![\"The](\"images/dir_semi_tutorial_graphics.jpg\")
\n"
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"* The top plane shows the black unit circle that represents the omnidirectional variogram. Within it, we see two ellipses: one is bright green and another is dark green.\n",
"* The bottom plane shows a unit circle and two ellipses: the brighter yellow, and darker purple.\n",
"* The long arrows within both circles are radii of the omnidirectional variogram or **the semi-major axis** of a directional ellipse. The `step_size` parameter controls its length. The shorter arrows are present only in the **directional variograms**. These represent the **semi-minor axes**. The `tolerance` parameter controls their length, and it is always:\n",
" - a fraction of `step_size`,\n",
" - **1**, in this case, a semivariogram is omnidirectional,\n",
" - a value very close to 0 (but not 0) - then ellipse falls into line.\n",
"* The good idea is to set `tolerance` to 0.5 and gradually make it smaller or larger (depending on the spatial properties of a dataset).\n",
"\n",
"---\n",
"\n",
"We can start the analysis by understanding how our parameters affect the semivariogram bins range. We will set tolerance to 0.2 in each case to better visualize the effects of the leading direction.\n",
"\n",
"### Case 1: West-East direction"
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"SEMI_MAJOR_AXIS_SIZE = 40000 # meters\n",
"MAX_RANGE = 400000 # meters\n",
"TOLERANCE = 0.2\n",
"WE_DIRECTION = 0 # or 180"
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"source": [
"exp_var = ExperimentalVariogram(\n",
" values=df[VALUE_COL],\n",
" geometries=df['geometry'],\n",
" step_size=SEMI_MAJOR_AXIS_SIZE,\n",
" max_range=MAX_RANGE,\n",
" direction=WE_DIRECTION,\n",
" tolerance=TOLERANCE\n",
")\n",
"exp_var.plot(semivariance=True, covariance=False, variance=False)"
],
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"