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forwardmodel_OIB_ALL.py
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forwardmodel_OIB_ALL.py
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# import sys
# import os
import time
# import numpy as np
# import matplotlib.pyplot as plt
# import matplotlib.cm as cm
# from datetime import datetime, date, time
from fatiando.gravmag import talwani
from fatiando.mesher import Polygon
from my_OIB_functions import *
def forward_oib(basedir, infile, dropturns=False, make_plots=False, diagnostics=False):
# type: (object, object, object, object) -> object
# -*- coding: utf-8 -*-
# %load_ext autoreload
# %autoreload 2
pd.options.mode.chained_assignment = None # None or 'warn' or 'raise'
pd.set_option("display.max_rows", 20)
# pd.set_option("precision",13)
pd.set_option('expand_frame_repr', False)
'''
Read in file
'''
# if os.path.isdir('/Volumes/C/'):
# basedir = '/Volumes/C/data/Antarctic/OIB/ATM/2009_AN_NASA_ATM'
# else:
# basedir = '/Volumes/BOOTCAMP/data/Antarctic/OIB/ATM/2009_AN_NASA_ATM'
# basedir = '/Users/dporter/Documents/data_local/Antarctica/OIB/'
# basedir = 'data'
outdir = os.path.join(basedir, 'integrated', 'forward')
df = pd.read_csv(infile)
'''
Preliminary Processing
'''
# Crossing Horizons
# df.ix[df['SURFACE_atm'] - df['ICEBASE'] <= 0, 'ICEBASE'] = (df['SURFACE_atm'] - 0.1)
# df.loc[df['surface_recalc'] - df['ICEBASE'] <= 0, 'ICEBASE'] = (df['surface_recalc'] - 0.1)
df.loc[df['surface_recalc'] - df['icebase_recalc'] <= 0, 'icebase_recalc'] = (df['surface_recalc'] - 0.1)
#
df = df.round({'RTOPO2_icemask': 0, 'D_gravmask': 0})
# TODO Add water block when HYDROAPPROX close to icebase_recalc OR use RTOPO icemask
if make_plots:
pdir = os.path.join('figs', str(df['DATE'].values[0]))
if not os.path.exists(pdir):
os.makedirs(pdir)
# Compute distance along transect
distance = np.zeros((np.size(df['LONG'])))
for i in range(2, np.size(df['LONG'])):
distance[i] = distance[i - 1] + haversine([df['LAT'].values[i - 1], df['LONG'].values[i - 1]],
[df['LAT'].values[i], df['LONG'].values[i]])
df['DIST'] = distance
# Drop where no gravity
if dropturns:
df = df.dropna(subset=['FAG070'])
'''
Run functions to read in each flight
'''
dflist = {}
dflst = [g for _, g in df.groupby(['LINE'])]
for dnum, dname in enumerate(dflst, start=0):
# print 'Mode of ATM is %.2f' % (mode)
print('L%s' % (str(dname['LINE'].values[0])))
dist = dname['DIST'][0:].values
fag070 = dname['FAG070'][0:].values
print('Distance of line is %i km' % (dist[-1] - dist[0]))
if np.isfinite(fag070).any() and (dist[-1] - dist[0] < 5e2) and (dname['surface_recalc'].any()):
if diagnostics:
print('Processing the line.')
'''
Extract data from DataFrame
'''
rho_i = 915
rho_w = 1005
rho_r = 2670
### Interpolate
if dname['icebase_recalc'].isnull().all():
print('No Icebase for this flight...')
dname['icebase_recalc'] = dname['surface_recalc'] - 1
# if dname['surface_recalc'].all():
# print('No surface for this flight...')
# TODO should these horizons be interpolated in previous script (read_OIB_ALL.py)?
dname['ICESFC_horizon'] = dname['surface_recalc']
dname['ICESFC_horizon'].interpolate(method='pad', inplace=True)
dname['ICEBASE_horizon'] = dname['icebase_recalc']
dname['ICEBASE_horizon'].interpolate(method='linear', limit_area='inside', axis=0, inplace=True)
# Find ice front position
firstmean = np.mean(dname['ICEBASE_horizon'].iloc[:5]) # or 'icebase_recalc', but this breaks when no bed
lastmean = np.mean(dname['ICEBASE_horizon'].iloc[-5:])
firsticepoint = dname['ICEBASE_horizon'].first_valid_index() - 1
lasticepoint = dname['ICEBASE_horizon'].last_valid_index() + 1
if np.isnan(firstmean):
if diagnostics:
print('Missing data at start of line.')
# firsticepoint = dname['icebase_recalc'].first_valid_index() - 1
if dname['ICESFC_horizon'].loc[firsticepoint] >= 120:
if diagnostics:
print('NOT floating.')
dname['ICEBASE_horizon'].interpolate(method='linear', limit_direction='backward', axis=0,
inplace=True)
else:
# make ice constant thick at end of line
if diagnostics:
print('LIKELY floating.')
dname['ICEBASE_horizon'].loc[firsticepoint] = dname['ICESFC_horizon'].loc[firsticepoint] - 1
dname['ICEBASE_horizon'].iloc[0] = dname['ICESFC_horizon'].iloc[0] - 1
else:
if diagnostics:
print('NO missing data at start of line.')
# Find ice front position
# lastmean = np.mean(dname['icebase_recalc'].iloc[-5:])
if np.isnan(lastmean):
if diagnostics:
print('Missing data at end of line.')
# lasticepoint = dname['icebase_recalc'].last_valid_index() + 1
if dname['ICESFC_horizon'].loc[lasticepoint] >= 120:
if diagnostics:
print('NOT floating.')
dname['ICEBASE_horizon'].interpolate(method='linear', limit_direction='forward', axis=0,
inplace=True)
else:
# make ice constant thick at end of line
if diagnostics:
print('LIKELY floating.')
dname['ICEBASE_horizon'].loc[lasticepoint] = dname['ICESFC_horizon'].loc[lasticepoint] - 1
dname['ICEBASE_horizon'].iloc[-1] = dname['ICESFC_horizon'].iloc[-1] - 1
else:
if diagnostics:
print('NO missing data at end of line.')
### Final INNER interpolation
dname['ICEBASE_horizon'].interpolate(method='linear', limit_area='inside', axis=0, inplace=True)
# Check for crossing horizons AGAIN?
# dname.loc[dname['ICESFC_horizon']-dname['ICEBASE_horizon'] <= 0, 'ICEBASE_horizon'] = (dname['ICESFC_horizon'] - 0.1)
# Create water base, set it to single depth below ice
dname['WATERBASE_horizon'] = dname['ICEBASE_horizon'] - 1
if np.mean(dname['ICESFC_horizon'].iloc[:5]) < 120:
print('Adding water block to START.')
dname['WATERBASE_horizon'].loc[dname['WATERBASE_horizon'].index[0]:firsticepoint] = \
dname['WATERBASE_horizon'].loc[
firsticepoint + 1]
if np.mean(dname['ICESFC_horizon'].iloc[-5:]) < 120:
print('Adding water block to END.')
dname['WATERBASE_horizon'].loc[lasticepoint:dname['WATERBASE_horizon'].index[-1]] = \
dname['WATERBASE_horizon'].loc[
lasticepoint - 1]
# if make_plots:
# pdir = os.path.join('figs', str(dname['DATE'].values[0]))
# print(pdir)
# if not os.path.exists(pdir):
# os.makedirs(pdir)
# oib_lineplot_all(dname, str(dname['DATE'].values[0])[:10] + '_L' + str(dname['LINE'].values[0]),
# os.path.join(pdir, str(dname['LINE'].values[0])+'_forward_lineplot.png'))
'''
Convert OIB data to polygon arrays
'''
icesfc = dname['ICESFC_horizon'][0:].values
icebase = dname['ICEBASE_horizon'][0:].values
# iceoutline = np.append(np.concatenate([icesfc, icebase[::-1]], axis=0), icesfc[0]) # no extension
iceoutline = make_outline(icesfc, icebase)
# plt.figure(facecolor='white'); plt.plot(iceoutline[1180:1220])
watertop = icebase
waterbase = dname['WATERBASE_horizon'][0:].values
wateroutline = make_outline(watertop, waterbase)
rocktop = waterbase
rockbase = -30000 * np.ones_like(waterbase)
# rockoutline = np.append(icebase[0], np.concatenate([icebase, z_r], axis=0), icebase[0], icebase[0])
rockoutline = make_outline(rocktop, rockbase)
### Distances
x = dist * 1000
xs = make_outline_dist(x, 1e6)
### Heights
elevation = dname['WGSHGT'][0:].values
# z = int(np.max(elevation))
# z = int(np.max(icesfc) + 1) * np.ones_like(x)
try:
z = elevation + int(np.max(icesfc) + 1)
except ValueError:
z = elevation + 50
# z = np.max(elevation) * np.ones_like(x)
# z = elevation + 50
'''
Build the Polygon
'''
props_i = {'density': rho_i}
props_r = {'density': rho_r}
props_w = {'density': rho_w}
# polygon = Polygon(np.transpose([xs, iceoutline]), props_i)
# polygons = [Polygon(np.transpose([xs, iceoutline]), props_i),
# Polygon(np.transpose([xs, rockoutline]), props_r)
# ]
polygons = [Polygon(np.transpose([xs, iceoutline]), props_i),
Polygon(np.transpose([xs, wateroutline]), props_w),
Polygon(np.transpose([xs, rockoutline]), props_r)
]
'''
Forward Model
'''
gz = talwani.gz(x, z, polygons)
gz_adj = (gz - np.nanmean(gz)) + np.nanmean(fag070)
n = len(gz_adj)
rmse = np.linalg.norm(gz_adj - fag070) / np.sqrt(n)
# print 'modeled'
'''
Make new channels
'''
# print 'make new channels'
try:
dname.loc[:, 'rmse'] = rmse
dname.loc[:, 'RESIDUAL'] = gz_adj - fag070
dname.loc[:, 'GZ'] = gz_adj
except:
dname.loc[:, 'rmse'] = 0
dname.loc[:, 'RESIDUAL'] = 0
dname.loc[:, 'GZ'] = 0
'''
Models of Intermediate Complexity
'''
# dnum = 7
# Interpolate across NaNs in compilation bedrock
dflst[dnum]['RTOPO2_bedrock'].interpolate(method='linear', axis=0, inplace=True)
# Remove Ice Mass Contribution
# Remove Water Mass Contribution
# Bouguer correction rock
# dflst[dnum]['BOUGUER_CORR_1'] = dflst[dnum]['RTOPO2_bedrock'] * 0.0419088 * (np.abs(rho_w - rho_r)) / 1000
dflst[dnum]['BOUGUER_CORR'] = np.where(dflst[dnum]['RTOPO2_bedrock'] > 0,
dflst[dnum]['RTOPO2_bedrock'] * 0.0419088 * (
np.abs(rho_r)) / 1000,
dflst[dnum]['RTOPO2_bedrock'] * 0.0419088 * (
np.abs(rho_w - rho_r)) / 1000)
# Bouguer Anamoly
dflst[dnum]['BOUGUER_ANOMALY'] = dflst[dnum]['FAG070'] - dflst[dnum]['BOUGUER_CORR']
# dflst[dnum][['BOUGUER_ANOMALY', 'BOUGUER_ANOMALY_1']].plot();
# plt.show()
# Low-pass filter (fill NaNs first)
dflst[dnum]['BOUGUER_ANOMALY'].interpolate(method='cubic', axis=0, inplace=True)
## Savitzky-Golay
# from scipy import signal
# dflst[dnum]['BOUGUER_ANOMALY_LP'] = signal.savgol_filter(dflst[dnum]['BOUGUER_ANOMALY'].values,
# window, 2, mode='nearest')
# dflst[dnum][['BOUGUER_ANOMALY', 'BOUGUER_ANOMALY_LP', 'FAG070']].plot();
# plt.show()
## Butterworth (filtfilt)
from scipy.signal import butter, filtfilt
import scipy.signal as signal
N = 1 # Filter order
# Wn = 0.4 # Cutoff frequency
lp_length_wallclock = 70
Wn = 1 / (lp_length_wallclock / np.nanmean(np.diff(dflst[dnum]['UNIX'])))
B, A = signal.butter(N, Wn, output='ba')
dflst[dnum]['BOUGUER_ANOMALY_LP'] = signal.filtfilt(B, A, dflst[dnum]['BOUGUER_ANOMALY'].values,
method="gust")
# dflst[dnum][['BOUGUER_ANOMALY', 'BOUGUER_ANOMALY_LP', 'FAG070']].plot();
# plt.show()
# Floating Ice
dflst[dnum]['D_floatice'] = np.where(dflst[dnum]['RTOPO2_icemask'] == 2, 1, np.nan)
# dflst[dnum][['RTOPO2_icemask', 'D_floatice']].plot();
# plt.show()
# Set bed beneath floating ice to -2000 AKA 'bedcomp'
# TODO samples SID and only do this to UNCONSTRAINED
dflst[dnum]['BEDCOMP'] = np.where((dflst[dnum]['D_floatice'] != 1),# & (dflst[dnum]['D_floatice'] != 1),
dflst[dnum]['ICEBASE_horizon'],
-2000)
# dflst[dnum][['RTOPO2_bedrock', 'ICESFC_horizon', 'ICEBASE_horizon', 'BEDCOMP']].plot();
# plt.show()
'''
Plot
'''
# diagnostics = True
if make_plots:
pdir = os.path.join('figs', str(dname['DATE'].values[0]))
if not os.path.exists(pdir):
os.makedirs(pdir)
if diagnostics:
fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(12, 8))
axes[0].plot(xs, iceoutline, color='cyan', marker='o')
axes[1].plot(xs, wateroutline, color='blue')
axes[2].plot(xs, rockoutline, color='orange')
plt.savefig(os.path.join(pdir, str(dname['LINE'].values[0]) + '_polygonsplot.pdf'))
plt.show()
# ### Horizons
# plt.figure(num=None, figsize=(8, 5), dpi=80, facecolor='w', edgecolor='k')
# plt.plot(xs, iceoutline)
# # plt.plot(x, icebase, linewidth=1, color='orange', alpha=0.5)
# plt.plot(x, z, '-r', linewidth=2, label='Modeled (constant)')
# plt.xlim(min(x), max(x))
# # plt.legend()
### Plot model results
if make_plots:
talwani_lineplot(x, fag070, gz_adj, polygons, rmse, np.nanmin(rocktop) - 300, np.nanmax(icesfc) + 300,
str(dname['DATE'].values[0]) + '_L' + str(dname['LINE'].values[0]),
os.path.join(pdir, str(dname['LINE'].values[0]) + '_Talwani_lineplot.pdf'))
if make_plots:
if diagnostics:
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(12, 8))
axes.fill(polygons[0].vertices[:, 0], polygons[0].vertices[:, 1], edgecolor='black', linewidth=1,
color='cyan', alpha=0.5)
# axes.fill(polygons[1], '.-k', linewidth=1, color='blue', alpha=0.5)
# axes.fill(polygons[2], '.-k', linewidth=1, color='orange', alpha=0.5)
plt.savefig(os.path.join(pdir, str(dname['LINE'].values[0]) + '_polygonsplot.pdf'))
plt.show()
elif not np.isfinite(fag070).any():
if diagnostics:
print('No FAG070 present: skipping.')
else:
if diagnostics:
print('Line over 1000 km: skipping.')
print(('\n' * 0))
'''
Merge back together
'''
dfout = {}
dfout = pd.concat(dflst, sort=True)
'''
Map
'''
### Entire flight
# if make_plots:
# oib_mapplot_flight(dfout['LONG'].where((dfout['D_gravmask'] != -1)),
# dfout['LAT'].where((dfout['D_gravmask'] != -1)),
# dfout['FLTENVIRO'].where((dfout['D_gravmask'] != -1)), '',
# 'FLTENVIRO ' + str(dfout['DATE'].values[0])[:10],
# os.path.join(pdir, str(dfout['DATE'].values[0])[:10] + '_mapplot_talwani_FLTENVIRO_ALL.png'))
#
# if make_plots:
# oib_mapplot(dfout['LONG'], dfout['LAT'], dfout['rmse'], 'm',
# 'rmse '+str(dfout['DATE'].values[0])[:10],
# os.path.join(pdir, str(dfout['DATE'].values[0])+'_mapplot_Talwani_rmse_ALL.pdf'))
if make_plots:
try:
oib_mapplot_zoom(dfout['LONG'].where((dfout['D_gravmask'] != -1)),
dfout['LAT'].where((dfout['D_gravmask'] != -1)),
dfout['BOUGUER_ANOMALY_LP'].where((dfout['D_gravmask'] != -1)), '', '',
os.path.join(pdir, str(dfout['DATE'].values[0])[:10] + '_oceanmapplot_BOUGUER_ALL.png'))
oib_mapplot_zoom(dfout['LONG'].where((dfout['D_gravmask'] != -1)),
dfout['LAT'].where((dfout['D_gravmask'] != -1)),
dfout['RESIDUAL'].where((dfout['D_gravmask'] != -1)), '', '',
os.path.join(pdir, str(dfout['DATE'].values[0])[:10] + '_oceanmapplot_RESIDUAL_ALL.png'))
except KeyError:
print("Can't make mapplot zoom for this flight. Sorry - do it yourself.")
'''
Save to CSV
'''
# TODO: rename some channels?
# Trim and rename
if not diagnostics:
print("Trimming output CSV for export")
dfout.drop(['FLT', 'FAG100', 'FAG140', 'FX', 'FY',
'BOTTOM', 'ELEVATION', 'NUMUSED', 'EOTGRAV', 'FACOR', 'ICEBASE',
'SURFACE_radar', 'TOPOGRAPHY_radar', 'INTCOR', 'QUALITY'],
axis=1, inplace=True)
else:
print("Exporting it all...")
# Change Precision of certain fields
dfout = dfout.round({'GZ': 1, 'DIST': 0, 'RESIDUAL': 2, 'rmse': 2, 'FAA': 1,
'BOUGUER_CORR': 1, 'BOUGUER_ANOMALY': 1, 'BOUGUER_ANOMALY_LP': 1,
'HYDROAPPX': 0, 'icebase_recalc': 1, 'surface_recalc': 1, 'BEDCOMP': 1,
'RTOPO2_icemask': 0, 'RTOPO2_bedrock': 0,
'ICESFC_horizon': 1, 'ICEBASE_horizon': 1, 'WATERBASE_horizon': 1})
# dfout['GZ_test'] = dfout['GZ'].map(lambda x: '%2.1f' % x)
# Write to CSV
if not os.path.exists(outdir):
os.makedirs(outdir)
dfout.to_csv(os.path.join(outdir, 'OIB_' + str(dfout['DATE'].values[0])[:10] + '_forward.csv'))
if __name__ == '__main__':
start_all = time.time()
basedir = '/Users/dporter/Documents/data_local/Antarctica/OIB/'
datadir = 'integrated'
suffix = '.csv'
### Run through each directory
pattern = os.path.join(basedir, datadir, 'OIB_*' + suffix)
# pattern = './data/NetCDF/10103/R10103_003*.nc'
filenames = sorted(glob(pattern)) # , key=alphanum_key)
print("Filelist:\n%s" % (filenames))
filecounter = len(filenames)
for fnum, filename in enumerate(filenames, start=0):
print("Data file %i is %s" % (fnum, filename))
# sys.exit(main(timedir))
# try:
forward_oib(basedir, filename, dropturns=True, make_plots=True, diagnostics=False)
# except IOError:
# print 'IOError - Data Not Found'
# except AttributeError:
# print 'Attribute Error'
end_all = time.time()
print('Processing took {}'.format(end_all - start_all))