text string |
|---|
<filename>sysopt/symbolic/symbols.py
"""Functions and factories to create symbolic variables."""
import weakref
from abc import ABCMeta, abstractmethod
from collections import defaultdict
from inspect import signature
import numpy as np
from scipy.sparse import dok_matrix, spmatrix
from typing import Union, List, Call... |
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 10 14:26:54 2019
@author: Mnemosyne
Functions to compute the features of the song
"""
import os
import shutil
import glob
import sys
import random
import re
import numpy as np
import scipy as sp
import scipy.io.wavfile as wav
from scipy.fftpack import fft, rfft
from sci... |
<filename>lesson5_lin_regression.py
import numpy as np
import scipy.optimize
def gradient_model(x, I_0, a, lam):
if np.any(np.array(x) < 0):
raise RuntimeError('x must be positive')
if np.any(np.array([I_0, a, lam] < 0)):
raise RuntimeError('all params must be positive')
return a + I_0 * np... |
<reponame>takaaki82/Java-Lessons<filename>AtCoder/CPSCO2019Session1/C.py
import bisect
import collections
import copy
import fractions
import heapq
import itertools
import math
import string
import sys
inf = float("inf")
mod = 10 ** 9 + 7
d4 = [(1, 0), (0, -1), (-1, 0), (0, 1)]
d8 = [(1, 0), (1, -1), (0, -1), (-1, -1)... |
import numpy as np
import pandas as pd
from scipy.signal import butter, filtfilt, find_peaks
def _butter_highpass(cutoff, fs, order=5):
nyq = 0.5 * fs
normal_cutoff = cutoff / nyq
b, a = butter(order, normal_cutoff, btype='high', analog=False)
return b, a
def highpass_filter(data, cutoff, fs, order=5)... |
<reponame>RINKESH0806/ga-learner-dsmp-repo<filename>ga-learner-dsmp-repo/code.py
# --------------
# Import packages
import numpy as np
import pandas as pd
from scipy.stats import mode
bank=pd.read_csv(path)
#print(bank)
categorical_var=bank.select_dtypes(include='object')
print(categorical_var)
numerical_var=bank.sele... |
import json
import traceback
from datetime import timedelta
from urllib.parse import quote
import numpy as np
import pandas as pd
import requests
from django.core.cache import cache
from django.utils import timezone
from loguru import logger
from pals.PLAGE import PLAGE
from pals.common import DATABASE_REACTOME_CHEBI,... |
import logging
import numpy as np
import scipy
import numba
import opt_einsum
import pyfftw
import mathx
from .. import bvar
from . import sa
logger = logging.getLogger(__name__)
fft = lambda Ar, axis=-1:pyfftw.interfaces.numpy_fft.fft(Ar, norm='ortho', axis=axis)
ifft = lambda Ak, axis=-1:pyfftw.interfaces.numpy_fft... |
###############################################################################
#
# PhenoPy is a Python 3.X library to process phenology indices derived from
# EarthObservation data.
#
###############################################################################
# libraries included in Python 3.X
from __future__ imp... |
#!/usr/bin/env python
"""
This module is a Keras/Tensorflow based implementation of Pyramid Scene Parsing Networks.
Original paper & code published by <NAME> et al. (2017)
"""
from __future__ import print_function
from __future__ import division
from os.path import splitext, join, isfile
from os import environ
from ma... |
import json
import random
import os
import time
import cv2
import numpy as np
from torch.utils.data import Dataset, DataLoader
import torch
from scipy import ndimage
from Configs import Config, features_clustering
from sklearn.metrics.cluster import normalized_mutual_info_score
import glob
from tqdm import tqdm
# Tra... |
<gh_stars>0
#Schrodinger 1D equation solved by finding eigenvectors of the hamiltonian
import numpy as np
import scipy
import scipy.integrate
import scipy.linalg
import scipy.misc
import scipy.special
import pylab as plt
l=10
N=1024
def v(x):
return m*w*x*x/2
m=1
w=1
hbar=1
X, dx=np.linspace(-l, l, N, retstep=True)
... |
<reponame>zhao-david/CDE-conformal
import numpy as np
import pandas as pd
import scipy.stats
import local_conformal as lc
def test_difference_validity_and_efficiency_basic():
"""
basic test for difference_validity_and_efficiency
"""
true_cde = np.ones((5,4))
predict_grid = np.arange(20).reshape(5,... |
from IPython.display import display
import numpy
from scipy.optimize import linprog
C=12 #numbers of RSUs
S=100
'''%Utility computing'''
'''W=randi([10 20],C);%average trip delay at intersection i after attack'''
W_B = numpy.random.random((1, C)) #its ndArray
#W = numpy.matrix(W_B)# it is matrix
W=... |
## Generates Figs 5, 6 & 7 of SEG 2016
## ============================================================================
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import quad
from matplotlib.patches import Ellipse
import flattened as fJ
from scipy.optimize import curve_fit
import seaborn as ... |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
from six import string_types
from scipy.spatial import distance
from Bio.PDB import PDBParser
from Bio.PDB import NeighborSearch
RESIDUELIST = ['ALA', 'ARG', 'ASP', 'ASN', 'CYS', 'GLU', 'GLN', 'GLY', 'HIS',
'ILE', 'LEU', 'LYS', 'MET', 'PHE', 'PRO', 'SER', ... |
<gh_stars>0
"""
Run this after run_analysis.py
"""
import logging
import numpy as np
import sys
import pickle
import tqdm
from collections import OrderedDict
from astropy.io import fits
from astropy.table import Table
from scipy.special import logsumexp
from scipy import stats
import npm_utils as npm
def lnprob(y... |
<reponame>charliewen95/flowNetwork<filename>current_flow_allostery/betweenness.py
"""
This is the function that does the betweenness calculation
"""
try:
import argparse
except ImportError:
raise ImportError("require argparse")
#numerical / data packages
try:
import numpy as np
np.set_printoptions(thre... |
<filename>data/Imageset/image_split.py
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 15 19:31:34 2017
@author: justjay
"""
#LIVE2 split images randomly
#Train: 17, Validation: 6, Test: 6
#Train: 23, Test: 6; train:test ~= 8:2
import numpy as np
import scipy.io as sio
names_mat = sio.loadmat('./refnames_all.mat')... |
import argparse
import functools
import json
import os
import numpy as np
import sys
from collections import Counter, defaultdict
from joblib import Parallel, delayed
from scipy.stats import pearsonr, spearmanr, kendalltau
sys.path.append('.')
from sacrerouge.commands.correlate import load_metrics, filter_metrics, me... |
<reponame>xa4a/unshred<filename>unshred/features/lines.py
import math
import cmath
import cv2
import numpy
from unshred import threshold
from unshred.features import AbstractShredFeature
DEBUG = False
MEAN, MEDIAN = range(2)
def _get_dominant_angle(lines, domination_type=MEDIAN):
"""Picks dominant angle of a... |
"""A few utility function for the pruner."""
import copy
import math
import numpy as np
import torch
from scipy import optimize
def _adapt_uniform_sample_size(probabilities, sample_size_target):
"""Do _adapt_sample_size for a uniform distribution (faster)."""
idx = probabilities.nonzero()
sample_size_tar... |
<gh_stars>0
import pandas as pd
from scipy.spatial.kdtree import KDTree
class NeighborsFinder:
def __init__(self,data):
"""Find neigbors
Args:
data (pd.DataFrame or dict): data with at columns id,pos,and eventually range
"""
if data is not None:
sel... |
<gh_stars>0
'''Main code with the analysis for the Methane project
Check the performance of EC vs SR vs WAVELET vs Flux-Variance
Also check flux variance relations for all runs
'''
# load dataframes with results
# and all stations
import os
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as... |
import random
import numpy as np
import scipy.optimize
import scipy.special
import math
import yaml
from enum import IntEnum
#import UWBsim
from UWBsim.utils import dataTypes
class RangingType(IntEnum):
NONE = 0
TWR = 1
TDOA = 2
class RangingSource(IntEnum):
NONE = 0
LOG = 1
GENERATE_GAU... |
<filename>spekpy/SpekPy.py
# For compatibility with Python2 #
from __future__ import print_function, division, absolute_import
##################################
from . import __version__
import spekpy.SpekConstants as Const
from spekpy.IO import find_file, path_file, read_spectrum_from_disk, \
get_script_path, is_... |
#encoding: utf-8
"""
grid.analysis.map_funcs -- A repository for standalone functions useful for
performing various computations on spatial map arrays.
Exported namespace: remap_quiver_plot, spatial_corr, linearize_spatial_corr,
peak_vs_neighbors, input_vs_output_norms, haff_vs_r_peaks,
field_comparison_... |
# import numpy as np
# import cv2
# from sympy import *
# import matplotlib.pyplot as plt
# image=plt.imread('1.jpeg')
# def normalize(data):
# _range = np.max(data) - np.min(data)
# return (data - np.min(data)) / _range
# # def GaussKernel(sig=5,m=0):
# # if m == 0:
# # m = int(sig * 2 * 3 + 1... |
import os, time, random
import numpy as np
import scipy
import tensorflow as tf
import tensorlayer as tl
from model import *
from utils import *
from config import *
import cv2
import math
import pdb #用于debug跟踪调试
oriImageName = "samples/test/test_origin.bmp"
resImageName = "samples/test/test_out.bmp"
if __name__ == ... |
<filename>autumn/economics.py
# external imports
import scipy
import numpy
def get_cost_from_coverage(coverage, inflection_cost, saturation, unit_cost, popsize, alpha=1.):
"""
Estimate the raw cost associated with a given coverage for a specific intervention.
Args:
coverage: The intervention's c... |
<gh_stars>0
import load_data
import os
import numpy as np
import scipy.ndimage
from sklearn import svm, preprocessing
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
def l... |
<filename>trainer.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
#【trainer】
#
# 概要: IMDB 推論 学習用クラス
#
# 更新履歴:
# 2019.03.31 新規作成
#
import pathlib
import tqdm
import logging
from statistics import mean
import torch
from torch import nn, optim
from torch.utils.data import Dataset, DataLoader, TensorDataset
fr... |
<filename>external/AR/ltr/dataset/sbd.py
from .base_image_dataset import BaseImageDataset
from ltr.data.image_loader import jpeg4py_loader_w_failsafe
import torch
from collections import OrderedDict
import os
from scipy.io import loadmat
from ltr.data.bounding_box_utils import masks_to_bboxes
from ltr.admin.environmen... |
<gh_stars>0
#!/usr/bin/env python3
import time
import statistics
import sys
import gzip
import string
import argparse
import re
import itertools
import yaml
import regex
from Bio import SeqIO
from Bio import Seq, SeqRecord
# TODO pass description to flags field, but this requires lots of warnings
# and caveats to th... |
<reponame>jasminwash/hostgal<filename>mask_objects.py
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 29 15:01:17 2018
@author: Dartoon
"""
import numpy as np
import astropy.io.fits as pyfits
import matplotlib.pyplot as plt
from photutils import detect_threshold
from astropy.convolution import Ga... |
import os
import warnings
import matplotlib as mpl
import seaborn as sb
import numpy as np
import matplotlib.pyplot as plt
import statistics
import pandas as pd
import logging
from df_creation import postprocess_vertex_df, get_users_map, get_file_tag
from dataset_paths import get_postprocess_folder_general, get_common... |
<filename>src/S1B_cc_pair_MPI.py
import gc
import sys
import time
import scipy
import obspy
import pyasdf
import datetime
import os, glob
import numpy as np
import pandas as pd
import noise_module
from mpi4py import MPI
from scipy.fftpack.helper import next_fast_len
import matplotlib.pyplot as plt
|
<filename>tests/test_transforms.py
"""
This module provides some tests of the transform, based on analytic transforms provided by Wikipedia.
Note that most of the tests in here are mirrored in test_known_integrals, in which k is set to 1.
There is a corresponding notebook in devel/ that runs each of these functions t... |
<reponame>sweigart/pygama<filename>experiments/mj60/calibration.py
#!/usr/bin/env python3
import argparse
import numpy as np
import pandas as pd
import tinydb as db
import matplotlib.pyplot as plt
import itertools as it
from scipy.stats import mode
from pprint import pprint
from pygama import DataSet
from pygama.utils ... |
# Copyright 2019 1QBit
#
# 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 ... |
import argparse
import collections
import gzip
import json
import logging
import math
import os
import statistics
import time
from typing import Dict, List, TypedDict
from roaringbitmap import RoaringBitmap
from sparklines import sparklines
from tdigest import RawTDigest
from engineering_notation import EngNumber
log... |
<reponame>atisor73/caulobacterpkg
import numpy as np
import pandas as pd
import random
import scipy.stats
import bokeh
import bokeh.palettes
from bokeh.transform import linear_cmap
import bokeh_catplot
import bokeh.models
import bebi103
import holoviews as hv
hv.extension('bokeh')
import bokeh.io
bebi103.hv.set_defau... |
"""
Not using
Download from https://raw.githubusercontent.com/EricYizhenWang/robust_nn_icml/master/robust_1nn.py
"""
from __future__ import division
import numpy as np
from sklearn.neighbors import NearestNeighbors, KNeighborsClassifier
from scipy.spatial.distance import cdist
from .eps_separation import find_eps_sep... |
# -*- coding: utf-8 -*-
"""
other_algorithms.py: Baseline algorithms to compare against.
"""
import pdb
import numpy as np
from scipy.optimize import least_squares
from pylocus.lateration import SRLS
from coordinate_fitting import fit_trajectory
from solvers import trajectory_recovery
EPS = 1e-10
def error_meas... |
# -*- coding: utf-8 -*-
import ETCetera.data.nonlinear_systems_datastructure as data
import sympy as sp
def create_template():
template_data = data.InputDataStructureNonLinear()
[template_data.hyperbox_states.append(list([i+0.2,i+3.4])) for i in range(1,4)]
[template_data.hyperbox_disturbances.append(list... |
from inspect import Traceback
from pickle import NONE
import pickle
from random import random, shuffle
from altair.vegalite.v4.api import value
from altair.vegalite.v4.schema.channels import Column
from numpy.core.fromnumeric import choose
from numpy.lib.npyio import save
import pandas,sys
from scipy.sparse import data... |
from scipy import stats
from matplotlib import pyplot as plt
from matplotlib.pyplot import figure
import numpy as np
import pandas as pd
import os
# Tries to load excel file from input folder
def loadfile():
try:
path = 'input/' + os.listdir('input')[0]
data = pd.read_excel(path)
except IndexEr... |
<filename>python/lm_layers.py
import sys
sys.path.insert(0, '/home/chenweiliang/caffe-windows-ms/python')
import caffe
import numpy as np
from PIL import Image
import scipy.io as sio
import os
import random
class LandmarkVisualLayer(caffe.Layer):
"""
Save images to file
"""
def setup(self, bottom, to... |
<filename>dpm_sheet.py<gh_stars>1-10
import argparse
from concurrent.futures import ProcessPoolExecutor
from itertools import product
from statistics.preset import get_preset_list
from dpmModule.character.characterTemplate import get_template_generator
from dpmModule.util.dpmgenerator import IndividualDPMGenerator
tr... |
<gh_stars>0
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from scipy.special import cbrt
from .config import *
_lim_val = np.finfo(np.float64).max
_lim_val_min = np.finfo(np.float64).min
_lim_val_exp = np.log(_lim_val)
_lim_val_squa... |
"""
Erase texture seams to prevent visible seams or tearing in various texture
maps (color, normal, displacement, ambient occlusion, etc.)
Written by <NAME>
"""
from __future__ import print_function
import sys
from multiprocessing import Process
from recordclass import recordclass
import scipy
import numpy
import... |
from typing import List, Optional
from dataclasses import dataclass
from scipy.stats import median_abs_deviation, mode
import numpy as np
import seaborn as sb
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import tikzplotlib
import pandas as pd
@dataclass
class TimeDelta:
d: float
h: float... |
<filename>12.1.py
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import minimize_scalar
def f(p):
def ff(p):
return 8 * np.exp(-(p/4.0)) + 6 * np.exp(-(p/2.0))
if (p < 0.96):
return (ff(p)-10) + p*(14 - ff(p))
elif (p>=0.96 and p<1.27):
return... |
""" Utilities for sympy. ***
SUffix conventions
*Arr - numpy array
*Mat - sympy.Matrix N X N
*Vec - sympy.Matrix N X 1
*s - list
Notes
1. By default, symbols are added to locals() of the caller.
This can be changed by using the dct optional keyword.
This means that all user callable functions must captu... |
import numpy as np
from scanorama import plt
from scipy.sparse import csr_matrix
from time import time
from geosketch import *
from utils import log
if __name__ == '__main__':
X_dimred = np.loadtxt('data/dimred/svd_zeisel.txt')
X_dimred = X_dimred[:500000]
sizes = [ 1000000, 2500000, 5000000, 100000... |
# coding: utf-8
# # 2-Variables and Basic Types
#
#
# Always run this statement first, when working with this book:
# In[1]:
from scipy import *
from matplotlib.pyplot import *
get_ipython().magic('matplotlib inline')
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivi... |
<filename>passiveRadar/signal_utils.py
import numpy as np
import scipy.signal as signal
def normalize(x):
'''normalize ndarray to unit mean'''
return x/np.mean(np.abs(x).flatten())
def decimate(x, q):
'''decimate x by a factor of q'''
return signal.decimate(x, q, n=255, ftype='fir', axis=0, zero_phase... |
#!/usr/bin/env python
# coding: utf-8
# # Scenario
#
# As an analyst for OilyGiant mining company our task is to find the best place for a new well.
#
# We will use several techniques, including machine learning and bootstrapping, to select the region with the highest profit margin.
#
# Machine learning prediction... |
"""
copyright (c) 2017 <NAME>
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. you should have received a copy
of the gnu genera... |
import numpy as np
from bananas.pipelines import mean_warszycki_logki
from bananas.worlds import (
MolPrint2D,
TanimotoMinMaxKernel,
TanimotoMinMaxSimilarity,
BalancedAgglomerativeClustering,
CrossValidation,
SubsetData,
StorageWorld,
Variable,
DataDict,
)
import crisper
from herbiv... |
<gh_stars>0
import numpy as np
from scipy import stats
from matplotlib import pyplot as plt
import time
import matplotlib.animation as animation
from astropy import convolution as conv
TIMESTEP = 0.1
def Gen_Static_Model(nmax=None):
count = 0
X, Y = np.meshgrid(np.linspace(-5, 5, 100), np.linspace(-5, 5, 10... |
# computes and graphs a PCA of a sample of light curves
# for the purposes of dimensionality reduction
import numpy as np
import pyfits as pf
import itertools
from scipy.io.idl import readsav
from scipy.spatial import distance
import pylab as pl
from sklearn import metrics
from sklearn.ensemble import RandomForestClas... |
<gh_stars>1-10
# -*- coding: utf-8 -*-
#
# halft_np.ones.py - Modules for halftone
#
#
# Autor: <NAME> [<EMAIL>]
# License: GPL V.2
#
# see: http://www.sawp.com.br
#
# Sep 2011
#
import halftones.compiled as _compiled
import numpy as _np
import scipy.misc as _spm
def __get_ordered_threshold(type="clustered dot"):
... |
import sys
import scipy.misc
from matplotlib import cm
from PIL import Image
#Parse command line arguments
filename = sys.argv[1]
basename = filename.split('.')
#Read input image
im = scipy.misc.imread(filename, flatten=True)
#Read crosshair overlay
overlay = Image.open("crosshair.png")
overlaycolor = Image.open("... |
<filename>asvgd/experiments.py
import sys
sys.path.append('../')
import time
import torch
import torch.nn.functional as F
from torch.distributions.normal import Normal
import numpy as np
from svgd.experiments import OneDimNormalMixture, generate100Dim
from svgd.svgd import RBF_kernel
from asvgd import asvgd
from scipy.... |
<filename>kge_from_text/bridges/table_linking_bridge.py
import pandas as pd
import itertools
from kge_from_text.utils import helpers
from scipy.spatial.distance import cosine
import numpy as np
class TableLinkingBridge:
"""
This class handles bridging from word to entities
"""
def __init__(self, bri... |
import scipy.misc
from visdom import Visdom
import numpy as np
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--datafile',
type=str,
default='',
help='Path to data file')
args = parser.parse_args()
# Image size of grid world
grid_ims... |
from __future__ import unicode_literals
import numpy as np
import json
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import spearmanr, kendalltau, shapiro, kstest
from sklearn.metrics import precision_score, recall_score
def get_sorted(sortKey, idx, time_array, reverse=Fa... |
<gh_stars>0
import pathlib
_2020 = pathlib.Path('2020')
try:
latest_day = max(_2020.glob('day??.py'))
new_day = int(latest_day.stem[-2:]) + 1
except ValueError:
# No days yet
new_day = 1
code = """\
import collections, datetime, decimal, difflib, fractions, functools, \\
itertools, math, oper... |
# -*- coding: utf-8 -*-
"""
LTF_ML_Notchfilter_py.py ==========================================
Kapitel "LTI-Systeme im Frequenzbereich"
Musterloesung zur Aufgabe "Notchfilter"
(c) 2014-Feb-04 <NAME> - Files zur Vorlesung "DSV auf FPGAs"
======================================================================... |
<gh_stars>1-10
import pandas as pd
import math
from .bayesian.main import binomial_ab_test, gaussian_ab_test
from scipy.stats.distributions import chi2
# Adjust metric stats to account for unconverted users
def get_adjusted_stats(x, sx, c, n, ignore_nulls=False, type="binomial"):
if type == "binomial":
re... |
import csv
import math
import scipy as sp
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import datetime
import networkx as nx
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from modules import *
#### databeses from NYC open city dataset
T = pd.r... |
# -*- coding: utf-8 -*-
# Released under the MIT License; see LICENSE.
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import trapz
from scipy.stats import norm
from scipy.misc import logsumexp
from sampler import slice_sample
# Some sample distributions, add your own!
def bimodal_normal():... |
#imports--------------------------------------------------------------------------------------------------------------------
import os
import glob
import sys
#import wget
import time
import subprocess
import shlex
import sys
import warnings
import random
import pickle
from Bio.SeqUtils import seq1
from Bio.PDB.PDBPars... |
<gh_stars>0
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 22 16:34:02 2019
This is an example of a parallelised script that does:
- Flatfield correction with SSIM
- 4-distance CTF-reconstruction (incl. correction for shifts)
- Stripe artifact reduction
- Automatic rotation axis detection
- Tomo-... |
<reponame>trevorcampbell/ubvi
import numpy as np
from scipy.stats import norm, multivariate_normal
import unittest
from distributions import Gaussian
class TestGaussian(unittest.TestCase):
def setUp(self):
self.g1f = Gaussian(1, False)
self.g2f = Gaussian(2, False)
self.g4t = Gaussian(... |
<reponame>Dorebom/robot_pybullet
import numpy as np
import pybullet as p
from scipy.spatial.transform import Rotation as R
class InverseKinematics6():
def __init__(self, robot_id, link_param, tool_pose):
self.robot_id = robot_id
self.link_param = link_param
self.tool_pose = tool_pose
... |
<filename>Face_recognition_02.py<gh_stars>0
from scipy.spatial.distance import cosine
import numpy as np
import cv2
import mtcnn
from keras.models import load_model
from utils import get_face, plt_show, get_encode, load_pickle, l2_normalizer
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
from sk... |
<gh_stars>0
import numpy as np
from scipy.integrate import fixed_quad
I_exact = np.log(3)
print(f"I_exact is {I_exact} (=ln(3))")
n = 1
while True:
I_approx = fixed_quad(lambda x : 1/(1-x), -1/2, 1/2, n=n)[0]
n += 1
print(f"I_approx is {I_approx} with order of quadrature integration n={n}")
if I_appro... |
<reponame>kbefus/ca_gw_slr
# -*- coding: utf-8 -*-
"""
Created on Wed May 11 13:53:40 2016
@author: kbefus
"""
from __future__ import print_function
import os,sys,shutil
from osgeo import osr, ogr
import numpy as np
import shapefile
from shapely.geometry import Polygon,MultiPoint,LineString,MultiPolygon#,Mu... |
<reponame>PatrickRatei/eVTOL
# Functions for rotor noise prediction
from __future__ import print_function
import numpy as np
import math
pi = math.pi
from scipy.special import jv
from gpkit import Model, ureg
from matplotlib import pyplot as plt
from aircraft_models import OnDemandAircraft
from mission_models import ... |
<reponame>Sharpiless/GraphGallery
import numpy as np
import scipy.sparse as sp
from graphgallery.transforms import Transform
class SparseAdjToSparseEdges(Transform):
def __call__(self, adj_matrix):
return sparse_adj_to_sparse_edges(adj_matrix)
def __repr__(self):
return f"{self.__class__.__na... |
# Copyright 2020 MIT Probabilistic Computing Project.
# See LICENSE.txt
from collections import OrderedDict
from itertools import chain
from itertools import combinations
from math import isinf
import numpy
import sympy
from sympy.core.relational import Relational
from .sets import FiniteReal
from .sets import Inte... |
<gh_stars>1-10
from __future__ import print_function, division
from matplotlib import pyplot as plt
import scipy.integrate as integrate
import numpy as np
from getMags import quasarMag
import scipy.stats
import subprocess
import sys
import os
import shutil
import argparse
import warnings
import datetime
import config... |
"""Reconstruction routines."""
import numpy as truenp
from prysm.mathops import np
def prepare_Zmm(zernike_chops, flux_mode=None, shear_chops=None, mask=None):
"""Assemble Zmm from chop images.
Parameters
----------
zernike_chops : `iterable` of numpy.ndarray
set of zernike chops. Assumed ... |
<filename>quantizer/pq.py
from __future__ import division
from __future__ import print_function
import numpy as np
from scipy.cluster.vq import vq, kmeans2
class PQ(object):
def __init__(self, M, Ks, verbose=True):
assert 0 < Ks <= 2 ** 32
self.M, self.Ks, self.verbose = M, Ks, verbose
sel... |
<gh_stars>1-10
import numpy as np
from numpy.linalg import inv, svd
from numpy.random import multivariate_normal, randn, rand
from logistic import logit
from optim_algo import (Newton_MR, Newton_CG, L_BFGS, Gauss_Newton,
MomentumSGD, Adagrad, Adadelta, RMSprop, Adam, SGD)
from loaddata import l... |
<gh_stars>1-10
# Siamese Architecture for face recognition
import random
import numpy as np
import time
import tensorflow as tf
import math
import pdb
import sys
import scipy.io as sio
from sklearn import *
import matplotlib.pyplot as plt
def Plot_PR_Fn(label,distance,phase):
precision, recall, thresholds = metr... |
<filename>testRegression.py
# _*_coding: utf-8_*_
import hashlib
import os
import tarfile
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn
from six.moves import urllib
DOWNLOAD_ROOT = 'https://raw.githubusercontent.com/ageron/handson-ml/master/'
# print('currect... |
# a number of utilities that can be used to store and analyze results of GLOFRIM
import fiona
import xarray as xr
import numpy as np
import rasterio
import rasterio.features
from scipy.signal import convolve2d
def perpendicular(line, reverse=False):
"""
determines the direction of a perpendicular line
inpu... |
<reponame>cactuskid/Fourier2Struct<filename>notebooks/ProFET/ProFET/feat_extract/Model_Parameters_CV.py
from __future__ import division
from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier, \
GradientBoostingClassifier
import numpy as np
from sklearn import metrics, cross_validation #, linear_... |
<reponame>acrellin/cesium
import numpy as np
import scipy.stats as stats
from ._lomb_scargle import lomb_scargle
def lomb_scargle_model(time, signal, error, sys_err=0.05, nharm=8, nfreq=3, tone_control=5.0):
"""Simultaneous fit of a sum of sinusoids by weighted least squares:
y(t) = Sum_k Ck*t^k + Sum_... |
import pandas as pd
import numpy as np
from scipy.stats import binned_statistic
from scipy.optimize import curve_fit, newton
import pmesh
#from halotools.mock_observables import tpcf
#from halotools import mock_observables as mo
from nbodykit.lab import *
from astrild.utils import read_hdf5
class SubFind:
def ... |
"""
Module containing functions to act on the Spatial Fourier Coefficients.
`BEMA`
Perform Bandwidth Extension for Microphone Arrays (BEMA) spatial
anti-aliasing.
`FFT`
Perform real-valued Fast Fourier Transform (FFT).
`iFFT`
Perform inverse real-valued (Fast) Fourier Transform (iFFT).
`spatFT`
Per... |
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from scipy import stats
plt.switch_backend('agg')
### 两个数据的比率图
# f, ax = plt.subplots(figsize=(16, 8))
# fig = sns.boxplot(x=var, y="SalePrice", data=data)
# fig.axis(ymin=0, ymax=800000)
# plt.xticks(rotation=90)
#### 画总体概览图
def f3():
#... |
#!/usr/bin/python3.6
import json
import pickle
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
from tqdm import tqdm
from scipy.stats import describe
from debug import dprint
with open('imagenet1000.txt') as f:
imagenet = eval(f.read())
categories = lis... |
import gurobipy as gp
from gurobipy import GRB
import numpy as np
import scipy.sparse as sp
try:
# 创建模型
m = gp.Model("model_name")
# 创建变量
x1 = m.addVar(vtype=GRB.BINARY, name="x1") # 0,1变量
x2 = m.addVar(vtype=GRB.BINARY, name="x2") # 0,1变量
'''x3 = m.addMVar(shape=3, vtype=GRB.BINARY , name ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from scipy import sparse
data = pd.read_excel('curated_df_11_3.xlsx')
SMILES = data['SMILES']
protein = data['Protein']
Ki = data['Ki']
SMILES_string_length = 100
protein_string_length = 1000
SMILES_n_encodings = 28
protein_n_encod... |
#Importo librerias
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
from scipy.optimize import curve_fit
import glob
# Altura de las mediciones
z=2.85
camino = 'D:\\Mediciones\\Filtrados\\*'
# Para calcular los otros espectros cambiar aca
direccion = glob.glob( camino + '\... |
# -*- coding: utf-8 -*-
import numpy as np
from scipy.stats.distributions import norm, lognorm, rv_frozen
class BrownianMotion:
'''Brownian Motion (Wiener Process) with optional drift.'''
def __init__(self, mu: float=0.0, sigma: float=1.0):
self.mu = mu
self.sigma = sigma
def simulate(se... |
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