Unpacking arrays into arrow plot-CodePudding

I have a strange looking function that calls the plots based on the attributes. So, if a function exists in the class then select that. Then I am trying to call it, in this example I use pyplot.arrow, however, I cannot seem to unpack all the values. It should take four parameters, but I get the following error:

ValueError: too many values to unpack (expected 2)

I cannot recognise how I am passing too many values given I unpack with *, here is what I have tried:

import numpy as np
import matplotlib.pyplot as plt

test_array = np.array([ [1, 2] , [5, 4] , [5, 2] , [1, 2] , [5, 2] , [1, 2]])
test_array = np.column_stack((test_array,[ [ 0 , 0 ] ]*test_array.shape[0]))
test_split = np.split(test_array, 6)
for b in test_split:
    b[ : , [ 0 , 1 , -2 , -1 ] ] = b[ : , [ -2 , -1 , 0 ,1] ]

def plot(size: list,plType, *args, **kwargs):
    figs, axs = plt.subplots(size[0], size[1], figsize=(8,8))
    xy = np.array(args)
    for A , ax in zip( xy , axs.flat ):
        X = np.hsplit( A , xy.shape[2] )
        if isinstance(X, list):
            for ind , Z in enumerate(zip(*X)):
                ax.__getattribute__( plType )( *Z, **kwargs)
    plt.show()

print(np.array(test_split).shape)
plot([2, 3], 'arrow', *test_split)

CodePudding user response：

I feel like you are doing several unnecessary things which has made it confusing.

The main point is you want to do ax.__getattribute__("arrow")(x, y, dx, dy, **kwargs). To keep it simple:

import numpy as np
import matplotlib.pyplot as plt

test_array = np.array([
    [1, 2],
    [5, 4],
    [5, 2],
    [1, 2],
    [5, 2],
    [1, 2]
])
test_array = np.column_stack((test_array, [[0, 0]] * test_array.shape[0]))
test_split = np.split(test_array, 6)
for b in test_split:
    b[:, [0, 1, -2 , -1]] = b[:, [-2, -1, 0 ,1]]

Here, test_split is a set of 6 arrays of size 4; assuming they represent (x, y, dx, dy).

[array([[0, 0, 1, 2]]),
 array([[0, 0, 5, 4]]),
 array([[0, 0, 5, 2]]),
 array([[0, 0, 1, 2]]),
 array([[0, 0, 5, 2]]),
 array([[0, 0, 1, 2]])]

We can plot them in the subplots as follows:

def plot(size: list, plType, *args, **kwargs):
    figs, axs = plt.subplots(size[0], size[1], figsize=(8, 8))
    for A, ax in zip(args, axs.flat):
        ax.__getattribute__(plType)(*A.ravel().tolist(), **kwargs)
    plt.show()

CodePudding user response：

You were missing another level of obfuscation.

import numpy as np
import matplotlib.pyplot as plt

test_array = np.array([ [1, 2] , [5, 4] , [5, 2] , [1, 2] , [5, 2] , [1, 2]])
test_array = np.column_stack((test_array,[ [ 0 , 0 ] ]*test_array.shape[0]))
test_split = np.split(test_array, 6)
for b in test_split:
    b[ : , [ 0 , 1 , -2 , -1 ] ] = b[ : , [ -2 , -1 , 0 ,1] ]

def plot(size: list,plType, *args, **kwargs):
    figs, axs = plt.subplots(size[0], size[1], figsize=(8,8))
    xy = np.array(args)
    for A , ax in zip( xy , axs.flat ):
        X = np.hsplit( A , xy.shape[2] )
        if isinstance(X, list):
            for i, Z in enumerate(zip(*X)_:
    ############################################################
                for j, z in enumerate(zip(*Z)):
    ############################################################
                    ax.__getattribute__( plType )( *z, **kwargs)
    plt.show()

print(np.array(test_split).shape)
plot([2, 3], 'arrow', *test_split)

I said obfuscation because, e.g., the enumerate is completely unnecessary — but this is just one of many examples.