import pandas
import os
import networkx as nx
import numpy
import numpy as np
from collections import defaultdict,Counter
import datetime
import json
import math
import pickle
from numpy import linspace, zeros, abs
import sympy as sp
import numpy as np
from operator import itemgetter
import dictlib
import math
import pickle
import random
import datetime
from scipy.stats import pearsonr
import json
import Input
import figures
from collections import defaultdict
from scipy.interpolate import CubicSpline
import scipy.optimize as opt
import scipy.stats
import pandas as pd
from sklearn import linear_model
import statsmodels.api as sm
import copy
from scipy.integrate import solve_ivp
from scipy.optimize import minimize
from scipy import interpolate
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from statsmodels.stats.outliers_influence import summary_table
from sklearn import linear_model
from itertools import accumulate
import ast
from sklearn.datasets import load_iris
from factor_analyzer import FactorAnalyzer
from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity
from factor_analyzer.factor_analyzer import calculate_kmo
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

import uncertainties as unc
import uncertainties.unumpy as unp

from sklearn.metrics import r2_score

from statsmodels.tools.eval_measures import rmse

from sklearn.cluster import KMeans

from sklearn.metrics import pairwise_distances_argmin

from mpl_toolkits import mplot3d

import plotly.graph_objects as go

from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix

import matplotlib.cm as cm
from scipy import stats
from scipy.optimize import minimize
from scipy import interpolate
from scipy.stats import truncnorm
import matplotlib as mpl
from itertools import permutations
from itertools import combinations_with_replacement


import itertools
import os
os.chdir("Documents")
os.environ['PROJ_LIB'] = '/Users/.conda/envs/PDE-COVID/share/proj'
path_external='Dataset'
