Physically labeled data: pyfocs single-ended examples¶
Finally, after all of that (probably confusing) work we can map the data to physical coordinates.
import xarray as xr
import pyfocs
import os
dir_example = os.path.join('../tests/data/')
# Grab a configuration file for the twisted pair pvc fiber and for the stainless steel fiber
config_names = [
'example_configuration_steelfiber.yml',
'example_twistedpair_bothwls.yml',
'example_twistedpair_p1wls.yml',
'example_twistedpair_p2wls.yml',
]
cfg_fname = os.path.join(dir_example, config_names[0])
cfg_ss, lib_ss = pyfocs.check.config(cfg_fname, ignore_flags=True)
cfg_fname = os.path.join(dir_example, config_names[1])
cfg_both, lib_both = pyfocs.check.config(cfg_fname, ignore_flags=True)
cfg_fname = os.path.join(dir_example, config_names[2])
cfg_p1, lib_p1 = pyfocs.check.config(cfg_fname, ignore_flags=True)
cfg_fname = os.path.join(dir_example, config_names[3])
cfg_p2, lib_p2 = pyfocs.check.config(cfg_fname, ignore_flags=True)
1.2 Data¶
- In this case we only use a single twisted pair, p1, since it is closer to the DTS device in LAF space yielding a less noisy signal.
- Additionally, we will load the paired heated-unheated stainless steel fiber that has been interpolated to a common spatial index.
ds_p1 = xr.open_dataset(os.path.join(dir_example, 'multifiledemo', 'final', 'multifiledemo_final_20190722-0000_p1-wls_unheated.nc'))
ds_p2 = xr.open_dataset(os.path.join(dir_example, 'multifiledemo', 'final', 'multifiledemo_final_20190722-0000_p2-wls_unheated.nc'))
ds_cold = xr.open_dataset(os.path.join(dir_example, 'multifiledemo', 'final', 'multifiledemo_final_20190722-0000_ss-wls_unheated.nc'))
ds_heat = xr.open_dataset(os.path.join(dir_example, 'multifiledemo', 'final', 'multifiledemo_final_20190722-0000_ss-wls_heated.nc'))
print('=================')
print('Unheated fibers - Twisted PVC fiber, pair 1')
print(ds_p1)
print('')
print('=================')
print('Unheated fibers - Twisted PVC fiber, pair 2')
print(ds_p2)
print('')
print('=================')
print('Unheated fibers - stainless steel')
print(ds_cold)
print('')
print('=================')
print('Heated fibers - stainless steel')
print(ds_heat)
print('')
Here we see that all datasets now have x, y, and z coordinates which are labeled using the xyz multiindex. Other quantities have been dropped.
The netcdf files are also now labeled differently. Channel information has been excluded and there is now a label on the location type at the end of the file name.
2. Calculate wind speed¶
2.1 Construct the power variable¶
Here I will construct a data variable of power. The details on what is happening here are not important besides power is a data variable with dimensions of LAF. The wind speed code can accept power as a DataArray with dimensions shared with cal_temp or as a single float.
import numpy as np
power_loc = {
'1': [1892.5, 2063.5],
'2': [2063.5, 2205.5],
'3': [2207.0, 2361.],
'4': [2361., 2524.]}
power_vals = {
'1': 6.1,
'2': 6.4,
'3': 4.7,
'4': 5.4,}
ds_heat['power'] = ('LAF', np.zeros_like(ds_heat.LAF))
for p in power_vals:
laf_mask = ((ds_heat.LAF > power_loc[p][0]) & (ds_heat.LAF < power_loc[p][1]))
ds_heat['power'] = xr.where(laf_mask, np.ones_like(ds_heat.LAF.values) * power_vals[p], ds_heat.power.values)
2.2 Calculate wind speed¶
wind_speed = pyfocs.wind_speed.calculate(ds_heat.cal_temp, ds_cold.cal_temp, ds_heat.power)
2.3 Split up wind speed based¶
Wind speed is most efficiently measured in the direction orthogonal to the fiber. Since we have fibers that are orthogonal to each other that means we effectively measured wind in two different directions. We represent that here by combining sections that are parallel to each other.
cross_valley_components = ['OR_SE', 'OR_NW']
logic = [wind_speed.unheated == l for l in cross_valley_components]
logic = xr.concat(logic, dim='locations').any(dim='locations')
wind_speed_cross_valley = wind_speed.where(logic, drop=True)
along_valley_components = ['OR_SW2', 'OR_SW1', 'OR_NE1', 'OR_NE2']
logic = [wind_speed.unheated == l for l in along_valley_components]
logic = xr.concat(logic, dim='locations').any(dim='locations')
wind_speed_along_valley = wind_speed.where(logic, drop=True)
2.4 Create a Dataset that contains all unheated data¶
unheated = xr.concat([ds_cold, ds_p1], dim='xyz', coords='different')
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(12, 6),)
spec = fig.add_gridspec(ncols=4,
nrows=2,
width_ratios=[1, 0.08, 0.04, 0.08],
hspace=0.18, wspace=0.25,
)
ax_ew_cbar = fig.add_subplot(spec[0, 3])
ax_ns_cbar = fig.add_subplot(spec[1, 3])
ax_t_cbar = fig.add_subplot(spec[:, 1])
ax_temp = fig.add_subplot(spec[:, 0])
im = ax_temp.scatter(unheated.x, unheated.y, s=10,
c=unheated.mean(dim='time').cal_temp.values,
cmap='viridis', vmin=8.5, vmax=10)
ax_temp.set_ylabel('Relative Northing (m)')
ax_temp.set_xlabel('Relative Easting (m)')
plt.colorbar(im, cax=ax_t_cbar, extend='both')
ax_t_cbar.set_ylabel('Temperature (C)')
ax_temp.set_title('a) LOVE19 Outer Array', loc='left')
im = ax_temp.scatter(wind_speed_along_valley.x * 1.1,
wind_speed_along_valley.y * 1.1,
s=10,
c=wind_speed_along_valley.mean(dim='time').values,
cmap='Oranges', vmin=0.5, vmax=4)
plt.colorbar(im, cax=ax_ew_cbar, extend='max')
ax_ew_cbar.set_ylabel('Along valley wind (m/s)')
im = ax_temp.scatter(wind_speed_cross_valley.x * 1.1,
wind_speed_cross_valley.y * 1.1,
s=10,
c=wind_speed_cross_valley.mean(dim='time').values,
cmap='Blues', vmin=0.5, vmax=4)
plt.colorbar(im, cax=ax_ns_cbar, extend='max')
ax_ns_cbar.set_ylabel('Cross valley wind (m/s)')
3.2 Biases in space¶
ds_p2 = ds_p2.interp_like(ds_p1)
fig = plt.figure(figsize=(8, 6),)
spec = fig.add_gridspec(ncols=2,
nrows=1,
width_ratios=[1, 0.1],
hspace=0.18, wspace=0.25,
)
ax_t_cbar = fig.add_subplot(spec[:, 1])
ax_temp = fig.add_subplot(spec[:, 0])
im = ax_temp.scatter(
ds_p1.x,
ds_p1.y,
s=10,
c=(ds_p1.cal_temp - ds_p2.cal_temp).mean(dim='time').values,
cmap='RdBu', vmin=-0.5, vmax=0.5)
ax_temp.set_ylabel('Relative Northing (m)')
ax_temp.set_xlabel('Relative Easting (m)')
plt.colorbar(im, cax=ax_t_cbar, extend='both')
ax_t_cbar.set_ylabel('p1 - p2 (K)')
ax_temp.set_title('LOVE19 Twisted PVC Fiber Bias', loc='left')
Here we can see that the reference sections are a bit misleading. While they evaluate to effectively zero bias, there are substantial biases between what should be replicate measurements. We have found this to be typical of DTS observations. The cause and correction is a subject of on-going research but we highlight as a final word of caution on DTS. The method is excpetionally powerful but is very far from a push-button operation. It requires a substantial investment in time for all steps: setting up the fiber takes much longer than other instruments, preparing the dataset is a long process even with the tools provided by pyfocs, and it is still a new technique that is subject to uncertainties that are not even known to the community.