tommct

Implements multiple, stacked plots with brushing. This extends the example at http://bl.ocks.org/mbostock/1667367 and allows for multiple panels where each subsequent panel zooms from the previous. Data points are also smoothed, permitting data with over 100,000 points to have an overview with subsequent telescoping while maintaining context.

tommct

This is Python code for updating the file modification date of a file on MacOSX or Linux. In this example, I had copied .dv files from my camcorder, which encoded the date in the filename, but had as the modification date, the time I transferred the file from the camcorder.

import os
import time
fpath = '/path/to/dv/files'
for root, dirs, files in os.walk(fpath):
    for name in files:

if name[-3:]=='.dv':

tommct

This is my own mods to http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/, which describes a scientific python environment install on Mac OS X. See also https://github.com/fonnesbeck/ScipySuperpack.

Install XCode and Commandline tools for XCode from Apple. Also install http://mxcl.github.io/homebrew/ and run brew update and brew doctor to make sure homebrew is installed correctly.

Then, you can run the following commands (I recommend one at a time so that you can deal with any errors or important warnings):

brew install python
brew install gfortran

pip install --upgrade pip

tommct

Implements constrained zooming of an image constructed from a data-driven ImageData object placed onto an HTML5 Canvas. Borrows heavily from https://gist.github.com/mbostock/3074470.

tommct

Implements constrained zooming of an image constructed from a data-driven ImageData object placed onto an HTML5 Canvas while giving the marginal distributions of the underlying data. Borrows heavily from https://gist.github.com/mbostock/3074470 and https://gist.github.com/tommct/8049508.

tommct

This D3 example demonstrates constrained zooming, much like http://bl.ocks.org/tommct/5671250, but also illustrates the use of hierarchical ordinal tick marks. It does this by using the normalized values that one gets when using a hierarchical partition layout.

tommct

This D3 example demonstrates using the zoom event and limits the bounds of the zooming to a specified domain. It is largely based on http://bl.ocks.org/jasondavies/3689931, but with bounds. Most of this bounding is done in the refresh function. You need to zoom in before you can pan or zoom out.

tommct

This is a recipe for using Sklearn to build a cosine similarity matrix and then to build dendrograms from it.

import numpy as np
import matplotlib.pyplot as plt
import scipy.cluster.hierarchy
import scipy.spatial.distance
from scipy.spatial.distance import pdist
from sklearn.metrics.pairwise import cosine_similarity

Make a "feature matrix" of 15 items that will be the binary representation of each index.

tommct

def columns_via_merge(df: pd.DataFrame, df2: pd.DataFrame, oncols: list, assigning: list):
    """
    Add (or replace) columns to df that map via a merge with df2.

    Examples:

        # Add the ord value to a subset of a DataFrame
        ABC = [chr(x) for x in range(ord('A'), ord('Z') + 1)]
        AABBCC = [chr(x)+chr(x) for x in range(ord('A'), ord('Z') + 1)]
        abc = [chr(x) for x in range(ord('a'), ord('z') + 1)]

tommct

This creates a normalized mass density histogram in matplotlib

bins = np.linspace(-1, 1, 101)
# To get a normalized mass density histogram, we have to do it this way...
hist, bins = np.histogram(df['some_column'], bins=bins, density=True)
hist /= len(bins)
width = bins[1]-bins[0]
fig = plt.figure(figsize=(8, 4))
ax = fig.add_axes([.15, .15, .75, .75])

plt.bar(left=bins[:-1], height=hist, width=width)