# Climatic variables spatial plotting Open you preferable Python IDE or Python console and initially import the required libraries. This example is quite more complex than the previous example [simple-climatic-variables-plotting](https://docs.answr.space/coding-examples/simple-climatic-variables-plotting "mention"). Beside the standard libraries also imported in the previous example, we are going to need a couple of geospatial related libraries to better handle coordinates. {% code overflow="wrap" lineNumbers="true" %} ```python import requests import json import numpy as np import geopandas as gpd import matplotlib.pyplot as plt import shapely.geometry ``` {% endcode %} After importing the libraries, we define all the needed variables to proceed. **Email**, **password**, **climatic*****\_variable*****\_1** and **coordinates** are the essential variables needed to get your responses from the API. The input coordinates of the API are single points, i.e. to time a call is made on the API it refers to specific location requested. Thus to have a collection of spatially distributed points, multiple requests are needed. {% hint style="info" %} It's a good idea to bring all variables that you are going to use up, in order to avoid "hard coding" values later in the code {% endhint %} {% code overflow="wrap" lineNumbers="true" %} ```python # Define the user credentials email = 'YOUR ACCOUNT EMAIL' password = 'YOUR PASSWORD' # Define the climatic variable to plot climatic_variable_1 = 'a-frost-days' # Average Frost Days # Define the coordinates of the polygon lat_point_list = [50.854457, 52.518172, 50.072651, 48.853033, 50.854457] lon_point_list = [4.377184, 13.407759, 14.435935, 2.349553, 4.377184] # Create a geopandas structure with the coordinates polygon_geom = shapely.geometry.Polygon(zip(lon_point_list, lat_point_list)) gdf = gpd.GeoDataFrame(index=[0], crs='epsg:4326', geometry=[polygon_geom]) # total area for the grid xmin, ymin, xmax, ymax= gdf.total_bounds # Define the grid n_cells=5 cell_size = (xmax-xmin)/n_cells # Projection of the grid crs = "+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs" ``` {% endcode %} So far, the **polygon**, **bounding box** and **projection** are defined. Following a **grid** is defined within this polygon. This grid contains the point coordinates to be used in the API calls {% code overflow="wrap" lineNumbers="true" %} ```python # Create the cells in a loop grid_cells = [] for x0 in np.arange(xmin, xmax+cell_size, cell_size): for y0 in np.arange(ymin, ymax+cell_size, cell_size): # bounds x1 = x0-cell_size y1 = y0+cell_size grid_cells.append(shapely.geometry.box(x0, y0, x1, y1)) cell = gpd.GeoDataFrame(grid_cells, columns=['geometry'], crs=crs) lng = cell.centroid.x lat = cell.centroid.y ``` {% endcode %} Following the process, the user needs to generate the temporally authkey to interact with the rest endpoints. {% code overflow="wrap" lineNumbers="true" %} ```python # Step 1: Generate a temporal API key to authentic the other API endpoints headers = { 'accept': 'application/json', # Already added when you pass json= but not when you pass data= # 'Content-Type': 'application/json', } json_data = { 'email': email, 'password': password, } # This the variable that holds the temporally API key auth_response = requests.post('https://api.answr.space/api:auth/auth/login', headers=headers, json=json_data) ``` {% endcode %} A for loop is used to loop over all points, make the calls to the API and store the responses into a list. {% code overflow="wrap" lineNumbers="true" %} ```python authkey = json.loads(auth_response.content) for i in range(len(cell)): # Step 2: Loop over the points and get the data from the answr.space API headers = { 'accept': 'application/json', 'Authorization': 'Bearer ' + authkey['authToken'] + '', } params = { 'Input_point': '{"type":"point","data":{"lng":' + str(lng[i]) + ',"lat":' + str(lat[i]) + '}}', } response_1 = requests.get('https://api.answr.space/api:climate-variables/' + climatic_variable_1 + '', params=params,headers=headers) # Convert byte responses to json format climatic_variable_response = json.loads(response_1.content) if response_1.content != b'null': # Rename json to key to months for better plot visualization. You can find the response keys in the API reference documentation for each endpoint new_key = {"January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"} old_key = {"CV76_M1", "CV76_M2", "CV76_M3", "CV76_M4", "CV76_M5", "CV76_M6", "CV76_M7", "CV76_M8", "CV76_M9", "CV76_M10", "CV76_M11", "CV76_M12"} responses.append(dict(zip(new_key, list(climatic_variable_response.values())))) else: responses.append(dict(zip(new_key, [0]*12))) ``` {% endcode %} Finally, we can plot the points to spatially understand the variation of the climate statistics. {% code overflow="wrap" lineNumbers="true" %} ```python df_responses = pd.DataFrame(responses) plt.scatter(lng, lat, c = df_responses['March'], cmap= 'viridis') ``` {% endcode %}
In case you want to run the entire process with a single command, bellow you can find the entire code. {% code overflow="wrap" lineNumbers="true" %} ```python import requests import json import numpy as np import geopandas as gpd import matplotlib.pyplot as plt import shapely.geometry import pandas as pd # Define the user credentials email = 'YOUR ACCOUNT EMAIL' password = 'YOUR PASSWORD' # Define the climatic variable to plot climatic_variable_1 = 'a-frost-days' # Average Frost Days # Define the coordinates of the polygon lat_point_list = [50.854457, 52.518172, 50.072651, 48.853033, 50.854457] lon_point_list = [4.377184, 13.407759, 14.435935, 2.349553, 4.377184] # Create a geopandas structure with the coordinates polygon_geom = shapely.geometry.Polygon(zip(lon_point_list, lat_point_list)) gdf = gpd.GeoDataFrame(index=[0], crs='epsg:4326', geometry=[polygon_geom]) # total area for the grid xmin, ymin, xmax, ymax= gdf.total_bounds # Define the grid n_cells=5 cell_size = (xmax-xmin)/n_cells # Projection of the grid crs = "+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs" # Create the cells in a loop grid_cells = [] for x0 in np.arange(xmin, xmax+cell_size, cell_size): for y0 in np.arange(ymin, ymax+cell_size, cell_size): # bounds x1 = x0-cell_size y1 = y0+cell_size grid_cells.append(shapely.geometry.box(x0, y0, x1, y1)) cell = gpd.GeoDataFrame(grid_cells, columns=['geometry'], crs=crs) lng = cell.centroid.x lat = cell.centroid.y # Step 1: Generate a temporal API key to authentic the other API endpoints headers = { 'accept': 'application/json', # Already added when you pass json= but not when you pass data= # 'Content-Type': 'application/json', } json_data = { 'email': email, 'password': password, } # This the variable that holds the temporally API key auth_response = requests.post('https://api.answr.space/api:auth/auth/login', headers=headers, json=json_data) responses = [] if auth_response.status_code == 200: authkey = json.loads(auth_response.content) for i in range(len(cell)): # Step 2: Loop over the points and get the data from the answr.space API headers = { 'accept': 'application/json', 'Authorization': 'Bearer ' + authkey['authToken'] + '', } params = { 'Input_point': '{"type":"point","data":{"lng":' + str(lng[i]) + ',"lat":' + str(lat[i]) + '}}', } response_1 = requests.get('https://api.answr.space/api:climate-variables/' + climatic_variable_1 + '', params=params, headers=headers) # Convert byte responses to json format climatic_variable_response = json.loads(response_1.content) if response_1.content != b'null': # Rename json to key to months for better plot visualization. You can find the response keys in the API reference documentation for each endpoint new_key = {"January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"} old_key = {"CV76_M1", "CV76_M2", "CV76_M3", "CV76_M4", "CV76_M5", "CV76_M6", "CV76_M7", "CV76_M8", "CV76_M9", "CV76_M10", "CV76_M11", "CV76_M12"} responses.append(dict(zip(new_key, list(climatic_variable_response.values())))) else: responses.append(dict(zip(new_key, [0]*12))) # Convert responses to data frame df_responses = pd.DataFrame(responses) plt.scatter(lng, lat, c = df_responses['March'], cmap= 'viridis') else: print('Return status code: ' + str(auth_response.status_code)) print(auth_response.content) ``` {% endcode %}