######################################################################## # # License: BSD # Created: September 21, 2002 # Author: Francesc Alted - faltet@pytables.com # # $Id$ # ######################################################################## """Cython interface between several PyTables classes and HDF5 library. Classes (type extensions): File AttributeSet Node Leaf Group Array VLArray UnImplemented Functions: Misc variables: """ import os import warnings from collections import namedtuple ObjInfo = namedtuple('ObjInfo', ['addr', 'rc']) ObjTimestamps = namedtuple('ObjTimestamps', ['atime', 'mtime', 'ctime', 'btime']) import pickle import numpy from .exceptions import HDF5ExtError, DataTypeWarning from .utils import (check_file_access, byteorders, correct_byteorder, SizeType) from .atom import Atom from .description import descr_from_dtype from .utilsextension import ( encode_filename, set_blosc_max_threads, set_blosc2_max_threads, atom_to_hdf5_type, atom_from_hdf5_type, hdf5_to_np_ext_type, create_nested_type, pttype_to_hdf5, pt_special_kinds, npext_prefixes_to_ptkinds, hdf5_class_to_string, platform_byteorder) # Types, constants, functions, classes & other objects from everywhere from libc.stdlib cimport malloc, free from libc.string cimport strdup, strlen from numpy cimport (import_array, ndarray, npy_intp, PyArray_BYTES, PyArray_DATA, PyArray_DIMS, PyArray_NDIM, PyArray_STRIDE) from cpython.bytes cimport (PyBytes_AsString, PyBytes_FromStringAndSize, PyBytes_Check) from cpython.unicode cimport PyUnicode_DecodeUTF8 from .definitions cimport (uintptr_t, hid_t, herr_t, hsize_t, hvl_t, H5S_seloper_t, H5D_FILL_VALUE_UNDEFINED, H5O_TYPE_UNKNOWN, H5O_TYPE_GROUP, H5O_TYPE_DATASET, H5O_TYPE_NAMED_DATATYPE, H5L_TYPE_ERROR, H5L_TYPE_HARD, H5L_TYPE_SOFT, H5L_TYPE_EXTERNAL, H5T_class_t, H5T_sign_t, H5T_NATIVE_INT, H5T_cset_t, H5T_CSET_ASCII, H5T_CSET_UTF8, H5F_SCOPE_GLOBAL, H5F_ACC_TRUNC, H5F_ACC_RDONLY, H5F_ACC_RDWR, H5P_DEFAULT, H5P_FILE_ACCESS, H5P_FILE_CREATE, H5T_DIR_DEFAULT, H5S_SELECT_SET, H5S_SELECT_AND, H5S_SELECT_NOTB, H5Fcreate, H5Fopen, H5Fclose, H5Fflush, H5Fget_vfd_handle, H5Fget_filesize, H5Fget_create_plist, H5Gcreate, H5Gopen, H5Gclose, H5Ldelete, H5Lmove, H5Dopen, H5Dclose, H5Dread, H5Dwrite, H5Dget_type, H5Dget_create_plist, H5Dget_space, H5Dvlen_reclaim, H5Dget_storage_size, H5Dvlen_get_buf_size, H5Tget_native_type, H5Tclose, H5Tis_variable_str, H5Tget_sign, H5Adelete, H5T_BITFIELD, H5T_INTEGER, H5T_FLOAT, H5T_STRING, H5Tget_order, H5Pcreate, H5Pset_cache, H5Pclose, H5Pget_userblock, H5Pset_userblock, H5Pset_fapl_sec2, H5Pset_fapl_log, H5Pset_fapl_stdio, H5Pset_fapl_core, H5Pset_fapl_split, H5Pget_obj_track_times, H5Sselect_all, H5Sselect_elements, H5Sselect_hyperslab, H5Screate_simple, H5Sclose, H5Oget_info, H5O_info_t, H5ATTRset_attribute, H5ATTRset_attribute_string, H5ATTRget_attribute, H5ATTRget_attribute_string, H5ATTRget_attribute_vlen_string_array, H5ATTRfind_attribute, H5ATTRget_type_ndims, H5ATTRget_dims, H5ARRAYget_ndims, H5ARRAYget_info, set_cache_size, get_objinfo, get_linkinfo, Giterate, Aiterate, H5UIget_info, get_len_of_range, conv_float64_timeval32, truncate_dset, H5_HAVE_DIRECT_DRIVER, pt_H5Pset_fapl_direct, H5_HAVE_WINDOWS_DRIVER, pt_H5Pset_fapl_windows, H5_HAVE_IMAGE_FILE, pt_H5Pset_file_image, pt_H5Fget_file_image, H5Tget_size, hobj_ref_t) cdef int H5T_CSET_DEFAULT = 16 from .utilsextension cimport malloc_dims, get_native_type, cstr_to_pystr, load_reference #------------------------------------------------------------------- cdef extern from "Python.h": object PyByteArray_FromStringAndSize(char *s, Py_ssize_t len) # Functions from HDF5 ARRAY (this is not part of HDF5 HL; it's private) cdef extern from "H5ARRAY.h" nogil: herr_t H5ARRAYmake(hid_t loc_id, char *dset_name, char *obversion, int rank, hsize_t *dims, int extdim, hid_t type_id, hsize_t *dims_chunk, void *fill_data, int complevel, char *complib, int shuffle, int fletcher32, hbool_t track_times, void *data) herr_t H5ARRAYappend_records(hid_t dataset_id, hid_t type_id, int rank, hsize_t *dims_orig, hsize_t *dims_new, int extdim, void *data ) herr_t H5ARRAYwrite_records(hid_t dataset_id, hid_t type_id, int rank, hsize_t *start, hsize_t *step, hsize_t *count, void *data) herr_t H5ARRAYread(hid_t dataset_id, hid_t type_id, hsize_t start, hsize_t nrows, hsize_t step, int extdim, void *data) herr_t H5ARRAYreadSlice(hid_t dataset_id, hid_t type_id, hsize_t *start, hsize_t *stop, hsize_t *step, void *data) herr_t H5ARRAYreadIndex(hid_t dataset_id, hid_t type_id, int notequal, hsize_t *start, hsize_t *stop, hsize_t *step, void *data) herr_t H5ARRAYget_chunkshape(hid_t dataset_id, int rank, hsize_t *dims_chunk) herr_t H5ARRAYget_fill_value( hid_t dataset_id, hid_t type_id, int *status, void *value) # Functions for dealing with VLArray objects cdef extern from "H5VLARRAY.h" nogil: herr_t H5VLARRAYmake( hid_t loc_id, char *dset_name, char *obversion, int rank, hsize_t *dims, hid_t type_id, hsize_t chunk_size, void *fill_data, int complevel, char *complib, int shuffle, int fletcher32, hbool_t track_times, void *data) herr_t H5VLARRAYappend_records( hid_t dataset_id, hid_t type_id, int nobjects, hsize_t nrecords, void *data ) herr_t H5VLARRAYmodify_records( hid_t dataset_id, hid_t type_id, hsize_t nrow, int nobjects, void *data ) herr_t H5VLARRAYget_info( hid_t dataset_id, hid_t type_id, hsize_t *nrecords, char *base_byteorder) #---------------------------------------------------------------------------- # Initialization code # The numpy API requires this function to be called before # using any numpy facilities in an extension module. import_array() #--------------------------------------------------------------------------- # Helper functions cdef hsize_t *npy_malloc_dims(int rank, npy_intp *pdims): """Returns a malloced hsize_t dims from a npy_intp *pdims.""" cdef int i cdef hsize_t *dims dims = NULL if rank > 0: dims = malloc(rank * sizeof(hsize_t)) for i from 0 <= i < rank: dims[i] = pdims[i] return dims cdef object getshape(int rank, hsize_t *dims): """Return a shape (tuple) from a dims C array of rank dimensions.""" cdef int i cdef object shape shape = [] for i from 0 <= i < rank: shape.append(SizeType(dims[i])) return tuple(shape) # Helper function for quickly fetch an attribute string cdef object get_attribute_string_or_none(hid_t node_id, char* attr_name): """Returns a string/unicode attribute if it exists in node_id. It returns ``None`` in case it don't exists (or there have been problems reading it). """ cdef char *attr_value cdef int cset = H5T_CSET_DEFAULT cdef object retvalue cdef hsize_t size attr_value = NULL retvalue = None # Default value if H5ATTRfind_attribute(node_id, attr_name): size = H5ATTRget_attribute_string(node_id, attr_name, &attr_value, &cset) if size == 0: if cset == H5T_CSET_UTF8: retvalue = numpy.unicode_('') else: retvalue = numpy.bytes_(b'') elif cset == H5T_CSET_UTF8: retvalue = PyUnicode_DecodeUTF8(attr_value, size, NULL) retvalue = numpy.unicode_(retvalue) else: retvalue = PyBytes_FromStringAndSize(attr_value, size) # AV: oct 2012 # since now we use the string size got form HDF5 we have to strip # trailing zeros used for padding. # The entire process is quite odd but due to a bug (??) in the way # numpy arrays are pickled in python 3 we can't assume that # strlen(attr_value) is the actual length of the attribute # and numpy.bytes_(attr_value) can give a truncated pickle string retvalue = retvalue.rstrip(b'\x00') retvalue = numpy.bytes_(retvalue) # Important to release attr_value, because it has been malloc'ed! if attr_value: free(attr_value) return retvalue # Get the numpy dtype scalar attribute from an HDF5 type as fast as possible cdef object get_dtype_scalar(hid_t type_id, H5T_class_t class_id, size_t itemsize): cdef H5T_sign_t sign cdef object stype if class_id == H5T_BITFIELD: stype = "b1" elif class_id == H5T_INTEGER: # Get the sign sign = H5Tget_sign(type_id) if (sign > 0): stype = "i%s" % (itemsize) else: stype = "u%s" % (itemsize) elif class_id == H5T_FLOAT: stype = "f%s" % (itemsize) elif class_id == H5T_STRING: if H5Tis_variable_str(type_id): raise TypeError("variable length strings are not supported yet") stype = "S%s" % (itemsize) # Try to get a NumPy type. If this can't be done, return None. try: ntype = numpy.dtype(stype) except TypeError: ntype = None return ntype _supported_drivers = ( "H5FD_SEC2", "H5FD_DIRECT", #"H5FD_LOG", "H5FD_WINDOWS", "H5FD_STDIO", "H5FD_CORE", #"H5FD_FAMILY", #"H5FD_MULTI", "H5FD_SPLIT", #"H5FD_MPIO", #"H5FD_MPIPOSIX", #"H5FD_STREAM", ) HAVE_DIRECT_DRIVER = bool(H5_HAVE_DIRECT_DRIVER) HAVE_WINDOWS_DRIVER = bool(H5_HAVE_WINDOWS_DRIVER) # Type extensions declarations (these are subclassed by PyTables # Python classes) cdef class File: cdef hid_t file_id cdef hid_t access_plist cdef object name def _g_new(self, name, pymode, **params): cdef herr_t err = 0 cdef hid_t access_plist, create_plist = H5P_DEFAULT cdef hid_t meta_plist_id = H5P_DEFAULT, raw_plist_id = H5P_DEFAULT cdef size_t img_buf_len = 0, user_block_size = 0 cdef void *img_buf_p = NULL cdef bytes encname #cdef bytes logfile_name # Check if we can handle the driver driver = params["DRIVER"] if driver is not None and driver not in _supported_drivers: raise ValueError("Invalid or not supported driver: '%s'" % driver) if driver == "H5FD_SPLIT": meta_ext = params.get("DRIVER_SPLIT_META_EXT", "-m.h5") raw_ext = params.get("DRIVER_SPLIT_RAW_EXT", "-r.h5") meta_name = meta_ext % name if "%s" in meta_ext else name + meta_ext raw_name = raw_ext % name if "%s" in raw_ext else name + raw_ext enc_meta_ext = encode_filename(meta_ext) enc_raw_ext = encode_filename(raw_ext) # Create a new file using default properties self.name = name # Encode the filename in case it is unicode encname = encode_filename(name) # These fields can be seen from Python. self._v_new = None # this will be computed later # """Is this file going to be created from scratch?""" self._isPTFile = True # assume a PyTables file by default # """Does this HDF5 file have a PyTables format?""" assert pymode in ('r', 'r+', 'a', 'w'), ("an invalid mode string ``%s`` " "passed the ``check_file_access()`` test; " "please report this to the authors" % pymode) image = params.get('DRIVER_CORE_IMAGE') if image: if driver != "H5FD_CORE": warnings.warn("The DRIVER_CORE_IMAGE parameter will be ignored by " "the '%s' driver" % driver) elif not PyBytes_Check(image): raise TypeError("The DRIVER_CORE_IMAGE must be a string of bytes") elif not H5_HAVE_IMAGE_FILE: raise RuntimeError("Support for image files is only available in " "HDF5 >= 1.8.9") # After the following check we can be quite sure # that the file or directory exists and permissions are right. if driver == "H5FD_SPLIT": for n in meta_name, raw_name: check_file_access(n, pymode) else: backing_store = params.get("DRIVER_CORE_BACKING_STORE", 1) if driver != "H5FD_CORE" or backing_store: check_file_access(name, pymode) # Should a new file be created? if image: exists = True elif driver == "H5FD_SPLIT": exists = os.path.exists(meta_name) and os.path.exists(raw_name) else: exists = os.path.exists(name) self._v_new = not (pymode in ('r', 'r+') or (pymode == 'a' and exists)) user_block_size = params.get("USER_BLOCK_SIZE", 0) if user_block_size and not self._v_new: warnings.warn("The HDF5 file already esists: the USER_BLOCK_SIZE " "will be ignored") elif user_block_size: user_block_size = int(user_block_size) is_pow_of_2 = ((user_block_size & (user_block_size - 1)) == 0) if user_block_size < 512 or not is_pow_of_2: raise ValueError("The USER_BLOCK_SIZE must be a power od 2 greather " "than 512 or zero") # File creation property list create_plist = H5Pcreate(H5P_FILE_CREATE) err = H5Pset_userblock(create_plist, user_block_size) if err < 0: H5Pclose(create_plist) raise HDF5ExtError("Unable to set the user block size") # File access property list access_plist = H5Pcreate(H5P_FILE_ACCESS) # Set parameters for chunk cache H5Pset_cache(access_plist, 0, params["CHUNK_CACHE_NELMTS"], params["CHUNK_CACHE_SIZE"], params["CHUNK_CACHE_PREEMPT"]) # Set the I/O driver if driver == "H5FD_SEC2": err = H5Pset_fapl_sec2(access_plist) elif driver == "H5FD_DIRECT": if not H5_HAVE_DIRECT_DRIVER: H5Pclose(create_plist) H5Pclose(access_plist) raise RuntimeError("The H5FD_DIRECT driver is not available") err = pt_H5Pset_fapl_direct(access_plist, params["DRIVER_DIRECT_ALIGNMENT"], params["DRIVER_DIRECT_BLOCK_SIZE"], params["DRIVER_DIRECT_CBUF_SIZE"]) #elif driver == "H5FD_LOG": # if "DRIVER_LOG_FILE" not in params: # H5Pclose(access_plist) # raise ValueError("The DRIVER_LOG_FILE parameter is required for " # "the H5FD_LOG driver") # logfile_name = encode_filename(params["DRIVER_LOG_FILE"]) # err = H5Pset_fapl_log(access_plist, # logfile_name, # params["DRIVER_LOG_FLAGS"], # params["DRIVER_LOG_BUF_SIZE"]) elif driver == "H5FD_WINDOWS": if not H5_HAVE_WINDOWS_DRIVER: H5Pclose(access_plist) H5Pclose(create_plist) raise RuntimeError("The H5FD_WINDOWS driver is not available") err = pt_H5Pset_fapl_windows(access_plist) elif driver == "H5FD_STDIO": err = H5Pset_fapl_stdio(access_plist) elif driver == "H5FD_CORE": err = H5Pset_fapl_core(access_plist, params["DRIVER_CORE_INCREMENT"], backing_store) if image: img_buf_len = len(image) img_buf_p = PyBytes_AsString(image) err = pt_H5Pset_file_image(access_plist, img_buf_p, img_buf_len) if err < 0: H5Pclose(create_plist) H5Pclose(access_plist) raise HDF5ExtError("Unable to set the file image") #elif driver == "H5FD_FAMILY": # H5Pset_fapl_family(access_plist, # params["DRIVER_FAMILY_MEMB_SIZE"], # fapl_id) #elif driver == "H5FD_MULTI": # err = H5Pset_fapl_multi(access_plist, memb_map, memb_fapl, memb_name, # memb_addr, relax) elif driver == "H5FD_SPLIT": err = H5Pset_fapl_split(access_plist, enc_meta_ext, meta_plist_id, enc_raw_ext, raw_plist_id) if err < 0: e = HDF5ExtError("Unable to set the file access property list") H5Pclose(create_plist) H5Pclose(access_plist) raise e if pymode == 'r': self.file_id = H5Fopen(encname, H5F_ACC_RDONLY, access_plist) elif pymode == 'r+': self.file_id = H5Fopen(encname, H5F_ACC_RDWR, access_plist) elif pymode == 'a': if exists: # A test for logging. ## H5Pset_sieve_buf_size(access_plist, 0) ## H5Pset_fapl_log (access_plist, "test.log", H5FD_LOG_LOC_WRITE, 0) self.file_id = H5Fopen(encname, H5F_ACC_RDWR, access_plist) else: self.file_id = H5Fcreate(encname, H5F_ACC_TRUNC, create_plist, access_plist) elif pymode == 'w': self.file_id = H5Fcreate(encname, H5F_ACC_TRUNC, create_plist, access_plist) if self.file_id < 0: e = HDF5ExtError("Unable to open/create file '%s'" % name) H5Pclose(create_plist) H5Pclose(access_plist) raise e H5Pclose(create_plist) H5Pclose(access_plist) # Set the cache size set_cache_size(self.file_id, params["METADATA_CACHE_SIZE"]) # Set the maximum number of threads for Blosc set_blosc_max_threads(params["MAX_BLOSC_THREADS"]) set_blosc2_max_threads(params["MAX_BLOSC_THREADS"]) # XXX: add the possibility to pass a pre-allocated buffer def get_file_image(self): """Retrieves an in-memory image of an existing, open HDF5 file. .. note:: this method requires HDF5 >= 1.8.9. .. versionadded:: 3.0 """ cdef ssize_t size = 0 cdef size_t buf_len = 0 cdef bytes image cdef char* cimage self.flush() # retrieve the size of the buffer for the file image size = pt_H5Fget_file_image(self.file_id, NULL, buf_len) if size < 0: raise HDF5ExtError("Unable to retrieve the size of the buffer for the " "file image. Plese note that not all drivers " "provide support for image files.") # allocate the memory buffer image = PyBytes_FromStringAndSize(NULL, size) if not image: raise RuntimeError("Unable to allecote meomory fir the file image") cimage = image buf_len = size size = pt_H5Fget_file_image(self.file_id, cimage, buf_len) if size < 0: raise HDF5ExtError("Unable to retrieve the file image. " "Plese note that not all drivers provide support " "for image files.") return image def get_filesize(self): """Returns the size of an HDF5 file. The returned size is that of the entire file, as opposed to only the HDF5 portion of the file. I.e., size includes the user block, if any, the HDF5 portion of the file, and any data that may have been appended beyond the data written through the HDF5 Library. .. versionadded:: 3.0 """ cdef herr_t err = 0 cdef hsize_t size = 0 err = H5Fget_filesize(self.file_id, &size) if err < 0: raise HDF5ExtError("Unable to retrieve the HDF5 file size") return size def get_userblock_size(self): """Retrieves the size of a user block. .. versionadded:: 3.0 """ cdef herr_t err = 0 cdef hsize_t size = 0 cdef hid_t create_plist create_plist = H5Fget_create_plist(self.file_id) if create_plist < 0: raise HDF5ExtError("Unable to get the creation property list") err = H5Pget_userblock(create_plist, &size) if err < 0: H5Pclose(create_plist) raise HDF5ExtError("unable to retrieve the user block size") H5Pclose(create_plist) return size # Accessor definitions def _get_file_id(self): return self.file_id def fileno(self): """Return the underlying OS integer file descriptor. This is needed for lower-level file interfaces, such as the ``fcntl`` module. """ cdef void *file_handle cdef uintptr_t *descriptor cdef herr_t err err = H5Fget_vfd_handle(self.file_id, H5P_DEFAULT, &file_handle) if err < 0: raise HDF5ExtError( "Problems getting file descriptor for file ``%s``" % self.name) # Convert the 'void *file_handle' into an 'int *descriptor' descriptor = file_handle return descriptor[0] def _flush_file(self, scope): # Close the file H5Fflush(self.file_id, scope) def _close_file(self): # Close the file H5Fclose( self.file_id ) self.file_id = 0 # Means file closed # This method is moved out of scope, until we provide code to delete # the memory booked by this extension types def __dealloc__(self): cdef int ret if self.file_id > 0: # Close the HDF5 file because user didn't do that! ret = H5Fclose(self.file_id) if ret < 0: raise HDF5ExtError("Problems closing the file '%s'" % self.name) cdef class AttributeSet: cdef object name def _g_new(self, node): self.name = node._v_name def _g_list_attr(self, node): """Return a tuple with the attribute list""" a = Aiterate(node._v_objectid) return a def _g_setattr(self, node, name, object value): """Save Python or NumPy objects as HDF5 attributes. Scalar Python objects, scalar NumPy & 0-dim NumPy objects will all be saved as H5T_SCALAR type. N-dim NumPy objects will be saved as H5T_ARRAY type. """ cdef int ret cdef hid_t dset_id, type_id cdef hsize_t *dims cdef ndarray ndv cdef object byteorder, rabyteorder, baseatom cdef char* cname = NULL cdef bytes encoded_name cdef int cset = H5T_CSET_DEFAULT encoded_name = name.encode('utf-8') # get the C pointer cname = encoded_name # The dataset id of the node dset_id = node._v_objectid # Convert a NumPy scalar into a NumPy 0-dim ndarray if isinstance(value, numpy.generic): value = numpy.array(value) # Check if value is a NumPy ndarray and of a supported type if (isinstance(value, numpy.ndarray) and value.dtype.kind in ('V', 'S', 'b', 'i', 'u', 'f', 'c')): # get a contiguous array: fixes #270 and gh-176 #value = numpy.ascontiguousarray(value) value = value.copy() if value.dtype.kind == 'V': description, rabyteorder = descr_from_dtype(value.dtype, ptparams=node._v_file.params) byteorder = byteorders[rabyteorder] type_id = create_nested_type(description, byteorder) # Make sure the value is consistent with offsets of the description value = value.astype(description._v_dtype) else: # Get the associated native HDF5 type of the scalar type baseatom = Atom.from_dtype(value.dtype.base) byteorder = byteorders[value.dtype.byteorder] type_id = atom_to_hdf5_type(baseatom, byteorder) # Get dimensionality info ndv = value dims = npy_malloc_dims(PyArray_NDIM(ndv), PyArray_DIMS(ndv)) # Actually write the attribute ret = H5ATTRset_attribute(dset_id, cname, type_id, PyArray_NDIM(ndv), dims, PyArray_BYTES(ndv)) if ret < 0: raise HDF5ExtError("Can't set attribute '%s' in node:\n %s." % (name, self._v_node)) # Release resources free(dims) H5Tclose(type_id) else: # Object cannot be natively represented in HDF5. if (isinstance(value, numpy.ndarray) and value.dtype.kind == 'U' and value.shape == ()): value = value[()].encode('utf-8') cset = H5T_CSET_UTF8 else: # Convert this object to a null-terminated string # (binary pickles are not supported at this moment) value = pickle.dumps(value, 0) ret = H5ATTRset_attribute_string(dset_id, cname, value, len(value), cset) if ret < 0: raise HDF5ExtError("Can't set attribute '%s' in node:\n %s." % (name, self._v_node)) # Get attributes def _g_getattr(self, node, attrname): """Get HDF5 attributes and retrieve them as NumPy objects. H5T_SCALAR types will be retrieved as scalar NumPy. H5T_ARRAY types will be retrieved as ndarray NumPy objects. """ cdef hsize_t *dims cdef H5T_class_t class_id cdef size_t type_size cdef hid_t mem_type, dset_id, type_id, native_type cdef int rank, ret, enumtype cdef void *rbuf cdef char *str_value cdef char **str_values = NULL cdef ndarray ndvalue cdef object shape, stype_atom, shape_atom, retvalue cdef int i, nelements cdef char* cattrname = NULL cdef bytes encoded_attrname cdef int cset = H5T_CSET_DEFAULT encoded_attrname = attrname.encode('utf-8') # Get the C pointer cattrname = encoded_attrname # The dataset id of the node dset_id = node._v_objectid dims = NULL ret = H5ATTRget_type_ndims(dset_id, cattrname, &type_id, &class_id, &type_size, &rank ) if ret < 0: raise HDF5ExtError("Can't get type info on attribute %s in node %s." % (attrname, self.name)) # Call a fast function for scalar values and typical class types if (rank == 0 and class_id == H5T_STRING): type_size = H5ATTRget_attribute_string(dset_id, cattrname, &str_value, &cset) if type_size == 0: if cset == H5T_CSET_UTF8: retvalue = numpy.unicode_('') else: retvalue = numpy.bytes_(b'') elif cset == H5T_CSET_UTF8: retvalue = PyUnicode_DecodeUTF8(str_value, type_size, NULL) retvalue = numpy.unicode_(retvalue) else: retvalue = PyBytes_FromStringAndSize(str_value, type_size) # AV: oct 2012 # since now we use the string size got form HDF5 we have to strip # trailing zeros used for padding. # The entire process is quite odd but due to a bug (??) in the way # numpy arrays are pickled in python 3 we can't assume that # strlen(attr_value) is the actual length of the attibute # and numpy.bytes_(attr_value) can give a truncated pickle sting retvalue = retvalue.rstrip(b'\x00') retvalue = numpy.bytes_(retvalue) # bytes # Important to release attr_value, because it has been malloc'ed! if str_value: free(str_value) H5Tclose(type_id) return retvalue elif (rank == 0 and class_id in (H5T_BITFIELD, H5T_INTEGER, H5T_FLOAT)): dtype_ = get_dtype_scalar(type_id, class_id, type_size) if dtype_ is None: warnings.warn("Unsupported type for attribute '%s' in node '%s'. " "Offending HDF5 class: %d" % (attrname, self.name, class_id), DataTypeWarning) self._v_unimplemented.append(attrname) return None shape = () else: # General case # Get the dimensional info dims = malloc(rank * sizeof(hsize_t)) ret = H5ATTRget_dims(dset_id, cattrname, dims) if ret < 0: raise HDF5ExtError("Can't get dims info on attribute %s in node %s." % (attrname, self.name)) shape = getshape(rank, dims) # dims is not needed anymore free( dims) # Get the NumPy dtype from the type_id try: stype_, shape_ = hdf5_to_np_ext_type(type_id, pure_numpy_types=True, ptparams=node._v_file.params) dtype_ = numpy.dtype(stype_, shape_) except TypeError: if class_id == H5T_STRING and H5Tis_variable_str(type_id): nelements = H5ATTRget_attribute_vlen_string_array(dset_id, cattrname, &str_values, &cset) if nelements < 0: raise HDF5ExtError("Can't read attribute %s in node %s." % (attrname, self.name)) # The following generator expressions do not work with Cython 0.15.1 if cset == H5T_CSET_UTF8: #retvalue = numpy.fromiter( # PyUnicode_DecodeUTF8(str_values[i], # strlen(str_values[i]), # NULL) # for i in range(nelements), "O8") retvalue = numpy.array([ PyUnicode_DecodeUTF8(str_values[i], strlen(str_values[i]), NULL) for i in range(nelements)], "O8") else: #retvalue = numpy.fromiter( # str_values[i] for i in range(nelements), "O8") retvalue = numpy.array( [str_values[i] for i in range(nelements)], "O8") retvalue.shape = shape # Important to release attr_value, because it has been malloc'ed! for i in range(nelements): free(str_values[i]) free(str_values) return retvalue # This class is not supported. Instead of raising a TypeError, issue a # warning explaining the problem. This will allow to continue browsing # native HDF5 files, while informing the user about the problem. warnings.warn("Unsupported type for attribute '%s' in node '%s'. " "Offending HDF5 class: %d" % (attrname, self.name, class_id), DataTypeWarning) self._v_unimplemented.append(attrname) return None # Get the container for data ndvalue = numpy.empty(dtype=dtype_, shape=shape) # Get the pointer to the buffer data area rbuf = PyArray_DATA(ndvalue) # Actually read the attribute from disk ret = H5ATTRget_attribute(dset_id, cattrname, type_id, rbuf) if ret < 0: raise HDF5ExtError("Attribute %s exists in node %s, but can't get it." % (attrname, self.name)) H5Tclose(type_id) if rank > 0: # multidimensional case retvalue = ndvalue else: retvalue = ndvalue[()] # 0-dim ndarray becomes a NumPy scalar return retvalue def _g_remove(self, node, attrname): cdef int ret cdef hid_t dset_id cdef char *cattrname = NULL cdef bytes encoded_attrname encoded_attrname = attrname.encode('utf-8') # Get the C pointer cattrname = encoded_attrname # The dataset id of the node dset_id = node._v_objectid ret = H5Adelete(dset_id, cattrname) if ret < 0: raise HDF5ExtError("Attribute '%s' exists in node '%s', but cannot be " "deleted." % (attrname, self.name)) cdef class Node: # Instance variables declared in .pxd def _g_new(self, where, name, init): self.name = name # """The name of this node in its parent group.""" self.parent_id = where._v_objectid # """The identifier of the parent group.""" def _g_delete(self, parent): cdef int ret cdef bytes encoded_name encoded_name = self.name.encode('utf-8') # Delete this node ret = H5Ldelete(parent._v_objectid, encoded_name, H5P_DEFAULT) if ret < 0: raise HDF5ExtError("problems deleting the node ``%s``" % self.name) return ret def __dealloc__(self): self.parent_id = 0 def _get_obj_info(self): cdef herr_t ret = 0 cdef H5O_info_t oinfo ret = H5Oget_info(self._v_objectid, &oinfo) if ret < 0: raise HDF5ExtError("Unable to get object info for '%s'" % self. _v_pathname) return ObjInfo(oinfo.addr, oinfo.rc) def _get_obj_timestamps(self): cdef herr_t ret = 0 cdef H5O_info_t oinfo ret = H5Oget_info(self._v_objectid, &oinfo) if ret < 0: raise HDF5ExtError("Unable to get object info for '%s'" % self. _v_pathname) return ObjTimestamps(oinfo.atime, oinfo.mtime, oinfo.ctime, oinfo.btime) cdef class Group(Node): cdef hid_t group_id def _g_create(self): cdef hid_t ret cdef bytes encoded_name encoded_name = self.name.encode('utf-8') # @TODO: set property list --> utf-8 # Create a new group ret = H5Gcreate(self.parent_id, encoded_name, H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT) if ret < 0: raise HDF5ExtError("Can't create the group %s." % self.name) self.group_id = ret return self.group_id def _g_open(self): cdef hid_t ret cdef bytes encoded_name encoded_name = self.name.encode('utf-8') ret = H5Gopen(self.parent_id, encoded_name, H5P_DEFAULT) if ret < 0: raise HDF5ExtError("Can't open the group: '%s'." % self.name) self.group_id = ret return self.group_id def _g_get_objinfo(self, object h5name): """Check whether 'name' is a children of 'self' and return its type.""" cdef int ret cdef object node_type cdef bytes encoded_name cdef char *cname encoded_name = h5name.encode('utf-8') # Get the C pointer cname = encoded_name ret = get_linkinfo(self.group_id, cname) if ret == -2 or ret == H5L_TYPE_ERROR: node_type = "NoSuchNode" elif ret == H5L_TYPE_SOFT: node_type = "SoftLink" elif ret == H5L_TYPE_EXTERNAL: node_type = "ExternalLink" elif ret == H5L_TYPE_HARD: ret = get_objinfo(self.group_id, cname) if ret == -2: node_type = "NoSuchNode" elif ret == H5O_TYPE_UNKNOWN: node_type = "Unknown" elif ret == H5O_TYPE_GROUP: node_type = "Group" elif ret == H5O_TYPE_DATASET: node_type = "Leaf" elif ret == H5O_TYPE_NAMED_DATATYPE: node_type = "NamedType" # Not supported yet #else H5O_TYPE_LINK: # # symbolic link # raise RuntimeError('unexpected object type') else: node_type = "Unknown" return node_type def _g_list_group(self, parent): """Return a tuple with the groups and the leaves hanging from self.""" cdef bytes encoded_name encoded_name = self.name.encode('utf-8') return Giterate(parent._v_objectid, self._v_objectid, encoded_name) def _g_get_gchild_attr(self, group_name, attr_name): """Return an attribute of a child `Group`. If the attribute does not exist, ``None`` is returned. """ cdef hid_t gchild_id cdef object retvalue cdef bytes encoded_group_name cdef bytes encoded_attr_name encoded_group_name = group_name.encode('utf-8') encoded_attr_name = attr_name.encode('utf-8') # Open the group retvalue = None # Default value gchild_id = H5Gopen(self.group_id, encoded_group_name, H5P_DEFAULT) if gchild_id < 0: raise HDF5ExtError("Non-existing node ``%s`` under ``%s``" % (group_name, self._v_pathname)) retvalue = get_attribute_string_or_none(gchild_id, encoded_attr_name) # Close child group H5Gclose(gchild_id) return retvalue def _g_get_lchild_attr(self, leaf_name, attr_name): """Return an attribute of a child `Leaf`. If the attribute does not exist, ``None`` is returned. """ cdef hid_t leaf_id cdef object retvalue cdef bytes encoded_leaf_name cdef bytes encoded_attr_name encoded_leaf_name = leaf_name.encode('utf-8') encoded_attr_name = attr_name.encode('utf-8') # Open the dataset leaf_id = H5Dopen(self.group_id, encoded_leaf_name, H5P_DEFAULT) if leaf_id < 0: raise HDF5ExtError("Non-existing node ``%s`` under ``%s``" % (leaf_name, self._v_pathname)) retvalue = get_attribute_string_or_none(leaf_id, encoded_attr_name) # Close the dataset H5Dclose(leaf_id) return retvalue def _g_flush_group(self): # Close the group H5Fflush(self.group_id, H5F_SCOPE_GLOBAL) def _g_close_group(self): cdef int ret ret = H5Gclose(self.group_id) if ret < 0: raise HDF5ExtError("Problems closing the Group %s" % self.name) self.group_id = 0 # indicate that this group is closed def _g_move_node(self, hid_t oldparent, oldname, hid_t newparent, newname, oldpathname, newpathname): cdef int ret cdef bytes encoded_oldname, encoded_newname encoded_oldname = oldname.encode('utf-8') encoded_newname = newname.encode('utf-8') ret = H5Lmove(oldparent, encoded_oldname, newparent, encoded_newname, H5P_DEFAULT, H5P_DEFAULT) if ret < 0: raise HDF5ExtError("Problems moving the node %s to %s" % (oldpathname, newpathname) ) return ret cdef class Leaf(Node): # Instance variables declared in .pxd def _get_storage_size(self): return H5Dget_storage_size(self.dataset_id) def _get_obj_track_times(self): """Get track_times boolean for dataset Uses H5Pget_obj_track_times to determine if the dataset was created with the track_times property. If the leaf is not a dataset, this will fail with HDF5ExtError. The track times dataset creation property does not seem to survive closing and reopening as of HDF5 1.8.17. Currently, it may be more accurate to test whether the ctime for the dataset is 0: track_times = (leaf._get_obj_timestamps().ctime == 0) """ cdef: hbool_t track_times = True if self.dataset_id < 0: raise ValueError('Invalid dataset id %s' % self.dataset_id) plist_id = H5Dget_create_plist(self.dataset_id) if plist_id < 0: raise HDF5ExtError("Could not get dataset creation property list " "from dataset id %s" % self.dataset_id) try: # Get track_times boolean for dataset if H5Pget_obj_track_times(plist_id, &track_times) < 0: raise HDF5ExtError("Could not get dataset track_times property " "from dataset id %s" % self.dataset_id) finally: H5Pclose(plist_id) return bool(track_times) def _g_new(self, where, name, init): if init: # Put this info to 0 just when the class is initialized self.dataset_id = -1 self.type_id = -1 self.base_type_id = -1 self.disk_type_id = -1 super()._g_new(where, name, init) cdef _get_type_ids(self): """Get the disk and native HDF5 types associated with this leaf. It is guaranteed that both disk and native types are not the same descriptor (so that it is safe to close them separately). """ cdef hid_t disk_type_id, native_type_id disk_type_id = H5Dget_type(self.dataset_id) native_type_id = get_native_type(disk_type_id) return disk_type_id, native_type_id cdef _convert_time64(self, ndarray nparr, int sense): """Converts a NumPy of Time64 elements between NumPy and HDF5 formats. NumPy to HDF5 conversion is performed when 'sense' is 0. Otherwise, HDF5 to NumPy conversion is performed. The conversion is done in place, i.e. 'nparr' is modified. """ cdef void *t64buf cdef long byteoffset, bytestride, nelements cdef hsize_t nrecords byteoffset = 0 # NumPy objects doesn't have an offset if (nparr).shape == (): # 0-dim array does contain *one* element nrecords = 1 bytestride = 8 else: nrecords = len(nparr) bytestride = PyArray_STRIDE(nparr, 0) # supports multi-dimensional recarray nelements = nparr.size // nrecords t64buf = PyArray_DATA(nparr) conv_float64_timeval32( t64buf, byteoffset, bytestride, nrecords, nelements, sense) # can't do since cdef'd def _g_truncate(self, hsize_t size): """Truncate a Leaf to `size` nrows.""" cdef hsize_t ret ret = truncate_dset(self.dataset_id, self.maindim, size) if ret < 0: raise HDF5ExtError("Problems truncating the leaf: %s" % self) classname = self.__class__.__name__ if classname in ('EArray', 'CArray'): # Update the new dimensionality self.dims[self.maindim] = size # Update the shape shape = list(self.shape) shape[self.maindim] = SizeType(size) self.shape = tuple(shape) elif classname in ('Table', 'VLArray'): self.nrows = size else: raise ValueError("Unexpected classname: %s" % classname) def _g_flush(self): # Flush the dataset (in fact, the entire buffers in file!) if self.dataset_id >= 0: H5Fflush(self.dataset_id, H5F_SCOPE_GLOBAL) def _g_close(self): # Close dataset in HDF5 space # Release resources if self.type_id >= 0: H5Tclose(self.type_id) if self.disk_type_id >= 0: H5Tclose(self.disk_type_id) if self.base_type_id >= 0: H5Tclose(self.base_type_id) if self.dataset_id >= 0: H5Dclose(self.dataset_id) cdef void* _array_data(ndarray arr): # When the object is not a 0-d ndarray and its strides == 0, that # means that the array does not contain actual data cdef npy_intp i, ndim ndim = PyArray_NDIM(arr) if ndim == 0: return PyArray_DATA(arr) for i in range(ndim): if PyArray_STRIDE(arr, i) > 0: return PyArray_DATA(arr) return NULL cdef class Array(Leaf): # Instance variables declared in .pxd def _create_array(self, ndarray nparr, object title, object atom): cdef int i cdef herr_t ret cdef void *rbuf cdef bytes complib, version, class_ cdef object dtype_, atom_, shape cdef ndarray dims cdef bytes encoded_title, encoded_name cdef H5T_cset_t cset = H5T_CSET_ASCII encoded_title = title.encode('utf-8') encoded_name = self.name.encode('utf-8') # Get the HDF5 type associated with this numpy type shape = (nparr).shape if atom is None or atom.shape == (): dtype_ = nparr.dtype.base atom_ = Atom.from_dtype(dtype_) else: atom_ = atom shape = shape[:-len(atom_.shape)] self.disk_type_id = atom_to_hdf5_type(atom_, self.byteorder) if self.disk_type_id < 0: raise HDF5ExtError( "Problems creating the %s: invalid disk type ID for atom %s" % ( self.__class__.__name__, atom_)) # Allocate space for the dimension axis info and fill it dims = numpy.array(shape, dtype=numpy.intp) self.rank = len(shape) self.dims = npy_malloc_dims(self.rank, PyArray_DATA(dims)) rbuf = _array_data(nparr) # Save the array complib = (self.filters.complib or '').encode('utf-8') version = self._v_version.encode('utf-8') class_ = self._c_classid.encode('utf-8') self.dataset_id = H5ARRAYmake(self.parent_id, encoded_name, version, self.rank, self.dims, self.extdim, self.disk_type_id, NULL, NULL, self.filters.complevel, complib, self.filters.shuffle_bitshuffle, self.filters.fletcher32, self._want_track_times, rbuf) if self.dataset_id < 0: raise HDF5ExtError("Problems creating the %s." % self.__class__.__name__) if self._v_file.params['PYTABLES_SYS_ATTRS']: cset = H5T_CSET_UTF8 # Set the conforming array attributes H5ATTRset_attribute_string(self.dataset_id, "CLASS", class_, len(class_), cset) H5ATTRset_attribute_string(self.dataset_id, "VERSION", version, len(version), cset) H5ATTRset_attribute_string(self.dataset_id, "TITLE", encoded_title, len(encoded_title), cset) # Get the native type (so that it is HDF5 who is the responsible to deal # with non-native byteorders on-disk) self.type_id = get_native_type(self.disk_type_id) return self.dataset_id, shape, atom_ def _create_carray(self, object title): cdef int i cdef herr_t ret cdef void *rbuf cdef bytes complib, version, class_ cdef ndarray dflts cdef void *fill_data cdef ndarray extdim cdef object atom cdef bytes encoded_title, encoded_name encoded_title = title.encode('utf-8') encoded_name = self.name.encode('utf-8') atom = self.atom self.disk_type_id = atom_to_hdf5_type(atom, self.byteorder) self.rank = len(self.shape) self.dims = malloc_dims(self.shape) if self.chunkshape: self.dims_chunk = malloc_dims(self.chunkshape) rbuf = NULL # The data pointer. We don't have data to save initially # Encode strings complib = (self.filters.complib or '').encode('utf-8') version = self._v_version.encode('utf-8') class_ = self._c_classid.encode('utf-8') # Get the fill values if isinstance(atom.dflt, numpy.ndarray) or atom.dflt: dflts = numpy.array(atom.dflt, dtype=atom.dtype) fill_data = PyArray_DATA(dflts) else: dflts = numpy.zeros((), dtype=atom.dtype) fill_data = NULL if atom.shape == (): # The default is preferred as a scalar value instead of 0-dim array atom.dflt = dflts[()] else: atom.dflt = dflts # Create the CArray/EArray self.dataset_id = H5ARRAYmake(self.parent_id, encoded_name, version, self.rank, self.dims, self.extdim, self.disk_type_id, self.dims_chunk, fill_data, self.filters.complevel, complib, self.filters.shuffle_bitshuffle, self.filters.fletcher32, self._want_track_times, rbuf) if self.dataset_id < 0: raise HDF5ExtError("Problems creating the %s." % self.__class__.__name__) if self._v_file.params['PYTABLES_SYS_ATTRS']: # Set the conforming array attributes H5ATTRset_attribute_string(self.dataset_id, "CLASS", class_, len(class_), H5T_CSET_ASCII) H5ATTRset_attribute_string(self.dataset_id, "VERSION", version, len(version), H5T_CSET_ASCII) H5ATTRset_attribute_string(self.dataset_id, "TITLE", encoded_title, len(encoded_title), H5T_CSET_ASCII) if self.extdim >= 0: extdim = numpy.array([self.extdim], dtype="int32") # Attach the EXTDIM attribute in case of enlargeable arrays H5ATTRset_attribute(self.dataset_id, "EXTDIM", H5T_NATIVE_INT, 0, NULL, PyArray_BYTES(extdim)) # Get the native type (so that it is HDF5 who is the responsible to deal # with non-native byteorders on-disk) self.type_id = get_native_type(self.disk_type_id) return self.dataset_id def _open_array(self): cdef size_t type_size, type_precision cdef H5T_class_t class_id cdef char cbyteorder[11] # "irrelevant" fits easily here cdef int i cdef int extdim cdef herr_t ret cdef object shape, chunkshapes, atom cdef int fill_status cdef ndarray dflts cdef void *fill_data cdef bytes encoded_name cdef str byteorder encoded_name = self.name.encode('utf-8') # Open the dataset self.dataset_id = H5Dopen(self.parent_id, encoded_name, H5P_DEFAULT) if self.dataset_id < 0: raise HDF5ExtError("Non-existing node ``%s`` under ``%s``" % (self.name, self._v_parent._v_pathname)) # Get the datatype handles self.disk_type_id, self.type_id = self._get_type_ids() # Get the atom for this type atom = atom_from_hdf5_type(self.type_id) # Get the rank for this array object if H5ARRAYget_ndims(self.dataset_id, &self.rank) < 0: raise HDF5ExtError("Problems getting ndims!") # Allocate space for the dimension axis info self.dims = malloc(self.rank * sizeof(hsize_t)) self.maxdims = malloc(self.rank * sizeof(hsize_t)) # Get info on dimensions, class and type (of base class) ret = H5ARRAYget_info(self.dataset_id, self.disk_type_id, self.dims, self.maxdims, &class_id, cbyteorder) if ret < 0: raise HDF5ExtError("Unable to get array info.") byteorder = cstr_to_pystr(cbyteorder) # Get the extendable dimension (if any) self.extdim = -1 # default is non-extensible Array for i from 0 <= i < self.rank: if self.maxdims[i] == -1: self.extdim = i break # Get the shape as a python tuple shape = getshape(self.rank, self.dims) # Allocate space for the dimension chunking info self.dims_chunk = malloc(self.rank * sizeof(hsize_t)) if H5ARRAYget_chunkshape(self.dataset_id, self.rank, self.dims_chunk) < 0: # The Array class is not chunked! chunkshapes = None else: # Get the chunkshape as a python tuple chunkshapes = getshape(self.rank, self.dims_chunk) # object arrays should not be read directly into memory if atom.dtype != object: # Get the fill value dflts = numpy.zeros((), dtype=atom.dtype) fill_data = PyArray_DATA(dflts) H5ARRAYget_fill_value(self.dataset_id, self.type_id, &fill_status, fill_data); if fill_status == H5D_FILL_VALUE_UNDEFINED: # This can only happen with datasets created with other libraries # than PyTables. dflts = None if dflts is not None and atom.shape == (): # The default is preferred as a scalar value instead of 0-dim array atom.dflt = dflts[()] else: atom.dflt = dflts # Get the byteorder self.byteorder = correct_byteorder(atom.type, byteorder) return self.dataset_id, atom, shape, chunkshapes def _append(self, ndarray nparr): cdef int ret, extdim cdef hsize_t *dims_arr cdef void *rbuf cdef object shape if self.atom.kind == "reference": raise ValueError("Cannot append to the reference types") # Allocate space for the dimension axis info dims_arr = npy_malloc_dims(self.rank, PyArray_DIMS(nparr)) # Get the pointer to the buffer data area rbuf = PyArray_DATA(nparr) # Convert some NumPy types to HDF5 before storing. if self.atom.type == 'time64': self._convert_time64(nparr, 0) # Append the records extdim = self.extdim with nogil: ret = H5ARRAYappend_records(self.dataset_id, self.type_id, self.rank, self.dims, dims_arr, extdim, rbuf) if ret < 0: raise HDF5ExtError("Problems appending the elements") free(dims_arr) # Update the new dimensionality shape = list(self.shape) shape[self.extdim] = SizeType(self.dims[self.extdim]) self.shape = tuple(shape) def _read_array(self, hsize_t start, hsize_t stop, hsize_t step, ndarray nparr): cdef herr_t ret cdef void *rbuf cdef hsize_t nrows cdef int extdim cdef size_t item_size = H5Tget_size(self.type_id) cdef void * refbuf = NULL # Number of rows to read nrows = get_len_of_range(start, stop, step) # Get the pointer to the buffer data area if self.atom.kind == "reference": refbuf = malloc(nrows * item_size) rbuf = refbuf else: rbuf = PyArray_DATA(nparr) if hasattr(self, "extdim"): extdim = self.extdim else: extdim = -1 # Do the physical read with nogil: ret = H5ARRAYread(self.dataset_id, self.type_id, start, nrows, step, extdim, rbuf) try: if ret < 0: raise HDF5ExtError("Problems reading the array data.") # Get the pointer to the buffer data area if self.atom.kind == "reference": load_reference(self.dataset_id, rbuf, item_size, nparr) finally: if refbuf: free(refbuf) refbuf = NULL if self.atom.kind == 'time': # Swap the byteorder by hand (this is not currently supported by HDF5) if H5Tget_order(self.type_id) != platform_byteorder: nparr.byteswap(True) # Convert some HDF5 types to NumPy after reading. if self.atom.type == 'time64': self._convert_time64(nparr, 1) return def _g_read_slice(self, ndarray startl, ndarray stopl, ndarray stepl, ndarray nparr): cdef herr_t ret cdef hsize_t *start cdef hsize_t *stop cdef hsize_t *step cdef void *rbuf cdef size_t item_size = H5Tget_size(self.type_id) cdef void * refbuf = NULL # Get the pointer to the buffer data area of startl, stopl and stepl arrays start = PyArray_DATA(startl) stop = PyArray_DATA(stopl) step = PyArray_DATA(stepl) # Get the pointer to the buffer data area if self.atom.kind == "reference": refbuf = malloc(nparr.size * item_size) rbuf = refbuf else: rbuf = PyArray_DATA(nparr) # Do the physical read with nogil: ret = H5ARRAYreadSlice(self.dataset_id, self.type_id, start, stop, step, rbuf) try: if ret < 0: raise HDF5ExtError("Problems reading the array data.") # Get the pointer to the buffer data area if self.atom.kind == "reference": load_reference(self.dataset_id, rbuf, item_size, nparr) finally: if refbuf: free(refbuf) refbuf = NULL if self.atom.kind == 'time': # Swap the byteorder by hand (this is not currently supported by HDF5) if H5Tget_order(self.type_id) != platform_byteorder: nparr.byteswap(True) # Convert some HDF5 types to NumPy after reading if self.atom.type == 'time64': self._convert_time64(nparr, 1) return def _g_read_coords(self, ndarray coords, ndarray nparr): """Read coordinates in an already created NumPy array.""" cdef herr_t ret cdef hid_t space_id cdef hid_t mem_space_id cdef hsize_t size cdef void *rbuf cdef object mode cdef size_t item_size = H5Tget_size(self.type_id) cdef void * refbuf = NULL # Get the dataspace handle space_id = H5Dget_space(self.dataset_id) # Create a memory dataspace handle size = nparr.size mem_space_id = H5Screate_simple(1, &size, NULL) # Select the dataspace to be read H5Sselect_elements(space_id, H5S_SELECT_SET, size, PyArray_DATA(coords)) # Get the pointer to the buffer data area if self.atom.kind == "reference": refbuf = malloc(nparr.size * item_size) rbuf = refbuf else: rbuf = PyArray_DATA(nparr) # Do the actual read with nogil: ret = H5Dread(self.dataset_id, self.type_id, mem_space_id, space_id, H5P_DEFAULT, rbuf) try: if ret < 0: raise HDF5ExtError("Problems reading the array data.") # Get the pointer to the buffer data area if self.atom.kind == "reference": load_reference(self.dataset_id, rbuf, item_size, nparr) finally: if refbuf: free(refbuf) refbuf = NULL # Terminate access to the memory dataspace H5Sclose(mem_space_id) # Terminate access to the dataspace H5Sclose(space_id) if self.atom.kind == 'time': # Swap the byteorder by hand (this is not currently supported by HDF5) if H5Tget_order(self.type_id) != platform_byteorder: nparr.byteswap(True) # Convert some HDF5 types to NumPy after reading if self.atom.type == 'time64': self._convert_time64(nparr, 1) return def perform_selection(self, space_id, start, count, step, idx, mode): """Performs a selection using start/count/step in the given axis. All other axes have their full range selected. The selection is added to the current `space_id` selection using the given mode. Note: This is a backport from the h5py project. """ cdef int select_mode cdef ndarray start_, count_, step_ cdef hsize_t *startp cdef hsize_t *countp cdef hsize_t *stepp # Build arrays for the selection parameters startl, countl, stepl = [], [], [] for i, x in enumerate(self.shape): if i != idx: startl.append(0) countl.append(x) stepl.append(1) else: startl.append(start) countl.append(count) stepl.append(step) start_ = numpy.array(startl, dtype="i8") count_ = numpy.array(countl, dtype="i8") step_ = numpy.array(stepl, dtype="i8") # Get the pointers to array data startp = PyArray_DATA(start_) countp = PyArray_DATA(count_) stepp = PyArray_DATA(step_) # Do the actual selection select_modes = {"AND": H5S_SELECT_AND, "NOTB": H5S_SELECT_NOTB} assert mode in select_modes select_mode = select_modes[mode] H5Sselect_hyperslab(space_id, select_mode, startp, stepp, countp, NULL) def _g_read_selection(self, object selection, ndarray nparr): """Read a selection in an already created NumPy array.""" cdef herr_t ret cdef hid_t space_id cdef hid_t mem_space_id cdef hsize_t size cdef void *rbuf cdef object mode cdef size_t item_size = H5Tget_size(self.type_id) cdef void * refbuf = NULL # Get the dataspace handle space_id = H5Dget_space(self.dataset_id) # Create a memory dataspace handle size = nparr.size mem_space_id = H5Screate_simple(1, &size, NULL) # Select the dataspace to be read # Start by selecting everything H5Sselect_all(space_id) # Now refine with outstanding selections for args in selection: self.perform_selection(space_id, *args) # Get the pointer to the buffer data area if self.atom.kind == "reference": refbuf = malloc(nparr.size * item_size) rbuf = refbuf else: rbuf = PyArray_DATA(nparr) # Do the actual read with nogil: ret = H5Dread(self.dataset_id, self.type_id, mem_space_id, space_id, H5P_DEFAULT, rbuf) try: if ret < 0: raise HDF5ExtError("Problems reading the array data.") # Get the pointer to the buffer data area if self.atom.kind == "reference": load_reference(self.dataset_id, rbuf, item_size, nparr) finally: if refbuf: free(refbuf) refbuf = NULL # Terminate access to the memory dataspace H5Sclose(mem_space_id) # Terminate access to the dataspace H5Sclose(space_id) if self.atom.kind == 'time': # Swap the byteorder by hand (this is not currently supported by HDF5) if H5Tget_order(self.type_id) != platform_byteorder: nparr.byteswap(True) # Convert some HDF5 types to NumPy after reading if self.atom.type == 'time64': self._convert_time64(nparr, 1) return def _g_write_slice(self, ndarray startl, ndarray stepl, ndarray countl, ndarray nparr): """Write a slice in an already created NumPy array.""" cdef int ret cdef void *rbuf cdef void *temp cdef hsize_t *start cdef hsize_t *step cdef hsize_t *count if self.atom.kind == "reference": raise ValueError("Cannot write reference types yet") # Get the pointer to the buffer data area rbuf = PyArray_DATA(nparr) # Get the start, step and count values start = PyArray_DATA(startl) step = PyArray_DATA(stepl) count = PyArray_DATA(countl) # Convert some NumPy types to HDF5 before storing. if self.atom.type == 'time64': self._convert_time64(nparr, 0) # Modify the elements: with nogil: ret = H5ARRAYwrite_records(self.dataset_id, self.type_id, self.rank, start, step, count, rbuf) if ret < 0: raise HDF5ExtError("Internal error modifying the elements " "(H5ARRAYwrite_records returned errorcode -%i)" % (-ret)) return def _g_write_coords(self, ndarray coords, ndarray nparr): """Write a selection in an already created NumPy array.""" cdef herr_t ret cdef hid_t space_id cdef hid_t mem_space_id cdef hsize_t size cdef void *rbuf cdef object mode if self.atom.kind == "reference": raise ValueError("Cannot write reference types yet") # Get the dataspace handle space_id = H5Dget_space(self.dataset_id) # Create a memory dataspace handle size = nparr.size mem_space_id = H5Screate_simple(1, &size, NULL) # Select the dataspace to be written H5Sselect_elements(space_id, H5S_SELECT_SET, size, PyArray_DATA(coords)) # Get the pointer to the buffer data area rbuf = PyArray_DATA(nparr) # Convert some NumPy types to HDF5 before storing. if self.atom.type == 'time64': self._convert_time64(nparr, 0) # Do the actual write with nogil: ret = H5Dwrite(self.dataset_id, self.type_id, mem_space_id, space_id, H5P_DEFAULT, rbuf) if ret < 0: raise HDF5ExtError("Problems writing the array data.") # Terminate access to the memory dataspace H5Sclose(mem_space_id) # Terminate access to the dataspace H5Sclose(space_id) return def _g_write_selection(self, object selection, ndarray nparr): """Write a selection in an already created NumPy array.""" cdef herr_t ret cdef hid_t space_id cdef hid_t mem_space_id cdef hsize_t size cdef void *rbuf cdef object mode if self.atom.kind == "reference": raise ValueError("Cannot write reference types yet") # Get the dataspace handle space_id = H5Dget_space(self.dataset_id) # Create a memory dataspace handle size = nparr.size mem_space_id = H5Screate_simple(1, &size, NULL) # Select the dataspace to be written # Start by selecting everything H5Sselect_all(space_id) # Now refine with outstanding selections for args in selection: self.perform_selection(space_id, *args) # Get the pointer to the buffer data area rbuf = PyArray_DATA(nparr) # Convert some NumPy types to HDF5 before storing. if self.atom.type == 'time64': self._convert_time64(nparr, 0) # Do the actual write with nogil: ret = H5Dwrite(self.dataset_id, self.type_id, mem_space_id, space_id, H5P_DEFAULT, rbuf) if ret < 0: raise HDF5ExtError("Problems writing the array data.") # Terminate access to the memory dataspace H5Sclose(mem_space_id) # Terminate access to the dataspace H5Sclose(space_id) return def __dealloc__(self): if self.dims: free(self.dims) if self.maxdims: free(self.maxdims) if self.dims_chunk: free(self.dims_chunk) cdef class VLArray(Leaf): # Instance variables cdef hsize_t nrecords def _create_array(self, object title): cdef int rank cdef hsize_t *dims cdef herr_t ret cdef void *rbuf cdef bytes complib, version, class_ cdef object type_, itemsize, atom, scatom cdef bytes encoded_title, encoded_name cdef H5T_cset_t cset = H5T_CSET_ASCII encoded_title = title.encode('utf-8') encoded_name = self.name.encode('utf-8') atom = self.atom if not hasattr(atom, 'size'): # it is a pseudo-atom atom = atom.base # Get the HDF5 type of the *scalar* atom scatom = atom.copy(shape=()) self.base_type_id = atom_to_hdf5_type(scatom, self.byteorder) if self.base_type_id < 0: raise HDF5ExtError( "Problems creating the %s: invalid base type ID for atom %s" % ( self.__class__.__name__, scatom)) # Allocate space for the dimension axis info rank = len(atom.shape) dims = malloc_dims(atom.shape) rbuf = NULL # We don't have data to save initially # Encode strings complib = (self.filters.complib or '').encode('utf-8') version = self._v_version.encode('utf-8') class_ = self._c_classid.encode('utf-8') # Create the vlarray self.dataset_id = H5VLARRAYmake(self.parent_id, encoded_name, version, rank, dims, self.base_type_id, self.chunkshape[0], rbuf, self.filters.complevel, complib, self.filters.shuffle_bitshuffle, self.filters.fletcher32, self._want_track_times, rbuf) if dims: free(dims) if self.dataset_id < 0: raise HDF5ExtError("Problems creating the VLArray.") self.nrecords = 0 # Initialize the number of records saved if self._v_file.params['PYTABLES_SYS_ATTRS']: cset = H5T_CSET_UTF8 # Set the conforming array attributes H5ATTRset_attribute_string(self.dataset_id, "CLASS", class_, len(class_), cset) H5ATTRset_attribute_string(self.dataset_id, "VERSION", version, len(version), cset) H5ATTRset_attribute_string(self.dataset_id, "TITLE", encoded_title, len(encoded_title), cset) # Get the datatype handles self.disk_type_id, self.type_id = self._get_type_ids() return self.dataset_id def _open_array(self): cdef char cbyteorder[11] # "irrelevant" fits easily here cdef int i, enumtype cdef int rank cdef herr_t ret cdef hsize_t nrecords, chunksize cdef object shape, type_ cdef bytes encoded_name cdef str byteorder encoded_name = self.name.encode('utf-8') # Open the dataset self.dataset_id = H5Dopen(self.parent_id, encoded_name, H5P_DEFAULT) if self.dataset_id < 0: raise HDF5ExtError("Non-existing node ``%s`` under ``%s``" % (self.name, self._v_parent._v_pathname)) # Get the datatype handles self.disk_type_id, self.type_id = self._get_type_ids() # Get the atom for this type atom = atom_from_hdf5_type(self.type_id) # Get info on dimensions & types (of base class) H5VLARRAYget_info(self.dataset_id, self.disk_type_id, &nrecords, cbyteorder) byteorder = cstr_to_pystr(cbyteorder) # Get some properties of the atomic type self._atomicdtype = atom.dtype self._atomictype = atom.type self._atomicshape = atom.shape self._atomicsize = atom.size # Get the byteorder self.byteorder = correct_byteorder(atom.type, byteorder) # Get the chunkshape (VLArrays are unidimensional entities) H5ARRAYget_chunkshape(self.dataset_id, 1, &chunksize) self.nrecords = nrecords # Initialize the number of records saved return self.dataset_id, SizeType(nrecords), (SizeType(chunksize),), atom def _append(self, ndarray nparr, int nobjects): cdef int ret cdef void *rbuf # Get the pointer to the buffer data area if nobjects: rbuf = PyArray_DATA(nparr) # Convert some NumPy types to HDF5 before storing. if self.atom.type == 'time64': self._convert_time64(nparr, 0) else: rbuf = NULL # Append the records: with nogil: ret = H5VLARRAYappend_records(self.dataset_id, self.type_id, nobjects, self.nrecords, rbuf) if ret < 0: raise HDF5ExtError("Problems appending the records.") self.nrecords = self.nrecords + 1 def _modify(self, hsize_t nrow, ndarray nparr, int nobjects): cdef int ret cdef void *rbuf # Get the pointer to the buffer data area rbuf = PyArray_DATA(nparr) if nobjects: # Convert some NumPy types to HDF5 before storing. if self.atom.type == 'time64': self._convert_time64(nparr, 0) # Append the records: with nogil: ret = H5VLARRAYmodify_records(self.dataset_id, self.type_id, nrow, nobjects, rbuf) if ret < 0: raise HDF5ExtError("Problems modifying the record.") return nobjects # Because the size of each "row" is unknown, there is no easy way to # calculate this value def _get_memory_size(self): cdef hid_t space_id cdef hsize_t size cdef herr_t ret if self.nrows == 0: size = 0 else: # Get the dataspace handle space_id = H5Dget_space(self.dataset_id) # Return the size of the entire dataset ret = H5Dvlen_get_buf_size(self.dataset_id, self.type_id, space_id, &size) if ret < 0: size = -1 # Terminate access to the dataspace H5Sclose(space_id) return size def _read_array(self, hsize_t start, hsize_t stop, hsize_t step): cdef int i cdef size_t vllen cdef herr_t ret cdef hvl_t *rdata cdef hsize_t nrows cdef hid_t space_id cdef hid_t mem_space_id cdef object buf, nparr, shape, datalist # Compute the number of rows to read nrows = get_len_of_range(start, stop, step) if start + nrows > self.nrows: raise HDF5ExtError( "Asking for a range of rows exceeding the available ones!.", h5bt=False) # Now, read the chunk of rows with nogil: # Allocate the necessary memory for keeping the row handlers rdata = malloc(nrows*sizeof(hvl_t)) # Get the dataspace handle space_id = H5Dget_space(self.dataset_id) # Create a memory dataspace handle mem_space_id = H5Screate_simple(1, &nrows, NULL) # Select the data to be read H5Sselect_hyperslab(space_id, H5S_SELECT_SET, &start, &step, &nrows, NULL) # Do the actual read ret = H5Dread(self.dataset_id, self.type_id, mem_space_id, space_id, H5P_DEFAULT, rdata) if ret < 0: raise HDF5ExtError( "VLArray._read_array: Problems reading the array data.") datalist = [] for i from 0 <= i < nrows: # Number of atoms in row vllen = rdata[i].len # Get the pointer to the buffer data area if vllen > 0: # Create a buffer to keep this info. It is important to do a # copy, because we will dispose the buffer memory later on by # calling the H5Dvlen_reclaim. PyByteArray_FromStringAndSize does this. buf = PyByteArray_FromStringAndSize(rdata[i].p, vllen*self._atomicsize) else: # Case where there is info with zero lentgh buf = None # Compute the shape for the read array shape = list(self._atomicshape) shape.insert(0, vllen) # put the length at the beginning of the shape nparr = numpy.ndarray( buffer=buf, dtype=self._atomicdtype.base, shape=shape) # Set the writeable flag for this ndarray object nparr.flags.writeable = True if self.atom.kind == 'time': # Swap the byteorder by hand (this is not currently supported by HDF5) if H5Tget_order(self.type_id) != platform_byteorder: nparr.byteswap(True) # Convert some HDF5 types to NumPy after reading. if self.atom.type == 'time64': self._convert_time64(nparr, 1) # Append this array to the output list datalist.append(nparr) # Release resources # Reclaim all the (nested) VL data ret = H5Dvlen_reclaim(self.type_id, mem_space_id, H5P_DEFAULT, rdata) if ret < 0: raise HDF5ExtError("VLArray._read_array: error freeing the data buffer.") # Terminate access to the memory dataspace H5Sclose(mem_space_id) # Terminate access to the dataspace H5Sclose(space_id) # Free the amount of row pointers to VL row data free(rdata) return datalist def get_row_size(self, row): """Return the total size in bytes of all the elements contained in a given row.""" cdef hid_t space_id cdef hsize_t size cdef herr_t ret cdef hsize_t offset[1] cdef hsize_t count[1] if row >= self.nrows: raise HDF5ExtError( "Asking for a range of rows exceeding the available ones!.", h5bt=False) # Get the dataspace handle space_id = H5Dget_space(self.dataset_id) offset[0] = row count[0] = 1 ret = H5Sselect_hyperslab(space_id, H5S_SELECT_SET, offset, NULL, count, NULL); if ret < 0: size = -1 ret = H5Dvlen_get_buf_size(self.dataset_id, self.type_id, space_id, &size) if ret < 0: size = -1 # Terminate access to the dataspace H5Sclose(space_id) return size cdef class UnImplemented(Leaf): def _open_unimplemented(self): cdef object shape cdef char cbyteorder[11] # "irrelevant" fits easily here cdef bytes encoded_name cdef str byteorder encoded_name = self.name.encode('utf-8') # Get info on dimensions shape = H5UIget_info(self.parent_id, encoded_name, cbyteorder) shape = tuple(map(SizeType, shape)) self.dataset_id = H5Dopen(self.parent_id, encoded_name, H5P_DEFAULT) byteorder = cstr_to_pystr(cbyteorder) return (shape, byteorder, self.dataset_id) def _g_close(self): H5Dclose(self.dataset_id) ## Local Variables: ## mode: python ## py-indent-offset: 2 ## tab-width: 2 ## fill-column: 78 ## End: