{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Chapter 5: Ta panda rhei\n", "\n", "### Generation of animations and interactive plots." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Earth is a dynamic system with changes occuring at any instance. Why not using the same philosophy when we create plots about our environment? \n", "\n", "In this tutorial we will:\n", "1. Search, download, and view data freely available in [Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/home).\n", "2. Use country shapefiles for generating country-average fields.\n", "3. Create dynamic outputs in the form of animations and interactive plots." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "NOTE: \n", "Before interacting with the following notebook, please ensure you've reviewed the How to Execute the Notebooks section.\n", "
" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "
Run the tutorial via free cloud platforms: \n", " \"Binder\"\n", " \"Kaggle\"\n", " \"Colab\"
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "------------------" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Section 1. Install & import the necessary packages.\n", "\n", "The first step for being able to analyse and plot the data is to download and import the necessary libraries for this tutorial. We categorized the libraries based on that they are used for: general libraries, libraries for data analysis, and plotting libraries." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# General libraries\n", "import requests # for getting data from url\n", "import os # operating system interfaces library\n", "import cdsapi # CDS API\n", "\n", "# Libraries for working with arrays\n", "import numpy as np # for n-d arrays\n", "import pandas as pd # for 2-d arrays\n", "import xarray as xr # for n-d arrays (including metadata for all the dimensions)\n", "import regionmask # library with stored polygons of countries, regions, etc. that can be used for masking xarray data\n", "\n", "# Libraries for plotting and visualising data\n", "import matplotlib.pyplot as plt\n", "from matplotlib.gridspec import GridSpec\n", "from matplotlib.animation import FuncAnimation\n", "import seaborn as sns\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "from IPython.display import HTML # for dispalying animations in the notebook\n", "import plotly.express as px # for interactive plots\n", "import plotly.graph_objects as go # for interactive plots\n", "from plotly.offline import plot, init_notebook_mode # for creating html of interactive plot, so it can be uploaded on jupyter book" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The below is for having a consistent plotting across all tutorials. It **will NOT work in Google Colab** or other cloud services, unless you include the file `copernicus.mplstyle` (available in the Github repository) in the cloud and in the same directory as this notebook, and use the correct path, e.g.\n", "`plt.style.use('copernicus.mplstyle')`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "plt.style.use('../copernicus.mplstyle') # use the predefined matplotlib style for consistent plotting across all tutorials" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Section 2. Download data." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Let's create a folder were all the data will be stored." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "dir_loc = 'data/' # assign folder for storing the downloaded data\n", "os.makedirs(f'{dir_loc}', exist_ok=True) # create the folder if not available" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's use the `regionmask` [package]((https://regionmask.readthedocs.io/en/stable/index.html#)) to select the region of interest and get the boundary box needed for deriving the data from CDS." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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0ZWZimbabwePOLYGON ((31.28789 -22.40205, 31.19727 -22.344...
1ZMZambiaPOLYGON ((30.39609 -15.64307, 30.25068 -15.643...
2YEYemenMULTIPOLYGON (((53.08564 16.64839, 52.58145 16...
3VNVietnamMULTIPOLYGON (((104.06396 10.39082, 104.08301 ...
4VEVenezuelaMULTIPOLYGON (((-60.82119 9.13838, -60.94141 9...
............
237AFAfghanistanPOLYGON ((66.52227 37.34849, 66.82773 37.37129...
238SGSiachen GlacierPOLYGON ((77.04863 35.10991, 77.00449 35.19634...
239AQAntarcticaMULTIPOLYGON (((-45.71777 -60.52090, -45.49971...
240SXSint MaartenPOLYGON ((-63.12305 18.06895, -63.01118 18.068...
241TVTuvaluPOLYGON ((179.21367 -8.52422, 179.20059 -8.534...
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" ], "text/plain": [ " abbrevs names \n", "numbers \n", "0 ZW Zimbabwe \\\n", "1 ZM Zambia \n", "2 YE Yemen \n", "3 VN Vietnam \n", "4 VE Venezuela \n", "... ... ... \n", "237 AF Afghanistan \n", "238 SG Siachen Glacier \n", "239 AQ Antarctica \n", "240 SX Sint Maarten \n", "241 TV Tuvalu \n", "\n", " geometry \n", "numbers \n", "0 POLYGON ((31.28789 -22.40205, 31.19727 -22.344... \n", "1 POLYGON ((30.39609 -15.64307, 30.25068 -15.643... \n", "2 MULTIPOLYGON (((53.08564 16.64839, 52.58145 16... \n", "3 MULTIPOLYGON (((104.06396 10.39082, 104.08301 ... \n", "4 MULTIPOLYGON (((-60.82119 9.13838, -60.94141 9... \n", "... ... \n", "237 POLYGON ((66.52227 37.34849, 66.82773 37.37129... \n", "238 POLYGON ((77.04863 35.10991, 77.00449 35.19634... \n", "239 MULTIPOLYGON (((-45.71777 -60.52090, -45.49971... \n", "240 POLYGON ((-63.12305 18.06895, -63.01118 18.068... \n", "241 POLYGON ((179.21367 -8.52422, 179.20059 -8.534... \n", "\n", "[242 rows x 3 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regions = regionmask.defined_regions.natural_earth_v5_0_0.countries_50 # use 50m resolution shapefiles (110 is too corase, and 10 is not working)\n", "regions_gdf = regions.to_geodataframe()\n", "regions_gdf" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "country_used = 'Cyprus'\n", "domains_selected = [i for i in regions_gdf.names.values if country_used in i] # take all regions that contain the country of interest, in case NaturalEarth splits the country in more domains\n", "regions_gdf.query('names in @domains_selected').plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the boundary box. Because ERA5 Land has a resolution of 0.1 degrees, we will modify the boundaries so that they are increments of 0.1." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([32.30097656, 34.56958008, 34.55605469, 35.66206055])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boundary_used = regions_gdf.query('names in @domains_selected').total_bounds\n", "boundary_used # the values are given as minx, miny, maxx, maxy" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "minx, miny = np.floor(boundary_used[:2]*10)/10 # for min values we use floor\n", "maxx, maxy = np.ceil(boundary_used[2:]*10)/10 # for max values we use ceil" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Enter CDS API key\n", "\n", "\n", "We will request data from the Climate Data Store (CDS) programmatically with the help of the CDS API. Let us make use of the option to manually set the CDS API credentials. First, you have to define two variables: `URL` and `KEY` which build together your CDS API key. The string of characters that make up your KEY include your personal User ID and CDS API key. To obtain these, first register or login to the CDS (http://cds.climate.copernicus.eu), then visit https://cds.climate.copernicus.eu/api-how-to and copy the string of characters listed after \"key:\". Replace the `#########` below with that string." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# CDS key\n", "cds_url = 'https://cds.climate.copernicus.eu/api/v2'\n", "cds_key = '########' # please add your key here the format should be as {uid}:{api-key}" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial we will work with [temperature data from ERA5 land](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview), that have finer resolution compared to ERA5." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2023-08-01 21:16:37,361 INFO Welcome to the CDS\n", "2023-08-01 21:16:37,361 INFO Sending request to https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-land-monthly-means\n", "2023-08-01 21:16:37,696 INFO Request is completed\n", "2023-08-01 21:16:37,697 INFO Downloading https://download-0008-clone.copernicus-climate.eu/cache-compute-0008/cache/data1/adaptor.mars.internal-1690750579.0248525-30650-12-5513b1c9-d723-4e6c-bdb3-e8a19500ea0f.grib to data//t2m_Cyprus.grib (310.1K)\n", "2023-08-01 21:16:38,686 INFO Download rate 313.8K/s\n" ] }, { "data": { "text/plain": [ "Result(content_length=317520,content_type=application/x-grib,location=https://download-0008-clone.copernicus-climate.eu/cache-compute-0008/cache/data1/adaptor.mars.internal-1690750579.0248525-30650-12-5513b1c9-d723-4e6c-bdb3-e8a19500ea0f.grib)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = cdsapi.Client(url=cds_url, key=cds_key)\n", "\n", "c.retrieve(\n", " 'reanalysis-era5-land-monthly-means',\n", " {\n", " 'area': [maxy, minx, miny, maxx], # North, West, South, East\n", " 'product_type': 'monthly_averaged_reanalysis',\n", " 'variable': '2m_temperature',\n", " 'year': list(range(1950, 2024)),\n", " 'month': [('0'+str(i))[-2:] for i in list(range(1, 13))], # the months should be given as 2 digit (e.g., '01', '12')\n", " 'time': '00:00',\n", " 'format': 'grib',\n", " },\n", " f'{dir_loc}/t2m_{country_used}.grib')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Read the file." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.DataArray 't2m' (time: 882, latitude: 13, longitude: 24)>\n",
       "[275184 values with dtype=float32]\n",
       "Coordinates:\n",
       "    number      int64 ...\n",
       "  * time        (time) datetime64[ns] 1950-01-01 1950-02-01 ... 2023-06-01\n",
       "    step        timedelta64[ns] ...\n",
       "    surface     float64 ...\n",
       "  * latitude    (latitude) float64 35.7 35.6 35.5 35.4 ... 34.8 34.7 34.6 34.5\n",
       "  * longitude   (longitude) float64 32.3 32.4 32.5 32.6 ... 34.3 34.4 34.5 34.6\n",
       "    valid_time  (time) datetime64[ns] ...\n",
       "Attributes: (12/30)\n",
       "    GRIB_paramId:                             167\n",
       "    GRIB_dataType:                            fc\n",
       "    GRIB_numberOfPoints:                      312\n",
       "    GRIB_typeOfLevel:                         surface\n",
       "    GRIB_stepUnits:                           1\n",
       "    GRIB_stepType:                            avgid\n",
       "    ...                                       ...\n",
       "    GRIB_shortName:                           2t\n",
       "    GRIB_totalNumber:                         0\n",
       "    GRIB_units:                               K\n",
       "    long_name:                                2 metre temperature\n",
       "    units:                                    K\n",
       "    standard_name:                            unknown
" ], "text/plain": [ "\n", "[275184 values with dtype=float32]\n", "Coordinates:\n", " number int64 ...\n", " * time (time) datetime64[ns] 1950-01-01 1950-02-01 ... 2023-06-01\n", " step timedelta64[ns] ...\n", " surface float64 ...\n", " * latitude (latitude) float64 35.7 35.6 35.5 35.4 ... 34.8 34.7 34.6 34.5\n", " * longitude (longitude) float64 32.3 32.4 32.5 32.6 ... 34.3 34.4 34.5 34.6\n", " valid_time (time) datetime64[ns] ...\n", "Attributes: (12/30)\n", " GRIB_paramId: 167\n", " GRIB_dataType: fc\n", " GRIB_numberOfPoints: 312\n", " GRIB_typeOfLevel: surface\n", " GRIB_stepUnits: 1\n", " GRIB_stepType: avgid\n", " ... ...\n", " GRIB_shortName: 2t\n", " GRIB_totalNumber: 0\n", " GRIB_units: K\n", " long_name: 2 metre temperature\n", " units: K\n", " standard_name: unknown" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t2m = xr.open_dataarray(f'{dir_loc}/t2m_{country_used}.grib', engine='cfgrib')\n", "t2m" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# \"quick and dirty\" plot of the data\n", "t2m.isel(time=0).plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ERA5 Land has the finest resolution possible for a global reanalysis model, nevetheless we can notice that it cannot fully depict the spatial domain of the country." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now use `regionask` for marking the temperature data so that only the grids over the selected country as used." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mask_countries = regionmask.defined_regions.natural_earth_v5_0_0.countries_50.mask_3D(t2m, lon_name='longitude', lat_name='latitude') # masks for the regions in the subdomain of the downloaded data\n", "masks_selected = [i for i,j in enumerate(mask_countries.names.values) if country_used in j] # keep the masks only for the domains of interest\n", "mask_country = mask_countries.isel(region=masks_selected).max('region') # in case the country is seperated in more regions, we combine the regions to one mask\n", "mask_country = mask_country*1 # convert to 0-1 from boolean\n", "mask_country.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Section 3. Data analysis and plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now use the country of interest for deriving the average temperature timeseries. The data are projected in lat/lon system. This system does not have equal areas for all grid cells, but as we move closer to the poles, the areas of the cells are reducing. These differences can be accounted when weighting the cells with the cosine of their latitude." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:     (time: 882)\n",
       "Coordinates:\n",
       "    number      int64 0\n",
       "  * time        (time) datetime64[ns] 1950-01-01 1950-02-01 ... 2023-06-01\n",
       "    step        timedelta64[ns] 1 days\n",
       "    surface     float64 0.0\n",
       "    valid_time  (time) datetime64[ns] 1950-01-02 1950-02-02 ... 2023-06-02\n",
       "Data variables:\n",
       "    t2m         (time) float64 281.3 282.4 286.0 291.0 ... 289.5 293.1 297.4
" ], "text/plain": [ "\n", "Dimensions: (time: 882)\n", "Coordinates:\n", " number int64 0\n", " * time (time) datetime64[ns] 1950-01-01 1950-02-01 ... 2023-06-01\n", " step timedelta64[ns] 1 days\n", " surface float64 0.0\n", " valid_time (time) datetime64[ns] 1950-01-02 1950-02-02 ... 2023-06-02\n", "Data variables:\n", " t2m (time) float64 281.3 282.4 286.0 291.0 ... 289.5 293.1 297.4" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weights = np.cos(np.deg2rad(t2m.latitude)) # get weights based on latitude\n", "average_t2m_country = t2m.weighted(mask_country * weights).mean(dim=(\"latitude\", \"longitude\")) # weigthed average\n", "average_t2m_country.to_dataset() # we use 'dataset' object instead of 'dataarray' so that the information are visually nicer " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's convert the temperature to Celsius from Kelvin, as the former is used more commonly." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "average_t2m_country = average_t2m_country - 273.15\n", "average_t2m_country.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have the average temperature for the country of interest it's time to generate the plots. But first let's have the country name in ISO format, so that we can attach on the plot the country's flag." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
geometrynamesCountryAlpha-2 codeAlpha-3 codeNumeric
0POLYGON ((34.02363 35.04556, 34.05020 34.98838...CyprusCyprusCYCYP196
\n", "
" ], "text/plain": [ " geometry names Country \n", "0 POLYGON ((34.02363 35.04556, 34.05020 34.98838... Cyprus Cyprus \\\n", "\n", " Alpha-2 code Alpha-3 code Numeric \n", "0 CY CYP 196 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get ISO names for the countries\n", "iso_codes = pd.read_html(\"https://www.iban.com/country-codes\")[0]\n", "\n", "# just in case there are more than 1 regions for one country from regionmask, combine the shapefiles\n", "country_final = regions_gdf.query('names in @domains_selected').drop(columns='abbrevs')\n", "country_final['combo'] = 0\n", "country_final = country_final.dissolve(by='combo')\n", "country_final['names'] = country_used\n", "\n", "# because names have differences, be more loose for preventing bugs (is not checked for all countries!)\n", "iso_codes = iso_codes.iloc[iso_codes.Country.str.contains(country_used).values]\n", "iso_codes['Country'] = country_used\n", "\n", "country_final = country_final.merge(iso_codes, left_on='names', right_on='Country')\n", "country_final" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the country's flag. We will use png files available in [country-flags repository](https://github.com/hampusborgos/country-flags)." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "iso_2digits_name = country_final['Alpha-2 code'].values[0].lower() # get the country's ISO 2-digit code and convert to lower\n", "url_address = f'https://cdn.jsdelivr.net/gh/hampusborgos/country-flags@main/png1000px/{iso_2digits_name}.png'\n", "img_data = requests.get(url_address).content\n", "with open(f'{dir_loc}/flag_{country_used}.png', 'wb') as handler:\n", " handler.write(img_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's create a plot using all yearly data for a specific month. We will give an option to create either a line plot with the actual values, or a bar plot with the anomalies. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def monthly_animation(month_of_interest, anomalies=False):\n", "\n", " # Get the subset for only the selected month\n", " final_timeseries = average_t2m_country.isel(time=(average_t2m_country.time.dt.strftime('%B')==month_of_interest)) # subset data\n", " final_timeseries = final_timeseries.assign_coords({'time': final_timeseries.time.dt.strftime('%Y').astype(int)}) # rename time as year only\n", "\n", " if anomalies:\n", " final_timeseries = final_timeseries - final_timeseries.mean() # get anomalies\n", " \n", " # use Red Blue colorbar\n", " my_cmap = plt.get_cmap(\"RdBu_r\")\n", " rescale = lambda y: (y - np.min(final_timeseries.values)) / (np.max(final_timeseries.values) - np.min(final_timeseries.values))\n", "\n", " # Let's calculate the linear trend in our data so we can add this information on the animation.\n", " fit_line = final_timeseries.polyfit(dim='time', deg=1)['polyfit_coefficients'] # fit linear trend\n", " linear_fit = fit_line[0]*final_timeseries.time + fit_line[1] # get fitted timeseries based on linear trend\n", "\n", " # get min and max values for the y-axis\n", " min_yaxis = np.floor(final_timeseries.min().values)\n", " max_yaxis = np.ceil(final_timeseries.max().values)\n", "\n", " # make the layout for the animation\n", " fig = plt.figure(figsize=(11, 5), layout='constrained') # generate the figure\n", " fig.patch.set_facecolor('.2') # change figure's background to black\n", "\n", " gs = GridSpec(18, 20, figure=fig) # use GridSpec for non-regular subplots of the figure\n", " bpax = plt.subplot(gs[3:, :17]) # create the axis for the basic plot\n", " gmax = plt.subplot(gs[3:9, 17:20], # axis used for plotting the country on the global map\n", " projection=ccrs.Orthographic(central_latitude=country_final.centroid.y.values[0],\n", " central_longitude=country_final.centroid.x.values[0])) \n", " cpax = plt.subplot(gs[9:15, 17:20]) # axis for adding the country's map\n", " flax = plt.subplot(gs[16:, 17:20]) # axis for adding the country's flag\n", "\n", " gmax.set_facecolor('grey') # change axis' background to black\n", " gmax.add_geometries(country_final.geometry, crs=ccrs.PlateCarree(), edgecolor='red', facecolor='red', linewidth=2, zorder =20)\n", " gmax.add_feature(cfeature.OCEAN, edgecolor='lightblue', lw=.5) # add the oceans as polygons from the cartopy library\n", " gmax.add_feature(cfeature.BORDERS, edgecolor='0.1', lw=.1)\n", " gmax.coastlines(edgecolor='0.1', lw=.3)\n", " gmax.set_global()\n", "\n", " txt_title_kws = dict(size=15, weight=500, ha='left', va='top', transform=bpax.transAxes) # arguments for text (as main title)\n", " if anomalies:\n", " year_title = bpax.text(.0, 1.3, f'2m temperature anomalies (\\u2103), month: {month_of_interest}, country: {country_used}', **txt_title_kws)\n", " else:\n", " year_title = bpax.text(.0, 1.3, f'2m temperature (\\u2103), month: {month_of_interest}, country: {country_used}', **txt_title_kws)\n", "\n", " dates = final_timeseries.coords['time'].values # get dates\n", "\n", " bpax.set_xlim(min(dates)-1, max(dates)+1) # specify the xlim of the plot (extend by 1 year both start/end)\n", " bpax.set_xticks(dates[::4]) # add ticks every 4 years\n", " bpax.set_ylim(min_yaxis, max_yaxis) # add limit of y-axis\n", " bpax.set_facecolor('black') # change axis' background to black\n", " sns.despine(ax=bpax, trim=True, offset=5) # trim the x and y lines\n", "\n", " cpax.set_facecolor('black') # change axis' background to black\n", " cpax.set_axis_off()\n", " country_final.plot(ax=cpax)\n", "\n", " country_flag = plt.imread(f'{dir_loc}flag_{country_used}.png', format='png')\n", " flax.imshow(country_flag, aspect='equal')\n", " flax.set_axis_off()\n", "\n", " def update(frame, use_anomalies=False):\n", " # for each frame, update the data stored on each artist.\n", " # we plot 1 more frame for adding the trend at the end, so for last frame, use the year of the previous frame\n", " if frame==final_timeseries.size:\n", " current_year = final_timeseries.coords['time'][frame-1].item() # get current year\n", " else:\n", " current_year = final_timeseries.coords['time'][frame].item() # get current year\n", " bpax.set_title(current_year, loc='left', pad=15) # add title of the axis plot\n", " \n", " if use_anomalies:\n", " bar = bpax.bar(final_timeseries.time[:frame], final_timeseries.values[:frame], \n", " color=my_cmap(rescale(final_timeseries.values[:frame])), zorder=10) # colored scatter\n", " # bpax.axhline(0, linestyle='--', color='white')\n", " else: \n", " line = bpax.plot(final_timeseries.time[:frame], final_timeseries.values[:frame], c='1', linestyle='--', zorder=1) # white dashed line \n", " scat = bpax.scatter(final_timeseries.time[:frame], final_timeseries.values[:frame], c='.3', s=50, zorder=10) \n", " \n", " # for the last frame add the linear trend\n", " if frame==final_timeseries.size:\n", " trend = bpax.plot(linear_fit.time, linear_fit.values, c='gold', linestyle='--', linewidth=3, zorder=20) # trend at the end of the animation\n", " bpax.text(0.99, 0.05, f'Linear trend: {10*fit_line[0].values: 0.2f} \\u2103/decade', \n", " size=12, transform=bpax.transAxes, color='black', ha='right', zorder=20, \n", " bbox=dict(facecolor='gold', edgecolor='black', pad=10.0)) # magnitude of linear trend\n", "\n", " plt.close() # close the initial static plot as it is redundant\n", "\n", " ani = FuncAnimation(fig, update, frames=final_timeseries.size+1, fargs=(anomalies,)) # Create the animation\n", "\n", " return HTML(ani.to_jshtml()) # Display the animation in a Jupyter notebook" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "monthly_animation('June', True) # plot the bar plot with the temperature anomalies" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The final plot will be an interactive plot. For that we will use pandas dataframes. We will create one with all values in one timeseries with additional columns with useful features. We will also create one more dataframe which is a pivot version of the first dataframe, with each year being a different columns. This latter dataframe will only have the temperature timeseries and no additional information." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "data_df = average_t2m_country.to_dataframe().drop(columns=['step', 'number', 'surface', 'valid_time'])\n", "data_df['month'] = data_df.index.month\n", "data_df['year'] = data_df.index.year\n", "data_df['date'] = data_df.index\n", "data_df['color'] = 'year'\n", "\n", "pivot_data_df = data_df.pivot_table(index='month', columns='year', values='t2m')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Order the years based on the average order of their values for all the months. In this way, the first year will be the year that on average has the smallest values for the 12 months from all years." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "pivot_data_df_colors = pivot_data_df.copy(deep=True).dropna(axis=1) # use only the years that have values for all months\n", "years_values_ordered = pivot_data_df_colors.apply(lambda x: np.argsort(x.argsort()), axis=1).mean(axis=0) # average order of the years" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's color the lines based on their \"temperature order\", giving stronger colors for the years that have in general higher temperatures for the different months." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# get colors used for the data. Use Green colorbar, and allocate the colors after sorting the values\n", "years_values_ordered = np.argsort(np.argsort(years_values_ordered.values)) # extract the order of the values for being sorted\n", "cmap_colors = sns.color_palette(\"Greens\", as_cmap=False, n_colors=len(years_values_ordered)) # get number of colors as the number of years\n", "cmap_colors = np.array(cmap_colors)[years_values_ordered] # order the colors so they align with the sorted values\n", "\n", "# plotly needs colors either from a predefined list or in specific RBG format. Let's use the RGB format since the former is not possible in our case\n", "cmap_colors = (cmap_colors*255).astype(int) # convert to 0-255 RGB\n", "cmap_colors = [f'rgb({i[0]}, {i[1]}, {i[2]})' for i in cmap_colors] # convert to string format\n", "\n", "# create a Series for adding a fixed color (gold) for the years that had missing values (e.g. the current year)\n", "cmap_colors = pd.Series(cmap_colors, index=pivot_data_df_colors.columns) # create the series\n", "cmap_colors = cmap_colors.reindex(pivot_data_df.columns).fillna('gold') # expand the series with all years and fill color\n", "cmap_colors = cmap_colors.values # get the RGB values of all the colors" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# plot all data as gray thin lines\n", "fig = px.line(data_df, x='month', y='t2m', color='year', markers=True, title=f'2m temperature (\\u2103), country: {country_used}')\n", "fig.update_traces(line_color='grey', line_width=.5)\n", "\n", "# make a slider so the user can select any of the available years, which is highlighted with a thick line, colored based on the temperature order\n", "# Add traces, one for each slider step\n", "for i_col, col in enumerate(pivot_data_df.columns):\n", " fig.add_trace(\n", " go.Scatter(\n", " visible=False,\n", " line=dict(color=cmap_colors[i_col], width=6),\n", " name=col,\n", " x=pivot_data_df.index,\n", " y=pivot_data_df[col],))\n", "\n", "# Make the first year from the go.Scatter visible\n", "fig.data[pivot_data_df.shape[1]].visible = True\n", "\n", "# Create and add slider\n", "steps = []\n", "for i in range(pivot_data_df.shape[1]): # len is half, since each year is plotted twice (in px.line and go.Scatter)\n", " step = dict(\n", " method=\"update\",\n", " args=[{\"visible\": [True] * pivot_data_df.shape[1] + [False] * pivot_data_df.shape[1]}], # px.lines always visible, and go.Scatter not visible\n", " label=str(pivot_data_df.columns[i])\n", " )\n", " step[\"args\"][0][\"visible\"][pivot_data_df.shape[1]+i] = True # make the go.Scatter of the selected year visible\n", " steps.append(step)\n", "\n", "sliders = [dict(\n", " active=0,\n", " currentvalue={\"prefix\": \"Year: \"},\n", " pad={\"t\": 50},\n", " steps=steps\n", ")]\n", "\n", "fig.update_layout(\n", " xaxis=dict(showline=True,\n", " showgrid=False,\n", " showticklabels=True,\n", " title='',\n", " linewidth=2,\n", " ticks='outside',\n", " tickvals = list(range(1, 13)),\n", " ticktext = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']), \n", " yaxis=dict(showgrid=False, title=''),\n", " showlegend=False,\n", " plot_bgcolor='black',\n", " sliders=sliders\n", ")\n", "\n", "plt.close() # don't show the figure here, because it is shown in the next cell" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \n", "NOTE:
\n", "For being able to view the interactive plot in your jupyter notebook, you need to activate the command `fig.show()`. The remaining 3 lines are needed for being able to show this plot in the jupyterbook.\n", "
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