joferkington

import matplotlib.pyplot as plt
import numpy as np

# Make dummy data with different ranges...
datas = [np.random.random() * np.random.random((10, 10)) for _ in range(6)]

fig, axes = plt.subplots(ncols=len(datas), figsize=(30, 5))

for i, (data, ax) in enumerate(zip(datas, axes.flat)):
    # It's important that we explicitly set vmin and vmax so they're the same

joferkington

#! /usr/bin/env python3

# Copyright 2023 Planet Labs, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#

joferkington

# There are two ways to clip data to irregular boundaries.
# One uses a masked array (and is most useful for "real" data)
# The other uses set_clip_path (and is most useful for graphics)
# This demonstrates the first (masked array)
import matplotlib.pyplot as plt
import scipy.ndimage
import numpy as np

import skimage.draw

joferkington

#!/usr/bin/env python
"""
# Copyright 2019 Planet Labs, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#

joferkington

"""
Silly auto-generated art. Meant to look like some sort of mold spores.
Deliberately slow to render -- meant to look good at high-res.
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.collections import LineCollection

joferkington

"""
Converts one or more zmap grids to ascii x,y,z tab-delimited files.  Exported
files will be saved to the current directory with a ".xyz" extension and the
same file name as the original.  Null grid values will not be included unless
the "--with_nulls" option is specified.

Usage:
    zmap2xyz.py [options] INPUT_FILES...

Options:

joferkington

import json

# https://mtgjson.com/v4/json/AllCards.json.zip
with open('AllCards.json', 'r') as infile:
    data = json.load(infile)

for name in data:
    card = data[name]
    cost = card.get('manaCost', '')
    if cost.count('{') > 6:

joferkington

import numpy as np                  
from scipy.interpolate import Rbf   
import matplotlib.pyplot as plt     
from matplotlib.colors import LinearSegmentedColormap
from mpl_toolkits.axes_grid1 import inset_locator    
from matplotlib.projections.polar import PolarAxes   
np.random.seed(1977)                                 

def main():
    x, y, z = generate_surface()

joferkington

import Tkinter as tk
import numpy as np  
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure                           

def main():
    root = tk.Tk()
    app = Application(root)
    tk.mainloop()          

joferkington

import numpy as np

# Our input data...
x = np.random.randint(0, 3200, (1000,1000))

# We're replacing something like
# struct.pack(">"+"hB"*x.size)
# Note that that's a 2-byte signed int followed by 1-byte unsigned

# We'll need to create the output 1D array and assign manually: