Open the app in focus mode: https://matlab.mathworks.com/open/github/v1?repo=yanndebray/weather&file=weather.mlapp&focus=true
Open the live script in focus mode: https://matlab.mathworks.com/open/github/v1?repo=yanndebray/weather&file=livescript.mlx&focus=true
apiKey ="b1b15e88fa797225412429c1c50c122a1";
% Define the location for which you want the weather forecast
cityName = "Muenchen";
countryCode = "DE";
% Create the URL for the API call
url = sprintf("http://samples.openweathermap.org/data/2.5/forecast?q=%s,%s&appid=%s", cityName, countryCode, apiKey);
% Send the web request to the OpenWeatherMap API
response = webread(url);
% Assuming "response" is the struct obtained from the OpenWeatherMap API
data = parseForecast(response.list);
data = struct2table(data);
data = table2timetable(data);
if false
data
end % Retime timetable
TT = retime(data(:,[1 3 4]),"regular","linear","TimeStep",hours(1));
% Retime each override variable to same time vector
temp2.Vars = ["weather","details"];
temp2.Methods = ["previous","previous"];
temp2.Locations = [2 5];
for k = 1:2
TT2 = retime(data(:,temp2.Vars(k)),TT.Properties.RowTimes, ...
temp2.Methods(k));
TT = addvars(TT,TT2.(1),'Before',temp2.Locations(k), ...
'NewVariableNames',temp2.Vars(k));
end
clear temp2 k TT2
% Display results
TT| date | temperature | weather | clouds | wind | details | |
|---|---|---|---|---|---|---|
| 1 | 16-Feb-2017 12:00:00 | 13.5200 | 'Clear' | 0 | 1.8100 | 'clear sky' |
| 2 | 16-Feb-2017 13:00:00 | 13.1833 | 'Clear' | 0 | 1.7367 | 'clear sky' |
| 3 | 16-Feb-2017 14:00:00 | 12.8467 | 'Clear' | 0 | 1.6633 | 'clear sky' |
| 4 | 16-Feb-2017 15:00:00 | 12.5100 | 'Clear' | 0 | 1.5900 | 'clear sky' |
| 5 | 16-Feb-2017 16:00:00 | 9.6400 | 'Clear' | 0 | 1.5300 | 'clear sky' |
| 6 | 16-Feb-2017 17:00:00 | 6.7700 | 'Clear' | 0 | 1.4700 | 'clear sky' |
| 7 | 16-Feb-2017 18:00:00 | 3.9000 | 'Clear' | 0 | 1.4100 | 'clear sky' |
| 8 | 16-Feb-2017 19:00:00 | 2.4767 | 'Clear' | 0 | 1.6867 | 'clear sky' |
| 9 | 16-Feb-2017 20:00:00 | 1.0533 | 'Clear' | 0 | 1.9633 | 'clear sky' |
| 10 | 16-Feb-2017 21:00:00 | -0.3700 | 'Clear' | 0 | 2.2400 | 'clear sky' |
| 11 | 16-Feb-2017 22:00:00 | -0.1830 | 'Clear' | 25.3333 | 2.6900 | 'clear sky' |
| 12 | 16-Feb-2017 23:00:00 | 0.0040 | 'Clear' | 50.6667 | 3.1400 | 'clear sky' |
| 13 | 17-Feb-2017 00:00:00 | 0.1910 | 'Clouds' | 76 | 3.5900 | 'broken clouds' |
| 14 | 17-Feb-2017 01:00:00 | 0.9333 | 'Clouds' | 76 | 3.6500 | 'broken clouds' |
X = linspace(1,height(TT),height(TT));
y = TT.temperature;
model1 = fitlm(X,y)model1 =
Linear regression model:
y ~ 1 + x1
Estimated Coefficients:
Estimate SE tStat pValue
_________ ________ _______ __________
(Intercept) 4.7728 0.71913 6.6369 1.4829e-09
x1 -0.061145 0.011668 -5.2404 8.4389e-07
Number of observations: 106, Error degrees of freedom: 104
Root Mean Squared Error: 3.68
R-squared: 0.209, Adjusted R-Squared: 0.201
F-statistic vs. constant model: 27.5, p-value = 8.44e-07
% plot(model1)
save("model1.mat","model1")
n = 6;
X = linspace(1,height(TT),height(TT));
y = TT.temperature;
p = polyfit(X,y,n);
y1 = polyval(p,X);
plot(X,y,X,y1)
function forecastData = parseForecast(responseList)
% Initialize an array of structs to hold the parsed forecast data
forecastData = struct("date", {}, "temperature", {}, "weather", {}, "clouds", {}, "wind", {}, "details", {});
% Loop through each entry in the responseList cell array
for i = 1:length(responseList)
% Get the current forecast struct
forecastStruct = responseList{i};
% Extract the date and time of the forecast
forecastData(i).date = datetime(forecastStruct.dt, "ConvertFrom", "posixtime");
% Extract main forecast data like temperature
forecastData(i).temperature = forecastStruct.main.temp - 273.15;
% Extract weather conditions
forecastData(i).weather = forecastStruct.weather(1).main;
% Extract cloud data
forecastData(i).clouds = forecastStruct.clouds.all;
% Extract wind data
forecastData(i).wind = forecastStruct.wind.speed;
% Extract additional details if needed
forecastData(i).details = forecastStruct.weather(1).description;
end
endfunction y = linReg(X)
mdl = load("model1.mat");
model = mdl.model1;
y = predict(model,X);
- Export Live Script to Markdown
export("livescript.mlx","README.md")
- Add Open in MATLAB Online badge
[](https://matlab.mathworks.com/open/github/v1?repo=yanndebray/weather&file=livescript.mlx)
- Add Focused View:
Open the app in focus mode: https://matlab.mathworks.com/open/github/v1?repo=yanndebray/weather&file=weather.mlapp&focus=true
Open the live script in focus mode: https://matlab.mathworks.com/open/github/v1?repo=yanndebray/weather&file=livescript.mlx&focus=true