numpy.nditer - python examples

Here are the examples of the python api numpy.nditer taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.

70 Examples 7

3 View Complete Implementation : cnn.py
Copyright GNU General Public License v3.0
Author : apachecn
def element_wise_op(array, op):
    '''
    Desc:
        element_wise_op函数实现了对numpy数组进行按元素操作,并将返回值写回到数组中
    '''
    for i in np.nditer(array,
                       op_flags=['readwrite']):
        i[...] = op(i)

3 View Complete Implementation : HiCKRy.py
Copyright MIT License
Author : ay-lab
def outputBias(biasCol, revFrag, outputFilePath):
    bpath = outputFilePath
    with gzip.open(bpath,'wt') as biasFile:
        ctr = 0
        for values in np.nditer(biasCol):
            chrommidTup = revFrag[ctr]
            chrom = chrommidTup[0]
            mid = chrommidTup[1]
            biasFile.write("%s\t%s\t%s\n" % (chrom, mid, values))
            ctr += 1

3 View Complete Implementation : churn_calc.py
Copyright MIT License
Author : carl24k
    def setup_group_column_names(self,load_mat_df):
        N_GROUP_CHAR=7
        MAX_GROUP_NAME=10
        num_weights = load_mat_df.astype(bool).sum(axis=0)
        weight_on_met = load_mat_df.sum(axis=1)
        solo_metics = (weight_on_met == 1).to_numpy().nonzero()[0]
        grouped_metrics = (num_weights > 1).to_numpy().nonzero()[0]
        self.grouped_columns = ['G%d_' % (d + 1) for d in np.nditer(grouped_metrics)]
        for m in grouped_metrics:
            group_cols = load_mat_df.iloc[:, m].to_numpy().nonzero()[0]
            self.grouped_columns[m] += '_'.join(
                [load_mat_df.index.values[i][:N_GROUP_CHAR].replace('_', '') for i in group_cols[:MAX_GROUP_NAME]])

        self.grouped_columns.extend([load_mat_df.index.values[i] for i in solo_metics])

3 View Complete Implementation : validation.py
Copyright GNU General Public License v3.0
Author : clcr
def build_clast_dict(clast_array, no_data=None):
    """Returns a dict of coordinates of the following shape:
    [clast, coord].
    WARNING: This will take up a LOT of memory!"""
    out_dict = {}
    it = np.nditer(clast_array, flags=['multi_index'])
    while not it.finished:
        this_clast = int(it.value)
        if this_clast == no_data:
            it.iternext()
            continue
        if this_clast in out_dict.keys():
            out_dict[this_clast].append(it.multi_index)
        else:
            out_dict.update({this_clast: [it.multi_index]})
        it.iternext()
    return out_dict

3 View Complete Implementation : utils.py
Copyright Apache License 2.0
Author : criteo-research
def compute_2i_regularization_id(prods, num_products):
    """Compute the ID for the regularization for the 2i approach"""

    reg_ids = []
    # Loop through batch and compute if the product ID is greater than the number of products
    for x in np.nditer(prods):
        if x >= num_products:
            reg_ids.append(x)
        elif x < num_products:
            reg_ids.append(x + num_products) # Add number of products to create the 2i representation 

    return np.asarray(reg_ids)

3 View Complete Implementation : utils.py
Copyright Apache License 2.0
Author : criteo-research
def compute_treatment_or_control(prods, num_products):
    """Compute if product is in treatment or control"""
    # Return the control product places and treatment places as 1's in a binary matrix.
    ids = []
    for x in np.nditer(prods):
        # Greater than the number of products
        if x >= num_products:
            ids.append(0)
        elif x < num_products:
            ids.append(1)
    # create the binary mask and return 
    return np.asarray(ids), np.logical_not(np.asarray(ids)).astype(int)

3 View Complete Implementation : png.py
Copyright The Unlicense
Author : DavidWilliams81
def write_frame(path, frame, palette):

    os.makedirs(path, exist_ok=True)
    (plane_count, row_count, col_count) = frame.shape
    
    for plane_idx, plane in enumerate(frame):
        
        imdata = bytearray()
        
        # Note, do we need to specify C vs. Fortran order here?
        # https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.nditer.html
        for mat_idx in np.nditer(plane):
            rgba = palette[mat_idx]
            imdata.extend(rgba)
                
        data = write_png(imdata, col_count, row_count)
        with open("{}/{}.png".format(path, plane_idx), 'wb') as fd:
            fd.write(data)

3 View Complete Implementation : axis_norm_test.py
Copyright Apache License 2.0
Author : deepmind
  def testSimpleCase(self):
    layer = axis_norm.LayerNorm([1, 2], create_scale=False, create_offset=False)
    inputs = tf.ones([2, 3, 3, 5])

    outputs = layer(inputs).numpy()
    for x in np.nditer(outputs):
      self.astertEqual(x, 0.0)

3 View Complete Implementation : axis_norm_test.py
Copyright Apache License 2.0
Author : deepmind
  def testSimpleCaseVar(self):
    layer = axis_norm.LayerNorm([1, 2],
                                create_scale=True,
                                create_offset=True,
                                scale_init=initializers.Constant(0.5),
                                offset_init=initializers.Constant(2.0))

    inputs = tf.ones([2, 3, 3, 5])

    outputs = layer(inputs).numpy()
    for x in np.nditer(outputs):
      self.astertEqual(x, 2.0)

3 View Complete Implementation : axis_norm_test.py
Copyright Apache License 2.0
Author : deepmind
  def testSimpleCaseNCHWVar(self):
    layer = axis_norm.LayerNorm([1, 2],
                                create_scale=True,
                                create_offset=True,
                                scale_init=initializers.Constant(0.5),
                                offset_init=initializers.Constant(2.0),
                                data_format="NCHW")

    inputs = tf.ones([2, 5, 3, 3])

    outputs = layer(inputs).numpy()
    for x in np.nditer(outputs):
      self.astertEqual(x, 2.0)