{ "cells": [ { "cell_type": "markdown", "id": "cardiac-graphic", "metadata": {}, "source": [ "# Processing Elmer/Ice output\n", "\n", "### Felicity Holmes" ] }, { "cell_type": "markdown", "id": "engaging-board", "metadata": {}, "source": [ "## General Project Description\n", "\n", "For my PhD project, I use an ice-sheet model called Elmer/Ice. Specifcally, I am simulating the glacier Kronebreen, Svalbard for the time period 2016 - 2017 and comparing the output to observational data in order to evaluate the applicability of Elmer/Ice in this location. The long-term goal is to use the tuned model to simulate the development of the glacier until 2100. \n", "\n", "After each simulation, I have the following output:\n", "\n", "1) A .csv file with point data for various variables (e.g. velocity, pressure, elevation) at each timestep\n", "\n", "2) A .dat file for each partition containing the mean velocity value after each timestep. Of particular interest is partition 0, which covers the front (and most dynamically exciting) part of the glacier. \n", "\n", "For the project, I want to explore this data and build up a processing chain that I can use to analyse the output. My current simulations in Elmer/Ice are not final as I am still tuning parameters etc, but I use my 'best yet' output for this project. This means that any conclusions drawn from the analysis are only preliminary, but provide a framework for exploration and analysis of the output.\n", "\n", "Specifically, my aims are:\n", "1) To convert the point data into a raster than I can then compare to observations. This would allow me to identify whether the model is performing well and highlight any areas of particulary good or bad performance. \n", "\n", "2) To look at the time series of mean velocities and understand how important surface mass balance (and its constituent components) are with regards to determining velocities. In order to do this, I want to explore the utility of a few techniques such as dynamic time warping and a random forest regressor.\n", "\n", "I will split up this report into parts 1 and 2; part 1 will deal with the point data (mainly geocomputation) and part 2 will deal with the data from the frontal partition (geocomputation and modelling)." ] }, { "cell_type": "markdown", "id": "complicated-regard", "metadata": {}, "source": [ "## Part 1: Point data exploration\n", "\n", "Although it is possible to export a .csv file for every timestep, I have opted to focus on the final timestep for this project. The initial conditions for the glacier are derived from observations, so it is of interest to see if the modelled and observed development of the glacier are similar. Although it would be of interest to conduct this analysis at regular intervals (e.g. once a month), the output that I am using for this project is preliminary and only spans 52 days. Therefore, this project will only compare the final model output to the appropriate observational data but forms a basis for more comprehensive analysis in the future.\n", "\n", "The .csv data is 3D with data being provided for the entire depth of the glacier. However, I am primarily interested in the surface values as the observational data only shows surface properties. The first steps are therefore to:\n", "\n", "1) Extract all the data when 'depth < 5'. I am using depth < 5 rather than depth == 0 as the surface nodes do not always lie precisely at 0m depth due to mesh evolution\n", "\n", "2) Investigate the available variables and the number of data points" ] }, { "cell_type": "code", "execution_count": 13, "id": "educational-siemens", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"depth\" \"temperature homologous\" \"smb\" \"velocity:0\" \"velocity:1\" \"velocity:2\" \"frontadvance:0\" \"frontadvance:1\" \"frontadvance:2\" \"calving:0\" \"calving:1\" \"calving:2\" \"Points:0\" \"Points:1\" \"Points:2\"\n", "8330 ./outputs/depth0_calvingVariables.txt\n", "14\n" ] } ], "source": [ "%%bash \n", "\n", "# Extract surface data. Using depth < 5 rather than depth == 0 as some surface nodes\n", "# are not exactly 0\n", "awk '{if ($0<5) print}' \"./inputs/calving_elmer_output.txt\" > ./outputs/depth0_calvingVariables.txt\n", "\n", "head -n 1 ./outputs/depth0_calvingVariables.txt\n", "\n", "wc -l ./outputs/depth0_calvingVariables.txt\n", "\n", "tail -n 1 ./outputs/depth0_calvingVariables.txt | grep -o \" \" | wc -l" ] }, { "cell_type": "markdown", "id": "bibliographic-momentum", "metadata": {}, "source": [ "It appears that the velocity variables are given in x, y, z but I would like to calculate the magnitude. \n", "\n", "The next step is therefore to calculate the magnitude of velocity in 2D (just taking into account velocity in the x and y directions)." ] }, { "cell_type": "code", "execution_count": 14, "id": "korean-speed", "metadata": { "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "import sys \n", "\n", "# Get the header line and add 'velocity_magnitude', then save this as a file\n", "\n", "fp=open('./outputs/depth0_calvingVariables.txt')\n", "header = fp.readline().strip().split(' ')\n", "header.append(\"velocity_magnitude\")\n", "np.savetxt('./outputs/calvingData_names.txt', header, delimiter=\",\", fmt=\"%s\")\n", "\n", "# Calculate the velocity magnitude and append to dataset\n", "\n", "dataset = np.genfromtxt('./outputs/depth0_calvingVariables.txt', skip_header=1)\n", "temp=[]\n", "\n", "for row in dataset:\n", " velocity_mag = np.sqrt((float(row[3]))**2+(float(row[4])**2)) \n", " temp.append(velocity_mag)\n", "\n", "temp_array = np.array(temp).reshape(8329,1)\n", "\n", "# Save the new datatset\n", "\n", "result = np.append(dataset, temp_array, axis=1) \n", "np.savetxt('./outputs/calving_velocity_mag.csv', result, delimiter=\",\", fmt=\"%s\")" ] }, { "cell_type": "markdown", "id": "given-bahrain", "metadata": {}, "source": [ "In order to double check that the operation was successful, the number of columns will be counted and the first few lines of data will be printed." ] }, { "cell_type": "code", "execution_count": 15, "id": "opposite-bernard", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "14\n", "15\n", "129.01,0.0,0.0,0.0,0.0,0.0,5.3266e-11,-5.3521e-11,-1.2158e-12,0.0,0.0,0.0,448350.0,8758700.0,7.1788,0.0\n", "192.91,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,449270.0,8758200.0,6.9542,0.0\n", "177.27,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,448800.0,8758500.0,10.856,0.0\n", "148.28,0.0,0.0,-781.79,782.08,20.19,-2.3741,2.3852,0.054103,0.0,0.0,0.0,448300.0,8758600.0,-1.6698e-09,1105.823100907193\n", "151.78,0.0,0.0,-1938.0,1134.2,-8.5292e-08,-5.922,3.506,1.2153e-10,0.0,0.0,0.0,448200.0,8758100.0,4.1405e-11,2245.4963014888267\n", "162.41,0.0,0.0,-1073.8,993.68,-4.7606e-08,-3.2781,3.0476,6.7156e-11,0.0,0.0,0.0,448270.0,8758400.0,-2.3167e-12,1463.0264462408054\n", "161.58,0.0,0.0,-1258.8,1170.4,-5.7489e-08,-3.8313,3.5924,7.3778e-11,0.0,0.0,0.0,448200.0,8758400.0,-2.2002e-11,1718.840772148485\n", "152.99,0.0,0.0,-1741.2,1127.1,-7.164e-08,-5.3043,3.4774,1.2594e-10,0.0,0.0,0.0,448210.0,8758200.0,2.8983e-10,2074.1581063168737\n", "155.49,0.0,0.0,-936.15,881.14,-5.2037e-08,-2.8785,2.6741,7.6945e-11,0.0,0.0,0.0,448290.0,8758500.0,4.1447e-11,1285.606674725983\n", "154.17,0.0,0.0,-1629.5,1150.1,-7.2964e-08,-4.9626,3.5429,9.8018e-11,0.0,0.0,0.0,448200.0,8758200.0,-2.6549e-12,1994.492481810849\n" ] } ], "source": [ "%%bash\n", "\n", "tail -n 1 ./outputs/depth0_calvingVariables.txt | grep -o \" \" | wc -l\n", "\n", "tail -n 1 ./outputs/calving_velocity_mag.csv | grep -o \",\" | wc -l\n", "\n", "head ./outputs/calving_velocity_mag.csv" ] }, { "cell_type": "markdown", "id": "hourly-douglas", "metadata": {}, "source": [ "To get a coherent view of what the velocities look like across the glacier, I want to convert the point data into a raster. \n", "\n", "This can be done using gdal_grid but, in order to do this, a vrt header must be made for the data file." ] }, { "cell_type": "code", "execution_count": 16, "id": "alpha-nature", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " \n", " CSV:./outputs/calving_velocity_mag.csv\n", " wkbPoint\n", " \n", " \n", "" ] } ], "source": [ "%%bash \n", "\n", "cat ./inputs/calving_velocity_mag.vrt" ] }, { "cell_type": "markdown", "id": "improved-defendant", "metadata": {}, "source": [ "The raster can now be created using gdal_grid. \n", "\n", "There are a number of different options available with regards to the interpolation algorithm. For this project, I have chosen to use the inverse distance to a power ('invdist') algorithm, which is a weighted average interpolator. \n", "\n", "Both the weighting power 'p' and the smoothing parameter 's' can be user defined. As velocities across a glacier do not vary abruptly, I have opted for a high smoothing paramater of 500.\n", "\n", "The raster is then clipped by extent using a shapefile that covers the area of Kronebreen (via gdalwarp), before being visualised using openev." ] }, { "cell_type": "code", "execution_count": 17, "id": "mature-utilization", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Grid data type is \"Float64\"\n", "Grid size = (400 400).\n", "Corner coordinates = (447926.775000 8764912.375000)-(466553.225000 8754987.625000).\n", "Grid cell size = (46.450000 24.750000).\n", "Source point count = 8329.\n", "Algorithm name: \"invdist\".\n", "Options are \"power=1.000000:smoothing=500.000000:radius1=0.000000:radius2=0.000000:angle=0.000000:max_points=0:min_points=0:nodata=0.000000\"\n", "\n", "0...10...20...30...40...50...60...70...80...90...100 - done.\n", "Creating output file that is 522P x 279L.\n", "Processing ./outputs/calving_velocity_mag.tiff [1/1] : 0...10...20...30...40...50...60...70...80...90...100 - done.\n", "Default software rendering mode (use -h if accelerated video card installed).\n", "Loading tools from /usr/bin/openev/tools/Tool_DriverList.py\n", "Loading tools from /usr/bin/openev/tools/Tool_Export.py\n", "Loading tools from /usr/bin/openev/tools/Tool_ShapesGrid.py\n" ] } ], "source": [ "%%bash \n", " \n", "gdal_grid -a invdist:power=1.0:smoothing=500.0 -outsize 400 400 -of GTiff -ot Float64 -l \\\n", " calving_velocity_mag ./inputs/calving_velocity_mag.vrt ./outputs/calving_velocity_mag.tiff\n", "\n", "gdalwarp -overwrite -cutline ./inputs/Domain.shp -crop_to_cutline -s_srs EPSG:32633 -t_srs EPSG:32633 \\\n", " ./outputs/calving_velocity_mag.tiff ./outputs/cropped_calving_velocity_mag.tiff -dstnodata 9999\n", "\n", "/usr/bin/openev/bin/openev ./outputs/cropped_calving_velocity_mag.tiff " ] }, { "cell_type": "markdown", "id": "about-scout", "metadata": {}, "source": [ "These modelled velocities are now ready to be compared to the observed velocities, which are also provided as a .tiff. \n", "\n", "For this operation, I use gdal_calc to subtract one raster from the other and thus create a map of difference. I first use gdalwarp to make sure the two rasters have the same extent and resample the rasters so the pixel size is consistent.\n", "\n", "The extents are still slightly different, but this can be worked around by using the --extent flag in gdal_calc. It is notable that the two rasters have different units; observations are given in m/day whereas the modelled velocities are in m/year. The observed velocities are therefore multiplied by 365.25 within gdal_calc. " ] }, { "cell_type": "code", "execution_count": 18, "id": "painful-conflict", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating output file that is 6309P x 3362L.\n", "Processing ./inputs/kroneVelo_Observed.tif [1/1] : 0Using internal nodata values (e.g. 0) for image ./inputs/kroneVelo_Observed.tif.\n", "...10...20...30...40...50...60...70...80...90...100 - done.\n", "Creating output file that is 1863P x 993L.\n", "Processing ./outputs/kroneVelo_Observed_crop.tiff [1/1] : 0Using internal nodata values (e.g. 9999) for image ./outputs/kroneVelo_Observed_crop.tiff.\n", "Copying nodata values from source ./outputs/kroneVelo_Observed_crop.tiff to destination ./outputs/kroneVelo_Observed_crop_resample.tiff.\n", "...10...20...30...40...50...60...70...80...90...100 - done.\n", "Creating output file that is 1859P x 993L.\n", "Processing ./outputs/cropped_calving_velocity_mag.tiff [1/1] : 0Using internal nodata values (e.g. 9999) for image ./outputs/cropped_calving_velocity_mag.tiff.\n", "Copying nodata values from source ./outputs/cropped_calving_velocity_mag.tiff to destination ./outputs/cropped_calving_velocity_mag_resample.tiff.\n", "...10...20...30...40...50...60...70...80...90...100 - done.\n", "0.. 0.. 1.. 2.. 3.. 4.. 5.. 5.. 6.. 7.. 8.. 9.. 10.. 10.. 11.. 12.. 13.. 14.. 15.. 15.. 16.. 17.. 18.. 19.. 20.. 20.. 21.. 22.. 23.. 24.. 25.. 25.. 26.. 27.. 28.. 29.. 30.. 30.. 31.. 32.. 33.. 34.. 35.. 35.. 36.. 37.. 38.. 39.. 40.. 40.. 41.. 42.. 43.. 44.. 45.. 45.. 46.. 47.. 48.. 49.. 50.. 50.. 51.. 52.. 53.. 54.. 55.. 55.. 56.. 57.. 58.. 59.. 60.. 60.. 61.. 62.. 63.. 64.. 65.. 65.. 66.. 67.. 68.. 69.. 70.. 70.. 71.. 72.. 73.. 74.. 75.. 75.. 76.. 77.. 78.. 79.. 80.. 80.. 81.. 82.. 83.. 84.. 85.. 85.. 86.. 87.. 88.. 89.. 90.. 90.. 91.. 92.. 93.. 94.. 95.. 95.. 96.. 97.. 98.. 99.. 100 - Done\n", "Default software rendering mode (use -h if accelerated video card installed).\n", "Loading tools from /usr/bin/openev/tools/Tool_DriverList.py\n", "Loading tools from /usr/bin/openev/tools/Tool_Export.py\n", "Loading tools from /usr/bin/openev/tools/Tool_ShapesGrid.py\n" ] } ], "source": [ "%%bash\n", "\n", "gdalwarp -overwrite -cutline ./inputs/Domain.shp -crop_to_cutline -s_srs EPSG:32633 -t_srs EPSG:32633 \\\n", "./inputs/kroneVelo_Observed.tif ./outputs/kroneVelo_Observed_crop.tiff -dstnodata 9999\n", "\n", "gdalwarp -tr 10 -10 ./outputs/kroneVelo_Observed_crop.tiff ./outputs/kroneVelo_Observed_crop_resample.tiff\n", "gdalwarp -tr 10 -10 ./outputs/cropped_calving_velocity_mag.tiff ./outputs/cropped_calving_velocity_mag_resample.tiff\n", "\n", "gdal_calc.py -A ./outputs/cropped_calving_velocity_mag_resample.tiff -B ./outputs/kroneVelo_Observed_crop_resample.tiff \\\n", "--outfile=./outputs/model_obs_comparison.tif --calc=\"A-(B*365.25)\" --extent=\"intersect\"\n", "\n", "/usr/bin/openev/bin/openev ./outputs/model_obs_comparison.tif\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 19, "id": "compound-province", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import rasterio\n", "from rasterio.plot import show\n", "from matplotlib import pyplot\n", "from matplotlib import colors, cm\n", "import numpy as np\n", "\n", "diff = rasterio.open(\"./outputs/model_obs_comparison.tif\")\n", "modelled = rasterio.open(\"./outputs/cropped_calving_velocity_mag_resample.tiff\")\n", "observed = rasterio.open(\"./outputs/kroneVelo_Observed_crop_resample.tiff\")\n", "\n", "cmap = cm.bwr.copy()\n", "cmap.set_over('w',1.)\n", "cmap.set_under('w',1.)\n", "\n", "pyplot.imshow(diff.read(1), vmin=700, vmax=2500, cmap=cmap)\n", "pyplot.colorbar()\n", "pyplot.title('Modelled - Observed velocities (m/yr)')\n", "pyplot.show()\n", "\n", "pyplot.imshow(modelled.read(1), vmin=1500, vmax=2500, cmap=cmap)\n", "pyplot.colorbar()\n", "pyplot.title('Modelled velocities (m/yr)')\n", "pyplot.show()\n", "\n", "pyplot.imshow((observed.read(1))*365.25, vmin=0, vmax=1100, cmap=cmap)\n", "pyplot.colorbar()\n", "pyplot.title('Observed velocities (m/yr)')\n", "pyplot.show()" ] }, { "cell_type": "markdown", "id": "extreme-hypothetical", "metadata": {}, "source": [ "The final results from Part 1 show that modelled velocities are systematically higher than the observed velocities. This suggests that there may be an issue with a parameter such as basal friction or ice viscosity. It should be noted that some of the differences are explainable:\n", "\n", "1) The very front of the glacier shows very low velocities. This is due to the retreat of the glacier and so this area should be ignored\n", "2) Observed velocities contain error (est. up to 150 m/yr). This is particlarly true for slow flowing regions where little change occurs being subsequent satellite images. Therefore, we expect greater error over the upper parts of the glacier and place less emphasis on getting perfect correspondaance here.\n", "\n", "Despite this, the above analysis is of great help in terms of tuning and evaluating my ice sheet model set-up and has identified that I need to look into why the glacier is flowing faster than expected." ] }, { "cell_type": "markdown", "id": "frequent-repeat", "metadata": {}, "source": [ "## Part 2: Time series of Velocity and Surface Mass Balance\n", "\n", "I want to analyse a time series of frontal velocities from Kronebreen, Svalbard.\n", "\n", "I have configured the Elmer/Ice model to output the average velocity for each partition, with partition 0 corresponding to the frontal section of the glacier. As this is the most dynamic and interesting part of the glacier, it is this area that will be focused on for this project. \n", "\n", "I first want to explore the data files and then begin with a simple visualisation of how velocity varies throughout the simulation.\n", "\n", "The data files do not have headers, but a separate file 'calving_averages.dat.names' provides a list of the variables included." ] }, { "cell_type": "code", "execution_count": 20, "id": "injured-worse", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Elmer version: 9.0\n", "Elmer revision: ae1635a\n", "Elmer compilation date: 2021-01-20\n", "Solver input file: krone_calving.sif\n", "File started at: 2021/05/19 17:31:54\n", " \n", "Variables in columns of matrix: ./calving_averages.dat\n", " 1: mean: velocity 1\n", " 2: mean: velocity 2\n", " 3: mean: velocity 3\n", " 4: mean: smb\n", " 5: res: solver cpu time distance\n", " 6: res: solver real time distance\n", " 7: res: solver cpu time navier-stokes\n", " 8: res: solver real time navier-stokes\n", " 9: res: solver cpu time stresssolver\n", " 10: res: solver real time stresssolver\n", " 11: res: solver cpu time longitudinal mesh update\n", " 12: res: solver real time longitudinal mesh update\n", " 13: res: solver cpu time free surface top\n", " 14: res: solver real time free surface top\n", " 15: res: solver cpu time free surface bottom\n", " 16: res: solver real time free surface bottom\n", " 17: res: solver cpu time vertical mesh update\n", " 18: res: solver real time vertical mesh update\n", " 19: res: solver cpu time 3d calving\n", " 20: res: solver real time 3d calving\n", " 21: res: solver cpu time remesh\n", " 22: res: solver real time remesh\n", " 23: mean: velocity magnitude\n", "-1.06E+03,-1.30E+02,-2.49E+03,-3.12E+00,8.07E-03,1.58E-02,4.50E+01,4.63E+01,8.03E-01,8.27E-01,1.67E-01,1.81E-01,4.62E-01,5.49E-01,1.45E-01,1.76E-01,3.54E-01,3.89E-01,1.20E-01,4.07E-01,2.38E+00,3.08E+00,1064.157847\n", "52 ./inputs/calving_averages/calving_averages.dat.0\n" ] } ], "source": [ "%%bash \n", "\n", "# View the metadata - variable names, where the data comes from etc\n", "\n", "cat ./inputs/calving_averages/calving_averages.dat.names\n", "\n", "# Look at the first row of data \n", "\n", "head -n 1 ./inputs/calving_averages/calving_averages.dat.0\n", "\n", "# Check the number of rows\n", "\n", "wc -l ./inputs/calving_averages/calving_averages.dat.0" ] }, { "cell_type": "code", "execution_count": 21, "id": "dedicated-disease", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "> \n", "> # Prepare the data and create an array 'timesteps'\n", "> #tseries = read.table(\"./inputs/longSpinUP_averages/longSpinUp_averages.dat.0\", sep=\",\" , header=FALSE)\n", "> tseries = read.table(\"./inputs/calving_averages/calving_averages.dat.0\", sep=\",\" , header=FALSE)\n", "> head(tseries)\n", " V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11\n", "1 -1060 -130.0 -2490.0 -3.120 0.00807 0.01580 45.00 46.30 0.803 0.827 0.167\n", "2 -3510 402.0 -53.7 5.930 0.00561 0.00937 5.84 6.08 0.172 0.186 0.108\n", "3 -2160 -6.7 -2490.0 0.955 0.00351 0.00887 4.69 4.99 0.171 0.193 0.102\n", "4 -3680 988.0 -66.2 -16.600 0.00569 0.00635 4.72 4.93 0.175 0.189 0.110\n", "5 -3640 969.0 -70.2 -23.400 0.00503 0.00979 8.45 8.91 0.170 0.187 0.111\n", "6 -3400 926.0 -59.0 -13.000 0.00435 0.01050 7.22 7.69 0.173 0.192 0.122\n", " V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22\n", "1 0.181 0.4620 0.5490 0.1450 0.1760 0.3540 0.3890 0.1200 0.407 2.380 3.08\n", "2 0.118 0.0349 0.0502 0.0201 0.0278 0.0675 0.0726 0.0868 0.216 0.713 1.04\n", "3 0.114 0.0322 0.0507 0.0131 0.0186 0.0664 0.0720 0.0796 0.203 0.626 1.00\n", "4 0.120 0.0383 0.0526 0.0272 0.0363 0.0660 0.0738 0.0764 0.206 0.720 1.76\n", "5 0.122 0.0380 0.0544 0.0165 0.0279 0.0680 0.0738 0.0871 0.202 0.591 1.70\n", "6 0.132 0.0423 0.0605 0.0157 0.0271 0.0702 0.0752 0.0876 0.221 0.781 1.09\n", " V23\n", "1 1064.158\n", "2 3529.869\n", "3 2163.036\n", "4 3814.982\n", "5 3767.336\n", "6 3523.405\n", "> timesteps = t(1:52)\n", "> \n", "> #Create the plot\n", "> png(\"./images/explore_velocities.png\")\n", "> plot(x=timesteps, y=tseries$V23, pch=4, cex=2, col=\"red\", type='l', ylab=\"Velocity magnitude (m/yr)\", xlab=\"Timestep\")\n", "> dev.off()\n", "null device \n", " 1 \n", "> \n", "> #Spare code to plot x and y velocities...\n", "> #plot(x=timesteps, y=tseries$V3, pch=4, cex=2, col=\"blue\", xlab=(\"Timestep\"), ylab=(\"Y Velocity (blue) (m/yr)\"), type='l', ylim=c(-550,1000))\n", "> #par(new=T)\n", "> #plot(x=timesteps, y=tseries$V2, pch=4, cex=2, col=\"red\", type='l', ylim=c(-3500,-600), axes=F, ylab=NA, xlab=NA)+axis(side=4)+mtext(\"X Velocity (red) (m/yr)\", side=4,line=-1.0)\n", "> \n" ] } ], "source": [ "%%bash\n", "# Plot velocity magnitude \n", "\n", "R --vanilla -q\n", "\n", "# Prepare the data and create an array 'timesteps'\n", "#tseries = read.table(\"./inputs/longSpinUP_averages/longSpinUp_averages.dat.0\", sep=\",\" , header=FALSE)\n", "tseries = read.table(\"./inputs/calving_averages/calving_averages.dat.0\", sep=\",\" , header=FALSE)\n", "head(tseries)\n", "timesteps = t(1:52)\n", "\n", "#Create the plot\n", "png(\"./images/explore_velocities.png\")\n", "plot(x=timesteps, y=tseries$V23, pch=4, cex=2, col=\"red\", type='l', ylab=\"Velocity magnitude (m/yr)\", xlab=\"Timestep\")\n", "dev.off()\n", "\n", "#Spare code to plot x and y velocities...\n", "#plot(x=timesteps, y=tseries$V3, pch=4, cex=2, col=\"blue\", xlab=(\"Timestep\"), ylab=(\"Y Velocity (blue) (m/yr)\"), type='l', ylim=c(-550,1000))\n", "#par(new=T)\n", "#plot(x=timesteps, y=tseries$V2, pch=4, cex=2, col=\"red\", type='l', ylim=c(-3500,-600), axes=F, ylab=NA, xlab=NA)+axis(side=4)+mtext(\"X Velocity (red) (m/yr)\", side=4,line=-1.0)" ] }, { "cell_type": "code", "execution_count": 22, "id": "insured-mystery", "metadata": {}, "outputs": [ { "data": { 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\n", "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(\"./images/explore_velocities.png\")" ] }, { "cell_type": "markdown", "id": "robust-confidentiality", "metadata": {}, "source": [ "It looks like velocity begins high (c. 3500 m/yr) at the beginning of the simulation - probably due to the initial conditions imposed by the simulation. After around 5 days, the velocity drops abd begins oscillating around 2000 m/yr.\n", "\n", "The velocity varies considerably between timesteps, with lows of 500 m/yr and highs of around 3000 m/yr. This may be due to differences in surface mass balance (SMB) or in internal dynamics due to the impacts of, for example, calving events.\n", "\n", "Each timestep is one day, and the above plot covers a 52 day period corresponding the the first 52 days of the calendar year in 2016.\n", "\n", "The model is forced by a surface mass balance (SMB) dataset which is calculated as follows:\n", "\n", "SMB = PR - RU - SU - ER\n", "\n", "where\n", "\n", "RU = ME + RA - RF\n", "\n", "PR is total precipitation (including snowfall and rainfall), RU is meltwater runoff, SU is total sublimation and ER is erosion from drifting snow. Runoff is composed of liquid water from rain (RA) and melt (ME) that is not retained or refrozen in firn (RF).\n", "\n", "I want to look into the relationship between velocity and SMB by comparing the time series of velocities with the time series of SMB. The first step in this analysis is to plot the time series against eachother and compare them qualitatively. \n", "\n", "The SMB data is from Noël et al. (2020), with some pre-processing having occured before use during this course project. " ] }, { "cell_type": "code", "execution_count": 23, "id": "chinese-nylon", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "> \n", "> # Prepare the velocity and SMB data and create an array 'timesteps'\n", "> input_series = read.table(\"./inputs/calving_averages/calving_averages.dat.0\", sep=\",\" , header=FALSE)\n", "> timesteps = t(1:52)\n", "> \n", "> #Create the plots\n", "> png(\"./images/velocities_SMB.png\")\n", "> plot(x=timesteps, y=input_series$V23, pch=4, cex=2, col=\"red\", type='l', ylim=c(0,4000), ylab=\"Velocity magnitude(red) (m/yr)\", xlab=\"Timestep\")\n", "> par(new=T)\n", "> plot(x=timesteps, y=input_series$V5, pch=4, cex=2, col=\"green\", type='l', axes=F, ylab=NA, xlab=NA)+axis(side=4)+mtext(\"SMB (green) (mm w.e./yr)\", side=4,line=-1.0)\n", "numeric(0)\n", "> dev.off()\n", "null device \n", " 1 \n", "> \n" ] } ], "source": [ "%%bash\n", "# Plot X velocity alongside SMB\n", "\n", "R --vanilla -q\n", "\n", "# Prepare the velocity and SMB data and create an array 'timesteps'\n", "input_series = read.table(\"./inputs/calving_averages/calving_averages.dat.0\", sep=\",\" , header=FALSE)\n", "timesteps = t(1:52)\n", "\n", "#Create the plots\n", "png(\"./images/velocities_SMB.png\")\n", "plot(x=timesteps, y=input_series$V23, pch=4, cex=2, col=\"red\", type='l', ylim=c(0,4000), ylab=\"Velocity magnitude(red) (m/yr)\", xlab=\"Timestep\")\n", "par(new=T)\n", "plot(x=timesteps, y=input_series$V5, pch=4, cex=2, col=\"green\", type='l', axes=F, ylab=NA, xlab=NA)+axis(side=4)+mtext(\"SMB (green) (mm w.e./yr)\", side=4,line=-1.0)\n", "dev.off()" ] }, { "cell_type": "code", "execution_count": 24, "id": "classical-saint", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(\"./images/velocities_SMB.png\")" ] }, { "cell_type": "markdown", "id": "accomplished-maintenance", "metadata": {}, "source": [ "I want to see if there is a correlation between these two time series, and will start with a simple linear regression:" ] }, { "cell_type": "code", "execution_count": 25, "id": "buried-laptop", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "> \n", "> input_series = read.table(\"./inputs/calving_averages/calving_averages.dat.0\", sep=\",\" , header=FALSE)\n", "> \n", "> regression = lm(V23 ~ V5, data = input_series)\n", "> \n", "> summary(regression)\n", "\n", "Call:\n", "lm(formula = V23 ~ V5, data = input_series)\n", "\n", "Residuals:\n", " Min 1Q Median 3Q Max \n", "-1457.71 -497.13 -53.78 452.40 1772.44 \n", "\n", "Coefficients:\n", " Estimate Std. Error t value Pr(>|t|) \n", "(Intercept) 2372.8 352.5 6.732 1.58e-08 ***\n", "V5 -58044.9 67071.9 -0.865 0.391 \n", "---\n", "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", "\n", "Residual standard error: 764.8 on 50 degrees of freedom\n", "Multiple R-squared: 0.01476,\tAdjusted R-squared: -0.004947 \n", "F-statistic: 0.7489 on 1 and 50 DF, p-value: 0.3909\n", "\n", "> \n" ] } ], "source": [ "%%bash\n", "# Regress SMB against X velocity\n", "\n", "R --vanilla -q\n", "\n", "input_series = read.table(\"./inputs/calving_averages/calving_averages.dat.0\", sep=\",\" , header=FALSE)\n", "\n", "regression = lm(V23 ~ V5, data = input_series)\n", "\n", "summary(regression)\n" ] }, { "cell_type": "markdown", "id": "typical-architect", "metadata": {}, "source": [ "The p value is poor (0.39) and shows the result is not statistically significant. The R^2 value is low at 0.15 suggesting that there is little correlation between surface mass balance and velocity magnitude. However, this is a very simply done analysis and is conducted only as an initial data exploration.\n", "\n", "I will now try dynamic time warping as an alternative method for comparing the two time series:" ] }, { "cell_type": "code", "execution_count": 26, "id": "spanish-spine", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "1374.9222062850727" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "from dtw import *\n", "import matplotlib.pyplot as plt\n", "\n", "# Get the data \n", "\n", "query = np.loadtxt(fname=\"./inputs/calving_averages/calving_averages.dat.0\", delimiter=',', usecols=22)\n", "# Smb in mm w.e. per YEAR converted to ice equiv. (as used in Elmer/Ice)\n", "reference = (np.loadtxt(fname=\"./inputs/calving_averages/calving_averages.dat.0\", delimiter=',', usecols=4))*365.25/(1000.0/910.0)\n", "\n", "\n", "# Perform the analysis\n", "\n", "alignment = dtw(query, reference, keep_internals=True)\n", "\n", "## Display the warping curve, i.e. the alignment curve\n", "alignment.plot(type=\"threeway\")\n", "plt.show()\n", "\n", "dtw(query, reference, keep_internals=True, window_type=\"sakoechiba\", window_args={\"window_size\":10},\n", " step_pattern=rabinerJuangStepPattern(6, \"c\")).plot(type=\"twoway\",offset=-1)\n", "\n", "alignment.normalizedDistance" ] }, { "cell_type": "markdown", "id": "novel-palace", "metadata": {}, "source": [ "The patterns of the two time series looked similar when plotted side by side but the distance between them is relatively large, which could be partially due to the very different units. \n", "\n", "A very close correspondance is not expected as ice dynamics do not react to external changes immediately but are partly controlled by preceding conditions. \n", "\n", "However, noise may still be impacting the result, and the warp path jumps (rather than being broadly diagonal) suggesting a less-than-optimal fit. In order to try and remove some noise, I will apply a wavelet transform to extract the key features of the two time series and then re-run the dynamic time warp." ] }, { "cell_type": "code", "execution_count": 27, "id": "considerable-today", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import pywt\n", "# Try out wavelet feature extraction ...\n", "\n", "#Velocity\n", "\n", "sLst = np.arange(1, 31)\n", "wav_transform_velocity, freqs = pywt.cwt(query, sLst, 'mexh')\n", "\n", "plt.plot(wav_transform_velocity[1])\n", "plt.plot(query)\n", "plt.show()\n", "\n", "#SMB\n", "\n", "wav_transform_smb, freqs = pywt.cwt(reference, sLst, 'mexh')\n", "\n", "plt.plot(wav_transform_smb[1])\n", "plt.plot(reference)\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 28, "id": "compound-poker", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "468.9828699904016" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "from dtw import *\n", "import matplotlib.pyplot as plt\n", "\n", "# Run dynamic time warp again\n", "\n", "alignment = dtw(wav_transform_velocity[1], wav_transform_smb[1], keep_internals=True)\n", "\n", "## Display the warping curve, i.e. the alignment curve\n", "alignment.plot(type=\"threeway\")\n", "plt.show()\n", "\n", "dtw(wav_transform_velocity[1], wav_transform_smb[1], keep_internals=True, window_type=\"sakoechiba\", window_args={\"window_size\":10},\n", " step_pattern=rabinerJuangStepPattern(6, \"c\")).plot(type=\"twoway\",offset=-1)\n", "\n", "alignment.normalizedDistance" ] }, { "cell_type": "markdown", "id": "experimental-clone", "metadata": {}, "source": [ "With the wavelet pre-processing, there is a much reduced distance between the two time series (1375 vs 469), suggesting that there may some connection between the two. However, the warp path still does'nt take a consistent, diagonal path. This means that the analysis is not overly successful and other techniques may be required to investigate the relationship. \n", "\n", "The lack of good correspondance points to the fact that other variables besides SMB are also influencing the velocity of the glacier. These could be indirect effects of SMB (for example through thinning and thickening of the glacier in different areas altering the gravitational driving stress), or unrelated variables such as bed geometry. \n", "\n", "It is also notable that SMB variations are not expected to immediately cause large velocity variations, as mentioned before. However, understanding how quickly the glacier can respond to external perturbations is of interest with regards to reducing uncertainty regarding how they will behave in the future and so in improving sea level rise estimates. \n", "\n", "The results of the qualitative and dynamic time warp analyses show that there may be a connection between the two, although nothing significant has been identified yet.\n", "\n", "I next want to split up SMB into its underlying components, so I can use these time series in my analysis. This will be useful for understanding whether frontal velocities are more sensitive to certain parameters and is also useful in investigating whether a single/few parameter(s) could be used to predict dynamical changes.\n", "\n", "The variables I will focus on are:\n", "1) Precipitation (mm w.e.)\n", "2) Air Temperature (K)\n", "3) Snowfall (mm w.e.)\n", "4) Snowmelt (mm w.e.\n", "\n", "The variables are given in separate .tiff files which each contain 365 bands with the mean value for each day in the period 2000-2019. Data will be extracted from each file for relevant location(s) using gdallocationinfo." ] }, { "cell_type": "markdown", "id": "moving-enterprise", "metadata": {}, "source": [ "Due to the poor results obtained by the dynamic time warp analysis, I want to instead move forward by using the Random Forest algorithm to try and fit a regression model on the data. In this way, I can understand the relative importance of the various factors for frontal velocity at Kronebreen." ] }, { "cell_type": "code", "execution_count": 29, "id": "comic-insight", "metadata": {}, "outputs": [], "source": [ "from osgeo import gdal \n", "import os\n", "import numpy as np\n", "import sys\n", "\n", "total=0.0\n", "with open(\"./outputs/smb_components.csv\", 'w') as myfile:\n", " for band in range(1, 53):\n", " for var in 'precip', 't2m', 'snowfall', 'snowmelt':\n", " for x in range(448209, 449110, 110):\n", " for y in range(8755649, 8758086, 250):\n", " temp=os.popen('gdallocationinfo -valonly -geoloc -b %s ./inputs/daily_%s_means_2000to2019_CROPPED.tif %s %s' % (band, var, x, y)).read()\n", " total=float(temp)+total\n", " average=total/100\n", " myfile.write(str(average)+', ')\n", " total=0.0\n", " myfile.write('\\n')\n", "myfile.close() " ] }, { "cell_type": "code", "execution_count": 30, "id": "first-concern", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from sklearn.ensemble import RandomForestRegressor as RFReg\n", "from sklearn.model_selection import train_test_split,GridSearchCV\n", "from sklearn.pipeline import Pipeline\n", "from scipy.stats.stats import pearsonr\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 31, "id": "increasing-tractor", "metadata": {}, "outputs": [], "source": [ "vel_dataset = pd.read_csv(\"./inputs/calving_averages/calving_averages.dat.0\", header=None)\n", "smb_dataset = pd.read_csv(\"./outputs/smb_components.csv\", header=None)\n", "vel_dataset.head()\n", "smb_dataset.head()\n", "\n", "#SMB components\n", "x = smb_dataset.iloc[:, 0:4].values\n", "# Velocity magnitude\n", "y = vel_dataset.iloc[:, 22:23].values\n", "\n", "variables = ['precip', 't2m', 'snowfall', 'snowmelt']\n" ] }, { "cell_type": "code", "execution_count": 32, "id": "spectacular-expense", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(52, 4)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.shape" ] }, { "cell_type": "code", "execution_count": 33, "id": "specialized-allen", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(52, 1)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.shape" ] }, { "cell_type": "markdown", "id": "loved-awareness", "metadata": {}, "source": [ "The data is split into training and testing datasets (various splits). After this, a pipeline is defined. This allows many different parameter combinations and scorings to be tested out.\n", "\n", "After initial tests (not shown here), I added lower values for max_features and max_depth as well as a higher number for n_estimators into my pipeline. \n", "\n", "In addition, I played around with different proportions of data used for training (20%, 50%, 70%) and different ways of scoring the regressions (R^2, explained variance, and negative root mean square error). The results from these tests are shown below:\n", "\n", "| Test size | Scoring | Train score (pearson) | Test score (pearson)|\n", "| --- | --- | --- | |\n", "| 0.2 | R^2 | 0.820 | -0.043 |\n", "| 0.5 | R^2 | 0.916 | -0.094 |\n", "| 0.7 | R^2 | 0.919 | 0.052 |\n", "| 0.2 | Explained variance | 0.922 | -0.087 |\n", "| 0.5 | Explained variance | 0.918 | -0.017 |\n", "| 0.7 | Explained variance | 0.931 | 0.095 |\n", "| 0.2 | Neg root mean square error | 0.816 | -0.068 |\n", "| 0.5 | Neg root mean square error | 0.937 | -0.079 |\n", "| 0.7 | Neg root mean square error | 0.951 | 0.091 |\n", "\n", "The parameter combination which gives the best fit will then be used for analysis. This corresponds to:\n", "1) A test size of **0.7**\n", "2) Scoring based on **negative root mean square error**\n", "3) Max depth: **25**\n", "4) Max features: **0.33**\n", "5) Max samples: **0.7**\n", "6) Number of estimators: **500**" ] }, { "cell_type": "code", "execution_count": 34, "id": "every-auction", "metadata": {}, "outputs": [], "source": [ "# Split into testing and training data sets\n", "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.7, random_state=24)\n", "y_train = np.ravel(Y_train)\n", "y_test = np.ravel(Y_test)" ] }, { "cell_type": "code", "execution_count": 35, "id": "other-complement", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting 3 folds for each of 256 candidates, totalling 768 fits\n" ] }, { "data": { "text/plain": [ "GridSearchCV(cv=3, estimator=Pipeline(steps=[('rf', RandomForestRegressor())]),\n", " n_jobs=-1,\n", " param_grid={'rf__max_depth': (25, 50, 100, 200),\n", " 'rf__max_features': ('log2', 'sqrt', 0.33, 0.2),\n", " 'rf__max_samples': (0.3, 0.5, 0.6, 0.7),\n", " 'rf__n_estimators': (500, 1000, 2000, 3000)},\n", " scoring='neg_root_mean_squared_error', verbose=1)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Determine a pipeline to test out different parameters and find the set of\n", "# parameters with the best fit. Various parameters are used to score the different fits.\n", "\n", "pipeline = Pipeline([('rf',RFReg())])\n", "\n", "parameters = {\n", " 'rf__max_features':(\"log2\",\"sqrt\",0.33, 0.2),\n", " 'rf__max_samples':(0.3, 0.5,0.6,0.7),\n", " 'rf__n_estimators':(500,1000,2000,3000),\n", " 'rf__max_depth':(25,50,100,200)}\n", "\n", "grid_search = GridSearchCV(pipeline,parameters,n_jobs=-1,cv=3,scoring='neg_root_mean_squared_error',verbose=1)\n", "grid_search.fit(X_train,y_train)\n" ] }, { "cell_type": "code", "execution_count": 36, "id": "mexican-morris", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-859.084465363649" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_search.best_score_" ] }, { "cell_type": "code", "execution_count": 37, "id": "monthly-military", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best Training score: -859.084\n", "Optimal parameters:\n", "\trf__max_depth: 25\n", "\trf__max_features: 0.33\n", "\trf__max_samples: 0.7\n", "\trf__n_estimators: 500\n" ] } ], "source": [ "# The best parameters are printed, and these are then used for the regression\n", "\n", "print ('Best Training score: %0.3f' % grid_search.best_score_)\n", "print ('Optimal parameters:')\n", "best_par = grid_search.best_estimator_.get_params()\n", "for par_name in sorted(parameters.keys()):\n", " print ('\\t%s: %r' % (par_name, best_par[par_name]))" ] }, { "cell_type": "code", "execution_count": 39, "id": "genuine-difficulty", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.950879197769439, 0.09090151937598724]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rfReg = RFReg(n_estimators=500,max_features=0.33,max_depth=25,max_samples=0.7,n_jobs=-1,random_state=24)\n", "rfReg.fit(X_train, y_train);\n", "dic_pred = {}\n", "dic_pred['train'] = rfReg.predict(X_train)\n", "dic_pred['test'] = rfReg.predict(X_test)\n", "[pearsonr(dic_pred['train'],y_train)[0],pearsonr(dic_pred['test'],y_test)[0]]" ] }, { "cell_type": "markdown", "id": "temporal-manhattan", "metadata": {}, "source": [ "Using Pearson's R, it can be seen that there is agood fit for the testing data (0.951) but a very poor fit for the testing data (0.091). \n", "\n", "This is likely due to overfitting and the following parameters could be altered to try and fix this:\n", "\n", "1) max_features - Reducing this number will reduce the number of features each tree is randomly assigned \n", "2) max_depth - Reducing this parameter lowers the complexity of the learned models, and thus minimises the risk of over fitting\n", "3) n_estimators - Overfitting is less likely with more estimators\n", "\n", "However,despite adding more possible parameters into my pipeline, no solution could be found to improve the results and prevent overfitting. A key limitation for this model is the lack of input data. Although this is a dissapointing realisation, it does give me hope that when I move on to running longer simulations (with e.g. over 1000 timesteps), I may see some progress.\n", "\n", "Despite this, I still want to visualise the results and look into the relative importance of all the various predictors for the best fit regression that I have:" ] }, { "cell_type": "code", "execution_count": 40, "id": "enormous-ethiopia", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(y_train,dic_pred['train'])\n", "plt.xlabel('orig')\n", "plt.ylabel('pred')\n", "ident = [0, 4000]\n", "plt.plot(ident,ident,'r--')" ] }, { "cell_type": "code", "execution_count": 41, "id": "conservative-opening", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(y_test,dic_pred['test'])\n", "plt.xlabel('orig')\n", "plt.ylabel('pred')\n", "ident = [0, 4000]\n", "plt.plot(ident,ident,'r--')" ] }, { "cell_type": "code", "execution_count": 42, "id": "careful-carry", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variable: precip Importance: 0.28\n", "Variable: t2m Importance: 0.25\n", "Variable: snowfall Importance: 0.24\n", "Variable: snowmelt Importance: 0.23\n" ] } ], "source": [ "# Get numerical feature importances\n", "importances = list(rfReg.feature_importances_)\n", "# List of tuples with variable and importance\n", "feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(variables, importances)]\n", "# Sort the feature importances by most important first\n", "feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n", "# Print out the feature and importances \n", "[print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances];" ] }, { "cell_type": "code", "execution_count": 45, "id": "industrial-theorem", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "([,\n", " ,\n", " ,\n", " ],\n", " [Text(0, 3, 'precipitation'),\n", " Text(0, 2, 'temperature'),\n", " Text(0, 1, 'Snowfall'),\n", " Text(0, 0, 'Snowmelt')])" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the importance of different predictors\n", "impt = [rfReg.feature_importances_, np.std([tree.feature_importances_ for tree in rfReg.estimators_],axis=1)]\n", "ind = np.argsort(impt[0])\n", "plt.rcParams[\"figure.figsize\"] = (10,6)\n", "plt.barh(range(len(variables)),impt[0][ind],color=\"r\", xerr=impt[1][ind], align=\"center\")\n", "plt.yticks([3, 2, 1, 0], ['precipitation', 'temperature', 'Snowfall', 'Snowmelt'])" ] }, { "cell_type": "markdown", "id": "czech-recognition", "metadata": {}, "source": [ "The final results show that the predictors used in this study are not sufficient to predict frontal velocity at Kronebreen (at least for this short time period). However, all the predictors did have some importance and so are not irrelevant parameters. The relative importance of the different parameters was similar, but snowmelt came out as being the most important predictor." ] }, { "cell_type": "markdown", "id": "special-pepper", "metadata": {}, "source": [ "Full reference for SMB data:\n", "\n", "\n", "Noël, Brice P Y; Jakobs, Constantijn L; van Pelt, Ward; Lhermitte, Stef; Wouters, Bert; Kohler, Jack; Hagen, Jon Ove; Luks, Bartłomiej; Reijmer, Carleen; van de Berg, Willem Jan; van den Broeke, Michiel R (2020): Annual surface mass balance (SMB) and components of Svalbard glaciers statistically downscaled to 500 m spatial resolution (1958-2018). PANGAEA, https://doi.org/10.1594/PANGAEA.920984" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }