Python numpy.savez() 使用实例

The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.

Example 1

def save(self, filepath):
    """ Save model parameters to file.
    """
    all_params = OrderedDict([(param.name, param.get_value()) for param in self.params])
    numpy.savez(filepath, **all_params)
    print('Saved model to: {}'.format(filepath)) 

Example 2

def tf2npz(tf_path, export_folder=FAST):
    vid_ids = []
    labels = []
    mean_rgb = []
    mean_audio = []
    tf_basename = os.path.basename(tf_path)
    npz_basename = tf_basename[:-len('.tfrecord')] + '.npz'
    isTrain = '/test' not in tf_path

    for example in tf.python_io.tf_record_iterator(tf_path):
        tf_example = tf.train.Example.FromString(example).features
        vid_ids.append(tf_example.feature['video_id'].bytes_list.value[0].decode(encoding='UTF-8'))
        if isTrain:
            labels.append(np.array(tf_example.feature['labels'].int64_list.value))
        mean_rgb.append(np.array(tf_example.feature['mean_rgb'].float_list.value).astype(np.float32))
        mean_audio.append(np.array(tf_example.feature['mean_audio'].float_list.value).astype(np.float32))

    save_path = export_folder + '/' + npz_basename
    np.savez(save_path,
             rgb=StandardScaler().fit_transform(np.array(mean_rgb)),
             audio=StandardScaler().fit_transform(np.array(mean_audio)),
             ids=np.array(vid_ids),
             labels=labels
             ) 

Example 3

def run(self):
        
        # generate dictionary of features
        features = {'tokens':{'n_list':self.ngrams.split(), 'blackfeats':self.blackfeats.split(), 'mt':self.minimum_token_frequency}}
        
        # format lines
        documents = [[doc] for doc in format_tokdoc(self.in_tokenized().path,self.lowercase)]

        # extract features
        ft = featurizer.Featurizer(documents, features)
        ft.fit_transform()
        instances, vocabulary = ft.return_instances(['tokens'])

        # write output
        numpy.savez(self.out_features().path, data=instances.data, indices=instances.indices, indptr=instances.indptr, shape=instances.shape)
        vocabulary = list(vocabulary)
        with open(self.out_vocabulary().path,'w',encoding='utf-8') as vocab_out:
            vocab_out.write('\n'.join(vocabulary))


# When the input is a directory with tokenized documents 

Example 4

def run(self):
        
        # generate dictionary of features
        features = {'tokens':{'n_list':self.ngrams.split(), 'blackfeats':self.blackfeats.split(), 'mt':self.minimum_token_frequency}}
        
        # read in files and put in right format for featurizer
        documents = []
        for infile in sorted(listdir(self.in_tokdir().path),key=keyfunc):
            documents.append(format_tokdoc(self.in_tokdir().path + '/' + infile,self.lowercase))
            
        # extract features
        ft = featurizer.Featurizer(documents, features) # to prevent ngrams across sentences, a featurizer is generated per document
        ft.fit_transform()
        instances, vocabulary = ft.return_instances(['tokens'])

        # write output
        numpy.savez(self.out_features().path, data=instances.data, indices=instances.indices, indptr=instances.indptr, shape=instances.shape)
        with open(self.out_vocabulary().path,'w',encoding='utf-8') as vocab_out:
            vocab_out.write('\n'.join(vocabulary))


# when the input is a file with frogged documents 

Example 5

def run(self):

        # read in vectors
        loader = numpy.load(self.in_vectors().path)
        instances = sparse.csr_matrix((loader['data'], loader['indices'], loader['indptr']), shape = loader['shape'])
        num_dimensions = instances.shape[1]

        # generate vectorpopulation
        random_vectorpopulation = ga_functions.random_vectorpopulation(num_dimensions, self.population_size)
        numpy.savez(self.out_vectorpopulation().path, data=random_vectorpopulation.data, indices=random_vectorpopulation.indices, indptr=random_vectorpopulation.indptr, shape=random_vectorpopulation.shape)

        # read in parameter options
        with open(self.in_parameter_options().path) as infile:
            lines = infile.read().rstrip().split('\n')
            parameter_options = [[i for i in range(len(line.split()))] for line in lines]

        # generate parameterpopulation
        random_parameterpopulation = ga_functions.random_parameterpopulation(parameter_options, self.population_size)
        numpy.savez(self.out_parameterpopulation().path, data=random_parameterpopulation.data, indices=random_parameterpopulation.indices, indptr=random_parameterpopulation.indptr, shape=random_parameterpopulation.shape)



################################################################################
###GA Iterator
################################################################################ 

Example 6

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
        File name of ``.npz`` file.
    args : Arguments
        Function arguments.
    kwds : Keyword arguments
        Keywords.

    See Also
    --------
    numpy.savez : Save several arrays into an uncompressed ``.npz`` file format
    numpy.load : Load the files created by savez_compressed.

    """
    _savez(file, args, kwds, True) 

Example 7

def test_closing_fid(self):
        # Test that issue #1517 (too many opened files) remains closed
        # It might be a "weak" test since failed to get triggered on
        # e.g. Debian sid of 2012 Jul 05 but was reported to
        # trigger the failure on Ubuntu 10.04:
        # http://projects.scipy.org/numpy/ticket/1517#comment:2
        with temppath(suffix='.npz') as tmp:
            np.savez(tmp, data='LOVELY LOAD')
            # We need to check if the garbage collector can properly close
            # numpy npz file returned by np.load when their reference count
            # goes to zero.  Python 3 running in debug mode raises a
            # ResourceWarning when file closing is left to the garbage
            # collector, so we catch the warnings.  Because ResourceWarning
            # is unknown in Python < 3.x, we take the easy way out and
            # catch all warnings.
            with warnings.catch_warnings():
                warnings.simplefilter("ignore")
                for i in range(1, 1025):
                    try:
                        np.load(tmp)["data"]
                    except Exception as e:
                        msg = "Failed to load data from a file: %s" % e
                        raise AssertionError(msg) 

Example 8

def test_npzfile_dict():
    s = BytesIO()
    x = np.zeros((3, 3))
    y = np.zeros((3, 3))

    np.savez(s, x=x, y=y)
    s.seek(0)

    z = np.load(s)

    assert_('x' in z)
    assert_('y' in z)
    assert_('x' in z.keys())
    assert_('y' in z.keys())

    for f, a in z.items():
        assert_(f in ['x', 'y'])
        assert_equal(a.shape, (3, 3))

    assert_(len(z.items()) == 2)

    for f in z:
        assert_(f in ['x', 'y'])

    assert_('x' in z.keys()) 

Example 9

def test_load_refcount():
    # Check that objects returned by np.load are directly freed based on
    # their refcount, rather than needing the gc to collect them.

    f = BytesIO()
    np.savez(f, [1, 2, 3])
    f.seek(0)

    assert_(gc.isenabled())
    gc.disable()
    try:
        gc.collect()
        np.load(f)
        # gc.collect returns the number of unreachable objects in cycles that
        # were found -- we are checking that no cycles were created by np.load
        n_objects_in_cycles = gc.collect()
    finally:
        gc.enable()
    assert_equal(n_objects_in_cycles, 0) 

Example 10

def savez(file, *args, **kwds):
    """Saves one or more arrays into a file in uncompressed ``.npz`` format.

    Arguments without keys are treated as arguments with automatic keys named
    ``arr_0``, ``arr_1``, etc. corresponding to the positions in the argument
    list. The keys of arguments are used as keys in the ``.npz`` file, which
    are used for accessing NpzFile object when the file is read by
    :func:`cupy.load` function.

    Args:
        file (file or str): File or filename to save.
        *args: Arrays with implicit keys.
        **kwds: Arrays with explicit keys.

    .. seealso:: :func:`numpy.savez`

    """
    args = map(cupy.asnumpy, args)
    for key in kwds:
        kwds[key] = cupy.asnumpy(kwds[key])
    numpy.savez(file, *args, **kwds) 

Example 11

def save_stored_rewards(self, file_name):
    """Saves the models stored rewards over time in a .npz file.

    Args:
      file_name: Name of the file that will be saved.
    """
    training_epochs = len(self.rewards_batched) * self.output_every_nth
    filename = os.path.join(self.output_dir,
                            file_name + '-' + str(training_epochs))
    np.savez(filename,
             train_rewards=self.rewards_batched,
             train_music_theory_rewards=self.music_theory_rewards_batched,
             train_note_rnn_rewards=self.note_rnn_rewards_batched,
             eval_rewards=self.eval_avg_reward,
             eval_music_theory_rewards=self.eval_avg_music_theory_reward,
             eval_note_rnn_rewards=self.eval_avg_note_rnn_reward,
             target_val_list=self.target_val_list) 

Example 12

def test(self):
        self.model.best_params = [(x.name, x.get_value()) for x in
                                  self.model.params]
        numpy.savez(self.state['prefix'] + '_best_params',
                    **dict(self.model.best_params))
        self.state['best_params_pos'] = self.step
        if self.test_data is not None:
            rvals = self.model.validate(self.test_data)
        else:
            rvals = []
        msg = '>>>         Test'
        pos = self.step // self.state['validFreq']
        for k, v in rvals:
            msg = msg + ' ' + k + ':%6.3f ' % v
            self.timings['test' + k][pos] = float(v)
            self.state['test' + k] = float(v)
        print msg
        self.state['testtime'] = float(time.time()-self.start_time)/60. 

Example 13

def save(self):
        start = time.time()
        print "Saving the model..."

        # ignore keyboard interrupt while saving
        s = signal.signal(signal.SIGINT, signal.SIG_IGN)
        numpy.savez(self.state['prefix']+'timing.npz',
                    **self.timings)
        if self.state['overwrite']:
            self.model.save(self.state['prefix']+'model.npz')
        else:
            self.model.save(self.state['prefix'] +
                            'model%d.npz' % self.save_iter)
        cPickle.dump(self.state, open(self.state['prefix']+'state.pkl', 'w'))
        self.save_iter += 1
        signal.signal(signal.SIGINT, s)

        print "Model saved, took {}".format(time.time() - start)

    # FIXME 

Example 14

def test(self):
        self.model.best_params = [(x.name, x.get_value()) for x in
                                  self.model.params]
        numpy.savez(self.state['prefix'] + '_best_params',
                    **dict(self.model.best_params))
        self.state['best_params_pos'] = self.step
        if self.test_data is not None:
            rvals = self.model.validate(self.test_data)
        else:
            rvals = []
        msg = '>>>         Test'
        pos = self.step // self.state['validFreq']
        for k, v in rvals:
            msg = msg + ' ' + k + ':%6.3f ' % v
            self.timings['test' + k][pos] = float(v)
            self.state['test' + k] = float(v)
        print msg
        self.state['testtime'] = float(time.time()-self.start_time)/60. 

Example 15

def save_total_power(data,times,SCADA_faults,filename):
    total_power = np.array([])
    new_times = np.array([])
    percent_active = np.array([])    
    for time in np.unique(times):
        state_fault = SCADA_faults[times == time]
        fault_mask = [state_fault == 2,state_fault == 1]
        fault_mask = reduce(np.logical_or,fault_mask)
        
        total_power = np.append(total_power,np.sum(data[times == time]))
        new_times = np.append(new_times,time)
        percent_active = np.append(percent_active,float(np.sum(fault_mask))/float(len(fault_mask)))
        
        
    total_dictionary = {}
    total_dictionary['total_power'] = total_power
    total_dictionary['time'] = new_times
    total_dictionary['percent_active'] = percent_active
        
    file_path = os.path.normpath('%s/FormattedData/%s' % (os.getcwd(),filename))
    np.savez(file_path,**total_dictionary) 

Example 16

def main():
    args = check_argv()

    print datetime.datetime.now()

    print "Reading HTK features from directory:", args.htk_dir
    npz_dict = {}
    n_feat_files = 0
    for feat_fn in glob.glob(path.join(args.htk_dir, "*." + args.extension)):
        hlist_output = shell("HList -r " + feat_fn)
        features = [
            [float(i) for i in line.split(" ") if i != ""] for line in
            hlist_output.split("\n") if line != ""
            ]
        key = path.splitext(path.split(feat_fn)[-1])[0]
        npz_dict[key] = np.array(features)
        n_feat_files += 1
    print "Read", n_feat_files, "feature files"

    print "Writing Numpy archive:", args.npz_fn
    np.savez(args.npz_fn, **npz_dict)

    print datetime.datetime.now() 

Example 17

def main():
    args = check_argv()
    
    print("Reading:", args.mat_fn)
    mat = tables.open_file(args.mat_fn)

    n_audio = mat.root.files_train[0].shape[0]
    print("No. audio files:", n_audio)

    filenames = []
    for i_audio in xrange(n_audio):
        filenames.append("".join([chr(i[0]) for i in mat.root.files_train[0][i_audio][0]]))
    audio_keys = [path.splitext(path.split(i)[-1])[0] for i in filenames]

    features_dict = {}
    for i_audio in xrange(n_audio):
        features = mat.root.F_train_iter[0][i_audio][0]
        features_dict[audio_keys[i_audio].replace("_", "-")] = features.T

    print("Writing:", args.npz_fn)
    np.savez(args.npz_fn, **features_dict) 

Example 18

def cleanup(self,*args, **kwargs):
        
        from theanompi.models.lstm import zipp, unzip, get_minibatches_idx, pred_error
        
        if self.best_p is not None:
            zipp(self.best_p, self.tparams)
        else:
            self.best_p = unzip(self.tparams)

        self.use_noise.set_value(0.)
        kf_train_sorted = get_minibatches_idx(len(self.train[0]), self.model_options['batch_size'])
        train_err = pred_error(self.f_pred, self.prepare_data, self.train, kf_train_sorted)
        valid_err = pred_error(self.f_pred, self.prepare_data, self.valid, kf_valid)
        test_err = pred_error(self.f_pred, self.prepare_data, self.test, kf_test)

        if self.rank==0: print( 'Train ', train_err, 'Valid ', valid_err, 'Test ', test_err )
        if saveto:
            numpy.savez(self.model_options['saveto'], train_err=train_err,
                        valid_err=valid_err, test_err=test_err,
                        history_errs=self.history_errs, **self.best_p)
        # print('The code run for %d epochs, with %f sec/epochs' % (
        #     (self.eidx + 1), (end_time - start_time) / (1. * (self.eidx + 1))))
        # print( ('Training took %.1fs' %
        #         (end_time - start_time)), file=sys.stderr) 

Example 19

def test_error_bars_stacked2(cmdopt, data_gen):

    output = skh_plt.hist([data_gen[0], data_gen[1]], bins=20, histtype='barstacked',
                          weights=[data_gen[2], data_gen[2]], errorbars=True, err_return=True,
                          normed=True, scale=2)

    if cmdopt == "generate":
        with open(answer_dir+'/answers_error_bars_stacked2.npz', 'wb') as f:
            np.savez(f, bc=output[0], be=output[1], berr=output[2])
        plt.title('test_error_bars_stacked2')
        plt.show()
    elif cmdopt == "test":
        answers = np.load(answer_dir+'/answers_error_bars_stacked2.npz')
        assert(np.all(output[0] == answers['bc']))
        assert(np.all(output[1] == answers['be']))
        assert(np.all(output[2] == answers['berr'])) 

Example 20

def test_error_bars_stacked3(cmdopt, data_gen):

    output = skh_plt.hist([data_gen[0], data_gen[1]], bins=20, histtype='step', stacked=True,
                          weights=[data_gen[2], data_gen[2]], errorbars=True, err_return=True,
                          normed=True, scale=2)

    if cmdopt == "generate":
        with open(answer_dir+'/answers_error_bars_stacked3.npz', 'wb') as f:
            np.savez(f, bc=output[0], be=output[1], berr=output[2])
        plt.title('test_error_bars_stacked2')
        plt.show()
    elif cmdopt == "test":
        answers = np.load(answer_dir+'/answers_error_bars_stacked3.npz')
        assert(np.all(output[0] == answers['bc']))
        assert(np.all(output[1] == answers['be']))
        assert(np.all(output[2] == answers['berr'])) 

Example 21

def test_ratio_plot(cmdopt, data_gen):

    output = skh_plt.ratio_plot(dict(x=data_gen[0], errorbars=True, histtype='marker'),
                                dict(x=data_gen[1], weights=data_gen[2], errorbars=True))

    if cmdopt == "generate":
        with open(answer_dir+'/answers_ratio_plot.npz', 'wb') as f:
            np.savez(f, bc1=output[1][0], be1=output[1][1],
                     bc2=output[2][0], be2=output[2][1])
        output[0][0].set_title('test_ratio_plot')
        plt.show()
    elif cmdopt == "test":
        answers = np.load(answer_dir+'/answers_ratio_plot.npz')
        assert(np.all(output[1][0] == answers['bc1']))
        assert(np.all(output[1][1] == answers['be1']))
        assert(np.all(output[2][0] == answers['bc2']))
        assert(np.all(output[2][1] == answers['be2'])) 

Example 22

def test_ratio_plot_log(cmdopt, data_gen):

    output = skh_plt.ratio_plot(dict(x=data_gen[0], errorbars=True, histtype='marker', log=True,
                                     err_x=False),
                                dict(x=data_gen[1], weights=data_gen[2], errorbars=True),
                                logx=True, ratio_range=(0, 10))

    if cmdopt == "generate":
        with open(answer_dir+'/answers_ratio_plot_log.npz', 'wb') as f:
            np.savez(f, bc1=output[1][0], be1=output[1][1],
                     bc2=output[2][0], be2=output[2][1])
        output[0][0].set_title('test_ratio_plot_log')
        plt.show()
    elif cmdopt == "test":
        answers = np.load(answer_dir+'/answers_ratio_plot_log.npz')
        assert(np.all(output[1][0] == answers['bc1']))
        assert(np.all(output[1][1] == answers['be1']))
        assert(np.all(output[2][0] == answers['bc2']))
        assert(np.all(output[2][1] == answers['be2'])) 

Example 23

def write_dndlnm(outfile, dndlnm, z, mass, clobber=False):
    """
    Write the halo mass distribution data into file in NumPy's ".npz"
    format, which packs the ``dndlnm``, ``z``, and ``mass`` arrays.

    Parameters
    ----------
    outfile : str
        The output file to store the dndlnm data, in ".npz" format.
    dndlnm : 2D float `~numpy.ndarray`
        Shape: (len(z), len(mass))
        Differential mass function in terms of natural log of M.
        Unit: [Mpc^-3] (the little "h" is folded into the values)
    z : 1D float `~numpy.ndarray`
        Redshifts where the halo mass distribution is calculated.
    mass : 1D float `~numpy.ndarray`
        (Logarithmic-distributed) masses points.
        Unit: [Msun] (the little "h" is folded into the values)
    clobber : bool, optional
        Whether to overwrite the existing output file?
    """
    _create_dir(outfile)
    _check_existence(outfile, clobber=clobber, remove=True)
    np.savez(outfile, dndlnm=dndlnm, z=z, mass=mass) 

Example 24

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("envid")
    parser.add_argument("outfile")
    parser.add_argument("--gymdir")

    args = parser.parse_args()
    if args.gymdir:
        sys.path.insert(0, args.gymdir)
    import gym
    from gym import utils
    print utils.colorize("gym directory: %s"%path.dirname(gym.__file__), "yellow")
    env = gym.make(args.envid)
    agent = RandomAgent(env.action_space)
    alldata = {}
    for i in xrange(2):
        np.random.seed(i)
        data = rollout(env, agent, env.spec.max_episode_steps)
        for (k, v) in data.items():
            alldata["%i-%s"%(i, k)] = v
    np.savez(args.outfile, **alldata) 

Example 25

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
        File name of ``.npz`` file.
    args : Arguments
        Function arguments.
    kwds : Keyword arguments
        Keywords.

    See Also
    --------
    numpy.savez : Save several arrays into an uncompressed ``.npz`` file format
    numpy.load : Load the files created by savez_compressed.

    """
    _savez(file, args, kwds, True) 

Example 26

def test_closing_fid(self):
        # Test that issue #1517 (too many opened files) remains closed
        # It might be a "weak" test since failed to get triggered on
        # e.g. Debian sid of 2012 Jul 05 but was reported to
        # trigger the failure on Ubuntu 10.04:
        # http://projects.scipy.org/numpy/ticket/1517#comment:2
        with temppath(suffix='.npz') as tmp:
            np.savez(tmp, data='LOVELY LOAD')
            # We need to check if the garbage collector can properly close
            # numpy npz file returned by np.load when their reference count
            # goes to zero.  Python 3 running in debug mode raises a
            # ResourceWarning when file closing is left to the garbage
            # collector, so we catch the warnings.  Because ResourceWarning
            # is unknown in Python < 3.x, we take the easy way out and
            # catch all warnings.
            with warnings.catch_warnings():
                warnings.simplefilter("ignore")
                for i in range(1, 1025):
                    try:
                        np.load(tmp)["data"]
                    except Exception as e:
                        msg = "Failed to load data from a file: %s" % e
                        raise AssertionError(msg) 

Example 27

def test_npzfile_dict():
    s = BytesIO()
    x = np.zeros((3, 3))
    y = np.zeros((3, 3))

    np.savez(s, x=x, y=y)
    s.seek(0)

    z = np.load(s)

    assert_('x' in z)
    assert_('y' in z)
    assert_('x' in z.keys())
    assert_('y' in z.keys())

    for f, a in z.items():
        assert_(f in ['x', 'y'])
        assert_equal(a.shape, (3, 3))

    assert_(len(z.items()) == 2)

    for f in z:
        assert_(f in ['x', 'y'])

    assert_('x' in z.keys()) 

Example 28

def test_load_refcount():
    # Check that objects returned by np.load are directly freed based on
    # their refcount, rather than needing the gc to collect them.

    f = BytesIO()
    np.savez(f, [1, 2, 3])
    f.seek(0)

    assert_(gc.isenabled())
    gc.disable()
    try:
        gc.collect()
        np.load(f)
        # gc.collect returns the number of unreachable objects in cycles that
        # were found -- we are checking that no cycles were created by np.load
        n_objects_in_cycles = gc.collect()
    finally:
        gc.enable()
    assert_equal(n_objects_in_cycles, 0) 

Example 29

def save(self, path):
        savedir = smartutils.create_folder(pjoin(path, type(self).__name__))

        hyperparameters = {'version': 2,
                           'image_shape': self.image_shape,
                           'nb_channels': self.nb_channels,
                           'ordering_seed': self.ordering_seed,
                           'use_mask_as_input': self.use_mask_as_input,
                           'hidden_activation': self.hidden_activation,
                           'has_convnet': self.has_convnet,
                           'has_fullnet': self.has_fullnet}
        smartutils.save_dict_to_json_file(pjoin(savedir, "meta.json"), {"name": self.__class__.__name__})
        smartutils.save_dict_to_json_file(pjoin(savedir, "hyperparams.json"), hyperparameters)

        # Save residual parameters for the projection shortcuts.
        np.savez(pjoin(savedir, "params.npz"), *self.parameters) 

Example 30

def __len__(self):
        return self.D

    # def save(self, savedir):
    #     state = {"version": 1,
    #              "seed": self.seed,
    #              "use_mask_as_input": self.use_mask_as_input,
    #              "batch_size": self.batch_size,
    #              "shared_batch_count": self.shared_batch_count.get_value(),
    #              "rng": pickle.dumps(self.rng),
    #              "shared_batch_mask": self._shared_mask_o_lt_d.get_value(),
    #              }

    #     np.savez(pjoin(savedir, 'mini_batch_scheduler_with_autoregressive_mask.npz'), **state)

    # def load(self, loaddir):
    #     state = np.load(pjoin(loaddir, 'mini_batch_scheduler_with_autoregressive_mask.npz'))
    #     self.batch_size = state["batch_size"]
    #     self.shared_batch_count.set_value(state["shared_batch_count"])
    #     self.rng = pickle.loads(state["rng"])
    #     self._shared_mask_o_lt_d.set_value(state["shared_batch_mask"]) 

Example 31

def writeToFile(self, filename, saveOpts=False):
        '''
        write the distortion coeffs to file
        saveOpts --> Whether so save calibration options (and not just results)
        '''
        try:
            if not filename.endswith('.%s' % self.ftype):
                filename += '.%s' % self.ftype
            s = {'coeffs': self.coeffs}
            if saveOpts:
                s['opts'] = self.opts
#             else:
#                 s['opts':{}]
            np.savez(filename, **s)
            return filename
        except AttributeError:
            raise Exception(
                'need to calibrate camera before calibration can be saved to file') 

Example 32

def _save_model(self, bleu_score):
        if self._is_valid_to_save(bleu_score):
            model = ModelInfo(bleu_score, self.config['saveto'])

            # Manage n-best model list first
            if len(self.best_models) >= self.track_n_models:
                old_model = self.best_models[0]
                if old_model.path and os.path.isfile(old_model.path):
                    logger.info("Deleting old model %s" % old_model.path)
                    os.remove(old_model.path)
                self.best_models.remove(old_model)

            self.best_models.append(model)
            self.best_models.sort(key=operator.attrgetter('bleu_score'))

            # Save the model here
            s = signal.signal(signal.SIGINT, signal.SIG_IGN)
            logger.info("Saving new model {}".format(model.path))
            numpy.savez(
                model.path, **self.main_loop.model.get_parameter_dict())
            numpy.savez(
                os.path.join(self.config['saveto'], 'val_bleu_scores.npz'),
                bleu_scores=self.val_bleu_curve)
            signal.signal(signal.SIGINT, s) 

Example 33

def save_training(self, filename):
        """
        Save traning set and labels of current model
        :param filename: filename of new data.npz, it will be saved in 'train/'
        """
        np.savez(os.path.join(TRAIN_DATA, filename),
                 train_set=self.train_set,
                 train_labels=self.train_labels) 

Example 34

def end(self, _session):
    np.savez(self.params["file"], **self._beam_accum) 

Example 35

def end(self, _session):
    scores_path = os.path.join(self.params["output_dir"],
                               "attention_scores.npz")
    np.savez(scores_path, *self._attention_scores_accum)
    tf.logging.info("Wrote %s", scores_path) 

Example 36

def save_path(self, path):
        self._model.save(path)
        self._feature_scaler.save_path(path.with_suffix('.feature_scaler'))

        with open(path.with_suffix('.pessimism'), 'wb') as f:
            np.savez(
                f,
                aim_pessimism_factor=self._aim_pessimism_factor,
                accuracy_pessimism_factor=self._accuracy_pessimism_factor,
            ) 

Example 37

def save_path(self, path):
        with open(path, 'wb') as f:
            np.savez(
                f,
                ndim=len(self._axes) + 1,
                mean=self.mean,
                std=self.std,
            ) 

Example 38

def dataset_to_file(dataset, ngrams, filename='dataset'):
    """Save a dataset to a file.

    Args:
        dataset (:class:`np.ndarray`): the dataset to save (built with :func:`dataset_tools.build_dataset`)
        ngrams (list of strings): the ngrams used to compute the features
        filename (string): the filename without extension (will be .npz)
    """
    num_samples, num_entries, num_features = dataset.shape
    # We rehaspe the ndarray from 3D to 2D in order to write it into a text file
    # Each line of the file will correspond to one cited paper
    # Therefore, on each there will be the `num_entries` sets of features
    dataset_sp = sparse.csr_matrix(dataset.reshape(num_samples*num_entries, num_features))
    np.savez(filename, num_entries=np.array([num_entries]), data=dataset_sp.data, indices=dataset_sp.indices,
             indptr=dataset_sp.indptr, shape=dataset_sp.shape, ngrams=ngrams) 

Example 39

def save_sparse_csr(filename,array):
    np.savez(filename,data = array.data ,indices=array.indices,
             indptr =array.indptr, shape=array.shape ) 

Example 40

def secure_numpy_save(params_dict, path):
    """Try saving into a temporary file and then move."""
    try:
        dirname = os.path.dirname(path)
        with tempfile.NamedTemporaryFile(delete=False, dir=dirname) as temp:
            numpy.savez(temp, **params_dict)
        shutil.move(temp.name, path)
    except Exception as e:
        # if "temp" in locals():
        #    os.remove(temp.name)
        logger.error(" Error {0}".format(str(e))) 

Example 41

def _save_params(self, model, params):

        # Rename accordingly for blocks compatibility
        params_to_save = dict(
            (k.replace('/', '-'), v) for k, v in params.items())

        numpy.savez(model.path, **params_to_save) 

Example 42

def _save_bleu_scores(self):
        numpy.savez(
            os.path.join(
                self.saveto,
                'val_bleu_scores{}_{}.npz'.format(self.enc_id, self.dec_id)),
            bleu_scores=self.val_bleu_curve) 

Example 43

def encode_npz(subvol):
    """
    This file format is unrelated to np.savez
    We are just saving as .npy and the compressing
    using zlib. 
    The .npy format contains metadata indicating
    shape and dtype, instead of np.tobytes which doesn't
    contain any metadata.
    """
    fileobj = io.BytesIO()
    if len(subvol.shape) == 3:
        subvol = np.expand_dims(subvol, 0)
    np.save(fileobj, subvol)
    cdz = zlib.compress(fileobj.getvalue())
    return cdz 

Example 44

def save_np(reader, output_filename):
    ts, data = reader.read_stack()
    np.savez(output_filename, timestamps=ts, data=data) 

Example 45

def write_test_file(self, variable='v', check=False):
        data, metadata = self.build_test_data(variable)
        metadata_array = np.array(sorted(metadata.items()))
        np.savez(self.test_file, data=data, metadata=metadata_array)
        if check:
            data1, metadata1 = read_test_file(self.test_file)
            assert metadata == metadata1, "%s != %s" % (metadata, metadata1)
            assert data.shape == data1.shape == (505, 2), \
                "%s, %s, (505, 2)" % (data.shape, data1.shape)
            assert (data == data1).all()
            assert metadata["n"] == 505 

Example 46

def _write_file_contents(self, data, metadata):
        # we explicitly set the dtype to ensure roundtrips preserve file contents exactly
        max_metadata_length = max(chain([len(k) for k in metadata.keys()],
                                        [len(str(v)) for v in metadata.values()]))
        if PY2:
            dtype = "S%d" % max_metadata_length
        else:
            dtype = "U%d" % max_metadata_length
        metadata_array = numpy.array(sorted(metadata.items()), dtype)
        numpy.savez(self.filename, data=data, metadata=metadata_array) 

Example 47

def write_test_file(self, variable='v', check=False):
        data, metadata = self.build_test_data(variable)
        metadata_array = np.array(sorted(metadata.items()))
        np.savez(self.test_file, data=data, metadata=metadata_array)
        if check:
            data1, metadata1 = read_test_file(self.test_file)
            assert metadata == metadata1, "%s != %s" % (metadata, metadata1)
            assert data.shape == data1.shape == (505, 2), \
                "%s, %s, (505, 2)" % (data.shape, data1.shape)
            assert (data == data1).all()
            assert metadata["n"] == 505 

Example 48

def _write_file_contents(self, data, metadata):
        # we explicitly set the dtype to ensure roundtrips preserve file contents exactly
        max_metadata_length = max(chain([len(k) for k in metadata.keys()],
                                        [len(str(v)) for v in metadata.values()]))
        if PY2:
            dtype = "S%d" % max_metadata_length
        else:
            dtype = "U%d" % max_metadata_length
        metadata_array = numpy.array(sorted(metadata.items()), dtype)
        numpy.savez(self.filename, data=data, metadata=metadata_array) 

Example 49

def save_model(self, file_name, directory):
		"""Saves a checkpoint of the model and a .npz file with stored rewards.

		Args:
		file_name: String name to use for the checkpoint and rewards files.
		Defaults to self.model_name if None is provided.
		"""
		if self.verbose: print "Saving model..."
		
		save_dir = directory + file_name
		os.mkdir(save_dir)
		directory = save_dir + '/'

		save_loc = os.path.join(directory, file_name + '.ckpt')
		training_epochs = len(self.training_val_results) * self.accuracy_logged_every_n
		self.saver.save(self.session, save_loc, global_step=training_epochs)
		
		
		npz_name = os.path.join(directory, file_name + '-' + str(training_epochs))
		
		if not self.print_per_task:
			np.savez(npz_name,
					training_val_results=self.training_val_results,
					l2_beta=self.l2_beta,
					dropout=self.dropout,
					hidden_sizes_shared=self.hidden_sizes_shared,
					hidden_size_task=self.hidden_size_task) 

Example 50

def save_model(self, file_name, directory):
		"""Saves a checkpoint of the model and a .npz file with stored rewards.

		Args:
		file_name: String name to use for the checkpoint and rewards files.
		Defaults to self.model_name if None is provided.
		"""
		if self.verbose: print "Saving model..."
		
		save_dir = directory + file_name
		os.mkdir(save_dir)
		directory = save_dir + '/'

		save_loc = os.path.join(directory, file_name + '.ckpt')
		training_epochs = len(self.training_val_results) * self.accuracy_logged_every_n
		self.saver.save(self.session, save_loc, global_step=training_epochs)
		
		
		npz_name = os.path.join(directory, file_name + '-' + str(training_epochs))
		
		if not self.print_per_task:
			np.savez(npz_name,
					training_val_results=self.training_val_results,
					l2_beta=self.l2_beta,
					dropout=self.dropout,
					hidden_sizes_shared=self.hidden_sizes_shared,
					hidden_size_task=self.hidden_size_task)
		else:
			np.savez(npz_name,
					training_val_results=self.training_val_results,
					training_val_results_per_task=self.training_val_results_per_task,
					l2_beta=self.l2_beta,
					dropout=self.dropout,
					hidden_sizes_shared=self.hidden_sizes_shared,
					hidden_size_task=self.hidden_size_task) 
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