prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#Script to do a grid search of gas dump mass and gas dump time
#Compares against 4 different sets of ages - linear correct form astroNN; lowess correct from astroNN; Sanders & Das; APOKASC
import numpy as np
import matplotlib.pyplot as plt
import math
import h5py
import json
from astropy.io import fits
from astropy.tab... | pd.isna(apokasc_data['rl']) | pandas.isna |
import numpy as np
import pandas as pd
import re
from itertools import chain
def import_csvs(pbp_cols, year=2018, weeks=16, encoding="ISO-8859-1"):
for week in range(1, weeks + 1):
df = pd.DataFrame(
pd.read_csv(f"../data/raw/pbp/{year} Week {week}.csv", encoding=encoding),
columns... | pd.DataFrame(columns=matchup_cols) | pandas.DataFrame |
from urllib.request import urlopen
from http.cookiejar import CookieJar
from io import StringIO
from app.extensions import cache
from app.api.constants import PERMIT_HOLDER_CACHE, DORMANT_WELLS_CACHE, LIABILITY_PER_WELL_CACHE, TIMEOUT_15_MINUTES, TIMEOUT_60_MINUTES, TIMEOUT_12_HOURS, TIMEOUT_1_YEAR
from flask import Fl... | pd.notnull(x) | pandas.notnull |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 4 14:39:07 2021
This scripts tests for the (in)dependence between tide and skew surge
@author: acn980
"""
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import os,sys,glob
import scipy.stats as sp
import statsmodels.api as sm
sys.path.insert(0,r... | pd.datetime.strptime(x, "%d-%m-%Y %H:%M:%S") | pandas.datetime.strptime |
from flask import render_template, flash, redirect, url_for, request, send_file, send_from_directory
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
from werkzeug.urls import url_parse
from app import app
from app.forms import LoginFor... | pd.merge(df_resTransform, dfGPS, on='fileName') | pandas.merge |
import time
import d2l.torch
import numpy as np
import pandas as pd
import torch.nn as nn
import torch
import torchsummary
import torchvision.io
from torchvision import datasets
from torchvision import transforms
from torch.utils import data
import matplotlib.pyplot as plt
import torch.nn.functional as F
from sklearn.p... | pd.concat((label, trainCSV), axis=1) | pandas.concat |
"""
Module for calling the FEWS REST API.
The module contains one class and methods corresponding with the FEWS PI-REST requests:
https://publicwiki.deltares.nl/display/FEWSDOC/FEWS+PI+REST+Web+Service#FEWSPIRESTWebService-GETtimeseries
"""
import pandas as pd
import geopandas as gpd
from shapely.geometry import Poin... | pd.to_numeric(df["value"]) | pandas.to_numeric |
# coding: utf-8
# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department
# Distributed under the terms of "New BSD License", see the LICENSE file.
import os
import posixpath
import numpy as np
import pandas
import tables
import warnings
from pyiron_base import Gener... | pandas.Series() | pandas.Series |
#--------------------------------------------------------
# Import Packages
#--------------------------------------------------------
from neorl.benchmarks import KP
from neorl import PPO2, DQN, ACER, ACKTR, A2C
from neorl import MlpPolicy, DQNPolicy
from neorl import RLLogger
import matplotlib.pyplot as plt
im... | pd.DataFrame(cb_acktr.r_hist) | pandas.DataFrame |
"""Module for data preprocessing.
You can consolidate data with `data_consolidation` and optimize it for example for machine learning models.
Then you can preprocess the data to be able to achieve even better results.
There are many small functions that you can use separately, but there is main function `prepr... | pd.DataFrame(data) | pandas.DataFrame |
#
# 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 ... | pd.DataFrame(self.results) | pandas.DataFrame |
#%%
import os
import xml
import heapq
import warnings
import numpy as np
import pandas as pd
from tqdm import tqdm
from shapely import wkt
import geopandas as gpd
from xml.dom import minidom
from collections import deque
import matplotlib.pyplot as plt
from haversine import haversine, Unit
from shapely.geometry import ... | pd.concat(res) | pandas.concat |
import pandas as pd
df = | pd.read_csv('data.csv', delimiter=',') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Data analysis for golf courses.
"""
# Import modules
import geopandas as gpd
import pandas as pd
import numpy as np
import glob
import matplotlib.pyplot as plt
from scipy import stats
# Define path to data
path = '/Users/jryan4/Dropbox (University of Oregon)/Parks... | pd.read_csv(infile) | pandas.read_csv |
import pandas as pd
import numpy as np
import re
from tqdm.notebook import tqdm
import random
import sklearn.metrics
from sklearn.pipeline import Pipeline
# For XGBoost Regression and Classification
import xgboost as xgb
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV, cross_val_... | pd.DataFrame(data) | pandas.DataFrame |
import pandas as pd
import numpy as np
from netCDF4 import Dataset
from PolutantsTable import PolutantsTable as pt
class DataManager:
# originalDF conté el dataframe amb les dades baixades de la XVPCA
# Ex: data/AirQualityData/QualitatAire2016TotCatalunya2016.csv
originalDF = pd.DataFrame()
'''
... | pd.datetime.strptime(x, '%d/%m/%Y') | pandas.datetime.strptime |
import folium
import pandas
df = | pandas.read_csv('oco.csv', delimiter='~') | pandas.read_csv |
# Loading Python libraries
import numpy as np
import pandas as pd
import scipy.stats as stats
import statsmodels.api as sm
import statsmodels.stats.multicomp as multi
from statsmodels.formula.api import ols
from IPython.display import Markdown
#%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
#s... | pd.DataFrame(chi2_result, columns=['Value']) | pandas.DataFrame |
#!/usr/bin/env python3
"""
Main file for processing data by age and week
"""
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from io import StringIO
from os.path import join
from sklearn.preprocessing import LabelEncoder
from matplotlib.patches import Patch
from loguru im... | pd.Series(output) | pandas.Series |
"""
Author: <NAME>
Created: 14/08/2020 11:04 AM
"""
import os
import numpy as np
import pandas as pd
from basgra_python import run_basgra_nz, _trans_manual_harv, get_month_day_to_nonleap_doy
from input_output_keys import matrix_weather_keys_pet
from check_basgra_python.support_for_tests import establish_org_input, g... | pd.read_csv(data_path, index_col=0) | pandas.read_csv |
from flask import Flask, render_template, request, redirect, url_for,session
import os
from os.path import join, dirname, realpath
from joblib import dump,load
import xgboost
import pandas as pd
import sklearn
import numpy as np
from flask import Flask, render_template, redirect, request, session
app = Fl... | pd.to_datetime('2009-12-01',format='%Y-%m-%d') | pandas.to_datetime |
'''
/*******************************************************************************
* Copyright 2016-2019 Exactpro (Exactpro Systems 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
... | pandas.to_datetime(frame['Created_tr']) | pandas.to_datetime |
#!/usr/bin/python3
import argparse
import os
import sys
import webbrowser
from datetime import timedelta
import numpy as np
import pandas as pd
import pandas_datareader.data as web
import requests_cache
from plotly import graph_objs as go
from plotly.subplots import make_subplots
from tqdm import tqdm
from finance_be... | pd.DataFrame() | pandas.DataFrame |
# coding: utf-8
# ### Import
# In[1]:
from bs4 import BeautifulSoup
import requests
import numpy as np
import pandas as pd
import xgboost
import xgboost as xgb
from xgboost.sklearn import XGBClassifier
from sklearn.metrics import *
from IPython.core.display import Image
from sklearn.datasets import make_classifi... | pd.DataFrame(columns=["date", "holiday"]) | pandas.DataFrame |
# %% 说明
# ------------------------------------------------------------------->>>>>>>>>>
# 最后更新ID name的时候用这个脚本，从师兄的list汇总完成替换
# os.chdir("/Users/zhaohuanan/NutstoreFiles/MyNutstore/Scientific_research/2021_DdCBE_topic/Manuscript/20220311_My_tables")
# ------------------------------------------------------------------->>... | pd.ExcelWriter('20220311_TargetSeqInfoForBarPlot_seqinfos.xlsx') | pandas.ExcelWriter |
#coding=utf-8
import os
import CSZLData
import CSZLFeatureEngineering as FE
import CSZLModel
import CSZLDisplay
import CSZLUtils
import pandas as pd
import datetime
import time
class CSZLWorkflow(object):
"""各种workflow 主要就是back testing"""
def BackTesting(self):
#Default_folder_path='./temp/'
... | pd.read_csv(resultpath,index_col=0,header=0) | pandas.read_csv |
import click
import os
import csv
import re
import functools
import pandas as pd
import numpy as np
import datetime
import common
import shutil
class InvalidSubscenario(Exception):pass
class CSVLocation(object):
"""Documentation for CSVLocation
class which acts as wrapper over folder, csv_location
"""
... | pd.to_datetime(s_.timestamp, format='%d-%m-%Y %H:%M') | pandas.to_datetime |
import xarray as _xr
import pathlib as _pl
import numpy as _np
# import cartopy.crs as ccrs
# import metpy
# from scipy import interpolate
# from datetime import datetime, timedelta
from mpl_toolkits.basemap import Basemap as _Basemap
from pyproj import Proj as _Proj
import urllib as _urllib
from pyquery import PyQuer... | _pd.DataFrame(self.list_of_files, columns=['fname_on_ftp']) | pandas.DataFrame |
from flask import Flask, render_template, url_for, request,jsonify
import numpy as np
import pandas as pd
import json
import operator
import time
import random
import glob
#Initialize Flask App
app = Flask(__name__)
@app.route('/')
def index():
return render_template('index.html')
@app.route('/rawdata')
def org... | pd.to_datetime(data['date_time']) | pandas.to_datetime |
#!/usr/bin/env python
"""Tests for `arcos_py` package."""
from numpy import int64
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from arcos4py import ARCOS
from arcos4py.tools._errors import noDataError
@pytest.fixture
def no_bin_data():
"""
pytest fixture t... | assert_frame_equal(out, df_true) | pandas.testing.assert_frame_equal |
# pylint: disable=E1101
from datetime import time, datetime
from datetime import timedelta
import numpy as np
from pandas.core.index import Index, Int64Index
from pandas.tseries.frequencies import infer_freq, to_offset
from pandas.tseries.offsets import DateOffset, generate_range, Tick
from pandas.tseries.tools impo... | normalize_date(end) | pandas.tseries.tools.normalize_date |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed May 6 09:28:00 2020
@author: <NAME>
PCA heterogenity plot
"""
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.gridspec as grid... | pd.DataFrame(bdata[:, genes_of_interest].X, index=bdata.obs.index, columns=bdata.var.index) | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
ROOT_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')
DATA_DIR = os.path.join(ROOT_DIR, 'data')
RESULTS_DIR = os.path.join(ROOT_DIR, 'results')
FIGURES_DIR = os.path.join(ROOT_DIR, 'figures')
DOWNLOAD_DIR = os.path.jo... | pd.concat(dfs) | pandas.concat |
import datetime as dt
import numpy as np
import pathlib
import pandas as pd
from functools import partial
from .deprecations import deprecated_kwargs
from . import utils
from copy import deepcopy
from collections import OrderedDict
from collections.abc import Iterable
from openpyxl import load_workbook
from openpyxl.... | pd.Index([indexes_to_style]) | pandas.Index |
# -*- coding: utf-8 -*-
"""
Created on set/2020
json a partir da tabela sqlite
@author: github rictom/rede-cnpj
2020-11-25 - Se uma tabela já existir, parece causar lentidão para o pandas pd.to_sql.
Não fazer Create table ou criar índice para uma tabela a ser criada ou modificada pelo pandas
"""
import os, s... | pd.Series(dfaux['descricao'].values, index=dfaux['codigo']) | pandas.Series |
"""A collections of functions to facilitate
analysis of HiC data based on the cooler and cooltools
interfaces."""
import warnings
from typing import Tuple, Dict, Callable
import cooltools.expected
import cooltools.snipping
import pandas as pd
import bioframe
import cooler
import pairtools
import numpy as np
... | pd.read_csv(pairs_body, sep="\t", names=cols) | pandas.read_csv |
import pandas as pd
import numpy as np
import scipy.stats
import matplotlib as plt
from scipy.stats import norm
from scipy.optimize import minimize
import ipywidgets as widgets
from IPython.display import display
import math
def drawdown(ret_ser: pd.Series):
"""
Lets Calculate it:
1. Compute wealth index... | pd.to_datetime(pfme_df.index, format="%Y%m") | pandas.to_datetime |
from sklearn.manifold import TSNE
from clustering import silhouette as sil
from data_processing import MulticlusteringExperimentUtils as expUtils
# Keep the clustering experiments that involve outliers here
from clustering.KMeansVariations import kMeans_baseline, kMeans_baseline_high_iteration, kMeans_baseline_random_... | pd.DataFrame(even_vectors) | pandas.DataFrame |
"""
BFR
"""
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
class BFR(object):
class Local(object):
def __init__(self, n_cluster, soft_n_cluster=None, shrink=0.5,
input_file_path=None, iter_func=None,
chunk_size=None, kmeans_params=None, ... | pd.DataFrame(columns=["cluster"]) | pandas.DataFrame |
# import csv
# with open('C:/Users/Eddie/Desktop/python-playground/Week 4/day 25 - CSV Data + Pandas Library/weather_data.csv') as data:
# weather_data = csv.reader(data)
# temperature = []
# for row in weather_data:
# if row[1] == 'temp':
# continue
# temperature.append(int(row[1]))
# print(tempe... | pandas.DataFrame(student_dict) | pandas.DataFrame |
from http.server import BaseHTTPRequestHandler, HTTPServer
import socketserver
import pickle
import urllib.request
import json
from pprint import pprint
from pandas.io.json import json_normalize
import pandas as pd
from sklearn import preprocessing
from sklearn.preprocessing import PolynomialFeatures
from sklearn impor... | pd.DataFrame(podNames_temp) | pandas.DataFrame |
import numpy as np
import pytest
from pandas import (
DataFrame,
IndexSlice,
NaT,
Timestamp,
)
import pandas._testing as tm
pytest.importorskip("jinja2")
from pandas.io.formats.style import Styler
from pandas.io.formats.style_render import _str_escape
@pytest.fixture
def df():
... | Styler(df, uuid_len=0) | pandas.io.formats.style.Styler |
from functools import partial
from unittest import TestCase, main as unittest_main
import numpy as np
import pandas as pd
from scipy.special import digamma
from scipy.stats import beta, norm
from gbstats.bayesian.dists import Beta, Norm
DECIMALS = 5
round_ = partial(np.round, decimals=DECIMALS)
def roundsum(x, dec... | pd.testing.assert_series_equal(res, out) | pandas.testing.assert_series_equal |
import os
import pandas as pd
import json
import cv2
def CSV_300W_LP(data_dir):
folders = [folder for folder in os.listdir(data_dir) if os.path.isdir(os.path.join(data_dir, folder))]
images = []
for idx, folder in enumerate(folders):
folder_path = os.path.join(data_dir, folder)
folder_ima... | pd.DataFrame(images) | pandas.DataFrame |
import numpy as np
from sympy import *
from scipy.integrate import odeint
import pandas as pd
import time
import matplotlib.pyplot as plt
from matplotlib import rcParams
import webbrowser
import random
import copy
import csv
import time
def coeff_vect(mtx):
"""Входные данные: mtx
Выходныее данные: mtx1
... | pd.DataFrame({'xi':xd,'yi':yd_euler,'zi':zd_euler}) | pandas.DataFrame |
import pytest
import os
from mapping import util
from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas as pd
from pandas import Timestamp as TS
import numpy as np
@pytest.fixture
def price_files():
cdir = os.path.dirname(__file__)
path = os.path.join(cdir, 'data/')
files = ... | TS('2015-01-03') | pandas.Timestamp |
from flask import Flask, render_template, jsonify, request
from flask_pymongo import PyMongo
from flask_cors import CORS, cross_origin
import json
import collections
import numpy as np
import re
from numpy import array
from statistics import mode
import pandas as pd
import warnings
import copy
from joblib import Mem... | pd.DataFrame.from_dict(dicSVC) | pandas.DataFrame.from_dict |
import pandas as pd
import numpy as np
from sklearn.cross_validation import StratifiedKFold, KFold
import xgboost
from sklearn.grid_search import ParameterGrid
from sklearn.metrics import mean_squared_error
CLASS = False # Whether classification or regression
SCORE_MIN = True # Optimizing score through minimum
k = ... | pd.concat([train] + dummy_train, axis=1) | pandas.concat |
import os
import pandas as pd
import numpy as np
import scipy
import openpyxl
from openpyxl import Workbook
import scipy.stats as stats
import file_functions
def sankey_chi_squared(detrended_dem, zs):
"""This function calculates a chi squared test comparing observed landform transitions vs expected, with expected... | pd.DataFrame.from_dict(out_dict) | pandas.DataFrame.from_dict |
import datetime
import warnings
from copy import copy
from types import MappingProxyType
from typing import Sequence, Callable, Mapping, Union, TypeVar, TYPE_CHECKING
import numpy as np
import pandas as pd
import sidekick as sk
from .clinical_acessor import Clinical
from .metaclass import ModelMeta
from .. import fit... | pd.concat([self.data, extra]) | pandas.concat |
from __future__ import print_function
import collections
import json
import logging
import os
import pickle
import sys
import numpy as np
import pandas as pd
import keras
from itertools import cycle, islice
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import StandardScaler, MinMaxScaler, Max... | pd.DataFrame(df_fp.loc[:, 'Drug']) | pandas.DataFrame |
import argparse
import numpy as np
import pandas as pd
import seaborn as sns
from pathlib import Path
import matplotlib.pyplot as plt
import context
from mhealth.utils.commons import print_title
from mhealth.utils.context_info import dump_context
from mhealth.utils.plotter_helper import save_figure, setup_plotting
d... | pd.concat([df_before, df_after], axis=0) | pandas.concat |
"""
Tests that work on both the Python and C engines but do not have a
specific classification into the other test modules.
"""
import csv
from io import StringIO
from pandas import DataFrame
import pandas._testing as tm
from pandas.io.parsers import TextParser
def test_read_data_list(all_parsers):
parser = all... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
import pytest
import os
from mapping import util
from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas as pd
from pandas import Timestamp as TS
import numpy as np
@pytest.fixture
def price_files():
cdir = os.path.dirname(__file__)
path = os.path.join(cdir, 'data/')
files = ... | TS('2015-01-04') | pandas.Timestamp |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 4 14:17:17 2021
@author: supokhrel
"""
from PyQt5 import QtWidgets, uic, QtCore
import sys
import os
import threading
File_Path = ''
init_dir = os.getcwd()
def BrowseFile():
global status
global File_Path
global init_dir
# print("Browsing...")
fi... | pd.to_numeric(df[df.columns[i]], errors='coerce') | pandas.to_numeric |
import pandas as pd
import numpy as np
import pytest
from kgextension.endpoints import DBpedia
from kgextension.schema_matching import (
relational_matching,
label_schema_matching,
value_overlap_matching,
string_similarity_matching
)
class TestRelationalMatching:
def test1_default(self):
... | pd.read_csv(path_expected) | pandas.read_csv |
#
# Copyright 2015 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... | pd.Timestamp('2013-1-1', tz='UTC') | pandas.Timestamp |
"""
"""
__version__='192.168.3.11.dev1'
import sys
import os
import logging
import pandas as pd
import re
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
logger = logging.getLogger('PT3S')
try:
from PT3S import Rm
except ImportError:... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
from .DensityFunctions import BaseDensityCalc
def raw_delta_calc(times):
'''
Given an array of times, this function calculates the deltas between them.
Arguments
---------
- times: array:
This is an array of times that will be used to calculate ... | pd.Timestamp(date) | pandas.Timestamp |
import pandas as pd
from kf_pedigree.common import get_logger
from kf_pedigree.family import find_family_from_family_list
logger = get_logger(__name__, testing_mode=False)
def gender(x):
if isinstance(x, str):
if x.lower() == "male":
return "1"
elif x.lower() == "female":
... | pd.concat([pedi_1_2, pedi_2_1]) | pandas.concat |
import os
import numpy as np
import pandas as pd
from pyuplift.utils import download_file
def download_hillstrom_email_marketing(
data_home=None,
url='http://www.minethatdata.com/Kevin_Hillstrom_MineThatData_E-MailAnalytics_DataMiningChallenge_2008.03.20.csv'
):
"""Downloading the Hillstrom Email Marketin... | pd.get_dummies(df, columns=[col_name], prefix=col_name) | pandas.get_dummies |
import os
import trimesh
import numpy as np
import pandas as pd
from enum import Enum
from matplotlib import cm
from urdfpy import URDF, JointLimit
from tools.utils import io
# from tools.visualization import Viewer
# override attributes to make effort, velocity optional
JointLimit._ATTRIBS = {
'effort': (float, ... | pd.concat(df_list, ignore_index=True) | pandas.concat |
import timeboard as tb
from timeboard.interval import Interval, _VoidInterval
from timeboard.workshift import Workshift
from timeboard.exceptions import (OutOfBoundsError, PartialOutOfBoundsError,
VoidIntervalError)
from timeboard.timeboard import _Location, OOB_LEFT, OOB_RIGHT, LOC_WI... | pd.Timestamp('08 Jan 2017 15:00') | pandas.Timestamp |
import os
import uuid
from datetime import datetime
import pathlib
import shutil
from send2trash import send2trash
from bs4 import (BeautifulSoup, Comment)
import lxml # 不一定用，但与bs4解析网页时相关模块有联系，作为模块预装的提示吧
import pandas as pd
import re
from wordcloud import WordCloud
import jieba
NOTEINDEXCOLS= ["type","title","path","c... | pd.read_json(self.info_path, typ="Series", convert_dates=["atime","ctime","mtime"]) | pandas.read_json |
"""test_ulogconv."""
from context import mathpandas as mpd
import pandas as pd
import numpy as np
from numpy.testing import assert_almost_equal
def test_norm_2d():
"""test pythagoras series."""
x = pd.Series([1, 2, 3, 4])
y = pd.Series([2, 3, 4, 5])
r = mpd.get_series_norm_2d(x, y, "test")
asser... | pd.Series([0]) | pandas.Series |
# -*- coding: utf-8 -*-
import os
import dash
import pandas as pd
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
#a
app = dash.Dash(__name__)
server = app.server
people = | pd.read_csv("BNOCSFINAL1.csv") | pandas.read_csv |
import os, sys
import collections
import pprint
import pandas as pd
import pysam
class Call:
def __init__(self, call, quality = None, is_error = False):
self.call = call
self.quality = quality
self.is_error = is_error
self.is_indel = len(call) > 1
def get_call_for_pileup_read(pile... | pd.concat(background_snps_list) | pandas.concat |
"""
The :mod:`hillmaker.bydatetime` module includes functions for computing occupancy,
arrival, and departure statistics by time bin of day and date.
"""
# Copyright 2022 <NAME>
#
import logging
import numpy as np
import pandas as pd
from pandas import DataFrame
from pandas import Series
from pandas import Timestamp
... | pd.concat([stops_df, occ_weight_df], axis=1) | pandas.concat |
import numpy as np
import pandas as pd
import itertools
import math
import re
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
plt.style.use('seaborn-white')
class MAM:
"""
MAM (Marketing Attribution Models) is a class inspired on the R Package ‘GameTheoryAllocation’ from <NAME>
and ‘... | pd.DataFrame(res) | pandas.DataFrame |
import pandas as pd
import numpy as np
def append_times(df, st, et):
df.insert(0, 'START_TIME', st)
df.insert(1, 'STOP_TIME', et)
df = df.set_index(['START_TIME', 'STOP_TIME'])
return df
def offset(df, offset_in_secs, start_time_col=0, stop_time_col=None):
df_copy = df.copy(deep=True)
if sta... | pd.concat((ledge_df, df, redge_df)) | pandas.concat |
import os
import glob
import numpy as np
import pylab as pl
import scipy.io as sio
# for_Jyotika.m
from copy import copy, deepcopy
import pickle
import matplotlib.cm as cm
import pdb
import h5py
import pandas as pd
import bct
from collections import Counter
import matplotlib.cm as cm
import sys
import seaborn as sns
i... | pd.read_csv(data_dir+"graph_properties_pandas_days_null_all.csv") | pandas.read_csv |
"""<NAME>., 2019 - 2020. All rights reserved."""
import os
import sys
import unittest
from unittest import mock
from io import StringIO
from test.test_support import TestResource
import pandas as pd
from pandas.util.testing import assert_frame_equal
from eaglevision.similarity_eagle import SimilarityEagle
class Simil... | assert_frame_equal(actual_dataframe[0], expected_dataframe[0]) | pandas.util.testing.assert_frame_equal |
#!/usr/bin/python
import warnings
warnings.filterwarnings("ignore")
import os,numpy,pandas,sys,scipy.io,scipy.sparse,time,numba
from optparse import OptionParser
#
#
opts = OptionParser()
usage = "Evaluate gene score by TSS peaks\nusage: %prog -s project --gtf hg19.gtf --distal 20000"
opts = OptionParser(usage=usage, v... | pandas.read_csv(options.s+'/peak/genes_tss_peaks.csv', sep='\t', index_col=0) | pandas.read_csv |
from bs4 import BeautifulSoup
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pypatent
import requests
from c0104_findLatLong import findLatLong
def search_pubs():
"""
Objective: List Rooster publication with metadata
Task 1: Identify search terms... | pd.read_csv(pub_file) | pandas.read_csv |
# -*- coding: UTF-8 -*-
"""
This module contains functions for calculating evaluation metrics for the generated service recommendations.
"""
import numpy
import pandas
runtime_metrics = ["Training time", "Overall testing time", "Individual testing time"]
quality_metrics = ["Recall", "Precision", "F1", "# of recommend... | pandas.concat(matrix, axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
import os
import glob
import pandas as pd
import numpy as np
from collections import Counter
from graphpype.utils_net import read_Pajek_corres_nodes
from graphpype.utils_dtype_coord import where_in_coords
from graphpype.utils_cor import where_in_labels
from graphpype.utils_mod import read_l... | pd.DataFrame(all_global_info_values) | pandas.DataFrame |
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
@pytest.mark.parametrize("align_axis", [0, 1, "index", "columns"])
def test_compare_axis(align_axis):
# GH#30429
df = pd.DataFrame(
{"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]},
... | pd.MultiIndex.from_arrays([["x", "y"], [0, 2]]) | pandas.MultiIndex.from_arrays |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import pandas as pd
from pandas.compat import long
from pandas.core.arrays import PeriodArray, DatetimeArrayMixin as DatetimeArray
@pytest.fixture(params=[1, np.array(1, dtype=np.int64)])
def one(request):
# zero-dim integer array behaves like an integer
... | pd.Timedelta('5m4s') | pandas.Timedelta |
#!/usr/bin/env python
# coding: utf-8
# # US Beveridge Curve Data
#
# Construct monthly unemploment rate and vacancy rate series for the US from April 1929 through the most recently available date. The methodology is based on the approach described in Petrosky-Nadeau and Zhang (2013): https://papers.ssrn.com/sol3/pa... | pd.concat([unemployment_rate_series,vacancy_rate_series,market_tightness_series], join='outer', axis = 1) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on 2017-7-7
@author: cheng.li
"""
import abc
import sys
import pandas as pd
from sqlalchemy import and_
from sqlalchemy import not_
from sqlalchemy import or_
from sqlalchemy import select
from alphamind.data.dbmodel.models import Universe as UniverseTable
class BaseUniverse(me... | pd.to_datetime(df["trade_date"]) | pandas.to_datetime |
"""
Detection Recipe - 192.168.3.11
References:
(1) 'Asteroseismic detection predictions: TESS' by Chaplin (2015)
(2) 'On the use of empirical bolometric corrections for stars' by Torres (2010)
(3) 'The amplitude of solar oscillations using stellar techniques' by Kjeldson (2008)
(4) 'An absolutely calibrated Teff ... | pd.to_numeric(data[:, 46]) | pandas.to_numeric |
from datetime import datetime, date
import sys
if sys.version_info >= (2, 7):
from nose.tools import assert_dict_equal
import xlwings as xw
try:
import numpy as np
from numpy.testing import assert_array_equal
def nparray_equal(a, b):
try:
assert_array_equal(a, b)
except Asse... | pd.DataFrame([[1., 2.], [3., 4.]]) | pandas.DataFrame |
from bs4 import BeautifulSoup
import logging
import pandas as pd
import re
import requests
from urllib.parse import urljoin
logging.basicConfig(format="%(asctime)s %(levelname)s:%(message)s", level=logging.INFO)
def get_html(url):
return requests.get(url).text
class CongressCrawler:
def __init__(self):
... | pd.DataFrame(self.congress) | pandas.DataFrame |
#!/usr/bin/env python3
"""Script for exporting tensorboard logs to csv."""
import re
import numpy as np
from collections import defaultdict
import pandas as pd
from tensorboard.backend.event_processing.event_multiplexer import EventMultiplexer
class TensorboardDataHelper():
"""Class to help extrat summary values ... | pd.DataFrame.from_dict(dict_of_values, orient='index') | pandas.DataFrame.from_dict |
import tempfile
import pytest
import pandas as pd
import numpy as np
import pytz
from eemeter.modeling.models.billing import BillingElasticNetCVModel
from eemeter.modeling.formatters import ModelDataBillingFormatter
from eemeter.structures import EnergyTrace
@pytest.fixture
def trace():
index = pd.date_range('6... | pd.date_range('2011-01-01', freq='D', periods=365, tz=pytz.UTC) | pandas.date_range |
"""
Evaluation of predictions againsts given dataset (in TXT format the same as training).
We expect that the predictions are in single folder and image names in dataset are the same
python evaluate.py \
--path_dataset ../model_data/VOC_2007_train.txt \
--path_results ../results \
--confide... | pd.DataFrame(columns=ANNOT_COLUMNS) | pandas.DataFrame |
import pandas as pd
from sodapy import Socrata
import datetime
import definitions
# global variables for main data:
hhs_data, test_data, nyt_data_us, nyt_data_state, max_hosp_date = [],[],[],[],[]
"""
get_data()
Fetches data from API, filters, cleans, and combines with provisional.
After running, global variables are... | pd.Timestamp(2020,1,1) | pandas.Timestamp |
import os
from pandas import DataFrame, read_csv
from networkx import DiGraph, write_gpickle, read_gpickle
from memory_profiler import profile
from app.decorators.number_decorators import fmt_n
from app.job import Job
from app.bq_service import BigQueryService
from app.file_storage import FileStorage
DATE = os.get... | read_csv(local_nodes_csv_filepath) | pandas.read_csv |
# Title: Weather Data Aggregator
# Description: Aggregates data from the weather station on Cockcroft from the OnCall API.
# Author: <NAME>
# Date: 17/12/2020
# Version: 1.0
# Import libraries
import pandas as pd
from pandas import json_normalize
import json
import requests
from datetime import datetime, timedelta
fr... | json_normalize(jsonLoad) | pandas.json_normalize |
"""
Enrich Stocks and ETF data with different indicators and generates a CSV file for analysis
"""
import argparse
from datetime import datetime
from pathlib import Path
import pandas as pd
from common.analyst import fetch_data_from_cache
from common.filesystem import output_dir
from common.market import load_all_ti... | pd.DataFrame(combined_db, copy=True) | pandas.DataFrame |
import unittest
import pandas as pd
import numpy as np
from autopandas_v2.ml.featurization.featurizer import RelationGraph
from autopandas_v2.ml.featurization.graph import GraphEdge, GraphEdgeType, GraphNodeType, GraphNode
from autopandas_v2.ml.featurization.options import GraphOptions
get_node_type = GraphNodeType.g... | pd.DataFrame([[5, 2], [2, 3], [2, 0]], columns=["A", "B"]) | pandas.DataFrame |
import pandas as pd
import sqlalchemy
from constants import DB_FOLDER, SYMBOL
import matplotlib.pyplot as plt
def create_engine(symbol):
engine = sqlalchemy.create_engine(f"sqlite:///{DB_FOLDER}/{symbol}-stream.db")
return engine
def fetch_dataframe(symbol, engine):
try:
return | pd.read_sql(symbol, engine) | pandas.read_sql |
import numpy as np
import pandas as pd
from scipy.io import loadmat
from tqdm import tqdm
ORIG_AU_NAMES = [
'AU1', 'AU1-2', 'AU2', 'AU2L', 'AU4', 'AU5', 'AU6', 'AU6L', 'AU6R', 'AU7L', 'AU7R', 'AU9',
'AU10Open', 'AU10LOpen', 'AU10ROpen', 'AU11L', 'AU11R', 'AU12', 'AU25-12', 'AU12L', 'AU12R',
'AU13', 'AU14',... | pd.DataFrame(au_data, columns=au_names, index=idx) | pandas.DataFrame |
# This scripts generates graphs for
# outputs of benchmarks
import argparse
import itertools
import os
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from matplotlib.lines import Line2D
LINE_STYLES = ["-", ":", "-.", "--"]
cmap = plt.cm.get_cmap('Dark2')
COLORS = [cmap(i) for i in range(5)]
... | pd.DataFrame(rd[f]) | pandas.DataFrame |
import json, os, logging
from typing import Tuple, Optional
import pandas as pd
from datetime import datetime
from jinja2 import Environment, FileSystemLoader, select_autoescape
from iplotter import ChartJSPlotter
from iplotter import GCPlotter
def read_bcl2fastq_stats_data_from_pandas(data: dict) -> Tuple[list, list... | pd.DataFrame(row_s) | pandas.DataFrame |
import time
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import savefig
from sklearn import preprocessing
from sklearn.model_selection import KFold
from sklearn.naive_bayes import MultinomialNB
from sklearn import svm
from sklearn.ensemble import RandomForestCl... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.preprocessing import StandardScaler
from scipy import signal
from scipy.io import loadmat
from sklearn.metrics import confusion_matrix
import os
from tensorflow.keras.models import Sequential, Model... | pd.DataFrame() | pandas.DataFrame |
"""Network rerouting loss maps
"""
import os
import sys
from collections import OrderedDict
import numpy as np
import geopandas as gpd
import pandas as pd
import cartopy.crs as ccrs
import matplotlib as mpl
import cartopy.io.shapereader as shpreader
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches... | pd.merge(region_file,flow_file,how='left', on=['edge_id']) | pandas.merge |
#!/home/bryanfeeney/anaconda3/bin/python3.6
#
# Simple script that uses the Microsoft Light Gradient-Boosted Machine-Learnign
# toolkit to make predictions *separately* for each value.
#
from datetime import date, timedelta, datetime
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_err... | pd.DataFrame() | pandas.DataFrame |
""" generates lists of SARS-CoV-2 samples which occurred before a particular date
Also generates a dictionary of reference compressed sequences
And a subset of these
Together, these can be passed to a ram_persistence object which
can be used instead of an fn3persistence object to test the performance of PCA, or for o... | pd.read_csv(f) | pandas.read_csv |
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