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AMR_feb_new_data.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 10 11:38:42 2020
@author: jha
"""
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 6 13:54:08 2020
@author: jha
"""
#!
#%%
import csv
import mysql.connector as mariadb
from tabulate import tabulate
import pandas as pd
amrdb= mariadb.connect(
host="localhost",
user="root",
passwd="Sukhoi@90",
database ="myamr"
)
cursor = amrdb.cursor(buffered=True) # else it fetches one row for everytime it is executed
print("We are at line 31 we have connection, lets begin")
#%%
#creating a table where all AST_phenotypes have values
#%%
new_table= cursor.execute("CREATE TABLE IF NOT EXISTS Candida_auris_nov_ast AS SELECT * FROM Candida_auris WHERE AST_phenotypes LIKE '%=S%' OR AST_phenotypes LIKE '%=R%' OR AST_phenotypes LIKE '%=I%' ")
print("Selected %s rows" %new_table)
print("Selected %s rows " %cursor.rowcount)
#rows =cursor.fetchall()#fetch all rows at once
#print(tabulate(rows, headers=['strain','AST_phenotype', 'AMR_genotype'], tablefmt='psql'))
#%%
#creating a table where all AST_phenotypes values are from USA
#%%
new_table= cursor.execute("CREATE TABLE IF NOT EXISTS Shigella_nov_ast_us AS SELECT * FROM ShigellaMerged WHERE geo_loc_name LIKE '%USA%' ")
print("Selected %s rows" %new_table)
print("Selected %s rows " %cursor.rowcount)
#rows =cursor.fetchall()#fetch all rows at once
#print(tabulate(rows, headers=['strain','AST_phenotype', 'AMR_genotype'], tablefmt='psql'))
#%%
#get the data from the ast table to make the dataframe
#%%
import pandas as pd
numrows= cursor.execute("SELECT AST_phenotypes FROM Shigella_nov_ast_us")
print("Selected %s rows" %numrows)
print("Selected %s rows " %cursor.rowcount)
rows =cursor.fetchall()#fetch all rows at once
#print(tabulate(rows, headers=['AST_phenotype'], tablefmt='psql'))
#%%
#splitting the AST_phenotype column values into separate columns of the table
#%%
df = pd.DataFrame(rows)
#print ("the pandas dataframe ",df
df.columns=["AST_phenotypes"]
df.head()
df['ASTphenotypes']=df['AST_phenotypes']
df=df.set_index('ASTphenotypes')
split_df= df['AST_phenotypes'].str.split(',', expand= True)
split_df.head()
split_dfT=split_df.T
#split_df = split_df.drop(split_df.columns[[23]], axis=1)
#%%
#we are making the column names for the AST dataframes
#%%
split_df_list=[]
split_df_list = split_df.values.tolist()
flat_list_sd = []
for sublist in split_df_list:
for item in sublist:
flat_list_sd.append(item)
#print ("ATline {0} in code split_df has been converted to a list{1}".format(lineno(),flat_list_sd))
split_flsd=[]
for element in flat_list_sd:
if element != None:
split_flsd.append(element.split('=', 1)[0])
#print ("ATline {0} in code split_df has been converted to a list".format(lineno()))
print("the list of all elements in split_df", split_flsd)
header_set=set(split_flsd)
header_list_AST=list(header_set)
print("at line 87 in code the list of header values are ")
df_AST=pd.DataFrame(header_list_AST)
df_AST.columns=["AST"]
df_AST=df_AST.T
df_AST=df_AST.reset_index()
df_AST=df_AST.T
df_AST=df_AST.reset_index()
df_AST=df_AST.drop(['index'], axis=1)
df_AST.columns=["AST"]
#%%
#%%
x=1
for tup in split_df.itertuples():
print (tup)
tuplist=list(tup)
tup_list_df=pd.DataFrame(tuplist)
colname=tup_list_df.iloc[0]
head= pd.DataFrame([colname])
head=head.reset_index()
head= head.T
head=head.reset_index()
head=head.drop(columns=['index'])
head=head.set_value(0,0, 'AST')
head.columns=["header"]
head_list=head['header'].tolist()
tup_list_df=tup_list_df.drop([0])
tup_list_df.columns=["AST"]
tup_list_df= tup_list_df.AST.str.split('=', expand= True)
tup_list_df.columns=head_list
tup_list_df=tup_list_df.T
tup_list_df=tup_list_df.reset_index()
tup_list_df=tup_list_df.T
tup_list_df.columns=["AST","AST_p"]
#tup_list_df_list= list(tup_list_df)
tup_list_df_list=list(tup_list_df.itertuples(index=False, name=None))
tup_list_df_list_dict=dict(tup_list_df_list)
#tup_list_df_list_dict=dict(tup_list_df_list)
df_AST[x]=df_AST['AST'].map(tup_list_df_list_dict)
x=x+1
#%%
#the list of Antimicrobial is populated by the R and S values forming an array. (the result is a list of lists called
#list_list_AST)
#%%
#def extractDigits(lst):
# return list(map(lambda el:[el], lst))
#list_list_AST= extractDigits(header_list_AST)
#for x in list_list_AST:
# print(x[0])
# for item in flat_list_sd:
# if item is not None and x[0] == item.split('=')[0] :
# x.append(item)
# print("printing if equal",x)
# #print("printing table item",item)
#print ("printing x",x)
#%%
#we are using regex to remove the anitibiotic name(ex: ampicillin=R and retain the "R" for each column= DONE)
#%%
#for col in list_list_AST:
# #print ("col",col)
# for row in col:
# #print("the row is",row)
# if "=" in row:
# #print("row", row)
# loc=col.index(row)
# row1=row.split("=")[-1]
# col.remove(row)
# col.insert(loc,row1)
# #print ("the row has been split to ",row)
#
#print("the rows have been split")
#%%
#%%
#df_AST= pd.DataFrame(list_list_AST)
#%%
#%%
df_ASTT=df_AST.T
df_ASTT.columns = df_ASTT.iloc[0] #grab the first row for the header
df_ASTT=df_ASTT.drop(['AST'])# drops the first row which is a header row from dataframe but this also changes the index of the dataframe
df_ASTT=df_ASTT.reset_index()# the change of index in the previous command causes error in the concatenation of the other dataframe
df_ASTT= df_ASTT.drop(columns=['index'])# so reset is used to create a new index and the old index is deleted
#df_ASTT = pd.concat([df,df_ASTT], axis=1, join_axes=[df.index])
#%%
#This removes all drugs which have only none values in all rows and columns
#%%
#df_ASTT = df_ASTT.replace('ND', np.nan)
#df_ASTT = df_ASTT.fillna('0')
#df_ASTT = df_ASTT.replace('0', np.nan)
#df_ASTT = df_ASTT.set_index('AST_phenotypes')
#df_ASTT = df_ASTT.dropna(axis = 0, how = 'all')
#df_ASTT = df_ASTT.dropna(axis = 1, how = 'all')
#df_ASTT = df_ASTT.reset_index()
#%%
#run this code only once to create the astt table
#%%
df_ASTT= df_ASTT.rename(columns={"AST_phenotypes":"AST"})
from sqlalchemy import create_engine
cnx = create_engine('mysql+pymysql://root:Sukhoi@90@localhost/myamr')
df_ASTT.to_sql(name='', con=cnx, if_exists = 'replace', index=False)
print("we are at line 186 in code")
#%%
##%%
#from sqlalchemy import create_engine
#
#cnx = create_engine('mysql+pymysql://root:Sukhoi@90@localhost/myamr')
##df3.to_sql(name='salall', con=cnx, if_exists = 'replace', index=False)
#df_sal_ast= pd.read_sql('Klebsiellaast',cnx)
#
##%%
#
#
#
#
#%%
numrows= cursor.execute("CREATE TABLE IF NOT EXISTS Ecoli_nov_merged AS SELECT * FROM Ecoli_nov_ast INNER JOIN Ecoli_nov_astt ON Ecoli_nov_ast.AST_phenotypes = Ecoli_nov_astt.AST")
print("Selected %s rows" %numrows)
print("Selected %s rows " %cursor.rowcount)
rows =cursor.fetchall()#fetch all rows at once
#print(tabulate(rows, headers=['strain','AST_phenotype', 'AMR_genotype'], tablefmt='psql'))
#dffinal = pd.DataFrame(rows)
#%%
#
#
#
#%%
#header_astt=[]
#header_astt= list(df_ASTT.columns)
#header_kleb_ast=[]
#header_kleb_ast= list(df_kleb.columns)
#header_final=[]
#header_final=header_astt+header_kleb_ast
#print(header_final)
#header_final = [s.replace('#label', 'Label') for s in header_final]
#dffinal.columns=header_final
#dffinal=dffinal.drop(dffinal.columns[[0]], axis=1)
##
#
#
#
#select * from Persons inner join data on persons.ID = dat
#
#
#%%
#
#
#
#run this code snippet just once to create the final table
#%%
from sqlalchemy import create_engine
cnx = create_engine('mysql+pymysql://root:Sukhoi@90@localhost/myamr')
dffinal.to_sql(name='Klebsiella_all_us', con=cnx, if_exists = 'replace', index=False)
print("we are at line 244 in code")
#
#
#%%
#
#this is the same as above why am I doing this again??
#%%
df_AB=df_AST
df_AB= df_AB.T
df_AB.columns= df_AB.iloc[0]
df_AB=df_AB.drop([0])
df_AB=df_AB.reset_index()
df_AB=df_AB.drop(columns=["index"])
#%%
#This removes all drugs which have only none values in all rows and columns
#%%
df_AB = df_AB.replace('ND', np.nan)
df_AB = df_AB.fillna('0')
df_AB = df_AB.replace('0', np.nan)
#df_AB = df_AB.set_index('AST_phenotypes')
df_AB = df_AB.dropna(axis = 0, how = 'all')
df_AB = df_AB.dropna(axis = 1, how = 'all')
#df_AB = df_AB.reset_index()
#%%
#%%
chunk=[]
chunk1=[]
for row in df_AB.columns:
try:
print(row)
#print(df_AB[row].value_counts().index.to_list())
#df_plot=(df_AB[row].value_counts().index.to_list())
#df_plot=pd.merge((df_AB[row].value_counts().to_frame()), on )
#print(df_AB[row].value_counts().to_frame())
#df_plot= df.append(df_AB[row].value_counts(), ignore_index=True)
#print("line 312")
print(df_AB[row].value_counts().reset_index())
df5=df_AB[row].value_counts().reset_index()
val=df5.loc[df5['index'] != "S"]
val=val.T
val=val.reset_index()
val=val.drop([0])
print("before saving to list)")
chunk1.append(val.values.tolist())
#val.columns=['antibiotic','number_of_S']
#df=pd.DataFrame('Antimicrobial': val[])
# print("yes",val)
#chunk= [df5.columns.values.tolist()] + df5.values.tolist()
# df_plot_C=pd.concat(series5)
except:
pass
#%%
#%%
list_chunk=[]
for x in chunk1:
print(x)
for y in x:
print(y)
list_chunk.append(y)
#%%
#%%
from matplotlib import pyplot as plt
import matplotlib.cm as cm
from matplotlib.colors import Normalize
import pandas as pd
import numpy as np
df_plotKNS= pd.DataFrame(list_chunk)
df_plotKNS.columns=["Antimicrobial","R","I"] #when there is all I in a column or all R then the I column is included in the R column
#df_plotKNS=df_plotKNS.drop(columns=["ND"])
df_plotKNS["non_susceptible"]=df_plotKNS.fillna(0)["R"]+df_plotKNS.fillna(0)["I"] # this line is needed to make non susceptible column by adding R and I columns
#df_plot.columns=['Antimicrobial','number_of_S']
#%%
#percentage resistant to each drug in klebsiella data
#%%
df_plotKNS['percent_nonS'] = ((df_plotKNS['non_susceptible']/(000000))*100)
df_plotKNS.percent_nonS=df_plotKNS.percent_nonS.round(2)
#df_plotKNS_sorted=df_plotKNS.sort_values('percent_nonS',ascending=False)
#ax = df_plotKNS_sorted.plot.bar(x='Antimicrobial', y='percent_nonS', rot=90,legend=False, # Turn the Legend off
# width=0.75, # Set bar width as 75% of space available
# figsize=(15,6), # Set size of plot in inches
# colormap='summer')
#for index,data in enumerate(df_plotKNS_sorted['percent_nonS']):
# plt.text(x=index , y =data , s=f"{data}",fontdict=dict(fontsize=8) )
#plt.title("Percentage of Klebsiella samples found non Susceptible to Antimicrobial (33,222 samples)")
#plt.ylabel("percentage of nonS")
#plt.plot(x,y)
#plt.show()
#%%
#for graphing the susceptible microbes
#%%
chunk2=[]
chunk3=[]
for row in df_AB.columns:
try:
print(row)
#print(df_AB[row].value_counts().index.to_list())
#df_plot=(df_AB[row].value_counts().index.to_list())
#df_plot=pd.merge((df_AB[row].value_counts().to_frame()), on )
#print(df_AB[row].value_counts().to_frame())
#df_plot= df.append(df_AB[row].value_counts(), ignore_index=True)
#print("line 312")
print(df_AB[row].value_counts().reset_index())
df6=df_AB[row].value_counts().reset_index()
val=df6.loc[df6['index'] == "S"]
val=val.T
val=val.reset_index()
val=val.drop([0])
print("before saving to list)")
chunk3.append(val.values.tolist())
#val.columns=['antibiotic','number_of_S']
#df=pd.DataFrame('Antimicrobial': val[])
# print("yes",val)
#chunk= [df5.columns.values.tolist()] + df5.values.tolist()
# df_plot_C=pd.concat(series5)
except:
pass
#%%
#%%
list_chunkS=[]
for x in chunk3:
print(x)
for y in x:
print(y)
list_chunkS.append(y)
#%%
#%%
from matplotlib import pyplot as plt
import matplotlib.cm as cm
from matplotlib.colors import Normalize
import pandas as pd
import numpy as np
df_plotKS= pd.DataFrame(list_chunkS)
df_plotKS.columns=["Antimicrobial","Susceptible"]
##
##print()
#%%
##plot for % susceptible Antimicrobial to klebsiella Antimicrobial
#%%
#df_plotKS['percent_S'] = ((df_plotKS['Susceptible']/16680)*100)
#df_plotKS.percent_S=df_plotKS.percent_S.round(2)
#df_plotKS_sorted=df_plotKS.sort_values('percent_S',ascending=False)
#ax = df_plotKS_sorted.plot.bar(x='Antimicrobial', y='percent_S', rot=90,legend=False, # Turn the Legend off
# width=0.75, # Set bar width as 75% of space available
# figsize=(15,6), # Set size of plot in inches
# colormap='summer')
#for index,data in enumerate(df_plotKS_sorted['percent_S']):
# plt.text(x=index , y =data , s=f"{data}",fontdict=dict(fontsize=8) )
#plt.title("Percentage of Klebsiella samples found Susceptible to Antimicrobial (33222 samples)")
#plt.ylabel("percentage of S")
#plt.plot(x,y)
#plt.show()
#
#
#
#
#%%
#making the table with each percentage non-susceptible is non-susceptible/(susceptible+non_susceptible) same for susceptible
#%%
df_plotKS_dict=df_plotKS.set_index('Antimicrobial')['Susceptible'].to_dict()
df_plotKNS['Susceptible'] = df_plotKNS['Antimicrobial'].map(df_plotKS_dict)
df_plotKNS["Total_Samples"]=df_plotKNS.fillna(0)["Susceptible"]+ df_plotKNS.fillna(0)["non_susceptible"]
df_plotKNS['percent_S'] = ((df_plotKNS['Susceptible']/df_plotKNS["Total_Samples"])*100)
df_plotKNS['percent_nonS'] = ((df_plotKNS['non_susceptible']/df_plotKNS["Total_Samples"])*100)
#%%
#Use only if plotting the ib=ncreasing percentage in graphs
#%%
df_plotKNS_sorted=df_plotKNS.sort_values('percent_S',ascending=False)
df_plotKNS_sorted.percent_nonS=df_plotKNS.percent_S.round(0)
df_plotKNS_sorted.percent_nonS=df_plotKNS.percent_nonS.round(0)
#%%
#percentage plot for increasing percentage
#%%
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
import matplotlib.patches as mpatches
r=[]
r= list(df_plotKNS_sorted["Antimicrobial"])
bars1=[]
bars1= list(df_plotKNS_sorted.fillna(0)["percent_nonS"])
bars2=[]
bars2=list(df_plotKNS_sorted.fillna(0)["percent_S"])
p1=plt.bar(r, bars1, color='rosybrown', edgecolor='white', width=1,label="non-Susceptible")
# Create green bars (middle), on top of the firs ones
p2=plt.bar(r, bars2, bottom=bars1, color='mistyrose', edgecolor='white', width=1,label="Susceptible")
plt.xticks(r,rotation=90)
plt.xlabel("Antimicrobial")
plt.legend(loc='upper left', bbox_to_anchor=(1,1), ncol=1)
plt.title("Percentage of Klebsiella samples tested ( samples)")
plt.ylabel("percentage")
rects1 = p1.patches
labels1 = ["%d" % i for i in (df_plotKNS_sorted.fillna(0)["Susceptible"])]
for rect, label in zip(rects1, labels1):
height = rect.get_height()
plt.text(rect.get_x() + rect.get_width() / 2., height, label,ha='center', va='bottom',color="black", fontsize=8,fontweight="bold", rotation= 90)
rects2 = p2.patches
labels2 = ["%d" % i for i in df_plotKNS_sorted.fillna(0)["non_susceptible"]]
for rect, label in zip(rects2, labels2):
height = rect.get_height()
plt.text(rect.get_x() + rect.get_width() / 2., 100-height, label,ha='center', va='top',color="blue", fontsize=8,fontweight="bold", rotation= 90)
rects1 = p1.patches
labels3 = ["%d" % i for i in df_plotKNS_sorted.fillna(0)["Total_Samples"]]
for rect, label in zip(rects2, labels3):
height= 20
plt.text(rect.get_x() + rect.get_width() / 2.,height, label,ha='center', va='bottom',color="maroon", fontsize=8,fontweight="bold", rotation= 90)
plt.show()
#%%
#writing data table into an excel file
#%%
import pandas as pd
numrows= cursor.execute("Describe Escherichia_MergedF")
print("Selected %s rows" %numrows)
print("Selected %s rows " %cursor.rowcount)
rows =cursor.fetchall()#fetch all rows at once
print(rows)
dfz= pd.DataFrame(rows)
listz=list(dfz[0])
numrows_k= cursor.execute("Select * from Escherichia_MergedF")
print("Selected %s rows" %numrows_k)
print("Selected %s rows " %cursor.rowcount)
rows_k =cursor.fetchall()#fetch all rows at once
df_kleb= pd.DataFrame(rows_k)
df_kleb.columns= listz
#%%
#This removes all drugs which have only none values in all rows and columns
#%%
df_kleb = df_kleb.replace('ND', np.nan)
df_kleb = df_kleb.replace('NONE',np.nan)
df_kleb = df_kleb.fillna('0')
df_kleb = df_kleb.replace('0', np.nan)
#df_kleb = df_kleb.set_index('AST_phenotypes')
df_kleb = df_kleb.dropna(axis = 0, how = 'all')
df_kleb = df_kleb.dropna(axis = 1, how = 'all')
#df_kleb = df_kleb.reset_index()
#%%
#run this code snippet just once to create the final table
#%%
from sqlalchemy import create_engine
cnx = create_engine('mysql+pymysql://root:Sukhoi@90@localhost/myamr')
df_kleb.to_sql(name='Escherichia_isolationepi', con=cnx, if_exists = 'replace', index=False)
print("we are at line 244 in code")
#
#
#%%
#%%
#import os
#
#outname = 'klebsiella.csv'
#
#outdir = '/Users/jha/Documents/Fall2019'
#if not os.path.exists(outdir):
# os.mkdir(outdir)
#
#fullname = os.path.join(outdir, outname)
#dffinal.to_excel("Klebsiella_all_us.xlsx")
df_kleb.to_excel("EscherichiamergedUS.xlsx")
#%%
#%%
#writing the list of drugs into an excel file for classification
# import xlsxwriter module
#import xlsxwriter
#
#workbook = xlsxwriter.Workbook('klebsiella_full.xlsx')
#worksheet = workbook.add_worksheet()
#
## Start from the first cell.
## Rows and columns are zero indexed.
#row = 0
#column = 0
#
#content = df_kleb
#
## iterating through content list
#for item in content :
#
# # write operation perform
# worksheet.write(row, column, item)
#
# # incrementing the value of row by one
# # with each iteratons.
# row += 1
#
#workbook.close()
#%%
#**************************************************************************************************
"""
Exploratory statistics on klebsiella
"""
#****************************************************************************************************
#%%
import csv
import mysql.connector as mariadb
from tabulate import tabulate
import pandas as pd
amrdb= mariadb.connect(
host="localhost",
user="root",
passwd="Sukhoi@90",
database ="myamr"
)
cursor = amrdb.cursor(buffered=True) # else it fetches one row for everytime it is executed
print("We are at line 31 we have connection, lets begin")
#%%
#%%
df_family = pd.read_excel('/Users/jha/Documents/spring2020/Klebsiella_Sneha_Jan_2020.xlsx', sheet_name='Sheet1')
df_family_mod= df_family
df_family=df_family.drop(["Intrinsicly_resistant","Antimicrobial_class","Antimicrobial_mod"], axis=1)
list_family=list(df_family.itertuples(index=False, name=None))
#list_family=list(df_family)
family_dict=dict(list_family)
df_plotKNS['Group']=df_plotKNS['Antimicrobial'].map(family_dict)
df_plotKNS_sorted=df_plotKNS.sort_values(by=['Group'])
df_plotKNS_sorted_ref=df_plotKNS_sorted
#%%
#%%
#df_plotKNS_sorted=df_plotKNS_sorted.set_index(['Antimicrobial_class', 'Antimicrobial'])
#%%
#%%
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
import matplotlib.patches as mpatches
r=[]
r2=[]
r= list(df_plotKNS_sorted.Antimicrobial.str.title())
#r = [sr.capitalize() for sr in r]
r2=list(df_plotKNS_sorted["Group"])
bars1=[]
bars1= list(df_plotKNS_sorted.fillna(0)["percent_nonS"])
bars2=[]
bars2=list(df_plotKNS_sorted.fillna(0)["percent_S"])
p1=plt.bar(r,bars1, color='rosybrown', edgecolor='white', width=1,label="Non-susceptible")
# Create green bars (middle), on top of the firs ones
p2=plt.bar(r, bars2, bottom=bars1, color='mistyrose', edgecolor='white', width=1,label="Susceptible")
plt.xticks(r,rotation=90)
plt.xlabel("Antimicrobials")
plt.legend(loc='upper left', bbox_to_anchor=(1,1), ncol=1)
plt.title("Comparative percentage of Klebsiella samples tested in 2015,grouped by family of antimicrobials")
plt.ylabel("Percentage")
rects1 = p1.patches
labels1 = ["%d" % i for i in (df_plotKNS_sorted.fillna(0)["non_susceptible"])]
for rect, label in zip(rects1, labels1):
height = rect.get_height()
plt.text(rect.get_x() + rect.get_width() / 2., height-1, label,ha='center', va='top',color="blue", fontsize=8,fontweight="bold", rotation= 90)
rects2 = p2.patches
labels2 = ["%d" % i for i in df_plotKNS_sorted.fillna(0)["Susceptible"]]
for rect, label in zip(rects2, labels2):
height = rect.get_height()
plt.text(rect.get_x() + rect.get_width() / 2., 100-height, label,ha='center', va='bottom',color="black", fontsize=8,fontweight="bold", rotation= 90)
rects1 = p1.patches
labels3 = ["%d" % i for i in df_plotKNS_sorted.fillna(0)["Total_Samples"]]
for rect, label in zip(rects2, labels3):
height= 0
plt.text(rect.get_x() + rect.get_width() / 2.,height, label,ha='center', va='bottom',color="black", fontsize=8,fontweight="bold", rotation= 90)
plt.show()
#%%
#%%
print(df_plotKNS_sorted.Antimicrobial_class.value_counts())
listp=[]
listp=list(df_plotKNS_sorted.Antimicrobial_class.unique())
#df_plotKNS_sorted_multi=df_plotKNS_sorted
df_plotKNS_sorted=df_plotKNS_sorted.set_index(['Antimicrobial_class', 'Antimicrobial'])
df_plotKNS_sorted_multi=df_plotKNS_sorted.sum(level =0)
#%%
#%%
# Libraries
import matplotlib.pyplot as plt
# Make data: I have 3 groups and 7 subgroups
group_names=listp
group_size=[]
group_size=list(df_plotKNS_sorted_multi["non_susceptible"])
subgroup_names=[]
subgroup_names=list(df_plotKNS_sorted_ref["Antimicrobial"])
subgroup_size=[]
subgroup_size=list(df_plotKNS_sorted_ref["non_susceptible"])
# Create colors
#a, b, c=[plt.cm.Blues, plt.cm.Reds, plt.cm.Greens]
cmap = plt.get_cmap('PuBuGn')
colors_grp = cmap(np.linspace(0., 1., len(group_names)))
cmap = plt.get_cmap('PuBuGn')
colors= cmap(np.linspace(0., 1., len(subgroup_names)))
print(colors_grp)
print(colors)
#First Ring (outside)
fig, ax = plt.subplots()
ax.axis('equal')
mypie, _ = ax.pie(group_size, radius=1.3, labels=group_names, colors=colors_grp)
plt.setp( mypie, width=0.3, edgecolor='white')
# Second Ring (Inside)
mypie2, _ = ax.pie(subgroup_size, radius=1.3-0.3, labels=subgroup_names, labeldistance=0.6, colors=colors,rotatelabels = True )
plt.setp( mypie2, width=0.4, edgecolor='white')
plt.margins(0,5)
#TOO FANCY
#wedges, texts = ax.pie(group_size, wedgeprops=dict(width=0.5), startangle=-40)
#
#bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
#kw = dict(arrowprops=dict(arrowstyle="-"),
# bbox=bbox_props, zorder=0, va="center")
#
#for i, p in enumerate(wedges):
# ang = (p.theta2 - p.theta1)/2. + p.theta1
# y = np.sin(np.deg2rad(ang))
# x = np.cos(np.deg2rad(ang))
# horizontalalignment = {-1: "right", 1: "left"}[int(np.sign(x))]
# connectionstyle = "angle,angleA=0,angleB={}".format(ang)
# kw["arrowprops"].update({"connectionstyle": connectionstyle})
# ax.annotate(group_names[i], xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y),
# horizontalalignment=horizontalalignment, **kw)
#
#ax.set_title("Antimicrobial Classes: A donut")
plt.title("Antimicrobial class on the outer doughnut and antimicrobials on the inner doughnut (values=number of non-susceptible samples", loc='left')
# show it
plt.show()
#%%
#%%
import pandas as pd
numrows= cursor.execute("SELECT host,count(host) FROM Klebsiellaast GROUP BY(host) ")
print("Selected %s rows" %numrows)
print("Selected %s rows " %cursor.rowcount)
rows =cursor.fetchall()#fetch all rows at once
#print(tabulate(rows, headers=['AST_phenotype'], tablefmt='psql'))
df_host = pd.DataFrame(rows)
df_host.columns=['host','count']
import matplotlib.pyplot as plt
# Data to plot
labels =[]
labels= list(df_host["host"])
count=[]
count=list(df_host["count"])
colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue','orange','brown','violet']
#explode = (0.1, 0, 0, 0) # explode 1st slice
def make_autopct(count):
def my_autopct(pct):
total = sum(count)
val = int(round(pct*total/100.0))
return '{p:.2f}% ({v:d})'.format(p=pct,v=val)
return my_autopct
# Plot
plt.pie(count, radius=1,labels=labels, colors=colors,autopct=make_autopct(count))
plt.title("Host vs number of samples from each host tested for AST", loc='left')
plt.axis('equal')
plt.show()
#%%
#%%
import pandas as pd
numrows= cursor.execute("select AST_phenotypes ,count(`#label`),host from Klebsiellaast group by AMR_genotypes")
print("Selected %s rows" %numrows)
print("Selected %s rows " %cursor.rowcount)
rows =cursor.fetchall()#fetch all rows at once
#print(tabulate(rows, headers=['AST_phenotype'], tablefmt='psql'))
df_host = pd.DataFrame(rows)
df_host.columns=['AST_phenotypes','AMR_genotypes' , 'count',]
total=df_host['count'].sum()
print(total)
df_host=df_host[df_host['count'] >= 160]
df_host=df_host.reset_index()
df_host=df_host.drop('index',axis=1)