#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This module has functions to manage data model
schema files and objects according to the
requirements of the data reader tool
"""
import os
import sys
import json
import logging
import shutil
from copy import deepcopy
import glob
from .. import properties
if sys.version_info[0] >= 3:
py3 = True
else:
py3 = False
toolPath = os.path.dirname(os.path.abspath(__file__))
schema_lib = os.path.join(toolPath,'lib')
templates_path = os.path.join(schema_lib,'templates','schemas')
def read_schema(schema_name = None, ext_schema_path = None):
"""
Reads a data model schema file to a dictionary and
completes it by adding explicitly information the
reader tool needs
Keyword Arguments
-----------------
schema_name : str, optional
The name of data model to read. This is for
data models included in the tool
ext_schema_path : str, optional
The path to the external data model schema file
Either schema_name or ext_schema_path must be provided.
Returns
-------
dict
Data model schema
"""
# 1. Validate input
if schema_name:
if schema_name not in properties.supported_data_models:
print('ERROR: \n\tInput data model "{}" not supported. See mdf_reader.properties.supported_data_models for supported data models'.format(schema_name))
return
else:
schema_path = os.path.join(schema_lib,schema_name)
else:
schema_path = os.path.abspath(ext_schema_path)
schema_name = os.path.basename(schema_path)
schema_file = os.path.join(schema_path, schema_name + '.json')
if not os.path.isfile(schema_file):
logging.error('Can\'t find input schema file {}'.format(schema_file))
return
# 2. Get schema
with open(schema_file) as fileObj:
schema = json.load(fileObj)
# 3. Expand schema
# Fill in the initial schema to "full complexity": to homogeneize schema,
# explicitly add info that is implicit to given situations/data models
# One report per record: make sure later changes are reflected in MULTIPLE
# REPORTS PER RECORD case below if we ever use it!
# Currently only supported case: one report per record (line)
# 3.1. First check for no header case: sequential sections
if not schema['header']:
if not schema['sections']:
logging.error('\'sections\' block needs to be defined in a schema with no header. Error in data model schema file {}'.format(schema_file))
return
schema['header'] = dict()
if not schema['header'].get('multiple_reports_per_line'):
# 3.2. Make no section formats be internally treated as 1 section format
if not schema.get('sections'):
if not schema.get('elements'):
logging.error('Data elements not defined in data model schema file {} under key \'elements\' '.format(schema_file))
return
schema['sections'] = {properties.dummy_level:{'header':{},'elements':schema.get('elements')}}
schema['header']['parsing_order'] = [{'s':[properties.dummy_level]}]
schema.pop('elements',None)
schema['sections'][properties.dummy_level]['header']['delimiter'] = schema['header'].get('delimiter')
schema['header'].pop('delimiter',None)
schema['sections'][properties.dummy_level]['header']['field_layout'] = schema['header'].get('field_layout')
schema['header'].pop('field_layout',None)
# 3.3. Make parsing order explicit
if not schema['header'].get('parsing_order'):# assume sequential
schema['header']['parsing_order'] = [{'s':list(schema['sections'].keys())}]
# 3.4. Make disable_read and field_layout explicit: this is ruled by delimiter being set,
# unless explicitly set
for section in schema['sections'].keys():
if schema['sections'][section]['header'].get('disable_read'):
continue
else:
schema['sections'][section]['header']['disable_read'] = False
if not schema['sections'][section]['header'].get('field_layout'):
delimiter = schema['sections'][section]['header'].get('delimiter')
schema['sections'][section]['header']['field_layout'] = 'delimited' if delimiter else 'fixed_width'
for element in schema['sections'][section]['elements'].keys():
if schema['sections'][section]['elements'][element].get('column_type') in properties.numpy_integers:
np_integer = schema['sections'][section]['elements'][element].get('column_type')
pd_integer = properties.pandas_nan_integers.get(np_integer)
schema['sections'][section]['elements'][element].update({'column_type':pd_integer})
return schema
else:
logging.error('Multile reports per line data model: not yet supported')
return
# 1X: MULTIPLE REPORTS PER RECORD
# !!!! NEED TO ADD SECTION LENS TO THE REPORT'S SECTION'S HEADER!!!
# CAN INFER FROM ELEMENTS LENGHT AND ADD, OR MAKE AS REQUIREMENT TO BE GIVEN
# global name_report_section
# Have to assess how the section splitting works when x sequential
# sections are declared, and only x-y are met.
#if not schema['header'].get('reports_per_line'):
# schema['header']['reports_per_line'] = 24
#if not schema.get('sections'):
# schema['sections'] = dict()
# schema['header']['parsing_order'] = [{'s':[]}]
# for i in range(1,schema['header']['reports_per_line'] + 1):
# schema['sections'].update({str(i):{'header':{},'elements':deepcopy(schema.get('elements'))}})
#else:
# name_report_section = list(schema['sections'].keys())[-1]
# schema['header']['name_report_section'] == name_report_section
# schema['header']['parsing_order'] = [{'s':list(schema['sections'].keys())[:-1]}]
# for i in range(1,schema['header']['reports_per_line'] + 1):
# schema['sections'].update({str(i):schema['sections'].get(name_report_section)})
# schema['sections'].pop(name_report_section,None)
#for i in range(1,schema['header']['reports_per_line'] + 1):
# schema['header']['parsing_order'][0]['s'].append(str(i))
#return schema
def df_schema(df_columns, schema):
"""
Creates a simple attribute dictionary for the elements
in a dataframe from its data model schema
Parameters
----------
df_columns : list
The columns in the data frame (data elements from
the data model)
schema : dict
The data model schema
Returns
-------
dict
Data elements attributes
"""
def clean_schema(columns,schema):
# Could optionally add cleaning of element descriptors that only apply
# to the initial reading of the data model: field_length, etc....
for element in list(schema):
if element not in columns:
schema.pop(element)
return
flat_schema = dict()
# Flatten main model schema
for section in schema.get('sections'):
if section == properties.dummy_level:
flat_schema.update(schema['sections'].get(section).get('elements'))
elif schema['sections'].get(section).get('header').get('disable_read'):
flat_schema.update( { (section, section): {'column_type':'object'} })
else:
flat_schema.update( { (section, x): schema['sections'].get(section).get('elements').get(x) for x in schema['sections'].get(section).get('elements') })
clean_schema(df_columns, flat_schema)
return flat_schema
def templates():
"""
Lists the name of the available schema file templates
Returns
-------
list
Schema file templates alias
"""
schemas = glob.glob(os.path.join(templates_path,'*.json'))
return [ os.path.basename(x).split(".")[0] for x in schemas ]
def copy_template(schema, out_dir = None,out_path = None):
"""
Copies a schema file template to an output
file or path
Parameters
----------
schema : str
Schema template name to copy
Keyword Arguments
-----------------
out_dir : dict, opt
Directory to copy schema file template to
out_path : dict, opt
Full filename to copy schema file template to
Either out_dir or out_path must be provided
"""
schemas = templates()
if schema in schemas:
schema_path = os.path.join(templates_path,schema + '.json')
schema_out = out_path if out_path else os.path.join(out_dir,schema + '.json')
shutil.copyfile(schema_path, schema_out)
if os.path.isfile( schema_out):
print('Schema template {0} copied to {1}'.format(schema, schema_out))
return
else:
print('copy_template ERROR:')
print('\tError copying schema template {0} copied to {1}'.format(schema, schema_out))
return
else:
print('copy_template ERROR:')
print('\tRequested template {} must be a valid name.'.format(schema))
print('\tValid names are: {}'.format(", ".join(schemas)))
return