import os import fire import h5py import glob import tqdm import numpy as np import pandas as pd import geopandas as gpd

requires fire, h5py, tqdm, numpy, pandas, and geopandas

def gedi_to_vector(file,variables=None,outFormat='CSV',filterBounds=None): # open hdf5 file data = h5py.File(file,'r')

# get full file name and extension
name,_ = os.path.splitext(file)

# create empty dataframe to append data to
df = pd.DataFrame()

# loop over all of the hdf5 groups
for k in list(data.keys()):
    # if BEAM in the group name
    if 'BEAM' in k:
        # get the geolocation subgroup
        geo = data[k]['geolocation']
        d = {}
        # loop through all of the variables defined earlier
        for var in variables:
            # assign variable array to dict key
            d[var] = np.array(geo[var])
        # convert dict of varaibles to dataframe
        tdf = pd.DataFrame(d)
        # concat to larger dataframe
        df = pd.concat([df,tdf],axis=0,sort=False)

# check if the the filterBounds is provided
if filterBounds is not None:
    w,s,e,n = filterBounds # expand list to individual variables
    # select features on X axis
    horizontalMask = (df.longitude_bin0 >= w) & (df.longitude_bin0 <= e)
    # select features on Y axis
    verticalMask = (df.latitude_bin0 >= s) & (df.latitude_bin0 <= n)
    # combines masks to select features that intersect
    spatialMask = verticalMask & horizontalMask
    # grab only masked values within the bounds provided
    df = df.loc[spatialMask]

# check to make sure that the dataframe has values.
# if not, then return from function without saving df
if df.size == 0:
    return

if outFormat in ['CSV','csv']:
    # save dataframe of parsed variables to CSV file
    df.to_csv('{}.{}'.format(name,outFormat.lower()),index=False)

else:
    # check if df has the geoinformation
    if ('latitude_bin0' not in df.columns) or ('longitude_bin0' not in df.columns):
        raise KeyError("Geospatial variables 'latitude_bin0' and/or 'longitude_bin0' were not found, "
            "please specify these variables to be extracted when writing to geospatial format")

    # convert to geodataframe
    gdf = gpd.GeoDataFrame(
        df, geometry=gpd.points_from_xy(df.longitude_bin0, df.latitude_bin0))
    # save the geodataframe of variables to file
    gdf.to_file('{}.{}'.format(name,outFormat.lower()))

return

def main(path,variables=None,verbose=False,outFormat='CSV',filterBounds=None): # check if the variables to extract have been defined if variables is None: raise ValueError("Please provide variables from the GEDI file to convert")

# if variables have been defined, check if provided in correct datetype
if type(variables) is not list:
    raise TypeError("Provided variables is not list, please provide argument as '[<var1>,<var2>,<var3>]'")

# check if filterBounds have been provided and in correct datatype
if (filterBounds is not None) and (type(filterBounds) is not list):
    raise TypeError("Provided filterBounds is not list, please provide argument as '[W,S,E,N]'")

# check if the output format provided is supported by script
availableFormats = ['CSV','SHP','GeoJSON','GPKG','csv','shp','geojson','gpkg']
if outFormat not in availableFormats:
    raise NotImplementedError('Selected output format is not support please select one of the following: "CSV","SHP","GeoJSON","GPKG"')

# check if path provided is a file or folder
if os.path.isfile(path):
    flist = [path]
else:
    # only search for h5 files in the path provided
    flist = glob.glob(os.path.join(path,'*.h5'))

if verbose:
    print('\n')
    t = tqdm.tqdm(total=len(flist))

# loop through the files and do the conversion
for i,f in enumerate(flist):
    if verbose:
        _, desc = os.path.split(f)
        t.set_description(desc="Processing {}".format(desc))

    gedi_to_vector(f,variables,outFormat,filterBounds)

    if verbose:
        t.update(i+1)

return

if name == "main": fire.Fire(main)