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| { | |
| "cells": [ | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-08T06:32:18.690266Z", | |
| "start_time": "2020-12-08T06:32:18.667229Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "import geopandas as gpd\nfrom shapely.geometry import Polygon\nfrom pathlib import Path\nimport os\nimport pandas as pd\nimport owslib.wms as wms\nimport owslib.wcs as wcs\nimport rasterio as rio\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom rasterio.warp import calculate_default_transform, reproject\nfrom rasterio.enums import Resampling\nimport rasterio.mask\nimport xarray as xr\nfrom typing import List, Tuple\nfrom pyproj.transformer import Transformer", | |
| "execution_count": 1, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:33:47.951543Z", | |
| "start_time": "2020-12-07T08:33:47.947957Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "def open_geotiff(fn, bands:int=None) -> np.ndarray:\n \"\"\"Open geotiff image from path, cast it to float and scale it to 0-1 range, optionally with only `bands` input bands.\"\n Returns numpy array of shape (C,W,H)\n \"\"\"\n with rio.open(str(fn)) as f:\n data = f.read()\n data = data.astype(np.float32)\n if bands is not None: data = data[bands]\n return data", | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-08T08:28:08.777059Z", | |
| "start_time": "2020-12-08T08:28:08.771948Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "def reproject_geotiff(infile:str, outfile:str, target_crs:str='EPSG:3857'):\n \"Reproject geotiff to some other coordinate system\"\n with rio.open(infile) as src:\n src_crs = src.crs\n tfm, w, h = calculate_default_transform(src_crs, target_crs, src.width, src.height, *src.bounds)\n kwargs = src.meta.copy()\n kwargs.update({\n 'crs':target_crs,\n 'transform': tfm,\n 'width': w,\n 'height': h\n })\n \n with rio.open(outfile, 'w', **kwargs) as dst:\n for i in range(1, src.count + 1):\n reproject(source=rio.band(src,i),\n destination=rio.band(dst,i),\n src_transform=src.transform,\n src_crs=src.crs,\n dst_transform=tfm,\n dst_crs=target_crs,\n resampling=Resampling.cubic)\n bounds = dst.bounds\n return bounds ", | |
| "execution_count": 3, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-08T08:28:09.329442Z", | |
| "start_time": "2020-12-08T08:28:09.322849Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "def download_and_preprocess(outpath, bounds:List[float], wms_url:str, layer:str):\n \"Download data from WCS and project it to EPSG:3857\"\n size = (int((bounds[2] - bounds[0])/10), int((bounds[3] - bounds[1])/10))\n wcs_url = 'https://data.nsdc.fmi.fi/geoserver/wcs'\n w = wcs.WebCoverageService(wcs_url, version='1.0.0')\n c = w[layer]\n t = c.timepositions\n for ts in t:\n if not os.path.exists(outpath/ts[:4]): os.makedirs(outpath/ts[:4])\n img = w.getCoverage(layer,\n crs='EPSG:32634',\n bbox=bounds, \n WIDTH=size[0],\n HEIGHT=size[1],\n format='GeoTIFF',\n time=[ts])\n #interpolations= 'bicubic')\n out = open(outpath/ts[:4]/f'{ts}.tif', 'wb')\n out.write(img.read())\n out.close()\n\n if not os.path.exists(outpath/f'{ts[:4]}_repro'): os.makedirs(outpath/f'{ts[:4]}_repro')\n reproject_geotiff(outpath/ts[:4]/f'{ts}.tif', outpath/f'{ts[:4]}_repro'/f'{ts}.tif', 'EPSG:3857')", | |
| "execution_count": 26, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:33:47.975498Z", | |
| "start_time": "2020-12-07T08:33:47.971748Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "def calc_yearly_median(path, year):\n images = [open_geotiff(path/str(year)/f)[0] for f in os.listdir(path/str(year))]\n masked_images = np.array([np.ma.masked_where(i == 0, i) for i in images])\n return np.ma.getdata(np.ma.median(masked_images, axis=0)).astype('uint8')", | |
| "execution_count": 5, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:33:47.982345Z", | |
| "start_time": "2020-12-07T08:33:47.977130Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "def make_yearly_medians(data_path):\n if not os.path.exists(data_path/'yearly_repro'): os.makedirs(data_path/'yearly_repro')\n for y in [2016,2017,2018,2019,2020]:\n files = os.listdir(data_path/str(y))\n with rio.open(data_path/f'{y}_repro'/files[0]) as src:\n prof = src.profile.copy()\n with rio.open(data_path/f'yearly_repro/{y}.tif', 'w', **prof) as dst:\n newvals = calc_yearly_median(data_path, y)\n dst.write(newvals, 1)", | |
| "execution_count": 6, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:33:47.989076Z", | |
| "start_time": "2020-12-07T08:33:47.984097Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "def mask_beaches(data_path:Path, beach_mask_path:str):\n \"Mask only `Hietikko` areas\"\n files = os.listdir(data_path)\n beach_mask = gpd.read_file(beach_mask_path)\n for f in files:\n with rio.open(data_path/f) as src:\n temp_beach_mask = beach_mask.to_crs(src.crs)\n temp_beach_mask = temp_beach_mask.cx[src.bounds[0]:src.bounds[2], src.bounds[1]:src.bounds[3]]\n out_im, out_tfm = rasterio.mask.mask(src, temp_beach_mask.geometry, crop=False, all_touched=True)\n out_meta = src.meta\n out_meta.update({'transform':out_tfm})\n with rio.open(data_path/f'{f[:-4]}_beach.tif', 'w', **out_meta) as dst:\n dst.write(out_im)\n", | |
| "execution_count": 39, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:33:47.995627Z", | |
| "start_time": "2020-12-07T08:33:47.991133Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "def make_graph_data(data_path:Path, outfile:str):\n \"Calculate means for each time step\"\n files = [f for f in os.listdir(data_path) if 'beach' in f]\n means = []\n for f in files:\n with rio.open(data_path/f) as src:\n vals = src.read(1)\n vals = np.ma.masked_where(vals==0, vals)\n stepmean = (np.ma.mean(vals)/100) - 1\n if np.isfinite(stepmean): \n means.append([f[:10]+'T00:00:00.000Z', stepmean])\n return means ", | |
| "execution_count": 8, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# Monitoring sandy beaches in the Baltic sea with yearly median NDVI" | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Data used:\n* `Maastotietokanta` polygon data from the year 2016\n* [Sentinel 2 NDVI mosaics](https://ckan.ymparisto.fi/dataset/sentinel-2-image-index-mosaics-s2ind-sentinel-2-kuvamosaiikit-s2ind)" | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# Filter Baltic sea beaches from Maastotietokanta data" | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Sandy beaches are defined here to be class `Hietikko`, id `34300` with maximum distance of 100 meters from `Meri`, id `36211` or `Järvi`, id `36200`" | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:35:35.030411Z", | |
| "start_time": "2020-12-07T08:33:50.007824Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "beaches_2016 = gpd.read_file('../sandy-beaches-data/MTKkoosteet_2016.gdb', layer='kivikalliohiekka')\nbeaches_2016 = beaches_2016[beaches_2016.LUOKKA == 34300.]\nbeaches_2016 = beaches_2016.to_crs('EPSG:32634')", | |
| "execution_count": 9, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:35:55.882976Z", | |
| "start_time": "2020-12-07T08:35:35.032314Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "waters = gpd.read_file('../sandy-beaches-data/waters.shp')\nwaters = waters[(waters.LUOKKA == 36211.) | (waters.LUOKKA == 36200.)]", | |
| "execution_count": 10, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-04T13:00:53.059016Z", | |
| "start_time": "2020-12-04T13:00:53.044495Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "#waters.LUOKKA.value_counts()", | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "36200.0 167596\n44300.0 44985\n36313.0 18599\n36211.0 680\nName: LUOKKA, dtype: int64" | |
| }, | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:35:55.888964Z", | |
| "start_time": "2020-12-07T08:35:55.885337Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "def dist_to_water(row, waters):\n \"Function to use with df.apply. Calculates minimum distance to water polygon\"\n nearest_idx = list(waters.sindex.nearest(row.geometry.bounds))[0]\n return waters.iloc[nearest_idx].geometry.distance(row.geometry)", | |
| "execution_count": 11, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:37:42.966271Z", | |
| "start_time": "2020-12-07T08:35:55.890923Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "beaches_2016['dist_to_water'] = beaches_2016.apply(lambda row: dist_to_water(row, waters), axis=1)\nbeaches_2016 = beaches_2016[beaches_2016.dist_to_water <= 100]", | |
| "execution_count": 12, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Reproject shapefile to `EPSG:3857` for folium/Leaflet maps." | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:37:45.042488Z", | |
| "start_time": "2020-12-07T08:37:42.969956Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "beaches_2016.to_crs('EPSG:3857').to_file('../streamlit_data/beaches_sea_lake_2016.shp')", | |
| "execution_count": 13, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# Download and preprocess data" | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "NDVI mosaics are be downloaded from National Satellite Data Centre." | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "beaches_2016 = gpd.read_file('../data/beaches_sea_lake_2016.shp').to_crs('EPSG:3857')", | |
| "execution_count": 24, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-08T08:19:53.581919Z", | |
| "start_time": "2020-12-08T08:19:53.579372Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "wcs_url = 'https://data.nsdc.fmi.fi/geoserver/wcs'\nlayer = 'PTA:s2m_ndvi'", | |
| "execution_count": 10, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Data is downloaded as `EPSG:32634` because it's easiest to control size. Basemaps for folium/Leaflet are in `EPSG:3857` **but** require coordinates as `EPSG:4326`." | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "## Vattaja" | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-08T08:28:11.789773Z", | |
| "start_time": "2020-12-08T08:28:11.787127Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "vattaja_path = Path('../streamlit_data/vattaja/')", | |
| "execution_count": 11, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Area bounds as `EPSG:32634`" | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-08T08:28:12.272039Z", | |
| "start_time": "2020-12-08T08:28:12.268903Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "vattaja_bounds = [615760., 7098940., 620880., 7109180.]", | |
| "execution_count": 12, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-08T08:29:35.457558Z", | |
| "start_time": "2020-12-08T08:28:25.396788Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "download_and_preprocess(vattaja_path, vattaja_bounds, wcs_url, layer)", | |
| "execution_count": 27, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Make yearly medians, excluding zeroes as they are nodata-values" | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:39:57.878868Z", | |
| "start_time": "2020-12-07T08:39:57.155194Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "make_yearly_medians(vattaja_path)", | |
| "execution_count": 28, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Mask images to only contain data in beaches." | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:40:12.930899Z", | |
| "start_time": "2020-12-07T08:39:58.739727Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "paths = ['2016_repro', '2017_repro', '2018_repro', '2019_repro', '2020_repro', 'yearly_repro']\nfor p in paths: mask_beaches(vattaja_path/p, '../data/beaches_sea_lake_2016.shp')", | |
| "execution_count": 40, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "Make csv data for mean median NDVIs in beach for each timestep:" | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:40:17.759686Z", | |
| "start_time": "2020-12-07T08:40:17.314379Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "retvals = []\nfor p in paths[:-1]: retvals.extend(make_graph_data(vattaja_path/p, 'def'))\n\noutf = pd.DataFrame(columns=['time', 'ndvi'], data=retvals)\noutf.to_csv(vattaja_path/'median_data.csv', index=False)", | |
| "execution_count": 41, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "## Hankoniemi" | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:40:18.612638Z", | |
| "start_time": "2020-12-07T08:40:18.610225Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "hankoniemi_path = Path('../streamlit_data/hankoniemi/')", | |
| "execution_count": 42, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:40:19.116234Z", | |
| "start_time": "2020-12-07T08:40:19.113612Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "hankoniemi_bounds = [605520., 6631740., 609360., 6634300.]", | |
| "execution_count": 43, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:41:07.081458Z", | |
| "start_time": "2020-12-07T08:40:19.520972Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "download_and_preprocess(hankoniemi_path, hankoniemi_bounds, wcs_url, layer)\n\nmake_yearly_medians(hankoniemi_path)\n\npaths = ['2016_repro', '2017_repro', '2018_repro', '2019_repro', '2020_repro', 'yearly_repro']\nfor p in paths: mask_beaches(hankoniemi_path/p, '../data/beaches_sea_lake_2016.shp')", | |
| "execution_count": 45, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:41:07.367034Z", | |
| "start_time": "2020-12-07T08:41:07.083653Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "retvals = []\nfor p in paths[:-1]: retvals.extend(make_graph_data(hankoniemi_path/p, 'def'))\n\noutf = pd.DataFrame(columns=['time', 'ndvi'], data=retvals)\noutf.to_csv(hankoniemi_path/'median_data.csv', index=False)", | |
| "execution_count": 46, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "## Kalajoki" | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:41:07.372935Z", | |
| "start_time": "2020-12-07T08:41:07.370027Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "kalajoki_path = Path('../streamlit_data/kalajoki/')", | |
| "execution_count": 47, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:41:07.381081Z", | |
| "start_time": "2020-12-07T08:41:07.375638Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "kalajoki_bounds = [ 633680., 7124540., 641360., 7133500.]", | |
| "execution_count": 48, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:42:59.070236Z", | |
| "start_time": "2020-12-07T08:41:07.384858Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "download_and_preprocess(kalajoki_path, kalajoki_bounds, wcs_url, layer)\n\nmake_yearly_medians(kalajoki_path)\n\npaths = ['2016_repro', '2017_repro', '2018_repro', '2019_repro', '2020_repro', 'yearly_repro']\nfor p in paths: mask_beaches(kalajoki_path/p, '../data/beaches_sea_lake_2016.shp')", | |
| "execution_count": 49, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-07T08:42:59.597704Z", | |
| "start_time": "2020-12-07T08:42:59.071668Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "retvals = []\nfor p in paths[:-1]: retvals.extend(make_graph_data(kalajoki_path/p, 'def'))\n\noutf = pd.DataFrame(columns=['time', 'ndvi'], data=retvals)\noutf.to_csv(kalajoki_path/'median_data.csv', index=False)", | |
| "execution_count": 50, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "## Livojärvi\n" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "livojarvi_path = Path('../streamlit_data/livojärvi')\nlivojarvi_bounds = [817220.,7334340.,826150,7339450]", | |
| "execution_count": 53, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "download_and_preprocess(livojarvi_path, livojarvi_bounds, wcs_url, layer)\n\nmake_yearly_medians(livojarvi_path)\n\npaths = ['2016_repro', '2017_repro', '2018_repro', '2019_repro', '2020_repro', 'yearly_repro']\nfor p in paths: mask_beaches(livojarvi_path/p, '../data/beaches_sea_lake_2016.shp')", | |
| "execution_count": 54, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "retvals = []\nfor p in paths[:-1]: retvals.extend(make_graph_data(livojarvi_path/p, 'def'))\n\noutf = pd.DataFrame(columns=['time', 'ndvi'], data=retvals)\noutf.to_csv(livojarvi_path/'median_data.csv', index=False)", | |
| "execution_count": 55, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "", | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ], | |
| "metadata": { | |
| "_draft": { | |
| "nbviewer_url": "https://gist.github.com/19bb57586e2c62053a5a762c902a3089" | |
| }, | |
| "gist": { | |
| "id": "19bb57586e2c62053a5a762c902a3089", | |
| "data": { | |
| "description": "Hiekkarantadata", | |
| "public": true | |
| } | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3", | |
| "language": "python" | |
| }, | |
| "language_info": { | |
| "name": "python", | |
| "version": "3.8.3", | |
| "mimetype": "text/x-python", | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "pygments_lexer": "ipython3", | |
| "nbconvert_exporter": "python", | |
| "file_extension": ".py" | |
| }, | |
| "latex_envs": { | |
| "LaTeX_envs_menu_present": true, | |
| "autoclose": false, | |
| "autocomplete": true, | |
| "bibliofile": "biblio.bib", | |
| "cite_by": "apalike", | |
| "current_citInitial": 1, | |
| "eqLabelWithNumbers": true, | |
| "eqNumInitial": 1, | |
| "hotkeys": { | |
| "equation": "Ctrl-E", | |
| "itemize": "Ctrl-I" | |
| }, | |
| "labels_anchors": false, | |
| "latex_user_defs": false, | |
| "report_style_numbering": false, | |
| "user_envs_cfg": false | |
| }, | |
| "toc": { | |
| "nav_menu": {}, | |
| "number_sections": true, | |
| "sideBar": true, | |
| "skip_h1_title": false, | |
| "base_numbering": 1, | |
| "title_cell": "Table of Contents", | |
| "title_sidebar": "Contents", | |
| "toc_cell": false, | |
| "toc_position": { | |
| "height": "calc(100% - 180px)", | |
| "left": "10px", | |
| "top": "150px", | |
| "width": "319px" | |
| }, | |
| "toc_section_display": true, | |
| "toc_window_display": true | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 4 | |
| } |
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