miguelsolorio

function term() {
  #!/bin/bash
  T='●●●'
  echo -e "\n                 40m     41m     42m     43m\
      44m     45m     46m     47m";
  for FGs in '    m' '   1m' '  30m' '1;30m' '  31m' '1;31m' '  32m' \
            '1;32m' '  33m' '1;33m' '  34m' '1;34m' '  35m' '1;35m' \
            '  36m' '1;36m' '  37m' '1;37m';
    do FG=${FGs// /}
    echo -en " $FGs \033[$FG  $T  "

miguelsolorio

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miguelsolorio

{
  "scripts": [],
  "styles": []
}

miguelsolorio

"workbench.colorCustomizations": {
	"[FireFly Pro]": {
		"activityBar.foreground": "#ff0000",
		"activityBarBadge.background": "#ff0000",
		"badge.background": "#ff0000",
		"breadcrumb.focusForeground": "#ff0000",
		"button.background": "#ff0000",
		"editor.inactiveSelectionBackground": "#ff0000",
		"list.activeSelectionForeground": "#ff0000",
		"panel.border": "#ff0000",

miguelsolorio

miguelsolorio

- let counter = 1
- let cells = [{group: 1, type: 'text', fold: true, heading: 'Common plotting pitfalls with large data', paragraph: 'When working with large datasets, visualizations are often the only way available to understand the properties of that dataset -- there are simply too many data points to examine each one!  Thus it is very important to be aware of some common plotting problems that are minor inconveniences with small datasets but very serious problems with larger ones.', git: 'modified'},{group: 1, type: 'text', paragraph: 'We\'ll first load the plotting libraries and set up some defaults:', git: 'modified'},{group: 1, type: 'code', string: 'conda install -c bokeh -c ioam holoviews colorcet matplotlib scikit-image', time: '0.1'},{group: 1, type: 'code', string: 'import numpy as np \n np.random.seed(42) \n \n import holoviews as hv \n hv.notebook_extension(\'matplotlib\') \n \n %opts Points [color_index=2] (cmap="bwr" edgecolors=\'k\' s=50 alpha=1.0) \n %opts Scatter3D [color_index=3 fig_size=2

miguelsolorio

‎‎​

miguelsolorio

- let counter = 1
- let cells = [{group: 1, type: 'text', heading: 'Common plotting pitfalls with large data', paragraph: 'When working with large datasets, visualizations are often the only way available to understand the properties of that dataset -- there are simply too many data points to examine each one!  Thus it is very important to be aware of some common plotting problems that are minor inconveniences with small datasets but very serious problems with larger ones.', git: 'modified'},{group: 1, type: 'code', string: 'import matplotlib.pyplot as plt\nimport numpy as np', time: '0.1'},{group: 1, type: 'code', string: 'print("Hello World")', time: "0.3", output: 'Hello World',git: 'added'},{group: 2, type: 'text', heading: '1. Overplotting', paragraph: 'Let\'s consider plotting some 2D data points that come from two separate categories, here plotted as blue and red in **A** and **B** below.  When the two categories are overlaid, the appearance of the result can be very different depending on which one is

miguelsolorio

- let counter = 1
- let cells = [{group: 1, type: 'text', heading: 'Common plotting pitfalls with large data', paragraph: 'When working with large datasets, visualizations are often the only way available to understand the properties of that dataset -- there are simply too many data points to examine each one!  Thus it is very important to be aware of some common plotting problems that are minor inconveniences with small datasets but very serious problems with larger ones.', git: 'modified'},{group: 1, type: 'code', string: 'import matplotlib.pyplot as plt\nimport numpy as np', time: '0.1'},{group: 1, type: 'code', string: 'print("Hello World")', time: "0.3", output: 'Hello World',git: 'added'},{group: 2, type: 'text', heading: '1. Overplotting', paragraph: 'Let\'s consider plotting some 2D data points that come from two separate categories, here plotted as blue and red in **A** and **B** below.  When the two categories are overlaid, the appearance of the result can be very different depending on which one is

miguelsolorio