotlib import pyplot as plt
from itertools import product
from spacetime.CA_Simulators.CAs import *
from spacetime.Local_Measures.Transducers import *
class CATestCase(TestCase):
'''
Tests for CAs.py
'''
def tes... | bsd-3-clause |
tomolaf/trading-with-python | cookbook/workingWithDatesAndTime.py | 77 | 1551 | # -*- coding: utf-8 -*-
"""
Created on Sun Oct 16 17:45:02 2011
@author: jev
"""
import time
import datetime as dt
from pandas import *
from pandas.core import datetools
# basic functions
print 'Epoch start: %s' % time.asctime(time.gmtime(0))
print 'Seconds from epoch: %.2f' % time.time()
t... | bsd-3-clause |
ky822/scikit-learn | examples/svm/plot_svm_kernels.py | 329 | 1971 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
SVM-Kernels
=========================================================
Three different types of SVM-Kernels are displayed below.
The polynomial and RBF are especially useful when the
data-points are not linearly sep... | bsd-3-clause |
iTech-/zds-defis | automated_text_generation/charabia/charabia.py | 2 | 3181 | from sys import argv
import operator
import random
import numpy as np
import matplotlib.pyplot as plt
lang_dict = {}
lang_str = ["fr", "de", "es", "pt", "eo", "it", "tr", "sv", "pl", "da", "is",
"fi", "cs"]
nb_lang = len(lang_str)
def generate_char():
"""
Generate a random character from a dist... | mit |
sanuj/shogun | examples/undocumented/python_modular/graphical/multiclass_qda.py | 26 | 3294 | """
Shogun demo
Fernando J. Iglesias Garcia
"""
import numpy as np
import matplotlib as mpl
import pylab
import util
from scipy import linalg
from modshogun import QDA
from modshogun import RealFeatures, MulticlassLabels
# colormap
cmap = mpl.colors.LinearSegmentedColormap('color_classes',
{'red': [(0, 1, 1),
... | gpl-3.0 |
aabadie/scikit-learn | examples/tree/plot_tree_regression.py | 95 | 1516 | """
===================================================================
Decision Tree Regression
===================================================================
A 1D regression with decision tree.
The :ref:`decision trees <tree>` is
used to fit a sine curve with addition noisy observation. As a result, it
learns ... | bsd-3-clause |
spookylukey/pandas-highcharts | pandas_highcharts/tests.py | 1 | 4925 | # -*- coding: utf-8 -*-
from __future__ import absolute_import
import datetime
import json
import pandas
from unittest import TestCase
from .core import serialize, json_encode
df = pandas.DataFrame([
{'a': 1, 'b': 2, 'c': 3, 't': datetime.datetime(2015, 1, 1), 's': 's1'},
{'a': 2, 'b': 4, 'c': 6, 't': datet... | mit |
WangWenjun559/Weiss | summary/sumy/sklearn/tests/test_calibration.py | 213 | 12219 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from sklearn.utils.testing import (assert_array_almost_equal, assert_equal,
assert_greater, assert_almost_equal,
... | apache-2.0 |
arahuja/scikit-learn | sklearn/feature_extraction/text.py | 4 | 49485 | # -*- coding: utf-8 -*-
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Robert Layton <robertlayton@gmail.com>
# Jochen Wersdörfer <jochen@wersdoerfer.de>
# Roman Sinayev <roman.sinayev@gma... | bsd-3-clause |
valexandersaulys/prudential_insurance_kaggle | venv/lib/python2.7/site-packages/pandas/io/tests/test_json_norm.py | 15 | 7823 | import nose
from pandas import DataFrame
import numpy as np
import pandas.util.testing as tm
from pandas.io.json import json_normalize, nested_to_record
def _assert_equal_data(left, right):
if not left.columns.equals(right.columns):
left = left.reindex(columns=right.columns)
tm.assert_frame_equal(l... | gpl-2.0 |
tum-camp/survival-support-vector-machine | survival/io/arffwrite.py | 1 | 4483 | # This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU 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,
# bu... | gpl-3.0 |
alexeyum/scikit-learn | examples/bicluster/bicluster_newsgroups.py | 142 | 7183 | """
================================================================
Biclustering documents with the Spectral Co-clustering algorithm
================================================================
This example demonstrates the Spectral Co-clustering algorithm on the
twenty newsgroups dataset. The 'comp.os.ms-windows... | bsd-3-clause |
ahardin015/programingworkshop | Python/pandas_and_parallel/single_meso_surface.py | 8 | 2232 | import pandas as pd
import os
import matplotlib.pyplot as plt
import datetime as dt
import numpy as np
from scipy import interpolate
from mpl_toolkits.basemap import Basemap, cm
import mesonet_calculations
''' Quick code for using the existing scripts to create a single
surface plot from mesonet data
'''
# Note tha... | mit |
Aasmi/scikit-learn | examples/plot_kernel_ridge_regression.py | 230 | 6222 | """
=============================================
Comparison of kernel ridge regression and SVR
=============================================
Both kernel ridge regression (KRR) and SVR learn a non-linear function by
employing the kernel trick, i.e., they learn a linear function in the space
induced by the respective k... | bsd-3-clause |
kjung/scikit-learn | sklearn/manifold/tests/test_t_sne.py | 8 | 21789 | import sys
from sklearn.externals.six.moves import cStringIO as StringIO
import numpy as np
import scipy.sparse as sp
from sklearn.neighbors import BallTree
from sklearn.utils.testing import assert_less_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from skle... | bsd-3-clause |
linearregression/airflow | setup.py | 1 | 2046 | from setuptools import setup, find_packages
# Kept manually in sync with airflow.__version__
version = '1.3.0'
doc = [
'sphinx>=1.2.3',
'sphinx-argparse>=0.1.13',
'sphinx-rtd-theme>=0.1.6',
'Sphinx-PyPI-upload>=0.2.1'
]
hive = [
'hive-thrift-py>=0.0.1',
'pyhive>=0.1.3',
'pyhs2>=0.6.0',
]
m... | apache-2.0 |
janmtl/pypsych | pypsych/data_sources/hrvstitcher.py | 1 | 10502 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Includes the HRV stitcher data source class
"""
import pandas as pd
import numpy as np
from scipy.io import loadmat
from scipy.interpolate import UnivariateSpline
from data_source import DataSource
from schema import Schema, Or, Optional
def _val(x, pos, label_bin):
... | bsd-3-clause |
WillArmentrout/galSims | plotting/PlotGalacticPlaneWiseSim.py | 1 | 1831 | import pylab as p
import math
from matplotlib.pyplot import Rectangle # Used to make dummy legend
scol = str('#FF0000') # Sets color of simulated regions
wcol = str('#5FB404') # Sets color of WISE regions
# Open CSV File
datafile = open('3DHiiRegions.csv', 'r')
csvFile = []
for row in datafile:
csvFile.append(row... | gpl-2.0 |
jackey-qiu/genx_pc_qiu | supportive_functions/backcor/background.py | 1 | 13401 | """
Methods to handle generic background determination
Authors/Modifications:
----------------------
* Tom Trainor (tptrainor@alaska.edu)
* The polynomial background model follows closley
the XRF background code by Mark Rivers
"""
#######################################################################
import ty... | gpl-3.0 |
pandas-ml/pandas-ml | pandas_ml/skaccessors/test/test_neural_network.py | 2 | 1115 | #!/usr/bin/env python
import pytest
import sklearn.datasets as datasets
import sklearn.neural_network as nn
import pandas_ml as pdml
import pandas_ml.util.testing as tm
class TestNeuralNtwork(tm.TestCase):
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.neur... | bsd-3-clause |
mbarylsk/goldbach-partition | goldbach-primes_gap.py | 1 | 9752 | #
# Copyright (c) 2017, Marcin Barylski
# All rights reserved.
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions... | gpl-3.0 |
EricSB/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/__init__.py | 69 | 28184 | """
This is an object-orient plotting library.
A procedural interface is provided by the companion pylab module,
which may be imported directly, e.g::
from pylab import *
or using ipython::
ipython -pylab
For the most part, direct use of the object-oriented library is
encouraged when programming rather tha... | agpl-3.0 |
marionleborgne/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_tkagg.py | 69 | 24593 | # Todd Miller jmiller@stsci.edu
from __future__ import division
import os, sys, math
import Tkinter as Tk, FileDialog
import tkagg # Paint image to Tk photo blitter extension
from backend_agg import FigureCanvasAgg
import os.path
import matplotlib
from matplotlib.cbook import is_string_like
from ... | agpl-3.0 |
sarahgrogan/scikit-learn | sklearn/metrics/__init__.py | 214 | 3440 | """
The :mod:`sklearn.metrics` module includes score functions, performance metrics
and pairwise metrics and distance computations.
"""
from .ranking import auc
from .ranking import average_precision_score
from .ranking import coverage_error
from .ranking import label_ranking_average_precision_score
from .ranking imp... | bsd-3-clause |
ElDeveloper/scikit-learn | sklearn/tests/test_calibration.py | 62 | 12288 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from sklearn.utils.testing import (assert_array_almost_equal, assert_equal,
assert_greater, assert_almost_equal,
... | bsd-3-clause |
canavandl/bokeh | bokeh/charts/builder/donut_builder.py | 31 | 8206 | """This is the Bokeh charts interface. It gives you a high level API to build
complex plot is a simple way.
This is the Donut class which lets you build your Donut charts just passing
the arguments to the Chart class and calling the proper functions.
It also add a new chained stacked method.
"""
#---------------------... | bsd-3-clause |
Horta/limix | limix/io/hdf5.py | 1 | 6093 | from __future__ import unicode_literals as _
# TODO: refactor this entire file. There are too many things here
def read_limix(filepath):
r"""Read the HDF5 limix file format.
Parameters
----------
filepath : str
File path.
Returns
-------
dict
Phenotype and genotype.
... | apache-2.0 |
LtGlahn/parsedau | parsedau.py | 1 | 10415 | # -*- coding: utf-8 -*-
"""
Created on Tue Dec 27 08:33:26 2016
@author: jajens
"""
import copy
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
class daufil():
"""Class for holding info from a single DAU file"""
def __init__(self, filename):
self.rawdata = se... | mit |
PrashntS/scikit-learn | doc/tutorial/text_analytics/skeletons/exercise_01_language_train_model.py | 254 | 2005 | """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 |
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