The following are 16 code examples for showing how to useh5py.File. They are extracted from open source Python projects.You can click
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Example 1
From project PyMVPA, under directory mvpa2/base, in source file hdf5.py.
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def h5save(filename, data, name=None, mode='w', mkdir=True, **kwargs): """Stores arbitrary data in an HDF5 file. This is a convenience wrapper around `obj2hdf()`. Please see its documentation for more details -- especially the warnings!! Parameters ---------- filename : str Name of the file the data shall be stored in. data : arbitrary Instance of an object that shall be stored in the file. name : str or None Name of the object. In case of a complex object that cannot be stored natively without disassembling them, this is going to be a new group, otherwise the name of the dataset. If None, no new group is created. mode : {'r', 'r+', 'w', 'w-', 'a'} IO mode of the HDF5 file. See `h5py.File` documentation for more information. mkdir : bool, optional Create target directory if it does not exist yet. **kwargs All additional arguments will be passed to `h5py.Group.create_dataset`. This could, for example, be `compression='gzip'`. """ if mkdir: target_dir = osp.dirname(filename) if target_dir and not osp.exists(target_dir): os.makedirs(target_dir) hdf = h5py.File(filename, mode) hdf.attrs.create('__pymvpa_hdf5_version__', '2') hdf.attrs.create('__pymvpa_version__', mvpa2.__version__) try: obj2hdf(hdf, data, name, **kwargs) finally: hdf.close() Example 2
From project PyMVPA, under directory mvpa/base, in source file hdf5.py.
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def h5save(filename, data, name=None, mode='w', **kwargs): """Stores arbitray data in an HDF5 file. This is a convenience wrapper around `obj2hdf()`. Please see its documentation for more details -- especially the warnings!! Parameters ---------- filename : str Name of the file the data shall be stored in. data : arbitrary Instance of an object that shall be stored in the file. name : str or None Name of the object. In case of a complex object that cannot be stored natively without disassembling them, this is going to be a new group, otherwise the name of the dataset. If None, no new group is created. mode : {'r', 'r+', 'w', 'w-', 'a'} IO mode of the HDF5 file. See `h5py.File` documentation for more information. **kwargs All additional arguments will be passed to `h5py.Group.create_dataset`. This could, for example, be `compression='gzip'`. """ hdf = h5py.File(filename, mode) hdf.attrs.create('__pymvpa_hdf5_version__', 1) try: obj2hdf(hdf, data, name, **kwargs) finally: hdf.close() Example 3
From project fos-legacy, under directory examples/neurons, in source file swc2hdf2.py.
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def create_hdf(pos, parents, labeling, colors): # create extendable hdf5 file f = h5py.File('neurons2.hdf5', 'w') neurons = f.create_group('neurons') neurons.create_dataset('position', data=pos) neurons.create_dataset('localtopology', data=parents.astype( np.int32 )) neurons.create_dataset('labeling', data=labeling) neurons.create_dataset('segmentcolors', data=colors) f.close() Example 4
From project fos-legacy, under directory examples/neurons, in source file swc2hdf.py.
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def create_hdf(pos, offset, parents, colors): # create extendable hdf5 file f = h5py.File('neurons.hdf5', 'w') neurons = f.create_group('neurons') neurons.create_dataset('positions', data=pos) neurons.create_dataset('offset', data=offset) neurons.create_dataset('parents', data=parents) neurons.create_dataset('colors', data=colors) f.close() Example 5
From project hifive-master, under directory test, in source file test_fivec_data.py.
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def setUp(self): self.data = h5py.File('test/data/test_import.fcd', 'r') self.frag_fname = 'test/data/test.frags' self.count_fname = 'test/data/test.counts' self.bam_fname1 = 'test/data/test_fivec_1.bam' self.bam_fname2 = 'test/data/test_fivec_2.bam' Example 6
From project hifive-master, under directory test, in source file test_hic_project.py.
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def test_hic_project_preanalysis(self): subprocess.call("./bin/hifive hic-project -q -m 20000 -f 10 -j 30000 -n 5 %s test/data/test_temp.hcp" % (self.data_fname), shell=True) project = h5py.File('test/data/test_temp.hcp', 'r') self.compare_hdf5_dicts(self.data, project, 'project') Example 7
From project hifive-master, under directory test, in source file test_fivec_project.py.
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def test_fivec_project_preanalysis(self): subprocess.call("./bin/hifive 5c-project -q -f 20 %s test/data/test_temp.fcp" % self.data_fname, shell=True) project = h5py.File('test/data/test_temp.fcp', 'r') self.compare_hdf5_dicts(self.raw, project, 'project') Example 8
From project hifive-master, under directory test, in source file test_fivec_binning.py.
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def test_generate_heatmap(self): subprocess.call("./bin/hifive 5c-heatmap -q -b 50000 -t -d fragment -a full %s test/data/test_temp.fch" % self.project_fname, shell=True) heatmap = h5py.File("test/data/test_temp.fch") self.compare_hdf5_dicts(self.heatmap, heatmap, 'heatmap') Example 9
From project fuel-master, under directory fuel/datasets, in source file hdf5.py.
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def __init__(self, file_or_path, which_sets, subset=None, load_in_memory=False, driver=None, sort_indices=True, **kwargs): if isinstance(file_or_path, h5py.File): self.path = file_or_path.filename self.external_file_handle = file_or_path else: self.path = file_or_path self.external_file_handle = None which_sets_invalid_value = ( isinstance(which_sets, six.string_types) or not all(isinstance(s, six.string_types) for s in which_sets)) if which_sets_invalid_value: raise ValueError('`which_sets` should be an iterable of strings') self.which_sets = which_sets self._subset_template = subset if subset else slice(None) self.load_in_memory = load_in_memory self.driver = driver self.sort_indices = sort_indices self._parse_dataset_info() kwargs.setdefault('axis_labels', self.default_axis_labels) super(H5PYDataset, self).__init__(**kwargs) Example 10
From project fuel-master, under directory tests, in source file test_converters.py.
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def setUp(self): self.h5file = h5py.File( 'file.hdf5', mode='w', driver='core', backing_store=False) self.train_features = numpy.arange( 16, dtype='uint8').reshape((4, 2, 2)) self.test_features = numpy.arange( 8, dtype='uint8').reshape((2, 2, 2)) + 3 self.train_targets = numpy.arange( 4, dtype='float32').reshape((4, 1)) self.test_targets = numpy.arange( 2, dtype='float32').reshape((2, 1)) + 3
Example 11
From project fuel-master, under directory tests, in source file test_svhn.py.
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def test_svhn(): data_path = config.data_path try: config.data_path = '.' f = h5py.File('svhn_format_2.hdf5', 'w') f['features'] = numpy.arange(100, dtype='uint8').reshape((10, 10)) f['targets'] = numpy.arange(10, dtype='uint8').reshape((10, 1)) split_dict = {'train': {'features': (0, 8), 'targets': (0, 8)}, 'test': {'features': (8, 10), 'targets': (8, 10)}} f.attrs['split'] = H5PYDataset.create_split_array(split_dict) f.close() dataset = SVHN(which_format=2, which_sets=('train',)) assert_equal(dataset.filename, 'svhn_format_2.hdf5') finally: config.data_path = data_path os.remove('svhn_format_2.hdf5') Example 12
From project ILTIS-master, under directory lib/pyqtgraph-master/examples, in source file hdf5.py.
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def createFile(finalSize=2000000000): """Create a large HDF5 data file for testing. Data consists of 1M random samples tiled through the end of the array. """ chunk = np.random.normal(size=1000000).astype(np.float32) f = h5py.File('test.hdf5', 'w') f.create_dataset('data', data=chunk, chunks=True, maxshape=(None,)) data = f['data'] nChunks = finalSize // (chunk.size * chunk.itemsize) with pg.ProgressDialog("Generating test.hdf5...", 0, nChunks) as dlg: for i in range(nChunks): newshape = [data.shape[0] + chunk.shape[0]] data.resize(newshape) data[-chunk.shape[0]:] = chunk dlg += 1 if dlg.wasCanceled(): f.close() os.remove('test.hdf5') sys.exit() dlg += 1 f.close() Example 13
From project dask-master, under directory dask/array, in source file core.py.
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def store(sources, targets, **kwargs): """ Store dask arrays in array-like objects, overwrite data in target This stores dask arrays into object that supports numpy-style setitem indexing. It stores values chunk by chunk so that it does not have to fill up memory. For best performance you can align the block size of the storage target with the block size of your array. If your data fits in memory then you may prefer calling ``np.array(myarray)`` instead. Parameters ---------- sources: Array or iterable of Arrays targets: array-like or iterable of array-likes These should support setitem syntax ``target[10:20] = ...`` Examples -------- >>> x = ... # doctest: +SKIP >>> import h5py # doctest: +SKIP >>> f = h5py.File('myfile.hdf5') # doctest: +SKIP >>> dset = f.create_dataset('/data', shape=x.shape, ... chunks=x.chunks, ... dtype='f8') # doctest: +SKIP >>> store(x, dset) # doctest: +SKIP Alternatively store many arrays at the same time >>> store([x, y, z], [dset1, dset2, dset3]) # doctest: +SKIP """ if isinstance(sources, Array): sources = [sources] targets = [targets] if any(not isinstance(s, Array) for s in sources): raise ValueError("All sources must be dask array objects") if len(sources) != len(targets): raise ValueError("Different number of sources [%d] and targets [%d]" % (len(sources), len(targets))) updates = [insert_to_ooc(tgt, src) for tgt, src in zip(targets, sources)] dsk = merge([src.dask for src in sources] + updates) keys = [key for u in updates for key in u] Array._get(dsk, keys, **kwargs) Example 14
From project SparseNet-master, under directory sparsenet/dataset, in source file svhn.py.
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def load_extra_torch(): myFile = h5py.File(nn.nas_address()+'/PSI-Share-no-backup/Ali/Dataset/SVHN/torch/svhn_extra_rgb_13.h5', 'r') # myFile = h5py.File('svhn_old.h5', 'r') X = np.array(myFile['X']) # temp = X[10000:10900,:,:,:] # nn.show_images(temp,(30,30)); plt.show() T_train_labels = np.array(myFile['T_train_labels']) T_train_labels = T_train_labels%10 # print T_train_labels[100000:1000010] print "dataset loaded" T = np.zeros((600000,10)) for i in range(600000): # if i%10000==0: # print i,T_train_labels[i:i+10] T[i,T_train_labels[i]]= 1 X_test = np.array(myFile['X_test'])[:10000,:,:,:] T_labels = np.array(myFile['T_labels'])[:10000] T_labels = T_labels%10 T_test = np.zeros((10000,10)) for i in range(10000): T_test[i,T_labels[i]]= 1 # if want_bw: # X = X[:,:1,:,:].reshape(70000,1024) # X_test = X_test[:,:1,:,:].reshape(70000,1024) # return X,T,X_test,T_test,T_train_labels,T_labels # if want_dense: # X = X.reshape(70000,3072) # X_test = X_test.reshape(10000,3072) return X,T,X_test,T_test,T_train_labels,T_labels Example 15
From project praxes, under directory praxes/combi, in source file XRDdefaults.py.
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def WAVESET1dFILE(mode='r'): if mode!='r': mode='r+'#to avoid 'w' return h5py.File('C:/Users/JohnnyG/Documents/CHESS/CHESSANALYSISARRAYS/waveset1d.h5',mode=mode) Example 16
From project hifive-master, under directory test, in source file test_fend.py.
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def setUp(self): self.fends = h5py.File('test/data/test.fends', 'r') self.bed_fname = 'test/data/test_fend.bed' 联系客服
