GeosysPy/notebook_utils.py at develop · earthdaily/GeosysPy · GitHub

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import boto3
import os
from urllib.parse import urlparse
from io import StringIO
import glob
import json
import pandas as pd
import matplotlib.pyplot as plt

def read_mrts_data(str_path):
    """ Read MRTS outputs dowloaded from AWS
        return dict containing path on files and dict containing data

    Args:
        str_path (_string_): path containing MRTS files

    Returns:
        data_files: data_files = {
                "metadata": metadata_file path,
                "raw_df" : raw data path,
                "df" : df denoised path,
                "timeserie" : ts path
            }
        data_files: dict_data = {
                "metadata" : metadata in json,
                "raw_df" : dataframe containing raw data,
                "df" : dataframe containing data denoised if no smmother,
                "ts" : dataframe containing timeserie denoised if smmother
            }

    """
    path_data = os.path.join(str_path.split('/')[-3],str_path.split('/')[-2],str_path.split('/')[-1])

    metadata_file = glob.glob(os.path.join(path_data,"*.json"))[0]
    with open(metadata_file, 'r') as f:
        metadata = json.load(f)

    str_index = metadata['parameters']['index']
    str_func = metadata['parameters']['aggregation']
    str_smoother = metadata['parameters']['smoother']
    raw_data = metadata['parameters']['raw_data']

    data_files = {
                "metadata": metadata_file,
                "raw_df" : glob.glob(os.path.join(path_data,"MRTS-" + str_index + str_func + "-Raw-2*.csv"))[0] if raw_data else None,
                "df" : glob.glob(os.path.join(path_data,"MRTS-" + str_index + str_func + "-2*.csv"))[0] if str_smoother == "false" else None,
                "timeserie" : glob.glob(os.path.join(path_data,"MRTS-" + str_index + str_func + "-2*.csv"))[0] if str_smoother != "false" else None
            }
    dict_data = {
        "metadata" : metadata,
        "raw_df" : pd.read_csv(data_files['raw_df'], sep=",") if raw_data else None,
        "df" : pd.read_csv(data_files['df'], sep=",") if str_smoother == "false" else None,
        "ts" : pd.read_csv(data_files['timeserie'], sep=",") if str_smoother != "false" else None
    }

    return data_files, dict_data

def plot_mrts_dict(dict_data):
    """ Plot MRTS outputs from the dict generated by the function read_mrts_data_from_awsS3
    or the function read_mrts_data

    Args:
        dict_data (_dictionnaire_): dict_data = {
                "metadata" : metadata in json,
                "raw_df" : dataframe containing raw data,
                "df" : dataframe containing data denoised if no smmother,
                "ts" : dataframe containing timeserie denoised if smmother
            }
    """
    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10,6))
    metadata = dict_data['metadata']
    index = metadata['parameters']['index']

    if index != 'reflectances':
        if dict_data['raw_df'] is not None:
            raw_df = dict_data['raw_df']
            raw_df['date'] = pd.to_datetime(raw_df['date'], format='%Y-%m-%d')
            raw_df = raw_df.set_index('date')
            plt.plot(raw_df[index] ,'.', markersize=12, label=index)
            plt.plot(raw_df[index].loc[raw_df['denoised'] == True] ,'x', color='r', markersize=12, label='Denoised')
        if dict_data['df'] is not None:
            df = dict_data['df']
            df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')
            df = df.set_index('date')
            plt.plot(df['vegetation_index'] ,'.', markersize=12, label=index)
        if dict_data['ts'] is not None:
            df_smooth = dict_data['ts']
            df_smooth['date'] = pd.to_datetime(df_smooth['date'], format='%Y-%m-%d')
            df_smooth = df_smooth.set_index('date')
            plt.plot(df_smooth, linewidth=2, label='Smooth ' + index)
    else:
        if dict_data['raw_df'] is not None:
            raw_df = dict_data['raw_df']
            raw_df['date'] = pd.to_datetime(raw_df['date'], format='%Y-%m-%d')
            raw_df = raw_df.set_index('date')
            plt.plot(raw_df['nir'] ,'.', markersize=12, label='nir')
            plt.plot(raw_df['nir'].loc[raw_df['denoised'] == True] ,'x', color='r', markersize=12, label='Denoised')
            plt.plot(raw_df['red'] ,'.', markersize=12, label='red')
            plt.plot(raw_df['red'].loc[raw_df['denoised'] == True] ,'x', color='r', markersize=12)
            plt.plot(raw_df['green'] ,'.', markersize=12, label='green')
            plt.plot(raw_df['green'].loc[raw_df['denoised'] == True] ,'x', color='r', markersize=12)
            plt.plot(raw_df['blue'] ,'.', markersize=12, label='blue')
            plt.plot(raw_df['blue'].loc[raw_df['denoised'] == True] ,'x', color='r', markersize=12)
        if dict_data['df'] is not None:
            df = dict_data['df']
            df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')
            df = df.set_index('date')
            plt.plot(df['nir'] ,'.', markersize=12, label=index)
            plt.plot(df['red'] ,'.', markersize=12, label=index)
            plt.plot(df['green'] ,'.', markersize=12, label=index)
            plt.plot(df['blue'] ,'.', markersize=12, label=index)
        if dict_data['ts'] is not None:
            df_smooth = dict_data['ts']
            df_smooth['date'] = pd.to_datetime(df_smooth['date'], format='%Y-%m-%d')
            df_smooth = df_smooth.set_index('date')
            plt.plot(df_smooth['nir'], linewidth=2, label='Smooth ' + 'nir')
            plt.plot(df_smooth['red'], linewidth=2, label='Smooth ' + 'red')
            plt.plot(df_smooth['green'], linewidth=2, label='Smooth ' + 'green')
            plt.plot(df_smooth['blue'], linewidth=2, label='Smooth ' + 'blue')

    if index == 'ndvi':
        plt.ylim((0,1))
    elif index == 'evi':
        plt.ylim((0,1))
    elif index == 'cvin':
        plt.ylim((0,1))
    elif index == 'gndvi':
        plt.ylim((0,1))
    elif index == 'ndwi':
        plt.ylim((0,1))
    elif index == 'reflectances':
        plt.ylim((0,1))
    elif index == 's2rep':
        plt.ylim((710,730))
    ax.set_ylabel(index)
    plt.legend()
    plt.grid(True)
    plt.show()

def read_mrts_from_awsS3(str_s3_path):
    """ Read MRTS outputs direclty from S3 AWS
        return dict containing path on files and dict containing data

    Args:
        str_s3_path (_string_): URI S3 (aws)

    Returns:
        data_files: dict_data = {
                "metadata" : metadata in json,
                "raw_df" : dataframe containing raw data,
                "df" : dataframe containing data denoised if no smmother,
                "ts" : dataframe containing timeserie denoised if smmother
            }

    """
    s3 = boto3.resource('s3')
    parsed_url = urlparse(str_s3_path)
    bucket_name = parsed_url.netloc
    directory_name = parsed_url.path.lstrip('/')
    bucket = s3.Bucket(bucket_name)
    s3 = boto3.client('s3')
    for obj in bucket.objects.filter(Prefix=directory_name):
        response = s3.get_object(Bucket=bucket_name, Key=obj.key)
        file_content = response['Body'].read().decode('utf-8')
        if obj.key.split('/')[-1].split('.')[-1] == 'json' :
            # Utilisez la méthode `get_object` pour récupérer l'objet du fichier
            metadata = json.load(StringIO(file_content))
        elif obj.key.split('/')[-1].split('.')[-1] == 'csv' :
            if 'Raw' in obj.key.split('/')[-1] :
                csv_data = StringIO(file_content)
                raw_df = pd.read_csv(csv_data, sep=",")
            else :
                csv_data = StringIO(file_content)
                df = pd.read_csv(csv_data, sep=",")

    str_smoother = metadata['parameters']['smoother']
    raw_data = metadata['parameters']['raw_data']

    dict_data = {
        "metadata" : metadata,
        "raw_df" : raw_df if raw_data else None,
        "df" : df if str_smoother == "false" else None,
        "ts" : df if str_smoother != "false" else None
    }
    return dict_data

def plot_mrts_from_aws(str_s3_path):
    """ Plot MRTS outputs stored on S3 AWS
    Args:
        str_s3_path (_string_): URI S3 (aws)
    """
    dict_data = read_mrts_from_awsS3(str_s3_path)
    plot_mrts_dict(dict_data)

def download_s3_files(str_s3_path):
    """download files stored on aws S3 bucket

    Args:
        str_s3_path (_string_): URI S3 (aws)
    """
    s3 = boto3.resource('s3')
    parsed_url = urlparse(str_s3_path)
    bucket_name = parsed_url.netloc
    directory_name = parsed_url.path.lstrip('/')
    bucket = s3.Bucket(bucket_name)
    for obj in bucket.objects.filter(Prefix=directory_name):
        if not os.path.exists(os.path.dirname(obj.key)):
            os.makedirs(os.path.dirname(obj.key))
        bucket.download_file(obj.key, obj.key)