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def calc_ime(plume_da):
    molar_volume = 22.4 # L/mol at STP
    molar_mass_ch4 = 0.01604 #kg/mol

    kg = plume_da * (1/1e6) * (60*60) * (1000) * (1/molar_volume) * molar_mass_ch4
    ime = np.nansum(kg)
    return ime

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import os
import pytorch_lightning as pl
from typing import Optional, Tuple, List

import kornia.augmentation as K
from torch.utils.data import DataLoader, WeightedRandomSampler
from starcop.data import dataset
import pandas as pd
from . import feature_extration
import rasterio.windows

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# more in https://github.com/spaceml-org/georeader/blob/main/notebooks/reading_overlapping_sentinel2_aviris.ipynb
from georeader.rasterio_reader import RasterioReader
from georeader import read

def same_location_as_source_from_target(source_tif, target_tif, show=False):
    
    src_reader = RasterioReader(source_tif)
    src_reader_in_memory = src_reader.load()

    target_reader = RasterioReader(target_tif)

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# from an example file with a plume event
#    EMIT_L1B_RAD_001_20220823T074504_2223505_021.nc


EMIT_wavelenghts = ['381.00558', '388.4092', '395.81583', '403.2254', '410.638', '418.0536', '425.47214', '432.8927', '440.31726', '447.7428', '455.17035', '462.59888', '470.0304', '477.46292', '484.89743', '492.33292', '499.77142', '507.2099', '514.6504', '522.0909', '529.5333', '536.9768', '544.42126', '551.8667', '559.3142', '566.7616', '574.20905', '581.6585', '589.108', '596.55835', '604.0098', '611.4622', '618.9146', '626.36804', '633.8215', '641.2759', '648.7303', '656.1857', '663.6411', '671.09753', '678.5539', '686.0103', '693.4677', '700.9251', '708.38354', '715.84094', '723.2993', '730.7587', '738.2171', '745.6765', '753.1359', '760.5963', '768.0557', '775.5161', '782.97754', '790.4379', '797.89935', '805.36176', '812.8232', '820.2846', '827.746', '835.2074', '842.66986', '850.1313', '857.5937', '865.0551', '872.5176', '879.98004', '887.44147', '894.90393', '902.3664', '909.82886', '917.2913', '924.7538

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The run.sh bash file should do everything for you, you just need to have an audio sample (in wav format) and the Nvidia docker installed (https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)

Download the run.sh command and give it executible rights:
chmod +x run.sh

Then simply run

./run.sh sample.wav 16

This will train a model for 16 epochs and then run an interactive real-time handler with it.

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from PIL import Image
from torchvision import datasets, transforms
import torch
import torch.utils.data as data
import urllib.request
import scipy.io
import os
import imageio
import numpy as np
from os import listdir

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# Based on implementations
# - vae core https://github.com/pytorch/examples/blob/master/vae/main.py
# - miwae https://github.com/yoonholee/pytorch-vae
# - notes on VAE from the article at https://iopscience.iop.org/article/10.3847/PSJ/ab9a52 (but can be taken from elsewhere too)

from __future__ import print_function
import argparse
import torch
import torch.utils.data
from torch import nn, optim

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# https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_gui/py_trackbar/py_trackbar.html#trackbar

import cv2
import numpy as np

class GUIHandler(object):

    def onChangeSend(self,x):

        self.A = cv2.getTrackbarPos('A', self.window_name)

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# v1

import mock
settings = mock.Mock()
settings.foo = 42


# v2
# works even when checking for non-existing entries...