# 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 = 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 = fstring.mmap_ptr self.source_ptr = fstring.mmap_ptr self.source = fstring self.tokenizer.source_len = 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 = ((source).mmap_ptr) self.source = source self.tokenizer.source_len = 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 = 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 = 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 = 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 = 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, 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 = 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, 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 = 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, 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 = 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, 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 = len(self.use_names) cdef int num_rows = 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 " "(, , , ...)") if read: return (fill_values, fill_empty) else: return fill_values # cache for empty values doesn't matter for writing