diff --git a/validate/validate.py b/validate/validate.py
index d306d4af6b6d5f0fdcaae099a465ed04695e96be..304811bf36bfe68f3d3e4ad4ceaa24f0fb97f09e 100644
--- a/validate/validate.py
+++ b/validate/validate.py
@@ -13,6 +13,9 @@ Validated elements are those with the following column_types:
they should already be NaT if they not validate as a valid datetime. The
correspoding mask is just created for them
+DEV notes:
+need to add tolerance to the numeric range validation
+
@author: iregon
"""
@@ -24,24 +27,24 @@ from .. import properties
from ..schemas import code_tables
from ..schemas import schemas
-def validate_numeric(elements,data,schema):
- # Find thresholds in schema. Flag if not available -> warn
+def validate_numeric(elements,data,schema):
+ # Find thresholds in schema. Flag if not available -> warn
mask = pd.DataFrame(index = data.index, data = False, columns = elements)
lower = { x:schema.get(x).get('valid_min', -np.inf) for x in elements }
upper = { x:schema.get(x).get('valid_max', np.inf) for x in elements }
-
+
set_elements = [ x for x in lower.keys() if lower.get(x) != -np.inf and upper.get(x) != np.inf ]
if len([ x for x in elements if x not in set_elements ]) > 0:
logging.warning('Data numeric elements with missing upper or lower threshold: {}'.format(",".join([ str(x) for x in elements if x not in set_elements ])))
logging.warning('Corresponding upper and/or lower bounds set to +/-inf for validation')
-
+
mask[elements] = ((data[elements] >= [ lower.get(x) for x in elements ] ) & (data[elements] <= [ upper.get(x) for x in elements ])) | data[elements].isna()
return mask
def validate_codes(elements, data, code_tables_path, schema, supp = False):
-
+
mask = pd.DataFrame(index = data.index, data = False, columns = elements)
-
+
if os.path.isdir(code_tables_path):
for element in elements:
code_table = schema.get(element).get('codetable')
@@ -63,10 +66,10 @@ def validate_codes(elements, data, code_tables_path, schema, supp = False):
key_elements = [ (element[0],x) for x in key_elements ]
else:
key_elements = [ (properties.dummy_level,x) if not isinstance(x,tuple) else x for x in key_elements ]
- dtypes = { x:properties.pandas_dtypes.get(schema.get(x).get('column_type')) for x in key_elements }
+ dtypes = { x:properties.pandas_dtypes.get(schema.get(x).get('column_type')) for x in key_elements }
table_keys = code_tables.table_keys(table)
table_keys_str = [ "∿".join(x) if isinstance(x,list) else x for x in table_keys ]
- validation_df = data[key_elements]
+ validation_df = data[key_elements]
imask = pd.Series(index = data.index, data =True)
imask.iloc[np.where(validation_df.notna().all(axis = 1))[0]] = validation_df.iloc[np.where(validation_df.notna().all(axis = 1))[0],:].astype(dtypes).astype('str').apply("∿".join, axis=1).isin(table_keys_str)
mask[element] = imask
@@ -80,42 +83,42 @@ def validate_codes(elements, data, code_tables_path, schema, supp = False):
logging.warning('Element mask set to False')
continue
else:
- logging.error('Code tables path {} not found'.format(code_tables_path))
+ logging.error('Code tables path {} not found'.format(code_tables_path))
logging.warning('All coded elements set to False')
return mask
-
-def validate(data, mask0, schema, code_tables_path):
+
+def validate(data, mask0, schema, code_tables_path):
logging.basicConfig(format='%(levelname)s\t[%(asctime)s](%(filename)s)\t%(message)s',
level=logging.INFO,datefmt='%Y%m%d %H:%M:%S',filename=None)
-
- # Check input
+
+ # Check input
if not isinstance(data,pd.DataFrame) or not isinstance(mask0,pd.DataFrame):
logging.error('Input data and mask must be a pandas data frame object')
return
-
+
# Get the data elements from the input data: might be just a subset of
# data model and flatten the schema to get a simple and sequential list
# of elements included in the input data
- elements = [ x for x in data ]
+ elements = [ x for x in data ]
element_atts = schemas.df_schema(elements, schema)
# See what elements we need to validate
numeric_elements = [ x for x in elements if element_atts.get(x).get('column_type') in properties.numeric_types ]
- datetime_elements = [ x for x in elements if element_atts.get(x).get('column_type') == 'datetime' ]
+ datetime_elements = [ x for x in elements if element_atts.get(x).get('column_type') == 'datetime' ]
coded_elements = [ x for x in elements if element_atts.get(x).get('column_type') == 'key' ]
-
+
if any([isinstance(x,tuple) for x in numeric_elements + datetime_elements + coded_elements ]):
validated_columns = pd.MultiIndex.from_tuples(list(set(numeric_elements + coded_elements + datetime_elements)))
else:
validated_columns = list(set(numeric_elements + coded_elements + datetime_elements))
-
+
mask = pd.DataFrame(index = data.index, columns = data.columns)
- # Validate elements by dtype:
+ # Validate elements by dtype:
# 1. Numeric elements
mask[numeric_elements] = validate_numeric(numeric_elements, data, element_atts)
-
+
# 2. Table coded elements
# See following: in multiple keys code tables, the non parameter element,
# won't have a code_table attribute in the element_atts:
@@ -127,15 +130,15 @@ def validate(data, mask0, schema, code_tables_path):
# Need to see how to grab the YEAR part of a datetime when YEAR comes from a datetime element
# pd.DatetimeIndex(df['_datetime']).year
if len(coded_elements)> 0:
- mask[coded_elements] = validate_codes(coded_elements, data, code_tables_path, element_atts)
-
+ mask[coded_elements] = validate_codes(coded_elements, data, code_tables_path, element_atts)
+
# 3. Datetime elements
# Those declared as such in element_atts
- # Because of the way they are converted, read into datetime,
+ # Because of the way they are converted, read into datetime,
# they should already be NaT if they not validate as a valid datetime;
# let's check: hurray! they are!
mask[datetime_elements] = data[datetime_elements].notna()
-
+
mask[validated_columns] = mask[validated_columns].mask(mask0[validated_columns] == False, False)
-
- return mask
\ No newline at end of file
+
+ return mask