NimishMishra

#Lists all subdatasets of any one file
file = gdal.Open('/Users/nimishmishra/Downloads/Kanpur MODIS Data/November/2018.11.18.hdf')
for path, desc in file.GetSubDatasets():
    print(desc)

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def load_data(FILEPATH):
    ds = gdal.Open(FILEPATH)
    return ds
    
# Opens the data HDF file and returns as a dataframe
def read_dataset(SUBDATASET_NAME, FILEPATH):
    dataset = load_data(FILEPATH)
    path = ''
    for sub, description in dataset.GetSubDatasets():
        if (description.endswith(SUBDATASET_NAME)):

NimishMishra

# Loads the HDF file, gets the Latitude subdataset and returns the information of the nearest pixel to specified position
def find_latitude_position(CITY_LATITUDE, FILEPATH):
    dataset_to_load = 'Latitude (32-bit floating-point)'
    latitude_dataframe = read_dataset(dataset_to_load, FILEPATH)
    min_diff = 100
    row_number = -1
    column_number = -1
    rows = latitude_dataframe.shape[0]
    columns = latitude_dataframe.shape[1]
    for i in range(rows):

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# Loads the HDF file, gets the Longitude subdataset and returns the information of the nearest pixel to specified position
def find_longitude_position(CITY_LONGITUDE, LATITUDE_ROW_NUMBER, FILEPATH):
    dataset_to_load = 'Longitude (32-bit floating-point)'
    longitude_dataframe = read_dataset(dataset_to_load, FILEPATH)
    min_diff = 100
    row_number = -1
    column_number = -1
    rows = longitude_dataframe.shape[0]
    columns = longitude_dataframe.shape[1]
    for j in range(columns):

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def find_product_value(LATITUDE_ROW_NUMBER, LONGITUDE_COLUMN_NUMBER, SUBDATASET, FILEPATH):
    dataset = read_dataset(SUBDATASET, FILEPATH)
    if(LATITUDE_ROW_NUMBER < 0 or LONGITUDE_COLUMN_NUMBER < 0):
        return -1
    return dataset.iloc[LATITUDE_ROW_NUMBER][LONGITUDE_COLUMN_NUMBER]

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# Creates a list of values of the product under observation across all datasets
def create_list(PRODUCT, CITY_LATITUDE, CITY_LONGITUDE, FILEPATH):
    latitude_dataframe, lat_row, lat_column = find_latitude_position(CITY_LATITUDE, FILEPATH)
    if lat_row == -1:
        return -1
    longitude_dataframe, lon_column = find_longitude_position(CITY_LONGITUDE, lat_row, FILEPATH)
    city_row_number = lat_row
    city_column_number = lon_column
    if(PRODUCT == 'AOD'):
        subdataset = 'Deep_Blue_Aerosol_Optical_Depth_550_Land mod04 (16-bit integer)'

NimishMishra

import glob
scattering_angle = []
deep_blue_AOD_Land = []
cloud_fraction = []
file_names = []
latitude = []
longitude = []
combined_AOD = []
angstrom_exponent = []
directory = '/Users/nimishmishra/Downloads/Kanpur MODIS Data/November' 

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def plot(x_axis, y_axis_1, y_axis_2, label1, label2, directory, month):
    figure = plt.figure()
    ax = sns.lineplot(x=date, y=y_axis_1)
    ax = sns.lineplot(x=x_axis, y=y_axis_2)
    figure.legend(labels = [label1, label2])
    plt.show()
    path = (directory + month + '/') + month + label1 + label2 + '.png'
    fig = ax.get_figure()
    fig.savefig(path)
    

NimishMishra

import glob
import seaborn as sns
import matplotlib.pyplot as plt
sns.set()
month_dict = {'Jan': 1, 'Feb': 2, 'Mar': 3, 'Apr': 4, 'May': 5, 'Jun': 6, 'Jul': 7, 'Aug': 8, 'Sep': 9, 'Oct': 10, 'Nov':11, 'Dec':12}
mean_month_list = []
mean_AOD_list = []
csv_directory = '/Users/nimishmishra/Downloads/Kanpur MODIS Data/CSV Files/'
for filename in glob.glob(csv_directory + '*.csv'):
    month = filename[63:66]

NimishMishra

import socket

connection = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

connection.bind((socket.gethostname(), 8000))

connection.listen(5)

while True:
    connectedsocket, address = connection.accept()