2022-01-23 08:55:28 +01:00
|
|
|
from pprint import pp
|
|
|
|
import logging
|
|
|
|
import configparser
|
|
|
|
import pathlib
|
|
|
|
import subprocess
|
|
|
|
from time import time
|
|
|
|
|
|
|
|
import pandas as pd
|
|
|
|
import numpy as np
|
|
|
|
from scipy.interpolate import griddata
|
|
|
|
|
|
|
|
_t0 = time()
|
|
|
|
config = configparser.ConfigParser()
|
|
|
|
config.read('config.ini')
|
|
|
|
|
|
|
|
if config['main']['plot'] == 'True': import matplotlib.pyplot as plt
|
|
|
|
|
|
|
|
logging.basicConfig(level=config['main']['logging'])
|
|
|
|
log = logging.getLogger('bathymetry')
|
|
|
|
|
|
|
|
|
|
|
|
# --- Initialization
|
|
|
|
log.info('Initialization')
|
|
|
|
folders = {
|
|
|
|
'data': pathlib.Path(config['data']['root']),
|
|
|
|
'scad': pathlib.Path(config['scad']['root']),
|
|
|
|
'stl': pathlib.Path(config['stl']['root']),
|
|
|
|
'pandas': pathlib.Path(config['pandas']['root']),
|
|
|
|
}
|
|
|
|
for path in folders.values():
|
|
|
|
path.mkdir(exist_ok=True)
|
|
|
|
|
|
|
|
bathy = pd.read_csv(
|
|
|
|
folders['data'].joinpath(config['data']['bathy']),
|
|
|
|
sep=' ',
|
|
|
|
names=('lon', 'lat', 'z'),
|
|
|
|
)
|
|
|
|
artha = pd.read_csv(
|
|
|
|
folders['data'].joinpath(config['data']['artha']),
|
|
|
|
)
|
|
|
|
|
|
|
|
earth_radius = 6371e3
|
|
|
|
L0 = float(config['main']['L0'])
|
|
|
|
L1 = float(config['main']['L1'])
|
|
|
|
direction = float(config['main']['dir'])
|
|
|
|
|
|
|
|
artha = artha.append(pd.DataFrame([
|
|
|
|
{
|
|
|
|
'lat': artha.lat.at[0] \
|
|
|
|
+ np.cos(direction*np.pi/180) * L0/earth_radius * 180/np.pi,
|
|
|
|
'lon': artha.lon.at[0] \
|
|
|
|
+ np.sin(direction*np.pi/180) * L0/earth_radius * 180/np.pi \
|
|
|
|
/ np.cos(artha.lat.at[0] * np.pi/180),
|
|
|
|
},
|
|
|
|
{
|
|
|
|
'lat': artha.lat.at[0] \
|
|
|
|
- np.cos(direction*np.pi/180) * L1/earth_radius * 180/np.pi,
|
|
|
|
'lon': artha.lon.at[0] \
|
|
|
|
- np.sin(direction*np.pi/180) * L1/earth_radius * 180/np.pi \
|
|
|
|
/ np.cos(artha.lat.at[0] * np.pi/180),
|
|
|
|
},
|
|
|
|
], index=[-1, 1]))
|
|
|
|
|
|
|
|
# --- Interpolation
|
|
|
|
log.info('Interpolating data')
|
|
|
|
N = int(config['main']['N'])
|
|
|
|
line = pd.DataFrame({
|
|
|
|
'x': np.linspace(0, L0+L1, N),
|
|
|
|
'lat': np.linspace(artha.lat.at[-1], artha.lat.at[1], N),
|
|
|
|
'lon': np.linspace(artha.lon.at[-1], artha.lon.at[1], N),
|
|
|
|
})
|
|
|
|
line.set_index('x', inplace=True)
|
|
|
|
|
|
|
|
line['z'] = griddata(
|
|
|
|
bathy[['lon','lat']],
|
|
|
|
bathy.z,
|
|
|
|
line[['lon','lat']],
|
|
|
|
method='linear',
|
|
|
|
)
|
|
|
|
|
|
|
|
# --- Adding blocs
|
|
|
|
log.info('Adding blocs')
|
|
|
|
b = config['data']['blocs'].split(',')
|
|
|
|
blocs = pd.Series([
|
|
|
|
pd.read_csv(
|
|
|
|
folders['data'].joinpath(path),
|
|
|
|
index_col='x',
|
|
|
|
) for path in b
|
|
|
|
])
|
|
|
|
for bloc in blocs:
|
|
|
|
bloc.index = L0 - bloc.index
|
|
|
|
|
|
|
|
|
|
|
|
#line.z = all_blocs.z.fillna(line.z)
|
|
|
|
|
|
|
|
lim = float(config['main']['bathy_max'])
|
|
|
|
bathy_line = line.z.clip(upper=lim)
|
|
|
|
rubble_line = line.z[line.z > lim]
|
|
|
|
|
|
|
|
data_dict = {}
|
|
|
|
for i in range(blocs.size):
|
|
|
|
data_dict[f'bloc{i}'] = blocs.iat[i].reset_index().values
|
|
|
|
for name, data in (
|
|
|
|
('bathy', bathy_line),
|
|
|
|
('rubble', rubble_line),
|
|
|
|
):
|
|
|
|
data_dict[name] = np.concatenate((
|
|
|
|
data.reset_index().values,
|
|
|
|
[[data.index.max(), data.min()],
|
|
|
|
[data.index.min(),data.min()]]
|
|
|
|
))
|
|
|
|
|
|
|
|
# --- Generating SCAD
|
|
|
|
|
|
|
|
with open(folders['data'].joinpath(config['data']['base_scad'])) as bsf:
|
|
|
|
base_scad = bsf.read()
|
|
|
|
for (name, data) in data_dict.items():
|
|
|
|
log.info(f'Generating {name}')
|
|
|
|
log.info('\tGenerating SCAD file')
|
2022-01-23 09:00:44 +01:00
|
|
|
scad_file = folders['scad'].joinpath(f'{name}.scad')
|
2022-01-23 08:55:28 +01:00
|
|
|
with open(scad_file, 'w') as osf:
|
|
|
|
osf.write(base_scad.format(
|
|
|
|
np.array2string(
|
|
|
|
data,
|
|
|
|
threshold=np.inf,
|
|
|
|
separator=','
|
|
|
|
)
|
|
|
|
))
|
|
|
|
log.info('\tGenerating STL file')
|
|
|
|
subprocess.run(('openscad', scad_file, '-o',
|
2022-01-23 09:00:44 +01:00
|
|
|
folders['stl'].joinpath(f'{name}.stl')),
|
2022-01-23 08:55:28 +01:00
|
|
|
check=True,
|
|
|
|
capture_output=True)
|
|
|
|
|
|
|
|
|
|
|
|
# --- Saving pandas
|
|
|
|
log.info('Saving Pandas')
|
|
|
|
|
|
|
|
with pd.HDFStore(
|
|
|
|
folders['pandas'].joinpath(config['pandas']['file']),
|
|
|
|
mode='w',
|
|
|
|
complib='blosc',
|
|
|
|
) as hdf:
|
|
|
|
for name, data in (
|
|
|
|
('bathy', bathy_line),
|
|
|
|
('rubble', rubble_line),
|
|
|
|
):
|
|
|
|
hdf.put(name, data)
|
|
|
|
for i, bloc in blocs.items():
|
|
|
|
hdf.put(f'bloc{i}', bloc)
|
|
|
|
|
|
|
|
|
|
|
|
_t1 = time()
|
|
|
|
log.info(f'Program ended successfully after {_t1-_t0:.2f}s')
|
|
|
|
|
|
|
|
# --- Plotting
|
|
|
|
|
|
|
|
if config['main']['plot'] == 'True':
|
|
|
|
log.info('Plotting data')
|
|
|
|
flt = (
|
|
|
|
((bathy.lon-artha.lon.at[0]).abs() < 0.002) & \
|
|
|
|
((bathy.lat-artha.lat.at[0]).abs() < 0.002)
|
|
|
|
)
|
|
|
|
|
|
|
|
fig, ax = plt.subplots()
|
|
|
|
ax.scatter(
|
|
|
|
bathy.lon[flt],
|
|
|
|
bathy.lat[flt],
|
|
|
|
c=bathy.z[flt],
|
|
|
|
marker='1',
|
|
|
|
lw=1,
|
|
|
|
)
|
|
|
|
ax.scatter(
|
|
|
|
artha.lon,
|
|
|
|
artha.lat,
|
|
|
|
color='k',
|
|
|
|
marker='+',
|
|
|
|
lw=1,
|
|
|
|
)
|
|
|
|
ax.set(
|
|
|
|
aspect='equal',
|
|
|
|
)
|
|
|
|
|
|
|
|
fig, ax = plt.subplots()
|
|
|
|
ax.plot(
|
|
|
|
bathy_line.index,
|
|
|
|
bathy_line,
|
|
|
|
color='k',
|
|
|
|
lw=1,
|
|
|
|
zorder=10,
|
|
|
|
label='Bathymetry',
|
|
|
|
)
|
|
|
|
ax.plot(
|
|
|
|
rubble_line.index,
|
|
|
|
rubble_line,
|
|
|
|
color='r',
|
|
|
|
lw=1,
|
|
|
|
zorder=11,
|
|
|
|
label='Rubble',
|
|
|
|
)
|
|
|
|
blocs.apply(lambda bloc: ax.fill_between(
|
|
|
|
bloc.index,
|
|
|
|
bloc.z,
|
|
|
|
color='k',
|
|
|
|
zorder=9,
|
|
|
|
alpha=.1,
|
|
|
|
label='Caisson',
|
|
|
|
))
|
|
|
|
ax.set(
|
|
|
|
aspect='equal',
|
|
|
|
xlim=(bathy_line.index.min(), bathy_line.index.max()),
|
|
|
|
)
|
|
|
|
ax.grid()
|
|
|
|
fig.legend()
|
|
|
|
|
|
|
|
plt.show(block=True)
|