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conservacao_uri.py
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conservacao_uri.py
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# -*- coding: utf-8 -*-
"""
Created on Tue May 5 22:21:24 2015
@author: João Frederico Vieira de Oliveira | R3 Patologia Clínica, UNICAMP
"""
import pandas as pd
import numpy as np
import scipy as sp
import statsmodels as sm
import random as ra
import seaborn as sns
import matplotlib.pyplot as plt
import csv
from pandas import DataFrame, Series, qcut, cut, crosstab, concat
from scipy.stats.stats import pearsonr, spearmanr
from statsmodels.stats.inter_rater import *
from statsmodels.sandbox.stats.runs import mcnemar
################################################################################################
#
# DATAFRAMES
#
################################################################################################
df = pd.read_csv("./URI_TOTAL_DATA.csv")
dfposneg = pd.read_csv("./URI_TOTAL_POSNEG.csv")
dfcontmelt2 = pd.read_csv("./posneg_sed.csv")
###########################################################
sg_amb = df[(df.EXAME == "SG") & (df.LOCAL == "AMB")]
sg_gel = df[(df.EXAME == "SG") & (df.LOCAL == "GEL")]
ph_amb = df[(df.EXAME == "PH") & (df.LOCAL == "AMB")]
ph_gel = df[(df.EXAME == "PH") & (df.LOCAL == "GEL")]
leu_amb = df[(df.EXAME == "LEU") & (df.LOCAL == "AMB")]
leu_gel = df[(df.EXAME == "LEU") & (df.LOCAL == "GEL")]
nit_amb = df[(df.EXAME == "NIT") & (df.LOCAL == "AMB")]
nit_gel = df[(df.EXAME == "NIT") & (df.LOCAL == "GEL")]
pro_amb = df[(df.EXAME == "PRO") & (df.LOCAL == "AMB")]
pro_gel = df[(df.EXAME == "PRO") & (df.LOCAL == "GEL")]
glu_amb = df[(df.EXAME == "GLU") & (df.LOCAL == "AMB")]
glu_gel = df[(df.EXAME == "GLU") & (df.LOCAL == "GEL")]
ket_amb = df[(df.EXAME == "KET") & (df.LOCAL == "AMB")]
ket_gel = df[(df.EXAME == "KET") & (df.LOCAL == "GEL")]
ubg_amb = df[(df.EXAME == "UBG") & (df.LOCAL == "AMB")]
ubg_gel = df[(df.EXAME == "UBG") & (df.LOCAL == "GEL")]
bil_amb = df[(df.EXAME == "BIL") & (df.LOCAL == "AMB")]
bil_gel = df[(df.EXAME == "BIL") & (df.LOCAL == "GEL")]
bld_amb = df[(df.EXAME == "BLD") & (df.LOCAL == "AMB")]
bld_gel = df[(df.EXAME == "BLD") & (df.LOCAL == "GEL")]
###########################################################
rbc_amb = df[(df.EXAME == "RBC") & (df.LOCAL == "AMB")]
rbc_gel = df[(df.EXAME == "RBC") & (df.LOCAL == "GEL")]
rbc_amb[["H0", "H4", "H8", "H12", "H24"]] = rbc_amb[["H0", "H4", "H8", "H12", "H24"]].astype(float)
rbc_gel[["H0", "H4", "H8", "H12", "H24"]] = rbc_gel[["H0", "H4", "H8", "H12", "H24"]].astype(float)
muc_amb = df[(df.EXAME == "MUC") & (df.LOCAL == "AMB")]
muc_gel = df[(df.EXAME == "MUC") & (df.LOCAL == "GEL")]
muc_amb[["H0", "H4", "H8", "H12", "H24"]] = muc_amb[["H0", "H4", "H8", "H12", "H24"]].astype(float)
muc_gel[["H0", "H4", "H8", "H12", "H24"]] = muc_gel[["H0", "H4", "H8", "H12", "H24"]].astype(float)
caoxd_amb = df[(df.EXAME == "CAOXD") & (df.LOCAL == "AMB")]
caoxd_gel = df[(df.EXAME == "CAOXD") & (df.LOCAL == "GEL")]
caoxd_amb[["H0", "H4", "H8", "H12", "H24"]] = caoxd_amb[["H0", "H4", "H8", "H12", "H24"]].astype(float)
caoxd_gel[["H0", "H4", "H8", "H12", "H24"]] = caoxd_gel[["H0", "H4", "H8", "H12", "H24"]].astype(float)
hya_amb = df[(df.EXAME == "HYA") & (df.LOCAL == "AMB")]
hya_gel = df[(df.EXAME == "HYA") & (df.LOCAL == "GEL")]
hya_amb[["H0", "H4", "H8", "H12", "H24"]] = hya_amb[["H0", "H4", "H8", "H12", "H24"]].astype(float)
hya_gel[["H0", "H4", "H8", "H12", "H24"]] = hya_gel[["H0", "H4", "H8", "H12", "H24"]].astype(float)
bac_amb = df[(df.EXAME == "BAC") & (df.LOCAL == "AMB")]
bac_gel = df[(df.EXAME == "BAC") & (df.LOCAL == "GEL")]
bac_amb[["H0", "H4", "H8", "H12", "H24"]] = bac_amb[["H0", "H4", "H8", "H12", "H24"]].astype(float)
bac_gel[["H0", "H4", "H8", "H12", "H24"]] = bac_gel[["H0", "H4", "H8", "H12", "H24"]].astype(float)
pat_amb = df[(df.EXAME == "PAT") & (df.LOCAL == "AMB")]
pat_gel = df[(df.EXAME == "PAT") & (df.LOCAL == "GEL")]
pat_amb[["H0", "H4", "H8", "H12", "H24"]] = pat_amb[["H0", "H4", "H8", "H12", "H24"]].astype(float)
pat_gel[["H0", "H4", "H8", "H12", "H24"]] = pat_gel[["H0", "H4", "H8", "H12", "H24"]].astype(float)
wbc_amb = df[(df.EXAME == "WBC") & (df.LOCAL == "AMB")]
wbc_gel = df[(df.EXAME == "WBC") & (df.LOCAL == "GEL")]
wbc_amb[["H0", "H4", "H8", "H12", "H24"]] = wbc_amb[["H0", "H4", "H8", "H12", "H24"]].astype(float)
wbc_gel[["H0", "H4", "H8", "H12", "H24"]] = wbc_gel[["H0", "H4", "H8", "H12", "H24"]].astype(float)
epi_amb = df[(df.EXAME == "EPI") & (df.LOCAL == "AMB")]
epi_gel = df[(df.EXAME == "EPI") & (df.LOCAL == "GEL")]
epi_amb[["H0", "H4", "H8", "H12", "H24"]] = epi_amb[["H0", "H4", "H8", "H12", "H24"]].astype(float)
epi_gel[["H0", "H4", "H8", "H12", "H24"]] = epi_gel[["H0", "H4", "H8", "H12", "H24"]].astype(float)
tri_amb = df[(df.EXAME == "TRI") & (df.LOCAL == "AMB")]
tri_gel = df[(df.EXAME == "TRI") & (df.LOCAL == "GEL")]
tri_amb[["H0", "H4", "H8", "H12", "H24"]] = tri_amb[["H0", "H4", "H8", "H12", "H24"]].astype(float)
tri_gel[["H0", "H4", "H8", "H12", "H24"]] = tri_gel[["H0", "H4", "H8", "H12", "H24"]].astype(float)
uri_amb = df[(df.EXAME == "URI") & (df.LOCAL == "AMB")]
uri_gel = df[(df.EXAME == "URI") & (df.LOCAL == "GEL")]
uri_amb[["H0", "H4", "H8", "H12", "H24"]] = uri_amb[["H0", "H4", "H8", "H12", "H24"]].astype(float)
uri_gel[["H0", "H4", "H8", "H12", "H24"]] = uri_gel[["H0", "H4", "H8", "H12", "H24"]].astype(float)
yea_amb = df[(df.EXAME == "YEA") & (df.LOCAL == "AMB")]
yea_gel = df[(df.EXAME == "YEA") & (df.LOCAL == "GEL")]
yea_amb[["H0", "H4", "H8", "H12", "H24"]] = yea_amb[["H0", "H4", "H8", "H12", "H24"]].astype(float)
yea_gel[["H0", "H4", "H8", "H12", "H24"]] = yea_gel[["H0", "H4", "H8", "H12", "H24"]].astype(float)
amo_amb = df[(df.EXAME == "AMO") & (df.LOCAL == "AMB")]
amo_gel = df[(df.EXAME == "AMO") & (df.LOCAL == "GEL")]
amo_amb[["H0", "H4", "H8", "H12", "H24"]] = amo_amb[["H0", "H4", "H8", "H12", "H24"]].astype(float)
amo_gel[["H0", "H4", "H8", "H12", "H24"]] = amo_gel[["H0", "H4", "H8", "H12", "H24"]].astype(float)
###########################################################
dfcont = df[df.EXAME.isin(["BAC", "RBC", "MUC", "CAOXD", "HYA", "PAT", "WBC",
"EPI","TRI", "URI", "YEA", "AMO"])]
dfcont[["H0", "H4", "H8", "H12", "H24"]] = dfcont[["H0", "H4", "H8", "H12", "H24"]].astype(float)
dfcontmelt = pd.melt(dfcont, id_vars=['KEY', 'EXAME', 'LOCAL'])
dfcontmelt.columns = ['KEY', 'EXAME', 'LOCAL', 'HORA', 'MEDIDA']
def caixas(exames=["BAC", "RBC", "MUC", "CAOXD", "HYA", "PAT", "WBC", "EPI","TRI", "URI", "YEA", "AMO"]):
for i in exames:
plt.clf()
plt.close()
filename = "boxplot"+i+".png"
sns.boxplot(x="HORA", y="MEDIDA", hue="LOCAL",
data=dfcontmelt[dfcontmelt.EXAME == i], palette="Blues", sym="")
plt.savefig(filename)
dfcate = df[df.EXAME.isin(["BLD", "BIL", "UBG", "KET", "GLU", "PRO", "NIT",
"LEU","PH", "SG"])]
dfcatemelt = pd.melt(dfcate, id_vars=['KEY', 'EXAME', 'LOCAL'])
dfcatemelt.columns = ['KEY', 'EXAME', 'LOCAL', 'HORA', 'MEDIDA']
def plotsbarra(exames = ["BLD", "BIL", "UBG", "KET", "GLU", "PRO", "NIT", "LEU","PH"]):
for i in exames:
filename = "barplot"+i+".png"
ordem = sorted(dfcatemelt[dfcatemelt.EXAME==i].MEDIDA.unique())
if "neg" in ordem:
ordem.remove("neg")
ordem.insert(0, "neg")
print(":: para",i)
sns.factorplot(x="MEDIDA", kind="count", hue="HORA",
data=dfcatemelt[dfcatemelt.EXAME == i],
order = ordem)
plt.savefig(filename)
def procurarduplicatas():
contador = 0
for i in sorted(df.KEY.unique()):
if len(df.KEY[df.KEY == i]) != 44:
print(":: Duplicata em",i)
contador += 1
print("total de contagens feitas:",contador)
'''
################################################################################################
DICIONÁRIOS E CONSTANTES
################################################################################################
'''
def namestr(obj, namespace=globals()): return([name for name in namespace if namespace[name] is obj][0])
_dicsed = {"RBC": 5, "WBC": 5, "CAOXD": 1.82, "TRI": 1.82, "AMO": 1.82,"HYA": 0.2,
"EPI": 0.37, "YEA": 0.2, "BAC": 8.33, "MUC": 25, "PAT": 0.3, "URI": 1.82}
_dic_cruzes = {"neg": 0, "pos": 1, "C1" : 1, "C2" : 2, "C3" : 3, "C4" : 4}
__l = "................................................................."
___l = "................................................................................"
_par_paula = [[wbc_amb, wbc_gel],[bac_amb, bac_gel],[rbc_amb, rbc_gel],[pat_amb, pat_gel],[amo_amb, amo_gel],]
_grupo_continuo = [wbc_amb, wbc_gel,bac_amb, bac_gel,rbc_amb, rbc_gel,pat_amb, pat_gel,amo_amb, amo_gel]
_grupo_discreto1 = [leu_amb, leu_gel, nit_amb, nit_gel, pro_amb, pro_gel, glu_amb,
glu_gel, ket_amb, ket_gel, bil_amb, bil_gel, bld_amb, bld_gel, ubg_gel, ubg_amb]
_grupo_discreto2 = [ph_gel, ph_amb, sg_amb, sg_gel]
_dic_continuo = {namestr(wbc_amb):"Leucócitos", namestr(wbc_gel): "Leucócitos", namestr(bac_amb):"Bactérias",
namestr(bac_gel):"Bactérias",namestr(rbc_amb):"Eritrócitos", namestr(rbc_gel): "Eritrócitos",
namestr(pat_amb):"Cilindros Pat.", namestr(pat_gel):"Cilindros Pat.",
namestr(amo_amb): "Cristais Amorfos", namestr(amo_gel): "Cristais Amorfos"}
_dic_discreto = {namestr(ph_amb):"pH", namestr(ph_gel): "pH", namestr(sg_amb):"Densidade", namestr(sg_gel):"Densidade",
namestr(leu_amb):"Leucócitos", namestr(leu_gel):"Leucócitos", namestr(nit_amb):"Nitrito",
namestr(nit_gel): "Nitrito", namestr(pro_amb): "Proteínas", namestr(pro_gel):"Proteínas",
namestr(glu_amb):"Glicose", namestr(glu_gel): "Glicose", namestr(ket_amb):"Corpos cetônicos",
namestr(ket_gel):"Corpos cetônicos", namestr(ubg_amb): "Urobilinogênio",
namestr(ubg_gel): "Urobilinogênio", namestr(bil_amb):"Bilirrubina", namestr(bil_gel): "Bilirrubina",
namestr(bld_amb): "Hemoglobina", namestr(bld_gel): "Hemoglobina"}
'''
################################################################################################
FUNÇÕES
################################################################################################
'''
fontetamanho = 20
def isfloat(value):
try:
float(value)
return True
except:
return False
def filtrasedimento(data):
tamanho = len(data)
for linha in data.index:
print("fazendo a linha", linha, "de",tamanho)
for coluna in ["H0", "H4", "H8", "H12", "H24"]:
if data.loc[linha,"EXAME"] in _dicsed:
if isfloat(data.loc[linha,coluna]):
if float(data.loc[linha,coluna]) < _dicsed[data.loc[linha,"EXAME"]]: data.loc[linha,coluna] ="neg"
elif float(data.loc[linha,coluna]) >= _dicsed[data.loc[linha,"EXAME"]]: data.loc[linha,coluna] ="pos"
else: print("deu erro em", linha)
else: data.loc[linha,coluna] = data.loc[linha,coluna]
else: pass
return(data)
#filtrasedimento(dfposneg)
def namestr(obj, namespace=globals()): return([name for name in namespace if namespace[name] is obj][0])
def grafico_j(conjunto, xl=None, yl=None, titulox="", tituloy="", filename="", tamanho=5, titulo=""):
a = np.array(conjunto[0].map(float))
b = np.array(conjunto[1].map(float))
c = DataFrame([a,b]).transpose()
c.columns = ["A", "B"]
g = sns.jointplot("A", "B", c, xlim=xl, ylim=yl, kind = "reg", stat_func=stats.spearmanr,
marginal_kws={"bins": 25}, size=tamanho)
# marginal_kws={"bins": 297}
sns.axlabel(titulox, tituloy, fontsize=fontetamanho)
plt.title(titulo, y=1.22, fontsize=20)
plt.savefig(filename, bbox_inches="tight", format = "png");
def grafico_l(conjunto, xl=None, yl=None, titulox="", tituloy="", titulo="", filename="", tamanho=5):
a = np.array(conjunto[0].map(float))
b = np.array(conjunto[1].map(float))
c = DataFrame([a,b]).transpose()
c.columns = ["A", "B"]
sns.lmplot("A", "B", c, x_jitter=0.2, y_jitter=0.3, size=tamanho)
plt.title(titulo, fontsize=16)
sns.axlabel(titulox, tituloy, fontsize=fontetamanho)
plt.savefig(filename);
def grafico_l2(conjunto, xl=None, yl=None, titulox="", tituloy="", titulo="", filename="", tamanho=5):
a = np.array(conjunto[0].map(_dic_cruzes))
b = np.array(conjunto[1].map(_dic_cruzes))
c = DataFrame([a,b]).transpose()
c.columns = ["A", "B"]
sns.lmplot("A", "B", c, x_jitter=0.2, y_jitter=0.3, size=tamanho)
plt.title(titulo, fontsize=16)
sns.axlabel(titulox, tituloy, fontsize=fontetamanho)
plt.savefig(filename);
def graficos_jp(grupo):
dic_horas = {"H4":"4h", "H8":"8h", "H12":"12h", "H24":"24h"}
for i in grupo:
for p in ["H4", "H8", "H12", "H24"]:
nome = namestr(i)
porextenso = _dic_continuo[nome]
if nome[-3:] == "amb": local = "Temperatura Ambiente"
if nome[-3:] == "gel": local = "Sob Refrigeração"
tity = porextenso+" | "+local+" ("+dic_horas[p]+")"
titx = porextenso+" | "+local+" (0h)"
titulomaior = ""
fn = "["+nome+"]_[H0 x"+p+"].png"
grafico_j([i["H0"],i[p]], titulox=titx, tituloy=tity,
titulo = titulomaior, filename=fn, tamanho=10)
def graficos_lp(grupo):
dic_horas = {"H4":"4h", "H8":"8h", "H12":"12h", "H24":"24h"}
for i in grupo:
for p in ["H4", "H8", "H12", "H24"]:
nome = namestr(i)
porextenso = _dic_discreto[nome]
if nome[-3:] == "amb": local = "Temperatura Ambiente"
if nome[-3:] == "gel": local = "Sob Refrigeração"
tity = porextenso+" | "+local+" ("+dic_horas[p]+")"
titx = porextenso+" | "+local+" (0h)"
titulomaior = ""
fn = "["+nome+"]_[H0 x"+p+"].png"
grafico_l2([i["H0"],i[p]], titulox=titx, tituloy=tity,
titulo = titulomaior, filename=fn, tamanho=10)
grupoo = [ph_amb]
################################################################################################
def meu_applymap(dataframe, dicionario):
fatia1, fatia2 = [0,1,2], [3, 4,5,6,7]
df1, df2 = dataframe[fatia1], dataframe[fatia2]
df3 = concat([df1, df2.applymap(lambda x: dicionario[x])], axis = 1)
return(df3)
def mycontingency(par, neg):
s1, s2 = np.array(par[0]), np.array(par[1])
cc, cd, dc, dd = 0,0,0,0
if len(s1) != len(s2): return("erro")
for i in range(len(s1)):
if s1[i] == s2[i]:
if s1[i] == neg: dd += 1
elif s1[i] != neg: cc += 1
elif s1[i] != s2[i]:
if s1[i] != neg: cd += 1
elif s1[i] == neg: dc += 1
return(np.array([[cc,cd],[dc,dd]]))
def checar_normalidade():
lista = [rbc_amb, rbc_gel, muc_amb, muc_gel, caoxd_amb, caoxd_gel, hya_amb, hya_gel, bac_amb, bac_gel,
pat_amb, pat_gel, wbc_amb, wbc_gel, epi_amb, epi_gel, tri_amb, tri_gel, uri_amb, uri_gel,
yea_amb, yea_gel, amo_amb, amo_gel]
for i in lista:
p = ""
ku, pv = stats.normaltest(i["H0"].map(float))
if pv > 0.05: p = "***"
print(list(Series(i["EXAME"]))[0], "", list(Series(i["LOCAL"]))[0],"| Curtose:",ku,"; Valor de p:",pv, p)
################################################################################################
def checarkappa1(t="H24"):
print()
print(__l)
print()
print("Avaliação de Kappa",t,"(GEL) x",t,"(AMB) para os diversos exames")
print()
for i in a_indice_cruzes_pares:
print(__l)
p, p1 = i[0], ""
k = [i[0][t], i[1][t]]
cokappa = cohens_kappa(mycontingency(k, "neg"))
if cokappa.pvalue_two_sided < 0.05: p1 = "(OBS: ESTE TESTE TEVE p < 0.05)"
print("RESULTADOS PARA ", list(Series(p["EXAME"]))[0], ":", p1)
print()
print(cokappa)
print(__l)
def checarkappa2(t="H24"):
print()
print(__l)
print()
print("Avaliação de Kappa H0 x",t,"para os diversos exames")
print()
for i in a_indice_cruzes_pares:
print(__l)
p = i[0]
k = [i[0]["H0"], i[0][t]]
l = [i[1]["H0"], i[1][t]]
print("RESULTADOS PARA ", list(Series(p["EXAME"]))[0], " EM TEMPERATURA AMBIENTE (AMB):")
print(cohens_kappa(mycontingency(k, "neg")))
print(__l)
print("RESULTADOS PARA ", list(Series(p["EXAME"]))[0], " SOB REFRIGERAÇÃO (GEL):")
print(cohens_kappa(mycontingency(l, "neg")))
print(__l)
def checarkappa_neu(t="H24"):
print()
print(__l)
print()
print("Avaliação de Kappa H0 x",t,"para os diversos exames")
print()
for i in a_indice_cruzes_pares:
print(__l)
p = i[0]
k = [i[0]["H0"], i[0][t]]
l = [i[1]["H0"], i[1][t]]
p11, p12, p21, p22 = "","","",""
amb, gel = cohens_kappa(mycontingency(k, "neg")), cohens_kappa(mycontingency(l, "neg"))
if amb.pvalue_one_sided > 0.05: p11 = "***"
if amb.pvalue_two_sided > 0.05: p12 = "***"
if gel.pvalue_one_sided > 0.05: p21 = "***"
if gel.pvalue_two_sided > 0.05: p22 = "***"
print("RESULTADOS PARA ", list(Series(p["EXAME"]))[0], " EM TEMPERATURA AMBIENTE (AMB):")
print("Kappa:",amb.kappa,"; Valor de p:",amb.pvalue_one_sided,p11,p12)
#print(__l)
print()
print("RESULTADOS PARA ", list(Series(p["EXAME"]))[0], " SOB REFRIGERAÇÃO (GEL):")
print("Kappa:",gel.kappa,"; Valor de p:",gel.pvalue_one_sided,p21,p22)
print(__l)
def checarkappa_reto(t=["H4", "H8", "H12", "H24"]):
print()
print(__l)
print()
print(" Relatório Kappa linear")
print()
for i in a_indice_cruzes_pares:
for v in t:
print(__l)
p = i[0]
k = [i[0]["H0"], i[0][v]]
l = [i[1]["H0"], i[1][v]]
p11, p12, p21, p22 = "","","",""
amb, gel = cohens_kappa(mycontingency(k, "neg")), cohens_kappa(mycontingency(l, "neg"))
if amb.pvalue_one_sided > 0.05: p11 = "***"
if amb.pvalue_two_sided > 0.05: p12 = "***"
if gel.pvalue_one_sided > 0.05: p21 = "***"
if gel.pvalue_two_sided > 0.05: p22 = "***"
print("RESULTADOS PARA ", list(Series(p["EXAME"]))[0], " EM TEMPERATURA AMBIENTE (AMB), EM,",v,":")
print("Kappa:",amb.kappa,"; Valor de p:",amb.pvalue_one_sided,p11,p12)
#print(__l)
print()
print("RESULTADOS PARA ", list(Series(p["EXAME"]))[0], " SOB REFRIGERAÇÃO (GEL), EM,",v,":")
print("Kappa:",gel.kappa,"; Valor de p:",gel.pvalue_one_sided,p21,p22)
print(__l)
def checarkappa_csv_cis():
t = ["H0", "H4", "H8", "H12", "H24"]
relatorio = csv.writer(open("./resultados_kappa_cis.csv", 'w'))
relatorio.writerow(["", "H0", "H4", "H8", "H12", "H24"])
for i in a_indice_cruzes:
loc, exa = list(i["LOCAL"])[0], list(i["EXAME"])[0]
abertura = exa+" "+loc+" | H0"
novalinha = [abertura]
for v in t:
k = [i["H0"], i[v]]
novalinha.append(cohens_kappa(mycontingency(k, "neg")).kappa)
relatorio.writerow(novalinha)
def geraposneg(data = dfposneg):
data = data[~((data.EXAME == "SG") | (data.EXAME == "PH"))]
listaposneg = []
for exame in data.EXAME.unique():
for local in data.LOCAL.unique():
listaposneg.append(data[(data.EXAME==exame) & (data.LOCAL == local)])
return(listaposneg)
def relatorio_kappa_csv():
t = ["H0", "H4", "H8", "H12", "H24"]
relatorio = csv.writer(open("./resultados_kappa_cis(pn).csv", 'w'))
relatorio.writerow(["", "H0", "H4", "H8", "H12", "H24"])
for i in geraposneg():
loc, exa = list(i["LOCAL"])[0], list(i["EXAME"])[0]
abertura = exa+" "+loc+" | H0"
novalinha = [abertura]
for v in t:
k = [i["H0"], i[v]]
novalinha.append(cohens_kappa(mycontingency(k, "neg")).kappa)
relatorio.writerow(novalinha)
def relatorio_spearman_csv():
t = ["H0", "H4", "H8", "H12", "H24"]
relatorio = csv.writer(open("./resultados_spearman_cis.csv", 'w'))
relatorio.writerow(["", "H0", "H4", "H8", "H12", "H24"])
lista = [rbc_amb, rbc_gel, muc_amb, muc_gel, caoxd_amb, caoxd_gel, hya_amb, hya_gel, bac_amb, bac_gel,
pat_amb, pat_gel, wbc_amb, wbc_gel, epi_amb, epi_gel, tri_amb, tri_gel, uri_amb, uri_gel,
yea_amb, yea_gel, amo_amb, amo_gel, sg_amb, sg_gel, ph_amb, ph_gel] #mudei aqui
for i in lista:
correlraw = checarspearman(i)
loc, exa = list(i["LOCAL"])[0], list(i["EXAME"])[0]
abertura = exa+" "+loc+" | H0"
novalinha = [abertura]
for v in correlraw:
novalinha.append(v[0])
relatorio.writerow(novalinha)
################################################################################################
def checarspearman(exame):
relatorio = []
## print()
## print("RESULTADOS DE ", list(Series(exame["EXAME"]))[0], "", list(Series(exame["LOCAL"]))[0], ":")
for i in ["H4", "H8", "H12", "H24"]:
p = ""
resultado = spearmanr(exame["H0"].map(float), exame[i].map(float))
relatorio.append(resultado)
if resultado[1] > 0.05: p = "***"
## print("Entre H0 e", i,":", resultado[0], "(Spearman r), e ", resultado[1],"(valor de p)", p)
## print(___l)
return(relatorio)
def checarspearman_paralelo_(par):
relatorio = []
for ex in par:
exame1, exame2 = ex[0], ex[1]
print()
print("RESULTADOS DE", list(Series(exame1["EXAME"]))[0],":")
for i in ["H4", "H8", "H12", "H24"]:
p = ""
resultado = spearmanr(exame1[i], exame2[i])
relatorio.append(resultado)
if resultado[1] < 0.05: p = "***"
print("Entre",i,"e", i,":", resultado[0], "(Spearman r), e ", resultado[1],"(valor de p)", p)
print(___l)
return(relatorio)
def checarspearman_paralelo():
lista = [[rbc_gel, rbc_amb], [muc_amb, muc_gel], [caoxd_amb, caoxd_gel], [hya_amb, hya_gel], [bac_amb, bac_gel],
[pat_amb, pat_gel], [wbc_amb, wbc_gel], [epi_amb, epi_gel], [tri_amb, tri_gel], [uri_amb, uri_gel],
[yea_amb, yea_gel], [amo_amb, amo_gel]]
relatorio = []
print(___l)
print()
print(" RELATÓRIO DE CORRELAÇÕES (SPEARMAN) PARALELO")
print()
print(___l)
for ex in lista:
exame1, exame2 = ex[0], ex[1]
print()
print("RESULTADOS DE", list(Series(exame1["EXAME"]))[0],":")
for i in ["H4", "H8", "H12", "H24"]:
p = ""
resultado = spearmanr(np.array(exame1[i].map(float)), np.array(exame2[i].map(float)))
relatorio.append(resultado)
if resultado[1] > 0.05: p = "***"
print("Entre",i,"e", i,":", resultado[0], "(Spearman ρ), e ", resultado[1],"(valor de p)", p)
print(___l)
return(relatorio)
def checarspearman_todos():
lista = [rbc_amb, rbc_gel, muc_amb, muc_gel, caoxd_amb, caoxd_gel, hya_amb, hya_gel, bac_amb, bac_gel,
pat_amb, pat_gel, wbc_amb, wbc_gel, epi_amb, epi_gel, tri_amb, tri_gel, uri_amb, uri_gel,
yea_amb, yea_gel, amo_amb, amo_gel]
relatorio = []
## print(___l)
## print()
## print(" RELATÓRIO DE CORRELAÇÕES (SPEARMAN)")
## print()
## print(___l)
for i in lista:
checarspearman(i)
def relatorios():
relatorio_spearman_csv()
relatorio_kappa_csv()
'''
################################################################################################
FÍSICO-QUÍMICA
################################################################################################
'''
a_indice_cruzes = [leu_amb, leu_gel, nit_amb, nit_gel, pro_amb, pro_gel, ket_amb, ket_gel, bil_amb, bil_gel,
bld_amb, bld_gel, glu_amb, glu_gel, ubg_amb, ubg_gel]
a_indice_cruzes_pares = [[leu_amb, leu_gel], [nit_amb, nit_gel], [pro_amb, pro_gel],
[ket_amb, ket_gel], [bil_amb, bil_gel], [bld_amb, bld_gel], [glu_amb, glu_gel], [ubg_amb, ubg_gel]]
a_indice_fq = [[sg_amb, sg_gel], [ph_amb, ph_gel], [leu_amb, leu_gel], [nit_amb, nit_gel], [pro_amb, pro_gel],
[glu_amb, glu_gel], [ket_amb, ket_gel], [ubg_amb, ubg_gel], [bil_amb, bil_gel], [bld_amb, bld_gel]]
'''
################################################################################################
SEDIMENTO
################################################################################################
'''
'''
################################################################################################
OUTROS
################################################################################################
'''