# 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](/coding-examples/simple-climatic-variables-plotting.md). 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 %}
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