def cm2inch(*tupl):
"""
convert length unit from cm to inch.
>>> cm2inch(10)
3.937007874015748
>>> cm2inch(2, 2)
(0.7874015748031495, 0.7874015748031495)
>>> cm2inch((1, 5))
(0.39370078740157477, 1.968503937007874)
"""
inch = 2.54
if isinstance(tupl[0], tuple):
return tuple(i / inch for i in tupl[0])
else:
if len(tupl) != 1:
return tuple(i / inch for i in tupl)
else:
return tupl[0] / inch
def rgb2hex(r, g, b):
"""
Convert rgb color to hex format.
>>> rgb2hex(129, 154, 70)
'#819a46'
>>> rgb2hex(-10, 256, -1)
Traceback (most recent call last):
...
AssertionError: (r, g, b) value must within range 0 ~ 255.
"""
assert (0, 0, 0) <= (r, g, b) <= (255, 255, 255), \
"(r, g, b) value must within range 0 ~ 255."
hex = "#{:02x}{:02x}{:02x}".format(r,g,b)
return hex
def hex2rgb(color_hex):
"""
Convert hex color code to rgb tuple.
>>> hex2rgb('#819a46')
(129, 154, 70)
"""
code = color_hex[1:]
r = int(code[0:2], 16)
g = int(code[2:4], 16)
b = int(code[4:6], 16)
return r, g, b
def shiftedColorMap(cmap, start=0, midpoint=0.5, stop=1.0, name='shiftedcmap'):
'''
This function is from: https://stackoverflow.com/a/20528097/8500469
Function to offset the "center" of a colormap. Useful for
data with a negative min and positive max and you want the
middle of the colormap's dynamic range to be at zero
Input
-----
cmap : The matplotlib colormap to be altered
start : Offset from lowest point in the colormap's range.
Defaults to 0.0 (no lower ofset). Should be between
0.0 and `midpoint`.
midpoint : The new center of the colormap. Defaults to
0.5 (no shift). Should be between 0.0 and 1.0. In
general, this should be 1 - vmax/(vmax + abs(vmin))
For example if your data range from -15.0 to +5.0 and
you want the center of the colormap at 0.0, `midpoint`
should be set to 1 - 5/(5 + 15)) or 0.75
stop : Offset from highets point in the colormap's range.
Defaults to 1.0 (no upper ofset). Should be between
`midpoint` and 1.0.
'''
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
cdict = {
'red': [],
'green': [],
'blue': [],
'alpha': []
}
# regular index to compute the colors
reg_index = np.linspace(start, stop, 257)
# shifted index to match the data
shift_index = np.hstack([
np.linspace(0.0, midpoint, 128, endpoint=False),
np.linspace(midpoint, 1.0, 129, endpoint=True)
])
for ri, si in zip(reg_index, shift_index):
r, g, b, a = cmap(ri)
cdict['red'].append((si, r, r))
cdict['green'].append((si, g, g))
cdict['blue'].append((si, b, b))
cdict['alpha'].append((si, a, a))
newcmap = matplotlib.colors.LinearSegmentedColormap(name, cdict)
plt.register_cmap(cmap=newcmap)
return newcmap
def get_size(obj, seen=None):
"""
Recursively finds size of objects
From:
https://stackoverflow.com/a/40880923/8500469
"""
import sys
size = sys.getsizeof(obj)
if seen is None:
seen = set()
obj_id = id(obj)
if obj_id in seen:
return 0
# Important mark as seen *before* entering recursion to gracefully handle
# self-referential objects
seen.add(obj_id)
if isinstance(obj, dict):
size += sum([get_size(v, seen) for v in obj.values()])
size += sum([get_size(k, seen) for k in obj.keys()])
elif hasattr(obj, '__dict__'):
size += get_size(obj.__dict__, seen)
elif hasattr(obj, '__iter__') and not isinstance(obj, (str, bytes, bytearray)):
size += sum([get_size(i, seen) for i in obj])
return size
def fig2bytes(fig, encode='svg', dpi=None):
"""
Convert matplotlib.figure.Figure object to image bytes.
"""
import io
buf = io.BytesIO()
fig.savefig(buf, format=encode, dpi=None)
buf.seek(0)
img_bytes = buf.read()
buf.close()
return img_bytes
