Compiled from 2012 publicly available data at http://www.open.georgia.gov/sta/viewMain.aud
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
#!/usr/bin/env python | |
""" | |
simple example script for running and testing notebook resulting in a new workbook. | |
Usage: `ipnbdoctest.py foo.ipynb foo_new.ipynb` | |
Each cell is submitted to the kernel, and the outputs are compared with those stored in the notebook. | |
""" | |
import io |
This example is a revision of Focus+Context via Brushing to demonstrate how to use D3's brush component to implement focus + context zooming but along the y-axis. Click and drag up/down in the narrow chart on the left to pan or zoom.
This line chart is an adaptation of Multi-Series Line Chart.
The adaptation highlights:
- Focus+Context via Brushing - Click and drag in the small chart to pan or zoom.
- Brush Transitions - Hover over legend items to refocus the chart on a single city.
- Touch Events - Mouseover effects simulated on tablets with touch events.
[ Launch: Tributary inlet ] 8232013 by davidshinn
[ Launch: bounded panning ] 8294897 by davidshinn
[ Launch: workbars ] 8423329 by davidshinn
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
{"nbformat_minor": 0, "cells": [{"source": "### Profile finding max in a vector versus finding max in matrix ", "cell_type": "markdown", "metadata": {}}, {"execution_count": 1, "cell_type": "code", "source": "import numpy as np", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 2, "cell_type": "code", "source": "%%time\nm = np.random.randn(500, 600, 500)", "outputs": [{"output_type": "stream", "name": "stdout", "text": "CPU times: user 4.37 s, sys: 309 ms, total: 4.68 s\nWall time: 4.68 s\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 3, "cell_type": "code", "source": "v = m[:, :, 1]", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"source": "Timing finding max in a vector\n\nHowever, realize you have to run this 300,000 times (500\\*600)", "cell_type": "markdown", "metadata": {}}, {"execution_count": 4, "cell_type": "code", "source": "%timeit np.max(v)", "outputs": [{"output_type": "stream", "name": "stdout", "text": "100 |
OlderNewer