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import pandas as pd
import numpy as np
import random
import json
from openpyxl import load_workbook
from openpyxl import Workbook
import numpy as np
#read excel
df = pd.read_excel("output.xlsx")[0:25]
#read Condition Name into 1D-array
name_arr = df.Name.unique()
name_arr = list(name_arr)
#Choose Params for disease
wb = load_workbook('output.xlsx') #Define workbook
ws = wb.get_sheet_by_name('Sheet1') #Define worksheet
A = np.array([[i.value for i in j] for j in ws['C1':'I1']]).ravel() #Read BiRads into list
B = np.array([[i.value for i in j] for j in ws['C2':'I2']]).ravel() #Read BiRads Probabilities into list
C = np.array([[i.value for i in j] for j in ws['O1':'Q1']]).ravel() #Read Params Probabilities into list
#Convert from np-arr to 1D arr
a = list(A) #BiRads list
b = list(B) #BiRad probs integer values
c = list(C) #Params probs integer values
#Define function to normalize arr values
def normalize(items):
sum_n = 0
for x in items:
sum_n += x
problist = [x/sum_n for x in items]
return(problist)
#Create random with parameter of report numbers
def report(num):
for i in range(num):
br_p = normalize(b)
a = list(A)
br = np.random.choice(a, 1, br_p)
name = df['Name'].values.tolist()[0:1]
"create list of values and slice empty entities from list"
cd = df['Condition description'].values.tolist()
rm = df['Relevant modalities'].values.tolist()
r = random.choice(rm)
#mammo params
if r == 'Mammography':
f_list = df['Relevant findings'].values.tolist()[0:9]
"random finding"
f = random.choice(f_list)
if f == 'Mass':
p = normalize(c)
s = np.array([[i.value for i in j] for j in ws['O2':'Q2']]).ravel()
s = list(s)
shape = np.random.choice(s, 1, p)
m = np.array([[i.value for i in j] for j in ws['O3':'Q3']]).ravel()
m = list(m)
margin = np.random.choice(m, 1, p)
d = np.array([[i.value for i in j] for j in ws['O4':'Q4']]).ravel()
d = list(d)
density = np.random.choice(d, 1, p)
print(name, br, r, f, shape, margin, density)
elif f == 'Calcifications':
p = normalize(c)
tb = np.array([[i.value for i in j] for j in ws['O5':'Q5']]).ravel()
tb = list(tb)
t_benigh = np.random.choice(tb, 1, p)
sm = np.array([[i.value for i in j] for j in ws['O6':'Q6']]).ravel()
sm = list(sm)
s_morph = np.random.choice(sm, 1, p)
d = np.array([[i.value for i in j] for j in ws['O7':'Q7']]).ravel()
d = list(d)
distrib = np.random.choice(d, 1, p)
print(name, br, r, f, tb, s_morph, distrib)
elif f == 'Assymetry':
p = normalize(c)
a = np.array([[i.value for i in j] for j in ws['O8':'Q8']]).ravel()
a = list(a)
assymetry = np.random.choice(a, 1, p)
print(name, br, r, f, assymetry)
else:
p = normalize(c)
ln = np.array([[i.value for i in j] for j in ws['O9':'Q9']]).ravel()
ln = list(ln)
print(name, br, r, f, ln)
#us params
elif r == 'US':
f_list = df['Relevant findings'].values.tolist()[10:16]
f = random.choice(f_list)
if f == 'Mass':
p = normalize(c)
s = np.array([[i.value for i in j] for j in ws['O10':'Q10']]).ravel()
s = list(s)
shape = np.random.choice(s, 1, p)
m = np.array([[i.value for i in j] for j in ws['O11':'Q11']]).ravel()
m = list(m)
margin = np.random.choice(m, 1, p)
e = np.array([[i.value for i in j] for j in ws['O12':'Q12']]).ravel()
e = list(e)
echo = np.random.choice(e, 1, p)
pos = np.array([[i.value for i in j] for j in ws['O13':'Q13']]).ravel()
pos = list(pos)
posterior = np.random.choice(pos, 1, p)
print(name, br, r, f, shape, margin, echo, posterior)
elif f == 'Calcifications US':
p = normalize(c)
calc = np.array([[i.value for i in j] for j in ws['O14':'Q14']]).ravel()
calc = list(calc)
calc_us = np.random.choice(calc, 1, p)
print(name, br, r, f, calc_us)
elif f == 'Lymph nodes':
p = normalize(c)
ln = np.array([[i.value for i in j] for j in ws['O15':'Q15']]).ravel()
ln = list(ln)
l_nodes = np.random.choice(ln, 1, p)
print(name, br, r, f, l_nodes)
else:
p = normalize(c)
sc = np.array([[i.value for i in j] for j in ws['O16':'Q16']]).ravel()
sc = list(sc)
sp_cases = np.random.choice(sc, 1, p)
print(name, br, r, f, sp_cases)
else:
f_list = df['Relevant findings'].values.tolist()[17:25]
f = random.choice(f_list)
if f == 'Mass':
p = normalize(c)
s = np.array([[i.value for i in j] for j in ws['O17':'Q17']]).ravel()
s = list(s)
shape = np.random.choice(m, 1, p)
m = np.array([[i.value for i in j] for j in ws['O18':'Q18']]).ravel()
m = list(m)
margin = np.random.choice(m, 1, p)
ie = np.array([[i.value for i in j] for j in ws['O19':'Q19']]).ravel()
ie = list(ie)
int_e = np.random.choice(ie, 1, p)
print(name, br, r, f, shape, margin, int_e)
elif f == 'MRI features':
p = normalize(c)
m_f = np.array([[i.value for i in j] for j in ws['O20':'Q20']]).ravel()
m_f = list(m_f)
mri_f = np.random.choice(m_f, 1, p)
print(name, br, r, f, mri_f)
elif f == 'Kinetic curve assessment':
p = normalize(c)
kca = np.array([[i.value for i in j] for j in ws['O21':'Q21']]).ravel()
kca = list(kca)
kin_ca = np.random.choice(kca, 1, p)
print(name, br, r, f, kin_ca)
elif f == 'Non-mass enhancement (NME)':
p = normalize(c)
distr = np.array([[i.value for i in j] for j in ws['O22':'Q22']]).ravel()
distr = list(distr)
distrib = np.random.choice(distr, 1, p)
iep = np.array([[i.value for i in j] for j in ws['O23':'Q23']]).ravel()
print(name, br, r, f, distrib, iep)
elif f == 'Non-enhancing findings':
p = normalize(c)
nef = np.array([[i.value for i in j] for j in ws['O21':'Q22']]).ravel()
nef = list(nef)
ne_f = np.random.choice(nef, 1, p)
print(name, br, r, f, ne_f)
elif f == 'Lymph nodes':
p = normalize(c)
ln = np.array([[i.value for i in j] for j in ws['O22':'Q22']]).ravel()
ln = list(ln)
l_nodes = np.random.choice(ln, 1, p)
print(name, br, r, f, l_nodes)
else:
p = normalize(c)
fcl = np.array([[i.value for i in j] for j in ws['O23':'Q23']]).ravel()
fcl= list(fcl)
fat_cl = np.random.choice(fcl, 1, p)
print(name, br, r, f, fat_cl)
un_list = df['Unique findings'].values.tolist()
un = random.choice(un_list)
p_list = df['Parameters'].values.tolist()
p = random.choice(p_list)
g_list = df['General'].values.tolist()
g = random.choice(g_list)
u_list = df['Unrelated'].values.tolist()
u = random.choice(u_list)
i_list = df['Ignore'].values.tolist()
i = random.choice(i_list)
a_list = df['Associated conditions'].values.tolist()
a = random.choice(a_list)
dd_list = df['Differential diagnosis'].values.tolist()
dd = random.choice(dd_list)
nt_list = df['Notes'].values.tolist()
nt = random.choice(nt_list)
params = ["name", "condition description", "relevant finding", "unique finding", "parameters", "general", "unrelated", "ignore", "associated conditions", "notes"]
keys = [name, cd, f, un, p, g, u, i, a, dd, nt]
rep = {}
for i in range(len(params)):
rep[params[i]] = keys[i]
rep = json.dumps(rep)
#print(rep)
##### information for the entire sheet ####
num_rows_in_cond = 25 #"height" of each condition
#width could be different for each condition - so not writing down
starting_column_for_typical = 15 # for all conditions, Typical, possible, none columns, start with typical at col 15
# possible at 15+1, and none at 15+2
first_row = 1 #this is the row with the numbers for typical, possible and none
def determine_width(starting_column):
# traverse first_row to determine width
width = 0
from IPython.core.debugger import Tracer; Tracer()()
item = df.iloc[first_row, starting_column_for_typical] #our first value
while isinstance(item, float) and (item!=0):
width = width + 1
item = df.iloc[first_row, starting_column_for_typical + width]
# each time incrementing a counter (count_numbers or width)
#for example, width should equal 3 at this point
print("width is: "+width)
determine_width(14)
def get_names_and_probs(row, width):
first_row = row
first_col = starting_column_for_typical
# this array_to_normalize will store all the values to normalize
array_to_normalize = [] #the "a" array
p_values = [] #the "p" array
#for each colmn from the first row until the last row (see "depth" of disease above
current_row = first_row
current_col = first_col #set the current_col to first_col to start
i = 0
for i in range(width):
#determine if cell has text, or is empty
if isinstance(df.iloc[current_row, current_col + i], float): #if cell is empty, python calls it a float
i = i+1
continue
#if we get to this point, there is text in the cell
temp_array = df.iloc[current_row, current_col + i].replace(" ","").split(",") #look at a cell
j = 0
for j in range(len(temp_array)):
p_values.append(df.iloc[1,current_col + i])
j = j+1
array_to_normalize.append(temp_array)
i = i+1
#array_to_normalize looks like [["oval","rectangular], "irregular"]
#p_values looks like [50, 50 , 1]
#flatten array_to_normalize
#https://stackoverflow.com/questions/952914/making-a-flat-list-out-of-list-of-lists-in-python
flat_list = [item for sublist in array_to_normalize for item in sublist]
#for sublist in array_to_normalize:
# for item in sublist:
# flat_list.append(item)
array_to_normalize = flat_list
#now array to normalize should look like ['Oval', 'Round', 'Irregular']
#a = ['Oval', 'Round', 'Irregular'] #for each row
#p = [50, 50, 1] #one for the entire sheeet
print("p_values:")
print(p_values)
#convert p_values from array of strings to array of ints
#https://stackoverflow.com/questions/5306079/python-how-do-i-convert-an-array-of-strings-to-an-array-of-numbers
#p_values = map(int, p_values)
#p_values = [int(numeric_string) for numeric_string in p_values]
#normalize p_values / "p array"
normalize(p_values)
return array_to_normalize, p_values
# ['Oval', 'Round', 'Irregular'] , [50, 50, 1]
#row = starting row of condition
#return array of arrays for condition
# pri[[a,p],[['Oval', 'Round', 'Irregular'],[50, 50, 1]],...25 times]
def cond_calculate(row):
cond_big_array[num_rows_in_cond]
return cond_big_array
my_width = determine_width(starting_column_for_typical) #my_width should equal 3
get_names_and_probs(2, my_width) #2 represents the 2nd row, aka, first row of Fibroadenoma
#wd