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colorMapNotebook.ipynb

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"r = np.sqrt(x**2 + y**2)"
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},
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{
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"cell_type": "markdown",
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"id": "07d69200-25e6-4101-9f5f-5a20f9c4cabd",
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"metadata": {},
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"source": [
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"### Some simple data\n",
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"Let's just start with some simple data: a double gaussian"
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]
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},
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"cell_type": "code",
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"execution_count": 3,
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"print(plt.colormaps())"
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{
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"cell_type": "markdown",
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"id": "0fa050dd-0de9-4388-aca1-27d64c5f8e77",
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"metadata": {},
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"source": [
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"### Why color maps matter!\n",
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"Here we compare the often used rainbow colormaps to other color maps.\n",
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"\n",
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"Note that the rainbow maps often lead to artifacts that seem to hint at some form of structure. What is smooth structures appear "
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]
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"cell_type": "code",
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"plt.show()"
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]
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"cell_type": "markdown",
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"id": "764d3639-4635-4909-8cf9-4d2ccc246142",
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"metadata": {},
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"source": [
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"### More realistic data\n",
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"Let's make a fractal (see the attached code for how to do this). This fractal is generated, theoretically, through inverse fourier transform of a pink noise spectrum (white noise + radial profile in fourier space). The function `gen_fractal(N, D)` returns an cube of (N,N,N) with fractal dimension D."
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]
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},
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"cell_type": "code",
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"execution_count": 16,
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e593a607-33fe-4d15-8efe-2232b52cd56a",
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"metadata": {},
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"source": [
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"This is in linear space, so lets see what happens if we go to log space. Notice how the rainbow spectrum and the diverging spectrum both create distinct features due to the coloring. Pink->Blue->Cyan->Green is less than order of magnitude difference! Similarly, for diverging maps, going from blue to red within a factor of 3-4."
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]
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},
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"cell_type": "code",
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"execution_count": 17,

scatterPlots.ipynb

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visualization_talk.pdf

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