Astro-Pi Experience Report

In this experience we verified whether there was a correlation between the strength of the Earth's magnetic field and the formation of low clouds.

Contributing

Thanks for the help provided:

• ESA for this wonderful initiative that allowed us to identify ourselves as two space explorers.
• Our supervisor Simone Conradi for the support given.
• ITIS Mario Delpozzo for the opportunity it has given us.
• The MyWay Team for providing us with their data.

Code Snippets

Snippet 1:

This is the function that has the task of extracting useful data from the images.

def calculateAreascaling(imagefile):
    img = cv.imread(imagefile, cv.IMREAD_COLOR)
    height,width=img.shape[:2]
    start_row,start_col=int(height*0.2),int(width*0.2)
    end_row,end_col=int(height*0.8),int(width*0.8)
    cropped=img[start_row:end_row,start_col:end_col]
    gray_img = cv.cvtColor(cropped, cv.COLOR_BGR2GRAY)
    img_area = np.shape(gray_img)[0]*np.shape(gray_img)[1]
    ret,thresh = cv.threshold(gray_img,130,255,cv.THRESH_BINARY)
    contours, _ = cv.findContours(thresh, cv.RETR_LIST, cv.CHAIN_APPROX_NONE )
    if len(contours)>0:
        contours = [c for c in contours if cv.arcLength(c,False)>50]
    else:
        contours = []
    if len(contours)>0:   
        tot_area = np.sum([cv.contourArea(cnt) for cnt in contours])
        tot_perimeter = np.sum([cv.arcLength(cnt,True) for cnt in contours])
        scaling = [np.log(cv.contourArea(cnt))/np.log(cv.arcLength(cnt,True)) for cnt in contours]
        mean_scaling = np.average(scaling)
        tot_num = len(contours)
        num_ge_11 = len([s for s in scaling if s >= 1.1])
        return [tot_perimeter/tot_num, tot_area/tot_num, mean_scaling, num_ge_11]
    else:
        return [0.0, 0.0 ,0.0 ,0.0]

Snippet 2:

This is the function that has the task of obtaining the value of the Earth's magnetic field given:

• latitude
• longitude
• acquisition of date and time of the data

Thanks to the help of the site NCEI Magnetic Field Calculators.

def getMagnetometricSensor():
    lat = getLatitude()
    lon = getLongitude()

    output = requests.get(f"https://www.ngdc.noaa.gov/geomag-web/calculators/calculateIgrfwmm?lat1={lat}&lon1={lon}&model=WMM&startYear=20{getDate(0)}&startMonth={getDate(2)}&startDay={getDate(4)}&endYear=20{getDate(0)}&endMonth={getDate(2)}&endDay={getDate(4)}&resultFormat=json")
    print(f'z={output.text.split(",")[4].split(":")[1]} y={output.text.split(",")[13].split(":")[1]} x={output.text.split(",")[21].split(":")[1]}')
    return f'z={output.text.split(",")[4].split(":")[1]} y={output.text.split(",")[13].split(":")[1]} x={output.text.split(",")[21].split(":")[1]}'

Snippet 3:

This is the function that has the purpose of creating graphics in the shape of a planisphere using a pandas dataframe.

def graficoPlanisfero(df):

    gdf = geopandas.GeoDataFrame(df, geometry=geopandas.points_from_xy(df["lon"], df["lat"]))

    world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))

    ax = world.plot(color='white', edgecolor='black', figsize=(40,40))

    gdf.plot(ax=ax, marker="o", markersize=15, color = df["colori"])

Insights

For machine learning we used the sklearn.ensemble library and in particular the RandomForestClassifier module which allowed us to recognize the type of clouds.

Graphs

Graphic CMC

Graphic MyWay

Planisphere CMC

Planisphere MyWay

Warning

To execute the code you need to have the images and arrange all the files in a single folder.

Conclusion

In conclusion, based on the data returned, we can say that there is a correlation between the intensity of the Earth's magnetic field and the formation of low clouds.

For more information see the document.

Authors

• Gabriele Ferrero
• Isabella Bianco