IMPORTANT!
IF YOU WANT TO RUN THE CODE RUN IT IN YOUR OWN JUPYTER NOTEBOOK IN YOUR PC AND NOT COURSERA LAB SINCE SOME MODULES NEED TO BE INSTALLED, ALSO KNOW I WON'T BE SHARING MY API FOR DOWNLOADING. THE DATA API KEY IS FREE TO GET ONE AT https://opendata.aemet.es/centrodedescargas/inicio SPANISH WEATHER AUTHORITY
IF YOU WANT TO RUN THE CODE RUN IT IN YOUR OWN JUPYTER NOTEBOOK IN YOUR PC AND NOT COURSERA LAB SINCE SOME MODULES NEED TO BE INSTALLED, ALSO KNOW I WON'T BE SHARING MY API FOR DOWNLOADING. THE DATA API KEY IS FREE TO GET ONE AT https://opendata.aemet.es/centrodedescargas/inicio SPANISH WEATHER AUTHORITY
Mean temperature difference between Monistrol de Montserrat and Barcelona
using AEMET API key and DATA
I live in small town outside Barcelona Spain called Monistrol de Montserrat which is around 50km from Barcelona. Since I lived here I've noticed what is a fact that temperature here is always a few degrees different from Barcelona few degrees colder in winter and few degrees warmer during summer. Which is normal due to coastal weather in Barcelona an Monistrol de Montserrat is 50km inland somehow north-east of Barcelona
But I've always wanted to put a number to this couple of degrees that I've felt. And why not used the little knowledge I have and the passion for data anlysis to get this done to pin point this difference.
- Barcelona Province, Spain
- Weather Data
- Mean temperature difference between Monistrol de Montserrat (Manresa) and Barcelona city in Spain.
The AEMET ("Agencia Estatal de Meteorología") Spanish Metereological Agency offers some of the meterological data for free on their website through API Key connection.
First I will download one dataset containing the location of the available weather stations where you get a json as a response into an URL, this dataset stations_df will be saved as stations_data.csv for later usage if requuired. It will be filtered out to use only weather stations located in Barcelona Province and map it out to choose the weather stations closest to my town and Barcelona City.
Station dataset variables:
- latitud : latitude coordinate of the weather station "413515N"
- provincia : province where the weather station is located
- altitud : weather station altitude meters above sea level
- indicativo : weather station code
- nombre : name of wather station
- indsinop : internal code
- logitud : longitude coordinate of the weather station "023224E"
After identifying the weather stations two datasets will be downloaded one for a barcelona weather station and the second for manresa weather station. Five years of records will be downloaded from JAN-2017 to DEC-2021 (5 year is the maximum span alowed on free API key). Two csv file will be saved for later usage if required.
Weather station dataset variables:
-
fecha : date in MM-DD-YYYY format
-
indicativo : weather station code
-
altitud : weather station altitude meters above sea level
-
nombre : name of wather station
-
prec : precipitation (not used)
-
tmed : daily mean temperature
-
tmin : daily min temperature (degrees C)
-
tmax : daily max temperature (degrees C)
#this cell is to load anonymously the API key to get the data from AEMET.
#you can get your own API key and replace for the variable AEMET_api.
%load_ext dotenv
%dotenv
import os
AEMET_api=os.getenv("AEMET_api")#requirements
import http.client
import json
import requests
from pandas import json_normalize
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors
import folium
from folium.features import DivIcon
from branca.element import Template, MacroElement
import statistics
import seaborn as sns
from matplotlib import cbook#let's get list of available weather stations
#connecting to AEMET site and excuting the proper request for more info you cna check https://opendata.aemet.es/dist/index.html?
#for this you can request an API key
connect_aemet = http.client.HTTPSConnection("opendata.aemet.es")
headers = {
'cache-control': "no-cache"
}
connect_aemet.request("GET", "https://opendata.aemet.es/opendata/api/valores/climatologicos/inventarioestaciones/todasestaciones/?api_key="+AEMET_api, headers=headers)
response = connect_aemet.getresponse()
data = response.read()
json_data = json.loads(data)
urldata = json_data['datos']
response = requests.get(urldata)
dictr = response.json()
stations_df = json_normalize(dictr)
stations_df.to_csv('stations_data.csv') #saving data to .csv file
#data cleaning and converting orgiginal lat and lon location format to proper format for later mapping.
stations_df['latitud Degrees']=stations_df['latitud'].str[:2]
stations_df['latitud Minutes']=stations_df['latitud'].str[2:4]
stations_df['latitud Seconds']=stations_df['latitud'].str[4:6]
stations_df['latitud Degrees']=stations_df['latitud Degrees'].astype(float)
stations_df['latitud Minutes']=stations_df['latitud Minutes'].astype(float)
stations_df['latitud Seconds']=stations_df['latitud Minutes'].astype(float)
stations_df['latitud']=stations_df['latitud Degrees']+((stations_df['latitud Minutes'])/60)+((stations_df['latitud Seconds'])/3600)
stations_df['longitud Degrees']=stations_df['longitud'].str[:2]
stations_df['longitud Minutes']=stations_df['longitud'].str[2:4]
stations_df['longitud Seconds']=stations_df['longitud'].str[4:6]
stations_df['longitud Degrees']=stations_df['longitud Degrees'].astype(float)
stations_df['longitud Minutes']=stations_df['longitud Minutes'].astype(float)
stations_df['longitud Seconds']=stations_df['longitud Minutes'].astype(float)
stations_df['longitud']=stations_df['longitud Degrees']+((stations_df['longitud Minutes'])/60)+((stations_df['longitud Seconds'])/3600)
stations_df=stations_df[['provincia','nombre','indicativo','indsinop','latitud','longitud','altitud']]
stations_df=stations_df.loc[stations_df['provincia']=='BARCELONA']
stations_df #checking df filtered to stations only in the Barcelona Province
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
</style>
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| provincia | nombre | indicativo | indsinop | latitud | longitud | altitud | |
|---|---|---|---|---|---|---|---|
| 0 | BARCELONA | ARENYS DE MAR | 0252D | 08186 | 41.593056 | 2.542222 | 74 |
| 1 | BARCELONA | BARCELONA AEROPUERTO | 0076 | 08181 | 41.288056 | 2.067778 | 4 |
| 2 | BARCELONA | BARCELONA, FABRA | 0200E | 41.423611 | 2.118611 | 408 | |
| 3 | BARCELONA | BARCELONA | 0201D | 08180 | 41.389722 | 2.203333 | 6 |
| 4 | BARCELONA | MANRESA | 0149X | 08174 | 41.728611 | 1.847222 | 291 |
| 5 | BARCELONA | SABADELL AEROPUERTO | 0229I | 08192 | 41.525278 | 2.101667 | 146 |
| 6 | BARCELONA | SANTA SUSANNA | 0255B | 08188 | 41.660833 | 2.694722 | 40 |
#for interactive mapping and later use of image in the final plot let's plot and highlight the point of interest
#using folium module and some hmtl and ccs to add the legend.
monistrol_lat=41.6095708
monistrol_lon=1.8427346
barcelona_station_lat=stations_df[stations_df['indicativo']=='0201D'].latitud
barcelona_station_lon=stations_df[stations_df['indicativo']=='0201D'].longitud
manresa_station_lat=stations_df[stations_df['indicativo']=='0149X'].latitud
manresa_station_lon=stations_df[stations_df['indicativo']=='0149X'].longitud
lats=stations_df['latitud']
lons=stations_df["longitud"]
lons_mean=statistics.mean(lons)
lats_mean=statistics.mean(lats)
m=folium.Map(location=[lats_mean,lons_mean],tiles="cartodbpositron")
for index, row in stations_df.iterrows():
folium.CircleMarker(location=[row.latitud,row.longitud], radius=5, color='red', fill=True, fill_color='black',fill_opacity=1,
popup=row.nombre+"-"+row.indicativo
).add_to(m)
folium.CircleMarker([monistrol_lat, monistrol_lon], 5, fill=True,color='red', fill_color='blue',fill_opacity=1,
popup='Monistrol de Montserrat',
).add_to(m)
folium.map.Marker(
[monistrol_lat+0.01, monistrol_lon-0.10],
icon=DivIcon(icon_size=(150,36),icon_anchor=(0,0),
html='<div style="font-size: 10pt;color: blue">%s</div>' % "Monistrol\n"+'<div style="font-size: 10pt;color: blue">%s</div>'%"de Montserrat",
)).add_to(m)
folium.map.Marker(
[barcelona_station_lat+0.02, barcelona_station_lon+0.02],
icon=DivIcon(icon_size=(150,36),icon_anchor=(0,0),
html='<div style="font-size: 10pt;color: red;font-weight:bold">%s</div>' % "Barcelona\n"+'<div style="font-size: 10pt;color: red;font-weight:bold">%s</div>'%"0201D",
)).add_to(m)
folium.map.Marker(
[manresa_station_lat+0.02, manresa_station_lon+0.02],
icon=DivIcon(icon_size=(150,36),icon_anchor=(0,0),
html='<div style="font-size: 10pt;color: red;font-weight:bold">%s</div>' % "Manresa\n"+'<div style="font-size: 10pt;color: red;font-weight:bold">%s</div>'%"0149X",
)).add_to(m)
template = """
{% macro html(this, kwargs) %}
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>jQuery UI Draggable - Default functionality</title>
<link rel="stylesheet" href="//code.jquery.com/ui/1.12.1/themes/base/jquery-ui.css">
<script src="https://code.jquery.com/jquery-1.12.4.js"></script>
<script src="https://code.jquery.com/ui/1.12.1/jquery-ui.js"></script>
<script>
$( function() {
$( "#maplegend" ).draggable({
start: function (event, ui) {
$(this).css({
right: "auto",
top: "auto",
bottom: "auto"
});
}
});
});
</script>
</head>
<body>
<div id='maplegend' class='maplegend'
style='position: absolute; z-index:9999; border:2px solid grey; background-color:rgba(255, 255, 255, 0.8);
border-radius:6px; padding: 10px; font-size:14px; right: 20px; bottom: 20px;'>
<div class='legend-title'>Legend</div>
<div class='legend-scale'>
<ul class='legend-labels'>
<li><span style='background:black;opacity:0.7;'></span>Weather station</li>
<li><span style='background:blue;opacity:0.7;'></span>Monistrol de Montserrat</li>
</ul>
</div>
</div>
</body>
</html>
<style type='text/css'>
.maplegend .legend-scale ul {
margin: 0;
margin-bottom: 5px;
padding: 0;
float: left;
list-style: none;
}
.maplegend .legend-scale ul li {
font-size: 80%;
list-style: none;
margin-left: 0;
line-height: 18px;
margin-bottom: 2px;
}
.maplegend ul.legend-labels li span {
display: block;
float: left;
width: 12px;
margin-right: 5px;
margin-left: 0;
margin-top:-2;
height: 12px;
border-radius: 100%;
border: 3px solid;
border-color: red;
background-color: black;
opacity: 1;
}
.maplegend .legend-source {
font-size: 80%;
color: #777;
clear: both;
}
.maplegend a {
color: #777;
}
</style>
{% endmacro %}"""
```python
macro = MacroElement()
macro._template = Template(template)
m.get_root().add_child(macro)
#map will be captured into map_final.png for later incorporation into the final visualization
m
Let´s get the historical data for the last five years in the two selected stations (five years is the maximum on free API key)
- Barcelona
- Manresa
#fetching daily weather data for the last five years from JAN-2017 until DEC-2021 and store into csv files.
conn = http.client.HTTPSConnection("opendata.aemet.es")
connect_aemet = http.client.HTTPSConnection("opendata.aemet.es")
headers = {
'cache-control': "no-cache"
}
initial_date="2017-01-01"
final_date="2021-12-31"
barcelona_station_id="0201D"
connect_aemet.request("GET", "https://opendata.aemet.es/opendata/api/valores/climatologicos/diarios/datos/fechaini/"+ initial_date +"T00:00:00UTC/fechafin/"+final_date + "T23:59:59UTC/estacion/"+barcelona_station_id+"/?api_key="+AEMET_api, headers=headers)
res = connect_aemet.getresponse()
data = res.read()
json_data = json.loads(data)
urldatos = json_data['datos']
response = requests.get(urldatos)
dictr = response.json()
recs = dictr[:]
barcelona_df = json_normalize(dictr)
manresa_station_id="0149X"
connect_aemet.request("GET", "https://opendata.aemet.es/opendata/api/valores/climatologicos/diarios/datos/fechaini/"+ initial_date +"T00:00:00UTC/fechafin/"+final_date + "T23:59:59UTC/estacion/"+manresa_station_id+"/?api_key="+AEMET_api, headers=headers)
res = connect_aemet.getresponse()
data = res.read()
json_data = json.loads(data)
urldatos = json_data['datos']
response = requests.get(urldatos)
dictr = response.json()
manresa_df = json_normalize(dictr)
#let´s save the data to csv file so we don't create requests everytime
manresa_df.to_csv('manresa_data.csv')
barcelona_df.to_csv('barcelona_data.csv')import warnings
from pandas.core.common import SettingWithCopyWarning
warnings.simplefilter(action="ignore", category=SettingWithCopyWarning)
manresa_mean=manresa_df[['fecha','tmed']]
barcelona_mean=barcelona_df[['fecha','tmed']]
manresa_min=manresa_df[['fecha','tmin']]
manresa_max=manresa_df[['fecha','tmax']]
barcelona_min=barcelona_df[['fecha','tmin']]
barcelona_max=barcelona_df[['fecha','tmax']]
df_list=[barcelona_min,barcelona_max,manresa_min,manresa_max,manresa_mean,barcelona_mean]
for df in df_list:
df.loc[:,:].dropna()
df.reset_index(drop=True)
df.loc[:]=df.loc[:].replace(',','.', regex=True)
df.iloc[:,-1]=df.iloc[:,-1].astype(float)
df.drop(df[df['fecha'].str.contains('02-29')].index, inplace=True)
df['fecha']=df['fecha'].str[5:]
manresa_min=manresa_min.groupby('fecha')['tmin'].min()
manresa_max=manresa_max.groupby('fecha')['tmax'].max()
manresa_mean_max=manresa_mean.groupby('fecha')['tmed'].max()
manresa_mean_min=manresa_mean.groupby('fecha')['tmed'].min()
barcelona_min=barcelona_min.groupby('fecha')['tmin'].min()
barcelona_max=barcelona_max.groupby('fecha')['tmax'].max()
barcelona_mean_max=barcelona_mean.groupby('fecha')['tmed'].max()
barcelona_mean_min=barcelona_mean.groupby('fecha')['tmed'].min()
barcelona_min = barcelona_min.rolling(5).mean()
barcelona_max = barcelona_max.rolling(5).mean()
manresa_min = manresa_min.rolling(5).mean()
manresa_max = manresa_max.rolling(5).mean()
months=['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
plt.figure(figsize=(12,6))
plt.style.use('bmh')
figure_gca=plt.gca()
plt.xlim([0, 365])
xtickvalues=[15,43,75,105,135,165,195,225,255,285,315,345]
ytickvalues=[-10,-5,0,5,10,20,30,40,50]
plt.xticks(ticks = xtickvalues ,labels = months, rotation = 'horizontal',fontsize=10)
#plt.yticks(ticks = ytickvalues ,fontsize=8)
#plt.plot(range(0,365), manresa_max, color='firebrick',label = "Record high temp" )
plt.plot(range(0,365), manresa_min, color='cornflowerblue', label = "Manresa tmin")
#plt.plot(range(0,365), barcelona_max, color='red',label = "Record high temp" )
plt.plot(range(0,365), barcelona_min, color='blue', label = "Barcelona tmin")
plt.gca().set_title('Record min temperature Manresa and Barcelona 2017-2021', fontsize=12, fontweight ='bold')
#plt.gca().set_xlabel('Day of the year')
plt.gca().set_ylabel('Temperature (°C)',fontsize=10)
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.legend(loc = 8, fontsize=10, frameon = False)<matplotlib.legend.Legend at 0x1caa803ddc0>
months=['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
plt.figure(figsize=(12,6));
plt.style.use('bmh')
figure_gca=plt.gca()
plt.xlim([0, 365])
xtickvalues=[15,43,75,105,135,165,195,225,255,285,315,345]
ytickvalues=[-10,-5,0,5,10,20,30,40,50]
plt.xticks(ticks = xtickvalues ,labels = months, rotation = 'horizontal',fontsize=10)
#plt.yticks(ticks = ytickvalues ,fontsize=8)
plt.plot(range(0,365), manresa_max, color='firebrick',label = "Manresa tmax" )
plt.plot(range(0,365), barcelona_max, color='red',label = "Barcelona tmax" )
plt.gca().set_title('Record max temperature Manresa and Barcelona 2017-2021', fontsize=12, fontweight ='bold')
#plt.gca().set_xlabel('Day of the year')
plt.gca().set_ylabel('Temperature (°C)',fontsize=10)
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.legend(loc = 8, fontsize=10, frameon = False)
<matplotlib.legend.Legend at 0x1caa7f33370>
diff_min=manresa_min-barcelona_min
diff_min = diff_min.rolling(1).mean()
mean_min=diff_min.mean()
diff_max=manresa_max-barcelona_max
diff_max = diff_max.rolling(1).mean()
mean_max=diff_max.mean()
mean_max
mean_min
print("Mean max Temperature Difference is: " + str(round(mean_max,2))+'°C' +'\n'
+ "Mean min Temperature Difference in (°C) is: " + str(round(mean_min,2))+'°C')
Mean max Temperature Difference is: 2.75°C
Mean min Temperature Difference in (°C) is: -6.17°C
months=['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
plt.figure(figsize=(12,6))
plt.style.use('bmh')
figure_gca=plt.gca()
plt.xlim([0, 365])
xtickvalues=[15,43,75,105,135,165,195,225,255,285,315,345]
ytickvalues=[-10,-5,0,5,10,20,30,40,50]
plt.xticks(ticks = xtickvalues ,labels = months, rotation = 'horizontal',fontsize=10)
#plt.yticks(ticks = ytickvalues ,fontsize=8)
#plt.plot(range(0,365), diff_max, color='firebrick',label = "Mean high temp diff" )
plt.plot(range(0,365), diff_min.rolling(5).mean(), color='cornflowerblue',label='Temperature diff')
plt.axhline(y=mean_min, color='blue', linestyle='dashed', label='Mean temp difference '+str(round(mean_min,2))+'°C')
plt.gca().set_title('Minimum Temp Difference Manresa vs Barcelona 2017-2021', fontsize=12, fontweight ='bold')
#plt.gca().set_xlabel('Day of the year')
plt.gca().set_ylabel('Temperature Difference (°C)',fontsize=10)
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.legend(loc = 8, fontsize=10, frameon = False)<matplotlib.legend.Legend at 0x1caa882ab20>
months=['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
plt.figure(figsize=(12,6))
plt.style.use('bmh')
figure_gca=plt.gca()
plt.xlim([0, 365])
xtickvalues=[15,43,75,105,135,165,195,225,255,285,315,345]
ytickvalues=[-10,-5,0,5,10,20,30,40,50]
plt.xticks(ticks = xtickvalues ,labels = months, rotation = 'horizontal',fontsize=10)
#plt.yticks(ticks = ytickvalues ,fontsize=8)
plt.plot(range(0,365), diff_max.rolling(5).mean(), color='red',label='Temperature diff')
plt.axhline(y=mean_max, color='r', linestyle='dashed', label='Mean temp difference '+str(round(mean_max,2))+'°C')
plt.gca().set_title('Temperature difference Manresa vs Barcelona\n comparing historical max records 2017-2021', fontsize=12, fontweight ='bold')
#plt.gca().set_xlabel('Day of the year')
plt.gca().set_ylabel('Temperature Difference (°C)',fontsize=10)
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.legend(loc = 8, fontsize=10, frameon = False)<matplotlib.legend.Legend at 0x1caa88bc520>
months=['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
plt.figure(figsize=(12,6));
plt.style.use('bmh')
figure_gca=plt.gca()
plt.xlim([0, 365])
xtickvalues=[15,43,75,105,135,165,195,225,255,285,315,345]
ytickvalues=[-10,-5,0,5,10,20,30,40,50]
plt.xticks(ticks = xtickvalues ,labels = months, rotation = 'horizontal',fontsize=8)
#plt.yticks(ticks = ytickvalues ,fontsize=8)
plt.plot(range(0,365), manresa_mean_max.rolling(5).mean(), color='firebrick',label = "Manresa tmax - 5 day MA" )
plt.plot(range(0,365), barcelona_mean_max.rolling(5).mean(), color='red',label = "Barcelona tmax - 5 day MA" )
plt.gca().set_title('Mean max temperature Manresa and Barcelona 2017-2021', fontsize=12, fontweight ='bold')
#plt.gca().set_xlabel('Day of the year')
plt.gca().set_ylabel('Temperature (°C)',fontsize=8)
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.legend(loc = 8, fontsize=8, frameon = False)<matplotlib.legend.Legend at 0x1caa8c2b2e0>
months=['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
plt.figure(figsize=(12,6))
plt.style.use('bmh')
figure_gca=plt.gca()
plt.xlim([0, 365])
xtickvalues=[15,43,75,105,135,165,195,225,255,285,315,345]
ytickvalues=[-10,-5,0,5,10,20,30,40,50]
plt.xticks(ticks = xtickvalues ,labels = months, rotation = 'horizontal',fontsize=8)
#plt.yticks(ticks = ytickvalues ,fontsize=8)
#plt.plot(range(0,365), manresa_max, color='firebrick',label = "Record high temp" )
plt.plot(range(0,365), manresa_mean_min.rolling(5).mean(), color='cornflowerblue', label = "Manresa tmin - 5 day MA")
#plt.plot(range(0,365), barcelona_max, color='red',label = "Record high temp" )
plt.plot(range(0,365), barcelona_mean_min.rolling(5).mean(), color='blue', label = "Barcelona tmin - 5 day MA")
plt.gca().set_title('Mean min temperature Manresa and Barcelona 2017-2021', fontsize=12, fontweight ='bold')
#plt.gca().set_xlabel('Day of the year')
plt.gca().set_ylabel('Temperature (°C)',fontsize=8)
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.legend(loc = 8, fontsize=8, frameon = False)<matplotlib.legend.Legend at 0x1caa8c7bf40>
diff_min=manresa_mean_min-barcelona_mean_min
diff_min = diff_min.rolling(1).mean()
mean_min=diff_min.mean()
diff_max=manresa_mean_max-barcelona_mean_max
diff_max = diff_max.rolling(1).mean()
mean_max=diff_max.mean()
mean_max
mean_min
print("Mean max Temperature Difference is: " + str(round(mean_max,2))+'°C' +'\n'
+ "Mean min Temperature Difference in (°C) is: " + str(round(mean_min,2))+'°C')Mean max Temperature Difference is: -1.33°C
Mean min Temperature Difference in (°C) is: -2.62°C
%matplotlib notebook
image = plt.imread('map_final.png')
fig, figsize=(14,8)
plt.subplots(constrained_layout=True)
#plt.tight_layout(pad=20, w_pad=0.5, h_pad=20)
ax1 = plt.subplot2grid(shape=(36, 12), loc=(0, 0), colspan=12, rowspan=12)
ax2 = plt.subplot2grid(shape=(36, 12), loc=(12, 0),rowspan=12,colspan=12)
ax3 = plt.subplot2grid(shape=(36, 12), loc=(24, 0), rowspan=12,colspan=12)
ax1.imshow(image)
ax1.axis('off')
ax1.set_title('Weather Station and Point Location', fontsize=8, fontweight ='bold')
ax2.set_xlim([0, 365])
ax2.set_xtickvalues=[15,43,75,105,135,165,195,225,255,285,315,345]
ax2.set_ytickvalues=[-8,-6,-4,-2,0,2,4]
ax2.set_xticks(xtickvalues)
ax2.set_xticklabels(months,fontsize=6)
ax2.set_yticks(np.arange(-8, 6, 2))
ax2.set_yticklabels(np.arange(-8, 6, 2),fontsize=6)
ax2.plot(range(0,365), diff_min.rolling(5).mean(), color='cornflowerblue',label='Temperature diff')
ax2.axhline(y=mean_min, color='blue', linestyle='dashed', label='Mean temp difference '+str(round(mean_min,2))+'°C')
ax2.set_title('Temperature Difference Manresa vs Barcelona \n using min temp records 2017-2021', fontsize=8, fontweight ='bold')
ax2.set_ylabel('Temperature Difference (°C)',fontsize=6)
ax2.legend(loc = 8, fontsize=6, frameon = False)
ax3.set_xlim([0, 365])
ax3.set_xtickvalues=[15,43,75,105,135,165,195,225,255,285,315,345]
ax3.set_ytickvalues=[-8,-6,-4,-2,0,2,4]
ax3.set_xticks(xtickvalues)
ax3.set_xticklabels(months,fontsize=6)
ax3.set_yticks(np.arange(-6, 6, 2))
ax3.set_yticklabels(np.arange(-6, 6, 2),fontsize=6)
ax3.plot(range(0,365), diff_max.rolling(5).mean(), color='lightcoral',label='Temperature diff')
ax3.axhline(y=mean_max, color='red', linestyle='dashed', label='Mean temp difference '+str(round(mean_max,2))+'°C')
ax3.set_title('Temperature Difference Manresa vs Barcelona \n using max temp records 2017-2021', fontsize=8, fontweight ='bold')
ax3.set_ylabel('Temperature Difference (°C)',fontsize=6)
ax3.legend(loc = 8, fontsize=6, frameon = False)
plt.savefig('final.png')