From 345669a19866b90e8ae751e8f4813f7facdca798 Mon Sep 17 00:00:00 2001 From: Alex Rojas <a.rojas8907@gmail.com> Date: Thu, 14 Mar 2024 18:03:29 -0700 Subject: [PATCH] updated build-env.sh to use conda instead of mamba. Edited the GapDS code using original code from forest server. --- .../GEDI-L1B-2021-URLS-checkpoint.txt | 524 ++++++ .../GEDI-L2A-2021-URLS-checkpoint.txt | 524 ++++++ ...MAINS-HSE-2020to2023-edited-checkpoint.csv | 20 + .ipynb_checkpoints/build-env-checkpoint.sh | 19 + .ipynb_checkpoints/environment-checkpoint.yml | 16 + .../get_gedi_data-checkpoint.py | 35 + .ipynb_checkpoints/main-checkpoint.py | 230 +++ .ipynb_checkpoints/pgap-checkpoint.py | 322 ++++ .ipynb_checkpoints/run-checkpoint.sh | 17 + GEDI-L1B-2021-URLS.txt | 4 - GEDI-L2A-2021-URLS.txt | 1050 ++++++------ NEON-DOMAINS-HSE-2020to2023-edited.csv | 2 +- __pycache__/get_gedi_data.cpython-312.pyc | Bin 1920 -> 1911 bytes __pycache__/pgap.cpython-312.pyc | Bin 13781 -> 13781 bytes build-env.sh | 9 +- main.py | 68 +- .../run-gedi-biomass-checkpoint.ipynb | 1508 ++++++++++++++++- notebooks/run-gedi-biomass.ipynb | 1215 ++++++++++++- pgap.py | 6 +- run.sh | 4 +- 20 files changed, 4938 insertions(+), 635 deletions(-) create mode 100644 .ipynb_checkpoints/GEDI-L1B-2021-URLS-checkpoint.txt create mode 100644 .ipynb_checkpoints/GEDI-L2A-2021-URLS-checkpoint.txt create mode 100644 .ipynb_checkpoints/NEON-DOMAINS-HSE-2020to2023-edited-checkpoint.csv create mode 100644 .ipynb_checkpoints/build-env-checkpoint.sh create mode 100644 .ipynb_checkpoints/environment-checkpoint.yml create mode 100644 .ipynb_checkpoints/get_gedi_data-checkpoint.py create mode 100644 .ipynb_checkpoints/main-checkpoint.py create mode 100644 .ipynb_checkpoints/pgap-checkpoint.py create mode 100644 .ipynb_checkpoints/run-checkpoint.sh diff --git a/.ipynb_checkpoints/GEDI-L1B-2021-URLS-checkpoint.txt b/.ipynb_checkpoints/GEDI-L1B-2021-URLS-checkpoint.txt new file mode 100644 index 0000000..5836dfd --- /dev/null +++ b/.ipynb_checkpoints/GEDI-L1B-2021-URLS-checkpoint.txt @@ -0,0 +1,524 @@ +https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.05.31/GEDI01_B_2021151223415_O13976_02_T00676_02_005_02_V002.h5 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/dev/null +++ b/.ipynb_checkpoints/NEON-DOMAINS-HSE-2020to2023-edited-checkpoint.csv @@ -0,0 +1,20 @@ +domain,alpha,beta,a,b,alpha_R2,alpha_rmse,beta_R2,beta_rmse,HSE,k +D01,2.1572,0.6012,0.3861,2.4172,0.7948,0.6186,0.6737,0.2475,2.1698,0.1374 +D02,2.271,0.6878,0.3844,1.8789,0.8567,0.6814,0.8163,0.2526,1.6368,0.4157 +D03,2.41,0.5978,0.1554,1.9862,0.7237,1.0622,0.6068,0.3779,1.9355,0.6481 +D04,3.4556,0.3366,0.0487,2.5282,0.553,1.2323,0.3851,0.2721,3.4865,0.0829 +D05,2.4488,0.6189,0.1375,2.3758,0.8349,0.6954,0.7739,0.2372,1.7829,0.4439 +D06,2.8204,0.5321,0.1342,2.0938,0.8734,0.7112,0.6918,0.3004,1.9383,0.4634 +D07,2.3704,0.631,0.2226,2.2403,0.8375,0.75,0.8015,0.2866,1.799,0.3486 +D08,2.6336,0.636,0.1501,2.1019,0.8372,0.8452,0.7857,0.2568,1.5109,0.6445 +D09,,,,,,,,,1.56, +D10,1.8393,0.5969,0.2969,1.6349,0.5313,0.8107,0.597,0.2815,2.5111,0.6488 +D11,2.9187,0.5095,0.1542,1.6502,0.8263,0.6523,0.6997,0.249,2.0065,0.9081 +D12,2.8231,0.5426,0.0492,1.9606,0.8793,0.7181,0.8643,0.2395,1.8628,1.7008 +D13,2.229,0.7632,0.1119,0.91,0.738,0.7827,0.6317,0.3532,1.3917,11.4434 +D14,,0.8936,,0.5258,,,0.4397,0.2172,1.56, +D15,2.3559,0.155,0.7807,2.3267,0.4221,0.7376,0.0872,0.2551,1.8628,0.0 +D16,2.1004,0.801,0.3335,1.3291,0.8866,0.6551,0.8748,0.2875,1.3965,1.4735 +D17,2.2601,0.7142,0.2322,1.0253,0.754,1.0605,0.813,0.3402,1.5404,4.0151 +D19,2.7256,0.6659,0.0487,1.4185,0.8095,0.7548,0.6888,0.2925,1.2777,7.1902 +D20,2.7748,0.5558,0.0535,2.0948,0.737,1.1261,0.7135,0.3204,1.8238,1.3068 diff --git a/.ipynb_checkpoints/build-env-checkpoint.sh b/.ipynb_checkpoints/build-env-checkpoint.sh new file mode 100644 index 0000000..802ebb2 --- /dev/null +++ b/.ipynb_checkpoints/build-env-checkpoint.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +# source activate base +basedir=$( cd "$(dirname "$0")" ; pwd -P) +echo installing environment... +mamba env create --name osgeo-env -f ${basedir}/environment.yml +# conda create --name osgeo-env --clone base +# source activate osgeo-env +# mamba install -c conda-forge scipy -y +# mamba install -c conda-forge h5py -y +# mamba install -c conda-forge geopandas -y + +# Install the maap.py environment +echo trying to install maap-py... +source activate osgeo-env +git clone --single-branch --branch v3.0.1 https://github.com/MAAP-Project/maap-py.git +cd maap-py +pip install -e . +echo installed maap-py package! diff --git a/.ipynb_checkpoints/environment-checkpoint.yml b/.ipynb_checkpoints/environment-checkpoint.yml new file mode 100644 index 0000000..e3512e4 --- /dev/null +++ b/.ipynb_checkpoints/environment-checkpoint.yml @@ -0,0 +1,16 @@ +name: osgeo-env +channels: + - conda-forge + - defaults +dependencies: + - boto3 + - botocore + - fsspec + - s3fs + - geopandas + - geopandas-base + - h5py + - numpy + - pandas + - python + - scipy \ No newline at end of file diff --git a/.ipynb_checkpoints/get_gedi_data-checkpoint.py b/.ipynb_checkpoints/get_gedi_data-checkpoint.py new file mode 100644 index 0000000..13606fb --- /dev/null +++ b/.ipynb_checkpoints/get_gedi_data-checkpoint.py @@ -0,0 +1,35 @@ + +import sys +import h5py +import boto3 +import botocore +import fsspec +import requests +from maap.maap import MAAP +maap = MAAP(maap_host="api.maap-project.org") +import os + + +def lpdaac_gedi_https_to_s3(url): + dir_comps = url.split("/") + return f"s3://lp-prod-protected/{dir_comps[6]}/{dir_comps[8].strip('.h5')}/{dir_comps[8]}" + +def get_gedi_data(url): + credentials = maap.aws.earthdata_s3_credentials( + 'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials' + ) + + s3 = fsspec.filesystem( + "s3", + key=credentials['accessKeyId'], + secret=credentials['secretAccessKey'], + token=credentials['sessionToken'] + ) + basename = os.path.basename(url) + outfp = f"output/{basename}" + gedi_ds = h5py.File(s3.open(lpdaac_gedi_https_to_s3(url), "rb"), "r") + with h5py.File(outfp, 'w') as dst: + for obj in gedi_ds.keys(): + gedi_ds.copy(obj, dst) + gedi_ds.close() + return outfp \ No newline at end of file diff --git a/.ipynb_checkpoints/main-checkpoint.py b/.ipynb_checkpoints/main-checkpoint.py new file mode 100644 index 0000000..5cef74c --- /dev/null +++ b/.ipynb_checkpoints/main-checkpoint.py @@ -0,0 +1,230 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Mon Oct 24 10:20:58 2022 + +@author: arojas + +""" + +import pandas as pd +import numpy as np +import os +import sys +import geopandas as gpd +import h5py +import multiprocessing as mp +from pgap import GapDS +from pgap import wf_smooth +import re +import glob +from get_gedi_data import get_gedi_data + +##################### +## Global Parameters +##################### + +## GET CWD of file to locate path +CWD = os.path.dirname(os.path.abspath(__file__)) + +##################### +##################### + +## Function to return beam dataframe with some meta data +def get_beam_gdf(beam): + # Create gdf to filter for GEDI returns + shot_number = l1b_ds[beam]["shot_number"][:] + lat = l1b_ds[beam]['geolocation']['latitude_bin0'][:] + lon = l1b_ds[beam]['geolocation']['longitude_bin0'][:] + degrade = l1b_ds[beam]['geolocation']['degrade'][:] + sf = l1b_ds[beam]['stale_return_flag'][:] + l2_qf = l2a_ds[beam]['quality_flag'][:] + modis_nonveg = l2a_ds[beam]['land_cover_data']["modis_nonvegetated"][:] + + # create geodataframe to filter for each domain + beam_gdf = gpd.GeoDataFrame({"shot_number":shot_number, "lat":lat, "lon":lon, + "sf":sf, "degrade":degrade, "qf":l2_qf, "modis_nonveg":modis_nonveg}, + geometry=gpd.points_from_xy(lon,lat), + crs="EPSG:4326") + return beam_gdf + + +def gedi_bioindex(index): + wfCount = l1b_ds[beam]['rx_sample_count'][index] # Number of samples in the waveform + wfStart = int(l1b_ds[beam]['rx_sample_start_index'][index] - 1) # Subtract one because python array indexing starts at 0 not 1 + noise_mean_corrected = l1b_ds[beam]['noise_mean_corrected'][index] + # Grab the elevation recorded at the start and end of the full waveform capture + zStart = l1b_ds[f'{beam}/geolocation/elevation_bin0'][index] # Height of the start of the rx window + zEnd = l1b_ds[f'{beam}/geolocation/elevation_lastbin'][index] + # level 2 metrics + zGround = l2a_ds[beam]['elev_lowestmode'][index] # ground + zTop = l2a_ds[beam]['elev_highestreturn'][index] # top of canopy (rh100) + rh100 = l2a_ds[beam]['rh'][index][-2] + + ## Calculate bioindex from waveform + elev_arr = np.linspace(zStart, zEnd, wfCount) + # convert elev to ht by subtracting ground + ht_arr = elev_arr - zGround + + # Retrieve the waveform sds layer using the sample start index and sample count information to slice the correct dimensions + waveform = l1b_ds[beam]['rxwaveform'][wfStart: wfStart + wfCount] + waveform_noisermvd = waveform - noise_mean_corrected + # smooth waveform + waveform_smooth = wf_smooth(waveform_noisermvd) + + # get bioindex using HSE from NEON db analysis + beam_domain = beam_filt.loc[index]['DomainID'] + try: + if pd.isnull(beam_domain): + cval = 1.63 # + else: + cval = allom_df[allom_df['domain']==beam_domain]['HSE'].values[0] + + # get pgap + pgap = GapDS(waveform_smooth, ht_arr, np.array([rh100]), + calc_refl_vg = False, + utm_x=None,utm_y=None,cval=cval) + except: + # bad data (fix for future) + return (np.nan,np.nan,np.nan,np.nan,np.nan) + # return a tuple of biwf and bfp + return (pgap.biWF[0], pgap.biFP[0], np.nanmin(pgap.gap), np.nanmax(pgap.lai), rh100, cval) + + +########## +## Process entire CONUS data +########## +if __name__ == '__main__': + + # File handling + l1b_url = sys.argv[1] # first index is python file name, second is arg1, etc + l2a_url = sys.argv[2] # e.g. 'GEDI01_B' or 'GEDI02_A' + outdir = sys.argv[3] + + # Get filenames for downloaded gedi + l1b_basename = os.path.basename(l1b_url) + l2a_basename = os.path.basename(l2a_url) + + # Read in domain polys and allom data + domain_poly_fp = os.path.join(CWD, "NEON_Domains/NEON_Domains.shp") + domain_polys = gpd.read_file(domain_poly_fp) + allom_fp = os.path.join(CWD, "NEON-DOMAINS-HSE-2020to2023-edited.csv") + allom_df = pd.read_csv(allom_fp) + + + try: + # get level 1 and level 2 data + print(l1b_basename) + print(l2a_basename) + l1b_fp = get_gedi_data(l1b_url) + l2a_fp = get_gedi_data(l2a_url) + l1b_ds = h5py.File(l1b_fp, "r") + l2a_ds = h5py.File(l2a_fp, "r") + except Exception as e: + # Some raw L1B files are corrupt? + print("Corrupt file: ", l1b_basename) + print(e) + sys.exit() + + + # Init output file path + orbit_num = re.findall("O[0-9]{5}", l1b_basename)[0] + track_num = re.findall("T[0-9]{5}", l1b_basename)[0] + date_str = re.findall("[0-9]{13}", l1b_basename)[0] + # outfp = os.path.join(outdir, "output",f"GEDI_bioindex_{date_str}_{orbit_num}_{track_num}.csv") + outfp = os.path.join(outdir, f"GEDI_bioindex_{date_str}_{orbit_num}_{track_num}.csv") + + + #################### + ## Loop through each beam + #################### + + # init df_list to concat afterwards + df_list = [] + beamNames = [g for g in l1b_ds.keys() if g.startswith('BEAM')] + for beam in beamNames: + + # get the beam gdf + try: + beam_gdf = get_beam_gdf(beam) + except: + print(f"File {l1b_basename} has an issue at {beam}") + continue + + # quality flag filter + beam_gdf = beam_gdf.loc[beam_gdf['qf']==1] + + # Spatially filter beam_gdf (fastest approach) + beam_filt = gpd.sjoin(beam_gdf, domain_polys[["DomainID", 'geometry']], + predicate='intersects', how="inner") + if len(beam_filt)==0: + continue + + # get shotnumber indices + idx = beam_filt.index.to_list() + if not isinstance(idx, list): + idx = [idx] + + # run function in parallel (not available in MAAP) + # pool = mp.Pool(10) + # results = pool.map(gedi_bioindex, idx) + # results = pool.starmap(gedi_bioindex, zip(idx, repeat(l1b_ds), repeat(l2a_ds))) + + # biWF_list = [bi[0] for bi in results] + # biFP_list = [bi[1] for bi in results] + # gap_list = [bi[2] for bi in results] + # lai_list = [bi[3] for bi in results] + # rh_list = [bi[4] for bi in results] + + + biWF_list = [] + biFP_list = [] + gap_list = [] + lai_list = [] + rh_list = [] + cval_list = [] + print("Running pgap iteratively...") + for i in idx: + results = gedi_bioindex(i) + # save to lists + biWF_list.append(results[0]) + biFP_list.append(results[1]) + gap_list.append(results[2]) + lai_list.append(results[3]) + rh_list.append(results[4]) + cval_list.append(results[5]) + print("Done!") + + # Create out_df to output as csv + new_df = beam_filt[['shot_number', "lat", "lon", 'qf',"DomainID"]].copy().reset_index(drop=True) + ## Add new variables + new_df['beam'] = beam + new_df['biwf'] = np.round(biWF_list,3) + new_df['bifp'] = np.round(biFP_list,3) + new_df['gap'] = np.round(gap_list,3) + new_df['lai'] = np.round(lai_list,3) + new_df['rh'] = np.round(rh_list,3) + new_df['cval'] = np.round(cval_list,3) + + # append to df_list + df_list.append(new_df) + + #################### + #################### + + ## Save results + try: + out_df = pd.concat(df_list, ignore_index=True) + out_df.to_csv(outfp, index=False) + # Close h5py files and delete so they are not saved! + l1b_ds.close() + l2a_ds.close() + os.remove(l1b_fp) + os.remove(l2a_fp) + except Exception as e: + print("Couldnt save file: ", l1b_basename) + print("outdir: ", outdir) + print("outfp: ", outfp) + print(e) + sys.exit() + diff --git a/.ipynb_checkpoints/pgap-checkpoint.py b/.ipynb_checkpoints/pgap-checkpoint.py new file mode 100644 index 0000000..f91dfbd --- /dev/null +++ b/.ipynb_checkpoints/pgap-checkpoint.py @@ -0,0 +1,322 @@ +# Import libraries +import numpy as np +from scipy.spatial import KDTree +import warnings +warnings.filterwarnings("ignore") +from scipy import ndimage + +# --------------------------DATA STRUCTURE FOR PGAP PROCESSED WAVEFORM-------------------------- # + +class GapDS: + + + """Calculates gap probability and bioindex using full waveform lidar data. + + Parameters + ---------- + wf : ndarray + Input directory with LVIS level 1 (.h5) and level 2 (.TXT) files to be joined. + ht : ndarray + Output directory to save the joined LVIS files. Default is None, which output to the same directory as the input files. + rh : array-like + 1d array or list of RH values indicating canopy top. + calc_refl_vg : bool + Calculate reflectance of vegetation / reflectance of ground. Default is set to False and refl_v/refl_g attribute is set to 1. + + + """ + + + def __init__(self, wf, ht, rh, calc_refl_vg = False, + utm_x=None,utm_y=None,cval=2, **kwargs): + + + """ + Constructor method + """ + + self.wf = np.copy(wf) + if wf.ndim == 1: + self.wf = np.expand_dims(wf, axis=0) + self.ht = np.copy(ht) + if wf.ndim == 1: + self.wf = np.expand_dims(wf, axis=0) + self.rh = rh + self.c = cval + self.refl_vg = np.array([1.0] * len(self.wf)) # init to 1 + self.dz = self.ht[0][0] - self.ht[0][1] + + if calc_refl_vg is True: + self.utmx=utm_x + self.utmy=utm_y + self.refl_v = np.array([1.0] * len(self.wf)) + self.refl_g = np.array([1.0] * len(self.wf)) + + self.calc_refl_vg = calc_refl_vg + + # Get the extra kwargs and save as attributes of same name + # (e.g szn=[15,30,45,60]) + for attr in kwargs.keys(): + self.__dict__[attr] = kwargs[attr] + + # call the functin below to get gap, etc. + self.lvisMainProcessFunc(c=cval) + + # Define properties for wf and ht data + @property + def wf(self): + return self._wf + @wf.setter + def wf(self, value): + if not isinstance(value, np.ndarray): + raise TypeError('wf must be an ndarray, where each row is a seperate waveform.') + if value.ndim == 1: + value = np.expand_dims(value, axis=0) + self._wf = value + + @property + def ht(self): + return self._ht + @ht.setter + def ht(self, value): + if not isinstance(value, np.ndarray): + raise TypeError('ht must be an ndarray of heights same shape as the waveforms.') + if value.ndim == 1: + value = np.expand_dims(value, axis=0) + if value.shape != self.wf.shape: + raise TypeError("wf and ht arrays are of different shapes.") + self._ht = value + + @property + def rh(self): + return self._rh + @rh.setter + def rh(self, value): + if (not isinstance(value, list)) and (not isinstance(value, np.ndarray)): + raise TypeError('rh must be a list or an array of values that correspond to the relative' \ + "heights for each waveform from which gap probability is calculated to the ground.") + if len(value) != len(self.wf): + raise TypeError("rh must be same length as the wf and ht arrays.") + self._rh = value + + + def lvisMainProcessFunc(self, c): + + ## First, seperate ground and vegetation return (use the function vegGroundSep()) + + # Call function to seperate vegetation from ground + self.vegGroundSep() + + if self.calc_refl_vg is True: + # concat the utmx and utmy values + utm_xy = np.concatenate([self.utmx.reshape(-1,1),self.utmy.reshape(-1,1)],axis=1) + # calculate nearest neighbor using sklearn KDtree + tree = KDTree(utm_xy) + nearest_dist, nearest_ind = tree.query(utm_xy, k=2) + self.nn = nearest_ind[:, 1] # drop id + + # call function to calculate gap and foliage + self.gap_fol_calc() + # Calculate biWF and biFP + self.bioWF() + self.bioFP() + + def vegGroundSep(self): + # Get the first vegetation indices (RH100) for each wf + in_bools = self.ht<=np.reshape(self.rh, (-1, 1)) + i_veg_first = np.argmax(in_bools,1) + + # Loop through the waveforms and seperate ground and vegetation + # init lists to save data + i_grd_list=[] + i_grd_last_list=[] + i_veg_last_list=[] + waveNorm_list=[] + dpdz_list=[] + for i in range(len(self.wf)): + # Save the wf and ht to variables + wf = self.wf[i] + ht = self.ht[i] + # Get the ground idx + grd = np.where(np.absolute(ht) == np.min(np.absolute(ht))) + i_grd = grd[0][0] + i_grd_list.append(i_grd) + + noise = 2 * np.std(wf[0:i_veg_first[i]]) + grnd_in = np.where(wf[i_grd:] < noise) + + try: + i_grd_last = i_grd + np.min(grnd_in) + except: + noise = 2 * np.std(wf[0:i_grd-1]) + grnd_in = np.where(wf[i_grd:] < noise) + i_grd_last = i_grd + np.min(grnd_in) + + i_grd_last_list.append(i_grd_last) + # Last veg return index + temp_veg = np.min([5, -ht[i_grd_last]]) + i_veg_last = np.max(np.where(ht >= temp_veg)) + i_veg_last_list.append(i_veg_last) + + ## Update waveform and heights + self.wf[i][0:i_veg_first[i]-1] = np.nan + self.wf[i][i_grd_last+1:] = np.nan + self.ht[i][0:i_veg_first[i]-1] = np.nan + self.ht[i][i_grd_last+1:] = np.nan + + # Normalize waveform (wf/sum(wf)) + temp_wfnorm = np.divide(self.wf[i], np.nansum(self.wf[i])) + waveNorm = temp_wfnorm + waveNorm_list.append(waveNorm) + # Add a variable dpdz + dpdz = temp_wfnorm / self.dz + # Correct for negative return values AR 4.12 + dpdz[dpdz<0]=0 + dpdz_list.append(dpdz) + + + # Add data to class (as arrays for easier data manipulation) + self.i_veg_first = i_veg_first + self.i_grd = np.array(i_grd_list) + self.i_grd_last = np.array(i_grd_last_list) + self.i_veg_last = np.array(i_veg_last_list) + self.waveNorm = np.array(waveNorm_list) + self.dpdz = np.array(dpdz_list) + + + def gap_fol_calc(self): + + ## Rho_vg calculation + # cur_wf = self.dpdz + cur_veg_first = self.i_veg_first + cur_veg_last = self.i_veg_last + cur_grd_last = self.i_grd_last + + ## Calculating sum of veg return and ground return + cols = np.arange(self.dpdz.shape[1]).reshape(-1, 1) + # Current + tmask = (cur_veg_first <= cols) & (cols<cur_veg_last) + R_cur_v = np.nansum(self.dpdz.T*tmask,axis=0) + tmask = (cur_veg_last+1 <= cols) & (cols<cur_grd_last) + R_cur_g = np.nansum(self.dpdz.T*tmask,axis=0) + + if self.calc_refl_vg is True: + + ## Calcualte Rv/Rg + # Get the nn wf and veg and ground indices + nearestn = self.nn + neb_wf = self.dpdz[nearestn] + nn_veg_first = self.i_veg_first[nearestn] + nn_veg_last = self.i_veg_last[nearestn] + nn_grd_last = self.i_grd_last[nearestn] + # Neighbor calculate sum of veg and ground return + tmask = (nn_veg_first <= cols) & (cols<nn_veg_last) + R_neb_v = np.nansum(neb_wf.T*tmask,axis=0) + tmask = (nn_veg_last+1 <= cols) & (cols<nn_grd_last) + R_neb_g = np.nansum(neb_wf.T*tmask,axis=0) + + del neb_wf + + R_vg=(R_cur_v - R_neb_v)/(R_neb_g - R_cur_g) + # Calculate R_v and R_g + R_v = (R_neb_g*R_cur_v - R_cur_g*R_neb_v) / (R_neb_g - R_cur_g) + R_g = (R_cur_g*R_neb_v - R_neb_g*R_cur_v) / (R_neb_v - R_cur_v) + + self.refl_v = R_v + self.refl_g = R_g + self.refl_vg = R_vg + + # Save Rv/Rg to variable + R_vg=self.refl_vg + + # Calculate vegetation cover + veg_cover = R_cur_v / (R_cur_v + (R_vg)*R_cur_g) + veg_cover2 = R_cur_v / (R_cur_v + R_cur_g) + + # Vegetation return dz + tmask = (cur_veg_first <= cols) & (cols<cur_veg_last) + R_cur_vz = np.nancumsum(self.dpdz.T*tmask,axis=0) + + # Calculate gap probability + pgap = 1-(R_cur_vz.T / (R_cur_v.reshape(-1,1) + R_vg.reshape(-1,1) * R_cur_g.reshape(-1,1))) + pgap[~tmask.T]=np.nan + + # Foliage accum and density + foliage_accum = -(np.log(pgap)) / 0.5 + foliage_dens = (self.dpdz * (1/pgap)) / 0.5 + + # Save vals to attributes + self.cov = veg_cover + self.covUncorr = veg_cover2 + self.gap = pgap + self.fdp = foliage_dens + self.lai = foliage_accum + + def bioWF(self): + ## Waveform based biomass index + dp = np.absolute(np.diff(self.gap, append=np.nan)) + # biowf = self.ht**self.c * dp + self.biWF = np.nansum(self.ht**self.c * dp, axis=1) + + def bioFP(self): + ## Foliage profile based biomass index + dp = np.absolute(np.diff(self.gap, append=np.nan)) + # biofp = (self.ht**self.c * dp) / self.gap + self.biFP = np.nansum((self.ht**self.c * dp) / self.gap, axis=1) + + # def biomassFP(self): + + # ## Testing for tropical forests (Mondah Forest, Gabon) + # beta = 0.5 + # b = np.exp(1.2) + # alpha = 3.854 + # a = np.exp(2.346) + # G = 0.5 + # gamma = 1 + # F = 0.6 + # rho = 0.55 + + + # k_coef = (np.pi / 40) * ((b**(-2/beta) * F * rho) / (a * G * gamma * Fa) + + +############# +## FullWF Utilities +############# + +def wf_smooth(wfarr,sigmean=None,sd=5): + + """ + Parameters + ---------- + wfarr : ndarray + Array of lvis data. 2d for multiple waveforms or 1d for single wf. + sigmean : array_like + Mean signal noise. + sd : None or int + Standard deviation for Gaussian kernel. (Default is 5) + The larger the smoother the function. + + Returns + ---------- + Waveforms with mean signal noise removed and + smoothed using a gaussian 1D kernel. + + """ + + # Read the waveforms and perform a gaussian smooth + if sigmean is not None: + if isinstance(sigmean, int): + wfarr = wfarr-sigmean + else: + if isinstance(sigmean, list): + sigmean = np.array(sigmean) + if wfarr.ndim != sigmean.ndim: + sigmean = sigmean.reshape(-1,1) + wfarr = wfarr - sigmean # 2 + smoothwfs = ndimage.gaussian_filter1d(wfarr,sd) + + # Return the smoothed wf + return smoothwfs + + diff --git a/.ipynb_checkpoints/run-checkpoint.sh b/.ipynb_checkpoints/run-checkpoint.sh new file mode 100644 index 0000000..4a8471e --- /dev/null +++ b/.ipynb_checkpoints/run-checkpoint.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +# Gets directory of .sh file +basedir=$( cd "$(dirname "$0")" ; pwd -P) + +# Activate environment that was created in the builed-env.sh file +# source activate osgeo-env + +# Create output dir +# OUTPUTDIR="${PWD}/output" +# mkdir -p output +# INPUT_FILES +L1B_URL=$1 +L2A_URL=$2 # e.g. GEDI01_B or GEDI02_A +# YR=$3 +# python ${basedir}/main.py ${L1B_URL} ${L2A_URL} ${OUTPUTDIR} +python ${basedir}/main.py ${L1B_URL} ${L2A_URL} output diff --git a/GEDI-L1B-2021-URLS.txt b/GEDI-L1B-2021-URLS.txt index 9ebe599..5836dfd 100644 --- a/GEDI-L1B-2021-URLS.txt +++ b/GEDI-L1B-2021-URLS.txt @@ -22,7 +22,6 @@ https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.06.03/GEDI01_B_20 https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.06.03/GEDI01_B_2021154232048_O14023_03_T07119_02_005_02_V002.h5 https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.06.04/GEDI01_B_2021155005343_O14024_03_T09966_02_005_02_V002.h5 https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.06.04/GEDI01_B_2021155022637_O14025_03_T09814_02_005_02_V002.h5 -https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.06.04/GEDI01_B_2021155162246_O14034_02_T09364_02_005_02_V002.h5 https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.06.04/GEDI01_B_2021155175540_O14035_02_T03520_02_005_02_V002.h5 https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.06.04/GEDI01_B_2021155192835_O14036_02_T00675_02_005_02_V002.h5 https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.06.05/GEDI01_B_2021156170914_O14050_02_T01653_02_005_02_V002.h5 @@ -72,7 +71,6 @@ https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.06.11/GEDI01_B_20 https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.06.11/GEDI01_B_2021162201506_O14145_03_T08389_02_005_02_V002.h5 https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.06.11/GEDI01_B_2021162214800_O14146_03_T11236_02_005_02_V002.h5 https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.06.11/GEDI01_B_2021162232055_O14147_03_T03969_02_005_02_V002.h5 -https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.06.12/GEDI01_B_2021163131702_O14156_02_T09211_02_005_02_V002.h5 https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.06.12/GEDI01_B_2021163144957_O14157_02_T06366_02_005_02_V002.h5 https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.06.12/GEDI01_B_2021163162251_O14158_02_T07790_02_005_02_V002.h5 https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.06.12/GEDI01_B_2021163175545_O14159_02_T09061_02_005_02_V002.h5 @@ -511,7 +509,6 @@ https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.08.28/GEDI01_B_20 https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.08.28/GEDI01_B_2021240124133_O15349_03_T03232_02_005_02_V002.h5 https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.08.28/GEDI01_B_2021240141429_O15350_03_T04656_02_005_02_V002.h5 https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.08.28/GEDI01_B_2021240154725_O15351_03_T08773_02_005_02_V002.h5 -https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.08.28/GEDI01_B_2021240172020_O15352_03_T05928_02_005_02_V002.h5 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--git a/main.py b/main.py index b0f0536..5cef74c 100644 --- a/main.py +++ b/main.py @@ -10,25 +10,28 @@ Created on Mon Oct 24 10:20:58 2022 import pandas as pd import numpy as np import os +import sys import geopandas as gpd import h5py +import multiprocessing as mp +from pgap import GapDS +from pgap import wf_smooth import re import glob -import sys -from itertools import repeat -# Import pgap function from wenge -from pgap import GapDS, wf_smooth -# import custom functions, etc. -# from download_gedi import download_gedi -# from get_gedi_data import get_gedi_data -from get_gedi_data_fsspec import get_gedi_data +from get_gedi_data import get_gedi_data +##################### +## Global Parameters +##################### ## GET CWD of file to locate path CWD = os.path.dirname(os.path.abspath(__file__)) +##################### +##################### + ## Function to return beam dataframe with some meta data -def get_beam_gdf(beam,l1b_ds,l2a_ds): +def get_beam_gdf(beam): # Create gdf to filter for GEDI returns shot_number = l1b_ds[beam]["shot_number"][:] lat = l1b_ds[beam]['geolocation']['latitude_bin0'][:] @@ -46,7 +49,7 @@ def get_beam_gdf(beam,l1b_ds,l2a_ds): return beam_gdf -def gedi_bioindex(index,l1b_ds,l2a_ds, beam, beam_filt, allom_df): +def gedi_bioindex(index): wfCount = l1b_ds[beam]['rx_sample_count'][index] # Number of samples in the waveform wfStart = int(l1b_ds[beam]['rx_sample_start_index'][index] - 1) # Subtract one because python array indexing starts at 0 not 1 noise_mean_corrected = l1b_ds[beam]['noise_mean_corrected'][index] @@ -73,20 +76,21 @@ def gedi_bioindex(index,l1b_ds,l2a_ds, beam, beam_filt, allom_df): beam_domain = beam_filt.loc[index]['DomainID'] try: if pd.isnull(beam_domain): - cval = 1.63 + cval = 1.63 # else: cval = allom_df[allom_df['domain']==beam_domain]['HSE'].values[0] + # get pgap - pgap = GapDS(waveform_smooth, ht_arr, np.array([rh100]), calc_refl_vg = False, - utm_x=None,utm_y=None,cval=cval) + pgap = GapDS(waveform_smooth, ht_arr, np.array([rh100]), + calc_refl_vg = False, + utm_x=None,utm_y=None,cval=cval) except: # bad data (fix for future) - return (np.nan,np.nan,np.nan,np.nan,np.nan,np.nan) + return (np.nan,np.nan,np.nan,np.nan,np.nan) # return a tuple of biwf and bfp return (pgap.biWF[0], pgap.biFP[0], np.nanmin(pgap.gap), np.nanmax(pgap.lai), rh100, cval) - ########## ## Process entire CONUS data ########## @@ -112,7 +116,6 @@ if __name__ == '__main__': # get level 1 and level 2 data print(l1b_basename) print(l2a_basename) - CWD = os.path.dirname(os.path.abspath(__file__)) l1b_fp = get_gedi_data(l1b_url) l2a_fp = get_gedi_data(l2a_url) l1b_ds = h5py.File(l1b_fp, "r") @@ -120,9 +123,11 @@ if __name__ == '__main__': except Exception as e: # Some raw L1B files are corrupt? print("Corrupt file: ", l1b_basename) + print(e) sys.exit() - - # Init output file + + + # Init output file path orbit_num = re.findall("O[0-9]{5}", l1b_basename)[0] track_num = re.findall("T[0-9]{5}", l1b_basename)[0] date_str = re.findall("[0-9]{13}", l1b_basename)[0] @@ -130,22 +135,28 @@ if __name__ == '__main__': outfp = os.path.join(outdir, f"GEDI_bioindex_{date_str}_{orbit_num}_{track_num}.csv") + #################### + ## Loop through each beam + #################### + # init df_list to concat afterwards df_list = [] - # Loop through each beam beamNames = [g for g in l1b_ds.keys() if g.startswith('BEAM')] for beam in beamNames: - + # get the beam gdf try: - beam_gdf = get_beam_gdf(beam,l1b_ds,l2a_ds) + beam_gdf = get_beam_gdf(beam) except: print(f"File {l1b_basename} has an issue at {beam}") continue - beam_gdf = beam_gdf.loc[beam_gdf['qf']==1] # quality flag filter + + # quality flag filter + beam_gdf = beam_gdf.loc[beam_gdf['qf']==1] # Spatially filter beam_gdf (fastest approach) - beam_filt = gpd.sjoin(beam_gdf, domain_polys[["DomainID", 'geometry']], op='intersects', how="inner") + beam_filt = gpd.sjoin(beam_gdf, domain_polys[["DomainID", 'geometry']], + predicate='intersects', how="inner") if len(beam_filt)==0: continue @@ -174,7 +185,7 @@ if __name__ == '__main__': cval_list = [] print("Running pgap iteratively...") for i in idx: - results = gedi_bioindex(i,l1b_ds,l2a_ds,beam,beam_filt, allom_df) + results = gedi_bioindex(i) # save to lists biWF_list.append(results[0]) biFP_list.append(results[1]) @@ -197,12 +208,15 @@ if __name__ == '__main__': # append to df_list df_list.append(new_df) + + #################### + #################### - # Save + ## Save results try: - out_df = pd.concat(df_list, axis=0, ignore_index=True) + out_df = pd.concat(df_list, ignore_index=True) out_df.to_csv(outfp, index=False) - # Close h5py files and delete so they are note saved! + # Close h5py files and delete so they are not saved! l1b_ds.close() l2a_ds.close() os.remove(l1b_fp) diff --git a/notebooks/.ipynb_checkpoints/run-gedi-biomass-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/run-gedi-biomass-checkpoint.ipynb index 363fcab..ca555ed 100644 --- a/notebooks/.ipynb_checkpoints/run-gedi-biomass-checkpoint.ipynb +++ b/notebooks/.ipynb_checkpoints/run-gedi-biomass-checkpoint.ipynb @@ -1,6 +1,1510 @@ { - "cells": [], - "metadata": {}, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "25172537-794b-4135-bb28-63c0fcefbb68", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "\n", + "import os\n", + "import re\n", + "from maap.maap import MAAP\n", + "import re\n", + "# maap = MAAP(maap_host='api.maap-project.org')\n", + "maap = MAAP(maap_host='api.maap-project.org')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f176e75b-5104-478b-ade6-a84006e966b0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f203a6c0-852b-4645-a3a5-4b65c462f4f5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b0d65f96-5dad-44e9-ae85-9d4086c4b29f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b39e76ac-d765-4b8c-baf1-fe41f3296de9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Change to appropriate filepaths for urls\n", + "l1b_urls_fpath = \"../GEDI-L1B-2021-URLS.txt\"\n", + "l2a_urls_fpath = \"../GEDI-L2A-2021-URLS.txt\"\n", + "# l1b_urls_fpath = \"/projects/biomass-gedi-conus/data/GEDI-L1B-2022-URLS.txt\"\n", + "# l2a_urls_fpath = \"/projects/biomass-gedi-conus/data/GEDI-L2A-2022-URLS.txt\"\n", + "\n", + "with open(l1b_urls_fpath) as f:\n", + " l1b_fpaths = f.read().splitlines()\n", + "with open(l2a_urls_fpath) as f:\n", + " l2a_fpaths = f.read().splitlines()\n", + "\n", + "# Get matching string pattern and run main.py\n", + "jobs_list = []\n", + "counter=1\n", + "for l1b_fp in l1b_fpaths:\n", + " # Get string pattern\n", + " str_pattern = re.findall(\"[0-9]{13}\", os.path.basename(l1b_fp))[0] \n", + " try:\n", + " l2a_fp = [s for s in l2a_fpaths if str_pattern in s][0]\n", + " except:\n", + " print(\"No matching L2 file!\", l1b_fp)\n", + " continue\n", + " \n", + " # print(l1b_fp)\n", + " # print(l2a_fp)\n", + " job = maap.submitJob(identifier=\"GEDI-biomass-2022\",\n", + " algo_id=\"arojas_biomass_gedi\",\n", + " version=\"master\",\n", + " username=\"arojearthdata\",\n", + " queue=\"maap-dps-worker-16gb\",\n", + " L1B_URL=l1b_fp,\n", + " L2A_URL=l2a_fp)\n", + " jobs_list.append(job)\n", + " \n", + " if counter%20==0:\n", + " print(\"on file num: \", counter, end='\\r')\n", + " break\n", + " # break\n", + " # time.sleep(1200)\n", + " counter+=1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8f0b9f73-ffb1-40b7-be0e-b31480571a4a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "529" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "counter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f6093c5-1f63-4556-abb8-02714f560ad1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7fe1c6f4-37c5-4fe7-b9ff-51e17b7a5cf0", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running\n", + "f2d0f231-4565-4f7b-a460-5b824de4639e\n" + ] + } + ], + "source": [ + "# View job status\n", + "for job in jobs_list:\n", + " print(job.retrieve_status())\n", + " print(job.id)\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2018077f-9640-4e70-bd01-a9d939b3626a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Succeeded\n", + "6152410f-1e33-4561-965a-83fd364e3767\n" + ] + } + ], + "source": [ + "# View job status\n", + "for job in jobs_list:\n", + " print(job.retrieve_status())\n", + " print(job.id)\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "873e9ea7-d6cb-4bb9-b082-5bffb4026615", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Succeeded 528\n" + ] + }, + { + "ename": "IndexError", + "evalue": "list index out of range", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[21], line 16\u001b[0m\n\u001b[1;32m 13\u001b[0m f_list\u001b[38;5;241m.\u001b[39mappend(status)\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28mprint\u001b[39m(s_list[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mlen\u001b[39m(s_list))\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mr_list\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m, \u001b[38;5;28mlen\u001b[39m(r_list))\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28mprint\u001b[39m(f_list[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mlen\u001b[39m(f_list))\n", + "\u001b[0;31mIndexError\u001b[0m: list index out of range" + ] + } + ], + "source": [ + "# View job status\n", + "f_list = []\n", + "s_list = []\n", + "r_list = []\n", + "\n", + "for job in jobs_list:\n", + " status = job.retrieve_status()\n", + " if status==\"Succeeded\":\n", + " s_list.append(status)\n", + " elif status==\"Running\":\n", + " r_list.append(status)\n", + " elif status==\"Failed\":\n", + " f_list.append(status)\n", + " \n", + "print(s_list[0], len(s_list))\n", + "print(r_list[0], len(r_list))\n", + "print(f_list[0], len(f_list))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8a40e13-dcdd-4b7f-a3b6-d060bb52eaf5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "988420a0-1145-499c-927c-eec059c0dde9", + "metadata": {}, + "outputs": [], + "source": [ + "################\n", + "###############\n", + "##############" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31626d2a-4edf-4572-81c2-7b68080e2dd3", + "metadata": {}, + "outputs": [], + "source": [ + "## Some files downloaded, other didnt (due to limit on download through USGS)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9ecb7ffc-319f-40c9-8f6e-d18b6ea1c890", + "metadata": {}, + "outputs": [], + "source": [ + "import glob\n", + "indir = \"/projects/my-private-bucket/dps_output/arojas_biomass_gedi/master/GEDI-biomass-2022\"\n", + "csv_list = []\n", + "for subdir, dirs, files in os.walk(indir):\n", + " for file in files:\n", + " if file.endswith(\".csv\"):\n", + " # fp = os.path.join(subdir, file)\n", + " # print(fp)\n", + " # csv_list.append(fp)\n", + " csv_list.append(file)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f3ae4e2c-fa90-41ae-8fa2-9e3fd1241157", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "637" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(csv_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5bb204a4-e7d4-4a83-898f-81db03d080aa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "done\n" + ] + } + ], + "source": [ + "print(\"done\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bb5b4efc", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running this cell now!\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.08.31/GEDI01_B_2022243142430_O21056_03_T06784_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.08.31/GEDI02_A_2022243142430_O21056_03_T06784_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.08.31/GEDI01_B_2022243125140_O21055_03_T02667_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.08.31/GEDI02_A_2022243125140_O21055_03_T02667_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.08.31/GEDI01_B_2022243111849_O21054_03_T05512_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.08.31/GEDI02_A_2022243111849_O21054_03_T05512_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.08.31/GEDI01_B_2022243094558_O21053_02_T01242_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.08.31/GEDI02_A_2022243094558_O21053_02_T01242_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.08.31/GEDI01_B_2022243064017_O21051_02_T02816_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.08.31/GEDI02_A_2022243064017_O21051_02_T02816_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.08.30/GEDI01_B_2022242151152_O21041_03_T05958_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.08.30/GEDI02_A_2022242151152_O21041_03_T05958_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.08.30/GEDI01_B_2022242072738_O21036_02_T10528_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.08.30/GEDI02_A_2022242072738_O21036_02_T10528_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.08.29/GEDI01_B_2022241155913_O21026_03_T05132_02_005_02_V002.h5\n", + "\n", + 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"https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.05/GEDI01_B_2022156211303_O19711_03_T08878_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.06.05/GEDI02_A_2022156211303_O19711_03_T08878_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.03/GEDI01_B_2022154180859_O19678_02_T02954_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.06.03/GEDI02_A_2022154180859_O19678_02_T02954_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.03/GEDI01_B_2022154163608_O19677_02_T01836_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.06.03/GEDI02_A_2022154163608_O19677_02_T01836_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.01/GEDI01_B_2022152194326_O19648_02_T04301_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.06.01/GEDI02_A_2022152194326_O19648_02_T04301_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.31/GEDI01_B_2022151032913_O19622_03_T09615_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.31/GEDI02_A_2022151032913_O19622_03_T09615_02_003_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.30/GEDI01_B_2022150211749_O19618_02_T11340_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.30/GEDI02_A_2022150211749_O19618_02_T11340_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.30/GEDI01_B_2022150194457_O19617_02_T08493_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.30/GEDI02_A_2022150194457_O19617_02_T08493_02_003_03_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.30/GEDI01_B_2022150041625_O19607_03_T08789_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.30/GEDI01_B_2022150024333_O19606_03_T04672_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.30/GEDI01_B_2022150011042_O19605_03_T00402_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.29/GEDI01_B_2022149233751_O19604_02_T03247_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.28/GEDI01_B_2022148225209_O19588_02_T11111_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.28/GEDI02_A_2022148225209_O19588_02_T11111_02_003_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.28/GEDI01_B_2022148011208_O19574_03_T01748_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.28/GEDI02_A_2022148011208_O19574_03_T01748_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.27/GEDI01_B_2022147050459_O19561_03_T06463_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.27/GEDI02_A_2022147050459_O19561_03_T06463_02_003_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.27/GEDI01_B_2022147015916_O19559_03_T10883_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.27/GEDI02_A_2022147015916_O19559_03_T10883_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.26/GEDI01_B_2022146212041_O19556_02_T09457_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.26/GEDI02_A_2022146212041_O19556_02_T09457_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.26/GEDI01_B_2022146055205_O19546_03_T04214_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.25/GEDI01_B_2022145020034_O19528_03_T07807_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.25/GEDI02_A_2022145020034_O19528_03_T07807_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.24/GEDI01_B_2022144225451_O19526_02_T05112_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.24/GEDI01_B_2022144212159_O19525_02_T00842_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.24/GEDI01_B_2022144011446_O19512_02_T04287_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.23/GEDI01_B_2022143220903_O19510_02_T02862_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.22/GEDI01_B_2022142225605_O19495_02_T09151_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.22/GEDI02_A_2022142225605_O19495_02_T09151_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.22/GEDI01_B_2022142212313_O19494_02_T05034_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.22/GEDI01_B_2022142055433_O19484_03_T11175_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.22/GEDI02_A_2022142055433_O19484_03_T11175_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.21/GEDI01_B_2022141234306_O19480_02_T11324_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.21/GEDI02_A_2022141234306_O19480_02_T11324_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.19/GEDI01_B_2022139064237_O19438_03_T04580_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.19/GEDI02_A_2022139064237_O19438_03_T04580_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.18/GEDI01_B_2022138090225_O19424_03_T05178_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.18/GEDI01_B_2022138042348_O19421_03_T11020_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.17/GEDI01_B_2022137064335_O19407_03_T10195_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.17/GEDI01_B_2022137051042_O19406_03_T07348_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.17/GEDI01_B_2022137033750_O19405_02_T03078_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.17/GEDI01_B_2022137020458_O19404_02_T01807_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.17/GEDI01_B_2022137003205_O19403_02_T10344_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.16/GEDI01_B_2022136090323_O19393_03_T05101_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.16/GEDI02_A_2022136090323_O19393_03_T05101_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.16/GEDI01_B_2022136025153_O19389_02_T08096_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.16/GEDI01_B_2022136011900_O19388_02_T06672_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.15/GEDI01_B_2022135234608_O19387_02_T09517_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.15/GEDI01_B_2022135064429_O19376_03_T08542_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.14/GEDI01_B_2022134103717_O19363_03_T09141_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.14/GEDI01_B_2022134073137_O19361_03_T07869_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.14/GEDI01_B_2022134055846_O19360_03_T09291_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.13/GEDI01_B_2022133081902_O19346_03_T08466_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.13/GEDI01_B_2022133064612_O19345_03_T07042_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.13/GEDI01_B_2022133051321_O19344_02_T04195_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.13/GEDI01_B_2022133034031_O19343_02_T01501_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.13/GEDI01_B_2022133020741_O19342_02_T05769_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.12/GEDI01_B_2022132060046_O19329_02_T07791_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.12/GEDI01_B_2022132042756_O19328_02_T09213_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.12/GEDI01_B_2022132025505_O19327_02_T10788_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.12/GEDI01_B_2022132012215_O19326_02_T06365_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.11/GEDI01_B_2022131082100_O19315_03_T09659_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.11/GEDI02_A_2022131082100_O19315_03_T09659_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.11/GEDI01_B_2022131064810_O19314_02_T11234_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.11/GEDI02_A_2022131064810_O19314_02_T11234_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.11/GEDI01_B_2022131034229_O19312_02_T08233_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.10/GEDI01_B_2022130090823_O19300_03_T05987_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.10/GEDI01_B_2022130073533_O19299_03_T05986_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.10/GEDI02_A_2022130073533_O19299_03_T05986_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.10/GEDI01_B_2022130060242_O19298_02_T10407_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.10/GEDI02_A_2022130060242_O19298_02_T10407_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.10/GEDI01_B_2022130025702_O19296_02_T06136_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.09/GEDI01_B_2022129095548_O19285_03_T06584_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.09/GEDI02_A_2022129095548_O19285_03_T06584_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.09/GEDI01_B_2022129065007_O19283_02_T08005_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.09/GEDI02_A_2022129065007_O19283_02_T08005_02_003_03_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.09/GEDI01_B_2022129034426_O19281_02_T02464_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.07/GEDI01_B_2022127130321_O19256_03_T04780_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.07/GEDI02_A_2022127130321_O19256_03_T04780_02_003_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.07/GEDI01_B_2022127113030_O19255_03_T09048_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.07/GEDI02_A_2022127113030_O19255_03_T09048_02_003_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.07/GEDI01_B_2022127082449_O19253_02_T04930_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.07/GEDI02_A_2022127082449_O19253_02_T04930_02_003_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.06/GEDI01_B_2022126121750_O19240_03_T01107_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.06/GEDI02_A_2022126121750_O19240_03_T01107_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.04/GEDI01_B_2022124121938_O19209_03_T09568_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.04/GEDI01_B_2022124104647_O19208_03_T10990_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.04/GEDI02_A_2022124104647_O19208_03_T10990_02_003_03_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.03/GEDI01_B_2022123082823_O19191_02_T00201_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.03/GEDI01_B_2022123065532_O19190_02_T03046_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.03/GEDI02_A_2022123065532_O19190_02_T03046_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.03/GEDI01_B_2022123052241_O19189_02_T04621_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.02/GEDI01_B_2022122074249_O19175_02_T09335_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.02/GEDI02_A_2022122074249_O19175_02_T09335_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.01/GEDI01_B_2022121144129_O19164_03_T04091_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.01/GEDI01_B_2022121130838_O19163_03_T02820_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.01/GEDI02_A_2022121130838_O19163_03_T02820_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.01/GEDI01_B_2022121113547_O19162_03_T05665_02_005_02_V002.h5\n", + "\n", + "Done\n" + ] + } + ], + "source": [ + "\n", + "# print(\"Waiting some time to run this cell...\")\n", + "# time.sleep(3600)\n", + "print(\"Running this cell now!\")\n", + "# Get all CSV files\n", + "indir = \"/projects/my-private-bucket/dps_output/arojas_biomass_gedi/master/GEDI-biomass-2022\"\n", + "csv_list = []\n", + "for subdir, dirs, files in os.walk(indir):\n", + " for file in files:\n", + " if file.endswith(\".csv\"):\n", + " # fp = os.path.join(subdir, file)\n", + " # print(fp)\n", + " # csv_list.append(fp)\n", + " csv_list.append(file)\n", + "\n", + "\n", + "# Change to appropriate filepaths for urls\n", + "l1b_urls_fpath = \"/projects/biomass-gedi-conus/data/GEDI-L1B-2022-URLS.txt\"\n", + "l2a_urls_fpath = \"/projects/biomass-gedi-conus/data/GEDI-L2A-2022-URLS.txt\"\n", + "\n", + "with open(l1b_urls_fpath) as f:\n", + " l1b_fpaths = f.readlines()\n", + "with open(l2a_urls_fpath) as f:\n", + " l2a_fpaths = f.readlines()\n", + "\n", + "# Get matching string pattern and run main.py\n", + "jobs_list = []\n", + "counter=1\n", + "for l1b_fp in l1b_fpaths:\n", + " # Get string pattern\n", + " str_pattern = re.findall(\"[0-9]{13}\", os.path.basename(l1b_fp))[0] \n", + " try:\n", + " l2a_fp = [s for s in l2a_fpaths if str_pattern in s][0]\n", + " except:\n", + " print(\"No matching L2 file!\", l1b_fp)\n", + " continue\n", + " \n", + " # Check if file was already downloaded\n", + " date_str = os.path.basename(l1b_fp).split(\"_\")[2]\n", + " if any(date_str in x for x in csv_list):\n", + " continue\n", + " \n", + " print(l1b_fp)\n", + " print(l2a_fp)\n", + " job = maap.submitJob(identifier=\"GEDI-biomass-2022\",\n", + " algo_id=\"arojas_biomass_gedi\",\n", + " version=\"master\",\n", + " username=\"arojearthdata\",\n", + " queue=\"maap-dps-worker-16gb\",\n", + " L1B_URL=l1b_fp,\n", + " L2A_URL=l2a_fp)\n", + " jobs_list.append(job)\n", + " \n", + " if counter%100==0:\n", + " print(\"on file num: \", counter, end='\\r')\n", + " time.sleep(1200)\n", + " counter+=1\n", + "\n", + "print('Done')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d8ef38d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da773e1f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ee0b897a", + "metadata": {}, + "outputs": [], + "source": [ + "#############\n", + "## SCRATCH\n", + "#############" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b364772a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<KeysViewHDF5 ['BEAM0000', 'BEAM0001', 'BEAM0010', 'BEAM0011', 'BEAM0101', 'BEAM0110', 'BEAM1000', 'BEAM1011', 'METADATA']>\n", + "[243.6951 244.02603 244.26328 244.186 243.79663 243.32755 243.1004\n", + " 243.24414 243.63077 243.96375]\n" + ] + } + ], + "source": [ + "import h5py\n", + "fp = \"../output/GEDI01_B_2022243142430_O21056_03_T06784_02_005_02_V002.h5\"\n", + "with h5py.File(fp, \"r\") as f:\n", + " print(f.keys())\n", + " print(f['BEAM0000'][\"rxwaveform\"][:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4a6460a7-4d65-4311-901c-e5685daeb2f0", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebb58d7e-c295-4955-a056-58fa8efc596f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "23b92856", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1804399", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/notebooks/run-gedi-biomass.ipynb b/notebooks/run-gedi-biomass.ipynb index 4b6438d..ca555ed 100644 --- a/notebooks/run-gedi-biomass.ipynb +++ b/notebooks/run-gedi-biomass.ipynb @@ -4,19 +4,12 @@ "cell_type": "code", "execution_count": 1, "id": "25172537-794b-4135-bb28-63c0fcefbb68", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Unable to load config file from source maap.cfg \n", - "Unable to load config file from source ./maap.cfg \n", - "Unable to load config file from source /projects/maap.cfg \n" - ] - } - ], + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ + "\n", "import os\n", "import re\n", "from maap.maap import MAAP\n", @@ -29,7 +22,9 @@ "cell_type": "code", "execution_count": 2, "id": "f176e75b-5104-478b-ade6-a84006e966b0", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "import time" @@ -39,7 +34,9 @@ "cell_type": "code", "execution_count": null, "id": "f203a6c0-852b-4645-a3a5-4b65c462f4f5", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [] }, @@ -53,22 +50,18 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "id": "b39e76ac-d765-4b8c-baf1-fe41f3296de9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "on file num: 100\r" - ] - } - ], + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "# Change to appropriate filepaths for urls\n", - "l1b_urls_fpath = \"/projects/biomass-gedi-conus/data/GEDI-L1B-2022-URLS.txt\"\n", - "l2a_urls_fpath = \"/projects/biomass-gedi-conus/data/GEDI-L2A-2022-URLS.txt\"\n", + "l1b_urls_fpath = \"../GEDI-L1B-2021-URLS.txt\"\n", + "l2a_urls_fpath = \"../GEDI-L2A-2021-URLS.txt\"\n", + "# l1b_urls_fpath = \"/projects/biomass-gedi-conus/data/GEDI-L1B-2022-URLS.txt\"\n", + "# l2a_urls_fpath = \"/projects/biomass-gedi-conus/data/GEDI-L2A-2022-URLS.txt\"\n", "\n", "with open(l1b_urls_fpath) as f:\n", " l1b_fpaths = f.read().splitlines()\n", @@ -78,7 +71,7 @@ "# Get matching string pattern and run main.py\n", "jobs_list = []\n", "counter=1\n", - "for l1b_fp in l1b_fpaths[200:]:\n", + "for l1b_fp in l1b_fpaths:\n", " # Get string pattern\n", " str_pattern = re.findall(\"[0-9]{13}\", os.path.basename(l1b_fp))[0] \n", " try:\n", @@ -98,20 +91,34 @@ " L2A_URL=l2a_fp)\n", " jobs_list.append(job)\n", " \n", - " if counter%100==0:\n", + " if counter%20==0:\n", " print(\"on file num: \", counter, end='\\r')\n", " break\n", - " time.sleep(1200)\n", + " # break\n", + " # time.sleep(1200)\n", " counter+=1" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "id": "8f0b9f73-ffb1-40b7-be0e-b31480571a4a", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "529" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "counter" + ] }, { "cell_type": "code", @@ -123,16 +130,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "id": "7fe1c6f4-37c5-4fe7-b9ff-51e17b7a5cf0", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Succeeded\n", - "62fd5122-a199-4ae7-bbd1-d86e11a04a7e\n" + "Running\n", + "f2d0f231-4565-4f7b-a460-5b824de4639e\n" ] } ], @@ -169,23 +178,50 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 21, "id": "873e9ea7-d6cb-4bb9-b082-5bffb4026615", - "metadata": {}, + "metadata": { + "scrolled": true, + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['http://maap-ops-workspace.s3-website-us-west-2.amazonaws.com/dataset/triaged_job/arojas_biomass_gedi/master/2023/10/19/edaa25c9-99e8-4b0f-aa52-bf843a6f9ada', 's3://s3-us-west-2.amazonaws.com:80/maap-ops-workspace/dataset/triaged_job/arojas_biomass_gedi/master/2023/10/19/edaa25c9-99e8-4b0f-aa52-bf843a6f9ada', 'https://s3.console.aws.amazon.com/s3/buckets/maap-ops-workspace/dataset/triaged_job/arojas_biomass_gedi/master/2023/10/19/edaa25c9-99e8-4b0f-aa52-bf843a6f9ada/?region=us-east-1&tab=overview', '++ python /app/get_username.py\\n+ USERNAME=arojearthdata\\n+ DPS_MACHINE_TOKEN=Y6hERQKLKs0oH0h5Kgi3d-gVVyHgW_DR77YeARdzd-a3KJDE-gYldJzhB94s635KySUXi4r9WOtD6Vo_F9cEbfNI_QUhOG8N47heXHVcW1k3vEw3d0StttXYMOnQPtTzSQLAYKQwJLgbtX-R8ug1XGVeMolh97P9nHcW8awN7yjyaR7g\\n++ curl -sb -H \\'Accept: application/json\\' -H \\'Content-Type: application/json\\' -H \\'dps-token: Y6hERQKLKs0oH0h5Kgi3d-gVVyHgW_DR77YeARdzd-a3KJDE-gYldJzhB94s635KySUXi4r9WOtD6Vo_F9cEbfNI_QUhOG8N47heXHVcW1k3vEw3d0StttXYMOnQPtTzSQLAYKQwJLgbtX-R8ug1XGVeMolh97P9nHcW8awN7yjyaR7g\\' https://api.maap-project.org/api/members/arojearthdata\\n++ jq -r .session_key\\n+ export MAAP_PGT=PGT-332-1NdFr4fQXYrHffIoE6zmdrON8fQBjz05-O6ycMomc8i2b7Xt7lZggJXaQaUsQjMs8Cs-8445019b059c\\n+ MAAP_PGT=PGT-332-1NdFr4fQXYrHffIoE6zmdrON8fQBjz05-O6ycMomc8i2b7Xt7lZggJXaQaUsQjMs8Cs-8445019b059c\\n+ unset DPS_MACHINE_TOKEN\\n+ /app/biomass-gedi-conus/run.sh \\'https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2021.05.31/GEDI01_B_2021151223415_O13976_02_T00676_02_005_02_V002.h5\\n\\' https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2021.05.31/GEDI02_A_2021151223415_O13976_02_T00676_02_003_02_V002.h5\\nTraceback (most recent call last):\\n File \"/app/biomass-gedi-conus/main.py\", line 197, in <module>\\n out_df.to_csv(outfp, index=False) \\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \"/opt/conda/envs/osgeo-env/lib/python3.11/site-packages/pandas/core/generic.py\", line 3902, in to_csv\\n return DataFrameRenderer(formatter).to_csv(\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \"/opt/conda/envs/osgeo-env/lib/python3.11/site-packages/pandas/io/formats/format.py\", line 1152, in to_csv\\n csv_formatter.save()\\n File \"/opt/conda/envs/osgeo-env/lib/python3.11/site-packages/pandas/io/formats/csvs.py\", line 247, in save\\n with get_handle(\\n ^^^^^^^^^^^\\n File \"/opt/conda/envs/osgeo-env/lib/python3.11/site-packages/pandas/io/common.py\", line 739, in get_handle\\n check_parent_directory(str(handle))\\n File \"/opt/conda/envs/osgeo-env/lib/python3.11/site-packages/pandas/io/common.py\", line 604, in check_parent_directory\\n raise OSError(rf\"Cannot save file into a non-existent directory: \\'{parent}\\'\")\\nOSError: Cannot save file into a non-existent directory: \\'/data/work/jobs/2023/10/19/03/14/job-arojas_biomass_gedi__master-20231019T030808.292586Z/output/output\\'\\n\\nDuring handling of the above exception, another exception occurred:\\n\\nTraceback (most recent call last):\\n File \"/app/biomass-gedi-conus/main.py\", line 199, in <module>\\n print(\"Couldnt save file: \", l1b_fp)\\n ^^^^^^\\nNameError: name \\'l1b_fp\\' is not defined. Did you mean: \\'l1b_ds\\'?\\n+ cp _stderr.txt _alt_traceback.txt\\n']\n" + "Succeeded 528\n" + ] + }, + { + "ename": "IndexError", + "evalue": "list index out of range", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[21], line 16\u001b[0m\n\u001b[1;32m 13\u001b[0m f_list\u001b[38;5;241m.\u001b[39mappend(status)\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28mprint\u001b[39m(s_list[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mlen\u001b[39m(s_list))\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mr_list\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m, \u001b[38;5;28mlen\u001b[39m(r_list))\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28mprint\u001b[39m(f_list[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;28mlen\u001b[39m(f_list))\n", + "\u001b[0;31mIndexError\u001b[0m: list index out of range" ] } ], "source": [ - "# Retrieve results\n", + "# View job status\n", + "f_list = []\n", + "s_list = []\n", + "r_list = []\n", + "\n", "for job in jobs_list:\n", - " print(job.retrieve_result())\n", - " break" + " status = job.retrieve_status()\n", + " if status==\"Succeeded\":\n", + " s_list.append(status)\n", + " elif status==\"Running\":\n", + " r_list.append(status)\n", + " elif status==\"Failed\":\n", + " f_list.append(status)\n", + " \n", + "print(s_list[0], len(s_list))\n", + "print(r_list[0], len(r_list))\n", + "print(f_list[0], len(f_list))" ] }, { @@ -220,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "id": "9ecb7ffc-319f-40c9-8f6e-d18b6ea1c890", "metadata": {}, "outputs": [], @@ -240,17 +276,17 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "id": "f3ae4e2c-fa90-41ae-8fa2-9e3fd1241157", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "169" + "637" ] }, - "execution_count": 4, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -261,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "id": "5bb204a4-e7d4-4a83-898f-81db03d080aa", "metadata": {}, "outputs": [ @@ -279,13 +315,1050 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "id": "bb5b4efc", - "metadata": {}, - "outputs": [], + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running this cell now!\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.08.31/GEDI01_B_2022243142430_O21056_03_T06784_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.08.31/GEDI02_A_2022243142430_O21056_03_T06784_02_003_02_V002.h5\n", + "\n", + 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"https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.10/GEDI01_B_2022161184925_O19787_02_T03048_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.06.10/GEDI02_A_2022161184925_O19787_02_T03048_02_003_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.10/GEDI01_B_2022161171634_O19786_02_T10315_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.06.10/GEDI02_A_2022161171634_O19786_02_T10315_02_003_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.09/GEDI01_B_2022160224226_O19774_03_T07916_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.06.09/GEDI02_A_2022160224226_O19774_03_T07916_02_003_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.09/GEDI01_B_2022160193644_O19772_03_T09337_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.06.09/GEDI02_A_2022160193644_O19772_03_T09337_02_003_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.08/GEDI01_B_2022159171822_O19755_02_T05816_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.06.08/GEDI02_A_2022159171822_O19755_02_T05816_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.08/GEDI01_B_2022159154531_O19754_02_T04392_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.06.08/GEDI02_A_2022159154531_O19754_02_T04392_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.08/GEDI01_B_2022159001702_O19744_03_T08957_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.07/GEDI01_B_2022158224411_O19743_03_T10532_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.07/GEDI01_B_2022158145957_O19738_02_T03565_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.06.07/GEDI02_A_2022158145957_O19738_02_T03565_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.06/GEDI01_B_2022157001845_O19713_03_T07457_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.06.06/GEDI02_A_2022157001845_O19713_03_T07457_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.05/GEDI01_B_2022156211303_O19711_03_T08878_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.06.05/GEDI02_A_2022156211303_O19711_03_T08878_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.03/GEDI01_B_2022154180859_O19678_02_T02954_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.06.03/GEDI02_A_2022154180859_O19678_02_T02954_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.03/GEDI01_B_2022154163608_O19677_02_T01836_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.06.03/GEDI02_A_2022154163608_O19677_02_T01836_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.06.01/GEDI01_B_2022152194326_O19648_02_T04301_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.06.01/GEDI02_A_2022152194326_O19648_02_T04301_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.31/GEDI01_B_2022151032913_O19622_03_T09615_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.31/GEDI02_A_2022151032913_O19622_03_T09615_02_003_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.30/GEDI01_B_2022150211749_O19618_02_T11340_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.30/GEDI02_A_2022150211749_O19618_02_T11340_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.30/GEDI01_B_2022150194457_O19617_02_T08493_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.30/GEDI02_A_2022150194457_O19617_02_T08493_02_003_03_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.30/GEDI01_B_2022150041625_O19607_03_T08789_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.30/GEDI01_B_2022150024333_O19606_03_T04672_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.30/GEDI01_B_2022150011042_O19605_03_T00402_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.29/GEDI01_B_2022149233751_O19604_02_T03247_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.28/GEDI01_B_2022148225209_O19588_02_T11111_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.28/GEDI02_A_2022148225209_O19588_02_T11111_02_003_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.28/GEDI01_B_2022148011208_O19574_03_T01748_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.28/GEDI02_A_2022148011208_O19574_03_T01748_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.27/GEDI01_B_2022147050459_O19561_03_T06463_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.27/GEDI02_A_2022147050459_O19561_03_T06463_02_003_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.27/GEDI01_B_2022147015916_O19559_03_T10883_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.27/GEDI02_A_2022147015916_O19559_03_T10883_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.26/GEDI01_B_2022146212041_O19556_02_T09457_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.26/GEDI02_A_2022146212041_O19556_02_T09457_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.26/GEDI01_B_2022146055205_O19546_03_T04214_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.25/GEDI01_B_2022145020034_O19528_03_T07807_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.25/GEDI02_A_2022145020034_O19528_03_T07807_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.24/GEDI01_B_2022144225451_O19526_02_T05112_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.24/GEDI01_B_2022144212159_O19525_02_T00842_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.24/GEDI01_B_2022144011446_O19512_02_T04287_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.23/GEDI01_B_2022143220903_O19510_02_T02862_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.22/GEDI01_B_2022142225605_O19495_02_T09151_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.22/GEDI02_A_2022142225605_O19495_02_T09151_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.22/GEDI01_B_2022142212313_O19494_02_T05034_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.22/GEDI01_B_2022142055433_O19484_03_T11175_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.22/GEDI02_A_2022142055433_O19484_03_T11175_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.21/GEDI01_B_2022141234306_O19480_02_T11324_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.21/GEDI02_A_2022141234306_O19480_02_T11324_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.19/GEDI01_B_2022139064237_O19438_03_T04580_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.19/GEDI02_A_2022139064237_O19438_03_T04580_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.18/GEDI01_B_2022138090225_O19424_03_T05178_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.18/GEDI01_B_2022138042348_O19421_03_T11020_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.17/GEDI01_B_2022137064335_O19407_03_T10195_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.17/GEDI01_B_2022137051042_O19406_03_T07348_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.17/GEDI01_B_2022137033750_O19405_02_T03078_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.17/GEDI01_B_2022137020458_O19404_02_T01807_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.17/GEDI01_B_2022137003205_O19403_02_T10344_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.16/GEDI01_B_2022136090323_O19393_03_T05101_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.16/GEDI02_A_2022136090323_O19393_03_T05101_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.16/GEDI01_B_2022136025153_O19389_02_T08096_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.16/GEDI01_B_2022136011900_O19388_02_T06672_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.15/GEDI01_B_2022135234608_O19387_02_T09517_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.15/GEDI01_B_2022135064429_O19376_03_T08542_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.14/GEDI01_B_2022134103717_O19363_03_T09141_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.14/GEDI01_B_2022134073137_O19361_03_T07869_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.14/GEDI01_B_2022134055846_O19360_03_T09291_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.13/GEDI01_B_2022133081902_O19346_03_T08466_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.13/GEDI01_B_2022133064612_O19345_03_T07042_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.13/GEDI01_B_2022133051321_O19344_02_T04195_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.13/GEDI01_B_2022133034031_O19343_02_T01501_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.13/GEDI01_B_2022133020741_O19342_02_T05769_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.12/GEDI01_B_2022132060046_O19329_02_T07791_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.12/GEDI01_B_2022132042756_O19328_02_T09213_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.12/GEDI01_B_2022132025505_O19327_02_T10788_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.12/GEDI01_B_2022132012215_O19326_02_T06365_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.11/GEDI01_B_2022131082100_O19315_03_T09659_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.11/GEDI02_A_2022131082100_O19315_03_T09659_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.11/GEDI01_B_2022131064810_O19314_02_T11234_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.11/GEDI02_A_2022131064810_O19314_02_T11234_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.11/GEDI01_B_2022131034229_O19312_02_T08233_02_005_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.10/GEDI01_B_2022130090823_O19300_03_T05987_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.10/GEDI01_B_2022130073533_O19299_03_T05986_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.10/GEDI02_A_2022130073533_O19299_03_T05986_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.10/GEDI01_B_2022130060242_O19298_02_T10407_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.10/GEDI02_A_2022130060242_O19298_02_T10407_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.10/GEDI01_B_2022130025702_O19296_02_T06136_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.09/GEDI01_B_2022129095548_O19285_03_T06584_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.09/GEDI02_A_2022129095548_O19285_03_T06584_02_003_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.09/GEDI01_B_2022129065007_O19283_02_T08005_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.09/GEDI02_A_2022129065007_O19283_02_T08005_02_003_03_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.09/GEDI01_B_2022129034426_O19281_02_T02464_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.07/GEDI01_B_2022127130321_O19256_03_T04780_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.07/GEDI02_A_2022127130321_O19256_03_T04780_02_003_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.07/GEDI01_B_2022127113030_O19255_03_T09048_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.07/GEDI02_A_2022127113030_O19255_03_T09048_02_003_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.07/GEDI01_B_2022127082449_O19253_02_T04930_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.07/GEDI02_A_2022127082449_O19253_02_T04930_02_003_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.06/GEDI01_B_2022126121750_O19240_03_T01107_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.06/GEDI02_A_2022126121750_O19240_03_T01107_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.04/GEDI01_B_2022124121938_O19209_03_T09568_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.04/GEDI01_B_2022124104647_O19208_03_T10990_02_005_03_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.04/GEDI02_A_2022124104647_O19208_03_T10990_02_003_03_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.03/GEDI01_B_2022123082823_O19191_02_T00201_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.03/GEDI01_B_2022123065532_O19190_02_T03046_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.03/GEDI02_A_2022123065532_O19190_02_T03046_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.03/GEDI01_B_2022123052241_O19189_02_T04621_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.02/GEDI01_B_2022122074249_O19175_02_T09335_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.02/GEDI02_A_2022122074249_O19175_02_T09335_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.01/GEDI01_B_2022121144129_O19164_03_T04091_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.01/GEDI01_B_2022121130838_O19163_03_T02820_02_005_02_V002.h5\n", + "\n", + "https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI02_A.002/2022.05.01/GEDI02_A_2022121130838_O19163_03_T02820_02_003_02_V002.h5\n", + "\n", + "No matching L2 file! https://e4ftl01.cr.usgs.gov//GEDI_L1_L2/GEDI/GEDI01_B.002/2022.05.01/GEDI01_B_2022121113547_O19162_03_T05665_02_005_02_V002.h5\n", + "\n", + "Done\n" + ] + } + ], "source": [ - "l1b_urls_fpath = \"../GEDI-L1B-2021-URLS.txt\"\n", - "l2a_urls_fpath = \"../GEDI-L2A-2021-URLS.txt\"\n", + "\n", + "# print(\"Waiting some time to run this cell...\")\n", + "# time.sleep(3600)\n", + "print(\"Running this cell now!\")\n", + "# Get all CSV files\n", + "indir = \"/projects/my-private-bucket/dps_output/arojas_biomass_gedi/master/GEDI-biomass-2022\"\n", + "csv_list = []\n", + "for subdir, dirs, files in os.walk(indir):\n", + " for file in files:\n", + " if file.endswith(\".csv\"):\n", + " # fp = os.path.join(subdir, file)\n", + " # print(fp)\n", + " # csv_list.append(fp)\n", + " csv_list.append(file)\n", + "\n", + "\n", + "# Change to appropriate filepaths for urls\n", + "l1b_urls_fpath = \"/projects/biomass-gedi-conus/data/GEDI-L1B-2022-URLS.txt\"\n", + "l2a_urls_fpath = \"/projects/biomass-gedi-conus/data/GEDI-L2A-2022-URLS.txt\"\n", "\n", "with open(l1b_urls_fpath) as f:\n", " l1b_fpaths = f.readlines()\n", @@ -309,9 +1382,9 @@ " if any(date_str in x for x in csv_list):\n", " continue\n", " \n", - " # print(l1b_fp)\n", - " # print(l2a_fp)\n", - " job = maap.submitJob(identifier=\"single_file_test_run\",\n", + " print(l1b_fp)\n", + " print(l2a_fp)\n", + " job = maap.submitJob(identifier=\"GEDI-biomass-2022\",\n", " algo_id=\"arojas_biomass_gedi\",\n", " version=\"master\",\n", " username=\"arojearthdata\",\n", @@ -320,10 +1393,12 @@ " L2A_URL=l2a_fp)\n", " jobs_list.append(job)\n", " \n", - " if counter%10==0:\n", + " if counter%100==0:\n", " print(\"on file num: \", counter, end='\\r')\n", - " time.sleep(600)\n", - " counter+=1" + " time.sleep(1200)\n", + " counter+=1\n", + "\n", + "print('Done')" ] }, { @@ -393,13 +1468,29 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "23b92856", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1804399", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "osgeo-env", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "osgeo-env" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -411,7 +1502,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/pgap.py b/pgap.py index a6b72fc..f91dfbd 100644 --- a/pgap.py +++ b/pgap.py @@ -1,9 +1,9 @@ # Import libraries import numpy as np from scipy.spatial import KDTree -from scipy import ndimage import warnings warnings.filterwarnings("ignore") +from scipy import ndimage # --------------------------DATA STRUCTURE FOR PGAP PROCESSED WAVEFORM-------------------------- # @@ -28,7 +28,7 @@ class GapDS: def __init__(self, wf, ht, rh, calc_refl_vg = False, - utm_x=None,utm_y=None,cval=2,**kwargs): + utm_x=None,utm_y=None,cval=2, **kwargs): """ @@ -313,7 +313,7 @@ def wf_smooth(wfarr,sigmean=None,sd=5): sigmean = np.array(sigmean) if wfarr.ndim != sigmean.ndim: sigmean = sigmean.reshape(-1,1) - wfarr = wfarr - sigmean + wfarr = wfarr - sigmean # 2 smoothwfs = ndimage.gaussian_filter1d(wfarr,sd) # Return the smoothed wf diff --git a/run.sh b/run.sh index 047fbb5..4a8471e 100644 --- a/run.sh +++ b/run.sh @@ -4,11 +4,11 @@ basedir=$( cd "$(dirname "$0")" ; pwd -P) # Activate environment that was created in the builed-env.sh file -source activate osgeo-env +# source activate osgeo-env # Create output dir # OUTPUTDIR="${PWD}/output" -mkdir -p output +# mkdir -p output # INPUT_FILES L1B_URL=$1 L2A_URL=$2 # e.g. GEDI01_B or GEDI02_A -- GitLab