forked from mhpicel/houston_cell_tracking
/
marcus_calcs.py
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/
marcus_calcs.py
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import pandas as pd
from dask import dataframe as dd
import numpy as np
import pyart
import gc
from datetime import datetime
def get_grid_size(grid_obj):
z_size = grid_obj.z['data'][1] - grid_obj.z['data'][0]
y_size = grid_obj.y['data'][1] - grid_obj.y['data'][0]
x_size = grid_obj.x['data'][1] - grid_obj.x['data'][0]
return np.array([z_size, y_size, x_size])
def get_grid_z(grid_size, alt_meters):
return np.int(np.round(alt_meters/grid_size[0]))
def parse_date_string(date_string):
return datetime.strptime(date_string, '%Y-%m-%d %H:%M:%S')
def filename_from_dt(dt, base):
date = dt.strftime('%Y%m%d')
file_name = '/KHGX_grid_' + dt.strftime('%Y%m%d.%H%M%S')
ext = '.nc'
return base + date + file_name + ext
def haversine(lat1, lon1, lat2, lon2):
R = 6372800. # Earth radius in kilometers
dLat = np.radians(lat2 - lat1)
dLon = np.radians(lon2 - lon1)
lat1 = np.radians(lat1)
lat2 = np.radians(lat2)
a = np.sin(dLat/2)**2 + np.cos(lat1)*np.cos(lat2)*np.sin(dLon/2)**2
c = 2*np.arcsin(np.sqrt(a))
return R * c
#%%
def check_keys(grid, scan_group):
keys = set(['cross_correlation_ratio', 'reflectivity',
'specific_differential_phase', 'differential_reflectivity'])
if keys <= set(grid.fields.keys()):
return False, None
else:
nan_row = pd.Series({'kdp_pct': np.nan,
'zdr_pct': np.nan,
'zhh_pct': np.nan,
'kdp_pet': np.nan,
'zdr_pet': np.nan,
'zhh_pet': np.nan})
nan_output = scan_group.apply(lambda row: nan_row, axis=1)
return True, pd.DataFrame(nan_output)
def preprocess_data(data, pars, rho=None, zhh=None):
data = np.ma.masked_values(data, pars['fill_value'])
if rho is not None:
data = np.ma.masked_where(rho < pars['rho_thresh'], data)
data = np.ma.masked_where(zhh < pars['dbz_thresh'], data)
return data
def get_neighborhood(dist_from_cent,
kdp_proc, kdp_int, pars, kdp_thresh=False):
circle = dist_from_cent < pars['radius']
circle = np.tile(circle, (kdp_proc.shape[0], 1, 1))
layers = np.zeros_like(kdp_proc.data)
layers[pars['zmelt']:pars['zmelt']+pars['nlayers']+1, :, :] = 1
neighborhood = np.logical_and(circle, layers.astype('bool'))
if kdp_thresh:
kdp_95 = np.percentile(kdp_proc[neighborhood], 95.)
filtered_circle = np.logical_and(circle, kdp_int > kdp_95)
neighborhood = np.logical_and(filtered_circle, layers.astype('bool'))
return neighborhood
def cell_calcs(lat, lon, kdp_proc, zdr_proc, zhh_proc,
kdp_pei, zdr_pei, zhh_pei, kdp_int, pars):
grid_ll = pars['grid_ll']
dist_from_cent = haversine(lat, lon, grid_ll[1], grid_ll[0])
neighborhood = get_neighborhood(dist_from_cent, kdp_proc, kdp_int, pars)
kdp_pct = np.percentile(kdp_proc[neighborhood], pars['pctile'])
zdr_pct = np.percentile(zdr_proc[neighborhood], pars['pctile'])
zhh_pct = np.percentile(zhh_proc[neighborhood], pars['pctile'])
if pars['use_kdp_thresh']:
neighborhood = get_neighborhood(dist_from_cent, kdp_proc,
kdp_int, kdp_thresh=True)
if not np.any(neighborhood):
print('isempty')
kdp_pet = 0
zdr_pet = 0
zhh_pet = 0
else:
kdp_pet = kdp_pei[neighborhood].sum()/neighborhood.sum()
zdr_pet = zdr_pei[neighborhood].sum()/neighborhood.sum()
zhh_pet = zhh_pei[neighborhood].sum()/neighborhood.sum()
return pd.Series({'kdp_pct': kdp_pct,
'zdr_pct': zdr_pct,
'zhh_pct': zhh_pct,
'kdp_pet': kdp_pet,
'zdr_pet': zdr_pet,
'zhh_pet': zhh_pet})
def marcus_stats(scan_group, pars):
file_name = scan_group['file'].iloc[0]
grid = pyart.io.read_grid(file_name)
bad_keys, nan_frame = check_keys(grid, scan_group)
if bad_keys:
return nan_frame
rho = grid.fields['cross_correlation_ratio']['data']
zhh = grid.fields['reflectivity']['data']
kdp = grid.fields['specific_differential_phase']['data']
zdr = grid.fields['differential_reflectivity']['data']
kdp_proc = preprocess_data(kdp, pars, rho=rho, zhh=zhh)
zdr_proc = preprocess_data(zdr, pars, rho=rho, zhh=zhh)
zhh_proc = preprocess_data(zhh, pars)
kdp_int = np.sum(
kdp_proc[pars['zmelt']:pars['zmelt']+pars['nlayers']+1, :, :],
axis=0
)
z_column = grid.z['data'][:, np.newaxis, np.newaxis]
kdp_pei = kdp_proc*z_column
zdr_pei = zdr_proc*z_column
zhh_pei = zhh_proc*z_column
def get_cell_calcs(cell_row):
"""Wraps cell_calcs to be passed to apply call."""
return cell_calcs(cell_row['lat'], cell_row['lon'],
kdp_proc, zdr_proc, zhh_proc,
kdp_pei, zdr_pei, zhh_pei,
kdp_int, pars)
output = scan_group.apply(get_cell_calcs, axis=1)
marcus_frame = pd.DataFrame(output)
print(marcus_frame)
del grid, rho, zhh, kdp, zdr, kdp_proc, zdr_proc, zhh_proc, kdp_int
del kdp_pei, zdr_pei, zhh_pei, nan_frame
gc.collect()
return marcus_frame
def attach_marcus_stats(tracks_frame, zmelt_meters=4000, layer_size=1500,
radius=5000, pctile=98., use_kdp_thresh=False,
rho_thresh=0.8, dbz_thresh=15, fill_value=-9999.):
# setup
file_name = tracks_frame['file'].iloc[0]
grid = pyart.io.read_grid(file_name)
zmelt, nlayers, grid_ll = get_gpars(grid, zmelt_meters, layer_size)
pars = {'zmelt': zmelt,
'nlayers': nlayers,
'grid_ll': grid_ll,
'radius': radius,
'pctile': pctile,
'use_kdp_thresh': use_kdp_thresh,
'rho_thresh': rho_thresh,
'dbz_thresh': dbz_thresh,
'fill_value': fill_value}
stats = tracks_frame.groupby(level='scan').apply(lambda scan:
marcus_stats(scan, pars))
return tracks_frame.join(stats)
def get_gpars(grid, zmelt_meters, layer_size):
grid_size = get_grid_size(grid)
zmelt = get_grid_z(grid_size, zmelt_meters)
nlayers = get_grid_z(grid_size, layer_size)
grid_ll = grid.get_point_longitude_latitude()
return zmelt, nlayers, grid_ll
#%%
if __name__ =='__main__':
grid_dir = '/home/mhpicel/blues_earthscience/radar/houston/data/'
test_tracks_path = '/home/mhpicel/NASA/july2015/refl_long_storm.csv'
test_tracks = pd.read_csv(test_tracks_path)
test_tracks.set_index(['scan', 'uid'], inplace=True)
test_tracks = test_tracks.loc[:10]
test_tracks['time'] = test_tracks['time'].apply(parse_date_string)
def get_filenames(dt):
return filename_from_dt(dt, grid_dir)
test_tracks['file'] = test_tracks['time'].apply(get_filenames)
start = datetime.now()
out_tracks = attach_marcus_stats(test_tracks)
print(datetime.now()-start)