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Binary files a/notebooks/data/out_of_bound_depth_prediction_train.dbf and b/notebooks/data/out_of_bound_depth_prediction_train.dbf differ diff --git a/notebooks/data/out_of_bound_depth_predictionscatter_plot.png b/notebooks/data/out_of_bound_depth_predictionscatter_plot.png new file mode 100644 index 0000000..61e52ef Binary files /dev/null and b/notebooks/data/out_of_bound_depth_predictionscatter_plot.png differ diff --git a/notebooks/sdb.ipynb b/notebooks/sdb.ipynb index 5ad9b5a..1e75877 100644 --- a/notebooks/sdb.ipynb +++ b/notebooks/sdb.ipynb @@ -89,6 +89,24 @@ "import os" ] }, + { + "cell_type": "markdown", + "id": "2213da44", + "metadata": {}, + "source": [ + "Import `pyplot` from `matpplotlib` to generate scatter plot from the results" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "82d9f50f", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, { "cell_type": "markdown", "id": "41b1ef33-994a-49c7-89e6-1c0ab6023e03", @@ -107,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "95d7ff77-3703-42ad-bb33-0eabbb9b2495", "metadata": {}, "outputs": [], @@ -127,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "48e9852a-061b-44db-89d6-085ee21a7f5c", "metadata": {}, "outputs": [], @@ -161,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "d6e7abfc-7089-4852-84d1-000b0ecc6e15", "metadata": {}, "outputs": [ @@ -191,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "3eb89c24-a4d9-42f4-ba42-4c117587e80d", "metadata": {}, "outputs": [ @@ -226,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "75d558a0-4665-4890-9b41-c01d074a2fd7", "metadata": {}, "outputs": [ @@ -370,7 +388,7 @@ "[4634 rows x 4 columns]" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -398,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "389ada11-da1a-424b-9754-40df2bac7714", "metadata": {}, "outputs": [ @@ -542,7 +560,7 @@ "[4634 rows x 4 columns]" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -569,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "e45bacbf-7609-41a1-a6ad-1ba8a0dbb069", "metadata": {}, "outputs": [], @@ -588,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "643f6bff-05d8-45c0-8a8b-6983273fac84", "metadata": {}, "outputs": [], @@ -607,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "f5703d7e-77ae-4ab0-8c3a-3dd0c11a4104", "metadata": {}, "outputs": [ @@ -774,7 +792,7 @@ "[4634 rows x 7 columns]" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -806,7 +824,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "7e4bae01-0283-482b-9c5c-08cd4c3ad78d", "metadata": { "tags": [] @@ -826,7 +844,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "0874e885-77ce-4ad9-ad12-8204a2da1b49", "metadata": {}, "outputs": [ @@ -993,7 +1011,7 @@ "[4634 rows x 7 columns]" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1015,7 +1033,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "b5f10674-e4bc-4c29-b75d-a35cbf84f784", "metadata": {}, "outputs": [ @@ -1182,7 +1200,7 @@ "[4634 rows x 7 columns]" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1204,7 +1222,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "df6cdd93-f60d-4938-8e7f-641223fc7348", "metadata": {}, "outputs": [], @@ -1223,7 +1241,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "id": "0aa50546-1535-40f9-98e3-5abe2c824346", "metadata": {}, "outputs": [ @@ -1289,7 +1307,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "46f060aa-88cd-458a-b47f-d4da8980a156", "metadata": {}, "outputs": [ @@ -1377,7 +1395,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "59a1157a-3c0c-47b2-bf2b-0d9929efa302", "metadata": {}, "outputs": [], @@ -1400,7 +1418,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "id": "cb2b04ea-b39f-4019-9231-72446c54a720", "metadata": {}, "outputs": [ @@ -1446,7 +1464,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "id": "61fb489b-db88-48dc-b4a7-136cbf4fd73e", "metadata": { "tags": [] @@ -1467,7 +1485,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "id": "6cc84c45-2c59-4cf2-be17-59b25f836bcd", "metadata": {}, "outputs": [ @@ -1508,7 +1526,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "id": "c4d046c9-3e34-4cd7-8e4b-e5de209d0ff6", "metadata": {}, "outputs": [ @@ -1549,7 +1567,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "id": "1c86d5a6-35d0-4b57-99cf-46e7e7222aea", "metadata": {}, "outputs": [], @@ -1580,7 +1598,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "id": "7cc4072a-0f4c-440a-aab1-de6e7c9ae54c", "metadata": {}, "outputs": [], @@ -1602,7 +1620,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "id": "3bf78782-7a56-4ffd-bd2e-68e63a5ab55c", "metadata": {}, "outputs": [], @@ -1621,7 +1639,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "id": "5d3886bb-4e08-4723-a1bd-1cdf0d578213", "metadata": {}, "outputs": [], @@ -1640,7 +1658,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "id": "b69cdb6c-c07e-4024-bb8a-d9cc8958f48e", "metadata": {}, "outputs": [], @@ -1667,7 +1685,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "id": "65c94b17-4cc5-4e1e-a285-efae669f453d", "metadata": {}, "outputs": [], @@ -1686,7 +1704,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "id": "3f41889b-734f-4baf-b690-7e3997faad8b", "metadata": {}, "outputs": [], @@ -1694,6 +1712,71 @@ "train_data_gdf.to_file(train_save_loc_shp)\n", "test_data_gdf.to_file(test_save_loc_shp)" ] + }, + { + "cell_type": "markdown", + "id": "fa1d8dc6", + "metadata": {}, + "source": [ + "Create file output path name for scatter plot" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "2e7a4d64", + "metadata": {}, + "outputs": [], + "source": [ + "scatter_plot_loc = (os.path.splitext(new_img_loc)[0] + 'scatter_plot.png')" + ] + }, + { + "cell_type": "markdown", + "id": "57abbc8a", + "metadata": {}, + "source": [ + "Create scatter plot using `matplotlib`" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "b4368289", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def scatter_plotter(x, y, plot_color='royalblue', line_color='r', title='Scatter Plot'):\n", + " fig, ax = plt.subplots(figsize=(5, 5))\n", + " ax.scatter(x, y, marker='.', color=plot_color, facecolors='none')\n", + " min_val, max_val = round(np.nanmin(x)), round(np.nanmax(x))\n", + " ax.plot([min_val, max_val], [min_val, max_val], color=line_color)\n", + " ax.set_xlabel('True Depth')\n", + " ax.set_ylabel('Predicted Depth')\n", + " ax.set_title(title)\n", + "\n", + " return fig, ax\n", + "\n", + "fig, ax = scatter_plotter(\n", + " x=test_data['z'],\n", + " y=test_data['z_validate']\n", + ")\n", + "\n", + "fig.savefig(scatter_plot_loc)" + ] } ], "metadata": { @@ -1712,7 +1795,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.13" + "version": "3.6.12" } }, "nbformat": 4, diff --git a/notebooks/sdb_out_of_bound.ipynb b/notebooks/sdb_out_of_bound.ipynb index 84fca8b..7399ba7 100644 --- a/notebooks/sdb_out_of_bound.ipynb +++ b/notebooks/sdb_out_of_bound.ipynb @@ -89,6 +89,24 @@ "import os" ] }, + { + "cell_type": "markdown", + "id": "0f1e7bc0", + "metadata": {}, + "source": [ + "Import `pyplot` from `matpplotlib` to generate scatter plot from the results" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "44469cfd", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, { "cell_type": "markdown", "id": "41b1ef33-994a-49c7-89e6-1c0ab6023e03", @@ -107,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "95d7ff77-3703-42ad-bb33-0eabbb9b2495", "metadata": {}, "outputs": [], @@ -127,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "48e9852a-061b-44db-89d6-085ee21a7f5c", "metadata": {}, "outputs": [], @@ -161,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "d6e7abfc-7089-4852-84d1-000b0ecc6e15", "metadata": {}, "outputs": [ @@ -191,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "3eb89c24-a4d9-42f4-ba42-4c117587e80d", "metadata": {}, "outputs": [ @@ -226,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "75d558a0-4665-4890-9b41-c01d074a2fd7", "metadata": {}, "outputs": [ @@ -370,7 +388,7 @@ "[10085 rows x 4 columns]" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -398,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "389ada11-da1a-424b-9754-40df2bac7714", "metadata": {}, "outputs": [ @@ -542,7 +560,7 @@ "[10085 rows x 4 columns]" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -569,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "c4c81210-f39a-440f-ac74-0788a9aa62f4", "metadata": {}, "outputs": [], @@ -588,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "6866e78a-b50b-406f-b18b-15bb5c514c81", "metadata": {}, "outputs": [ @@ -732,7 +750,7 @@ "[4634 rows x 4 columns]" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -758,7 +776,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "e45bacbf-7609-41a1-a6ad-1ba8a0dbb069", "metadata": {}, "outputs": [], @@ -777,7 +795,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "643f6bff-05d8-45c0-8a8b-6983273fac84", "metadata": {}, "outputs": [], @@ -796,7 +814,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "f5703d7e-77ae-4ab0-8c3a-3dd0c11a4104", "metadata": {}, "outputs": [ @@ -963,7 +981,7 @@ "[4634 rows x 7 columns]" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -995,7 +1013,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "67bc329e-fb4c-4cb6-a38e-de82c5b2a8f0", "metadata": { "tags": [] @@ -1015,7 +1033,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "b084605a-2cfa-4feb-acda-610dc7a4dea0", "metadata": {}, "outputs": [ @@ -1182,7 +1200,7 @@ "[4634 rows x 7 columns]" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1204,7 +1222,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "id": "b5f10674-e4bc-4c29-b75d-a35cbf84f784", "metadata": {}, "outputs": [ @@ -1371,7 +1389,7 @@ "[4634 rows x 7 columns]" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1393,7 +1411,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "df6cdd93-f60d-4938-8e7f-641223fc7348", "metadata": {}, "outputs": [], @@ -1412,7 +1430,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "0aa50546-1535-40f9-98e3-5abe2c824346", "metadata": {}, "outputs": [ @@ -1478,7 +1496,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "id": "46f060aa-88cd-458a-b47f-d4da8980a156", "metadata": {}, "outputs": [ @@ -1564,7 +1582,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "id": "59a1157a-3c0c-47b2-bf2b-0d9929efa302", "metadata": {}, "outputs": [], @@ -1587,7 +1605,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "id": "cb2b04ea-b39f-4019-9231-72446c54a720", "metadata": {}, "outputs": [ @@ -1631,7 +1649,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "id": "ecaaae15-943e-4dbd-9a42-6ebbceb74102", "metadata": {}, "outputs": [], @@ -1650,7 +1668,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "id": "6cc84c45-2c59-4cf2-be17-59b25f836bcd", "metadata": {}, "outputs": [ @@ -1691,7 +1709,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "id": "c4d046c9-3e34-4cd7-8e4b-e5de209d0ff6", "metadata": {}, "outputs": [ @@ -1732,7 +1750,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "id": "1c86d5a6-35d0-4b57-99cf-46e7e7222aea", "metadata": {}, "outputs": [], @@ -1763,7 +1781,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "id": "7cc4072a-0f4c-440a-aab1-de6e7c9ae54c", "metadata": {}, "outputs": [], @@ -1785,7 +1803,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "id": "3bf78782-7a56-4ffd-bd2e-68e63a5ab55c", "metadata": {}, "outputs": [], @@ -1804,7 +1822,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "id": "5d3886bb-4e08-4723-a1bd-1cdf0d578213", "metadata": {}, "outputs": [], @@ -1823,7 +1841,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "id": "b69cdb6c-c07e-4024-bb8a-d9cc8958f48e", "metadata": {}, "outputs": [], @@ -1850,7 +1868,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "id": "65c94b17-4cc5-4e1e-a285-efae669f453d", "metadata": {}, "outputs": [], @@ -1869,7 +1887,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "id": "3f41889b-734f-4baf-b690-7e3997faad8b", "metadata": {}, "outputs": [], @@ -1877,6 +1895,71 @@ "train_data_gdf.to_file(train_save_loc_shp)\n", "test_data_gdf.to_file(test_save_loc_shp)" ] + }, + { + "cell_type": "markdown", + "id": "2719ebc5", + "metadata": {}, + "source": [ + "Create file output path name for scatter plot" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "28429664", + "metadata": {}, + "outputs": [], + "source": [ + "scatter_plot_loc = (os.path.splitext(new_img_loc)[0] + 'scatter_plot.png')" + ] + }, + { + "cell_type": "markdown", + "id": "e9e4f121", + "metadata": {}, + "source": [ + "Create scatter plot using `matplotlib`" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "e206dd41", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def scatter_plotter(x, y, plot_color='royalblue', line_color='r', title='Scatter Plot'):\n", + " fig, ax = plt.subplots(figsize=(5, 5))\n", + " ax.scatter(x, y, marker='.', color=plot_color, facecolors='none')\n", + " min_val, max_val = round(np.nanmin(x)), round(np.nanmax(x))\n", + " ax.plot([min_val, max_val], [min_val, max_val], color=line_color)\n", + " ax.set_xlabel('True Depth')\n", + " ax.set_ylabel('Predicted Depth')\n", + " ax.set_title(title)\n", + "\n", + " return fig, ax\n", + "\n", + "fig, ax = scatter_plotter(\n", + " x=test_data['z'],\n", + " y=test_data['z_validate']\n", + ")\n", + "\n", + "fig.savefig(scatter_plot_loc)" + ] } ], "metadata": { @@ -1895,7 +1978,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.13" + "version": "3.6.12" } }, "nbformat": 4,