File: C:/Users/fred/anaconda3/Lib/site-packages/astropy/io/ascii/cparser.pyx
# Licensed under a 3-clause BSD style license - see LICENSE.rst
#cython: language_level=3
import csv
import math
import mmap
import multiprocessing
import os
import queue as Queue
import warnings
import numpy as np
cimport numpy as np
from numpy import ma
from cpython.buffer cimport (
Py_buffer,
PyBUF_SIMPLE,
PyBuffer_Release,
PyObject_GetBuffer,
)
from libc cimport stdio
from libc.stdint cimport int64_t
from astropy.utils.data import get_readable_fileobj
from astropy.utils.exceptions import AstropyWarning
from . import core
np.import_array()
cdef extern from "src/tokenizer.h":
ctypedef enum tokenizer_state:
START_LINE
START_FIELD
START_QUOTED_FIELD
FIELD
QUOTED_FIELD
COMMENT
CARRIAGE_RETURN
ctypedef enum err_code:
NO_ERROR
INVALID_LINE
TOO_MANY_COLS
NOT_ENOUGH_COLS
CONVERSION_ERROR
OVERFLOW_ERROR
ctypedef struct tokenizer_t:
char *source # single string containing all of the input
size_t source_len # length of the input
size_t source_pos # current index in source for tokenization
char delimiter # delimiter character
char comment # comment character
char quotechar # quote character
char expchar # exponential character in scientific notation
char newline # EOL character
char **output_cols # array of output strings for each column
char **col_ptrs # array of pointers to current output position for each col
int *output_len # length of each output column string
int num_cols # number of table columns
int num_rows # number of table rows
int fill_extra_cols # represents whether or not to fill rows with too few values
tokenizer_state state # current state of the tokenizer
err_code code # represents the latest error that has occurred
int iter_col # index of the column being iterated over
char *curr_pos # current iteration position
char *buf # buffer for empty data
int strip_whitespace_lines # whether to strip whitespace at the beginning and end of lines
int strip_whitespace_fields # whether to strip whitespace at the beginning and end of fields
int use_fast_converter # whether to use the fast converter for floats
char *comment_lines # single null-delimited string containing comment lines
int comment_lines_len # length of comment_lines in memory
int comment_pos # current index in comment_lines
# Example input/output
# --------------------
# source: "A,B,C\n10,5.,6\n1,2,3"
# output_cols: ["A\x0010\x001", "B\x005.\x002", "C\x006\x003"]
ctypedef struct memory_map:
char *ptr
int len
void *file_ptr
void *handle
tokenizer_t *create_tokenizer(char delimiter, char comment, char quotechar, char expchar,
int fill_extra_cols, int strip_whitespace_lines,
int strip_whitespace_fields, int use_fast_converter)
void delete_tokenizer(tokenizer_t *tokenizer)
int skip_lines(tokenizer_t *self, int offset, int header)
int tokenize(tokenizer_t *self, int end, int header, int num_cols)
int64_t str_to_int64_t(tokenizer_t *self, char *str)
double fast_str_to_double(tokenizer_t *self, char *str)
double str_to_double(tokenizer_t *self, char *str)
void start_iteration(tokenizer_t *self, int col)
char *next_field(tokenizer_t *self, int *size)
char *get_line(char *ptr, size_t *len, size_t map_len)
void reset_comments(tokenizer_t *self)
cdef extern from "Python.h":
int PyObject_AsReadBuffer(object obj, const void **buffer, Py_ssize_t *buffer_len)
class CParserError(Exception):
"""
An instance of this class is thrown when an error occurs
during C parsing.
"""
ERR_CODES = dict(enumerate([
"no error",
"invalid line supplied",
lambda line: "too many columns found in line {0} of data".format(line),
lambda line: "not enough columns found in line {0} of data".format(line),
"type conversion error",
"overflow error"
]))
cdef class FileString:
"""
A wrapper class for a memory-mapped file pointer.
"""
cdef:
object fhandle
object mmap
const void *mmap_ptr
Py_buffer buf
def __cinit__(self, fname):
self.fhandle = open(fname, 'r')
if not self.fhandle:
raise OSError('File "{0}" could not be opened'.format(fname))
self.mmap = mmap.mmap(self.fhandle.fileno(), 0, access=mmap.ACCESS_READ)
cdef Py_ssize_t buf_len = len(self.mmap)
PyObject_GetBuffer(self.mmap, &self.buf, PyBUF_SIMPLE)
self.mmap_ptr = self.buf.buf
def __dealloc__(self):
if self.mmap:
PyBuffer_Release(&self.buf)
self.mmap.close()
self.fhandle.close()
def __len__(self):
return len(self.mmap)
def __getitem__(self, i):
return self.mmap[i]
def splitlines(self):
"""
Return a generator yielding lines from the memory map.
"""
cdef char *ptr = <char *>self.mmap_ptr
cdef char *tmp
cdef size_t line_len
cdef size_t map_len = len(self.mmap)
while ptr:
tmp = get_line(ptr, &line_len, map_len)
yield ptr[:line_len].decode('ascii')
ptr = tmp
cdef class CParser:
"""
A fast Cython parser class which uses underlying C code
for tokenization.
"""
cdef:
tokenizer_t *tokenizer
object names
object header_names
int data_start
object data_end
object include_names
object exclude_names
object fill_values
object fill_empty
object fill_include_names
object fill_exclude_names
object fill_names
int fill_extra_cols
bytes source_bytes
char *source_ptr
set use_cols
cdef public:
int width
object source
object header_start
object header_chars
def __cinit__(self, source, strip_line_whitespace, strip_line_fields,
delimiter=',',
comment=None,
quotechar='"',
header_start=0,
data_start=1,
data_end=None,
names=None,
include_names=None,
exclude_names=None,
fill_values=('', '0'),
fill_include_names=None,
fill_exclude_names=None,
fill_extra_cols=0,
fast_reader=None):
if fast_reader is None:
fast_reader = {}
# Handle fast_reader parameter
expchar = fast_reader.pop('exponent_style', 'E').upper()
# use_fast_reader are False by default, and it
# supports Fortran double precision notation
if expchar == 'E':
use_fast_converter = fast_reader.pop('use_fast_converter', False)
else:
use_fast_converter = fast_reader.pop('use_fast_converter', True)
if not use_fast_converter:
raise core.FastOptionsError("fast_reader: exponent_style requires use_fast_converter")
if expchar.startswith('FORT'):
expchar = 'A'
if fast_reader:
raise core.FastOptionsError("Invalid parameter in fast_reader dict")
if comment is None:
comment = '\x00' # tokenizer ignores all comments if comment='\x00'
self.tokenizer = create_tokenizer(ord(delimiter), ord(comment),
ord(quotechar), ord(expchar),
fill_extra_cols,
strip_line_whitespace,
strip_line_fields,
use_fast_converter)
self.source = None
if source is not None:
self.setup_tokenizer(source)
self.header_start = header_start
self.data_start = data_start
self.data_end = data_end
self.names = names
self.include_names = include_names
self.exclude_names = exclude_names
self.fill_values = fill_values
self.fill_include_names = fill_include_names
self.fill_exclude_names = fill_exclude_names
self.fill_names = None
self.fill_extra_cols = fill_extra_cols
if self.names is not None:
if None in self.names:
raise TypeError('Cannot have None for column name')
if len(set(self.names)) != len(self.names):
raise ValueError('Duplicate column names')
def __dealloc__(self):
if self.tokenizer:
delete_tokenizer(self.tokenizer) # perform C memory cleanup
cdef get_error(self, code, num_rows, msg):
err_msg = ERR_CODES.get(code, "unknown error")
# error code is lambda function taking current line as input
if callable(err_msg):
err_msg = err_msg(num_rows + 1)
return CParserError("{0}: {1}".format(msg, err_msg))
cdef raise_error(self, msg):
raise self.get_error(self.tokenizer.code, self.tokenizer.num_rows, msg)
cpdef setup_tokenizer(self, source):
cdef FileString fstring
if isinstance(source, str): # filename or data
if '\n' not in source and '\r' not in source: # filename
fstring = FileString(source)
self.tokenizer.source = <char *>fstring.mmap_ptr
self.source_ptr = <char *>fstring.mmap_ptr
self.source = fstring
self.tokenizer.source_len = <size_t>len(fstring)
return
# Otherwise, source is the actual data so we leave it be
elif hasattr(source, 'read'): # file-like object
with get_readable_fileobj(source) as file_obj:
source = file_obj.read()
elif isinstance(source, FileString):
self.tokenizer.source = <char *>((<FileString>source).mmap_ptr)
self.source = source
self.tokenizer.source_len = <size_t>len(source)
return
else:
# Iterable sequence of lines, merge with newline character
try:
if self.tokenizer.delimiter == ord('\n'):
newline = '\r'
else:
newline = '\n'
source = newline.join(source)
except TypeError:
raise TypeError('Input "table" must be a file-like object, a '
'string (filename or data), or an iterable')
# Create a reference to the Python object so its char * pointer remains valid
self.source = source
# encode in ASCII for char * handling
self.source_bytes = self.source.encode('ascii')
self.tokenizer.source = self.source_bytes
self.tokenizer.source_len = <size_t>len(self.source_bytes)
def read_header(self, deduplicate=True, filter_names=True):
self.tokenizer.source_pos = 0
# header_start is a valid line number
if self.header_start is not None and self.header_start >= 0:
if skip_lines(self.tokenizer, self.header_start, 1) != 0:
self.raise_error("an error occurred while advancing to the "
"first header line")
if tokenize(self.tokenizer, -1, 1, 0) != 0:
self.raise_error("an error occurred while tokenizing the header line")
self.header_names = []
name = ''
for i in range(self.tokenizer.output_len[0]): # header is in first col string
c = self.tokenizer.output_cols[0][i] # next char in header string
if not c: # zero byte -- field terminator
if name:
# replace empty placeholder with ''
self.header_names.append(name.replace('\x01', ''))
name = ''
else:
break # end of string
else:
name += chr(c)
self.width = <int>len(self.header_names)
if deduplicate and not self.names: # skip if custom names were provided
self._deduplicate_names()
else:
# Get number of columns from first data row
if tokenize(self.tokenizer, -1, 1, 0) != 0:
self.raise_error("an error occurred while tokenizing the first line of data")
self.width = 0
for i in range(self.tokenizer.output_len[0]): # header is in first col string
# zero byte -- field terminator
if not self.tokenizer.output_cols[0][i]:
# ends valid field
if i > 0 and self.tokenizer.output_cols[0][i - 1]:
self.width += 1
else: # end of line
break
if self.width == 0: # no data
raise core.InconsistentTableError('No data lines found, C reader '
'cannot autogenerate column names')
# auto-generate names
self.header_names = ['col{0}'.format(i + 1) for i in range(self.width)]
if self.names:
self.width = <int>len(self.names)
else:
self.names = self.header_names
# self.use_cols should only contain columns included in output
self.use_cols = set(self.names)
if filter_names and self.include_names is not None:
self.use_cols.intersection_update(self.include_names)
if filter_names and self.exclude_names is not None:
self.use_cols.difference_update(self.exclude_names)
self.width = <int>len(self.names)
def read(self, try_int, try_float, try_string):
# Read in a single process
self.tokenizer.source_pos = 0
if skip_lines(self.tokenizer, self.data_start, 0) != 0:
self.raise_error("an error occurred while advancing to the first "
"line of data")
self.header_chars = self.source[:self.tokenizer.source_pos]
cdef int data_end = -1 # keep reading data until the end
if self.data_end is not None and self.data_end >= 0:
data_end = max(self.data_end - self.data_start, 0) # read nothing if data_end < 0
if tokenize(self.tokenizer, data_end, 0, <int>len(self.names)) != 0:
if self.tokenizer.code in (NOT_ENOUGH_COLS, TOO_MANY_COLS):
raise core.InconsistentTableError("Number of header columns " +
"({0}) inconsistent with data columns in data line {1}"
.format(self.tokenizer.num_cols, self.tokenizer.num_rows))
else:
self.raise_error("an error occurred while parsing table data")
elif self.tokenizer.num_rows == 0: # no data
return ([np.array([], dtype=np.int_)] * self.width,
self._get_comments(self.tokenizer))
self._set_fill_values()
cdef int num_rows = self.tokenizer.num_rows
if self.data_end is not None and self.data_end < 0: # negative indexing
num_rows += self.data_end
return self._convert_data(self.tokenizer, try_int, try_float,
try_string, num_rows)
cdef _set_fill_values(self):
if self.fill_names is None:
self.fill_names = set(self.names)
if self.fill_include_names is not None:
self.fill_names.intersection_update(self.fill_include_names)
if self.fill_exclude_names is not None:
self.fill_names.difference_update(self.fill_exclude_names)
self.fill_values, self.fill_empty = get_fill_values(self.fill_values)
cdef _get_comments(self, tokenizer_t *t):
line_comments = []
comment = ''
for i in range(t.comment_pos):
c = t.comment_lines[i] # next char in comment string
if not c: # zero byte -- line terminator
# replace empty placeholder with ''
line_comments.append(comment.replace('\x01', '').strip())
comment = ''
else:
comment += chr(c)
return line_comments
cdef _convert_data(self, tokenizer_t *t, try_int, try_float, try_string, num_rows):
cols = {}
for i, name in enumerate(self.names):
if name not in self.use_cols:
continue
# Try int first, then float, then string
try:
if try_int and not try_int[name]:
raise ValueError()
cols[name] = self._convert_int(t, i, num_rows)
except ValueError:
try:
if t.code == OVERFLOW_ERROR:
# Overflow during int conversion (extending range)
warnings.warn("OverflowError converting to {0} in column {1}, reverting to String."
.format('IntType', name), AstropyWarning)
if try_string and not try_string[name]:
raise ValueError('Column {0} failed to convert'.format(name))
t.code = NO_ERROR
cols[name] = self._convert_str(t, i, num_rows)
else:
if try_float and not try_float[name]:
raise ValueError()
t.code = NO_ERROR
cols[name] = self._convert_float(t, i, num_rows)
if t.code == OVERFLOW_ERROR:
# Overflow during float conversion (extending range)
warnings.warn("OverflowError converting to {0} in column {1}, possibly resulting in degraded precision."
.format('FloatType', name), AstropyWarning)
t.code = NO_ERROR
except ValueError:
if try_string and not try_string[name]:
raise ValueError('Column {0} failed to convert'.format(name))
cols[name] = self._convert_str(t, i, num_rows)
return cols, self._get_comments(t)
cdef np.ndarray _convert_int(self, tokenizer_t *t, int i, int nrows):
cdef int num_rows = t.num_rows
if nrows != -1:
num_rows = nrows
# initialize ndarray
# use `int64_t` for integers to ensure large integers are converted correctly
# on some platforms, e.g. Windows (where `long` is 32 bits)
# https://github.com/astropy/astropy/issues/5744
cdef np.ndarray col = np.empty(num_rows, dtype=np.int64)
cdef int64_t converted
cdef int row = 0
cdef int64_t *data = <int64_t*> col.data # pointer to raw data
cdef char *field
cdef char *empty_field = t.buf # memory address of designated empty buffer
cdef bytes new_value
mask = set() # set of indices for masked values
start_iteration(t, i) # begin the iteration process in C
for row in range(num_rows):
# retrieve the next field as a C pointer
field = next_field(t, <int *>0)
replace_info = None
if field == empty_field and self.fill_empty:
replace_info = self.fill_empty
# hopefully this implicit char * -> byte conversion for fill values
# checking can be avoided in most cases, since self.fill_values will
# be empty in the default case (self.fill_empty will do the work
# instead)
elif field != empty_field and self.fill_values and field in self.fill_values:
replace_info = self.fill_values[field]
if replace_info is not None:
# Either this column applies to the field as specified in the
# fill_values parameter, or no specific columns are specified
# and this column should apply fill_values.
if (len(replace_info) > 1 and self.names[i] in replace_info[1:]) \
or (len(replace_info) == 1 and self.names[i] in self.fill_names):
mask.add(row)
new_value = str(replace_info[0]).encode('ascii')
# try converting the new value
converted = str_to_int64_t(t, new_value)
else:
converted = str_to_int64_t(t, field)
else:
# convert the field to long (widest integer type)
converted = str_to_int64_t(t, field)
if t.code in (CONVERSION_ERROR, OVERFLOW_ERROR):
# no dice
if t.code == CONVERSION_ERROR:
t.code = NO_ERROR
raise ValueError()
data[row] = converted
row += 1
if mask:
# convert to masked_array
return ma.masked_array(col, mask=[1 if i in mask else 0 for i in
range(row)])
else:
return col
cdef np.ndarray _convert_float(self, tokenizer_t *t, int i, int nrows):
# very similar to _convert_int()
cdef int num_rows = t.num_rows
if nrows != -1:
num_rows = nrows
cdef np.ndarray col = np.empty(num_rows, dtype=np.float64)
cdef double converted
cdef int row = 0
cdef double *data = <double *> col.data
cdef char *field
cdef char *empty_field = t.buf
cdef bytes new_value
cdef int replacing
cdef err_code overflown = NO_ERROR # store any OVERFLOW to raise warning
mask = set()
start_iteration(t, i)
for row in range(num_rows):
field = next_field(t, <int *>0)
replace_info = None
replacing = False
if field == empty_field and self.fill_empty:
replace_info = self.fill_empty
elif field != empty_field and self.fill_values and field in self.fill_values:
replace_info = self.fill_values[field]
if replace_info is not None:
if (len(replace_info) > 1 and self.names[i] in replace_info[1:]) \
or (len(replace_info) == 1 and self.names[i] in self.fill_names):
mask.add(row)
new_value = str(replace_info[0]).encode('ascii')
replacing = True
converted = str_to_double(t, new_value)
else:
converted = str_to_double(t, field)
else:
converted = str_to_double(t, field)
if t.code == CONVERSION_ERROR:
t.code = NO_ERROR
raise ValueError()
else:
data[row] = converted
if t.code == OVERFLOW_ERROR:
t.code = NO_ERROR
overflown = OVERFLOW_ERROR
row += 1
t.code = overflown
if mask:
return ma.masked_array(col, mask=[1 if i in mask else 0 for i in
range(row)])
else:
return col
cdef _convert_str(self, tokenizer_t *t, int i, int nrows):
# similar to _convert_int, but no actual conversion
cdef int num_rows = t.num_rows
if nrows != -1:
num_rows = nrows
cdef int row = 0
cdef bytes field
cdef int field_len
cdef int max_len = 0
cdef list fields_list = []
mask = set()
start_iteration(t, i)
for row in range(num_rows):
field = next_field(t, &field_len)
replace_info = None
if field_len == 0 and self.fill_empty:
replace_info = self.fill_empty
elif field_len > 0 and self.fill_values and field in self.fill_values:
replace_info = self.fill_values[field]
if replace_info is not None:
el = replace_info[0].encode('ascii')
if (len(replace_info) > 1 and self.names[i] in replace_info[1:]) \
or (len(replace_info) == 1 and self.names[i] in self.fill_names):
mask.add(row)
field = el
fields_list.append(field)
if field_len > max_len:
max_len = field_len
row += 1
cdef np.ndarray col = np.array(fields_list, dtype=(str, max_len))
if mask:
return ma.masked_array(col, mask=[1 if i in mask else 0 for i in
range(row)])
else:
return col
def get_names(self):
# ignore excluded columns
return [name for name in self.names if name in self.use_cols]
def set_names(self, names):
self.names = names
def get_header_names(self):
return self.header_names
def _deduplicate_names(self):
"""Ensure there are no duplicates in ``self.header_names``
Cythonic version of core._deduplicate_names.
"""
cdef int i
new_names = []
existing_names = set()
for name in self.header_names:
base_name = name + '_'
i = 1
while name in existing_names:
# Iterate until a unique name is found
name = base_name + str(i)
i += 1
new_names.append(name)
existing_names.add(name)
self.header_names = new_names
def __reduce__(self):
cdef bytes source = self.source_ptr if self.source_ptr else self.source_bytes
fast_reader = dict(exponent_style=chr(self.tokenizer.expchar),
use_fast_converter=self.tokenizer.use_fast_converter)
return (_copy_cparser, (source, self.use_cols, self.fill_names,
self.fill_values, self.fill_empty, self.tokenizer.strip_whitespace_lines,
self.tokenizer.strip_whitespace_fields,
dict(delimiter=chr(self.tokenizer.delimiter),
comment=chr(self.tokenizer.comment),
quotechar=chr(self.tokenizer.quotechar),
header_start=self.header_start,
data_start=self.data_start,
data_end=self.data_end,
names=self.names,
include_names=self.include_names,
exclude_names=self.exclude_names,
fill_values=None,
fill_include_names=self.fill_include_names,
fill_exclude_names=self.fill_exclude_names,
fill_extra_cols=self.tokenizer.fill_extra_cols,
fast_reader=fast_reader)))
def _copy_cparser(bytes source, use_cols, fill_names, fill_values,
fill_empty, strip_whitespace_lines, strip_whitespace_fields, kwargs):
parser = CParser(None, strip_whitespace_lines, strip_whitespace_fields, **kwargs)
parser.use_cols = use_cols
parser.fill_names = fill_names
parser.fill_values = fill_values
parser.fill_empty = fill_empty
parser.tokenizer.source = source
parser.tokenizer.source_len = <size_t>len(source)
parser.source_bytes = source
return parser
def _read_chunk(CParser self, start, end, try_int,
try_float, try_string, queue, reconvert_queue, i):
cdef tokenizer_t *chunk_tokenizer = self.tokenizer
chunk_tokenizer.source_len = end
chunk_tokenizer.source_pos = start
reset_comments(chunk_tokenizer)
data = None
err = None
if tokenize(chunk_tokenizer, -1, 0, <int>len(self.names)) != 0:
err = (chunk_tokenizer.code, chunk_tokenizer.num_rows)
if chunk_tokenizer.num_rows == 0: # no data
data = dict((name, np.array([], np.int_)) for name in self.get_names())
line_comments = self._get_comments(chunk_tokenizer)
else:
try:
data, line_comments = self._convert_data(chunk_tokenizer,
try_int, try_float, try_string, -1)
except Exception as e:
delete_tokenizer(chunk_tokenizer)
self.tokenizer = NULL # prevent another de-allocation in __dalloc__
queue.put((None, e, i))
return
try:
queue.put(((line_comments, data), err, i))
except Queue.Full as e:
# hopefully this shouldn't happen
delete_tokenizer(chunk_tokenizer)
self.tokenizer = NULL # prevent another de-allocation in __dalloc__
queue.pop()
queue.put((None, e, i))
return
reconvert_cols = reconvert_queue.get()
for col in reconvert_cols:
queue.put((self._convert_str(chunk_tokenizer, col, -1), i, col))
delete_tokenizer(chunk_tokenizer)
self.tokenizer = NULL # prevent another de-allocation in __dalloc__
reconvert_queue.put(reconvert_cols) # return to the queue for other processes
cdef class FastWriter:
"""
A fast Cython writing class for writing tables
as ASCII data.
"""
cdef:
object table
list use_names
dict fill_values
set fill_cols
list col_iters
list formats
list format_funcs
list types
list line_comments
str quotechar
str expchar
str delimiter
int strip_whitespace
object comment
def __cinit__(self, table,
delimiter=',',
comment='# ',
quotechar='"',
expchar='e',
formats=None,
strip_whitespace=True,
names=None, # ignore, already used in _get_writer
include_names=None,
exclude_names=None,
fill_values=[],
fill_include_names=None,
fill_exclude_names=None,
fast_writer=True):
from astropy.table import pprint # Here to avoid circular import
if fast_writer is True:
fast_writer = {}
# fast_writer might contain custom writing options
self.table = table
self.comment = comment
self.strip_whitespace = strip_whitespace
use_names = set(table.colnames)
# Apply include_names before exclude_names
if include_names is not None:
use_names.intersection_update(include_names)
if exclude_names is not None:
use_names.difference_update(exclude_names)
# preserve column ordering via list
self.use_names = [x for x in table.colnames if x in use_names]
fill_values = get_fill_values(fill_values, False)
self.fill_values = fill_values.copy()
# Add int/float versions of each fill value (if applicable)
# to the fill_values dict. This prevents the writer from having
# to call unicode() on every value, which is a major
# performance hit.
for key, val in fill_values.items():
try:
self.fill_values[int(key)] = val
self.fill_values[float(key)] = val
except (ValueError, np.ma.MaskError):
pass
fill_names = set(self.use_names)
# Apply fill_include_names before fill_exclude_names
if fill_include_names is not None:
fill_names.intersection_update(fill_include_names)
if fill_exclude_names is not None:
fill_names.difference_update(fill_exclude_names)
# Preserve column ordering
self.fill_cols = set([i for i, name in enumerate(self.use_names) if
name in fill_names])
# formats in user-specified dict should override
# existing column formats
if formats is not None:
for name in self.use_names:
if name in formats:
self.table[name].format = formats[name]
self.col_iters = []
self.formats = []
self.format_funcs = []
self.line_comments = table.meta.get('comments', [])
for col in table.columns.values():
if col.name in self.use_names: # iterate over included columns
# If col.format is None then don't use any formatter to improve
# speed. However, if the column is a byte string and this
# is Py3, then use the default formatter (which in this case
# does val.decode('utf-8')) in order to avoid a leading 'b'.
if col.format is None and not col.dtype.kind == 'S':
self.format_funcs.append(None)
else:
self.format_funcs.append(col.info._format_funcs.get(
col.format, pprint.get_auto_format_func(col)))
# col is a numpy.ndarray, so we convert it to
# an ordinary list because csv.writer will call
# np.array_str() on each numpy value, which is
# very inefficient
self.col_iters.append(iter(col.tolist()))
self.formats.append(col.format)
self.quotechar = None if quotechar is None else str(quotechar)
self.delimiter = ' ' if delimiter is None else str(delimiter)
# 'S' for string types, 'N' for numeric types
self.types = ['S' if self.table[name].dtype.kind in ('S', 'U') else 'N'
for name in self.use_names]
cdef _write_comments(self, output):
if self.comment not in (False, None):
for comment_line in self.line_comments:
output.write(self.comment + comment_line + '\n')
def _write_header(self, output, writer, header_output, output_types):
if header_output is not None and header_output == 'comment':
output.write(self.comment)
writer.writerow([x.strip() for x in self.use_names] if
self.strip_whitespace else self.use_names)
self._write_comments(output)
else:
self._write_comments(output)
if header_output is not None:
writer.writerow([x.strip() for x in self.use_names] if
self.strip_whitespace else self.use_names)
if output_types:
writer.writerow(self.types)
def write(self, output, header_output, output_types):
opened_file = False
if not hasattr(output, 'write'): # output is a filename
# NOTE: we need to specify newline='', otherwise the default
# behavior is for Python to translate \r\n (which we write because
# of os.linesep) into \r\r\n. Specifying newline='' disables any
output = open(output, 'w', newline='')
opened_file = True # remember to close file afterwards
writer = core.CsvWriter(output,
delimiter=self.delimiter,
doublequote=True,
escapechar=None,
quotechar=self.quotechar,
quoting=csv.QUOTE_MINIMAL,
lineterminator=os.linesep)
self._write_header(output, writer, header_output, output_types)
# Split rows into N-sized chunks, since we don't want to
# store all the rows in memory at one time (inefficient)
# or fail to take advantage of the speed boost of writerows()
# over writerow().
cdef int i = -1
cdef int N = 100
cdef int num_cols = <int>len(self.use_names)
cdef int num_rows = <int>len(self.table)
# cache string columns beforehand
cdef set string_rows = set([i for i, type in enumerate(self.types) if
type == 'S'])
cdef list rows = [[None] * num_cols for i in range(N)]
for i in range(num_rows):
for j in range(num_cols):
orig_field = next(self.col_iters[j]) # get field
# str_val monitors whether we should check if the field
# should be stripped
str_val = True
if orig_field is None: # tolist() converts ma.masked to None
field = core.masked
rows[i % N][j] = ''
elif self.format_funcs[j] is not None:
field = self.format_funcs[j](self.formats[j], orig_field)
rows[i % N][j] = field
else:
field = orig_field
rows[i % N][j] = field
str_val = j in string_rows
if field in self.fill_values:
new_val = self.fill_values[field][0]
# Either this column applies to the field as specified in
# the fill_values parameter, or no specific columns are
# specified and this column should apply fill_values.
if (len(self.fill_values[field]) > 1 and self.use_names[j] in self.fill_values[field][1:]) \
or (len(self.fill_values[field]) == 1 and j in self.fill_cols):
str_val = True
rows[i % N][j] = new_val
if self.strip_whitespace: # new_val should be a string
rows[i % N][j] = rows[i % N][j].strip()
if str_val and self.strip_whitespace:
rows[i % N][j] = rows[i % N][j].strip(' \t')
if i >= N - 1 and i % N == N - 1: # rows is now full
writer.writerows(rows)
# Write leftover rows not included in previous chunks
if i >= 0 and i % N != N - 1:
writer.writerows(rows[:i % N + 1])
if opened_file:
output.close()
def get_fill_values(fill_values, read=True):
if len(fill_values) > 0 and isinstance(fill_values[0], str):
# e.g. fill_values=('999', '0')
fill_values = [fill_values]
else:
fill_values = fill_values
# look for an empty replacement to cache for speedy conversion
fill_empty = None
for el in fill_values:
if el[0] == '':
fill_empty = el[1:]
break
try:
# Create a dict with the values to be replaced as keys
if read:
fill_values = dict([(l[0].encode('ascii'), l[1:]) for
l in fill_values if l[0] != ''])
else:
# don't worry about encoding for writing
fill_values = dict([(l[0], l[1:]) for l in fill_values])
except IndexError:
raise ValueError("Format of fill_values must be "
"(<bad>, <fill>, <optional col1>, ...)")
if read:
return (fill_values, fill_empty)
else:
return fill_values # cache for empty values doesn't matter for writing