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Commit b6eee4f

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adding output cells and date update
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‎notebooks/variogramanisotropy/variogramanisotropy.ipynb

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" <font style=\"color:#00797C;padding-bottom:50px\"><center><b><font size=\"+2\">A Companion Notebook to Geostatistics Lessons</font></b></center></p> \n",
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"</div>\n",
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"\n",
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"<p class=\"author\" style=\"padding: 0;\"><center><b>Haoze Zhang, Luis Davila and Ryan Barnett</b></center></p>\n",
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"<p class=\"author\" style=\"padding: 0;\"><center><b>Haoze Zhang, Luis Davila, and Ryan Barnett</b></center></p>\n",
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"<p class=\"date\" style=\"padding: 0;\"><center><b>September 9, 2025</b></center></p>\n",
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"\n",
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"---\n",
@@ -957,22 +957,32 @@
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"id": "74",
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"metadata": {},
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"outputs": [],
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"source": [
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"expvario_neutral.data[\"Lag Distance\"].describe()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "75",
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"metadata": {},
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"outputs": [],
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"source": [
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"angles = rmsp.calc_moi_angles(expvario_neutral, sill=1.0, power=5)\n",
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"angles"
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]
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},
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{
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"cell_type": "markdown",
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"id": "75",
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"id": "76",
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"metadata": {},
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"source": [
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"## Calculate and visualize variogram along principal directions"
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]
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},
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{
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"cell_type": "markdown",
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"id": "76",
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"id": "77",
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"metadata": {},
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"source": [
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"Create principal directions from MOI inferred direction"
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "77",
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"id": "78",
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "markdown",
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"id": "78",
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"id": "79",
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"metadata": {},
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"source": [
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"Fit variogram"
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "79",
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"id": "80",
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"metadata": {},
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"outputs": [],
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"source": [
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},
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"cell_type": "markdown",
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"id": "80",
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"id": "81",
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"metadata": {},
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"source": [
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"## Visually compare\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "81",
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"id": "82",
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Inspect visually against the circle based variogram and true variogram. The MOI approach is shown to outperform the circle based approach with sparse data. Also note, however, that that the MOI based angles underperform direct fitting of the variogram model to the neutral based variogram volume (prior section), which is case dependent (here MOI angles over influenced by distracting features along the covariance volume edges)."
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "83",
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"id": "84",
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "markdown",
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"id": "84",
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"id": "85",
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"metadata": {},
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"source": [
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"--- \n",
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},
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{
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"cell_type": "markdown",
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"id": "86",
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"metadata": {},
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"source": [
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"<footer>\n",

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