diff --git a/DEMO 2.1 - Generate a uniform design - two metrics.ipynb b/DEMO 2.1 - Generate a uniform design - two metrics.ipynb deleted file mode 100644 index cf5efee..0000000 --- a/DEMO 2.1 - Generate a uniform design - two metrics.ipynb +++ /dev/null @@ -1,1305 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import required libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from osgeo import gdal, osr\n", - "import pyproj as pyproj\n", - "import numpy as np\n", - "from random import randint\n", - "from scipy import ndimage\n", - "from copy import copy\n", - "import time\n", - "import pandas as pd\n", - "from matplotlib import pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "def extract_rasters(raster_path):\n", - " raster_raw = gdal.Open(raster_path)\n", - " raster = raster_raw.ReadAsArray()\n", - " return raster" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### INPUTS: Enter own values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Specify a unique name for the project as `save_folder`. A folder with your chosen name will be created in the results folder, and all outputs for the design will be saved there. E.g:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "save_folder = 'Uniform_Design_Demo'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Enter path to habitat map (`hab_path`). This should be in GeoTiff format and saved in the 'raw' data folder. E.g:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "hab_path = 'raw/HabitatMap.tif'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Enter path to invalid areas mask (if there are any you wish to exclude). This can be used to mask out inaccessible areas, or habitat categories which you do not wish to sample in. E.g:" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "mask_path = 'raw/InvalidAreasMask.tif'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you do not wish to enter a mask, run this line:" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "mask_path = None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Enter fragmentation metric maps. This design aims to optimise the placement of sites based on different measures of fragmentation. This could be, for example, a distance to nearest habitat edge map. More than one metric can be entered. Please specify the number of maps you will enter below. E.g:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "n_metrics = 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Enter path to metric map one. E.g:" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "metric1_path = 'raw/DistanceToEdgeLog2.tif'" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [], - "source": [ - "bins1 = 9" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To spread sites evenly across the metric values, we break continous metrics into intervals. " - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [], - "source": [ - "def discretize_metric(metric, mask, nbins):\n", - " \"\"\"Convert continuous metrics into discrete, using the specified number of bins\"\"\"\n", - " imheight, imwidth = metric.shape\n", - " # Mask out invalid areas of metric\n", - " metric_mask = np.ma.masked_array(metric, mask=mask)\n", - " # Break range of unmasked metric values into 'nbin' intervals\n", - " hist, breaks, patches = plt.hist(metric_mask.compressed(), bins = nbins)\n", - " # Assign each bin a unique integer ID\n", - " ids = range(nbins)\n", - " ones = np.ones((imheight, imwidth)); metric_bin = np.zeros((imheight, imwidth))\n", - " # Loop through ID's and convert all values in each bin to corresponding id\n", - " for ID in ids:\n", - " # Closed on the lower bound, open on the top\n", - " lower_lim=np.where(ones, metric_mask>=breaks[ID], 0)\n", - " upper_lim=np.where(ones, metric_mask" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "metric1 = extract_rasters(metric1_path)\n", - "metric1_binned, metric1_id, metric1_breaks = discretize_metric(metric1, mask, bins1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see from the histogram, some metric values are represented by larger areas of the landscape. Some, for example the high distance to edges, are only present in a small area. We wish to sample these areas uniformly with our sample sites.\n", - "Try altering the number of bins and see how the distribution changes" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(7, 7))\n", - "plt.imshow(metric1_binned)\n", - "plt.colorbar()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Enter path to metric map two. E.g:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "metric2_path = 'raw/FragmentAreaLog10.tif'" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "bins2 = 8" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, convert the metric to discrete" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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wRZIVc7oxyR8Ax6rq0NBzWWJXVdXlwLXAre308bJmaJyq67ElWv7aef7PAJ+sqs8OPZ+lVFU/AP4d2DLwVBbTVcAftnP+9wBvT/LPw05p8VXV0fZ+DPgcM6fIlzVD41Q+tmQFaBeJ7wIOV9VHh57PUkgykeSCtnwu8HvAt4ed1eKpqg9U1dqqWs/M/8MvVdUfDzytRZXkvHajBknOAzYDy/6uRkNjRFW9BJx8bMlh4N6V9tiSJJ8CHgJ+I8l0kluGntMSuAp4FzO/nT7SXtcNPalFtgZ4MMmjzPyyc6CqVuRtqSvYxcBXknwT+Bpwf1V9YeA5zclbbiVJ3TzSkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LU7f8Al1hAL9gzVBcAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "metric2 = extract_rasters(metric2_path)\n", - "metric2_binned, metric2_id, metric2_breaks = discretize_metric(metric2, mask, bins2)" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(7, 7))\n", - "plt.imshow(metric2_binned)\n", - "plt.colorbar()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For ```n_metrics``` larger than two, add extra paths and bin numbers in the same format. E.g:\n", - "\n", - "```metric3_path = ```\n", - "\n", - "```bins3 = ```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Enter an integer number of sample sites (`nsp`). E.g" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "nsp = 80" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Combine metrics for processing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now extend this analysis to look at combinations between multiple metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [], - "source": [ - "def build_df(bin_ids):\n", - " \"\"\"Create a dataframe of all combinations of IDs\"\"\"\n", - " # Create array of all combinations\n", - " id_mesh = np.meshgrid(*bin_ids)\n", - " # Convert to dataframe, each row represents a unique combination of the ID's\n", - " combo_df = pd.DataFrame([ids.flatten() for ids in id_mesh]).T\n", - " return combo_df\n", - "\n", - "\n", - "def bin_metrics(input_metrics, mask, n_metrics, nbins):\n", - " \"\"\"Bin all input metric arrays into discrete ID arrays, and create a dataframe of all the\n", - " unique combinations of these IDs\"\"\"\n", - " binned_metrics = []\n", - " bin_ids = []\n", - " bin_breaks = []\n", - " # Generate a binned version of all input metric arrays\n", - " for i in range(n_metrics):\n", - " metric_bin, ids, breaks = discretize_metric(input_metrics[i + 1], mask, nbins[i + 1])\n", - " binned_metrics.append(metric_bin) # Save all new (discretized) metric arrays\n", - " bin_ids.append(ids) # Save the list of all IDs present for each array\n", - " bin_breaks.append(breaks) # Save the bin breaks for each metric\n", - " # Generate dataframe of all combinations of IDs between the input metric arrays\n", - " combo_df = build_df(bin_ids)\n", - " return binned_metrics, combo_df, bin_breaks" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "ename": "IndexError", - "evalue": "list index out of range", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mbinned_metrics\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcombo_df\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbin_breaks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbin_metrics\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimmetrics\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_metrics\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnbins\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m\u001b[0m in \u001b[0;36mbin_metrics\u001b[1;34m(input_metrics, mask, n_metrics, nbins)\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;31m# Generate a binned version of all input metric arrays\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_metrics\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m \u001b[0mmetric_bin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mids\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbreaks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdiscretize_metric\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_metrics\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnbins\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 19\u001b[0m \u001b[0mbinned_metrics\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmetric_bin\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Save all new (discretized) metric arrays\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[0mbin_ids\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mids\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Save the list of all IDs present for each array\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mIndexError\u001b[0m: list index out of range" - ] - } - ], - "source": [ - "binned_metrics, combo_df, bin_breaks = bin_metrics(immetrics, mask, n_metrics, nbins)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_all_layers(binned_metrics, n_metrics, mask_inv, combo_df, nvars, nsp):\n", - " \"\"\"Create single combined ID array and calculate the optimum number of sample sites in each ID \"\"\"\n", - " imheight, imwidth = mask_inv.shape\n", - " combo_num = len(combo_df)\n", - " # create 3d array to store ID combo layers in\n", - " all_layers = np.zeros((combo_num, imheight, imwidth))\n", - " counts = []\n", - " # Iterate through all unique ID combinations\n", - " for i in range(combo_num):\n", - " im_layer = np.ones((imheight, imwidth))\n", - " for j in range(n_metrics):\n", - " # Convert selected ID to binary for each metric, and multiply together to see where\n", - " # combinations are in the landscape image\n", - " im_layer = np.where(binned_metrics[j] == combo_df.iloc[i][j], 1, 0) * im_layer\n", - " # Make sure invalid areas are set as zero\n", - " layer_mask = im_layer * mask_inv\n", - " counts.append(np.sum(layer_mask)) # Store the number of pixels in each unique combo layer\n", - " all_layers[i, :, :] = layer_mask # Save combo Id layer in 3d array\n", - " combo_df['Counts'] = counts\n", - " ID_df = combo_df[combo_df.Counts != 0] # remove empty bins to create ID dataframe\n", - " s_opt = np.float(nsp) / len(ID_df) # optimum sample sites in each ID\n", - " ID_df = ID_df[ID_df.Counts >= 10 * np.ceil(s_opt)] # remove IDs with too few pixels\n", - " s_opt = np.float(nsp) / len(ID_df)\n", - " return all_layers, ID_df, s_opt" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'binned_metrics' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mall_layers\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mID_df\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms_opt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgenerate_all_layers\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbinned_metrics\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_metrics\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcombo_df\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_metrics\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnsp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mNameError\u001b[0m: name 'binned_metrics' is not defined" - ] - } - ], - "source": [ - "all_layers, ID_df, s_opt = generate_all_layers(binned_metrics, n_metrics, mask, combo_df, n_metrics, nsp)" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_ID_im(all_layers, ID_df, nsp, savepath):\n", - " imdepth, imheight, imwidth = all_layers.shape\n", - " ID_im = np.zeros((imheight, imwidth))\n", - " store_masks = np.zeros((len(ID_df), imheight, imwidth))\n", - " counter = 0;\n", - " unique_IDs = []\n", - " for k in ID_df.index.values:\n", - " store_masks[counter, :, :] = all_layers[k, :, :]\n", - " ID_im += all_layers[k, :, :] * (counter + 1)\n", - " unique_IDs.append(counter + 1)\n", - " counter += 1\n", - " # Save an ID image for adapted uniform designs\n", - " # np.save(\"{0}/{1}Site_Uniform_IDim\".format(savepath, nsp), ID_im)\n", - " return store_masks, ID_im, unique_IDs" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'all_layers' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mstore_masks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mID_im\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munique_IDs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgenerate_ID_im\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_layers\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mID_df\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnsp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msavepath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mNameError\u001b[0m: name 'all_layers' is not defined" - ] - } - ], - "source": [ - "store_masks, ID_im, unique_IDs = generate_ID_im(all_layers, ID_df, nsp, savepath)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### check the number of sample sites needed to have a uniform number of sample sites in each metric bin. " - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [], - "source": [ - "def upper_lower_suggest(nsp, ID_df):\n", - " \"\"\"Calculate upper and lower number of sample sites to meet uniform distribution \"\"\"\n", - " nsp = float(nsp)\n", - " n_bins = len(ID_df)\n", - " nsp_lower = np.floor(nsp / n_bins) * n_bins\n", - " nsp_upper = np.ceil(nsp / n_bins) * n_bins\n", - " return nsp_lower.astype(int), nsp_upper.astype(int)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "if mask_path:\n", - " mask = extract_rasters(mask_path)\n", - "else:\n", - " mask = np.ones_like(habmap)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.imshow(mask)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load data" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "def get_file_info(file_path):\n", - " file_raw = gdal.Open(file_path)\n", - " prj = file_raw.GetProjection(); srs = osr.SpatialReference(wkt=prj)\n", - " auth_code = srs.GetAuthorityCode(None)\n", - " GeoT = file_raw.GetGeoTransform(); res = GeoT[1]\n", - " file_map = file_raw.ReadAsArray()\n", - " nbins = len(np.unique(file_map))\n", - " return file_map, nbins, res, GeoT, auth_code" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading raw/HabitatMap.tif ...\n", - "\n", - "Number of habitat categories in map: 2\n", - "Pixel resolution (m): 30.0\n", - "GeoT info: (604343.5084861958, 30.0, 0.0, 5302852.190465175, 0.0, -30.0)\n", - "Authority code: 32759\n" - ] - } - ], - "source": [ - "print('Loading {} ...'.format(hab_path))\n", - "\n", - "habmap, hab_bins, res, GeoT, auth_code = get_file_info(hab_path)\n", - "\n", - "print('\\nNumber of habitat categories in map: {}'.format(hab_bins))\n", - "print('Pixel resolution (m): {}'.format(res))\n", - "print('GeoT info: {}'.format(GeoT))\n", - "print('Authority code: {}'.format(auth_code))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Plot habitat map**: Check habitat map, there should be integer values denoting the different categories." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(7, 7))\n", - "plt.imshow(habmap)\n", - "plt.title('Habitat Map: {} categories'.format(len(np.unique(habmap))))\n", - "cbar = plt.colorbar()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'metric_path' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m 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"\u001b[1;31mNameError\u001b[0m: name 'metric_path' is not defined" - ] - } - ], - "source": [ - "immetrics = [habmap]\n", - "nbins = [hab_bins]\n", - "\n", - "for i in range(n_metrics):\n", - " metric = extract_rasters(metric_path[i])\n", - " immetrics.append(metric)\n", - " nbins.append(bins[i])\n", - " \n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_id_list(unique_IDs, s_opt, nsp, ID_df):\n", - " \"\"\"Create a list where each unique ID is repeated by s_opt\"\"\"\n", - " id_rep = np.repeat(unique_IDs, np.floor(s_opt))\n", - " # If there is a difference in the number of sample sites and the number of elements\n", - " # in id_rep then add extras randomly from the list of IDs, without replacement\n", - " diff = nsp - len(id_rep)\n", - " while diff >= len(unique_IDs):\n", - " id_rep = np.hstack([id_rep, unique_IDs])\n", - " diff = nsp - len(id_rep)\n", - " extra_sites = np.random.choice(unique_IDs, nsp - len(id_rep), replace=False)\n", - " id_list = np.hstack([id_rep, extra_sites]) # Now list of length nsp\n", - " # Randomly permute list\n", - " id_mix = np.random.choice(id_list, nsp, replace=False)\n", - " # If nsp is less than the number of IDs (i.e one or less sample per ID),\n", - " # then create a reduced dataframe\n", - " if len(id_mix) < len(unique_IDs):\n", - " id_mix.sort()\n", - " ID_df = ID_df.iloc[id_mix]\n", - " ID_df['Freq'] = np.unique(id_mix, return_counts=True)[1]\n", - " return id_mix, ID_df" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'generate_id_list' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mid_mix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mID_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgenerate_id_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0munique_IDs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms_opt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnsp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mID_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mNameError\u001b[0m: name 'generate_id_list' is not defined" - ] - } - ], - "source": [ - "id_mix, ID_df = generate_id_list(unique_IDs, s_opt, nsp, ID_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate uniform design" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_uniform_design(id_mix, unique_IDs, store_masks):\n", - "\n", - " imdepth, imheight, imwidth = store_masks.shape\n", - " dist_im = np.ones((imheight, imwidth))\n", - " sites = np.ones((imheight, imwidth))\n", - " x_vals = []\n", - " y_vals = []\n", - " loop_count = 0\n", - " nsp = len(id_mix)\n", - "\n", - " for i in id_mix:\n", - "\n", - " # Select binary map relating to selected ID\n", - " maskID = unique_IDs.index(i)\n", - " # Mask out any regions of EDT not in ID\n", - " layer = store_masks[maskID, :, :] * dist_im\n", - "\n", - " # Extract coords of pixels with maximum distance value and choose one at random\n", - " dist_mx = zip(*np.where(layer == layer.max()))\n", - " idx = randint(0, len(dist_mx) - 1)\n", - " x, y = dist_mx[idx]\n", - "\n", - " # Save coordinates\n", - " x_vals = np.append(x_vals, x)\n", - " y_vals = np.append(y_vals, y)\n", - "\n", - " # Add selected site to sites array as 0 (feature pixel)\n", - " sites[x, y] = 0\n", - "\n", - " # Compute EDT from all placed sites\n", - " dist_im = ndimage.distance_transform_edt(sites)\n", - " loop_count += 1\n", - "\n", - " xy0 = np.hstack([x_vals, y_vals])\n", - " return xy0" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Plotting site 1\n", - "Plotting site 2\n", - "Plotting site 3\n", - "Plotting site 4\n", - "Plotting site 5\n", - "Plotting site 6\n", - "Plotting site 7\n", - "Plotting site 8\n", - "Plotting site 9\n", - "Plotting site 10\n", - "Plotting site 11\n", - "Plotting site 12\n", - "Plotting site 13\n", - "Plotting site 14\n", - "Plotting site 15\n", - "Plotting site 16\n", - "Plotting site 17\n", - "Plotting site 18\n", - "Plotting site 19\n", - "Plotting site 20\n", - "Plotting site 21\n", - "Plotting site 22\n", - "Plotting site 23\n", - "Plotting site 24\n", - "Plotting site 25\n", - "Plotting site 26\n", - "Plotting site 27\n", - "Plotting site 28\n", - "Plotting site 29\n", - "Plotting site 30\n", - "Stratified sample design complete!\n" - ] - } - ], - "source": [ - "x_unif, y_unif = generate_uniform_design(id_mix, unique_IDs, store_masks)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate results folder for given project (if the folder already exists it will not overwrite, additional results will just be added to the existing folder)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results will be saved in the folder results/Stratified_Design_Demo\n", - "Files will start with the time stamp 2019_09_27_165245\n" - ] - } - ], - "source": [ - "directory = 'results/{}'.format(save_folder)\n", - "if not os.path.exists(directory):\n", - " os.makedirs(directory)\n", - "\n", - "# Create unique time stamp to add to file names (to avoid overwriting)\n", - "ts = time.gmtime()\n", - "ts = time.strftime(\"%Y_%m_%d_%H%M%S\", ts)\n", - "\n", - "print('Results will be saved in the folder {}'.format(directory))\n", - "print('Files will start with the time stamp {}'.format(ts))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot Design" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(7, 9))\n", - "plt.imshow(mask, cmap=plt.cm.get_cmap('Accent_r', 2))\n", - "plt.title('{} design with {} sample sites'.format('Stratified', len(x_strat)))\n", - "plt.axis('off')\n", - "cbar = plt.colorbar(fraction=0.02, orientation='horizontal', pad=0.01)\n", - "cbar.set_ticks([0, 1])\n", - "cbar.set_ticklabels(['0: Invalid', '1: Valid'])\n", - "plt.scatter(y_strat, x_strat, c='black', marker='x', linewidth=2)\n", - "plt.savefig('{}/{}_{}Site_Stratified_Plot.tif'.format(directory, ts, nsp))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Stratified Design Algorithm (SDA) produces a layout where sites are spread evenly spatially, given the constraints of the valid sample areas. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Save the design" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The below function converts the (x, y) coordinates specifying pixels in the image to longitude and latitude coordinates. Geo-information extracted from the input \"invalid areas mask\" is used to transform the points to long-lat. " - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "file_raw = gdal.Open(mask_path)\n", - "\n", - "srs = osr.SpatialReference()\n", - "srs.ImportFromWkt(file_raw.GetProjection())" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "srsLatLong = srs.CloneGeogCS()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "ct = osr.CoordinateTransformation(srs, srsLatLong)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " >" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ct" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(80.88127412159001, -85.53507302812842, 0.0)\n" - ] - } - ], - "source": [ - "print(ct.TransformPoint(1000, 1000))" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "indataset = gdal.Open( mask_path, gdal.GA_ReadOnly)\n", - "geomatrix = indataset.GetGeoTransform()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'line' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mpixel_x\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m;\u001b[0m \u001b[0mpixel_y\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgeomatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mgeomatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mpixel_x\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mgeomatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mY\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgeomatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mgeomatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mpixel_y\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mgeomatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mNameError\u001b[0m: name 'line' is not defined" - ] - } - ], - "source": [ - "pixel_x = 1; pixel_y = 1\n", - "line \n", - "\n", - "X = geomatrix[0] + geomatrix[1] * pixel_x + geomatrix[2] * line\n", - "Y = geomatrix[3] + geomatrix[4] * pixel_y + geomatrix[5] * line\n", - "\n", - "# shift to the center of the pixel\n", - "X += geomatrix[1] / 2.0\n", - "Y += geomatrix[5] / 2.0\n", - "\n", - "# build spatial coordinate system \n", - "srs = osr.SpatialReference()\n", - "if srs.ImportFromWkt(indataset.GetPojection()) != 0:\n", - " print(\"Error: cannot import projection '%s'\" % indataset.GetProjection())\n", - " sys.exit(1)\n", - " \n", - "srsLatLong = srs.CloneGeogCS()\n", - "ct = osr.CoordinateTransformation(srs, srsLatLong)\n", - "(int, lat, height) = ct.TransformPoint(X, Y)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "def LongLatConvert(x, y, GeoT, auth_code):\n", - " x_proj = x * GeoT[1] + GeoT[0] + (GeoT[1] / 2)\n", - " y_proj = y * GeoT[5] + GeoT[3] + (GeoT[5] / 2)\n", - " p1 = pyproj.Proj(init='EPSG:'+auth_code)\n", - " longlat = p1(x_proj, y_proj, inverse=True)\n", - " return longlat" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "b'no arguments in initialization list'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Convert from row/col to projected\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mlonglat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLongLatConvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_strat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_strat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mGeoT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mauth_code\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m\u001b[0m in \u001b[0;36mLongLatConvert\u001b[1;34m(x, y, GeoT, auth_code)\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mx_proj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mGeoT\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mGeoT\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mGeoT\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0my_proj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mGeoT\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mGeoT\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mGeoT\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mp1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpyproj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mProj\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'EPSG:'\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mauth_code\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mlonglat\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_proj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_proj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mlonglat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyproj\\__init__.py\u001b[0m in \u001b[0;36m__new__\u001b[1;34m(self, projparams, preserve_units, **kwargs)\u001b[0m\n\u001b[0;32m 360\u001b[0m \u001b[1;31m# on case-insensitive filesystems).\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 361\u001b[0m \u001b[0mprojstring\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprojstring\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'EPSG'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'epsg'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 362\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_proj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mProj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__new__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprojstring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 363\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 364\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m_proj.pyx\u001b[0m in \u001b[0;36m_proj.Proj.__cinit__\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mRuntimeError\u001b[0m: b'no arguments in initialization list'" - ] - } - ], - "source": [ - "# Convert from row/col to projected\n", - "longlat = LongLatConvert(x_strat, y_strat, GeoT, auth_code)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Save the coordinates in a csv file..." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'longlat' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Reformat and save to csv\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mlonglat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'longitude'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'latitude'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m;\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'row'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx_strat\u001b[0m\u001b[1;33m;\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'col'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my_strat\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'sampled'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnsp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mcsv_filename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'{}_{}Site_Stratified.csv'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnsp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mNameError\u001b[0m: name 'longlat' is not defined" - ] - } - ], - "source": [ - "# Reformat and save to csv\n", - "result = pd.DataFrame(list(zip(*longlat)), columns = ['longitude','latitude'])\n", - "result.index += 1; result['row'] = x_strat; result['col'] = y_strat\n", - "result['sampled'] = np.array([0]*nsp)\n", - "csv_filename = '{}_{}Site_Stratified.csv'.format(ts, nsp)\n", - "result.to_csv('{}/{}'.format(directory, csv_filename), index_label='site')\n", - "print('Design results saved as a csv in {} directory \\nFile name: {}'.format(directory, csv_filename))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example output:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Print the first few lines of the csv file..." - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [], - "source": [ - "view_csv = pd.read_csv('{}/{}'.format(directory, csv_filename))" - ] - }, - { - "cell_type": "code", - 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sitelongitudelatituderowcolsampled
01172.575026-42.517812834.0377.00
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45172.638446-42.4150691015.00.00
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" - ], - "text/plain": [ - " site longitude latitude row col sampled\n", - "0 1 172.575026 -42.517812 834.0 377.0 0\n", - "1 2 172.268461 -42.419761 0.0 0.0 0\n", - "2 3 172.336065 -42.615136 174.0 726.0 0\n", - "3 4 172.447568 -42.422772 491.0 19.0 0\n", - "4 5 172.638446 -42.415069 1015.0 0.0 0" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "view_csv.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Columns consist of:\n", - "* A **site** ID\n", - "* The coordinates in **longitude** and **latitude**\n", - "* The coordinates in **row** and **column** (corresponding to pixels in image)\n", - "* A **sampled** column: This is used when generating an *adapted design*. When conducting the survey sites which have been sampled can be tagged with a 1 in this column. Unsampled sites remain tagged with a 0. **For example see demo_adapted_stratified notebook**. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.6.10" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/aux_imgs/csv-tag-opt1.png b/aux_imgs/csv-tag-opt1.png new file mode 100644 index 0000000..067a8ec Binary files /dev/null and b/aux_imgs/csv-tag-opt1.png differ diff --git a/aux_imgs/csv-tag-opt2.png b/aux_imgs/csv-tag-opt2.png new file mode 100644 index 0000000..855f409 Binary files /dev/null and b/aux_imgs/csv-tag-opt2.png differ