diff --git a/read.py b/read.py
index 877f38fc9f9835697de835c14ad402044dbbedbc..d67a546bc4282ea71a023af463c2c2cc548ac309 100644
--- a/read.py
+++ b/read.py
@@ -22,9 +22,9 @@ import json
import copy
from io import StringIO as StringIO
-from . import schemas
-from . import properties
-from .common import pandas_TextParser_hdlr
+from . import schemas
+from . import properties
+from .common import pandas_TextParser_hdlr
from .reader import import_data
from .reader import get_sections
from .reader import read_sections
@@ -34,6 +34,7 @@ toolPath = os.path.dirname(os.path.abspath(__file__))
schema_lib = os.path.join(toolPath,'schemas','lib')
def ERV(TextParser,read_sections_list, schema, code_tables_path):
+
data_buffer = StringIO()
valid_buffer = StringIO()
@@ -44,23 +45,23 @@ def ERV(TextParser,read_sections_list, schema, code_tables_path):
# - requested NA sections as NaN columns
# - columns order as in read_sections_list
sections_df = get_sections.get_sections(string_df, schema, read_sections_list)
-
+
# b. Read elements from sections: along data chunks, resulting data types
# may vary if gaps, keep track of data types!
# Sections as parsed in the same order as sections_df.columns
[data_df, valid_df, out_dtypesi ] = read_sections.read_sections(sections_df, schema)
if i_chunk == 0:
out_dtypes = copy.deepcopy(out_dtypesi)
-
- for k in out_dtypesi:
+
+ for k in out_dtypesi:
if out_dtypesi in properties.numpy_floats:
out_dtypes.update({ k:out_dtypesi.get(k) })
-
- valid_df = validate.validate(data_df, valid_df, schema, code_tables_path)
+
+ valid_df = validate.validate(data_df, valid_df, schema, code_tables_path)
# Save to buffer
data_df.to_csv(data_buffer,header = False, mode = 'a', encoding = 'utf-8',index = False)
valid_df.to_csv(valid_buffer,header = False, mode = 'a', encoding = 'utf-8',index = False)
- # Create the output
+ # Create the output
# WE'LL NEED TO POSPROCESS THIS WHEN READING MULTIPLE REPORTS PER LINE
data_buffer.seek(0)
valid_buffer.seek(0)
@@ -69,21 +70,22 @@ def ERV(TextParser,read_sections_list, schema, code_tables_path):
# (source is either pd.io.parsers.TextFileReader or a file with chunksize specified on input):
# This way it supports direct chunksize property inheritance if the input source was a pd.io.parsers.TextFileReader
chunksize = TextParser.orig_options['chunksize'] if isinstance(TextParser,pd.io.parsers.TextFileReader) else None
- # 'datetime' is not a valid pandas dtype: Only on output (on reading) will be then converted (via parse_dates) to datetime64[ns] type, cannot specify 'datetime' (of any kind) here: will fail
+ # 'datetime' is not a valid pandas dtype: Only on output (on reading) will be then converted (via parse_dates) to datetime64[ns] type,
+ # cannot specify 'datetime' (of any kind) here: would fail
date_columns = [] # Needs to be the numeric index of the column, as seems not to be able to work with tupples....
for i,element in enumerate(list(out_dtypes)):
if out_dtypes.get(element) == 'datetime':
date_columns.append(i)
-
+
data = pd.read_csv(data_buffer,names = data_df.columns, chunksize = chunksize, dtype = out_dtypes, parse_dates = date_columns)
valid = pd.read_csv(valid_buffer,names = data_df.columns, chunksize = chunksize)
-
+
return data, valid
def validate_arg(arg_name,arg_value,arg_type):
-
+
if arg_value and not isinstance(arg_value,arg_type):
- logging.error('Argument {0} must be {1}, input type is {2}'.format(arg_name,arg_type,type(arg_value)))
+ logging.error('Argument {0} must be {1}, input type is {2}'.format(arg_name,arg_type,type(arg_value)))
return False
else:
return True
@@ -104,14 +106,14 @@ def read(source, data_model = None, data_model_path = None, sections = None,chun
return
elif not os.path.isfile(source):
logging.error('Could not open data source file {}'.format(source))
- logging.info('Otherwise, supported in-memory data sources are {}'.format(",".join([ str(x) for x in properties.supported_sources])))
+ logging.info('If input source was not a file: supported in-memory data sources are {}'.format(",".join([ str(x) for x in properties.supported_sources])))
return
if not validate_arg('sections',sections,list):
return
if not validate_arg('chunksize',chunksize,int):
- return
+ return
if not validate_arg('skiprows',skiprows,int):
- return
+ return
# 1. Read data model
# Schema reader will return None if schema does not validate
@@ -122,37 +124,37 @@ def read(source, data_model = None, data_model_path = None, sections = None,chun
if data_model:
model_path = os.path.join(schema_lib,data_model)
else:
- model_path = data_model_path
+ model_path = data_model_path
code_tables_path = os.path.join(model_path,'code_tables')
+
# For future use: some work already done in schema reading
if schema['header'].get('multiple_reports_per_line'):
logging.error('File format not yet supported')
sys.exit(1)
-
+
# 2. Read and validate data
imodel = data_model if data_model else data_model_path
- logging.info("EXTRACTING DATA FROM MODEL: {}".format(imodel))
-
- # 2.1. Define output
- # Subset data model sections to requested sections
+ logging.info("EXTRACTING DATA FROM MODEL: {}".format(imodel))
+
+ # 2.1. Subset data model sections to requested sections
parsing_order = schema['header'].get('parsing_order')
if not sections:
sections = [ x.get(y) for x in parsing_order for y in x ]
read_sections_list = [y for x in sections for y in x]
else:
- read_sections_list = sections
-
+ read_sections_list = sections
+
# 2.2 Homogeneize input data to an iterable with dataframes:
# a list with a single dataframe or a pd.io.parsers.TextFileReader
logging.info("Getting data string from source...")
TextParser = import_data.import_data(source, chunksize = chunksize, skiprows = skiprows)
-
+
# 2.3. Extract, read and validate data in same loop
logging.info("Extracting and reading sections")
data,valid = ERV(TextParser,read_sections_list, schema, code_tables_path)
-
+
# 3. Create out data attributes
- logging.info("CREATING OUTPUT DATA ATTRIBUTES FROM DATA MODEL(S)")
+ logging.info("CREATING OUTPUT DATA ATTRIBUTES FROM DATA MODEL")
data_columns = [ x for x in data ] if isinstance(data,pd.DataFrame) else data.orig_options['names']
out_atts = schemas.df_schema(data_columns, schema)
@@ -178,7 +180,7 @@ def read(source, data_model = None, data_model_path = None, sections = None,chun
header = cols
out_atts_json = out_atts
data_df.to_csv(os.path.join(out_path,'data.csv'), header = header, mode = mode, encoding = 'utf-8',index = True, index_label='index')
- valid_df.to_csv(os.path.join(out_path,'valid_mask.csv'), header = header, mode = mode, encoding = 'utf-8',index = True, index_label='index')
+ valid_df.to_csv(os.path.join(out_path,'valid_mask.csv'), header = header, mode = mode, encoding = 'utf-8',index = True, index_label='index')
if enlisted:
data = data[0]
valid = valid[0]