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16 | 16 | " <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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17 | 17 | "</div>\n",
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18 | 18 | "\n",
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19 | | - "<p class=\"author\" style=\"padding: 0;\"><center><b>Haoze Zhang, Luis Davila and Ryan Barnett</b></center></p>\n", |
| 19 | + "<p class=\"author\" style=\"padding: 0;\"><center><b>Haoze Zhang, Luis Davila, and Ryan Barnett</b></center></p>\n", |
20 | 20 | "<p class=\"date\" style=\"padding: 0;\"><center><b>September 9, 2025</b></center></p>\n",
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21 | 21 | "\n",
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22 | 22 | "---\n",
|
|
957 | 957 | "id": "74",
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958 | 958 | "metadata": {},
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959 | 959 | "outputs": [],
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| 960 | + "source": [ |
| 961 | + "expvario_neutral.data[\"Lag Distance\"].describe()" |
| 962 | + ] |
| 963 | + }, |
| 964 | + { |
| 965 | + "cell_type": "code", |
| 966 | + "execution_count": null, |
| 967 | + "id": "75", |
| 968 | + "metadata": {}, |
| 969 | + "outputs": [], |
960 | 970 | "source": [
|
961 | 971 | "angles = rmsp.calc_moi_angles(expvario_neutral, sill=1.0, power=5)\n",
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962 | 972 | "angles"
|
963 | 973 | ]
|
964 | 974 | },
|
965 | 975 | {
|
966 | 976 | "cell_type": "markdown",
|
967 | | - "id": "75", |
| 977 | + "id": "76", |
968 | 978 | "metadata": {},
|
969 | 979 | "source": [
|
970 | 980 | "## Calculate and visualize variogram along principal directions"
|
971 | 981 | ]
|
972 | 982 | },
|
973 | 983 | {
|
974 | 984 | "cell_type": "markdown",
|
975 | | - "id": "76", |
| 985 | + "id": "77", |
976 | 986 | "metadata": {},
|
977 | 987 | "source": [
|
978 | 988 | "Create principal directions from MOI inferred direction"
|
|
981 | 991 | {
|
982 | 992 | "cell_type": "code",
|
983 | 993 | "execution_count": null,
|
984 | | - "id": "77", |
| 994 | + "id": "78", |
985 | 995 | "metadata": {},
|
986 | 996 | "outputs": [],
|
987 | 997 | "source": [
|
|
994 | 1004 | },
|
995 | 1005 | {
|
996 | 1006 | "cell_type": "markdown",
|
997 | | - "id": "78", |
| 1007 | + "id": "79", |
998 | 1008 | "metadata": {},
|
999 | 1009 | "source": [
|
1000 | 1010 | "Fit variogram"
|
|
1003 | 1013 | {
|
1004 | 1014 | "cell_type": "code",
|
1005 | 1015 | "execution_count": null,
|
1006 | | - "id": "79", |
| 1016 | + "id": "80", |
1007 | 1017 | "metadata": {},
|
1008 | 1018 | "outputs": [],
|
1009 | 1019 | "source": [
|
|
1015 | 1025 | },
|
1016 | 1026 | {
|
1017 | 1027 | "cell_type": "markdown",
|
1018 | | - "id": "80", |
| 1028 | + "id": "81", |
1019 | 1029 | "metadata": {},
|
1020 | 1030 | "source": [
|
1021 | 1031 | "## Visually compare\n",
|
|
1025 | 1035 | {
|
1026 | 1036 | "cell_type": "code",
|
1027 | 1037 | "execution_count": null,
|
1028 | | - "id": "81", |
| 1038 | + "id": "82", |
1029 | 1039 | "metadata": {},
|
1030 | 1040 | "outputs": [],
|
1031 | 1041 | "source": [
|
|
1037 | 1047 | },
|
1038 | 1048 | {
|
1039 | 1049 | "cell_type": "markdown",
|
1040 | | - "id": "82", |
| 1050 | + "id": "83", |
1041 | 1051 | "metadata": {},
|
1042 | 1052 | "source": [
|
1043 | 1053 | "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)."
|
|
1046 | 1056 | {
|
1047 | 1057 | "cell_type": "code",
|
1048 | 1058 | "execution_count": null,
|
1049 | | - "id": "83", |
| 1059 | + "id": "84", |
1050 | 1060 | "metadata": {},
|
1051 | 1061 | "outputs": [],
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1052 | 1062 | "source": [
|
|
1066 | 1076 | },
|
1067 | 1077 | {
|
1068 | 1078 | "cell_type": "markdown",
|
1069 | | - "id": "84", |
| 1079 | + "id": "85", |
1070 | 1080 | "metadata": {},
|
1071 | 1081 | "source": [
|
1072 | 1082 | "--- \n",
|
|
1079 | 1089 | },
|
1080 | 1090 | {
|
1081 | 1091 | "cell_type": "markdown",
|
1082 | | - "id": "85", |
| 1092 | + "id": "86", |
1083 | 1093 | "metadata": {},
|
1084 | 1094 | "source": [
|
1085 | 1095 | "<footer>\n",
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