def cancer_table(conn, cursor, file_number):
table_cancer = "previous_cancer_history"
type_of_cancer_list = []
year_diagnosis_list = []
treat_all = []
type_all = []
duration_all = []
data_return = []
add_cancer = True
while add_cancer:
type_of_cancer = input("Type of Cancer: ")
type_of_cancer_list.append(type_of_cancer)
year_diagnosis = input("Year of diagnosis: ")
year_diagnosis_list.append(year_diagnosis)
col = ("File_number, Type_Cancer, Year_diagnosis")
data = file_number, type_of_cancer, year_diagnosis
sql.insert(conn, cursor, table_cancer, col, data)
print("Please enter the type of treatment used: ")
treat_list = []
type_list = []
duration_list = []
treated, type, duration = ("NA", ) * 3
for treatment in PatientInfo.previous_cancer_treatment:
treat = ask_y_n(treatment)
if treat:
treat_list.append(treatment)
type_treat = input("Type of " + treatment)
type_list.append(type_treat)
duration_treat = input("Duration of " + treatment)
duration_list.append(duration_treat)
data = treatment, type_treat, duration_treat
columns = [
treatment, ("type_" + treatment), ("duration_" + treatment)
]
treated = "; ".join(treat_list)
type = "; ".join(type_list)
duration = "; ".join(duration_list)
sql.update_multiple(conn, cursor, table_cancer, columns,
file_number, data)
elif not treat:
index_no = "No " + treatment
type_treat, duration_treat = ("NA", ) * 2
data = index_no, type_treat, duration_treat
columns = [
treatment, ("type_" + treatment), ("duration_" + treatment)
]
sql.update_multiple(conn, cursor, table_cancer, columns,
file_number, data)
treat_all.append(treated)
type_all.append(type)
duration_all.append(duration)
add_cancer = ask_y_n("Additional cancer history")
all_data = [
type_of_cancer_list, year_diagnosis_list, treat_all, type_all,
duration_all
]
for index in all_data:
data_joint = "|".join(index)
data_return.append(data_joint)
return tuple(data_return)
def cancer_table(conn, cursor, file_number):
table_cancer = "Previous_Cancer_History"
type_of_cancer_list = []
year_diagnosis_list = []
treat_all = []
type_all = []
duration_all = []
data_return = []
add_cancer = True
while add_cancer:
type_of_cancer = input("Type of Cancer: ")
type_of_cancer_list.append(type_of_cancer)
year_diagnosis = input("Year of diagnosis: ")
year_diagnosis_list.append(year_diagnosis)
col = ("File_number, Type_Cancer, Year_diagnosis")
data = file_number, type_of_cancer, year_diagnosis
insert(conn, cursor, table_cancer, col, data)
print("Please enter the type of treatment used: ")
treatment = [
"Surgery", "Radiation", "Chemotherapy", "Hormone", "Alternative",
"HomeRemedy"
]
treat_list = []
type_list = []
duration_list = []
treated, type, duration = ("NA", ) * 3
for index in treatment:
treat = ask_y_n(index)
if treat:
treat_list.append(index)
type_treat = input("Type of " + index)
type_list.append(type_treat)
duration_treat = input("Duration of " + index)
duration_list.append(duration_treat)
data = index, type_treat, duration_treat
columns = [index, ("Type_" + index), ("Duration_" + index)]
treated = "; ".join(treat_list)
type = "; ".join(type_list)
duration = "; ".join(duration_list)
update_multiple(conn, cursor, table_cancer, columns,
file_number, data)
elif not treat:
index_no = "No " + index
type_treat, duration_treat = ("NA", ) * 2
data = index_no, type_treat, duration_treat
columns = [index, ("Type_" + index), ("Duration_" + index)]
update_multiple(conn, cursor, table_cancer, columns,
file_number, data)
treat_all.append(treated)
type_all.append(type)
duration_all.append(duration)
add_cancer = ask_y_n("Additional cancer history")
all_data = [
type_of_cancer_list, year_diagnosis_list, treat_all, type_all,
duration_all
]
for index in all_data:
data_joint = "|".join(index)
data_return.append(data_joint)
return tuple(data_return)
def family_cancer_table(conn, cursor, file_number):
add_family = True
type_cancer_list, relation_degree_list, type_relation_list, age_detect_list = [], [], [], []
all_data = []
while add_family:
type_of_cancer = input("Type of Cancer: ")
type_cancer_list.append(type_of_cancer)
relation_to_patient = ask_option(
"Relation to patient",
["Immediate Family", "Maternal Family", "Paternal Family"])
relation_degree_list.append(relation_to_patient)
type_relation = input("Specific Relationship:")
type_relation_list.append(type_relation)
age_at_detection_yrs = input('Age at detection (yrs) :')
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)
columns = 'File_number, Type_Cancer, Relation_to_Patient, Type_Relation, Age_at_detection_yrs'
table = "Family_Cancer_History"
insert(conn, cursor, table, columns, family_history)
add_family = ask_y_n("Add more family cancer history? ")
all_data_flat = "|".join(all_data)
return (all_data_flat)
def physical_activity_table(conn, cursor, file_number):
table_act = "Physical_Activity"
columns = ", ".join(pccm_names.names_info("phys_act_table"))
add_act = True
type_phys_list, freq_phys_list = [], []
while add_act:
type_phys_act = input("Type of physical activity: ")
type_phys_list.append(type_phys_act)
freq_phys_act = input("Frequency of physical activity: ")
freq_phys_list.append(freq_phys_act)
data = file_number, type_phys_act, freq_phys_act
insert(conn, cursor, table_act, columns, data)
add_act = ask_y_n("Add further activities?")
type_phys = '; '.join(type_phys_list)
freq_phys = '; '.join(freq_phys_list)
return (type_phys, freq_phys)
def med_history_table(conn, cursor, file_number):
add_history = True
diagnosis_date_list, treatment_list, condition_list = [], [], []
while add_history:
condition = input("Condition : ")
condition_list.append(condition)
diagnosis_date = input("Date of diagnosis: ")
diagnosis_date_list.append(diagnosis_date)
treatment = input("Treatment: ")
treatment_list.append(treatment)
history = file_number, condition, diagnosis_date, treatment
table_med = "General_Medical_History"
columns = "File_number, Condition, Diagnosis_date, Treatment"
insert(conn, cursor, table_med, columns, history)
add_history = ask_y_n('Add more history')
condition_hist = '; '.join(condition_list)
treatment_hist = '; '.join(treatment_list)
diagnosis_date_hist = "; ".join(diagnosis_date_list)
return (condition_hist, diagnosis_date_hist, treatment_hist)
def feed_duration(conn, cursor, 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 = input("Breast feeding duration for " + kid_add +
" (months) ?")
feeding_duration_list.append(feeding_duration)
feeding_details = ask_option("Breast feeding for child " + kid,
PatientInfo.breast_feeding)
feeding_details_list.append(feeding_details)
columns = 'file_number, child_number, feeding_duration, breast_usage_feeding'
data = file_number, kid, feeding_duration, feeding_details
sql.insert(conn, cursor, table, columns, data)
data_list = [child_list, feeding_duration_list, feeding_details_list]
data_return = join_lists(data_list, "; ")
return (data_return)
def nut_supp_table(conn, cursor, file_number):
type_nut_list, quant_nut_list, duration_nut_list = [], [], []
add_supp = True
table_nut = "Nutritional_Supplements"
columns = ", ".join(pccm_names.names_info("nut_sup"))
while add_supp:
nut_supplements_type = input("Type of nutritional supplements taken: ")
type_nut_list.append(nut_supplements_type)
nut_supplements_quant = input(
"Quantity of nutritional supplements taken per day: ")
quant_nut_list.append(nut_supplements_quant)
nut_supplements_duration = input(
"Duration of nutritional supplements use: ")
duration_nut_list.append(nut_supplements_duration)
new_data = file_number, nut_supplements_type, nut_supplements_quant, nut_supplements_duration
insert(conn, cursor, table_nut, columns, new_data)
add_supp = ask_y_n('Add more nutritional supplements?')
type_nut = '; '.join(type_nut_list)
quant_nut = '; '.join(quant_nut_list)
duration_nut = "; ".join(duration_nut_list)
return (type_nut, quant_nut, duration_nut)
def feed_duration(conn, cursor, 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 = input("Breast feeding duration for " + kid_add +
" (months) ?")
feeding_duration_list.append(feeding_duration)
category = "Breast feeding for child " + kid
options = ["Right Breast", "Left Breast", "Both Breasts", "Other"]
feeding_details = ask_option(category, options)
feeding_details_list.append(feeding_details)
columns = 'File_number, Child_number, Feeding_duration, Breast_usage_feeding'
data = file_number, kid, feeding_duration, feeding_details
insert(conn, cursor, table, columns, data)
data_list = [child_list, feeding_duration_list, feeding_details_list]
data_return = join_lists(data_list, "; ")
return (data_return)
#read from excel with same col names and distribution as in table
file_to_read = "D:/Documents/IISER/Prashanti_docs/Breast_Cancer_FFPE_blocks_database_Biopsy_dk08062018.xlsx"
data = pd.read_excel(file_to_read, header=1, dtype='object', usecols='A:AB')
update_by = "dk from ruhi/shaheen data"
last_update = datetime.now().strftime("%Y-%b-%d %H:%M")
module_names = ["biopsy_report_info", "tumour_biopsy_data", "lymphnode_biopsy"]
col_list = ["File_number"]
for index in module_names:
col_list = col_list + pccm_names.names_biopsy(index)
columns = ", ".join(col_list)
for index in range(0, len(data)):
data_list = list(data.loc[index])
data_list.append(update_by)
data_list.append(last_update)
sql.insert(conn_all, cursor_all, table, columns, data_list)
#for surgery_report
table = "Surgery_Block_Report_Data"
if sql.table_check(cursor_all, table) == 0:
cursor_all.execute('CREATE TABLE {tn}({nf})'.format(tn=table,
nf=file_number))
module_names = [
"surgery_block_information_1", "surgery_block_information_2",
"surgery_block_information_3", "path_stage"
]
for index in module_names:
col_name = pccm_names.names_surgery(index)
sql.add_columns(cursor_all, table, col_name)
#add data
def multiple_mass(table, conn, cursor, file_number):
import helper_function.ask_y_n_statement as ask
import sql.add_update_sql as add_update_sql
import helper_function.pccm_names as pccm_names
mass_number = int(input("Number of masses detected: "))
if table == "SonnoMammography_Multiple_Mass":
sonno_quad, sonno_location, sonno_clock, sonno_shape, sonno_depth, sonno_distance, sonno_pect,sonno_orientation,\
sonno_margin, sonno_echo, sonno_posterior = [list([]) for _ in range(11)]
if table == "Mammography_Multiple_Mass":
location, quad, pect, depth, distance, shape, margin, density = [
list([]) for _ in range(8)
]
if table == "MRI_Multiple_Mass":
location, quad, shape, margin, internal = [list([]) for _ in range(5)]
for index in range(0, mass_number):
check = False
while not check:
mass_id = index + 1
if table == "Mammography_Multiple_Mass":
mass_location = ask_y_n_statement.ask_option(
"Location of mass " + str(mass_id),
["Right Breast", "Left Breast"])
location_quad = lesion_location(mass_location)
mass_depth = ask_y_n_statement.ask_option(
"Depth of " + str(mass_id),
["Anterior", "Middle", "Posterior", "Other"])
mass_dist = ask_y_n_statement.ask_option(
"Distance from nipple", ["<0.5 cm", ">0.5 cm", "Other"])
pect_check = ask_y_n_statement.ask_y_n(
"Is distance from Pectoralis Major described for " +
str(mass_id))
if pect_check:
mass_pect = input("Distance from Pectoralis Major (cm): ")
else:
mass_pect = "Distance from Pectoralis Major not described"
depth.append(mass_depth)
location.append(mass_location)
distance.append(mass_dist)
quad.append(location_quad)
pect.append(mass_pect)
mammo_mass_shape = ask_y_n_statement.ask_option(
"Shape of mass", ["Oval", "Round", "Irregular", "Other"])
shape.append(mammo_mass_shape)
mammo_mass_margin = ask_y_n_statement.ask_option(
"Margins of mass", [
"Circumscribed", "Obscured", "Microlobulated",
"Indistinct", "Spiculated", "Other"
])
margin.append(mammo_mass_margin)
mammo_mass_density = ask_y_n_statement.ask_option(
"Density of mass", [
"High density", "Equal density", "Low density",
"Fat-containing", "Other"
])
density.append(mammo_mass_density)
mass_id = "Mass " + str(index + 1)
data_list = [
file_number, mass_id, mass_location, location_quad,
mass_depth, mass_dist, mass_pect, mammo_mass_shape,
mammo_mass_margin, mammo_mass_density
]
elif table == "MRI_Multiple_Mass":
mass_location = ask_y_n_statement.ask_option(
"Location of mass " + str(mass_id),
["Right Breast", "Left Breast"])
location.append(mass_location)
location_quad = lesion_location(mass_location)
quad.append(location_quad)
mri_mass_shape = ask_y_n_statement.ask_option(
"Shape of mass", ["Oval", "Round", "Irregular", "Other"])
shape.append(mri_mass_shape)
mri_mass_margin = ask_y_n_statement.ask_option(
"Margins of mass",
["Circumscribed", "Not circumscribed", "Other"])
if mri_mass_margin == "Not circumscribed":
mri_mass_notc = ask_y_n_statement.ask_option(
"Not circumscribed margins of mass",
["Irregular", "Spiculated"])
mri_mass_margin = mri_mass_margin + ": " + mri_mass_notc
margin.append(mri_mass_margin)
mri_mass_internal = ask_y_n_statement.ask_option(
"Internal enhancement characteristics", [
"Homogeneous", "Heterogeneous", "Rim enhancement",
"Dark internal septations"
])
internal.append(mri_mass_internal)
mass_id = "Mass " + str(index + 1)
data_list = [
file_number, mass_id, mass_location, location_quad,
mri_mass_shape, mri_mass_margin, mri_mass_internal
]
elif table == "SonnoMammography_Multiple_Mass":
mass_location = ask_y_n_statement.ask_option(
"Location of mass " + str(mass_id),
["Right Breast", "Left Breast"])
sonno_location.append(mass_location)
location_quad = lesion_location(mass_location)
sonno_quad.append(location_quad)
location_clock = input("What is the clock position of mass " +
str(mass_id) + "?")
location_clock = location_clock + " o'clock"
sonno_clock.append(location_clock)
mass_shape = ask_y_n_statement.ask_option(
"Shape of mass " + str(mass_id),
["Oval", "Round", "Irregular", "Other"])
mass_depth = input("Depth of mass " + str(mass_id) + "(cm): ")
mass_dist = ask_y_n_statement.ask_option(
"Distance from nipple", ["<0.5 cm", ">0.5 cm", "Other"])
pect_check = ask_y_n_statement.ask_y_n(
"Is distance from Pectoralis Major described for mass " +
str(mass_id))
if pect_check:
mass_pect = input("Distance from Pectoralis Major (cm): ")
else:
mass_pect = "NA"
sonno_depth.append(mass_depth)
sonno_distance.append(mass_dist)
sonno_pect.append(mass_pect)
sonno_shape.append(mass_shape)
mass_orientation = ask_y_n_statement.ask_option(
"Orientation of mass " + str(mass_id),
["Parallel", "Not parallel"])
sonno_orientation.append(mass_orientation)
mass_margin = ask_y_n_statement.ask_option(
"Margin of mass " + str(mass_id),
["Circumscribed", "Not circumscribed"])
if mass_margin == "Not circumscribed":
mass_margin = ask_y_n_statement.ask_option(
"Is Not circumscribed margin", [
"Indistinct", "Angular", "Microlobulated",
"Spiculated"
])
sonno_margin.append(mass_margin)
mass_echo = ask_y_n_statement.ask_option(
"Echo pattern of mass " + str(mass_id), [
"Anechoic", "Hyperechoic", "Complex cystic "
"and solid", "Hypoechoic", "Isoechoic",
"Heterogeneous", "Other"
])
sonno_echo.append(mass_echo)
mass_posterior = ask_y_n_statement.ask_option(
"Posterior Acoustic features", [
"No posterior features", "Enhancement", "Shadowing",
"Combined pattern", "Other"
])
sonno_posterior.append(mass_posterior)
mass_id = "Mass " + str(index + 1)
data_list = [
file_number, mass_id, mass_location, location_quad,
location_clock, mass_depth, mass_location, mass_dist,
mass_shape, mass_orientation, mass_margin, mass_echo,
mass_posterior
]
col_list = pccm_names.names_radio(table)
check = add_update_sql.review_input(file_number, col_list,
data_list)
columns = ", ".join(col_list)
add_update_sql.insert(conn, cursor, table, columns, tuple(data_list))
if table == "SonnoMammography_Multiple_Mass":
all_data = [[str(mass_number)], sonno_quad, sonno_location,
sonno_clock, sonno_depth, sonno_distance, sonno_pect,
sonno_shape, sonno_orientation, sonno_margin, sonno_echo,
sonno_posterior]
elif table == "Mammography_Multiple_Mass":
all_data = [[str(mass_number)], location, quad, depth, distance, pect,
shape, margin, density]
elif table == "MRI_Multiple_Mass":
all_data = [[str(mass_number)], location, quad, shape, margin,
internal]
else:
all_data = []
data_return = ask_y_n_statement.join_lists(all_data, "; ")
return tuple(data_return)
def write_file(self, new_data):
for i in range(0, new_data.shape[0]):
data_add = list(new_data.loc[i, :]) + [self.file_name]
sql.insert(self.conn, self.cursor, self.table, self.columns,
data_add)
#read from excel with same col names and distribution as in table
file_to_read = "D:/Documents/IISER/Prashanti_docs/Breast_Cancer_FFPE_blocks_database_Biopsy_dk08062018.xlsx"
data = pd.read_excel(file_to_read, header=1, dtype='object', usecols='A:AB')
update_by = "dk from ruhi/shaheen data"
module_names = ["biopsy_report_info", "tumour_biopsy_data", "lymphnode_biopsy"]
col_list = ["File_number"]
for index in module_names:
col_list = col_list + pccm_names.names_biopsy(index)
columns = ", ".join(col_list)
for index in range(0, len(data)):
data_list = list(data.loc[index])
data_list.append(update_by)
data_list.append(sql.last_update())
sql.insert(conn_all, cursor_all, table, columns, data_list)
#for surgery_report
table = "Surgery_Block_Report_Data"
if sql.table_check(cursor_all, table) == 0:
cursor_all.execute('CREATE TABLE {tn}({nf})'.format(tn=table,
nf=file_number))
module_names = [
"surgery_block_information_1", "surgery_block_information_2",
"surgery_block_information_3", "path_stage"
]
for index in module_names:
col_name = pccm_names.names_surgery(index)
sql.add_columns(cursor_all, table, col_name)
#add data
