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gristlabs_grist-core/sandbox/grist/imports/import_csv.py

201 lines
6.8 KiB

"""
Plugin for importing CSV files
"""
import os
import logging
import chardet
import messytables
import six
from six.moves import zip
import parse_data
from imports import import_utils
log = logging.getLogger(__name__)
SCHEMA = [
{
'name': 'lineterminator',
'label': 'Line terminator',
'type': 'string',
'visible': True,
},
{
'name': 'include_col_names_as_headers',
'label': 'First row contains headers',
'type': 'boolean',
'visible': True,
},
{
'name': 'delimiter',
'label': 'Field separator',
'type': 'string',
'visible': True,
},
{
'name': 'skipinitialspace',
'label': 'Skip leading whitespace',
'type': 'boolean',
'visible': True,
},
{
'name': 'quotechar',
'label': 'Quote character',
'type': 'string',
'visible': True,
},
{
'name': 'doublequote',
'label': 'Quotes in fields are doubled',
'type': 'boolean',
'visible': True,
},
{
'name': 'quoting',
'label': 'Convert quoted fields',
'type': 'number',
'visible': False, # Not supported by messytables
},
{
'name': 'escapechar',
'label': 'Escape character',
'type': 'string',
'visible': False, # Not supported by messytables
},
{
'name': 'start_with_row',
'label': 'Start with row',
'type': 'number',
'visible': False, # Not yet implemented
},
{
'name': 'NUM_ROWS',
'label': 'Number of rows',
'type': 'number',
'visible': False,
}]
def parse_file_source(file_source, options):
parsing_options, export_list = parse_file(import_utils.get_path(file_source["path"]), options)
return {"parseOptions": parsing_options, "tables": export_list}
def parse_file(file_path, parse_options=None):
"""
Reads a file path and parse options that are passed in using ActiveDoc.importFile()
and returns a tuple with parsing options (users' or guessed) and an object formatted so that
it can be used by grist for a bulk add records action.
"""
parse_options = parse_options or {}
with open(file_path, "rb") as f:
parsing_options, export_list = _parse_open_file(f, parse_options=parse_options)
return parsing_options, export_list
def _parse_open_file(file_obj, parse_options=None):
options = {}
csv_keys = ['delimiter', 'quotechar', 'lineterminator', 'doublequote', 'skipinitialspace']
csv_options = {k: parse_options.get(k) for k in csv_keys}
if six.PY2:
csv_options = {k: v.encode('utf8') if isinstance(v, six.text_type) else v
for k, v in csv_options.items()}
table_set = messytables.CSVTableSet(file_obj,
delimiter=csv_options['delimiter'],
quotechar=csv_options['quotechar'],
lineterminator=csv_options['lineterminator'],
doublequote=csv_options['doublequote'],
skipinitialspace=csv_options['skipinitialspace'])
num_rows = parse_options.get('NUM_ROWS', 0)
# Messytable's encoding detection uses too small a sample, so we override it here.
sample = file_obj.read(100000)
table_set.encoding = chardet.detect(sample)['encoding']
# In addition, always prefer UTF8 over ASCII.
if table_set.encoding == 'ascii':
table_set.encoding = 'utf8'
export_list = []
# A table set is a collection of tables:
for row_set in table_set.tables:
table_name = None
sample_rows = list(row_set.sample)
# Messytables doesn't guess whether headers are present, so we need to step in.
data_offset, headers = import_utils.headers_guess(sample_rows)
# Make sure all header values are strings.
for i, header in enumerate(headers):
if not isinstance(header, six.string_types):
headers[i] = six.text_type(header)
log.info("Guessed data_offset as %s", data_offset)
log.info("Guessed headers as: %s", headers)
have_guessed_headers = any(headers)
include_col_names_as_headers = parse_options.get('include_col_names_as_headers',
have_guessed_headers)
if include_col_names_as_headers and not have_guessed_headers:
# use first line as headers
data_offset, first_row = import_utils.find_first_non_empty_row(sample_rows)
headers = import_utils.expand_headers(first_row, data_offset, sample_rows)
elif not include_col_names_as_headers and have_guessed_headers:
# move guessed headers to data
data_offset -= 1
headers = [''] * len(headers)
row_set.register_processor(messytables.offset_processor(data_offset))
rows = [
[cell.value for cell in row]
for row in row_set
]
table_data_with_types = parse_data.get_table_data(rows, len(headers), num_rows)
# Identify and remove empty columns, and populate separate metadata and data lists.
column_metadata = []
table_data = []
for col_data, header in zip(table_data_with_types, headers):
if not header and all(val == "" for val in col_data["data"]):
continue # empty column
data = col_data.pop("data")
col_data["id"] = header
column_metadata.append(col_data)
table_data.append(data)
if not table_data:
# Don't add tables with no columns.
continue
guessed = row_set._dialect
quoting = parse_options.get('quoting')
options = {"delimiter": parse_options.get('delimiter', guessed.delimiter),
"doublequote": parse_options.get('doublequote', guessed.doublequote),
"lineterminator": parse_options.get('lineterminator', guessed.lineterminator),
"quotechar": parse_options.get('quotechar', guessed.quotechar),
"skipinitialspace": parse_options.get('skipinitialspace', guessed.skipinitialspace),
"include_col_names_as_headers": include_col_names_as_headers,
"start_with_row": 1,
"NUM_ROWS": num_rows,
"SCHEMA": SCHEMA
}
log.info("Output table %r with %d columns", table_name, len(column_metadata))
for c in column_metadata:
log.debug("Output column %s", c)
export_list.append({
"table_name": table_name,
"column_metadata": column_metadata,
"table_data": table_data
})
return options, export_list
def get_version():
""" Return name and version of plug-in"""
pass