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delivery_plume_tiler.py
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delivery_plume_tiler.py
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import argparse
import subprocess
import datetime
import masked_plume_delineator
import logging
from spectral.io import envi
import numpy as np
import os
from utils import envi_header
from osgeo import gdal
import pandas as pd
import time
import json
import glob
from scrape_refine_upload import write_color_plume, rawspace_coordinate_conversion
from apply_glt import single_image_ortho
from copy import deepcopy
from rasterio.features import rasterize
from shapely.geometry import Polygon
import matplotlib.pyplot as plt
import requests
if os.environ.get("GHG_DEBUG"):
logging.info("Using internal ray")
import rray as ray
else:
import ray
class SerialEncoder(json.JSONEncoder):
"""Encoder for json to help ensure json objects can be passed to the workflow manager.
"""
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
else:
return super(SerialEncoder, self).default(obj)
def write_science_cog(output_img, output_file, geotransform, projection, metadata):
tmp_file = os.path.splitext(output_file)[0] + '_tmp.tif'
driver = gdal.GetDriverByName('GTiff')
driver.Register()
outDataset = driver.Create(tmp_file,output_img.shape[1],output_img.shape[0],1,gdal.GDT_Float32,options = ['COMPRESS=LZW'])
md = outDataset.GetMetadata()
md.update(metadata)
outDataset.SetMetadata(md)
outDataset.GetRasterBand(1).WriteArray(output_img)
outDataset.GetRasterBand(1).SetNoDataValue(-9999)
outDataset.SetProjection(projection)
outDataset.SetGeoTransform(geotransform)
del outDataset
subprocess.call(f'sh /home/brodrick/bin/cog.sh {tmp_file} {output_file} 1',shell=True)
subprocess.call(f'rm {tmp_file}',shell=True)
def tile_dcid(features, outdir, datadir):
dcid = features[0]["properties"]["DCID"]
ds = gdal.Open(os.path.join(datadir, f'dcid_{dcid}_mf_ort.tif'))
dat = ds.ReadAsArray().squeeze()
plume_mask = np.zeros(dat.shape,dtype=bool)
for feat in features:
outmask_ort_file = os.path.join(datadir, f'{feat["properties"]["Plume ID"]}_mask_ort.tif')
loc_dcid_mask = np.squeeze(gdal.Open(outmask_ort_file).ReadAsArray()).astype(bool)
plume_mask[loc_dcid_mask] = 1
color_ort_file = os.path.join(outdir, f'{dcid}_color_ort.tif')
write_color_plume(dat, plume_mask, ds, color_ort_file, style='ch4')
fids = np.unique([sublist for feat in features for sublist in feat['properties']['Scene FIDs']])
start_date=fids[0][4:]
start_ftime=fids[0].split('t')[-1].split('_')[0]
end_date=fids[-1][4:]
end_ftime=fids[-1].split('t')[-1].split('_')[0]
od_date = f'{start_date[:4]}-{start_date[4:6]}-{start_date[6:8]}T{start_ftime[:2]}_{start_ftime[2:4]}_{start_ftime[4:]}Z-to-{end_date[:4]}-{end_date[4:6]}-{end_date[6:8]}T{end_ftime[:2]}_{end_ftime[2:4]}_{str(int(end_ftime[4:6])+1):02}Z'
cmd_str = f'gdal2tiles.py -z 2-12 --srcnodata 0 --processes=40 -r antialias {color_ort_file} {outdir}/{od_date} -x'
subprocess.call(cmd_str,shell=True)
@ray.remote
def single_plume_proc(all_plume_meta, index, output_base, dcid_sourcedir, source_dir, extra_metadata):
plume_dict = {"crs": {"properties": {"name": "urn:ogc:def:crs:OGC:1.3:CRS84" }, "type": "name"},"features":[],"name":"methane_metadata","type":"FeatureCollection" }
plume_dict['features'] = [deepcopy(all_plume_meta['features'][index])]
plume_id = plume_dict['features'][0]['properties']['Plume ID']
# rasterize that polygon
ds = gdal.Open(os.path.join(dcid_sourcedir, f'dcid_{plume_dict["features"][0]["properties"]["DCID"]}_mf_ort.tif'))
dat = ds.ReadAsArray().squeeze()
rawspace_coords = rawspace_coordinate_conversion([], plume_dict['features'][0]['geometry']['coordinates'][0], ds.GetGeoTransform(), ortho=True)
manual_mask = rasterize(shapes=[Polygon(rawspace_coords)], out_shape=(dat.shape[0],dat.shape[1])) # numpy binary mask for manual IDs
y_locs = np.where(np.sum(manual_mask > 0, axis=1))[0]
x_locs = np.where(np.sum(manual_mask > 0, axis=0))[0]
dat[manual_mask < 1] = -9999
dat = dat[y_locs[0]:y_locs[-1],x_locs[0]:x_locs[-1]]
outtrans = list(ds.GetGeoTransform())
outtrans[0] = outtrans[0] + x_locs[0]*outtrans[1]
outtrans[3] = outtrans[3] + y_locs[0]*outtrans[5]
scene_names = []
for _s in range(len(plume_dict['features'][0]['properties']['Scene FIDs'])):
fid =plume_dict['features'][0]['properties']['Scene FIDs'][_s]
scene =plume_dict['features'][0]['properties']['DAAC Scene Numbers'][_s]
orbit =plume_dict['features'][0]['properties']['Orbit']
scene_names.append(f'EMIT_L2B_CH4ENH_{extra_metadata["product_version"]}_{fid[4:12]}T{fid[13:19]}_{orbit}_{scene}')
metadata = {
'Plume_Complex': plume_dict['features'][0]['properties']['Plume ID'],
'Estimated_Uncertainty_ppmm': plume_dict['features'][0]['properties']['Concentration Uncertainty (ppm m)'],
'UTC_Time_Observed': plume_dict['features'][0]['properties']['UTC Time Observed'],
#Source_Scenes - match full conventions j
'Source_Scenes': ','.join(scene_names),
'Latitude of max concentration': plume_dict['features'][0]['properties']['Latitude of max concentration'],
'Longitude of max concentration': plume_dict['features'][0]['properties']['Longitude of max concentration'],
'Max Plume Concentration (ppm m)': plume_dict['features'][0]['properties']['Max Plume Concentration (ppm m)'],
}
metadata.update(extra_metadata)
write_science_cog(dat, output_base + '.tif', outtrans, ds.GetProjection(), metadata)
write_color_quicklook(dat, output_base + '.png')
plume_output_file = os.path.join(output_base + '.json')
# conger the DAAC Scene Numbers to full dac names, as above
plume_dict['features'][0]['properties']['DAAC Scene Names'] = scene_names
del plume_dict['features'][0]['properties']['style']
del plume_dict['features'][0]['properties']['Data Download']
with open(plume_output_file, 'w') as fout:
fout.write(json.dumps(plume_dict, cls=SerialEncoder))
def write_color_quicklook(indat, output_file):
dat = indat.copy()
mask = dat != -9999
dat[dat < 0] = 0
dat = dat /1500.
output = np.zeros((indat.shape[0],indat.shape[1],3),dtype=np.uint8)
output[mask,:] = np.round(plt.cm.plasma(dat[mask])[...,:3] * 255).astype(np.uint8)
output[mask,:] = np.maximum(1, output[mask])
memdriver = gdal.GetDriverByName('MEM')
memdriver.Register()
outDataset = memdriver.Create('',dat.shape[1],dat.shape[0],3,gdal.GDT_Byte)
for n in range(1,4):
outDataset.GetRasterBand(n).WriteArray(output[...,n-1])
outDataset.GetRasterBand(n).SetNoDataValue(0)
driver = gdal.GetDriverByName('PNG')
driver.Register()
dst_ds = driver.CreateCopy(output_file, outDataset, strict=0)
del dst_ds, outDataset
@ray.remote
def single_scene_proc(input_file, output_file, extra_metadata):
ds = gdal.Open(input_file)
dat = ds.ReadAsArray().squeeze()
write_science_cog(dat, output_file, ds.GetGeoTransform(), ds.GetProjection(), extra_metadata)
write_color_quicklook(dat, output_file.replace('.tif','.png'))
def get_daac_link(feature, product_version, outbasedir):
prod_v = product_version.split('V')[-1]
fid=feature['Scene FIDs'][0]
cid= feature['Plume ID'].split('-')[-1].zfill(6)
link = f'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/EMITL2BCH4PLM.{prod_v}/EMIT_L2B_CH4PLM_{prod_v}_{fid[4:12]}T{fid[13:19]}_{cid}/EMIT_L2B_CH4PLM_{prod_v}_{fid[4:12]}T{fid[13:19]}_{cid}.tif'
if len(glob.glob(os.path.join(outbasedir, fid[4:12], 'l2bch4plm', f'EMIT_L2B_CH4PLM_{prod_v}_{fid[4:12]}T{fid[13:19]}_{cid}*.json'))) > 0:
return link
else:
return 'Coming soon'
def main(input_args=None):
parser = argparse.ArgumentParser(description="Delineate/colorize plume")
parser.add_argument('--source_dir', type=str, default='methane_20230813')
parser.add_argument('--dest_dir', type=str, default='visions_delivery')
parser.add_argument('--manual_del_dir', type=str, default='/beegfs/scratch/brodrick/methane/ch4_plumedir_scenetest/')
parser.add_argument('--software_version', type=str, default=None)
parser.add_argument('--data_version', type=str, default=None)
parser.add_argument('--visions_delivery', type=int, choices=[0,1,2],default=0)
parser.add_argument('--n_cores', type=int, default=1)
parser.add_argument('--overwrite', action='store_true')
parser.add_argument('--loglevel', type=str, default='DEBUG', help='logging verbosity')
parser.add_argument('--logfile', type=str, default=None, help='output file to write log to')
args = parser.parse_args(input_args)
logging.basicConfig(format='%(levelname)s:%(asctime)s ||| %(message)s', level=args.loglevel,
filename=args.logfile, datefmt='%Y-%m-%d,%H:%M:%S')
tile_dir = os.path.join(args.dest_dir, 'ch4_plume_tiles')
all_plume_meta = json.load(open(f'{args.manual_del_dir}/combined_plume_metadata.json'))
unique_fids = np.unique([sublist for feat in all_plume_meta['features'] for sublist in feat['properties']['Scene FIDs']])
dcids = np.array([feat['properties']['DCID'] for feat in all_plume_meta['features']])
unique_dcids = np.unique(dcids)
valid_plume_idx = [x for x, feat in enumerate(all_plume_meta['features']) if feat['properties']['style']['color'] == 'white' and feat['geometry']['type'] == 'Polygon']
valid_point_idx = [x for x, feat in enumerate(all_plume_meta['features']) if feat['properties']['style']['color'] == 'white' and feat['geometry']['type'] == 'Point']
plume_count = 1
ray.init(num_cpus=args.n_cores)
extra_metadata = {}
if args.software_version:
extra_metadata['software_build_version'] = args.software_version
else:
cmd = ["git", "symbolic-ref", "-q", "--short", "HEAD", "||", "git", "describe", "--tags", "--exact-match"]
output = subprocess.run(" ".join(cmd), shell=True, capture_output=True)
if output.returncode != 0:
raise RuntimeError(output.stderr.decode("utf-8"))
extra_metadata['software_build_version'] = output.stdout.decode("utf-8").replace("\n", "")
if args.data_version:
extra_metadata['product_version'] = args.data_version
extra_metadata['keywords'] = "Imaging Spectroscopy, minerals, EMIT, dust, radiative forcing"
extra_metadata['sensor'] = "EMIT (Earth Surface Mineral Dust Source Investigation)"
extra_metadata['instrument'] = "EMIT"
extra_metadata['platform'] = "ISS"
extra_metadata['Conventions'] = "CF-1.63"
extra_metadata['institution'] = "NASA Jet Propulsion Laboratory/California Institute of Technology"
extra_metadata['license'] = "https://science.nasa.gov/earth-science/earth-science-data/data-information-policy/"
extra_metadata['naming_authority'] = "LPDAAC"
extra_metadata['date_created'] = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%SZ")
extra_metadata['keywords_vocabulary'] = "NASA Global Change Master Directory (GCMD) Science Keywords"
extra_metadata['stdname_vocabulary'] = "NetCDF Climate and Forecast (CF) Metadata Convention"
extra_metadata['creator_name'] = "Jet Propulsion Laboratory/California Institute of Technology"
extra_metadata['creator_url'] = "https://earth.jpl.nasa.gov/emit/"
extra_metadata['project'] = "Earth Surface Mineral Dust Source Investigation"
extra_metadata['project_url'] = "https://earth.jpl.nasa.gov/emit/"
extra_metadata['publisher_name'] = "NASA LPDAAC"
extra_metadata['publisher_url'] = "https://lpdaac.usgs.gov"
extra_metadata['publisher_email'] = "[email protected]"
extra_metadata['identifier_product_doi_authority'] = "https://doi.org"
extra_metadata['title'] = "EMIT"
extra_metadata['Units']= 'ppm m'
if args.visions_delivery != 2:
jobs = []
for _feat, feat in enumerate(all_plume_meta['features']):
if _feat not in valid_plume_idx:
continue
logging.info(f'Processing plume {_feat+1}/{len(all_plume_meta["features"])}')
if feat['geometry']['type'] == 'Polygon':
outdir=os.path.join(args.dest_dir, feat['properties']['Scene FIDs'][0][4:12], 'l2bch4plm')
if os.path.isdir(outdir) is False:
subprocess.call(f'mkdir -p {outdir}',shell=True)
output_base = os.path.join(outdir, feat['properties']['Scene FIDs'][0] + '_' + feat['properties']['Plume ID'])
if args.overwrite or os.path.isfile(output_base) is False:
jobs.append(single_plume_proc.remote(all_plume_meta, _feat, output_base, args.manual_del_dir, args.source_dir, extra_metadata))
rreturn = [ray.get(jid) for jid in jobs]
jobs = []
for fid in unique_fids:
outdir = os.path.join(args.dest_dir, fid[4:12], 'l2bch4enh')
if os.path.isdir(outdir) is False:
subprocess.call(f'mkdir -p {outdir}',shell=True)
of = os.path.join(outdir, fid + 'ch4_enh.tif')
if args.overwrite or os.path.isfile(of) is False:
jobs.append(single_scene_proc.remote(os.path.join(args.source_dir, fid[4:12], fid + '_ch4_mf_ort'), of, extra_metadata))
rreturn = [ray.get(jid) for jid in jobs]
if args.visions_delivery == 1 or args.visions_delivery == 2:
outdir = os.path.join(args.dest_dir, 'visions_ch4_tiles')
if os.path.isdir(outdir) is False:
subprocess.call(f'mkdir -p {outdir}',shell=True)
logging.info('Build output geojson')
outdict = {"crs": {"properties": {"name": "urn:ogc:def:crs:OGC:1.3:CRS84" }, "type": "name"},"features":[],"name":"methane_metadata","type":"FeatureCollection" }
for nmi in valid_plume_idx:
newfeat = all_plume_meta['features'][nmi].copy()
pc = newfeat['properties']['Plume ID']
if newfeat['geometry']['type'] == 'Polygon':
newfeat['properties']['plume_complex_count'] = plume_count
newfeat['properties']['Data Download'] = get_daac_link(newfeat['properties'], extra_metadata['product_version'], args.dest_dir)
outdict['features'].append(newfeat)
for npi in valid_point_idx:
pointfeat = all_plume_meta['features'][npi].copy()
if pointfeat['properties']['Plume ID'] == newfeat['properties']['Plume ID']:
pointfeat['properties']['plume_complex_count'] = plume_count
pointfeat['properties']['Data Download'] = newfeat['properties']['Data Download']
pointfeat['properties']['style'] = {'color': 'red','fillOpacity':0,'maxZoom':9,'minZoom':0,'opacity':1,'radius':10,'weight':2}
outdict['features'].append(pointfeat)
break
plume_count += 1
with open(os.path.join(args.dest_dir, 'combined_plume_metadata.json'), 'w') as fout:
fout.write(json.dumps(outdict, cls=SerialEncoder))
subprocess.call("rsync visions_delivery/combined_plume_metadata.json brodrick@${EMIT_SCIENCE_IP}:/data/emit/mmgis/coverage/combined_plume_metadata.json",shell=True)
logging.info('Tile output')
for _dcid, dcid in enumerate(unique_dcids):
logging.info(f'Tiling {_dcid + 1} / {len(unique_dcids)}')
match_idx = np.where(dcids == dcid)[0]
subfeatures = [feat for _feat, feat in enumerate(all_plume_meta['features']) if _feat in match_idx and _feat in valid_plume_idx]
if len(subfeatures) > 0:
tile_dcid(subfeatures, outdir, args.manual_del_dir)
subprocess.call("rsync -a --info=progress2 visions_delivery/visions_ch4_tiles/ brodrick@${EMIT_SCIENCE_IP}:/data/emit/mmgis/mosaics/ch4_plume_tiles/",shell=True)
if __name__ == '__main__':
main()