joferkington

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider

def main():
    data = np.random.random((10,10,10))
    ex = Explorer(data)
    ex.show()

class Explorer(object):

joferkington

import numpy as np
import scipy.ndimage as ndimage

# The array you gave above
data = np.array( 
        [
           [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
           [0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 

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:

joferkington

"""MIT license"""

import xlrd
import re

def col2index(name):
    name = name.upper()
    col = -1
    for i, letter in enumerate(name[::-1]):
        col += (ord(letter) - ord('A') + 1) * 26**i

joferkington

import numpy as np

def process_file(filename, num_cols, delimiter='\t'):
    def items(infile):
        for line in infile:
            for item in line.rstrip().split(delimiter):
                yield float(item)

    with open(filename, 'r') as infile:
        data = np.fromiter(items(infile))

joferkington

# Make up some data in nested lists 
strain_data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]

# To make things a bit more readable, let's define a function that operates
# on a single list...
def zero(data):
    """Returns the difference between the items in "data" and its first item.""" 
    # This is a "list comprehension". It's basically a 1-line for loop
    return [item - data[0] for item in data]

joferkington

import matplotlib.pyplot as plt

def main():
    resized = (8, 3)

    basic_example()
    basic_example(resized)

    fixed_aspect()
    fixed_aspect(resized)

joferkington

import numpy as np
import pandas as pd

#-- Generate some data similar to yours
idx = np.arange(20)

np.random.shuffle(idx)
idx1 = idx[:15]
np.random.shuffle(idx)
idx2 = idx[:10]

joferkington

import matplotlib.pyplot as plt
from matplotlib.mlab import csv2rec
from matplotlib.cbook import get_sample_data

#fname = get_sample_data('percent_bachelors_degrees_women_usa.csv')
fname = 'percent_bachelors_degrees_women_usa.csv'
gender_degree_data = csv2rec(fname)

# These are the colors that will be used in the plot

joferkington

import random
import matplotlib.pyplot as plt
from mpldatacursor import datacursor

def main():
    accounts = generate_accounts()
    lookup = plot(accounts)
    datacursor(formatter=Formatter(lookup), bbox=dict(alpha=1))
    plt.show()