repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
morrellk/openelections-data-or | src/progress.py | 1 | 3077 | #!/usr/local/bin/python3
# -*- coding: utf-8 -*-
# The MIT License (MIT)
# Copyright (c) 2017 Nick Kocharhook
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including... | mit |
indhub/mxnet | example/named_entity_recognition/src/preprocess.py | 10 | 2002 | # !/usr/bin/env python
# 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
# "... | apache-2.0 |
clemkoa/scikit-learn | benchmarks/bench_mnist.py | 45 | 6977 | """
=======================
MNIST dataset benchmark
=======================
Benchmark on the MNIST dataset. The dataset comprises 70,000 samples
and 784 features. Here, we consider the task of predicting
10 classes - digits from 0 to 9 from their raw images. By contrast to the
covertype dataset, the feature space is... | bsd-3-clause |
marcusmueller/gnuradio | gnuradio-runtime/examples/volk_benchmark/volk_plot.py | 6 | 6198 | #!/usr/bin/env python
from __future__ import division
from __future__ import unicode_literals
import sys, math
import argparse
from volk_test_funcs import *
try:
import matplotlib
import matplotlib.pyplot as plt
except ImportError:
sys.stderr.write("Could not import Matplotlib (http://matplotlib.sourcefor... | gpl-3.0 |
krez13/scikit-learn | examples/plot_johnson_lindenstrauss_bound.py | 127 | 7477 | r"""
=====================================================================
The Johnson-Lindenstrauss bound for embedding with random projections
=====================================================================
The `Johnson-Lindenstrauss lemma`_ states that any high dimensional
dataset can be randomly projected i... | bsd-3-clause |
lazywei/scikit-learn | examples/gaussian_process/gp_diabetes_dataset.py | 223 | 1976 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
========================================================================
Gaussian Processes regression: goodness-of-fit on the 'diabetes' dataset
========================================================================
In this example, we fit a Gaussian Process model onto... | bsd-3-clause |
466152112/scikit-learn | examples/cluster/plot_kmeans_digits.py | 230 | 4524 | """
===========================================================
A demo of K-Means clustering on the handwritten digits data
===========================================================
In this example we compare the various initialization strategies for
K-means in terms of runtime and quality of the results.
As the gr... | bsd-3-clause |
NYU-CAL/Disco | Python/floopAnalysis.py | 1 | 1101 | import sys
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import discopy.util as util
import discopy.geom as geom
def analyzeSingle(filename):
opts = util.loadOpts(filename)
pars = util.loadPars(filename)
print("Loading " + filename)
t, x1, x2, x3, prim, dat = util.loadChe... | gpl-3.0 |
yavalvas/yav_com | build/matplotlib/lib/matplotlib/artist.py | 11 | 41588 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import re
import warnings
import inspect
import matplotlib
import matplotlib.cbook as cbook
from matplotlib import docstring, rcParams
from .transforms import Bbox, IdentityTransform, TransformedBbo... | mit |
dkainer/pyms | Display/Class.py | 7 | 10046 | """
Class to Display Ion Chromatograms and TIC
"""
#############################################################################
# #
# PyMS software for processing of metabolomic mass-spectrometry data #
# Copyright (C) 2005-2012 Vladi... | gpl-2.0 |
cogstat/cogstat | cogstat/cogstat_dialogs.py | 1 | 35802 | # -*- coding: utf-8 -*-
import gettext
import os
from PyQt5 import QtWidgets, QtCore, QtGui
from . import cogstat_config as csc
QString = str
t = gettext.translation('cogstat', os.path.dirname(os.path.abspath(__file__))+'/locale/', [csc.language], fallback=True)
_ = t.gettext
# Overwrite the qt _translate function... | gpl-3.0 |
precedenceguo/mxnet | example/reinforcement-learning/ddpg/strategies.py | 42 | 2473 | # 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 u... | apache-2.0 |
zheminzhou/GrapeTree | simulations/plot_sens_pre.py | 2 | 1747 | import numpy as np
from numpy import median
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
import pandas as pd
import collections
import sys
sum_file = sys.argv[1]
df = pd.read_csv(sum_file,header=None,sep='\t')
df = df[df[4] <= 0.007]
#del df[4]
#colors =... | gpl-3.0 |
jseabold/statsmodels | examples/python/quantile_regression.py | 5 | 4049 | # coding: utf-8
# DO NOT EDIT
# Autogenerated from the notebook quantile_regression.ipynb.
# Edit the notebook and then sync the output with this file.
#
# flake8: noqa
# DO NOT EDIT
# # Quantile regression
#
# This example page shows how to use ``statsmodels``' ``QuantReg`` class
# to replicate parts of the analysi... | bsd-3-clause |
gticket/scikit-neuralnetwork | docs/conf.py | 5 | 1814 | # -*- coding: utf-8 -*-
#
# scikit-neuralnetwork documentation build configuration file, created by
# sphinx-quickstart on Tue Mar 31 20:28:10 2015.
import sys
import os
project = u'scikit-neuralnetwork'
copyright = u'2015, scikit-neuralnetwork developers (BSD License)'
# -- Configuration of documentation --------... | bsd-3-clause |
apdjustino/urbansim | urbansim/models/transition.py | 4 | 17258 | """
Use the ``TransitionModel`` class with the different transitioners to
add or remove agents based on growth rates or target totals.
"""
from __future__ import division
import logging
import numpy as np
import pandas as pd
from . import util
from ..utils.logutil import log_start_finish
from ..utils.sampling impor... | bsd-3-clause |
BinRoot/TensorFlow-Book | ch09_cnn/conv_visuals.py | 1 | 1920 | import numpy as np
import matplotlib.pyplot as plt
import cifar_tools
import tensorflow as tf
names, data, labels = \
cifar_tools.read_data('/home/binroot/res/cifar-10-batches-py')
def show_conv_results(data, filename=None):
plt.figure()
rows, cols = 4, 8
for i in range(np.shape(data)[3]):
im... | mit |
areeda/gwpy | examples/frequencyseries/transfer_function.py | 3 | 2451 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (C) Duncan Macleod (2014-2020)
#
# This file is part of GWpy.
#
# GWpy 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,... | gpl-3.0 |
chase-qi/workload-automation | wlauto/instrumentation/energy_model/__init__.py | 2 | 42026 | # Copyright 2015 ARM Limited
#
# 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 writin... | apache-2.0 |
garibaldu/radioblobs | other_scores/kl3.py | 1 | 2900 | import sys
import numpy as np, numpy.random as rng
import pylab as pl, matplotlib.cm as cm
def do_exit():
print ('usage: python %s [image_size num_sources noise_size output_image_name]' % (sys.argv[0]))
sys.exit('eg: python %s 100 3 2.0 mytestimage' % (sys.argv[0]))
def calc_score_everywhere(model_sigma)... | gpl-2.0 |
EducationalTestingService/rsmtool | tests/test_utils_prmse.py | 1 | 11698 | import os
import warnings
from pathlib import Path
import numpy as np
import pandas as pd
from nose.tools import assert_almost_equal, eq_, ok_, raises
from numpy.testing import assert_array_equal
from pandas.testing import assert_frame_equal
from rsmtool.utils.prmse import (get_n_human_scores,
... | apache-2.0 |
sunzhxjs/JobGIS | lib/python2.7/site-packages/pandas/io/tests/test_json/test_ujson.py | 9 | 54415 | # -*- coding: utf-8 -*-
from unittest import TestCase
try:
import json
except ImportError:
import simplejson as json
import math
import nose
import platform
import sys
import time
import datetime
import calendar
import re
import decimal
from functools import partial
from pandas.compat import range, zip, Strin... | mit |
gautam1858/tensorflow | tensorflow/tools/dist_test/python/census_widendeep.py | 48 | 11896 | # 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 |
Geertex/midioke | midiokeDOTexe.py | 1 | 1777 | # -*- coding: utf-8 -*-
"""
Created on a Sunday
@author: G-man
"""
import pyaudio
import wave
import struct
import math
import matplotlib.pyplot as plt
import numpy as np
from midiutil import MIDIFile
print("It is Recording.....")
print("If you want to stop just press 'Ctrl + C' ")
CHUNK = 4096
FORMAT = pyaudio.paIn... | gpl-3.0 |
mgarbanzo/radarphysics | simplecapons.py | 1 | 2107 | #!/usr/bin/python
import numpy as np
from scipy import fftpack, pi
import matplotlib.pyplot as plt
#Frequencies to be used in the signal
freqs = 0.09, -0.2, 0.2, -0.3, 0.3, 0.08, -0.21, 0.22, -0.31, 0.32, 0.093, -0.24, 0.25, -0.34, 0.35, 0.098
time = np.arange(0,64,1)
sgn = np.zeros_like(time)+np.zeros_like(time,com... | gpl-3.0 |
yan9yu/NWD | src/newwords.py | 1 | 6475 | #!/usr/bin/python
# -*- coding: utf-8 -*-
from __future__ import division
import re
import os
import math
import config
import pandas as pd
from collections import Counter
from collections import defaultdict
__author__ = 'yan9yu'
class NewWordsDetector:
def __init__(self, content):
self.content = content... | mit |
rougier/Neurosciences | superior-colliculus/taouali-et-at-2014/fig-accuracy.py | 1 | 3999 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright INRIA
# Contributors: Wahiba Taouali (Wahiba.Taouali@inria.fr)
# Nicolas P. Rougier (Nicolas.Rougier@inria.fr)
#
# This software is governed by the CeCILL license under... | bsd-3-clause |
ryfeus/lambda-packs | Keras_tensorflow_nightly/source2.7/tensorflow/contrib/learn/python/learn/learn_io/__init__.py | 42 | 2656 | # 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... | mit |
nelango/ViralityAnalysis | model/lib/pandas/core/strings.py | 9 | 50351 | import numpy as np
from pandas.compat import zip
from pandas.core.common import (isnull, _values_from_object, is_bool_dtype, is_list_like,
is_categorical_dtype, is_object_dtype, take_1d)
import pandas.compat as compat
from pandas.core.base import AccessorProperty, NoNewAttributesMixin
f... | mit |
wright-group/WrightTools | tests/artists/test_pcolor.py | 1 | 1139 | #! /usr/bin/env python3
import WrightTools as wt
from WrightTools import datasets
from matplotlib import pyplot as plt
import shutil
import os
def test_pcolor():
p = datasets.wt5.v1p0p1_MoS2_TrEE_movie
p = shutil.copy(p, "./test_pcolor.wt5")
data = wt.open(p)
os.unlink(p)
data.level(0, 2, -3)
... | mit |
lyuboshen/Pose-Estimation-on-Depth-Images-of-Clinical-Patients-V2.0 | src/test.py | 1 | 6673 | import cv2
import os
import numpy as np
import copy
from openpyxl import Workbook
from openpyxl import load_workbook
import xlrd
import xlwt
import json
image_rows = 424
image_cols = 512
data_path = 'images/trial_2/p3+5/middle/'
image_prefix = 'p3s1d_'
image_path = 'images/'
label_path = 'annotations/'
# depth_image... | mit |
robbymeals/scikit-learn | sklearn/neighbors/tests/test_kd_tree.py | 129 | 7848 | import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.neighbors.kd_tree import (KDTree, NeighborsHeap,
simultaneous_sort, kernel_norm,
nodeheap_sort, DTYPE, ITYPE)
from sklearn.neighbors.dist_metrics import Dista... | bsd-3-clause |
yl565/statsmodels | statsmodels/examples/ex_generic_mle.py | 32 | 16462 |
from __future__ import print_function
import numpy as np
from scipy import stats
import statsmodels.api as sm
from statsmodels.base.model import GenericLikelihoodModel
data = sm.datasets.spector.load()
data.exog = sm.add_constant(data.exog, prepend=False)
# in this dir
probit_mod = sm.Probit(data.endog, data.exog)
... | bsd-3-clause |
Canpio/Paddle | benchmark/paddle/image/plotlog.py | 7 | 3298 | # Copyright (c) 2016 PaddlePaddle 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 applic... | apache-2.0 |
bundgus/python-playground | matplotlib-playground/examples/event_handling/data_browser.py | 3 | 2345 | import numpy as np
class PointBrowser(object):
"""
Click on a point to select and highlight it -- the data that
generated the point will be shown in the lower axes. Use the 'n'
and 'p' keys to browse through the next and previous points
"""
def __init__(self):
self.lastind = 0
... | mit |
nwillemse/misc-scripts | ib-downloader/ib-downloader2.py | 1 | 8772 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
ib-downloader.py
Created on Tue Jul 5 15:53:45 2016
@author: nwillemse
"""
import click
import time
import pandas as pd
from datetime import datetime
from ib.ext.Contract import Contract
from ib.opt import Connection
class Downloader:
def __init__(
s... | mit |
huzq/scikit-learn | examples/linear_model/plot_sgd_penalties.py | 23 | 1405 | """
==============
SGD: Penalties
==============
Contours of where the penalty is equal to 1
for the three penalties L1, L2 and elastic-net.
All of the above are supported by :class:`~sklearn.linear_model.SGDClassifier`
and :class:`~sklearn.linear_model.SGDRegressor`.
"""
print(__doc__)
import numpy as np
import ma... | bsd-3-clause |
cl4rke/scikit-learn | examples/model_selection/grid_search_digits.py | 227 | 2665 | """
============================================================
Parameter estimation using grid search with cross-validation
============================================================
This examples shows how a classifier is optimized by cross-validation,
which is done using the :class:`sklearn.grid_search.GridSearc... | bsd-3-clause |
intel-analytics/analytics-zoo | pyzoo/zoo/chronos/model/tcmf/local_model.py | 1 | 24645 | # Copyright 2018 Analytics Zoo Authors.
#
# 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 i... | apache-2.0 |
tawsifkhan/scikit-learn | examples/cluster/plot_adjusted_for_chance_measures.py | 286 | 4353 | """
==========================================================
Adjustment for chance in clustering performance evaluation
==========================================================
The following plots demonstrate the impact of the number of clusters and
number of samples on various clustering performance evaluation me... | bsd-3-clause |
indhub/mxnet | docs/mxdoc.py | 2 | 13330 | # 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 u... | apache-2.0 |
bo-yang/stock_analysis | symbol.py | 1 | 53489 | from stock_analysis.utils import *
# conda install -c conda-forge selenium=3.0.1
from selenium import webdriver
def parse_google_financial_table(tables, keyword=None):
"""
Parse Google Financial table into DataFrame.
tables - selenium.webdriver.remote.webelement.WebElement
"""
tbl = None
for t... | mit |
ual/urbansim | urbansim/models/tests/test_dcm.py | 6 | 22108 | import numpy as np
import numpy.testing as npt
import pandas as pd
import pytest
import os
import tempfile
import yaml
from pandas.util import testing as pdt
from ...utils import testing
from .. import dcm
@pytest.fixture
def seed(request):
current = np.random.get_state()
def fin():
np.random.set_s... | bsd-3-clause |
OpenSourcePolicyCenter/dynamic | ogusa/get_micro_data.py | 1 | 10394 | '''
------------------------------------------------------------------------
This program extracts tax rate and income data from the microsimulation
model (Tax-Calculator).
------------------------------------------------------------------------
'''
from taxcalc import Records, Calculator, Policy
from pandas import Dat... | mit |
DiCarloLab-Delft/PycQED_py3 | pycqed/simulations/cz_superoperator_simulation_new2.py | 1 | 31649 | import adaptive
from pycqed.measurement import measurement_control as mc
from pycqed.instrument_drivers.meta_instrument.LutMans import flux_lutman as flm
from pycqed.instrument_drivers.virtual_instruments import sim_control_CZ as scCZ
from pycqed.simulations import cz_superoperator_simulation_new_functions as czf
imp... | mit |
dhimmel/networkx | networkx/tests/test_convert_pandas.py | 43 | 2177 | from nose import SkipTest
from nose.tools import assert_true
import networkx as nx
class TestConvertPandas(object):
numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
@classmethod
def setupClass(cls):
try:
import pandas as pd
except ImportError:
... | bsd-3-clause |
yavalvas/yav_com | build/matplotlib/examples/api/histogram_path_demo.py | 6 | 1444 | """
This example shows how to use a path patch to draw a bunch of
rectangles. The technique of using lots of Rectangle instances, or
the faster method of using PolyCollections, were implemented before we
had proper paths with moveto/lineto, closepoly etc in mpl. Now that
we have them, we can draw collections of regul... | mit |
kiyoto/statsmodels | statsmodels/discrete/tests/test_constrained.py | 26 | 19635 | # -*- coding: utf-8 -*-
"""
Created on Fri May 30 16:22:29 2014
Author: Josef Perktold
License: BSD-3
"""
from statsmodels.compat.python import StringIO
import numpy as np
from numpy.testing import assert_allclose, assert_equal, assert_
from nose import SkipTest
import pandas as pd
import patsy
from statsmodels.d... | bsd-3-clause |
walterreade/scikit-learn | examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py | 87 | 3903 | """
==============================================
Feature agglomeration vs. univariate selection
==============================================
This example compares 2 dimensionality reduction strategies:
- univariate feature selection with Anova
- feature agglomeration with Ward hierarchical clustering
Both metho... | bsd-3-clause |
jonyroda97/redbot-amigosprovaveis | lib/matplotlib/backends/backend_template.py | 2 | 9623 | """
This is a fully functional do nothing backend to provide a template to
backend writers. It is fully functional in that you can select it as
a backend with
import matplotlib
matplotlib.use('Template')
and your matplotlib scripts will (should!) run without error, though
no output is produced. This provides a ... | gpl-3.0 |
alexandrejaguar/strata-sv-2015-tutorial | resources/vizarray.py | 2 | 3634 | # encoding: utf-8
"""Vizualize NumPy arrays using ipythonblocks.
To enable the automatic vizualization of arrays::
import vizarray
vizarray.enable()
To disable this::
vizarray.disable()
To set the colormap (to any valid matplotlib colormap name)::
vizarray.set_cmap('jet')
To set the block_size in... | bsd-3-clause |
toobaz/pandas | asv_bench/benchmarks/io/sql.py | 1 | 4950 | import sqlite3
import numpy as np
import pandas.util.testing as tm
from pandas import DataFrame, date_range, read_sql_query, read_sql_table
from sqlalchemy import create_engine
class SQL:
params = ["sqlalchemy", "sqlite"]
param_names = ["connection"]
def setup(self, connection):
N = 10000
... | bsd-3-clause |
blink1073/scikit-image | doc/examples/edges/plot_convex_hull.py | 9 | 1487 | """
===========
Convex Hull
===========
The convex hull of a binary image is the set of pixels included in the
smallest convex polygon that surround all white pixels in the input.
In this example, we show how the input pixels (white) get filled in by the
convex hull (white and grey).
A good overview of the algorithm... | bsd-3-clause |
kerrpy/kerrpy | kerrpy/utils/draw.py | 2 | 2556 | from ..universe import universe
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.patches import Circle
import mpl_toolkits.mplot3d.art3d as art3d
def spher2cart(points):
# Retrieve the actual data
r = points[:, 0]
theta = points[:, 1]
phi = points[:, 2]
cosT = np.cos(theta... | gpl-3.0 |
tosolveit/scikit-learn | sklearn/decomposition/pca.py | 192 | 23117 | """ Principal Component Analysis
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis A. Engemann <d.engemann@fz-juelich.de>
# Michael Eickenberg <michael.eickenberg@inria.fr>
#
# Lice... | bsd-3-clause |
fabioticconi/scikit-learn | examples/bicluster/plot_spectral_biclustering.py | 403 | 2011 | """
=============================================
A demo of the Spectral Biclustering algorithm
=============================================
This example demonstrates how to generate a checkerboard dataset and
bicluster it using the Spectral Biclustering algorithm.
The data is generated with the ``make_checkerboard`... | bsd-3-clause |
nathania/pysal | pysal/contrib/spint/tests/test_gravity_stats.py | 8 | 12472 | """
Tests for statistics for gravity-style spatial interaction models
"""
__author__ = 'toshan'
import unittest
import numpy as np
import pandas as pd
import gravity as grav
import mle_stats as stats
class SingleParameter(unittest.TestCase):
"""Unit tests statistics when there is a single parameters estimated""... | bsd-3-clause |
micmn/shogun | applications/tapkee/swissroll_embedding.py | 12 | 2600 | import numpy
numpy.random.seed(40)
tt = numpy.genfromtxt('../../data/toy/swissroll_color.dat',unpack=True).T
X = numpy.genfromtxt('../../data/toy/swissroll.dat',unpack=True).T
N = X.shape[1]
converters = []
from shogun import LocallyLinearEmbedding
lle = LocallyLinearEmbedding()
lle.set_k(9)
converters.append((lle, "L... | gpl-3.0 |
georgetown-analytics/skidmarks | bin/playin.py | 1 | 1374 | import csv
import pandas as pd
df = pd.read_csv('./output/trip/1_1.csv')
'''
#This is the pandas library code to index and return the values of a column; the first number is rows, second is columns.
The ':' is used to represent "through these values". For example, 1:10 symbolizes numbers 1 through values 10.
''... | mit |
alexsavio/scikit-learn | examples/cluster/plot_kmeans_digits.py | 42 | 4491 | """
===========================================================
A demo of K-Means clustering on the handwritten digits data
===========================================================
In this example we compare the various initialization strategies for
K-means in terms of runtime and quality of the results.
As the gr... | bsd-3-clause |
adyavanapalli/Muon-Mass | Gabe_Owen_.py | 1 | 3641 | import numpy as np
from matplotlib import pyplot as plt
import math
import random as ran
from scipy.integrate import quad
from scipy import interpolate
pi = np.pi
sqrt = np.sqrt
cos = np.cos
sin = np.sin
#Assign constant
hbar = 6.582*10**(-24)
gw = 10**(-6)
MW = .0008039 #MeV
N = 10000
Nexp = 43.
tp = 0.9525 #cm
R ... | mit |
466152112/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 |
TNT-Samuel/Coding-Projects | DNS Server/Source - Copy/Lib/site-packages/dask/dataframe/tests/test_dataframe.py | 2 | 108508 | import sys
import textwrap
from distutils.version import LooseVersion
from itertools import product
from operator import add
import pandas as pd
import pandas.util.testing as tm
import numpy as np
import pytest
import dask
import dask.array as da
import dask.dataframe as dd
from dask.base import compute_as_if_collect... | gpl-3.0 |
halflings/crosscultural-media | pca_transformer.py | 1 | 1628 | from itertools import cycle
import mongoengine
#import matplotlib.pyplot as plt
import sklearn.decomposition
import config
from crawler import enqueue_query, process_query
import sys
# Converts the 'array' type returned from pca.transform to a Python array
def toArray(projection):
data = []
for projPoint in... | apache-2.0 |
Froff/TFY4115-Simulering | python/main.py | 1 | 1573 | #!/usr/bin/python3
import matplotlib.pyplot as plt
import matplotlib.text as txt
import numpy as np
import math
import sys
from Slope import Slope
from SlopeDict import slopeDict
from Simulation import Simulation
from PhysicalSeries import PhysicalSeries
import os.path
slope_type = "linje"
if __name__ == "__main__":
... | mit |
MartinDelzant/scikit-learn | examples/neural_networks/plot_rbm_logistic_classification.py | 258 | 4609 | """
==============================================================
Restricted Boltzmann Machine features for digit classification
==============================================================
For greyscale image data where pixel values can be interpreted as degrees of
blackness on a white background, like handwritten... | bsd-3-clause |
RPGOne/Skynet | scikit-learn-0.18.1/examples/ensemble/plot_gradient_boosting_oob.py | 82 | 4768 | """
======================================
Gradient Boosting Out-of-Bag estimates
======================================
Out-of-bag (OOB) estimates can be a useful heuristic to estimate
the "optimal" number of boosting iterations.
OOB estimates are almost identical to cross-validation estimates but
they can be compute... | bsd-3-clause |
carlgogo/vip_exoplanets | vip_hci/negfc/simplex_optim.py | 2 | 25988 | #! /usr/bin/env python
"""
Module with simplex (Nelder-Mead) optimization for defining the flux and
position of a companion using the Negative Fake Companion.
"""
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import minimize
from .simplex_fmerit import chisquare, get_mu_and_sigma
from ..pca... | bsd-3-clause |
DailyActie/Surrogate-Model | 01-codes/scikit-learn-master/setup.py | 1 | 11778 | #! /usr/bin/env python
#
# Copyright (C) 2007-2009 Cournapeau David <cournape@gmail.com>
# 2010 Fabian Pedregosa <fabian.pedregosa@inria.fr>
# License: 3-clause BSD
import subprocess
descr = """A set of python modules for machine learning and data mining"""
import sys
import os
import shutil
from distut... | mit |
ueshin/apache-spark | python/pyspark/pandas/indexes/category.py | 15 | 7766 | #
# 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 |
eastmallingresearch/crosslink | scripts/check_map_order.py | 1 | 4311 | #!/usr/bin/python
#Crosslink Copyright (C) 2016 NIAB EMR see included NOTICE file for details
'''
plot estimated versus correct map positions
colour coded to show marker type: maternal-only/paternal-only/both
'''
import argparse
ap = argparse.ArgumentParser(description=__doc__,formatter_class=argparse.ArgumentDefault... | gpl-2.0 |
nitin-cherian/LifeLongLearning | Python/PythonProgrammingLanguage/Encapsulation/encap_env/lib/python3.5/site-packages/IPython/core/magics/basic.py | 2 | 21310 | """Implementation of basic magic functions."""
import argparse
import textwrap
import io
import sys
from pprint import pformat
from IPython.core import magic_arguments, page
from IPython.core.error import UsageError
from IPython.core.magic import Magics, magics_class, line_magic, magic_escapes
from IPython.utils.tex... | mit |
linebp/pandas | pandas/tests/frame/test_sorting.py | 4 | 20958 | # -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
import random
import numpy as np
import pandas as pd
from pandas.compat import lrange
from pandas import (DataFrame, Series, MultiIndex, Timestamp,
date_range, NaT, IntervalIndex)
from pandas.util.testing import assert_s... | bsd-3-clause |
zaxliu/deepnap | experiments/kdd-exps/experiment_message_2016-6-12_G5_BUF2_AR1_b65_legacy.py | 1 | 4374 | # System built-in modules
import time
from datetime import datetime
import sys
import os
from multiprocessing import Pool
# Project dependency modules
import pandas as pd
pd.set_option('mode.chained_assignment', None) # block warnings due to DataFrame value assignment
import lasagne
# Project modules
sys.path.append('... | bsd-3-clause |
anurag313/scikit-learn | examples/ensemble/plot_adaboost_hastie_10_2.py | 355 | 3576 | """
=============================
Discrete versus Real AdaBoost
=============================
This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates
the difference in performance between the discrete SAMME [2] boosting
algorithm and real SAMME.R boosting algorithm. Both algorithms are evaluate... | bsd-3-clause |
rneher/FitnessInference | flu/src/clade_frequency_correlations.py | 1 | 5578 | #!/ebio/ag-neher/share/programs/bin/python2.7
################################################################################
#
# author: Richard Neher
# email: richard.neher@tuebingen.mpg.de
#
# Reference: Richard A. Neher, Colin A Russell, Boris I Shraiman.
# "Predicting evolution from the shape of gene... | mit |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_time_frequency_simulated.py | 5 | 8402 | """
======================================================================
Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell)
======================================================================
This example demonstrates the different time-frequency estimation methods
on simulated data. It shows ... | bsd-3-clause |
kylerbrown/scikit-learn | sklearn/linear_model/tests/test_randomized_l1.py | 214 | 4690 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.linear_model.randomized_l1 i... | bsd-3-clause |
jgliss/pydoas | pydoas/helpers.py | 1 | 3793 | # -*- coding: utf-8 -*-
#
# Pydoas is a Python library for the post-analysis of DOAS result data
# Copyright (C) 2017 Jonas Gliß (jonasgliss@gmail.com)
#
# This program is free software: you can redistribute it and/or
# modify it under the terms of the BSD 3-Clause License
#
# This program is distributed in the hope th... | bsd-3-clause |
js7558/pyBinance | tests/test-getOpenOrders.py | 1 | 2221 | #!/usr/bin/python
import pandas as pd
import sys
sys.path.append('../')
from Binance import Binance
import logging.config
import logging.handlers
import logging
import os
# this logging configuration is sketchy
binance = logging.getLogger(__name__)
logging.config.fileConfig('logging.ini')
# create Binance object
bn... | mit |
joshloyal/scikit-learn | doc/tutorial/text_analytics/solutions/exercise_02_sentiment.py | 104 | 3139 | """Build a sentiment analysis / polarity model
Sentiment analysis can be casted as a binary text classification problem,
that is fitting a linear classifier on features extracted from the text
of the user messages so as to guess wether the opinion of the author is
positive or negative.
In this examples we will use a ... | bsd-3-clause |
cloudera/ibis | ibis/backends/spark/udf.py | 1 | 6563 | """
APIs for creating user-defined element-wise, reduction and analytic
functions.
"""
import collections
import functools
import itertools
import pyspark.sql.functions as f
import pyspark.sql.types as pt
import ibis.common.exceptions as com
import ibis.expr.datatypes as dt
import ibis.expr.signature as sig
import i... | apache-2.0 |
zrhans/python | exemplos/Examples.lnk/bokeh/compat/mpl/lc_offsets.py | 13 | 1067 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from bokeh import mpl
from bokeh.plotting import show
# Simulate a series of ocean current profiles, successively
# offset by 0.1 m/s so that they form what is sometimes called
# a "waterfall" plot or a "stagger" plot.... | gpl-2.0 |
nmayorov/scikit-learn | examples/linear_model/plot_logistic_l1_l2_sparsity.py | 384 | 2601 | """
==============================================
L1 Penalty and Sparsity in Logistic Regression
==============================================
Comparison of the sparsity (percentage of zero coefficients) of solutions when
L1 and L2 penalty are used for different values of C. We can see that large
values of C give mo... | bsd-3-clause |
lenovor/scikit-learn | sklearn/tree/tree.py | 113 | 34767 | """
This module gathers tree-based methods, including decision, regression and
randomized trees. Single and multi-output problems are both handled.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Da... | bsd-3-clause |
t20100/sandbox | curves/CurvesView.py | 1 | 12272 | # coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2017 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
#... | mit |
0asa/scikit-learn | sklearn/pipeline.py | 2 | 19361 | """
The :mod:`sklearn.pipeline` module implements utilities to build a composite
estimator, as a chain of transforms and estimators.
"""
# Author: Edouard Duchesnay
# Gael Varoquaux
# Virgile Fritsch
# Alexandre Gramfort
# Lars Buitinck
# Licence: BSD
from collections import defaultdict... | bsd-3-clause |
eg-zhang/scikit-learn | sklearn/utils/tests/test_murmurhash.py | 261 | 2836 | # Author: Olivier Grisel <olivier.grisel@ensta.org>
#
# License: BSD 3 clause
import numpy as np
from sklearn.externals.six import b, u
from sklearn.utils.murmurhash import murmurhash3_32
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
from nose.tools import assert_equa... | bsd-3-clause |
barajasr/Baseball-Reference-Plotting | Plot.py | 1 | 9927 | import os
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
import Auxiliary as aux
import BrefScraper as brf
class Plot(object):
""" With data obtained from BrefScraper, Plot clean the raw
data and saves it to file.
"""
def __init__(self, scraper=brf.BrefScrape... | bsd-2-clause |
mathdd/numpy | numpy/core/code_generators/ufunc_docstrings.py | 51 | 90047 | """
Docstrings for generated ufuncs
The syntax is designed to look like the function add_newdoc is being
called from numpy.lib, but in this file add_newdoc puts the docstrings
in a dictionary. This dictionary is used in
numpy/core/code_generators/generate_umath.py to generate the docstrings
for the ufuncs in numpy.co... | bsd-3-clause |
ammarkhann/FinalSeniorCode | lib/python2.7/site-packages/pandas/core/computation/pytables.py | 7 | 18930 | """ manage PyTables query interface via Expressions """
import ast
from functools import partial
import numpy as np
import pandas as pd
from pandas.core.dtypes.common import is_list_like
import pandas.core.common as com
from pandas.compat import u, string_types, DeepChainMap
from pandas.core.base import StringMixin
f... | mit |
magic2du/contact_matrix | Contact_maps/DeepLearning/DeepLearningTool/DL_contact_matrix_load2-new10fold_12_15_2014_server.py | 1 | 43156 |
# coding: utf-8
# In[3]:
import sys, os
sys.path.append('../../../libs/')
import os.path
import IO_class
from IO_class import FileOperator
from sklearn import cross_validation
import sklearn
import numpy as np
import csv
from dateutil import parser
from datetime import timedelta
from sklearn import svm
import numpy ... | gpl-2.0 |
justincassidy/scikit-learn | examples/ensemble/plot_adaboost_multiclass.py | 354 | 4124 | """
=====================================
Multi-class AdaBoosted Decision Trees
=====================================
This example reproduces Figure 1 of Zhu et al [1] and shows how boosting can
improve prediction accuracy on a multi-class problem. The classification
dataset is constructed by taking a ten-dimensional ... | bsd-3-clause |
rajegannathan/grasp-lift-eeg-cat-dog-solution-updated | python-packages/nolearn-0.5/nolearn/tests/test_lasagne.py | 2 | 5731 | from mock import patch
from lasagne.layers import DenseLayer
from lasagne.layers import DropoutLayer
from lasagne.layers import InputLayer
from lasagne.nonlinearities import identity
from lasagne.nonlinearities import softmax
from lasagne.updates import nesterov_momentum
import numpy as np
import pytest
from sklearn.ba... | bsd-3-clause |
marionleborgne/nupic.research | htmresearch/frameworks/capybara/embedding.py | 7 | 3195 | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2017, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This progra... | agpl-3.0 |
revanthkolli/osf.io | scripts/analytics/addons.py | 15 | 2140 | # -*- coding: utf-8 -*-
import os
import re
import matplotlib.pyplot as plt
from framework.mongo import database
from website import settings
from website.app import init_app
from .utils import plot_dates, oid_to_datetime, mkdirp
log_collection = database['nodelog']
FIG_PATH = os.path.join(settings.ANALYTICS_PATH... | apache-2.0 |
trmznt/fatools | fatools/lib/fautil/_xxx/fautils.py | 2 | 43844 |
# re-imagining the peakutils
import numpy as np
from scipy.signal import find_peaks_cwt
from scipy.optimize import leastsq, curve_fit
from scipy.interpolate import UnivariateSpline
from matplotlib import pyplot as plt
from bisect import bisect_left
from operator import itemgetter
from pprint import pprint
from .d... | lgpl-3.0 |
pepcio03/python | pdftosplitandpdf.py | 1 | 2753 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# pdfsplitandpdf.py
#
# Copyright 2014 pepcio <piotrk0303@gmail.com>
#
# 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 2 o... | gpl-2.0 |
ryandougherty/mwa-capstone | MWA_Tools/build/matplotlib/lib/mpl_examples/user_interfaces/wxcursor_demo.py | 4 | 2166 | """
Example to draw a cursor and report the data coords in wx
"""
import matplotlib
matplotlib.use('WXAgg')
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigureCanvas
from matplotlib.backends.backend_wx import NavigationToolbar2Wx
from matplotlib.figure import Figure
from numpy import arange, sin... | gpl-2.0 |
TinghuiWang/pyActLearn | examples/CASAS_Single_Test/b1_randomforest.py | 1 | 7254 | import os
import pickle
import logging
import argparse
from sklearn.ensemble import RandomForestClassifier
from datetime import datetime
from pyActLearn.CASAS.data import CASASData
from pyActLearn.CASAS.fuel import CASASFuel
from pyActLearn.performance.record import LearningResult
from pyActLearn.performance import get... | bsd-3-clause |
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