def sds_if_parameters(decimal_age, measurement_method, measurement_value, sex):
if decimal_age and measurement_method and measurement_value and sex:
try:
return rcpchgrowth.sds(decimal_age, measurement_method, measurement_value, sex)
except:
print('could not calculate this value')
return None
else:
return None
def return_calculated_data_as_measurement_objects(data_frame, unique=True):
## receives a validated dataframe
## iterates to return an array of measurement objects and unique_child flag
## check columns data-type correct
data_frame['gestation_days'] = data_frame['gestation_days'].fillna(0).astype(int)
data_frame['gestation_weeks'] = data_frame['gestation_weeks'].fillna(0).astype(int)
data_frame['birth_date']=pd.to_datetime(data_frame['birth_date'], dayfirst=True)
data_frame['observation_date']=pd.to_datetime(data_frame['observation_date'], dayfirst=True) #.astype('datetime64[ns]')
data_frame['measurement_method']=data_frame['measurement_method'].astype(str)
data_frame['measurement_method']=data_frame.apply(lambda row: row['measurement_method'].lower(), axis=1) ## ensure sex and measurement_method are lowercase
data_frame['sex']=data_frame['sex'].astype(str)
data_frame['sex']=data_frame.apply(lambda row: row['sex'].lower(), axis=1) ## ensure sex and measurement_method are lowercase
## add the calculated columns (SDS and Centile, corrected and chronological decimal age)
data_frame['corrected_decimal_age']=data_frame.apply(lambda row: rcpchgrowth.corrected_decimal_age(row['birth_date'], row['observation_date'], row['gestation_weeks'], row['gestation_days']), axis=1)
data_frame['chronological_decimal_age']=data_frame.apply(lambda row: rcpchgrowth.chronological_decimal_age(row['birth_date'], row['observation_date']), axis=1)
data_frame['estimated_date_delivery']=data_frame.apply(lambda row: rcpchgrowth.estimated_date_delivery(row['birth_date'], row['gestation_weeks'], row['gestation_days']), axis=1)
data_frame['sds']=data_frame.apply(lambda row: sds_if_parameters(row['corrected_decimal_age'], row['measurement_method'], row['measurement_value'], row['sex']), axis=1)
data_frame['centile']=data_frame.apply(lambda row: rcpchgrowth.centile(row['sds']), axis=1)
data_frame['height'] = data_frame.apply(lambda row: value_for_measurement('height', row['measurement_method'], row['measurement_value']), axis=1)
data_frame['weight'] = data_frame.apply(lambda row: value_for_measurement('weight', row['measurement_method'], row['measurement_value']), axis=1)
same_date_data_frame = data_frame[data_frame.duplicated(['birth_date', 'observation_date'], keep=False)]
height = 0.0
weight = 0.0
weight_date = ''
height_date = ''
weight_birth_date = ''
height_birth_date = ''
extra_rows = []
for index, row in same_date_data_frame.iterrows():
if pd.notnull(row['height']):
height = row['height']
height_date = row['observation_date']
height_birth_date = row['birth_date']
if pd.notnull(row['weight']):
weight = row['weight']
weight_date = row['observation_date']
weight_birth_date = row['birth_date']
if height > 0.0 and weight > 0.0 and height_date == weight_date and height_birth_date == weight_birth_date:
bmi = rcpchgrowth.bmi_from_height_weight(height, weight)
if row['corrected_decimal_age'] > 0.038329911:
bmi_sds = rcpchgrowth.sds(row['corrected_decimal_age'], 'bmi', bmi, row['sex'])
bmi_centile = rcpchgrowth.centile(bmi_sds)
else:
bmi_sds = None
bmi_centile = None
new_row = {'birth_date': height_birth_date, 'observation_date': height_date, 'gestation_weeks': row['gestation_weeks'], 'gestation_days': row['gestation_days'], 'estimated_date_delivery': row['estimated_date_delivery'], 'chronological_decimal_age': row['chronological_decimal_age'], 'corrected_decimal_age': row['corrected_decimal_age'], 'sex': row['sex'], 'measurement_method': 'bmi', 'measurement_value': bmi, 'sds': bmi_sds, 'centile': bmi_centile, 'height': None, 'weight': None}
extra_rows.append(new_row)
if len(extra_rows) > 0:
data_frame = data_frame.append(extra_rows, ignore_index=True)
data_frame = data_frame.drop_duplicates(ignore_index=True)
if data_frame['birth_date'].nunique() > 1:
print('these are not all data from the same patient. They cannot be charted.') #do not chart these values
unique = False
## create measurement objects
measurement_object_array = []
for index, row in data_frame.iterrows():
new_measurement_type = rcpchgrowth.Measurement_Type(measurement_method=row['measurement_method'], measurement_value=row['measurement_value'])
new_measurement = rcpchgrowth.Measurement(sex=row['sex'], birth_date=row['birth_date'], observation_date=row['observation_date'], measurement_type=new_measurement_type, gestation_weeks=row['gestation_weeks'], gestation_days=row['gestation_days'])
measurement_object_array.append(new_measurement.measurement)
return {
'data': measurement_object_array,
'unique_child': unique
}
def return_calculated_data(data_frame, unique=True):
# receives a dataframe, returns a dataframe of same structure to excel sheet plus calculated rows for bmi and columns for
# sds, centiles and date calculations, with unique_child flag
## check columns data-type correct
data_frame['gestation_days'] = data_frame['gestation_days'].fillna(0).astype(int)
data_frame['gestation_weeks'] = data_frame['gestation_weeks'].fillna(0).astype(int)
# data_frame['birth_date']=data_frame['birth_date'].astype('datetime64[ns]')
# data_frame['observation_date']=data_frame['observation_date'].astype('datetime64[ns]')
data_frame['birth_date']=pd.to_datetime(data_frame['birth_date'], infer_datetime_format=True)
data_frame['observation_date']=pd.to_datetime(data_frame['observation_date'], infer_datetime_format=True)
data_frame['measurement_method']=data_frame['measurement_method'].astype(str)
data_frame['measurement_method']=data_frame.apply(lambda row: row['measurement_method'].lower(), axis=1) ## ensure sex and measurement_method are lowercase
data_frame['sex']=data_frame['sex'].astype(str)
data_frame['sex']=data_frame.apply(lambda row: row['sex'].lower(), axis=1) ## ensure sex and measurement_method are lowercase
## add the calculated columns (SDS and Centile, corrected and chronological decimal age)
data_frame['corrected_decimal_age']=data_frame.apply(lambda row: rcpchgrowth.corrected_decimal_age(row['birth_date'], row['observation_date'], row['gestation_weeks'], row['gestation_days']), axis=1)
data_frame['chronological_decimal_age']=data_frame.apply(lambda row: rcpchgrowth.chronological_decimal_age(row['birth_date'], row['observation_date']), axis=1)
data_frame['estimated_date_delivery']=data_frame.apply(lambda row: rcpchgrowth.estimated_date_delivery(row['birth_date'], row['gestation_weeks'], row['gestation_days']), axis=1)
data_frame['sds']=data_frame.apply(lambda row: sds_if_parameters(row['corrected_decimal_age'], row['measurement_method'], row['measurement_value'], row['sex']), axis=1)
data_frame['centile']=data_frame.apply(lambda row: rcpchgrowth.centile(row['sds']), axis=1)
data_frame['height'] = data_frame.apply(lambda row: value_for_measurement('height', row['measurement_method'], row['measurement_value']), axis=1)
data_frame['weight'] = data_frame.apply(lambda row: value_for_measurement('weight', row['measurement_method'], row['measurement_value']), axis=1)
same_date_data_frame = data_frame[data_frame.duplicated(['birth_date', 'observation_date'], keep=False)]
height = 0.0
weight = 0.0
weight_date = ''
height_date = ''
weight_birth_date = ''
height_birth_date = ''
extra_rows = []
for index, row in same_date_data_frame.iterrows():
if pd.notnull(row['height']):
height = row['height']
height_date = row['observation_date']
height_birth_date = row['birth_date']
if pd.notnull(row['weight']):
weight = row['weight']
weight_date = row['observation_date']
weight_birth_date = row['birth_date']
if height > 0.0 and weight > 0.0 and height_date == weight_date and height_birth_date == weight_birth_date:
bmi = rcpchgrowth.bmi_from_height_weight(height, weight)
if row['corrected_decimal_age'] > 0.038329911:
bmi_sds = rcpchgrowth.sds(row['corrected_decimal_age'], 'bmi', bmi, row['sex'])
bmi_centile = rcpchgrowth.centile(bmi_sds)
else:
bmi_sds = None
bmi_centile = None
new_row = {'birth_date': height_birth_date, 'observation_date': height_date, 'gestation_weeks': row['gestation_weeks'], 'gestation_days': row['gestation_days'], 'estimated_date_delivery': row['estimated_date_delivery'], 'chronological_decimal_age': row['chronological_decimal_age'], 'corrected_decimal_age': row['corrected_decimal_age'], 'sex': row['sex'], 'measurement_method': 'bmi', 'measurement_value': bmi, 'sds': bmi_sds, 'centile': bmi_centile, 'height': None, 'weight': None}
extra_rows.append(new_row)
if len(extra_rows) > 0:
data_frame = data_frame.append(extra_rows, ignore_index=True)
data_frame = data_frame.drop_duplicates(ignore_index=True)
if data_frame['birth_date'].nunique() > 1:
print('these are not all data from the same patient. They cannot be charted.') #do not chart these values
unique = False
return {
'data': data_frame.to_json(orient='records', date_format='epoch'),
'unique_child': unique
}