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common_functions.py
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#-- GEO1001.2020--hw01
#-- [Runnan Fu]
#-- [5213045]
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
#common functions
#lesson A1
def mean(data):
mean = data.mean(axis=0)
return mean
def var(data):
var = data.var(axis=0)
return var
def std(data):
std = data.std(axis=0)
return std
def draw_hist(data,xlabel,nb,ax):
fs= 10
ax.hist(x=data,bins=nb, facecolor='lightblue', edgecolor = 'black', alpha=0.7)
ax.set_xlabel(xlabel,fontsize=fs)
ax.set_ylabel('Frequency',fontsize=fs)
ax.tick_params(labelsize=fs)
ax.yaxis.grid(True)
def draw_fp(data, line_color, line_label, nb, ax):
fs = 10
y , edges, _ = ax.hist(data, bins=nb, color='w',histtype='step', linewidth=0)
midpoint = 0.5*(edges[1:]+edges[:-1])
ax.plot(midpoint,y, line_color, label=line_label, linewidth=1.5, alpha=1)
ax.set_xlabel('Temperature ($^\circ$C)',fontsize=fs)
ax.set_ylabel('Frequency',fontsize=fs)
ax.tick_params(labelsize=fs)
ax.yaxis.grid(True)
def draw_boxplot(data,ylabel,ax):
fs = 10
labels = ['A', 'B', 'C', 'D', 'E']
ax.boxplot(data,showmeans=True,patch_artist=True,labels=labels)
ax.set_xlabel('Five separate samples',fontsize=fs)
ax.set_ylabel(ylabel,fontsize=fs)
ax.tick_params(labelsize=fs)
ax.yaxis.grid(True)
#lesson A2 #pdf、cdf、pmf、kde
def draw_PMF(data,xlabel,ax):
fs = 10
def pmf(sample):
c = sample.value_counts()
p = c/len(sample)
return p
df = pmf(data)
df1 = df.to_frame()
df1.reset_index(inplace=True)
df1 = df1.astype(float)
c = df1.sort_values(by=['index'])
ax.bar(c.iloc[ : ,0],c.iloc[ : ,1],width=0.1, facecolor='lightblue',edgecolor='k', alpha=0.5)
# df = pmf(data.astype(float))
# c = df.sort_index()
# ax.bar(c.index,c,width=0.1, color='blue', alpha=0.5)
ax.set_xlabel(xlabel,fontsize=fs)
ax.set_ylabel('PMF',fontsize=fs)
ax.tick_params(labelsize=fs)
ax.yaxis.grid(True)
def draw_PDF(data,xlabel,nb,ax):
fs = 10
y , edges, _ =ax.hist(x=data, bins=nb, density=True,color='lightblue', alpha=0.7,label = 'Histogram with Densities')
midpoint = 0.5*(edges[1:]+edges[:-1])
ax.plot(midpoint,y, color='blue', linewidth=1.5, alpha=0.7, label = 'PDF')
#sns.distplot(data, bins=nb, color='b',ax=ax)
ax.set_xlabel(xlabel, fontsize=fs)
ax.set_ylabel('Probability Density')
ax.tick_params(labelsize=fs)
ax.yaxis.grid(True)
def draw_CDF(data,xlabel,nb,ax):
fs = 10
a=ax.hist(x=data, bins=nb, density=True, cumulative=True, color='lightblue', alpha=0.7)
ax.plot(a[1][1:]-(a[1][1:]-a[1][:-1])/2,a[0], color='k')
ax.set_xlabel(xlabel,fontsize=fs)
ax.set_ylabel('CDF',fontsize=fs)
ax.tick_params(labelsize=fs)
ax.yaxis.grid(True)
def draw_KDE(data,xlabel,ax):
fs = 10
sns.kdeplot(data, ax=ax ,color='r',label = 'KDE')
ax.tick_params(labelsize=fs)
ax.yaxis.grid(True)
#Lesson A3
def equal_size(data1,data2):
return np.interp(np.linspace(0,len(data2),len(data2)),np.linspace(0,len(data1),len(data1)),data1)
def draw_scatter(data1,data2,ax,xlabel,ylabel,unit):
fs = 10
data1 = equal_size(data1,data2)
ax.scatter(data1, data2, c='b',marker='.',s=1)
ax.set_xlabel(xlabel+unit,fontsize=fs)
ax.set_ylabel(ylabel+unit,fontsize=fs)
ax.set_title(xlabel+' vs '+ylabel,fontsize=fs+3)
ax.tick_params(labelsize=fs)
ax.yaxis.grid(True)
#Lesson A4
def draw_interval(data, ax):
# xquantx = ((np.quantile(data, 0.05), np.quantile(data, 0.95)))
data_mean = data.mean()
data_std = np.std(data, ddof = 1)
# n=len(data)
# data_se = data_std/np.sqrt(n)
confi_inte = stats.norm.interval(0.95, data_mean, data_std)
ax.axvline(confi_inte[0],color='r',linewidth = 0.5)
ax.axvline(confi_inte[1],color='r',linewidth = 0.5)
def save_interval(data):
data_mean = data.mean(axis=1)
data_std = np.std(data,ddof = 1,axis=1)
# n=data.shape[0]
# data_se = data_std/np.sqrt(n)
confi_inte = stats.norm.interval(0.95, data_mean, data_std)
return confi_inte
def creat_df(data,label):
a=[]
b=[]
for i in save_interval(data)[0]:
a.append(i)
for i in save_interval(data)[1]:
b.append(i)
c ={label+"_lower boundary" : a,
label+"_upper boundary" : b}
return pd.DataFrame(c)
def hy_test(data1,data2):
t, p_two= stats.ttest_ind(data1, data2, equal_var=False)
return p_two