from __future__ import division, absolute_import, print_function import sys import os import re import itertools import warnings import weakref from operator import itemgetter, index as opindex import numpy as np from . import format from ._datasource import DataSource from numpy.core.multiarray import packbits, unpackbits from ._iotools import ( LineSplitter, NameValidator, StringConverter, ConverterError, ConverterLockError, ConversionWarning, _is_string_like, has_nested_fields, flatten_dtype, easy_dtype, _bytes_to_name ) from numpy.compat import ( asbytes, asstr, asbytes_nested, bytes, basestring, unicode, is_pathlib_path ) if sys.version_info[0] >= 3: import pickle else: import cPickle as pickle from future_builtins import map loads = pickle.loads __all__ = [ 'savetxt', 'loadtxt', 'genfromtxt', 'ndfromtxt', 'mafromtxt', 'recfromtxt', 'recfromcsv', 'load', 'loads', 'save', 'savez', 'savez_compressed', 'packbits', 'unpackbits', 'fromregex', 'DataSource' ] class BagObj(object): """ BagObj(obj) Convert attribute look-ups to getitems on the object passed in. Parameters ---------- obj : class instance Object on which attribute look-up is performed. Examples -------- >>> from numpy.lib.npyio import BagObj as BO >>> class BagDemo(object): ... def __getitem__(self, key): # An instance of BagObj(BagDemo) ... # will call this method when any ... # attribute look-up is required ... result = "Doesn't matter what you want, " ... return result + "you're gonna get this" ... >>> demo_obj = BagDemo() >>> bagobj = BO(demo_obj) >>> bagobj.hello_there "Doesn't matter what you want, you're gonna get this" >>> bagobj.I_can_be_anything "Doesn't matter what you want, you're gonna get this" """ def __init__(self, obj): # Use weakref to make NpzFile objects collectable by refcount self._obj = weakref.proxy(obj) def __getattribute__(self, key): try: return object.__getattribute__(self, '_obj')[key] except KeyError: raise AttributeError(key) def __dir__(self): """ Enables dir(bagobj) to list the files in an NpzFile. This also enables tab-completion in an interpreter or IPython. """ return object.__getattribute__(self, '_obj').keys() def zipfile_factory(file, *args, **kwargs): """ Create a ZipFile. Allows for Zip64, and the `file` argument can accept file, str, or pathlib.Path objects. `args` and `kwargs` are passed to the zipfile.ZipFile constructor. """ if is_pathlib_path(file): file = str(file) import zipfile kwargs['allowZip64'] = True return zipfile.ZipFile(file, *args, **kwargs) class NpzFile(object): """ NpzFile(fid) A dictionary-like object with lazy-loading of files in the zipped archive provided on construction. `NpzFile` is used to load files in the NumPy ``.npz`` data archive format. It assumes that files in the archive have a ``.npy`` extension, other files are ignored. The arrays and file strings are lazily loaded on either getitem access using ``obj['key']`` or attribute lookup using ``obj.f.key``. A list of all files (without ``.npy`` extensions) can be obtained with ``obj.files`` and the ZipFile object itself using ``obj.zip``. Attributes ---------- files : list of str List of all files in the archive with a ``.npy`` extension. zip : ZipFile instance The ZipFile object initialized with the zipped archive. f : BagObj instance An object on which attribute can be performed as an alternative to getitem access on the `NpzFile` instance itself. allow_pickle : bool, optional Allow loading pickled data. Default: True pickle_kwargs : dict, optional Additional keyword arguments to pass on to pickle.load. These are only useful when loading object arrays saved on Python 2 when using Python 3. Parameters ---------- fid : file or str The zipped archive to open. This is either a file-like object or a string containing the path to the archive. own_fid : bool, optional Whether NpzFile should close the file handle. Requires that `fid` is a file-like object. Examples -------- >>> from tempfile import TemporaryFile >>> outfile = TemporaryFile() >>> x = np.arange(10) >>> y = np.sin(x) >>> np.savez(outfile, x=x, y=y) >>> outfile.seek(0) >>> npz = np.load(outfile) >>> isinstance(npz, np.lib.io.NpzFile) True >>> npz.files ['y', 'x'] >>> npz['x'] # getitem access array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> npz.f.x # attribute lookup array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) """ def __init__(self, fid, own_fid=False, allow_pickle=True, pickle_kwargs=None): # Import is postponed to here since zipfile depends on gzip, an # optional component of the so-called standard library. _zip = zipfile_factory(fid) self._files = _zip.namelist() self.files = [] self.allow_pickle = allow_pickle self.pickle_kwargs = pickle_kwargs for x in self._files: if x.endswith('.npy'): self.files.append(x[:-4]) else: self.files.append(x) self.zip = _zip self.f = BagObj(self) if own_fid: self.fid = fid else: self.fid = None def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self.close() def close(self): """ Close the file. """ if self.zip is not None: self.zip.close() self.zip = None if self.fid is not None: self.fid.close() self.fid = None self.f = None # break reference cycle def __del__(self): self.close() def __getitem__(self, key): # FIXME: This seems like it will copy strings around # more than is strictly necessary. The zipfile # will read the string and then # the format.read_array will copy the string # to another place in memory. # It would be better if the zipfile could read # (or at least uncompress) the data # directly into the array memory. member = 0 if key in self._files: member = 1 elif key in self.files: member = 1 key += '.npy' if member: bytes = self.zip.open(key) magic = bytes.read(len(format.MAGIC_PREFIX)) bytes.close() if magic == format.MAGIC_PREFIX: bytes = self.zip.open(key) return format.read_array(bytes, allow_pickle=self.allow_pickle, pickle_kwargs=self.pickle_kwargs) else: return self.zip.read(key) else: raise KeyError("%s is not a file in the archive" % key) def __iter__(self): return iter(self.files) def items(self): """ Return a list of tuples, with each tuple (filename, array in file). """ return [(f, self[f]) for f in self.files] def iteritems(self): """Generator that returns tuples (filename, array in file).""" for f in self.files: yield (f, self[f]) def keys(self): """Return files in the archive with a ``.npy`` extension.""" return self.files def iterkeys(self): """Return an iterator over the files in the archive.""" return self.__iter__() def __contains__(self, key): return self.files.__contains__(key) def load(file, mmap_mode=None, allow_pickle=True, fix_imports=True, encoding='ASCII'): """ Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files. Parameters ---------- file : file-like object, string, or pathlib.Path The file to read. File-like objects must support the ``seek()`` and ``read()`` methods. Pickled files require that the file-like object support the ``readline()`` method as well. mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional If not None, then memory-map the file, using the given mode (see `numpy.memmap` for a detailed description of the modes). A memory-mapped array is kept on disk. However, it can be accessed and sliced like any ndarray. Memory mapping is especially useful for accessing small fragments of large files without reading the entire file into memory. allow_pickle : bool, optional Allow loading pickled object arrays stored in npy files. Reasons for disallowing pickles include security, as loading pickled data can execute arbitrary code. If pickles are disallowed, loading object arrays will fail. Default: True fix_imports : bool, optional Only useful when loading Python 2 generated pickled files on Python 3, which includes npy/npz files containing object arrays. If `fix_imports` is True, pickle will try to map the old Python 2 names to the new names used in Python 3. encoding : str, optional What encoding to use when reading Python 2 strings. Only useful when loading Python 2 generated pickled files on Python 3, which includes npy/npz files containing object arrays. Values other than 'latin1', 'ASCII', and 'bytes' are not allowed, as they can corrupt numerical data. Default: 'ASCII' Returns ------- result : array, tuple, dict, etc. Data stored in the file. For ``.npz`` files, the returned instance of NpzFile class must be closed to avoid leaking file descriptors. Raises ------ IOError If the input file does not exist or cannot be read. ValueError The file contains an object array, but allow_pickle=False given. See Also -------- save, savez, savez_compressed, loadtxt memmap : Create a memory-map to an array stored in a file on disk. lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file. Notes ----- - If the file contains pickle data, then whatever object is stored in the pickle is returned. - If the file is a ``.npy`` file, then a single array is returned. - If the file is a ``.npz`` file, then a dictionary-like object is returned, containing ``{filename: array}`` key-value pairs, one for each file in the archive. - If the file is a ``.npz`` file, the returned value supports the context manager protocol in a similar fashion to the open function:: with load('foo.npz') as data: a = data['a'] The underlying file descriptor is closed when exiting the 'with' block. Examples -------- Store data to disk, and load it again: >>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]])) >>> np.load('/tmp/123.npy') array([[1, 2, 3], [4, 5, 6]]) Store compressed data to disk, and load it again: >>> a=np.array([[1, 2, 3], [4, 5, 6]]) >>> b=np.array([1, 2]) >>> np.savez('/tmp/123.npz', a=a, b=b) >>> data = np.load('/tmp/123.npz') >>> data['a'] array([[1, 2, 3], [4, 5, 6]]) >>> data['b'] array([1, 2]) >>> data.close() Mem-map the stored array, and then access the second row directly from disk: >>> X = np.load('/tmp/123.npy', mmap_mode='r') >>> X[1, :] memmap([4, 5, 6]) """ own_fid = False if isinstance(file, basestring): fid = open(file, "rb") own_fid = True elif is_pathlib_path(file): fid = file.open("rb") own_fid = True else: fid = file if encoding not in ('ASCII', 'latin1', 'bytes'): # The 'encoding' value for pickle also affects what encoding # the serialized binary data of NumPy arrays is loaded # in. Pickle does not pass on the encoding information to # NumPy. The unpickling code in numpy.core.multiarray is # written to assume that unicode data appearing where binary # should be is in 'latin1'. 'bytes' is also safe, as is 'ASCII'. # # Other encoding values can corrupt binary data, and we # purposefully disallow them. For the same reason, the errors= # argument is not exposed, as values other than 'strict' # result can similarly silently corrupt numerical data. raise ValueError("encoding must be 'ASCII', 'latin1', or 'bytes'") if sys.version_info[0] >= 3: pickle_kwargs = dict(encoding=encoding, fix_imports=fix_imports) else: # Nothing to do on Python 2 pickle_kwargs = {} try: # Code to distinguish from NumPy binary files and pickles. _ZIP_PREFIX = b'PK\x03\x04' N = len(format.MAGIC_PREFIX) magic = fid.read(N) # If the file size is less than N, we need to make sure not # to seek past the beginning of the file fid.seek(-min(N, len(magic)), 1) # back-up if magic.startswith(_ZIP_PREFIX): # zip-file (assume .npz) # Transfer file ownership to NpzFile tmp = own_fid own_fid = False return NpzFile(fid, own_fid=tmp, allow_pickle=allow_pickle, pickle_kwargs=pickle_kwargs) elif magic == format.MAGIC_PREFIX: # .npy file if mmap_mode: return format.open_memmap(file, mode=mmap_mode) else: return format.read_array(fid, allow_pickle=allow_pickle, pickle_kwargs=pickle_kwargs) else: # Try a pickle if not allow_pickle: raise ValueError("allow_pickle=False, but file does not contain " "non-pickled data") try: return pickle.load(fid, **pickle_kwargs) except: raise IOError( "Failed to interpret file %s as a pickle" % repr(file)) finally: if own_fid: fid.close() def save(file, arr, allow_pickle=True, fix_imports=True): """ Save an array to a binary file in NumPy ``.npy`` format. Parameters ---------- file : file, str, or pathlib.Path File or filename to which the data is saved. If file is a file-object, then the filename is unchanged. If file is a string or Path, a ``.npy`` extension will be appended to the file name if it does not already have one. allow_pickle : bool, optional Allow saving object arrays using Python pickles. Reasons for disallowing pickles include security (loading pickled data can execute arbitrary code) and portability (pickled objects may not be loadable on different Python installations, for example if the stored objects require libraries that are not available, and not all pickled data is compatible between Python 2 and Python 3). Default: True fix_imports : bool, optional Only useful in forcing objects in object arrays on Python 3 to be pickled in a Python 2 compatible way. If `fix_imports` is True, pickle will try to map the new Python 3 names to the old module names used in Python 2, so that the pickle data stream is readable with Python 2. arr : array_like Array data to be saved. See Also -------- savez : Save several arrays into a ``.npz`` archive savetxt, load Notes ----- For a description of the ``.npy`` format, see the module docstring of `numpy.lib.format` or the NumPy Enhancement Proposal http://docs.scipy.org/doc/numpy/neps/npy-format.html Examples -------- >>> from tempfile import TemporaryFile >>> outfile = TemporaryFile() >>> x = np.arange(10) >>> np.save(outfile, x) >>> outfile.seek(0) # Only needed here to simulate closing & reopening file >>> np.load(outfile) array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) """ own_fid = False if isinstance(file, basestring): if not file.endswith('.npy'): file = file + '.npy' fid = open(file, "wb") own_fid = True elif is_pathlib_path(file): if not file.name.endswith('.npy'): file = file.parent / (file.name + '.npy') fid = file.open("wb") own_fid = True else: fid = file if sys.version_info[0] >= 3: pickle_kwargs = dict(fix_imports=fix_imports) else: # Nothing to do on Python 2 pickle_kwargs = None try: arr = np.asanyarray(arr) format.write_array(fid, arr, allow_pickle=allow_pickle, pickle_kwargs=pickle_kwargs) finally: if own_fid: fid.close() def savez(file, *args, **kwds): """ Save several arrays into a single file in uncompressed ``.npz`` format. If arguments are passed in with no keywords, the corresponding variable names, in the ``.npz`` file, are 'arr_0', 'arr_1', etc. If keyword arguments are given, the corresponding variable names, in the ``.npz`` file will match the keyword names. Parameters ---------- file : str or file Either the file name (string) or an open file (file-like object) where the data will be saved. If file is a string or a Path, the ``.npz`` extension will be appended to the file name if it is not already there. args : Arguments, optional Arrays to save to the file. Since it is not possible for Python to know the names of the arrays outside `savez`, the arrays will be saved with names "arr_0", "arr_1", and so on. These arguments can be any expression. kwds : Keyword arguments, optional Arrays to save to the file. Arrays will be saved in the file with the keyword names. Returns ------- None See Also -------- save : Save a single array to a binary file in NumPy format. savetxt : Save an array to a file as plain text. savez_compressed : Save several arrays into a compressed ``.npz`` archive Notes ----- The ``.npz`` file format is a zipped archive of files named after the variables they contain. The archive is not compressed and each file in the archive contains one variable in ``.npy`` format. For a description of the ``.npy`` format, see `numpy.lib.format` or the NumPy Enhancement Proposal http://docs.scipy.org/doc/numpy/neps/npy-format.html When opening the saved ``.npz`` file with `load` a `NpzFile` object is returned. This is a dictionary-like object which can be queried for its list of arrays (with the ``.files`` attribute), and for the arrays themselves. Examples -------- >>> from tempfile import TemporaryFile >>> outfile = TemporaryFile() >>> x = np.arange(10) >>> y = np.sin(x) Using `savez` with \\*args, the arrays are saved with default names. >>> np.savez(outfile, x, y) >>> outfile.seek(0) # Only needed here to simulate closing & reopening file >>> npzfile = np.load(outfile) >>> npzfile.files ['arr_1', 'arr_0'] >>> npzfile['arr_0'] array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) Using `savez` with \\**kwds, the arrays are saved with the keyword names. >>> outfile = TemporaryFile() >>> np.savez(outfile, x=x, y=y) >>> outfile.seek(0) >>> npzfile = np.load(outfile) >>> npzfile.files ['y', 'x'] >>> npzfile['x'] array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) """ _savez(file, args, kwds, False) def savez_compressed(file, *args, **kwds): """ Save several arrays into a single file in compressed ``.npz`` format. If keyword arguments are given, then filenames are taken from the keywords. If arguments are passed in with no keywords, then stored file names are arr_0, arr_1, etc. Parameters ---------- file : str or file Either the file name (string) or an open file (file-like object) where the data will be saved. If file is a string or a Path, the ``.npz`` extension will be appended to the file name if it is not already there. args : Arguments, optional Arrays to save to the file. Since it is not possible for Python to know the names of the arrays outside `savez`, the arrays will be saved with names "arr_0", "arr_1", and so on. These arguments can be any expression. kwds : Keyword arguments, optional Arrays to save to the file. Arrays will be saved in the file with the keyword names. Returns ------- None See Also -------- numpy.save : Save a single array to a binary file in NumPy format. numpy.savetxt : Save an array to a file as plain text. numpy.savez : Save several arrays into an uncompressed ``.npz`` file format numpy.load : Load the files created by savez_compressed. Notes ----- The ``.npz`` file format is a zipped archive of files named after the variables they contain. The archive is compressed with ``zipfile.ZIP_DEFLATED`` and each file in the archive contains one variable in ``.npy`` format. For a description of the ``.npy`` format, see `numpy.lib.format` or the NumPy Enhancement Proposal http://docs.scipy.org/doc/numpy/neps/npy-format.html When opening the saved ``.npz`` file with `load` a `NpzFile` object is returned. This is a dictionary-like object which can be queried for its list of arrays (with the ``.files`` attribute), and for the arrays themselves. Examples -------- >>> test_array = np.random.rand(3, 2) >>> test_vector = np.random.rand(4) >>> np.savez_compressed('/tmp/123', a=test_array, b=test_vector) >>> loaded = np.load('/tmp/123.npz') >>> print(np.array_equal(test_array, loaded['a'])) True >>> print(np.array_equal(test_vector, loaded['b'])) True """ _savez(file, args, kwds, True) def _savez(file, args, kwds, compress, allow_pickle=True, pickle_kwargs=None): # Import is postponed to here since zipfile depends on gzip, an optional # component of the so-called standard library. import zipfile # Import deferred for startup time improvement import tempfile if isinstance(file, basestring): if not file.endswith('.npz'): file = file + '.npz' elif is_pathlib_path(file): if not file.name.endswith('.npz'): file = file.parent / (file.name + '.npz') namedict = kwds for i, val in enumerate(args): key = 'arr_%d' % i if key in namedict.keys(): raise ValueError( "Cannot use un-named variables and keyword %s" % key) namedict[key] = val if compress: compression = zipfile.ZIP_DEFLATED else: compression = zipfile.ZIP_STORED zipf = zipfile_factory(file, mode="w", compression=compression) # Stage arrays in a temporary file on disk, before writing to zip. # Since target file might be big enough to exceed capacity of a global # temporary directory, create temp file side-by-side with the target file. file_dir, file_prefix = os.path.split(file) if _is_string_like(file) else (None, 'tmp') fd, tmpfile = tempfile.mkstemp(prefix=file_prefix, dir=file_dir, suffix='-numpy.npy') os.close(fd) try: for key, val in namedict.items(): fname = key + '.npy' fid = open(tmpfile, 'wb') try: format.write_array(fid, np.asanyarray(val), allow_pickle=allow_pickle, pickle_kwargs=pickle_kwargs) fid.close() fid = None zipf.write(tmpfile, arcname=fname) except IOError as exc: raise IOError("Failed to write to %s: %s" % (tmpfile, exc)) finally: if fid: fid.close() finally: os.remove(tmpfile) zipf.close() def _getconv(dtype): """ Find the correct dtype converter. Adapted from matplotlib """ def floatconv(x): x.lower() if b'0x' in x: return float.fromhex(asstr(x)) return float(x) typ = dtype.type if issubclass(typ, np.bool_): return lambda x: bool(int(x)) if issubclass(typ, np.uint64): return np.uint64 if issubclass(typ, np.int64): return np.int64 if issubclass(typ, np.integer): return lambda x: int(float(x)) elif issubclass(typ, np.longdouble): return np.longdouble elif issubclass(typ, np.floating): return floatconv elif issubclass(typ, np.complex): return lambda x: complex(asstr(x)) elif issubclass(typ, np.bytes_): return asbytes else: return asstr def loadtxt(fname, dtype=float, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0): """ Load data from a text file. Each row in the text file must have the same number of values. Parameters ---------- fname : file, str, or pathlib.Path File, filename, or generator to read. If the filename extension is ``.gz`` or ``.bz2``, the file is first decompressed. Note that generators should return byte strings for Python 3k. dtype : data-type, optional Data-type of the resulting array; default: float. If this is a structured data-type, the resulting array will be 1-dimensional, and each row will be interpreted as an element of the array. In this case, the number of columns used must match the number of fields in the data-type. comments : str or sequence, optional The characters or list of characters used to indicate the start of a comment; default: '#'. delimiter : str, optional The string used to separate values. By default, this is any whitespace. converters : dict, optional A dictionary mapping column number to a function that will convert that column to a float. E.g., if column 0 is a date string: ``converters = {0: datestr2num}``. Converters can also be used to provide a default value for missing data (but see also `genfromtxt`): ``converters = {3: lambda s: float(s.strip() or 0)}``. Default: None. skiprows : int, optional Skip the first `skiprows` lines; default: 0. usecols : int or sequence, optional Which columns to read, with 0 being the first. For example, usecols = (1,4,5) will extract the 2nd, 5th and 6th columns. The default, None, results in all columns being read. .. versionadded:: 1.11.0 Also when a single column has to be read it is possible to use an integer instead of a tuple. E.g ``usecols = 3`` reads the fourth column the same way as `usecols = (3,)`` would. unpack : bool, optional If True, the returned array is transposed, so that arguments may be unpacked using ``x, y, z = loadtxt(...)``. When used with a structured data-type, arrays are returned for each field. Default is False. ndmin : int, optional The returned array will have at least `ndmin` dimensions. Otherwise mono-dimensional axes will be squeezed. Legal values: 0 (default), 1 or 2. .. versionadded:: 1.6.0 Returns ------- out : ndarray Data read from the text file. See Also -------- load, fromstring, fromregex genfromtxt : Load data with missing values handled as specified. scipy.io.loadmat : reads MATLAB data files Notes ----- This function aims to be a fast reader for simply formatted files. The `genfromtxt` function provides more sophisticated handling of, e.g., lines with missing values. .. versionadded:: 1.10.0 The strings produced by the Python float.hex method can be used as input for floats. Examples -------- >>> from io import StringIO # StringIO behaves like a file object >>> c = StringIO("0 1\\n2 3") >>> np.loadtxt(c) array([[ 0., 1.], [ 2., 3.]]) >>> d = StringIO("M 21 72\\nF 35 58") >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'), ... 'formats': ('S1', 'i4', 'f4')}) array([('M', 21, 72.0), ('F', 35, 58.0)], dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')]) >>> c = StringIO("1,0,2\\n3,0,4") >>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True) >>> x array([ 1., 3.]) >>> y array([ 2., 4.]) """ # Type conversions for Py3 convenience if comments is not None: if isinstance(comments, (basestring, bytes)): comments = [asbytes(comments)] else: comments = [asbytes(comment) for comment in comments] # Compile regex for comments beforehand comments = (re.escape(comment) for comment in comments) regex_comments = re.compile(b'|'.join(comments)) user_converters = converters if delimiter is not None: delimiter = asbytes(delimiter) if usecols is not None: # Allow usecols to be a single int or a sequence of ints try: usecols_as_list = list(usecols) except TypeError: usecols_as_list = [usecols] for col_idx in usecols_as_list: try: opindex(col_idx) except TypeError as e: e.args = ( "usecols must be an int or a sequence of ints but " "it contains at least one element of type %s" % type(col_idx), ) raise # Fall back to existing code usecols = usecols_as_list fown = False try: if is_pathlib_path(fname): fname = str(fname) if _is_string_like(fname): fown = True if fname.endswith('.gz'): import gzip fh = iter(gzip.GzipFile(fname)) elif fname.endswith('.bz2'): import bz2 fh = iter(bz2.BZ2File(fname)) elif sys.version_info[0] == 2: fh = iter(open(fname, 'U')) else: fh = iter(open(fname)) else: fh = iter(fname) except TypeError: raise ValueError('fname must be a string, file handle, or generator') X = [] # not to be confused with the flatten_dtype we import... def flatten_dtype_internal(dt): """Unpack a structured data-type, and produce re-packing info.""" if dt.names is None: # If the dtype is flattened, return. # If the dtype has a shape, the dtype occurs # in the list more than once. shape = dt.shape if len(shape) == 0: return ([dt.base], None) else: packing = [(shape[-1], list)] if len(shape) > 1: for dim in dt.shape[-2::-1]: packing = [(dim*packing[0][0], packing*dim)] return ([dt.base] * int(np.prod(dt.shape)), packing) else: types = [] packing = [] for field in dt.names: tp, bytes = dt.fields[field] flat_dt, flat_packing = flatten_dtype_internal(tp) types.extend(flat_dt) # Avoid extra nesting for subarrays if tp.ndim > 0: packing.extend(flat_packing) else: packing.append((len(flat_dt), flat_packing)) return (types, packing) def pack_items(items, packing): """Pack items into nested lists based on re-packing info.""" if packing is None: return items[0] elif packing is tuple: return tuple(items) elif packing is list: return list(items) else: start = 0 ret = [] for length, subpacking in packing: ret.append(pack_items(items[start:start+length], subpacking)) start += length return tuple(ret) def split_line(line): """Chop off comments, strip, and split at delimiter. Note that although the file is opened as text, this function returns bytes. """ line = asbytes(line) if comments is not None: line = regex_comments.split(asbytes(line), maxsplit=1)[0] line = line.strip(b'\r\n') if line: return line.split(delimiter) else: return [] try: # Make sure we're dealing with a proper dtype dtype = np.dtype(dtype) defconv = _getconv(dtype) # Skip the first `skiprows` lines for i in range(skiprows): next(fh) # Read until we find a line with some values, and use # it to estimate the number of columns, N. first_vals = None try: while not first_vals: first_line = next(fh) first_vals = split_line(first_line) except StopIteration: # End of lines reached first_line = '' first_vals = [] warnings.warn('loadtxt: Empty input file: "%s"' % fname, stacklevel=2) N = len(usecols or first_vals) dtype_types, packing = flatten_dtype_internal(dtype) if len(dtype_types) > 1: # We're dealing with a structured array, each field of # the dtype matches a column converters = [_getconv(dt) for dt in dtype_types] else: # All fields have the same dtype converters = [defconv for i in range(N)] if N > 1: packing = [(N, tuple)] # By preference, use the converters specified by the user for i, conv in (user_converters or {}).items(): if usecols: try: i = usecols.index(i) except ValueError: # Unused converter specified continue converters[i] = conv # Parse each line, including the first for i, line in enumerate(itertools.chain([first_line], fh)): vals = split_line(line) if len(vals) == 0: continue if usecols: vals = [vals[i] for i in usecols] if len(vals) != N: line_num = i + skiprows + 1 raise ValueError("Wrong number of columns at line %d" % line_num) # Convert each value according to its column and store items = [conv(val) for (conv, val) in zip(converters, vals)] # Then pack it according to the dtype's nesting items = pack_items(items, packing) X.append(items) finally: if fown: fh.close() X = np.array(X, dtype) # Multicolumn data are returned with shape (1, N, M), i.e. # (1, 1, M) for a single row - remove the singleton dimension there if X.ndim == 3 and X.shape[:2] == (1, 1): X.shape = (1, -1) # Verify that the array has at least dimensions `ndmin`. # Check correctness of the values of `ndmin` if ndmin not in [0, 1, 2]: raise ValueError('Illegal value of ndmin keyword: %s' % ndmin) # Tweak the size and shape of the arrays - remove extraneous dimensions if X.ndim > ndmin: X = np.squeeze(X) # and ensure we have the minimum number of dimensions asked for # - has to be in this order for the odd case ndmin=1, X.squeeze().ndim=0 if X.ndim < ndmin: if ndmin == 1: X = np.atleast_1d(X) elif ndmin == 2: X = np.atleast_2d(X).T if unpack: if len(dtype_types) > 1: # For structured arrays, return an array for each field. return [X[field] for field in dtype.names] else: return X.T else: return X def savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='', footer='', comments='# '): """ Save an array to a text file. Parameters ---------- fname : filename or file handle If the filename ends in ``.gz``, the file is automatically saved in compressed gzip format. `loadtxt` understands gzipped files transparently. X : array_like Data to be saved to a text file. fmt : str or sequence of strs, optional A single format (%10.5f), a sequence of formats, or a multi-format string, e.g. 'Iteration %d -- %10.5f', in which case `delimiter` is ignored. For complex `X`, the legal options for `fmt` are: a) a single specifier, `fmt='%.4e'`, resulting in numbers formatted like `' (%s+%sj)' % (fmt, fmt)` b) a full string specifying every real and imaginary part, e.g. `' %.4e %+.4ej %.4e %+.4ej %.4e %+.4ej'` for 3 columns c) a list of specifiers, one per column - in this case, the real and imaginary part must have separate specifiers, e.g. `['%.3e + %.3ej', '(%.15e%+.15ej)']` for 2 columns delimiter : str, optional String or character separating columns. newline : str, optional String or character separating lines. .. versionadded:: 1.5.0 header : str, optional String that will be written at the beginning of the file. .. versionadded:: 1.7.0 footer : str, optional String that will be written at the end of the file. .. versionadded:: 1.7.0 comments : str, optional String that will be prepended to the ``header`` and ``footer`` strings, to mark them as comments. Default: '# ', as expected by e.g. ``numpy.loadtxt``. .. versionadded:: 1.7.0 See Also -------- save : Save an array to a binary file in NumPy ``.npy`` format savez : Save several arrays into an uncompressed ``.npz`` archive savez_compressed : Save several arrays into a compressed ``.npz`` archive Notes ----- Further explanation of the `fmt` parameter (``%[flag]width[.precision]specifier``): flags: ``-`` : left justify ``+`` : Forces to precede result with + or -. ``0`` : Left pad the number with zeros instead of space (see width). width: Minimum number of characters to be printed. The value is not truncated if it has more characters. precision: - For integer specifiers (eg. ``d,i,o,x``), the minimum number of digits. - For ``e, E`` and ``f`` specifiers, the number of digits to print after the decimal point. - For ``g`` and ``G``, the maximum number of significant digits. - For ``s``, the maximum number of characters. specifiers: ``c`` : character ``d`` or ``i`` : signed decimal integer ``e`` or ``E`` : scientific notation with ``e`` or ``E``. ``f`` : decimal floating point ``g,G`` : use the shorter of ``e,E`` or ``f`` ``o`` : signed octal ``s`` : string of characters ``u`` : unsigned decimal integer ``x,X`` : unsigned hexadecimal integer This explanation of ``fmt`` is not complete, for an exhaustive specification see [1]_. References ---------- .. [1] `Format Specification Mini-Language <http://docs.python.org/library/string.html# format-specification-mini-language>`_, Python Documentation. Examples -------- >>> x = y = z = np.arange(0.0,5.0,1.0) >>> np.savetxt('test.out', x, delimiter=',') # X is an array >>> np.savetxt('test.out', (x,y,z)) # x,y,z equal sized 1D arrays >>> np.savetxt('test.out', x, fmt='%1.4e') # use exponential notation """ # Py3 conversions first if isinstance(fmt, bytes): fmt = asstr(fmt) delimiter = asstr(delimiter) own_fh = False if is_pathlib_path(fname): fname = str(fname) if _is_string_like(fname): own_fh = True if fname.endswith('.gz'): import gzip fh = gzip.open(fname, 'wb') else: if sys.version_info[0] >= 3: fh = open(fname, 'wb') else: fh = open(fname, 'w') elif hasattr(fname, 'write'): fh = fname else: raise ValueError('fname must be a string or file handle') try: X = np.asarray(X) # Handle 1-dimensional arrays if X.ndim == 1: # Common case -- 1d array of numbers if X.dtype.names is None: X = np.atleast_2d(X).T ncol = 1 # Complex dtype -- each field indicates a separate column else: ncol = len(X.dtype.descr) else: ncol = X.shape[1] iscomplex_X = np.iscomplexobj(X) # `fmt` can be a string with multiple insertion points or a # list of formats. E.g. '%10.5f\t%10d' or ('%10.5f', '$10d') if type(fmt) in (list, tuple): if len(fmt) != ncol: raise AttributeError('fmt has wrong shape. %s' % str(fmt)) format = asstr(delimiter).join(map(asstr, fmt)) elif isinstance(fmt, str): n_fmt_chars = fmt.count('%') error = ValueError('fmt has wrong number of %% formats: %s' % fmt) if n_fmt_chars == 1: if iscomplex_X: fmt = [' (%s+%sj)' % (fmt, fmt), ] * ncol else: fmt = [fmt, ] * ncol format = delimiter.join(fmt) elif iscomplex_X and n_fmt_chars != (2 * ncol): raise error elif ((not iscomplex_X) and n_fmt_chars != ncol): raise error else: format = fmt else: raise ValueError('invalid fmt: %r' % (fmt,)) if len(header) > 0: header = header.replace('\n', '\n' + comments) fh.write(asbytes(comments + header + newline)) if iscomplex_X: for row in X: row2 = [] for number in row: row2.append(number.real) row2.append(number.imag) fh.write(asbytes(format % tuple(row2) + newline)) else: for row in X: try: fh.write(asbytes(format % tuple(row) + newline)) except TypeError: raise TypeError("Mismatch between array dtype ('%s') and " "format specifier ('%s')" % (str(X.dtype), format)) if len(footer) > 0: footer = footer.replace('\n', '\n' + comments) fh.write(asbytes(comments + footer + newline)) finally: if own_fh: fh.close() def fromregex(file, regexp, dtype): """ Construct an array from a text file, using regular expression parsing. The returned array is always a structured array, and is constructed from all matches of the regular expression in the file. Groups in the regular expression are converted to fields of the structured array. Parameters ---------- file : str or file File name or file object to read. regexp : str or regexp Regular expression used to parse the file. Groups in the regular expression correspond to fields in the dtype. dtype : dtype or list of dtypes Dtype for the structured array. Returns ------- output : ndarray The output array, containing the part of the content of `file` that was matched by `regexp`. `output` is always a structured array. Raises ------ TypeError When `dtype` is not a valid dtype for a structured array. See Also -------- fromstring, loadtxt Notes ----- Dtypes for structured arrays can be specified in several forms, but all forms specify at least the data type and field name. For details see `doc.structured_arrays`. Examples -------- >>> f = open('test.dat', 'w') >>> f.write("1312 foo\\n1534 bar\\n444 qux") >>> f.close() >>> regexp = r"(\\d+)\\s+(...)" # match [digits, whitespace, anything] >>> output = np.fromregex('test.dat', regexp, ... [('num', np.int64), ('key', 'S3')]) >>> output array([(1312L, 'foo'), (1534L, 'bar'), (444L, 'qux')], dtype=[('num', '<i8'), ('key', '|S3')]) >>> output['num'] array([1312, 1534, 444], dtype=int64) """ own_fh = False if not hasattr(file, "read"): file = open(file, 'rb') own_fh = True try: if not hasattr(regexp, 'match'): regexp = re.compile(asbytes(regexp)) if not isinstance(dtype, np.dtype): dtype = np.dtype(dtype) seq = regexp.findall(file.read()) if seq and not isinstance(seq[0], tuple): # Only one group is in the regexp. # Create the new array as a single data-type and then # re-interpret as a single-field structured array. newdtype = np.dtype(dtype[dtype.names[0]]) output = np.array(seq, dtype=newdtype) output.dtype = dtype else: output = np.array(seq, dtype=dtype) return output finally: if own_fh: file.close() #####-------------------------------------------------------------------------- #---- --- ASCII functions --- #####-------------------------------------------------------------------------- def genfromtxt(fname, dtype=float, comments='#', delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=None, replace_space='_', autostrip=False, case_sensitive=True, defaultfmt="f%i", unpack=None, usemask=False, loose=True, invalid_raise=True, max_rows=None): """ Load data from a text file, with missing values handled as specified. Each line past the first `skip_header` lines is split at the `delimiter` character, and characters following the `comments` character are discarded. Parameters ---------- fname : file, str, pathlib.Path, list of str, generator File, filename, list, or generator to read. If the filename extension is `.gz` or `.bz2`, the file is first decompressed. Note that generators must return byte strings in Python 3k. The strings in a list or produced by a generator are treated as lines. dtype : dtype, optional Data type of the resulting array. If None, the dtypes will be determined by the contents of each column, individually. comments : str, optional The character used to indicate the start of a comment. All the characters occurring on a line after a comment are discarded delimiter : str, int, or sequence, optional The string used to separate values. By default, any consecutive whitespaces act as delimiter. An integer or sequence of integers can also be provided as width(s) of each field. skiprows : int, optional `skiprows` was removed in numpy 1.10. Please use `skip_header` instead. skip_header : int, optional The number of lines to skip at the beginning of the file. skip_footer : int, optional The number of lines to skip at the end of the file. converters : variable, optional The set of functions that convert the data of a column to a value. The converters can also be used to provide a default value for missing data: ``converters = {3: lambda s: float(s or 0)}``. missing : variable, optional `missing` was removed in numpy 1.10. Please use `missing_values` instead. missing_values : variable, optional The set of strings corresponding to missing data. filling_values : variable, optional The set of values to be used as default when the data are missing. usecols : sequence, optional Which columns to read, with 0 being the first. For example, ``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns. names : {None, True, str, sequence}, optional If `names` is True, the field names are read from the first valid line after the first `skip_header` lines. If `names` is a sequence or a single-string of comma-separated names, the names will be used to define the field names in a structured dtype. If `names` is None, the names of the dtype fields will be used, if any. excludelist : sequence, optional A list of names to exclude. This list is appended to the default list ['return','file','print']. Excluded names are appended an underscore: for example, `file` would become `file_`. deletechars : str, optional A string combining invalid characters that must be deleted from the names. defaultfmt : str, optional A format used to define default field names, such as "f%i" or "f_%02i". autostrip : bool, optional Whether to automatically strip white spaces from the variables. replace_space : char, optional Character(s) used in replacement of white spaces in the variables names. By default, use a '_'. case_sensitive : {True, False, 'upper', 'lower'}, optional If True, field names are case sensitive. If False or 'upper', field names are converted to upper case. If 'lower', field names are converted to lower case. unpack : bool, optional If True, the returned array is transposed, so that arguments may be unpacked using ``x, y, z = loadtxt(...)`` usemask : bool, optional If True, return a masked array. If False, return a regular array. loose : bool, optional If True, do not raise errors for invalid values. invalid_raise : bool, optional If True, an exception is raised if an inconsistency is detected in the number of columns. If False, a warning is emitted and the offending lines are skipped. max_rows : int, optional The maximum number of rows to read. Must not be used with skip_footer at the same time. If given, the value must be at least 1. Default is to read the entire file. .. versionadded:: 1.10.0 Returns ------- out : ndarray Data read from the text file. If `usemask` is True, this is a masked array. See Also -------- numpy.loadtxt : equivalent function when no data is missing. Notes ----- * When spaces are used as delimiters, or when no delimiter has been given as input, there should not be any missing data between two fields. * When the variables are named (either by a flexible dtype or with `names`, there must not be any header in the file (else a ValueError exception is raised). * Individual values are not stripped of spaces by default. When using a custom converter, make sure the function does remove spaces. References ---------- .. [1] NumPy User Guide, section `I/O with NumPy <http://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html>`_. Examples --------- >>> from io import StringIO >>> import numpy as np Comma delimited file with mixed dtype >>> s = StringIO("1,1.3,abcde") >>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'), ... ('mystring','S5')], delimiter=",") >>> data array((1, 1.3, 'abcde'), dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')]) Using dtype = None >>> s.seek(0) # needed for StringIO example only >>> data = np.genfromtxt(s, dtype=None, ... names = ['myint','myfloat','mystring'], delimiter=",") >>> data array((1, 1.3, 'abcde'), dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')]) Specifying dtype and names >>> s.seek(0) >>> data = np.genfromtxt(s, dtype="i8,f8,S5", ... names=['myint','myfloat','mystring'], delimiter=",") >>> data array((1, 1.3, 'abcde'), dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')]) An example with fixed-width columns >>> s = StringIO("11.3abcde") >>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'], ... delimiter=[1,3,5]) >>> data array((1, 1.3, 'abcde'), dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', '|S5')]) """ if max_rows is not None: if skip_footer: raise ValueError( "The keywords 'skip_footer' and 'max_rows' can not be " "specified at the same time.") if max_rows < 1: raise ValueError("'max_rows' must be at least 1.") # Py3 data conversions to bytes, for convenience if comments is not None: comments = asbytes(comments) if isinstance(delimiter, unicode): delimiter = asbytes(delimiter) if isinstance(missing_values, (unicode, list, tuple)): missing_values = asbytes_nested(missing_values) # if usemask: from numpy.ma import MaskedArray, make_mask_descr # Check the input dictionary of converters user_converters = converters or {} if not isinstance(user_converters, dict): raise TypeError( "The input argument 'converter' should be a valid dictionary " "(got '%s' instead)" % type(user_converters)) # Initialize the filehandle, the LineSplitter and the NameValidator own_fhd = False try: if is_pathlib_path(fname): fname = str(fname) if isinstance(fname, basestring): if sys.version_info[0] == 2: fhd = iter(np.lib._datasource.open(fname, 'rbU')) else: fhd = iter(np.lib._datasource.open(fname, 'rb')) own_fhd = True else: fhd = iter(fname) except TypeError: raise TypeError( "fname must be a string, filehandle, list of strings, " "or generator. Got %s instead." % type(fname)) split_line = LineSplitter(delimiter=delimiter, comments=comments, autostrip=autostrip)._handyman validate_names = NameValidator(excludelist=excludelist, deletechars=deletechars, case_sensitive=case_sensitive, replace_space=replace_space) # Skip the first `skip_header` rows for i in range(skip_header): next(fhd) # Keep on until we find the first valid values first_values = None try: while not first_values: first_line = next(fhd) if names is True: if comments in first_line: first_line = ( b''.join(first_line.split(comments)[1:])) first_values = split_line(first_line) except StopIteration: # return an empty array if the datafile is empty first_line = b'' first_values = [] warnings.warn('genfromtxt: Empty input file: "%s"' % fname, stacklevel=2) # Should we take the first values as names ? if names is True: fval = first_values[0].strip() if fval in comments: del first_values[0] # Check the columns to use: make sure `usecols` is a list if usecols is not None: try: usecols = [_.strip() for _ in usecols.split(",")] except AttributeError: try: usecols = list(usecols) except TypeError: usecols = [usecols, ] nbcols = len(usecols or first_values) # Check the names and overwrite the dtype.names if needed if names is True: names = validate_names([_bytes_to_name(_.strip()) for _ in first_values]) first_line = b'' elif _is_string_like(names): names = validate_names([_.strip() for _ in names.split(',')]) elif names: names = validate_names(names) # Get the dtype if dtype is not None: dtype = easy_dtype(dtype, defaultfmt=defaultfmt, names=names, excludelist=excludelist, deletechars=deletechars, case_sensitive=case_sensitive, replace_space=replace_space) # Make sure the names is a list (for 2.5) if names is not None: names = list(names) if usecols: for (i, current) in enumerate(usecols): # if usecols is a list of names, convert to a list of indices if _is_string_like(current): usecols[i] = names.index(current) elif current < 0: usecols[i] = current + len(first_values) # If the dtype is not None, make sure we update it if (dtype is not None) and (len(dtype) > nbcols): descr = dtype.descr dtype = np.dtype([descr[_] for _ in usecols]) names = list(dtype.names) # If `names` is not None, update the names elif (names is not None) and (len(names) > nbcols): names = [names[_] for _ in usecols] elif (names is not None) and (dtype is not None): names = list(dtype.names) # Process the missing values ............................... # Rename missing_values for convenience user_missing_values = missing_values or () # Define the list of missing_values (one column: one list) missing_values = [list([b'']) for _ in range(nbcols)] # We have a dictionary: process it field by field if isinstance(user_missing_values, dict): # Loop on the items for (key, val) in user_missing_values.items(): # Is the key a string ? if _is_string_like(key): try: # Transform it into an integer key = names.index(key) except ValueError: # We couldn't find it: the name must have been dropped continue # Redefine the key as needed if it's a column number if usecols: try: key = usecols.index(key) except ValueError: pass # Transform the value as a list of string if isinstance(val, (list, tuple)): val = [str(_) for _ in val] else: val = [str(val), ] # Add the value(s) to the current list of missing if key is None: # None acts as default for miss in missing_values: miss.extend(val) else: missing_values[key].extend(val) # We have a sequence : each item matches a column elif isinstance(user_missing_values, (list, tuple)): for (value, entry) in zip(user_missing_values, missing_values): value = str(value) if value not in entry: entry.append(value) # We have a string : apply it to all entries elif isinstance(user_missing_values, bytes): user_value = user_missing_values.split(b",") for entry in missing_values: entry.extend(user_value) # We have something else: apply it to all entries else: for entry in missing_values: entry.extend([str(user_missing_values)]) # Process the filling_values ............................... # Rename the input for convenience user_filling_values = filling_values if user_filling_values is None: user_filling_values = [] # Define the default filling_values = [None] * nbcols # We have a dictionary : update each entry individually if isinstance(user_filling_values, dict): for (key, val) in user_filling_values.items(): if _is_string_like(key): try: # Transform it into an integer key = names.index(key) except ValueError: # We couldn't find it: the name must have been dropped, continue # Redefine the key if it's a column number and usecols is defined if usecols: try: key = usecols.index(key) except ValueError: pass # Add the value to the list filling_values[key] = val # We have a sequence : update on a one-to-one basis elif isinstance(user_filling_values, (list, tuple)): n = len(user_filling_values) if (n <= nbcols): filling_values[:n] = user_filling_values else: filling_values = user_filling_values[:nbcols] # We have something else : use it for all entries else: filling_values = [user_filling_values] * nbcols # Initialize the converters ................................ if dtype is None: # Note: we can't use a [...]*nbcols, as we would have 3 times the same # ... converter, instead of 3 different converters. converters = [StringConverter(None, missing_values=miss, default=fill) for (miss, fill) in zip(missing_values, filling_values)] else: dtype_flat = flatten_dtype(dtype, flatten_base=True) # Initialize the converters if len(dtype_flat) > 1: # Flexible type : get a converter from each dtype zipit = zip(dtype_flat, missing_values, filling_values) converters = [StringConverter(dt, locked=True, missing_values=miss, default=fill) for (dt, miss, fill) in zipit] else: # Set to a default converter (but w/ different missing values) zipit = zip(missing_values, filling_values) converters = [StringConverter(dtype, locked=True, missing_values=miss, default=fill) for (miss, fill) in zipit] # Update the converters to use the user-defined ones uc_update = [] for (j, conv) in user_converters.items(): # If the converter is specified by column names, use the index instead if _is_string_like(j): try: j = names.index(j) i = j except ValueError: continue elif usecols: try: i = usecols.index(j) except ValueError: # Unused converter specified continue else: i = j # Find the value to test - first_line is not filtered by usecols: if len(first_line): testing_value = first_values[j] else: testing_value = None converters[i].update(conv, locked=True, testing_value=testing_value, default=filling_values[i], missing_values=missing_values[i],) uc_update.append((i, conv)) # Make sure we have the corrected keys in user_converters... user_converters.update(uc_update) # Fixme: possible error as following variable never used. #miss_chars = [_.missing_values for _ in converters] # Initialize the output lists ... # ... rows rows = [] append_to_rows = rows.append # ... masks if usemask: masks = [] append_to_masks = masks.append # ... invalid invalid = [] append_to_invalid = invalid.append # Parse each line for (i, line) in enumerate(itertools.chain([first_line, ], fhd)): values = split_line(line) nbvalues = len(values) # Skip an empty line if nbvalues == 0: continue if usecols: # Select only the columns we need try: values = [values[_] for _ in usecols] except IndexError: append_to_invalid((i + skip_header + 1, nbvalues)) continue elif nbvalues != nbcols: append_to_invalid((i + skip_header + 1, nbvalues)) continue # Store the values append_to_rows(tuple(values)) if usemask: append_to_masks(tuple([v.strip() in m for (v, m) in zip(values, missing_values)])) if len(rows) == max_rows: break if own_fhd: fhd.close() # Upgrade the converters (if needed) if dtype is None: for (i, converter) in enumerate(converters): current_column = [itemgetter(i)(_m) for _m in rows] try: converter.iterupgrade(current_column) except ConverterLockError: errmsg = "Converter #%i is locked and cannot be upgraded: " % i current_column = map(itemgetter(i), rows) for (j, value) in enumerate(current_column): try: converter.upgrade(value) except (ConverterError, ValueError): errmsg += "(occurred line #%i for value '%s')" errmsg %= (j + 1 + skip_header, value) raise ConverterError(errmsg) # Check that we don't have invalid values nbinvalid = len(invalid) if nbinvalid > 0: nbrows = len(rows) + nbinvalid - skip_footer # Construct the error message template = " Line #%%i (got %%i columns instead of %i)" % nbcols if skip_footer > 0: nbinvalid_skipped = len([_ for _ in invalid if _[0] > nbrows + skip_header]) invalid = invalid[:nbinvalid - nbinvalid_skipped] skip_footer -= nbinvalid_skipped # # nbrows -= skip_footer # errmsg = [template % (i, nb) # for (i, nb) in invalid if i < nbrows] # else: errmsg = [template % (i, nb) for (i, nb) in invalid] if len(errmsg): errmsg.insert(0, "Some errors were detected !") errmsg = "\n".join(errmsg) # Raise an exception ? if invalid_raise: raise ValueError(errmsg) # Issue a warning ? else: warnings.warn(errmsg, ConversionWarning, stacklevel=2) # Strip the last skip_footer data if skip_footer > 0: rows = rows[:-skip_footer] if usemask: masks = masks[:-skip_footer] # Convert each value according to the converter: # We want to modify the list in place to avoid creating a new one... if loose: rows = list( zip(*[[conv._loose_call(_r) for _r in map(itemgetter(i), rows)] for (i, conv) in enumerate(converters)])) else: rows = list( zip(*[[conv._strict_call(_r) for _r in map(itemgetter(i), rows)] for (i, conv) in enumerate(converters)])) # Reset the dtype data = rows if dtype is None: # Get the dtypes from the types of the converters column_types = [conv.type for conv in converters] # Find the columns with strings... strcolidx = [i for (i, v) in enumerate(column_types) if v in (type('S'), np.string_)] # ... and take the largest number of chars. for i in strcolidx: column_types[i] = "|S%i" % max(len(row[i]) for row in data) # if names is None: # If the dtype is uniform, don't define names, else use '' base = set([c.type for c in converters if c._checked]) if len(base) == 1: (ddtype, mdtype) = (list(base)[0], np.bool) else: ddtype = [(defaultfmt % i, dt) for (i, dt) in enumerate(column_types)] if usemask: mdtype = [(defaultfmt % i, np.bool) for (i, dt) in enumerate(column_types)] else: ddtype = list(zip(names, column_types)) mdtype = list(zip(names, [np.bool] * len(column_types))) output = np.array(data, dtype=ddtype) if usemask: outputmask = np.array(masks, dtype=mdtype) else: # Overwrite the initial dtype names if needed if names and dtype.names: dtype.names = names # Case 1. We have a structured type if len(dtype_flat) > 1: # Nested dtype, eg [('a', int), ('b', [('b0', int), ('b1', 'f4')])] # First, create the array using a flattened dtype: # [('a', int), ('b1', int), ('b2', float)] # Then, view the array using the specified dtype. if 'O' in (_.char for _ in dtype_flat): if has_nested_fields(dtype): raise NotImplementedError( "Nested fields involving objects are not supported...") else: output = np.array(data, dtype=dtype) else: rows = np.array(data, dtype=[('', _) for _ in dtype_flat]) output = rows.view(dtype) # Now, process the rowmasks the same way if usemask: rowmasks = np.array( masks, dtype=np.dtype([('', np.bool) for t in dtype_flat])) # Construct the new dtype mdtype = make_mask_descr(dtype) outputmask = rowmasks.view(mdtype) # Case #2. We have a basic dtype else: # We used some user-defined converters if user_converters: ishomogeneous = True descr = [] for i, ttype in enumerate([conv.type for conv in converters]): # Keep the dtype of the current converter if i in user_converters: ishomogeneous &= (ttype == dtype.type) if ttype == np.string_: ttype = "|S%i" % max(len(row[i]) for row in data) descr.append(('', ttype)) else: descr.append(('', dtype)) # So we changed the dtype ? if not ishomogeneous: # We have more than one field if len(descr) > 1: dtype = np.dtype(descr) # We have only one field: drop the name if not needed. else: dtype = np.dtype(ttype) # output = np.array(data, dtype) if usemask: if dtype.names: mdtype = [(_, np.bool) for _ in dtype.names] else: mdtype = np.bool outputmask = np.array(masks, dtype=mdtype) # Try to take care of the missing data we missed names = output.dtype.names if usemask and names: for (name, conv) in zip(names or (), converters): missing_values = [conv(_) for _ in conv.missing_values if _ != b''] for mval in missing_values: outputmask[name] |= (output[name] == mval) # Construct the final array if usemask: output = output.view(MaskedArray) output._mask = outputmask if unpack: return output.squeeze().T return output.squeeze() def ndfromtxt(fname, **kwargs): """ Load ASCII data stored in a file and return it as a single array. Parameters ---------- fname, kwargs : For a description of input parameters, see `genfromtxt`. See Also -------- numpy.genfromtxt : generic function. """ kwargs['usemask'] = False return genfromtxt(fname, **kwargs) def mafromtxt(fname, **kwargs): """ Load ASCII data stored in a text file and return a masked array. Parameters ---------- fname, kwargs : For a description of input parameters, see `genfromtxt`. See Also -------- numpy.genfromtxt : generic function to load ASCII data. """ kwargs['usemask'] = True return genfromtxt(fname, **kwargs) def recfromtxt(fname, **kwargs): """ Load ASCII data from a file and return it in a record array. If ``usemask=False`` a standard `recarray` is returned, if ``usemask=True`` a MaskedRecords array is returned. Parameters ---------- fname, kwargs : For a description of input parameters, see `genfromtxt`. See Also -------- numpy.genfromtxt : generic function Notes ----- By default, `dtype` is None, which means that the data-type of the output array will be determined from the data. """ kwargs.setdefault("dtype", None) usemask = kwargs.get('usemask', False) output = genfromtxt(fname, **kwargs) if usemask: from numpy.ma.mrecords import MaskedRecords output = output.view(MaskedRecords) else: output = output.view(np.recarray) return output def recfromcsv(fname, **kwargs): """ Load ASCII data stored in a comma-separated file. The returned array is a record array (if ``usemask=False``, see `recarray`) or a masked record array (if ``usemask=True``, see `ma.mrecords.MaskedRecords`). Parameters ---------- fname, kwargs : For a description of input parameters, see `genfromtxt`. See Also -------- numpy.genfromtxt : generic function to load ASCII data. Notes ----- By default, `dtype` is None, which means that the data-type of the output array will be determined from the data. """ # Set default kwargs for genfromtxt as relevant to csv import. kwargs.setdefault("case_sensitive", "lower") kwargs.setdefault("names", True) kwargs.setdefault("delimiter", ",") kwargs.setdefault("dtype", None) output = genfromtxt(fname, **kwargs) usemask = kwargs.get("usemask", False) if usemask: from numpy.ma.mrecords import MaskedRecords output = output.view(MaskedRecords) else: output = output.view(np.recarray) return output