prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#!usr/bin/env python
"""
Evaluate the performance of the generative model on multiple aspects:
to be filled
"""
import pandas as pd
import numpy as np
from post_processing import data
from rdkit import Chem, DataStructs
import scipy.stats as ss
import math
from rdkit import Chem
from rdkit.Chem.Draw import IPythonCons... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (C) 2015 by <NAME>
import argparse
import pandas as pd
import numpy as np
from tqdm import trange
def parse_args():
"""
Parse command-line arguments.
"""
parser = argparse.ArgumentParser('Extract user sessions from log.')
parser.add_argum... | pd.Series(sip1, name='ipdst', dtype=str) | pandas.Series |
import unittest
import os
import tempfile
from collections import namedtuple
from blotter import blotter
from pandas.util.testing import assert_frame_equal, assert_series_equal, \
assert_dict_equal
import pandas as pd
import numpy as np
class TestBlotter(unittest.TestCase):
def setUp(self):
cdir = os... | pd.Series(["PNL", "INTEREST", "PNL", "INTEREST"], index=idx) | pandas.Series |
import os
from typing import Union
import math
import pandas as pd
import numpy as np
import sha_calc as sha
from gmhazard_calc import site
from gmhazard_calc import gm_data
from gmhazard_calc import constants as const
from gmhazard_calc.im import IMComponent
from .NZTAResult import NZTAResult
from qcore import geo
... | pd.read_csv(NZTA_LOOKUP_FFP, header=0, index_col=0) | pandas.read_csv |
import geopandas as gpd
import pandas as pd
from shapely.geometry import Polygon,Point
import math
import numpy as np
def rect_grids(bounds,accuracy = 500):
'''
Generate the rectangular grids in the bounds
Parameters
-------
bounds : List
Create the bounds, [lon1, lat1, lon2, lat2](WGS84... | pd.concat([df1,df2]) | pandas.concat |
import numpy as np
import pandas as pd
from fox_toolbox.utils.rates import Curve, RateCurve, Swap, Swaption, Volatility
from collections import namedtuple
swap_rate_model = namedtuple('swap_rate_model', 'mtype a b neff')
cms_result = namedtuple('cms_result', 'swap_fwd disc_Tf_Tp')
csvCMSFlow = namedtuple('csvCMSFlow',... | pd.DataFrame(columns=tsr_columns) | pandas.DataFrame |
import numpy as np
import os
import pickle
import scipy.sparse as sp
from pathlib import Path
import wget
import pickle
import os
import pandas as pd
import numpy as np
import torch
def get_project_root() -> Path:
return Path(__file__).parent.parent
PROJECT_ROOT = get_project_root()
def double_transition_ma... | pd.DataFrame(feature, index=timestamp) | pandas.DataFrame |
# =============================================================================
# Imports
# =============================================================================
# Standard
import argparse
import os
import sys
import glob
import math
import pandas as pd
import json
import numpy as np
import datetime
# =======... | pd.read_csv(file) | pandas.read_csv |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
import nose
import numpy as np
from numpy import nan
import pandas as pd
from distutils.version import LooseVersion
from pandas import (Index, Series, DataFrame, Panel, isnull,
date_range, period_range)
from pandas.core.index import MultiIn... | DataFrame({"A": [1, 2, 3]}) | pandas.DataFrame |
# standard modules
import os
import shutil
import argparse
# aliased standard modules
import pandas as pd
# modules of sanity checker
import lib.paths as paths
import lib.utils as utils
import lib.logger_config as logger_config
# standalone imports
from lib.logger_config import log
from lib.test_config import get_co... | pd.read_csv(f_exp_descr, sep=';') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 6 08:51:19 2019
@author: dipesh
"""
# Import necessary libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import preprocessing
from sklearn.model_selection import train_test_split... | pd.read_csv('bank-additional-full.csv') | pandas.read_csv |
import sqlite3
import sqlalchemy as sa
import pandas as pd
from powergenome.params import DATA_PATHS
from powergenome.util import init_pudl_connection
GENS860_COLS = [
"report_date",
"plant_id_eia",
"generator_id",
# "associated_combined_heat_power",
# "balancing_authority_code_eia",
# "bypass... | pd.read_sql_query(s, pudl_engine, parse_dates=["report_date"]) | pandas.read_sql_query |
"""
Created on Wed May 12 15:36:53 2021
@author: Lenovo
"""
import cv2
import numpy as np
import os
from csv import writer
import pandas as pd
def register():
t=1
lst=[]
directory = r'singleShot'
while(t):
print("Enter 1 to register a new student / 2 to remove an existing studen... | pd.read_csv("Details.csv") | pandas.read_csv |
import pandas as pd
from ..utils import constants, plot, utils
import numpy as np
from warnings import warn
from shapely.geometry import Polygon, Point
import geopandas as gpd
from .flowdataframe import FlowDataFrame
from skmob.preprocessing import routing
class TrajSeries(pd.Series):
@property
def _construc... | pd.core.dtypes.common.is_float_dtype(self[constants.LONGITUDE]) | pandas.core.dtypes.common.is_float_dtype |
#coding: utf-8
import struct
from pytdx.reader.base_reader import BaseReader
from collections import OrderedDict
import pandas as pd
import os
from io import BytesIO
"""
参考这个 http://blog.csdn.net/Metal1/article/details/44352639
"""
BlockReader_TYPE_FLAT = 0
BlockReader_TYPE_GROUP = 1
class BlockReader(BaseReader):
... | pd.DataFrame(result) | pandas.DataFrame |
import pandas as pd
dataset_train=pd.read_csv('../input/train.csv')
dataset_test=pd.read_csv('../input/test.csv')
dataset_train.head()
dataset_test.head()
dataset_train.isnull().values.any()
dataset_test.isnull().values.any()
dataset_train.info()
dataset_test.info()
dataset_train.describe()
dataset_test.describe()
data... | pd.DataFrame(y_new,dataset_test['Id']) | pandas.DataFrame |
#
# Adaptation of spontaneous activity 2 in the developing visual cortex
# M. E. Wosniack et al.
#
# Data analysis codes
# Auxiliar functions file: extra_functions.py
#
# Author: <NAME>
# Max Planck Institute for Brain Research
# <EMAIL>
# June 2020
#
import numpy as np
import pandas as pd
from sklearn.utils import re... | pd.DataFrame.from_dict(amp_diff_recordings, orient='index') | pandas.DataFrame.from_dict |
from collections import Counter
import pandas as pd
import sys
data =sys.argv[1] or open("POS.train", "r")
ready_sentence = []
sentence = []
tag_counter = Counter()
word_tag_counter = Counter()
for n in data:
f = n.split()
for m in f:
v = m.split("/")
sentence.append(v)
ready_sentence.appe... | pd.DataFrame(test_out) | pandas.DataFrame |
"""This code implements the GEO mean predictor from the paper:
Estimating Query Representativeness for Query-Performance Prediction
by Sondak et al."""
import argparse
import pandas as pd
from qpputils import dataparser as dp
from Timer import Timer
parser = argparse.ArgumentParser(description='RSD(wig) predictor',... | pd.concat([df, qdf['qlen']], axis=1, sort=True) | pandas.concat |
import logging
import time
import pandas as pd
from .featurize import FeaturizedDataset
from .learn import RepairModel
from dataset import AuxTables
class RepairEngine:
def __init__(self, env, dataset):
self.ds = dataset
self.env = env
def setup_featurized_ds(self, featurizers, iteration_nu... | pd.DataFrame(data=infer_val) | pandas.DataFrame |
"""
Contains the ligand similarity search class.
"""
from pathlib import Path
from typing_extensions import ParamSpecKwargs
import pandas as pd # for creating dataframes and handling data
from .consts import Consts
from .ligand import Ligand
from .helpers import pubchem, rdkit
class LigandSimilaritySearch:
""... | pd.DataFrame(analogs_info) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Analize the SRAG data and export the statistics to generate the figure 1
Needs the filter_SRAG.py csv output to run
"""
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt
from scipy.stats import norm, binom
def median_estimate(X, C... | pd.isna(data[ycol]) | pandas.isna |
import logging
import geopandas as gpd
import numpy as np
import pandas as pd
from shapely.geometry import LineString
from rasterstats import zonal_stats
from delft3dfmpy.core import checks, geometry
from delft3dfmpy.datamodels.common import ExtendedDataFrame
import rasterio
import warnings
from rasterio.transform imp... | pd.DataFrame(arr,columns=['ms_'+areas.iloc[0,0]]) | pandas.DataFrame |
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
#The Data
with open('kddcup.names', 'r') as infile:
kdd_names = infile.readlines()
kdd_cols = [x.split(':')[0] for x in kdd_names[1:]]
kdd_cols += ['class', 'difficulty']
kdd = pd.read_csv('nsl-KDDTrain+.txt', names=kd... | pd.get_dummies(y_test) | pandas.get_dummies |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 10 19:01:55 2019
@author: ashik
"""
import pandas as pd
import time
import os
import glob
import datetime
from datetime import timedelta
import scipy.stats
import math
#articleDF = pd.read_excel("data/ajb9b3.xlsx")
#time.strftime("%A %Y-%m-%d %H:%... | pd.DataFrame() | pandas.DataFrame |
import os
import operator
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def collapse_phn(char):
collapse_dict = {"b":"b", "bcl":"h#", "d":"d", "dcl":"h#", "g":"g", "gcl":"h#", "p":"p", "pcl":"h#", "t":"t", "tcl":"h#", "k":"k", "kcl":"h#", "dx":"dx", "q":"q", "jh":"jh", "ch":"ch", "s":"s", "s... | pd.read_csv('data/original1/original1.csv', index_col=0) | pandas.read_csv |
"""Kodoja pipeline."""
from __future__ import print_function
import subprocess
import pandas as pd
import random
import os
import pickle
from math import isnan
from Bio import SeqIO
from Bio.SeqIO.FastaIO import SimpleFastaParser
from Bio.SeqIO.QualityIO import FastqGeneralIterator
# The user-facing scripts will all... | pd.merge(seq_data, seq_labelData, on='Seq_ID', how='outer') | pandas.merge |
import numpy as np
import pandas as pd
import pytest
from hypothesis import given, settings
from pandas.testing import assert_frame_equal
from janitor.testing_utils.strategies import (
conditional_df,
conditional_right,
conditional_series,
)
@pytest.mark.xfail(reason="empty object will pass thru")
@given(... | pd.Int64Dtype() | pandas.Int64Dtype |
import os
import sys
sys.path.insert(0, '.') # make runable from src/
# external libraries
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
# For path referencing
from config.definitions... | pd.DataFrame(X_pca) | pandas.DataFrame |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# *****************************************************************************/
# * Authors: <NAME>
# *****************************************************************************/
"""transformCSV.py
This module contains the basic functions for creating the content of... | pandas.StringDtype() | pandas.StringDtype |
import numpy as np
import pandas as pd
import tarfile
import sys
import os
import scipy.spatial
from scipy.cluster.hierarchy import linkage, dendrogram, fcluster
import collections
import json
import warnings
import pickle
import multiprocessing
import parasail
import pwseqdist
from zipdist.zip2 import Zipdist2
from ... | pd.DataFrame(cluster_summary) | pandas.DataFrame |
import pickle
import numpy as np
import pandas as pd
from rdkit import Chem
from rdkit.Chem import AllChem, Descriptors
def get_feature_array(mols):
"""
Return an pd.DataFrame of molecule properties given an array (or array-like) of molecule objects
Parameters
----------
mols: array-like, array ... | pd.DataFrame(data=entries, dtype=float) | pandas.DataFrame |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2020-2021 Alibaba Group Holding Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | pd.DataFrame(columns) | pandas.DataFrame |
from os.path import join, exists, dirname, basename
from os import makedirs
import sys
import pandas as pd
from glob import glob
import seaborn as sns
import numpy as np
from scipy import stats
import xlsxwriter
import matplotlib.pyplot as plt
from scripts.parse_samplesheet import get_min_coverage, get_role, add_aliass... | pd.isnull(samplesheets['spike_entity_role']) | pandas.isnull |
import math
from collections import OrderedDict, defaultdict
import numpy as np
import pandas as pd
from bcns import Durations, sim, Simulator, SimulatorCoordinated
from bcns.sim import Equ_LatD, Equ_pooled_LatD, Exp_LatD, Exp_pooled_LatD
def distance_between_2_points(a: tuple, b: tuple) -> float:
x1, y1 = a
... | pd.concat(miner_df) | pandas.concat |
import pandas as pd
dataframe = pd.read_csv("C:\\bank-additional-full.csv", sep=";")
cols =['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'poutcome']
data_1 = dataframe[cols]
data_dummies = pd.get_dummies(data_1)
result_df = | pd.concat([data_dummies, dataframe], axis=1) | pandas.concat |
"""
This code generate features based on the topic of papers that the author has wrote.
It take two files:
- paper_embeddings_64.txt
- author_papers.txt
Then we will apply clustering on the embeddings of the papers in such way that we group papers that have similar topic (Before doing so, we first need to lower the d... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import regex
class Matrix:
@staticmethod
def compile_r1_passed(r1_passed: dict) -> dict:
r1_compiled = {header[1:41] : labels[0] + labels[1] + labels[2] + labels[3] \
for header, labels in r1_passed.items()}
return r1_compiled
... | pd.DataFrame(index=cell_index, columns=gene_symbols) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import warnings
warnings.filterwarnings('ignore')
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# In[2]:
pd.set_option('display.max_columns', None)
np.set_printoptions(suppres... | pd.read_csv("E:/Study/ML tuts/Case Studies/fifa/players_20_classification.csv") | pandas.read_csv |
import pandas as pd
import numpy as np
import talib
def load_data(ticker):
"""
"""
path_to_data = 'https://stooq.pl/q/d/l/?s={ticker}&i=d'.format(
ticker=ticker)
return pd.read_csv(path_to_data)
def train_test_split(X, y, test_size = 0.3):
"""
Returns data split in train and test pa... | pd.DataFrame(X, columns=colnames) | pandas.DataFrame |
import pandas as pd
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
import seaborn as sns
import shap
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import r2_score
# from .utils import Boba_Utils as u
class Boba_Model_Diagn... | pd.qcut(y_temp['pred'], 10) | pandas.qcut |
import pandas as pd
import numpy as np
import scipy
import os, sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pylab
import matplotlib as mpl
import seaborn as sns
import analysis_utils
from multiprocessing import Pool
sys.path.append('../utils/')
from game_utils import *
in_d... | pd.DataFrame({'Game':games,'Score':scores,'Number of Players':ns,function_names[func_ind]:values,'Source':sources,'Lengths':lengths}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# formats: ipynb,py
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.9.1+dev
# kernelspec:
# display_name: Python [conda env:core_acc] *
# language: python
# nam... | pd.read_csv(pao1_regulon_filename, index_col=0, header=0) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# # Import Dependencies
# In[1]:
get_ipython().run_line_magic('matplotlib', 'inline')
from matplotlib import style
style.use('fivethirtyeight')
import matplotlib.pyplot as plt
# In[2]:
import numpy as np
import pandas as pd
# In[3]:
import datetime as dt
# In[173]:
... | pd.DataFrame(trip_rain, columns=['Station','Avg_Precipitation']) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from pandas.testing import assert_series_equal
from sid.config import INDEX_NAMES
from sid.update_states import _kill_people_over_icu_limit
from sid.update_states import _update_immunity_level
from sid.update_states impor... | pd.DataFrame({"needs_icu": [False] * 5 + [True] * 5, "cd_dead_true": -1}) | pandas.DataFrame |
"""Tests models
"""
import numpy as np
import pandas as pd
#import matplotlib.pyplot as plt
from dsutils.models import InterpolatingPredictor
def test_InterpolatingPredictor():
"""Tests ensembling.EnsembleRegressor"""
# Make dummy data
N = 100
D = 3
X = pd.DataFrame(data=np.random.randn(N,D))
... | pd.Series(index=X.index) | pandas.Series |
import pandas as pd
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=[0, 1, 2, 3])
df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
... | pd.merge(left_comp, right_comp, on=['key1', 'key2']) | pandas.merge |
import pandas as pd
#############
###Helpers###
#############
def format_mat(flavor_mat):
flavor_frame = | pd.DataFrame(flavor_mat) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Zerodha Kite Connect - candlestick pattern scanner
@author: <NAME> (http://rasuquant.com/wp/)
"""
from kiteconnect import KiteConnect
import pandas as pd
import datetime as dt
import os
import time
import numpy as np
from technicalta import *
#cwd = os.chdir("D:\\Udemy\\Zerodha KiteConnect... | pd.to_numeric(df['high']) | pandas.to_numeric |
import streamlit as st
import numpy as np
import pandas as pd
from matplotlib.image import imread
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import seaborn as sns
import requests
import joblib
import shap
# import streamlit.components.v1 as components
shap.initjs()
st.set_option('deprecation.sho... | pd.read_csv("./dashboard_data/df_test_num_features.csv") | pandas.read_csv |
#! /usr/bin/env python3
import argparse
import re,sys,os,math,gc
import numpy as np
import pandas as pd
import matplotlib as mpl
import copy
import math
from math import pi
mpl.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
f... | pd.merge(df1,df2,on='start',how='inner') | pandas.merge |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 15 23:22:49 2021
@author: maita
"""
# load libraries
import pandas as pd
import os, re
import datetime as dt
# Welches Jahr?
jahr = "2021"
# define paths
workingdir = "/mnt/c/Users/maita.schade/Nextcloud/Documents/Work/Gap_Map/"
# workingdir = "/h... | pd.read_csv(trips_path) | pandas.read_csv |
"""
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):
... | tm.assert_frame_equal(chunks[0], expected[:2]) | pandas._testing.assert_frame_equal |
import os
import glob
import scanpy as sc
import numpy as np
import pandas as pd
from scipy.stats import gaussian_kde
import seaborn as sns
import matplotlib.pyplot as plt
import time
import datetime
import pickle
from scipy.stats import zscore
from sklearn.linear_model import LogisticRegression
from sklearn impo... | pd.DataFrame() | pandas.DataFrame |
import time
import random
import numpy as np
import pandas as pd
import hdbscan
import sklearn.datasets
from sklearn import metrics
from classix import CLASSIX
from sklearn.cluster import KMeans
from sklearn.cluster import DBSCAN
from sklearn import preprocessing
from tqdm import tqdm
from sklearn.cluster import MeanSh... | pd.read_csv("data/Real_data/Ecoli.csv") | pandas.read_csv |
def setup_fs(s3, key="", secret="", endpoint="", cert="", passwords={}):
"""Given a boolean specifying whether to use local disk or S3, setup filesystem
Syntax examples: AWS (http://s3.us-east-2.amazonaws.com), MinIO (http://192.168.0.1:9000)
The cert input is relevant if you're using MinIO with TLS enabled... | pd.DataFrame(frame_list, columns=base_frame.index, index=frame_timestamp_list) | pandas.DataFrame |
import os
import torch
import pickle
import collections
import math
import pandas as pd
import numpy as np
import networkx as nx
from rdkit import Chem
from rdkit.Chem import Descriptors
from rdkit.Chem import AllChem
from rdkit import DataStructs
from rdkit.Chem.rdMolDescriptors import GetMorganFingerprintAsBitVect
fr... | pd.read_csv(input_path, sep=',') | pandas.read_csv |
import os
import yaml
import json
import pandas as pd
import matplotlib.pyplot as plt
from pylab import rcParams
import seaborn as sns
import numpy as np
from sklearn.linear_model import LinearRegression
import glob
import time
###############################################################################... | pd.merge(a1, b1, how='outer', on='batsman') | pandas.merge |
# -*- coding: utf-8 -*-
import re
import numpy as np
import pytest
from pandas.core.dtypes.common import (
is_bool_dtype, is_categorical, is_categorical_dtype,
is_datetime64_any_dtype, is_datetime64_dtype, is_datetime64_ns_dtype,
is_datetime64tz_dtype, is_datetimetz, is_dtype_equal, is_interval_dtype,
... | IntervalDtype(subtype) | pandas.core.dtypes.dtypes.IntervalDtype |
import re
from typing import Optional
import warnings
import numpy as np
from pandas.errors import AbstractMethodError
from pandas.util._decorators import cache_readonly
from pandas.core.dtypes.common import (
is_hashable,
is_integer,
is_iterator,
is_list_like,
is_number,
)
from p... | pprint_thing(y) | pandas.io.formats.printing.pprint_thing |
from re import A
import matplotlib.pyplot as plt
# %matplotlib inline
import seaborn as sns
sns.set()
import pandas as pd
import numpy as np
import os, sys
from scripts.constants import *
# https://abseil.io/docs/python/guides/flags
from absl import flags
FLAGS = flags.FLAGS
## Hparams
flags.DEFINE_string("merged_... | pd.concat([run_down_ours_std, run_down_others_std]) | pandas.concat |
# flake8: noqa: F841
import tempfile
from pathlib import Path
from typing import List
from pandas._typing import Scalar, ArrayLike
import pandas as pd
import numpy as np
from pandas.core.window import ExponentialMovingWindow
def test_types_init() -> None:
pd.Series(1)
pd.Series((1, 2, 3))
pd.Series(np... | pd.Series([-10, 2, 3, 10]) | pandas.Series |
import unittest
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from yitian.datasource import *
from yitian.datasource import preprocess
class Test(unittest.TestCase):
# def test_standardize_date(self):
# data_pd = pd.DataFrame([
# ['01/01/2019', 11.11],... | pd.Timestamp('2019-04-04 04:44:44') | pandas.Timestamp |
# coding=utf-8
#
# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis-python
#
# Most of this work is copyright (C) 2013-2018 <NAME>
# (<EMAIL>), but it contains contributions by others. See
# CONTRIBUTING.rst for a full list of people who may hold copyright, and
# co... | is_categorical_dtype(dtype) | pandas.api.types.is_categorical_dtype |
import os
import numpy as np
import pandas as pd
import torch
from skimage import io, img_as_uint
from skimage.morphology import skeletonize_3d
from numbers import Number
from itertools import product
from torch.autograd import Variable
from torch.utils.data import DataLoader, Dataset
from torch.nn.functional import... | pd.DataFrame(scores) | pandas.DataFrame |
import os
import re
import json
import numpy as np
import pandas as pd
import operator
import base64
os.environ['DJANGO_SETTINGS_MODULE'] = 'zazz_site.settings'
import django
django.setup()
from django.core.exceptions import ObjectDoesNotExist
from django.core import serializers
from zazz import models
from time i... | pd.isnull(x) | pandas.isnull |
"""
This file contains methods to visualize EKG data, clean EKG data and run EKG analyses.
Classes
-------
EKG
Notes
-----
All R peak detections should be manually inspected with EKG.plotpeaks method and
false detections manually removed with rm_peak method. After rpeak examination,
NaN data can be accounted for by ... | pd.Series() | pandas.Series |
# coding: utf-8
# Import libraries
import pandas as pd
from pandas import ExcelWriter
from openpyxl import load_workbook
import pickle
import numpy as np
def summarize_reg(gene_set, n_data_matrix):
"""
The SUMMARIZE_REG operation summarizes all the data analysis results, by collecting them in convenient tables th... | pd.read_excel('./5_Data_Analysis/'+gene_set+'/Relevant_Features-Gene_'+gene_ID+'_['+current_gene+'].xlsx',sheetname='M'+model,header=0) | pandas.read_excel |
# Bamadrew95's stat compiler. Uses Beautiful Soup and Panda to grab stats from web and compile and sort them by team.
# Based on Bamaham93's FBS Scraper program
######################################################################################################
# This will be used to store static html for a singl... | pd.DataFrame(all_stats, columns=stat_titles) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#"""
#Copyright [2020] [Indian Institute of Science, Bangalore & Tata Institute of Fundamental Research, Mumbai]
#SPDX-License-Identifier: Apache-2.0
#"""
__name__ = "Script for generating city files - instantiation of a synthetic city"
import os
import sys
import math
im... | pd.read_csv(demographicsfile) | pandas.read_csv |
import numpy as np
def interp1d_(x, y, x_new):
from scipy.interpolate import interp1d, pchip_interpolate
# return interp1d(x,y,kind='cubic')(x_new)
return pchip_interpolate(x, y, x_new)
def get_baseline_dff(fmean, fneuropil, cont_ratio=0.7, win_=3000, q=0.1):
import pandas as pd
fmean_comp = fme... | pd.Series(fmean_comp) | pandas.Series |
import getpass
import math
import pickle
from kivy.clock import Clock
from kivy.uix.textinput import TextInput
from kivymd.app import MDApp
from kivymd.uix.datatables import MDDataTable
from kivy.lang.builder import Builder
from kivy.uix.screenmanager import ScreenManager, Screen
from kivy.metrics import dp
import os
f... | pd.DataFrame(data_serv) | pandas.DataFrame |
### import used modules first
from TPM.localization import select_folder
from glob import glob
import random
import string
import numpy as np
import os
import datetime
import pandas as pd
import scipy.linalg as la
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d i... | pd.concat([df_dict[f'{sheet_name}'], df], axis=axis) | pandas.concat |
from apiclient.discovery import build
import pandas as pd
import sys
from datetime import datetime
time = datetime.now().strftime('_%Y-%m-%d_%H_%M_%S')
# CREDENTIALS
DEVELOPER_KEY = "YOUR API KEY"
YOUTUBE_API_SERVICE_NAME = "youtube"
YOUTUBE_API_VERSION = "v3"
def youtube_search(q, max_results=50,order="relevance", ... | pd.DataFrame(data=video_data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 5 16:37:53 2019
@author: sdenaro
"""
import pandas as pd
import numpy as np
def setup(year,operating_horizon,perfect_foresight):
#read generator parameters into DataFrame
df_gen = pd.read_csv('PNW_data_file/generators.csv',header=0)
zone = ['PNW']
##t... | pd.read_csv('Path_setup/PNW_exports65.csv',header=0) | pandas.read_csv |
import numpy as np
#exec(open(r'D:\OneDrive\documents\Projects\trader\trendln\trendln\__init__.py').read())
def datefmt(xdate, cal=None):
from pandas.tseries.holiday import AbstractHolidayCalendar, Holiday, nearest_workday, \
USMartinLutherKingJr, USPresidentsDay, GoodFriday, USMemorialDay, \
USLab... | Holiday('NewYearsDay', month=1, day=1, observance=nearest_workday) | pandas.tseries.holiday.Holiday |
"""
Tests the usecols functionality during parsing
for all of the parsers defined in parsers.py
"""
from io import StringIO
import numpy as np
import pytest
from pandas._libs.tslib import Timestamp
from pandas import DataFrame, Index
import pandas._testing as tm
_msg_validate_usecols_arg = (
"'usecols' must eit... | DataFrame([[19, 29, 39], [19, 29, 39], [10, 20, 30]]) | pandas.DataFrame |
import pandas as pd
import numpy as np
from statsmodels.discrete.discrete_model import Probit
#First regression table
def table2_reg(df_reg, disp_it):
"""Function to create the tables for the first probit models.
Args:
dataFrame containing the categorial variables as dummies and the... | pd.DataFrame({'(1)': [], '(2)': [], '(3)': [], '(4)': []}) | pandas.DataFrame |
import filecmp
import os
import pandas as pd
import pytest
import sas7bdat_converter.converter as converter
import shutil
import xlrd
from pathlib import Path
from glob import glob
current_dir = Path().absolute()
def test_batch_to_csv(tmpdir, sas_file_1, sas_file_2, sas_file_3):
converted_file_1 = Path(tmpdir)... | pd.DataFrame(data=d) | pandas.DataFrame |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, time, timedelta, date
import sys
import os
import operator
from distutils.version import LooseVersion
import nose
import numpy as np
randn = np.random.randn
from pandas import (Index, Series, TimeSeries, DataFrame,
isnull, date_ran... | pd.Index(rng) | pandas.Index |
# -*- coding: utf-8 -*-
"""Compute statistical description of datasets"""
import multiprocessing
import itertools
from functools import partial
import numpy as np
import pandas as pd
import matplotlib
from pkg_resources import resource_filename
import pandas_profiling.formatters as formatters
import pandas_profiling.b... | pd.Series(result, index=names, name=series.name) | pandas.Series |
#***************************************************************
# climo_4.ncl
#
# Concepts illustrated:
# - Drawing a latitude/time contour plot
# - Calculating a zonally averaged annual cycle
# - Setting contour colors using RGB triplets
# - Explicitly setting tickmarks and labels on the bottom X axis
# - E... | pd.to_datetime(times) | pandas.to_datetime |
#!/usr/bin/env python
# coding: utf-8
# ## 17 - AgriPV - Jack Solar Site Modeling
# Modeling Jack Solar AgriPV site in Longmonth CO, for crop season May September. The site has two configurations:
#
#
# <b> Configuration A: </b>
# * Under 6 ft panels : 1.8288m
# * Hub height: 6 ft : 1.8288m
#
#
# Configura... | pd.to_datetime('2021-09-30 18:0:0 -7') | pandas.to_datetime |
import pandas as pd
from textblob import TextBlob
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from nltk.util import ngrams
import string
from progress.bar import Bar
# filepath = '../data/filtered_train_data_all.csv'
# # filepath = 'toy_... | pd.DataFrame(sentiment_dict) | pandas.DataFrame |
from torchvision import transforms, datasets
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import torch
import pandas as pd
import time
import os
def load_dataset(config, logger):
logger.info('Loading Dataset {IF Data present use it else download}')
path = os.path.join(config['direct... | pd.DataFrame(data={'Loss': loss, 'Accuracy': acc}) | pandas.DataFrame |
"""Tests for Safegraph process functions."""
from datetime import date
import tempfile
import os
import time
import numpy as np
import pandas as pd
from delphi_safegraph.process import (
aggregate,
construct_signals,
get_daily_source_files,
process,
process_window
)
from delphi_safegraph.run impor... | pd.testing.assert_frame_equal(expected, actual) | pandas.testing.assert_frame_equal |
from dash.dependencies import Input, Output, State
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import dash_table
import numpy as np
import plotly.express as px
from apps.app import dash_app
from apps.template import app_layout
import datetime as dt
import re... | pd.read_json(data) | pandas.read_json |
"""
Convert MIMIC III data to CCDEF (hdf5 based)
"""
import numpy as np
import pandas as pd
#import sqlite3
import h5py
import json
import wfdb
from ccdef._utils import df_to_sarray
def patient_id_from_file(filename):
return int(os.path.basename(filename).split('p')[1].split('-')[0])
def labs_to_df (dset)... | pd.to_datetime(row['dischtime']) | pandas.to_datetime |
import gensim
import numpy as np
import pandas as pd
import re
import os
import time
import jieba
import cv2
import json
import urllib
import random
import hashlib
from snownlp import sentiment
from snownlp import SnowNLP
import jieba.posseg as pseg
from gensim.models import word2vec
import logging
import torch
import ... | pd.isna(text_content) | pandas.isna |
import re
import os
import pandas as pd
import numpy as np
import pickle as pkl
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
from nltk import word_tokenize
from nltk.stem import WordNetLemmatizer
from sklearn.utils import resample
from sklearn.utils import shuffle
from variables import tr... | pd.read_csv(train_data_path) | pandas.read_csv |
import datetime
import re
import empyrical as em
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pyfolio as pf
import pymongo
import QUANTAXIS as QA
from qaenv import mongo_ip
#mongo_ip = '127.0.0.1'
def mergex(dict1, dict2):
dict1.update(dict2)
return dict1
def promise_list(... | pd.DataFrame(b) | pandas.DataFrame |
#! /bin/bash
# -*- coding: utf-8 -*-
import logging
import pandas as pd
import numpy as np
import click
from datetime import datetime
logger = logging.getLogger(__name__)
_COLS_TO_CONVERT = [
'market_data_current_price_usd',
'market_data_circulating_supply',
'market_data_ath_usd',
'market_data_high_24... | pd.read_csv(path_test_df, encoding="ISO-8859-1") | pandas.read_csv |
"""Module to provide generic utilities for other accelerometer modules."""
from collections import OrderedDict
import datetime
import json
import math
import os
import pandas as pd
import re
DAYS = ['mon', 'tue', 'wed', 'thur', 'fri', 'sat', 'sun']
TIME_SERIES_COL = 'time'
def formatNum(num, decimalPlaces):
"""... | pd.DataFrame.from_dict(jdicts) | pandas.DataFrame.from_dict |
import glob
import pandas as pd
import sys
files = sys.argv[1]
out_file = sys.argv[2]
data_frame = pd.read_csv(files.split(',')[0],sep='\t')
for file in files.split(',')[1:]:
df1 = | pd.read_csv(file,sep='\t') | pandas.read_csv |
#-- -- -- -- Intermediate Python
# Used for Data Scientist Training Path
#FYI it's a compilation of how to work
#with different commands.
####### -----> Matplotlib
### --------------------------------------------------------
## Line plot - ex#0
# Print the last item from year and pop
print(year[-1])
p... | pd.read_csv('cars.csv') | pandas.read_csv |
"""
generate paper figures
"""
from __future__ import print_function
import ast
import datetime
import os
import numpy as np
import pandas as pd
from ccdc.cavity import Cavity
from ccdc.io import MoleculeReader
from pipeline import HotspotPipeline
from hotspots.hs_io import HotspotReader
from hotspots.grid_extensi... | pd.concat(reports, ignore_index=True) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from constants import *
import numpy as np
import pandas as pd
import utils
import time
from collections import deque, defaultdict
from scipy.spatial.distance import cosine
from scipy import stats
import math
seed = SEED
cur_stage = CUR_STAGE
mode = cur_mode... | pd.DataFrame(right_result,index=feat_right.index,columns=['right_allitem_item_imagesim_max','right_allitem_item_imagesim_sum']) | pandas.DataFrame |
import time
import pandas as pd
import scrapping
def Items(items):
# intiate results items dataframe
Results = pd.DataFrame()
for counter in range(len(items['Items'])):
# print(items[counter])
GetItem = {'item':items['Items'][counter],
'link':'https://www.alibaba.com/trade/s... | pd.read_csv(file) | pandas.read_csv |
# -*- coding: utf-8
"""Test the created constraints against approved constraints.
This file is part of project oemof (github.com/oemof/oemof-thermal).
It's copyrighted by the contributors recorded in the version control
history of the file, available from its original location
oemof-thermal/tests/constraint_tests.py
... | pd.DataFrame(data=d) | pandas.DataFrame |
import os
import pandas as pd
import numpy as np
import math
import collections
import pymongo
import json
import copy
import hashlib
from io import StringIO
import warnings
warnings.filterwarnings("ignore")
home_path = os.getenv("HOME")
desktop_path = f"{home_path}/Desktop"
class UtilsPandas():
def __init__(s... | pd.to_datetime(df["date"]) | pandas.to_datetime |
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