pyaibox.utils package

Submodules

pyaibox.utils.colormaps module

pyaibox.utils.colors module

pyaibox.utils.colors.gray2rgb(gray, cmap, drange=[0, 255], fmtstr=False)

Converts gray image values to rgb image

converts gray image values to rgb image according to the specified colormap string ‘cmap’, supported are: 'parula', 'jet', 'hsv', 'hot', 'cool', 'spring', 'summer', 'autumn', 'winter', 'gray', 'bone', 'copper', 'pink', 'lines', 'colorcube', 'prism', 'flag', 'white'.

Parameters

gray (numpy array) – The gray image.
cmap (str or colormap) – colormap string
drange (list or tuple) – The low and high value, default is [0, 255]
fmtstr (bool or str) – Specifies the format ('int', 'uint8', 'uint16', 'uint32', 'float16', 'float32', , 'float64') for the output rgb matrix. (default is False, don’t change)

Examples

import pyaibox as pl
import matplotlib.pyplot as plt
import numpy as np

cmap = 'jet'
# cmap = 'hsv'
# cmap = 'hot'
# cmap = 'parula'
gray = pb.imread('../../data/images/LenaGRAY256.png')
print(gray.shape)

rgb = gray2rgb(gray, cmap, [0, 1], False)  # rgb --> double, [0, 1]
# rgb = gray2rgb(gray, cmap, [0, 255], False)  # rgb --> double, [0., 255.]
# rgb = gray2rgb(gray, cmap, [0, 255], True)  # rgb --> uint8, [0, 255]

print(gray.shape, np.min(gray), np.max(gray), gray.dtype)
print(rgb.shape, np.min(rgb), np.max(rgb), rgb.dtype)

plt.figure()
plt.subplot(121)
plt.imshow(gray, cmap=pb.parula if cmap == 'parula' else cmap)
plt.subplot(122)
plt.imshow(rgb)
plt.show()

pyaibox.utils.colors.rgb2gray(rgb, fmt='chnllast')

Converts RGB image to GRAY image

Converts RGB image to GRAY image according to

\[G = 0.2989R + 0.5870G + 0.1140B \]

see matlab’s rgb2gray function for details.

Parameters

rgb (numpy array) – Original RGB tensor.
fmt (str, optional) – Specifies the position of channels in rgb tensor, surpported are: - 'chnllast' (default) - 'chnlfirst'

pyaibox.utils.const module

pyaibox.utils.convert module

pyaibox.utils.convert.bstr2int(b, endian='<', signed=True)

convert binary string data to integer

Parameters

b (bstr) – an integer in binary format
endian (str, optional) – The order of the bytes, supported are little endian: '<' (the default), big endian: '>'.
signed (bool, optional) – signed or unsigned, by default True

Returns

The integer in decimal.

Return type

int

Examples

n = -123

bs = int2bstr(n, 4, '<', signed=True)
print(bs)
print(hex(n))
print(bstr2int(bs, '<'))

bs = int2bstr(n, 4, '>', signed=True)
print(bs)
print(hex(n))
print(bstr2int(bs, '>'))

# ---output
b'\x85\xff\xff\xff'
-0x7b
-123
b'\xff\xff\xff\x85'
-0x7b
-123

pyaibox.utils.convert.dict2str(ddict, indent=' ', linebreak='\n', nindent=0)

dump dict object to str

Parameters

ddict (dict) – The dict object to be converted
indent (str, optional) – The dict identifier, by default ' '
linebreak (str, optional) – The line break character, by default ‘n’
nindent (int, optional) – the number of initial indent characters, by default 0

Returns

The converted string.

Return type

str

pyaibox.utils.convert.file2hash(file, hmode='sha256', tohex=True)

convert contents of a file to hash code

Parameters

file (str) – the input file path string.
hmode (str or hash function, optional) –

must either be a hash algorithm name as a str, a hash constructor, or a callable that returns a hash object.
'sha1', 'sha224', 'sha256', 'sha384', 'sha512', 'md5', …,

see hashlib , by default ‘sha256’
tohex (bool, optional) – return hex code?, by default True.

Returns

hash code hex string or hash object

Return type

str or object

pyaibox.utils.convert.int2bstr(n, nbytes, endian='<', signed=True)

converts integer to bytes string

Parameters

n (int) – the input integer
nbytes (int) – the number of bytes
endian (str, optional) – byte order, supported are little endian: '<' (the default), big endian: '>'.
signed (bool, optional) – signed or unsigned, by default True

Returns

The integer in binary string format.

Return type

bstr

Examples

n = -123

bs = int2bstr(n, 4, '<', signed=True)
print(bs)
print(hex(n))
print(bstr2int(bs, '<'))

bs = int2bstr(n, 4, '>', signed=True)
print(bs)
print(hex(n))
print(bstr2int(bs, '>'))

# ---output
b'\x85\xff\xff\xff'
-0x7b
-123
b'\xff\xff\xff\x85'
-0x7b
-123

pyaibox.utils.convert.str2hash(s, hmode='sha256', enc='utf-8', tohex=True)

convert a string to hash code

Parameters

s (str) – the input.
hmode (str or hash function, optional) –

must either be a hash algorithm name as a str, a hash constructor, or a callable that returns a hash object.
'sha1', 'sha224', 'sha256', 'sha384', 'sha512', 'md5', …,

see hashlib , by default ‘sha256’
enc (str, optional) – encoding type, by default ‘utf-8’.
tohex (bool, optional) – return hex code?, by default True.

Returns

hash code hex string or hash object.

Return type

str or object

pyaibox.utils.convert.str2list(s)

Converts string with [ and ] to list

Parameters: s (str) – The string.

Examples

s = '[0, [[[[1], 2.], 33], 4], [5, [6, 2.E-3]], 7, [8]], 1e-3'
print(str2list(s))

# ---output
([0, [[[[1], 2.0], 33], 4], [5, [6, 0.002]], 7, [8]], 0.001)

pyaibox.utils.convert.str2num(s, tfunc=None)

Extracts numbers in a string.

Parameters

s (str) – The string.
tfunc (None, optional) – formating function, such as int, float or 'auto'.

Returns

The number list.

Return type

list

Examples

print(str2num(s, int))
print(str2num(s, float))
print(str2num(s, 'auto'))

print(2**(str2num('int8', int)[0]))
print(str2num('int', int) == [])

# ---output
[0, 1, 2, 33, 4, 5, 6, 0, 7, 8, 0]
[0.0, 1.0, 2.0, 33.0, 4.0, 5.0, 6.0, 0.002, 7.0, 8.0, 0.001]
[0, 1, 2.0, 33, 4, 5, 6, 0.002, 7, 8, 0.001]
256
True

pyaibox.utils.convert.str2sec(x, sep=':')

Extracts second in a time string.

hh:mm:ss –> hh*3600 + mm*60 + ss

Parameters

s (str) – The string or string list/tuple.
sep (str) – The separator between hour, minute and seconds, default is ':'.

Returns

y – The seconds.

Return type

int

Examples

print(str2sec('1:00:0'))
print(str2sec('1:10:0'))
print(str2sec('1:10:6'))
print(str2sec('1:10:30'))

# ---output
3600
4200
4206
4230

pyaibox.utils.docstr module

pyaibox.utils.docstr.gpyi(pkgdir, autoskip=True)

generates pyi files

Parameters

pkgdir (str) – package root directory
autoskip (bool, optional) – skip __init__.py, by default True

pyaibox.utils.docstr.rmcache(pkgdir, exts='.c')

remove cache files

Parameters

pkgdir (str) – package root directory
ext (str, optional) – file extension

pyaibox.utils.file module

pyaibox.utils.file.copyfile(srcfile, dstfile)

copy file

copy file from srcfile to dstfile.

Parameters

srcfile (str) – the source file path string
dstfile (str) – the destnation file path string

pyaibox.utils.file.copyfiles(srcdir, dstdir, filenames)

copy files

copy files from source directory to destnation directory

Parameters

srcdir (str) – the source directory path string
dstdir (str) – the destnation directory path string
filenames (list or tuple) – filenames to be copied

pyaibox.utils.file.data_path(dsname=None)

obtain data source path

Parameters: dsname (str, optional) – dataset name ('character', 'shape', 'optical', 'remote', 'mstar'), by default None
Returns: the path string.
Return type: str

pyaibox.utils.file.fileparts(file)

Filename parts

Returns the path, file name, and file name extension for the specified file. The file input is the name of a file or folder, and can include a path and file name extension.

Parameters

file (str) – The name string of a file or folder.

Returns

filepath (str) – The path string of the file or folder.
name (str) – The name string of the file or folder.
ext (str) – The extension string of the file.

pyaibox.utils.file.listxfile(listdir=None, exts=None, recursive=False)

List the files in a directory.

Parameters

listdir (None, optional) – The directory for listing. The default is None, which means return filelist directly.
exts (str, list or None, optional) – File extension string or extension string list, such as '.png' or ['.png', 'jpg']. The default is None, which means any extension.
recursive (bool, optional) – Recursive search? The default is False, which means only list the root directory (listdir).

Returns

The list of file path with extension exts. Sometimes you need to sort the list using sorted.

Return type

list

pyaibox.utils.file.pathjoin(*kwargs)

Joint strings to a path.

Parameters: *kwargs – strings.
Returns: The joined path string.
Return type: str

pyaibox.utils.file.pkg_path()

obtain this package’s path

Returns: package path string.
Return type: str

pyaibox.utils.file.readcsv(filepath, sep=None, vfn=None, nlines=None)

Read a csv file and extract numbers in it.

Parameters

filepath (str) – The path string of the file.
sep (str, optional) – The separation character. Such as ',' or ' '. If None (default) or '' (empty) return a list of all the lines.
vfn (function or None, optional) – The function for formating the numbers. float –> convert to float number; int –> convert to integer number…, The default is None, which means won’t converted, string format.
nlines (None, optional) – The number of lines for reading, the default is None, which means all the lines.

Returns

The list of numbers or strings.

Return type

list

pyaibox.utils.file.readnum(filepath, pmain='Train', psub='loss: ', vfn=<class 'float'>, nshots=None)

Read a file and extract numbers in it.

Parameters

filepath (str) – The path string of the file.
pmain (str, optional) – The matching pattern string, such as ‘—>Train’.
psub (str, optional) – The sub-matching pattern string, such as ‘loss’.
vfn (function, optional) – The function for formating the numbers. float –> convert to float number; int –> convert to integer number…, The default is float.
nshots (None, optional) – The number of shots of sub-matching pattern.

Returns

The list of numbers.

Return type

list

pyaibox.utils.file.readsec(filepath, pmain='Train', psub='time: ', vfn=<class 'int'>, nshots=None)

Read a file and extract seconds in it.

hh:mm:ss –> hh*3600 + mm*60 + ss

Parameters

filepath (str) – The path string of the file.
pmain (str, optional) – The matching pattern string, such as ‘—>Train’.
psub (str, optional) – The sub-matching pattern string, such as ‘loss’.
vfn (function or None, optional) – The function for formating the numbers. float –> convert to float number; int –> convert to integer number.
nshots (None, optional) – The number of shots of sub-matching pattern.

Returns

The list of seconds.

Return type

list

pyaibox.utils.file.readtxt(filepath, mode=None)

Read a text file.

Parameters

filepath (str) – The path string of the file.
mode (str or None, optional) – 'line' –> reading line-by-line. 'all' –> reading all content. (default)

Returns

File content.

Return type

str

pyaibox.utils.image module

pyaibox.utils.image.histeq(img, nbins=256)

pyaibox.utils.image.imread(imgfile)

pyaibox.utils.image.imresize(img, oshape=None, odtype=None, order=1, mode='constant', cval=0, clip=True, preserve_range=False)

resize image to oshape

see skimage.transform.resize().

Parameters

img (ndarray) – Input image.
oshape (tulpe, optional) – output shape (the default is None, which is the same as the input)
odtype (str, optional) – output data type, 'uint8', 'uint16', 'int8', ... (the default is None, float)
order (int, optional) – The order of the spline interpolation, default is 1. The order has to be in the range 0-5. See skimage.transform.warp for detail.
mode (str, optional) – Points outside the boundaries of the input are filled according to the given mode. {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, Modes match the behaviour of numpy.pad. The default mode is ‘constant’.
cval (float, optional) – Used in conjunction with mode ‘constant’, the value outside the image boundaries.
clip (bool, optional) – Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range.
preserve_range (bool, optional) – Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float.

Returns

resized – Resized version of the input.

Return type

ndarray

Notes

Modes ‘reflect’ and ‘symmetric’ are similar, but differ in whether the edge pixels are duplicated during the reflection. As an example, if an array has values [0, 1, 2] and was padded to the right by four values using symmetric, the result would be [0, 1, 2, 2, 1, 0, 0], while for reflect it would be [0, 1, 2, 1, 0, 1, 2].

Examples

>>> from skimage import data
>>> from skimage.transform import resize
>>> image = data.camera()
>>> resize(image, (100, 100), mode='reflect').shape
(100, 100)

pyaibox.utils.image.imsave(outfile, img)

pyaibox.utils.ios module

pyaibox.utils.ios.loadbin(file, dbsize=4, dtype='i', endian='little', offsets=0, nshots=None)

load binary file

load data from binary file

Parameters

file (str) – the binary file path
dbsize (int, optional) – number pf bits of each number, by default 4
dtype (str, optional) – the type of data. - 'c':char, 'b':schar, 'B':uchar, 's':char[], 'p':char[], 'P':void* - 'h':short, 'H':ushort, 'i':int, 'I':uint, 'l':long, 'L':ulong, 'q':longlong, 'Q':ulonglong - 'f':float, 'd':double, by default 'i'
endian (str, optional) – byte order 'little' / 'l', 'big' / 'b', by default ‘little’
offsets (int, optional) – start reading offsets index, by default 0
nshots (int, optional) – the number of data points to be read.

Returns

loaded data.

Return type

list

Examples

import pyaibox as pb

datafile = 'data/data.bin'

x = [1, 3, 6, 111]
pb.savebin('./data.bin', x, dtype='i', endian='L', mode='o')

y = pb.loadbin('./data.bin', dbsize=4, dtype='i', endian='L')

print(y)

x = (1.3, 3.6)
pb.savebin('./data.bin', x, dtype='f', endian='B', offsets=16, mode='a')

y = pb.loadbin('./data.bin', dbsize=4, dtype='f', endian='B', offsets=16)

print(y)

pyaibox.utils.ios.loadh5(filename, keys=None)

load h5 file

load data from a h5 file. (None will be replaced by None)

Parameters

filename (str) – File’s full path string.
keys (list) – list of keys.

Returns

D – The loaded data in dict type.

Return type

dict

pyaibox.utils.ios.loadjson(filepath, field=None)

load a json file

Parameters

filepath (str) – The file path string.
field (None, optional) – The string of field that want to be loaded.

pyaibox.utils.ios.loadmat(filepath)

load data from an .mat file

load data from an .mat file (None will be replaced by None)

see https://stackoverflow.com/questions/7008608/scipy-io-loadmat-nested-structures-i-e-dictionaries

Parameters: filepath (str) – The file path string.

pyaibox.utils.ios.loadyaml(filepath, field=None)

Load a yaml file.

Parameters

filepath (str) – The file path string.
field (None, optional) – The string of field that want to be loaded.

pyaibox.utils.ios.mvkeyh5(filepath, ksf, kst, sep='.')

rename keys in .h5 file

Parameters

filepath (str) – The file path string
ksf (list) – keys from list, e.g. [‘a.x’, ‘b.y’]
kst (list) – keys to list, e.g. [‘a.1’, ‘b.2’]
sep (str, optional) – The separate pattern, default is '.'

Returns

All is ok!

Return type

0

pyaibox.utils.ios.savebin(file, x, dtype='i', endian='little', offsets=0, mode='o')

load binary file

load data from binary file

Parameters

file (str) – the binary file path
x (any) – data to be written (iterable)
dtype (str, optional) – the type of data. - 'c':char, 'b':schar, 'B':uchar, 's':char[], 'p':char[], 'P':void* - 'h':short, 'H':ushort, 'i':int, 'I':uint, 'l':long, 'L':ulong, 'q':longlong, 'Q':ulonglong - 'f':float, 'd':double, by default 'i'
endian (str, optional) – byte order 'little' / 'l', 'big' / 'b', by default ‘little’
offsets (int, optional) – start reading offsets index, by default 0
mode (int, optional) –
- 'append' / 'a' –> append data to the end of the file
- 'overwrite' / 'o' –> overwrite the file. (default)

Examples

import pyaibox as pb

datafile = 'data/data.bin'

x = [1, 3, 6, 111]
pb.savebin('./data.bin', x, dtype='i', endian='L', mode='o')

y = pb.loadbin('./data.bin', dbsize=4, dtype='i', endian='L')

print(y)

x = (1.3, 3.6)
pb.savebin('./data.bin', x, dtype='f', endian='B', offsets=16, mode='a')

y = pb.loadbin('./data.bin', dbsize=4, dtype='f', endian='B', offsets=16)

print(y)

pyaibox.utils.ios.saveh5(filename, mdict, mode='w')

save data to h5 file

save data to h5 file (None will be replaced by None)

Parameters

filename (str) – filename string
mdict (dict) – each dict is store in group, the elements in dict are store in dataset
mode (str) – save mode, 'w' for write, 'a' for add.

Returns

0 –> all is well.

Return type

number

pyaibox.utils.ios.savemat(filepath, mdict, fmt='5', long_field_names=True, do_compression=True, oned_as='row')

save data to an .mat file

save data to .mat file (None will be replaced by 'None')

Parameters

filepath (str) – savefile path
mdict (dict) – data in dict formation.
fmt (str, optional) – mat formation, by default ‘5’
long_field_names (bool, optional) – False (the default) - maximum field name length in a structure is 31 characters which is the documented maximum length. True - maximum field name length in a structure is 63 characters which works for MATLAB 7.6+.
do_compression (bool, optional) – Whether or not to compress matrices on write. Default is False.
oned_as ({'row', 'column'}, optional) – If ‘column’, write 1-D NumPy arrays as column vectors. If ‘row’, write 1-D NumPy arrays as row vectors.

Returns

all is ok!

Return type

0

pyaibox.utils.ios.saveyaml(filepath, ddict=None, indent='-', mode='w')

Load a yaml file.

Parameters

filepath (str) – The file path string.
ddict (dict) – The data to be written is in dict format, {‘field1’: value1, …}
indent (str) – The indent, (the default is ' ')
mode (str) – save mode, 'w' for overwrite, 'a' for add.

Returns

all is ok!

Return type

0

pyaibox.utils.plot_show module

class pyaibox.utils.plot_show.Plots(plotdir='./', xlabel='x', ylabel='y', title='', figname=None, issave=False, isshow=True): Bases: object

pyaibox.utils.plot_show.cplot(ca, lmod=None)

pyaibox.utils.plot_show.imshow(Xs, nrows=None, ncols=None, xlabels=None, ylabels=None, titles=None, figsize=None, outfile=None, **kwargs)

show images

This function create an figure and show images in \(a\) rows and \(b\) columns.

Parameters

Xs (array, list or tuple) – list/tuple of image arrays, if the type is not list or tuple, wrap it.
nrows (int, optional) – show in nrows rows, by default None (auto computed).
ncols (int, optional) – show in ncols columns, by default None (auto computed).
xlabels (str, optional) – labels of x-axis
ylabels (str, optional) – labels of y-axis
titles (str, optional) – titles
figsize (tuple, optional) – figure size, by default None
outfile (str, optional) – save image to file, by default None (do not save).
kwargs –

figfigure handle
sunch as fig = plt.figure()

see matplotlib.pyplot.imshow()

Returns

plot handle

Return type

plt

Examples

x = np.random.rand(3, 100, 100)
plt = imshow([xi for xi in x])
plt.show()

# ---animation
x = np.random.rand(10, 128, 128)
y = np.random.rand(10, 128, 128)
fig = plt.figure()
for n in range(10):
    fig.clf()
    plt = imshow([x[n], y[n]], 1, 2, fig=fig)
    plt.pause(0.5)

pyaibox.utils.plot_show.mesh(Zs, nrows=None, ncols=None, xlabels=None, ylabels=None, zlabels=None, titles=None, figsize=None, outfile=None, **kwargs)

This function create an figure and show some 2d-arrays in \(a\) rows and \(b\) columns with 3d projection.

Parameters

Zs (array, list or tuple) – list/tuple of image arrays, if the type is not list or tuple, wrap it.
nrows (int, optional) – show in nrows rows, by default None (auto computed).
ncols (int, optional) – show in ncols columns, by default None (auto computed).
xlabels (str, optional) – labels of x-axis
ylabels (str, optional) – labels of y-axis
zlabels (str, optional) – labels of z-axis
titles (str, optional) – titles
figsize (tuple, optional) – figure size, by default None
outfile (str, optional) – save image to file, by default None (do not save).
kwargs –

Xslist or tuple
X-axis values

Yslist or tuple
Y-axis values

figfigure handle
sunch as fig = plt.figure()

for others, see matplotlib.pyplot.plot_surface()

Returns

plot handle

Return type

plt

Examples

x, y = np.meshgrid(np.arange(0, 10), np.arange(0, 20))
z = np.random.rand(20, 10)

plt = mesh(z, 1, 2)
plt.show()

plt = mesh(z, 1, 2, Xs=[np.arange(30, 40)])
plt.show()

# ---animation
x = np.random.rand(10, 128, 128)
y = np.random.rand(10, 128, 128)
fig = plt.figure()
for n in range(10):
    fig.clf()
    plt = mesh([x[n], y[n]], 1, 2, fig=fig)
    plt.pause(0.5)

pyaibox.utils.plot_show.mshow(Zs, nrows=None, ncols=None, xlabels=None, ylabels=None, zlabels=None, titles=None, projections=None, figsize=None, outfile=None, **kwargs)

show tensors

This function create an figure and show some 2d-arrays in \(a\) rows and \(b\) columns with 2d/3d projection.

Parameters

Zs (array, list or tuple) – list/tuple of image arrays, if the type is not list or tuple, wrap it.
nrows (int, optional) – show in nrows rows, by default None (auto computed).
ncols (int, optional) – show in ncols columns, by default None (auto computed).
xlabels (str, optional) – labels of x-axis
ylabels (str, optional) – labels of y-axis
zlabels (str, optional) – labels of z-axis
titles (str, optional) – titles
figsize (tuple, optional) – figure size, by default None
outfile (str, optional) – save image to file, by default None (do not save).
kwargs –
Xs : list or tuple Ys : list or tuple fig : figure handle

sunch as fig = plt.figure()

for others, see matplotlib.pyplot.plot_surface()

Returns

plot handle

Return type

plt

Examples

x, y = np.meshgrid(np.arange(0, 10), np.arange(0, 20))
z1 = np.random.rand(20, 10)
z2 = np.random.randn(60, 60)

plt = mshow([z1, z2], 1, 2, Xs=[np.arange(30, 40)], projections=['3d', '2d'])
plt.show()

# ---animation
x = np.random.rand(10, 128, 128)
y = np.random.rand(10, 128, 128)
fig = plt.figure()
for n in range(10):
    fig.clf()
    plt = mshow([x[n], y[n]], 1, 2, fig=plt)
    plt.pause(0.5)

pyaibox.utils.plot_show.plot(Ys, nrows=None, ncols=None, styles=None, legends=None, grids=False, xlabels=None, ylabels=None, titles=None, figsize=None, outfile=None, **kwargs)

show images

This function create an figure and show images in \(a\) rows and \(b\) columns.

Parameters

Ys (array, list or tuple) – list/tuple of image arrays, if the type is not list or tuple, wrap it.
nrows (int, optional) – show in nrows rows, by default None (auto computed).
ncols (int, optional) – show in ncols columns, by default None (auto computed).
styles (str or list, optional) – line style
legends (str or list, optional) – legend str list
grids (bool, optional) – If True plot grid, default False.
xlabels (str, optional) – labels of x-axis
ylabels (str, optional) – labels of y-axis
titles (str, optional) – titles
figsize (tuple, optional) – figure size, by default None
outfile (str, optional) – save image to file, by default None (do not save).
kwargs –

figfigure handle
sunch as fig = plt.figure()

Xslist or tuple
Y-axis values

see matplotlib.pyplot.imshow()

Returns

plot handle

Return type

plt

Examples

x1 = np.random.rand(2, 100)
x2 = np.random.rand(2, 100)
plt = plot([[xi for xi in x1], [xi for xi in x2]])
plt.show()

x1 = np.random.rand(2, 100)
x2 = np.random.rand(2, 100)
plt = plot([[xi for xi in x1], [xi for xi in x2]], styles=[['-b', '-r'], ['-b', '-r']], legends=[['real', 'imag'], ['real', 'imag']], grids=True)
plt.show()

pyaibox.utils.plot_show.plots(x, ydict, plotdir='./', xlabel='x', ylabel='y', title='', issave=False, isshow=True)

pyaibox.utils package

Submodules

pyaibox.utils.colormaps module

pyaibox.utils.colors module

pyaibox.utils.const module

pyaibox.utils.convert module

pyaibox.utils.docstr module

pyaibox.utils.file module

pyaibox.utils.image module

pyaibox.utils.ios module

pyaibox.utils.plot_show module

Module contents