def mammo_mass(self, mass_id):
data_list = ask.default_data_list(self.col_list)
mass_location = self.mammo_breast
if self.mammo_breast == "bilateral":
mass_location = gf.get_choice(RadioTables.breast)
mass_quadrant = gf.get_choice(Radio.lesion_quadrant)
mammo_mass_shape = gf.get_choice(RadioTables.mass_shape)
mammo_mass_margin = gf.get_choice(RadioTables.mass_margin)
mass_nipple = gf.get_number(10)
size_input = gf.get_choice(['3d', '2d'])
x = gf.get_number_lt(1, 10)
y = gf.get_number_lt(1, 10)
z = gf.get_number_lt(1, 10)
if size_input == '3d':
mass_dimension = [str(x) + ' x ' + str(y) + ' x ' + str(z)]
mass_longest_dimension = max([x, y, z])
elif size_input == '2d':
mass_dimension = [str(x) + ' x ' + str(y)]
mass_longest_dimension = max([x, y])
mass_size_unit = gf.get_choice(RadioTables.mass_units)
mass_name = "lesion_" + str(mass_id)
modality = self.table
data_list = [
self.file_number, mass_location, mass_name, mass_quadrant,
mammo_mass_shape, mammo_mass_margin, mass_nipple, mass_dimension,
mass_longest_dimension, mass_size_unit, modality
]
data_list = data_list + [self.user_name, sql.last_update()]
return data_list
def bio_info(file_number):
module_name = "bio_info"
mr_number = gf.get_number_lt(500, 2000)
name = names.get_full_name(gender='female')
aadhaar_card = 'data_not_available'
date_first = gf.gen_date(base_date='2010-12-12')
permanent_address = 'data_not_available'
current_address = permanent_address
phone = gf.get_number_lt(900000000, 999999999)
email_id = 'data_not_available'
gender = gf.get_choice(["Female", "Male"])
date_of_birth = gf.get_dob()
age_yrs = gf.get_age(date_of_birth)
age_diag = gf.get_years(date_of_birth, date_first)
place_birth = 'data_not_available'
height_cm = gf.get_number_lt(140, 180)
weight_kg = gf.get_number_lt(50, 90)
weight = float(weight_kg)
height = float(height_cm) / 100
bmi = str(round(weight / (height * height)))
columns_list = pccm_names.names_info(module_name)
new_data = [
mr_number, name, aadhaar_card, date_first, permanent_address,
current_address, phone, email_id, gender, age_yrs, age_diag,
date_of_birth, place_birth, height_cm, weight_kg, bmi
]
return (age_diag, tuple(new_data))
def habits(file_number, age):
module_name = "habits"
diet = gf.get_choice(PatientInfo.diet)
alcohol = gf.get_yes_no_na()
if alcohol == 'yes':
alcohol_consump = "Alcohol Consumption"
alcohol_age = gf.get_number_lt(21, age)
alcohol_quant = gf.get_number_lt(1, 10)
alcohol_duration = gf.get_choice(PatientInfo.frequency)
alcohol_comments = 'na'
elif alcohol == 'no':
alcohol_consump = "No Alcohol Consumption"
alcohol_age, alcohol_quant, alcohol_duration, alcohol_comments = (
"No Alcohol Consumption", ) * 4
else:
alcohol_consump, alcohol_age, alcohol_quant, alcohol_duration, alcohol_comments = (
alcohol, ) * 5
data_list_alc = [
diet, alcohol_consump, alcohol_age, alcohol_quant, alcohol_duration,
alcohol_comments
]
tobacco = gf.get_yes_no_na()
if tobacco == 'yes':
tobacco = "Tobacco consumption"
exposure_type = gf.get_choice(PatientInfo.tobacco_exposure)
if exposure_type == "Passive":
tobacco_type_partic = gf.get_choice(PatientInfo.passive_tobacco)
if tobacco_type_partic == "Home":
tobacco_type_who = 'family member'
tobacco_passive = tobacco_type_partic + (
" : ") + tobacco_type_who
else:
tobacco_passive = tobacco_type_partic
else:
tobacco_passive = "NA"
tobacco_type = gf.get_choice(PatientInfo.type_tobacco)
tobacco_age = gf.get_number_lt(15, age)
tobacco_freq = gf.get_choice(PatientInfo.frequency)
tobacco_quant = gf.get_number(5)
tobacco_duration = age - tobacco_age
tobacco_comments = 'na'
elif tobacco == 'no':
tobacco = "No Tobacco Consumption"
exposure_type, tobacco_type, tobacco_passive, tobacco_age, tobacco_freq, tobacco_quant, tobacco_duration, \
tobacco_comments = ("No Tobacco Consumption",) * 8
else:
exposure_type, tobacco_type, tobacco_passive, tobacco_age, tobacco_freq, tobacco_quant, tobacco_duration, \
tobacco_comments = (tobacco,) * 8
other_del_habits = 'na'
data_list_tob = [
tobacco, exposure_type, tobacco_passive, tobacco_type, tobacco_age,
tobacco_freq, tobacco_quant, tobacco_duration, tobacco_comments,
other_del_habits
]
data_list = data_list_alc + data_list_tob
return tuple(data_list)
def cal_table(file_number, mammo_breast):
import helper_function.pccm_names as pccm_names
table = "calcification_mammography"
mass_number = gf.get_number_lt(1, 3)
try:
number_calc = int(mass_number)
except ValueError:
number_calc = 1
location, calc_type, calicification_comments = [list([]) for _ in range(3)]
for index in range(0, number_calc):
mass_id = index + 1
if mammo_breast == "Bilateral":
mass_location = gf.get_choice(["Right Breast", "Left Breast"])
else:
mass_location = mammo_breast
location.append(mass_location)
mammo_calcification = gf.get_choice([
"Skin", "Vascular", "Coarse or 'Popcorn-like'", "Large Rod-like",
"Round and punctate", "Eggshell or Rim", "Dystrophic", "Suture",
"Amorphous", "Coarse Heterogeneous", "Fine Pleomorphic",
"Fine Linear or Fine Linear Branching", "Other"
])
calc_type.append(mammo_calcification)
mass_id = "Group " + str(index + 1)
comment = 'na'
calicification_comments.append(comment)
data_list = [
file_number, mass_id,
str(mass_location), mammo_calcification, comment
]
col_list = pccm_names.names_radio_mass(table)
all_data = [[str(mass_number)], location, calc_type,
calicification_comments]
data_return = ask.join_lists(all_data, "; ")
return tuple(data_return)
def multiple_mass(self):
number_mass = gf.get_number_lt(1, 4)
mass_df = pd.DataFrame(columns=self.col_list, index=range(number_mass))
for i in range(number_mass):
mass_id = 'mass' + str(1 + i)
dat = self.mammo_mass(mass_id)
dat = [str(data) for data in dat]
mass_df.loc[i] = dat
mass_dat = []
for col in list(mass_df.columns):
i = "; ".join(list(mass_df[col]))
mass_dat.append(i)
return number_mass, mass_dat[:-2]
def feed_duration(file_number, children_number):
table = "breast_feeding"
child_list, feeding_duration_list, feeding_details_list = [], [], []
child_number = int(children_number)
for index in range(0, child_number):
kid = str(index + 1)
kid_add = "Child " + kid
child_list.append(kid_add)
feeding_duration = str(gf.get_number_lt(1, 36))
feeding_duration_list.append(feeding_duration)
feeding_details = gf.get_choice(PatientInfo.breast_feeding)
feeding_details_list.append(feeding_details)
data_list = [child_list, feeding_duration_list, feeding_details_list]
data_return = join_lists(data_list, "; ")
return (data_return)
def family_cancer_table(file_number):
type_cancer_list, relation_degree_list, type_relation_list, age_detect_list = [], [], [], []
all_data = []
type_of_cancer = 'data_not_available'
type_cancer_list.append(type_of_cancer)
relation_to_patient = gf.get_choice(PatientInfo.family)
relation_degree_list.append(relation_to_patient)
type_relation = 'data_not_available'
type_relation_list.append(type_relation)
age_at_detection_yrs = str(gf.get_number_lt(15, 80))
age_detect_list.append(age_at_detection_yrs)
family_history = file_number, type_of_cancer, relation_to_patient, type_relation, age_at_detection_yrs
family_history_list = "; ".join([
type_of_cancer, relation_to_patient, type_relation,
age_at_detection_yrs
])
all_data.append(family_history_list)
all_data_flat = "|".join(all_data)
return (all_data_flat)
def report(self):
data_list = ask.default_data_list(self.col_list_all[1:])
check_mammo = gf.get_choice(MultiTest.test_reason)
reason_report = check_mammo
mammo = gf.get_bool()
if mammo:
tomo = gf.get_bool()
if tomo:
tomography_y_n = "Yes"
else:
tomography_y_n = "No"
report_date = gf.gen_date('2019-03-13')
mammo_place = gf.get_choice(["PCCM", "Outside"])
if mammo_place == "Outside":
mammography_place = names.get_full_name()
else:
mammography_place = mammo_place
mammography_indication = gf.get_choice(Radiology.mammo_indication)
mammography_breast = gf.get_choice(MultiTest.breast_cancer)
mammo_mass = gf.get_bool()
mass_all = []
mass_data = ['no_mass_detected']
mass_all.append(mass_data)
mammography_massnumber = "No mass detected"
mammography_masslocation = "No mass detected"
mammography_massshape = "No mass detected"
mammography_massmargin = "No mass detected"
mammography_massnipple_cm = "No mass detected"
mammography_masssize = "No mass detected"
mammography_masssize_unit = "No mass detected"
mass_longest_dimension = 'na'
if mammo_mass:
mass_breast = gf.get_choice(MultiTest.breast_cancer)
mass = radio_tables.MassCalcification(self.table, mass_breast,
self.file_number,
self.user_name)
mammography_massnumber, mass_dat = mass.multiple_mass()
[
mammography_masslocation, mass_name, mass_quadrant,
mammography_massshape, mammography_massmargin,
mammography_massnipple_cm, mammography_masssize,
mass_longest_dimension, mammography_masssize_unit, modality
] = mass_dat[1:]
mammography_calcificationnumber = "No Calcification detected"
mammography_calcificationlocation = "No Calcification detected"
mammography_calcificationtype = "No Calcification detected"
mammography_calcification_comments = "No Calcification detected"
calc = gf.get_bool()
if calc:
mammography_calcification = radio_tables.cal_table(
self.file_number, mammography_breast)
(mammography_calcificationnumber,
mammography_calcificationlocation,
mammography_calcificationtype,
mammography_calcification_comments
) = mammography_calcification
mammography_skin_involvement = gf.get_choice(
Radiology.skin_involvement)
mammography_node_description = 'nodes_not_described'
mammography_node_size = 'nodes_not_described'
mammography_node_size_unit = 'nodes_not_described'
node_description = gf.get_bool()
if node_description:
mammography_node_description = gf.get_choice(
Radiology.node_description)
mammography_node_size = gf.get_number_lt(3, 10)
mammography_node_size_unit = 'cm'
mammo_birad = gf.get_bool()
if mammo_birad:
mammography_birad = radio_tables.birads()
else:
mammography_birad = "BI-RAD not assigned in report"
mammography_impression = gf.get_choice(Radiology.impression)
data_list = [
reason_report, report_date, mammography_place,
mammography_indication, mammography_breast,
mammography_massnumber, mammography_masslocation,
mammography_massshape, mammography_massmargin,
mammography_massnipple_cm, mammography_masssize[0],
mammography_masssize_unit, mammography_calcificationnumber,
mammography_calcificationlocation,
mammography_calcificationtype,
mammography_calcification_comments,
mammography_skin_involvement, mammography_node_description,
mammography_node_size, mammography_node_size_unit,
mammography_birad, mammography_impression, tomography_y_n
]
data_list = [str(dat) for dat in data_list]
data_list = [self.file_number] + data_list + [
self.user_name, sql.last_update()
]
return data_list
def family_details(file_number, age_mother):
module_name = "family_details"
col_list = pccm_names.names_info(module_name)
marital_status = 'data_not_available'
siblings = gf.get_yes_no_na()
if siblings == 'yes':
siblings_number = gf.get_number(5)
sisters = gf.get_number(siblings_number)
brothers = siblings_number - sisters
elif siblings == 'no':
siblings_number, sisters, brothers = "No Siblings", "0", "0"
else:
siblings_number, sisters, brothers = (siblings, ) * 3
children_y_n = gf.get_yes_no_na()
if children_y_n == 'yes':
children_number = gf.get_number(5)
daughters = gf.get_number(children_number)
sons = children_number - daughters
elif children_y_n == 'no':
children_number, daughters, sons = 0, 0, 0
else:
children_number, daughters, sons = (children_y_n, ) * 3
menarche = gf.get_number_lt(11, 20)
menopause = gf.get_choice(PatientInfo.menopause_status)
menopause_age = menopause
if menopause == "Post-Menopausal":
menopause_age = gf.get_number_lt(menarche, 60)
lmp = "Last menstrual period " + str(menopause_age) + " yrs"
else:
lmp = gf.gen_date(base_date='2020-01-01')
# category = "Type of Period"
options = ["Regular", "Irregular", "Other"]
period_type = gf.get_choice(options)
number_pregnancy = gf.get_number(8)
if number_pregnancy == "0":
age_first_preg = 'No children'
age_last_preg = 'No children'
number_term = 'No children'
number_abortion = 'No children'
age_first = 'No children'
age_last = 'No children'
twice_birth = 'No children'
breast_feeding_data = 'No children'
kid_feeding = 'No children'
duration_feeding = 'No children'
breast_usage = 'No children'
elif number_pregnancy == "Requires Follow-up":
age_first_preg = number_pregnancy
age_last_preg = number_pregnancy
number_term = number_pregnancy
number_abortion = number_pregnancy
age_first = number_pregnancy
age_last = number_pregnancy
twice_birth = number_pregnancy
breast_feeding_data = number_pregnancy
kid_feeding = number_pregnancy
duration_feeding = number_pregnancy
breast_usage = number_pregnancy
else:
number_term = gf.get_number(5)
number_abortion = gf.get_number(5)
age_first_preg = gf.get_number_lt(menarche, 55)
age_last_preg = "NA"
age_first = gf.get_number_lt(0, age_mother)
age_last = age_first
if children_number == 0:
age_first = children_number
age_last = children_number
twice_birth = children_number
breast_feeding_data = children_number
kid_feeding = children_number
duration_feeding = children_number
breast_usage = children_number
elif children_y_n == 'yes':
age_first = gf.get_number_lt(0, age_mother)
if age_first_preg == "NA":
age_first_preg = int(age_mother) - int(age_first)
if int(children_number) > 1:
age_last = gf.get_number_lt(0, age_first_preg)
age_last_preg = age_mother - age_last
if age_last_preg == "NA":
age_last_preg = str(int(age_mother) - int(age_last))
twice_birth = gf.get_choice(
["Two births in a year", "No two births in a year"])
else:
age_last = age_first
age_last_preg = 'NA'
twice_birth = "NA"
breast_feeding = gf.get_bool()
if breast_feeding:
breast_feeding_data = "Breast feeding"
feed_details = bct.feed_duration(file_number, children_number)
else:
breast_feeding_data = "No Breast feeding"
feed_details = ("NA", ) * 3
kid_feeding, duration_feeding, breast_usage = feed_details
else:
age_first = str(children_y_n)
age_last = str(children_y_n)
twice_birth = str(children_y_n)
breast_feeding_data = str(children_y_n)
kid_feeding = str(children_y_n)
duration_feeding = str(children_y_n)
breast_usage = str(children_y_n)
fert_treat_y_n = gf.get_yes_no()
if fert_treat_y_n == 'yes':
fert_treat = "Fertility Treatment used"
type_fert = gf.get_choice(PatientInfo.fert_treat)
detail_fert = 'data_not_available'
cycles_fert = gf.get_number_lt(1, 4)
success_fert = gf.get_choice(
["Pregnancy from Treatment", "No pregnancy from treatment"])
elif fert_treat_y_n == "no":
fert_treat = "No Fertility Treatment used"
type_fert = fert_treat
detail_fert = fert_treat
cycles_fert = fert_treat
success_fert = fert_treat
else:
fert_treat = str(fert_treat_y_n)
type_fert = str(fert_treat_y_n)
detail_fert = str(fert_treat_y_n)
cycles_fert = str(fert_treat_y_n)
success_fert = str(fert_treat_y_n)
birth_control = gf.get_yes_no_na()
if birth_control == 'yes':
type_birth_control = gf.get_choice(
["Birth control pills", "Copper-T", "Condom"])
detail_birth_control = 'data_not_available'
duration_birth_control = gf.get_number(max_no=age_mother)
elif birth_control == 'no':
type_birth_control = "No birth control used"
detail_birth_control = "No birth control used"
duration_birth_control = "No birth control used" * 3
else:
type_birth_control = birth_control
detail_birth_control = birth_control
duration_birth_control = birth_control
data_list = [
marital_status, siblings_number, sisters, brothers, children_number,
daughters, sons, menarche, menopause, menopause_age, lmp, period_type,
number_pregnancy, number_term, number_abortion, age_first,
age_first_preg, age_last, age_last_preg, twice_birth,
breast_feeding_data, kid_feeding, duration_feeding, breast_usage,
fert_treat, type_fert, detail_fert, cycles_fert, success_fert,
type_birth_control, detail_birth_control, duration_birth_control
]
return tuple(data_list)