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 |
|---|---|---|---|---|---|
nest/nest-simulator | pynest/examples/balancedneuron.py | 8 | 7344 | # -*- coding: utf-8 -*-
#
# balancedneuron.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 2 of the License,... | gpl-2.0 |
mmottahedi/neuralnilm_prototype | scripts/e280.py | 2 | 4987 | from __future__ import print_function, division
import matplotlib
import logging
from sys import stdout
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
from neuralnilm import (Net, RealApplianceSource,
BLSTMLayer, DimshuffleLayer,
Bidirectio... | mit |
jseabold/scikit-learn | examples/neighbors/plot_kde_1d.py | 347 | 5100 | """
===================================
Simple 1D Kernel Density Estimation
===================================
This example uses the :class:`sklearn.neighbors.KernelDensity` class to
demonstrate the principles of Kernel Density Estimation in one dimension.
The first plot shows one of the problems with using histogram... | bsd-3-clause |
louisLouL/pair_trading | capstone_env/lib/python3.6/site-packages/matplotlib/backends/backend_gtk3.py | 2 | 32330 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import os, sys
try:
import gi
except ImportError:
raise ImportError("Gtk3 backend requires pygobject to be installed.")
try:
gi.require_version("Gtk", "3.0")
except AttributeError:
... | mit |
Windy-Ground/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 |
Eric89GXL/scikit-learn | benchmarks/bench_plot_lasso_path.py | 301 | 4003 | """Benchmarks of Lasso regularization path computation using Lars and CD
The input data is mostly low rank but is a fat infinite tail.
"""
from __future__ import print_function
from collections import defaultdict
import gc
import sys
from time import time
import numpy as np
from sklearn.linear_model import lars_pat... | bsd-3-clause |
nelango/ViralityAnalysis | model/lib/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... | mit |
pratapvardhan/scikit-image | doc/examples/segmentation/plot_join_segmentations.py | 10 | 1998 | """
==========================================
Find the intersection of two segmentations
==========================================
When segmenting an image, you may want to combine multiple alternative
segmentations. The `skimage.segmentation.join_segmentations` function
computes the join of two segmentations, in wh... | bsd-3-clause |
yonglehou/scikit-learn | sklearn/__check_build/__init__.py | 345 | 1671 | """ Module to give helpful messages to the user that did not
compile the scikit properly.
"""
import os
INPLACE_MSG = """
It appears that you are importing a local scikit-learn source tree. For
this, you need to have an inplace install. Maybe you are in the source
directory and you need to try from another location.""... | bsd-3-clause |
SciTools/cartopy | lib/cartopy/tests/mpl/test_feature_artist.py | 2 | 4353 | # Copyright Cartopy Contributors
#
# This file is part of Cartopy and is released under the LGPL license.
# See COPYING and COPYING.LESSER in the root of the repository for full
# licensing details.
from unittest import mock
import numpy as np
import pytest
import shapely.geometry as sgeom
from matplotlib.transforms ... | lgpl-3.0 |
dannyjacobs/PRISim | main/MWA_interferometer_array_observing_run_data_simulation.mpi.py | 1 | 90520 | from mpi4py import MPI
import argparse
import numpy as NP
from astropy.io import fits
from astropy.io import ascii
import scipy.constants as FCNST
from scipy import interpolate
import matplotlib.pyplot as PLT
import matplotlib.colors as PLTC
import matplotlib.animation as MOV
from scipy.interpolate import griddata
im... | mit |
mxjl620/scikit-learn | benchmarks/bench_plot_incremental_pca.py | 374 | 6430 | """
========================
IncrementalPCA benchmark
========================
Benchmarks for IncrementalPCA
"""
import numpy as np
import gc
from time import time
from collections import defaultdict
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_lfw_people
from sklearn.decomposition import Incre... | bsd-3-clause |
pianomania/scikit-learn | sklearn/utils/tests/test_metaestimators.py | 86 | 2304 | from sklearn.utils.testing import assert_true, assert_false
from sklearn.utils.metaestimators import if_delegate_has_method
class Prefix(object):
def func(self):
pass
class MockMetaEstimator(object):
"""This is a mock meta estimator"""
a_prefix = Prefix()
@if_delegate_has_method(delegate="a... | bsd-3-clause |
gdooper/scipy | scipy/special/c_misc/struve_convergence.py | 23 | 3678 | """
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 |
UptakeOpenSource/uptasticsearch | py-pkg/uptasticsearch/fetch_all.py | 1 | 1666 | """Functions for Pulling data from Elasticsearch and unpacking into a table
"""
import pandas as pd
import json
from uptasticsearch.clients import uptasticsearch_factory
def es_search(es_host, es_index, query_body="{}", size=10000, max_hits=None,
scroll="5m"):
"""
Execute a query to elasticsea... | bsd-3-clause |
wlamond/scikit-learn | examples/linear_model/plot_sparse_logistic_regression_mnist.py | 18 | 2701 | """
=====================================================
MNIST classfification using multinomial logistic + L1
=====================================================
Here we fit a multinomial logistic regression with L1 penalty on a subset of
the MNIST digits classification task. We use the SAGA algorithm for this
pur... | bsd-3-clause |
gfyoung/pandas | pandas/tests/indexes/timedeltas/methods/test_shift.py | 6 | 2751 | import pytest
from pandas.errors import NullFrequencyError
import pandas as pd
from pandas import TimedeltaIndex
import pandas._testing as tm
class TestTimedeltaIndexShift:
# -------------------------------------------------------------
# TimedeltaIndex.shift is used by __add__/__sub__
def test_tdi_sh... | bsd-3-clause |
Lx37/pyqtgraph | pyqtgraph/exporters/Matplotlib.py | 39 | 4821 | from ..Qt import QtGui, QtCore
from .Exporter import Exporter
from .. import PlotItem
from .. import functions as fn
__all__ = ['MatplotlibExporter']
"""
It is helpful when using the matplotlib Exporter if your
.matplotlib/matplotlibrc file is configured appropriately.
The following are suggested for getting usable P... | mit |
jiangjinjinyxt/vnpy | docker/dockerTrader/ctaStrategy/strategy/strategyDualThrust.py | 5 | 9485 | # encoding: UTF-8
"""
DualThrust交易策略
"""
from datetime import time
from ..ctaBase import *
from ..ctaTemplate import CtaTemplate
########################################################################
class DualThrustStrategy(CtaTemplate):
"""DualThrust交易策略"""
className = 'DualThrustStrategy'
author =... | mit |
nelson-liu/scikit-learn | sklearn/feature_selection/tests/test_mutual_info.py | 56 | 6268 | from __future__ import division
import numpy as np
from numpy.testing import run_module_suite
from scipy.sparse import csr_matrix
from sklearn.utils.testing import (assert_array_equal, assert_almost_equal,
assert_false, assert_raises, assert_equal)
from sklearn.feature_selection.mut... | bsd-3-clause |
jonathanlxy/miaozhen_hackathon | lib/classifier.py | 1 | 1312 | import pickle
import numpy as np
from math import ceil
class Classifier:
def __init__(self, model_pickle):
# Load the pickled sklearn model
with open(model_pickle, 'rb') as f:
self.model = pickle.load(f)
def predict(self, X, diff_rate=0.35):
'''
Input: F... | mit |
petosegan/scikit-learn | sklearn/semi_supervised/label_propagation.py | 128 | 15312 | # 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 |
unnikrishnankgs/va | venv/lib/python3.5/site-packages/matplotlib/dviread.py | 10 | 33393 | """
An experimental module for reading dvi files output by TeX. Several
limitations make this not (currently) useful as a general-purpose dvi
preprocessor, but it is currently used by the pdf backend for
processing usetex text.
Interface::
dvi = Dvi(filename, 72)
# iterate over pages (but only one page is support... | bsd-2-clause |
dmd/stabilitycalc | stabilitysummary.py | 1 | 8521 | #!/usr/bin/env python
import os
from os.path import join as pjoin
import shutil
import time
import logging
import matplotlib
matplotlib.use('Agg', warn=False)
import numpy as np
import matplotlib.pyplot as plt
import seaborn
seaborn.set_style("dark")
import pandas as pd
from collections import OrderedDict
from datetim... | apache-2.0 |
jayflo/scikit-learn | benchmarks/bench_plot_lasso_path.py | 301 | 4003 | """Benchmarks of Lasso regularization path computation using Lars and CD
The input data is mostly low rank but is a fat infinite tail.
"""
from __future__ import print_function
from collections import defaultdict
import gc
import sys
from time import time
import numpy as np
from sklearn.linear_model import lars_pat... | bsd-3-clause |
rrozewsk/OurProject | UnitTests/test_corporateRates.py | 2 | 1403 | from datetime import date
from unittest import TestCase
import numpy as np
import pandas as pd
from Curves.Corporates.CorporateDaily import CorporateRates
from parameters import WORKING_DIR
periods = '1Y'
freq = '1M'
t_step = 1.0 / 365.0
simNumber = 10
start = date(2005, 3, 30)
trim_start = date(2000, 1, 1)
trim_en... | mit |
zorroblue/scikit-learn | sklearn/kernel_ridge.py | 16 | 6766 | """Module :mod:`sklearn.kernel_ridge` implements kernel ridge regression."""
# Authors: Mathieu Blondel <mathieu@mblondel.org>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: BSD 3 clause
import numpy as np
from .base import BaseEstimator, RegressorMixin
from .metrics.pairwise import pairwise... | bsd-3-clause |
tedmeeds/tcga_encoder | tcga_encoder/models/pytorch/weibull_survival_sandbox.py | 1 | 42931 | from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn import Parameter
import torch.optim as optim
import pylab as pp
import sklearn
from sklearn.model_selection import KFold
import pdb
from lifelines... | mit |
jkarnows/scikit-learn | sklearn/covariance/__init__.py | 389 | 1157 | """
The :mod:`sklearn.covariance` module includes methods and algorithms to
robustly estimate the covariance of features given a set of points. The
precision matrix defined as the inverse of the covariance is also estimated.
Covariance estimation is closely related to the theory of Gaussian Graphical
Models.
"""
from ... | bsd-3-clause |
wzbozon/scikit-learn | sklearn/linear_model/tests/test_ransac.py | 216 | 13290 | import numpy as np
from numpy.testing import assert_equal, assert_raises
from numpy.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises_regexp
from scipy import sparse
from sklearn.utils.testing import assert_less
from sklearn.linear_model import LinearRegression, RANSACRegressor
f... | bsd-3-clause |
cainiaocome/scikit-learn | examples/applications/plot_prediction_latency.py | 234 | 11277 | """
==================
Prediction Latency
==================
This is an example showing the prediction latency of various scikit-learn
estimators.
The goal is to measure the latency one can expect when doing predictions
either in bulk or atomic (i.e. one by one) mode.
The plots represent the distribution of the pred... | bsd-3-clause |
casawa/mdtraj | mdtraj/formats/pdb/pdbfile.py | 3 | 27208 | ##############################################################################
# MDTraj: A Python Library for Loading, Saving, and Manipulating
# Molecular Dynamics Trajectories.
# Copyright 2012-2013 Stanford University and the Authors
#
# Authors: Peter Eastman, Robert McGibbon
# Contributors: Carlos Hernande... | lgpl-2.1 |
Designist/sympy | sympy/external/tests/test_importtools.py | 91 | 1215 | from sympy.external import import_module
# fixes issue that arose in addressing issue 6533
def test_no_stdlib_collections():
'''
make sure we get the right collections when it is not part of a
larger list
'''
import collections
matplotlib = import_module('matplotlib',
__import__kwargs={... | bsd-3-clause |
0asa/scikit-learn | sklearn/metrics/cluster/__init__.py | 312 | 1322 | """
The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for
cluster analysis results. There are two forms of evaluation:
- supervised, which uses a ground truth class values for each sample.
- unsupervised, which does not and measures the 'quality' of the model itself.
"""
from .supervised import ... | bsd-3-clause |
andrej5elin/ddm | ddm/video/misc.py | 1 | 4991 | #python 3 compatibility stuff
from __future__ import division, print_function, absolute_import
import numpy as np
import time
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button
from matplotlib.animation import FuncAnimation
from matplotlib.patches import Circle
from ddm.core.ddm_tools impor... | gpl-3.0 |
plotly/python-api | packages/python/plotly/plotly/graph_objs/barpolar/marker/_colorbar.py | 1 | 69658 | from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class ColorBar(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "barpolar.marker"
_path_str = "barpolar.marker.colorbar"
_valid_props = {
"bgcolor"... | mit |
fpetitjean/DBA | cython/test.py | 1 | 1196 | from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from DBA import performDBA
def main():
#generating synthetic data
n_series = 20
length = 200
series = list()
padding_length=30
indices = range(0, length-padding_length)
main_profile_gen = np.array(list(map(l... | gpl-3.0 |
RobertABT/heightmap | build/matplotlib/examples/pylab_examples/barb_demo.py | 13 | 1712 | '''
Demonstration of wind barb plots
'''
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-5, 5, 5)
X,Y = np.meshgrid(x, x)
U, V = 12*X, 12*Y
data = [(-1.5, .5, -6, -6),
(1, -1, -46, 46),
(-3, -1, 11, -11),
(1, 1.5, 80, 80),
(0.5, 0.25, 25, 15),
(-1.5, -0.5, -... | mit |
uri-mog/dreampie | dreampielib/subprocess/__init__.py | 1 | 37102 | # Copyright 2010 Noam Yorav-Raphael
#
# This file is part of DreamPie.
#
# DreamPie 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.
# ... | gpl-3.0 |
aashish24/seaborn | seaborn/miscplot.py | 34 | 1498 | from __future__ import division
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
def palplot(pal, size=1):
"""Plot the values in a color palette as a horizontal array.
Parameters
----------
pal : sequence of matplotlib colors
colors, i.e. as returned by seaborn.colo... | bsd-3-clause |
harisbal/pandas | pandas/tests/groupby/test_function.py | 3 | 38905 | import pytest
import numpy as np
import pandas as pd
from pandas import (DataFrame, Index, compat, isna,
Series, MultiIndex, Timestamp, date_range)
from pandas.errors import UnsupportedFunctionCall
from pandas.util import testing as tm
import pandas.core.nanops as nanops
from string import ascii_lo... | bsd-3-clause |
Sentient07/scikit-learn | examples/linear_model/plot_sgd_comparison.py | 112 | 1819 | """
==================================
Comparing various online solvers
==================================
An example showing how different online solvers perform
on the hand-written digits dataset.
"""
# Author: Rob Zinkov <rob at zinkov dot com>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot a... | bsd-3-clause |
mbayon/TFG-MachineLearning | vbig/lib/python2.7/site-packages/sklearn/linear_model/ridge.py | 8 | 52900 | """
Ridge regression
"""
# Author: Mathieu Blondel <mathieu@mblondel.org>
# Reuben Fletcher-Costin <reuben.fletchercostin@gmail.com>
# Fabian Pedregosa <fabian@fseoane.net>
# Michael Eickenberg <michael.eickenberg@nsup.org>
# License: BSD 3 clause
from abc import ABCMeta, abstractmethod
impor... | mit |
akucukelbir/stanhelper | stanhelper/stanhelper.py | 1 | 13902 | import re
import linecache
import os
import subprocess
import numpy as np
import pandas as pd
from functools import reduce
from operator import mul
from tempfile import NamedTemporaryFile
def run(stan_binary_path: str,
input_data: dict,
method: str,
init_data: dict=None,
method_params:... | gpl-3.0 |
ephes/scikit-learn | sklearn/neural_network/rbm.py | 206 | 12292 | """Restricted Boltzmann Machine
"""
# Authors: Yann N. Dauphin <dauphiya@iro.umontreal.ca>
# Vlad Niculae
# Gabriel Synnaeve
# Lars Buitinck
# License: BSD 3 clause
import time
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator
from ..base import TransformerMixi... | bsd-3-clause |
beepee14/scikit-learn | examples/svm/plot_svm_nonlinear.py | 268 | 1091 | """
==============
Non-linear SVM
==============
Perform binary classification using non-linear SVC
with RBF kernel. The target to predict is a XOR of the
inputs.
The color map illustrates the decision function learned by the SVC.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn imp... | bsd-3-clause |
Obus/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 |
Ziqi-Li/bknqgis | Shapely/docs/sphinxext/inheritance_diagram.py | 98 | 13648 | """
Defines a docutils directive for inserting inheritance diagrams.
Provide the directive with one or more classes or modules (separated
by whitespace). For modules, all of the classes in that module will
be used.
Example::
Given the following classes:
class A: pass
class B(A): pass
class C(A): pass
... | gpl-2.0 |
datapythonista/pandas | pandas/tests/frame/methods/test_set_index.py | 2 | 25979 | """
See also: test_reindex.py:TestReindexSetIndex
"""
from datetime import (
datetime,
timedelta,
)
import numpy as np
import pytest
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
Index,
MultiIndex,
Series,
date_range,
period_range,
to_datetime,
)
import panda... | bsd-3-clause |
gbellandi/bubble_size_analysis | bubblekicker/bubblekicker.py | 1 | 13842 | """
S. Van Hoey
2016-06-06
"""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
from skimage.feature import canny
from skimage.segmentation import clear_border
from skimage.morphology import dilation, rectangle
from skimage.me... | mit |
BerkeleyAutomation/vtsc | scripts/background_subtraction.py | 1 | 2070 | import numpy as np
import cv2
import argparse
import sys
import matplotlib.pyplot as plt
import IPython
import parser
import utils
import constants
def run_video_with_bsub(cap, func, kernel = None, params = None):
if params is not None:
fgbg = func(params[0], params[1], params[2], params[3])
else:
fgbg = func()... | mit |
HazyResearch/dd-genomics | document_classifier/classification/classify.py | 1 | 2019 | #! /usr/bin/python
# -*- coding: utf-8 -*-
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
import sys
from bunch import *
import numpy as np
import random
from sklearn.linear_model import LogisticRegression
from nltk.stem import WordNetLemmatizer... | apache-2.0 |
hitszxp/scikit-learn | examples/gaussian_process/plot_gp_probabilistic_classification_after_regression.py | 252 | 3490 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
==============================================================================
Gaussian Processes classification example: exploiting the probabilistic output
==============================================================================
A two-dimensional regression exerci... | bsd-3-clause |
bmcage/stickproject | stick/stickApp.py | 1 | 3341 | #!/usr/bin/env python
# Copyright (C) 2009 B. Malengier
#
# 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 of the License, or
# (at your option) any later version.
#
# This pro... | gpl-2.0 |
phihes/sds-models | examples/verbose_test.py | 1 | 2928 | import pandas as pd
import matplotlib.pyplot as plt
import sdsModels as sdsm
#data = pd.io.excel.read_excel('../data/data_klaus_feb_2015.xls')
#data = pd.read_csv('../data/complete_april_2014.csv')
data = pd.read_csv('../data/data_klaus_feb_2015.csv')
data['label'] = data['user'].astype(int).astype('str') + data['t... | mit |
ablifedev/ABLIRC | ABLIRC/bin/public/iv_cluster/get_CTSS.py | 1 | 10604 | #!/usr/bin/env python2.7
# -*- coding: utf-8 -*-
####################################################################################
### Copyright (C) 2015-2019 by ABLIFE
####################################################################################
########################################################... | mit |
mit-crpg/openmc | tests/regression_tests/mgxs_library_nuclides/test.py | 6 | 2524 | import hashlib
import openmc
import openmc.mgxs
from openmc.examples import pwr_pin_cell
from tests.testing_harness import PyAPITestHarness
class MGXSTestHarness(PyAPITestHarness):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# Initialize a two-group structure
... | mit |
JosmanPS/scikit-learn | sklearn/utils/tests/test_class_weight.py | 140 | 11909 | import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_blobs
from sklearn.utils.class_weight import compute_class_weight
from sklearn.utils.class_weight import compute_sample_weight
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testin... | bsd-3-clause |
zorojean/scikit-learn | sklearn/feature_extraction/tests/test_text.py | 110 | 34127 | from __future__ import unicode_literals
import warnings
from sklearn.feature_extraction.text import strip_tags
from sklearn.feature_extraction.text import strip_accents_unicode
from sklearn.feature_extraction.text import strip_accents_ascii
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.fe... | bsd-3-clause |
CIFASIS/vdiscover-workshop | split.py | 2 | 3072 | #!/usr/bin/python2
import os
import sys
import csv
import random
import gzip
from sklearn import cross_validation
data_filename = sys.argv[1]
seed = sys.argv[2]
data_dir = "data/"+seed
train_filename = data_dir+"/train.csv"
test_filename = data_dir+"/test.csv"
os.system("mkdir -p "+data_dir)
random.seed(seed)
pr... | gpl-3.0 |
aerler/WRF-Projects | src/archive/plotDomain2.py | 1 | 6131 | '''
Created on 2012-09-29
A simple script that reads a WRF netcdf-4 file and displays a 2D field in a proper geographic projection;
application here is plotting precipitation in the inner WRF domain.
@author: Andre R. Erler
'''
## includes
# matplotlib config: size etc.
import numpy as np
import matplotlib.pylab as ... | gpl-3.0 |
huzq/scikit-learn | sklearn/decomposition/tests/test_incremental_pca.py | 7 | 14464 | """Tests for Incremental PCA."""
import numpy as np
import pytest
from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_allclose_dense_sparse
from sklearn import datasets
from sklearn.decomposition import PCA, Incr... | bsd-3-clause |
softEcon/talks | intro_structural_econometrics/images/entropy.py | 1 | 1676 | #!/usr/bin/env python
""" This module collects all tools to explore the entropy.
"""
# standard library
import matplotlib.pyplot as plt
import shapely.geometry as sg
import descartes
# Module-wide variables
NUM_POINTS = 3
KL_MAX = 0.02
# Translate to circle coordinates.
center_coordinates = (0.0, 0.0)
radius = KL_MAX... | mit |
detrout/debian-statsmodels | statsmodels/sandbox/examples/ex_mixed_lls_timecorr.py | 34 | 7824 | # -*- coding: utf-8 -*-
"""Example using OneWayMixed with within group intertemporal correlation
Created on Sat Dec 03 10:15:55 2011
Author: Josef Perktold
This example constructs a linear model with individual specific random
effects, and uses OneWayMixed to estimate it.
This is a variation on ex_mixed_lls_0.py.
... | bsd-3-clause |
dkushner/zipline | tests/test_batchtransform.py | 18 | 9891 | #
# Copyright 2013 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | apache-2.0 |
AlexanderFabisch/scikit-learn | examples/cluster/plot_kmeans_silhouette_analysis.py | 242 | 5885 | """
===============================================================================
Selecting the number of clusters with silhouette analysis on KMeans clustering
===============================================================================
Silhouette analysis can be used to study the separation distance between the... | bsd-3-clause |
glorizen/nupic | nupic/math/roc_utils.py | 49 | 8308 | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, 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 |
JosmanPS/scikit-learn | examples/exercises/plot_cv_diabetes.py | 231 | 2527 | """
===============================================
Cross-validation on diabetes Dataset Exercise
===============================================
A tutorial exercise which uses cross-validation with linear models.
This exercise is used in the :ref:`cv_estimators_tut` part of the
:ref:`model_selection_tut` section of ... | bsd-3-clause |
hainn8x/gnuradio | gr-filter/examples/channelize.py | 58 | 7003 | #!/usr/bin/env python
#
# Copyright 2009,2012,2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio 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, or (at your ... | gpl-3.0 |
Bioh4z4rd/scapy | scapy/plist.py | 7 | 20977 | ## This file is part of Scapy
## See http://www.secdev.org/projects/scapy for more informations
## Copyright (C) Philippe Biondi <phil@secdev.org>
## This program is published under a GPLv2 license
"""
PacketList: holds several packets and allows to do operations on them.
"""
import os,subprocess
from .config import... | gpl-2.0 |
bnoi/scikit-tracker | sktracker/data/__init__.py | 1 | 3796 |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from __future__ import division
from __future__ import absolute_import
from __future__ import print_function
"""Test dataset and fake auto generated trajectories.
When data function end with _temp. The file is being copied to a temporary
directory be... | bsd-3-clause |
jgraving/pinpoint | pinpoint/utils.py | 1 | 33939 | """
Copyright 2015-2018 Jacob M. Graving <jgraving@gmail.com>
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 ag... | apache-2.0 |
web-masons/holocron-api | holocron_api/api/views.py | 2 | 4775 | from api.models import * # noqa
from rest_framework import viewsets
from api.serializers import * # noqa
from rest_framework import filters
from rest_pandas import PandasViewSet
import django_filters
class ProgramViewSet(viewsets.ModelViewSet):
queryset = Program.objects.all()
serializer_class = ProgramSeri... | apache-2.0 |
cainiaocome/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 |
frank-tancf/scikit-learn | examples/gaussian_process/plot_gpc_iris.py | 81 | 2231 | """
=====================================================
Gaussian process classification (GPC) on iris dataset
=====================================================
This example illustrates the predicted probability of GPC for an isotropic
and anisotropic RBF kernel on a two-dimensional version for the iris-dataset.
... | bsd-3-clause |
sanketloke/scikit-learn | sklearn/linear_model/tests/test_ridge.py | 32 | 26520 | import numpy as np
import scipy.sparse as sp
from scipy import linalg
from itertools import product
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn... | bsd-3-clause |
trankmichael/scikit-learn | examples/decomposition/plot_pca_vs_fa_model_selection.py | 78 | 4510 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
===============================================================
Model selection with Probabilistic PCA and Factor Analysis (FA)
===============================================================
Probabilistic PCA and Factor Analysis are probabilistic models.
The consequence ... | bsd-3-clause |
boada/ICD | sandbox/lowerSN/plot_sersic_vs_icd_vs_mass_box_width.py | 1 | 6738 | #!/usr/bin/env python
import pylab as pyl
from mpl_toolkits.axes_grid1 import AxesGrid
import cPickle as pickle
from colsort import colsort
def plot_uvj_vs_icd():
galaxies = pickle.load(open('galaxies.pickle','rb'))
galaxies = filter(lambda galaxy: galaxy.ICD_IH != None, galaxies)
galaxies = filter(lambda ... | mit |
JackKelly/neuralnilm_prototype | scripts/e433.py | 2 | 7273 | from __future__ import print_function, division
import matplotlib
import logging
from sys import stdout
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
from neuralnilm import (Net, RealApplianceSource,
BLSTMLayer, DimshuffleLayer,
Bidirectio... | mit |
dataplumber/nexus | analysis/webservice/algorithms_spark/HofMoellerSpark.py | 1 | 13208 | """
Copyright (c) 2016 Jet Propulsion Laboratory,
California Institute of Technology. All rights reserved
"""
import itertools
import logging
import traceback
from cStringIO import StringIO
from datetime import datetime
from multiprocessing.dummy import Pool, Manager
import matplotlib.pyplot as plt
import mpld3
impor... | apache-2.0 |
andyraib/data-storage | python_scripts/env/lib/python3.6/site-packages/pandas/tests/indexes/test_multi.py | 7 | 102429 | # -*- coding: utf-8 -*-
from datetime import timedelta
from itertools import product
import nose
import re
import warnings
from pandas import (DataFrame, date_range, period_range, MultiIndex, Index,
CategoricalIndex, compat)
from pandas.core.common import PerformanceWarning
from pandas.indexes.bas... | apache-2.0 |
akionakamura/scikit-learn | examples/cluster/plot_kmeans_stability_low_dim_dense.py | 338 | 4324 | """
============================================================
Empirical evaluation of the impact of k-means initialization
============================================================
Evaluate the ability of k-means initializations strategies to make
the algorithm convergence robust as measured by the relative stan... | bsd-3-clause |
macks22/scikit-learn | sklearn/neighbors/tests/test_nearest_centroid.py | 305 | 4121 | """
Testing for the nearest centroid module.
"""
import numpy as np
from scipy import sparse as sp
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from sklearn.neighbors import NearestCentroid
from sklearn import datasets
from sklearn.metrics.pairwise import pairwise_distances
# t... | bsd-3-clause |
satonreb/machine-learning-using-tensorflow | scripts/02_linear_regression.py | 1 | 6206 | import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
# Data Preparation =====================================================================================================
# Synthetic D... | gpl-3.0 |
jpautom/scikit-learn | examples/ensemble/plot_bias_variance.py | 357 | 7324 | """
============================================================
Single estimator versus bagging: bias-variance decomposition
============================================================
This example illustrates and compares the bias-variance decomposition of the
expected mean squared error of a single estimator again... | bsd-3-clause |
okadate/bgc | plot/plot_profiles.py | 1 | 1896 | # -*- coding: utf-8 -*-
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import os
from bgc_userconfig import *
def plot_profiles(outdir, outfile, pngfile, obsfile):
outfile = os.path.join(outdir, outfile)
pngfile = os.path.join(outdir, pngfile)
print outfile
out = pd.... | mit |
huobaowangxi/scikit-learn | sklearn/feature_extraction/dict_vectorizer.py | 234 | 12267 | # Authors: Lars Buitinck
# Dan Blanchard <dblanchard@ets.org>
# License: BSD 3 clause
from array import array
from collections import Mapping
from operator import itemgetter
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..ext... | bsd-3-clause |
LargePanda/LearnPy | learnpy/models/NaiveBayes.py | 1 | 4229 | __author__ = 'Jiarui Xu'
from learnpy.models.Model import Model
import pandas as pd
from learnpy.models.ModelUtil import *
import operator
import time
# to be implemented next week
class NaiveBayes(Model):
def __init__(self, data, class_column):
"""
Constructor for the Naive Bayes Model
... | mit |
saiwing-yeung/scikit-learn | examples/svm/plot_separating_hyperplane.py | 294 | 1273 | """
=========================================
SVM: Maximum margin separating hyperplane
=========================================
Plot the maximum margin separating hyperplane within a two-class
separable dataset using a Support Vector Machine classifier with
linear kernel.
"""
print(__doc__)
import numpy as np
impor... | bsd-3-clause |
costypetrisor/scikit-learn | sklearn/ensemble/tests/test_partial_dependence.py | 365 | 6996 | """
Testing for the partial dependence module.
"""
import numpy as np
from numpy.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import if_matplotlib
from sklearn.ensemble.partial_dependence import partial_dependence
from sklearn.ensemble.partial_dependence... | bsd-3-clause |
YuxingZhang/prescription | visualize/tsne.py | 1 | 5462 | #
# tsne.py
#
# Implementation of t-SNE in Python. The implementation was tested on Python 2.7.10, and it requires a working
# installation of NumPy. The implementation comes with an example on the MNIST dataset. In order to plot the
# results of this example, a working installation of matplotlib is required.
#
# The ... | bsd-3-clause |
JohanComparat/pySU | spm/bin/combine_model_spectra.py | 1 | 10387 | import time
t0t=time.time()
from os.path import join
import os
import numpy as n
import glob
import sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as p
import astropy.io.fits as fits
from scipy.interpolate import interp1d
from scipy.stats import norm as gaussD
plate = sys.argv[1]
mjd =... | cc0-1.0 |
Myasuka/scikit-learn | sklearn/feature_selection/tests/test_rfe.py | 209 | 11733 | """
Testing Recursive feature elimination
"""
import warnings
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
from nose.tools import assert_equal, assert_true
from scipy import sparse
from sklearn.feature_selection.rfe import RFE, RFECV
from sklearn.datasets import load_iris,... | bsd-3-clause |
abitofalchemy/hrg_nets | generalized_exact_phrg.py | 1 | 18116 | #!/usr/bin/env python
# make the other metrics work
# generate the txt files, then work on the pdf otuput
# TODO:load_edgelist
from vador.load_edgelist import print_treewidth
__version__ = "0.1.0"
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use('pdf')
import matplotlib.pyplot as plt
import sys... | gpl-3.0 |
pmorissette/ffn | setup.py | 1 | 1415 | import os
import re
import setuptools
with open(os.path.join(os.path.dirname(__file__), "ffn", "__init__.py"), "r") as fp:
version = re.search(
"^__version__ = \\((\\d+), (\\d+), (\\d+)\\)$", fp.read(), re.MULTILINE
).groups()
with open(os.path.join(os.path.dirname(__file__), "README.rst"), "r") as ... | mit |
evgchz/scikit-learn | sklearn/neighbors/tests/test_dist_metrics.py | 48 | 4949 | import itertools
import numpy as np
from numpy.testing import assert_array_almost_equal
import scipy
from scipy.spatial.distance import cdist
from sklearn.neighbors.dist_metrics import DistanceMetric
from nose import SkipTest
def cmp_version(version1, version2):
version1 = tuple(map(int, version1.split('.')[:2]... | bsd-3-clause |
saildata/data-science-from-scratch | code-python3/visualizing_data.py | 12 | 5036 | import matplotlib.pyplot as plt
from collections import Counter
def make_chart_simple_line_chart():
years = [1950, 1960, 1970, 1980, 1990, 2000, 2010]
gdp = [300.2, 543.3, 1075.9, 2862.5, 5979.6, 10289.7, 14958.3]
# create a line chart, years on x-axis, gdp on y-axis
plt.plot(years, gdp, color='green... | unlicense |
jenniew/BigDL | pyspark/bigdl/optim/optimizer.py | 1 | 29556 | #
# Copyright 2016 The BigDL 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 in ... | apache-2.0 |
robbymeals/scikit-learn | examples/ensemble/plot_adaboost_twoclass.py | 347 | 3268 | """
==================
Two-class AdaBoost
==================
This example fits an AdaBoosted decision stump on a non-linearly separable
classification dataset composed of two "Gaussian quantiles" clusters
(see :func:`sklearn.datasets.make_gaussian_quantiles`) and plots the decision
boundary and decision scores. The di... | bsd-3-clause |
clemkoa/scikit-learn | examples/applications/plot_tomography_l1_reconstruction.py | 27 | 5478 | """
======================================================================
Compressive sensing: tomography reconstruction with L1 prior (Lasso)
======================================================================
This example shows the reconstruction of an image from a set of parallel
projections, acquired along dif... | bsd-3-clause |
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