"""
This is a container to hold:
1. TransferFunctionHeader
2. Dictionary of TransferFunction Objects
Note that a single transfer function object is associated with a station,
which we call the "local_station". In a database of TFs we could add a column
for local_station and one for reference station.
"""
import numpy as np
import xarray as xr
from loguru import logger
from aurora.transfer_function.plot.rho_phi_helpers import plot_phi
from aurora.transfer_function.plot.rho_phi_helpers import plot_rho
from aurora.general_helper_functions import FIGURES_PATH
EMTF_REGRESSION_ENGINE_LABELS = {}
EMTF_REGRESSION_ENGINE_LABELS["RME"] = "Robust Single Station"
EMTF_REGRESSION_ENGINE_LABELS["RME_RR"] = "Robust Remote Reference"
[docs]class TransferFunctionCollection(object):
def __init__(self, **kwargs):
"""
Parameters
----------
kwargs
tf_dict: dict
This is a dictionary of TTFZ objects, one per decimation level. They are
keyed by the decimation_level_id, usually integers 0, 1, 2... n_dec
"""
self.tf_dict = kwargs.get("tf_dict", None)
self.processing_config = kwargs.get("processing_config", None)
self.labelled_tf = None
self.merged_tf = None
self.merged_cov_nn = None
self.merged_cov_ss_inv = None
@property
def header(self):
return self.tf_dict[0].tf_header
@property
def local_station_id(self):
return self.header.local_station.id
@property
def remote_station_id(self):
if self.header.remote_station:
return self.header.remote_station[0].id
else:
return ""
@property
def total_number_of_frequencies(self):
num_frequecies = 0
for dec_level in self.tf_dict.keys():
num_frequecies += len(self.tf_dict[dec_level].periods)
return num_frequecies
@property
def channel_list(self):
tf_input_chs = self.tf_dict[0].transfer_function.input_channel.data.tolist()
tf_output_chs = self.tf_dict[0].transfer_function.output_channel.data.tolist()
all_channels = tf_input_chs + tf_output_chs
return all_channels
@property
def total_number_of_channels(self):
num_channels = 0
num_channels += len(self.header.input_channels)
num_channels += len(self.header.output_channels)
return num_channels
@property
def number_of_decimation_levels(self):
return len(self.tf_dict)
[docs] def get_merged_dict(self, channel_nomenclature):
output = {}
self._merge_decimation_levels()
self.check_all_channels_present(channel_nomenclature)
output["tf"] = self.merged_tf
output["tf_xarray"] = self.labelled_tf
output["cov_ss_inv"] = self.merged_cov_ss_inv
output["cov_nn"] = self.merged_cov_nn
return output
def _merge_decimation_levels(self):
"""
Will merge all decimation levels into a single xarray for export.
The output of this may become its own class, MergedTransferFunction
One concern here is that the same period can be estiamted at more then one
decimation level, making the frequency or period axis not of the same order
as the number of estimates. Ways around this:
1. We can multi-index the period axis with decimation level
2. Make sure that the processing config does not dupliacate periods
3. Take the average estimate over all periods heving the same value
4. Drop all but one estimate accoring to a rule (keep most observations say)
Does TFXML support multiple estimates? How is it dealt with there?
There is an issue here -
Flow:
starting from self.tf_dict, we cast each decimation level's tf, which is
nativley an xr.DataArray to xr.Dataset, forming a list of datasets.
This dataset list is then combined over all periods forming a representation
of the TTFZ which is merged over all the decimation levels.
result is to build:
merged_tf
merged_cov_ss_inv
merged_cov_nn
"""
n_dec = self.number_of_decimation_levels
tmp = [self.tf_dict[i].tf.to_dataset("period") for i in range(n_dec)]
merged_tf = xr.combine_by_coords(tmp)
merged_tf = merged_tf.to_array(dim="period")
self.merged_tf = merged_tf
tmp = [self.tf_dict[i].cov_ss_inv.to_dataset("period") for i in range(n_dec)]
merged_cov_ss_inv = xr.combine_by_coords(tmp)
merged_cov_ss_inv = merged_cov_ss_inv.to_array("period")
self.merged_cov_ss_inv = merged_cov_ss_inv
tmp = [self.tf_dict[i].cov_nn.to_dataset("period") for i in range(n_dec)]
merged_cov_nn = xr.combine_by_coords(tmp)
merged_cov_nn = merged_cov_nn.to_array("period")
self.merged_cov_nn = merged_cov_nn
return
[docs] def check_all_channels_present(self, channel_nomenclature):
"""
Parameters
----------
channel_nomenclature: mt_metadata.transfer_functions.processing.aurora.channel_nomenclature.ChannelNomenclature
Scheme according to how channels are named
"""
ex, ey, hx, hy, hz = channel_nomenclature.unpack()
if hz not in self.merged_tf.output_channel.data.tolist():
output_channels_original = self.merged_tf.output_channel.data.tolist()
output_channels = [f"tmp___{x}" for x in output_channels_original]
output_channels[0] = hz
tmp = self.merged_tf.copy(deep=True)
tmp = tmp.assign_coords({"output_channel": output_channels})
tmp.data *= np.nan
tmp = tmp.to_dataset("period")
tmp = tmp.merge(self.merged_tf.to_dataset("period"))
tmp = tmp.to_array("period")
output_channels = tmp.output_channel.data.tolist()
output_channels = [x for x in output_channels if x[0:6] != "tmp___"]
tmp = tmp.sel(output_channel=output_channels)
self.merged_tf = tmp
n_output_ch = len(self.merged_tf.output_channel) # 3
n_periods = len(self.merged_tf.period)
cov_nn_dims = (n_output_ch, n_output_ch, n_periods)
cov_nn_data = np.zeros(cov_nn_dims, dtype=np.complex128)
import xarray as xr
cov_nn = xr.DataArray(
cov_nn_data,
dims=["output_channel_1", "output_channel_2", "period"],
coords={
"output_channel_1": self.merged_tf.output_channel.data,
"output_channel_2": self.merged_tf.output_channel.data,
"period": self.merged_tf.period,
},
)
# to dataset and back makes the coords line up -- this does not seem robust
cov_nn = cov_nn.to_dataset("period").to_array("period")
for out_ch_1 in cov_nn.output_channel_1.data.tolist():
for out_ch_2 in cov_nn.output_channel_1.data.tolist():
try:
values = self.merged_cov_nn.loc[:, out_ch_1, out_ch_2]
cov_nn.loc[:, out_ch_1, out_ch_2] = values
except KeyError:
pass
self.merged_cov_nn = cov_nn
[docs] def rho_phi_plot(
self,
show=True,
aux_data=None,
xy_or_yx="xy",
ttl_str="",
x_axis_fontsize=25,
y_axis_fontsize=25,
ttl_fontsize=16,
markersize=10,
rho_ylims=[10, 1000],
phi_ylims=[0, 90],
figures_path=FIGURES_PATH,
**kwargs,
):
"""
One-off plotting method intended only for the synthetic test data for aurora dev
Parameters
----------
show
aux_data
xy_or_yx
ttl_str
Returns
-------
"""
import matplotlib.pyplot as plt
fig, axs = plt.subplots(nrows=2, figsize=(11, 8.5), dpi=300)
# plotter.rho_sub_plot(axs[0], ttl_str=ttl_str)
# plotter.phase_sub_plot(axs[1], ttl_str=ttl_str)
color_cyc = {0: "r", 1: "orange", 2: "g", 3: "b", 4: "k"}
for i_dec in self.tf_dict.keys():
tf = self.tf_dict[i_dec]
if xy_or_yx == "xy":
aurora_rho = tf.rho[:, 0]
else:
aurora_rho = tf.rho[:, 1]
plot_rho(
axs[0],
tf.periods,
aurora_rho,
marker="o",
color=color_cyc[i_dec],
linestyle="None",
label=f"aurora {i_dec}",
markersize=markersize,
)
if xy_or_yx == "xy":
aurora_phi = tf.phi[:, 0]
else:
aurora_phi = tf.phi[:, 1]
# rotate phases so all are positive:
negative_phi_indices = np.where(aurora_phi < 0)[0]
aurora_phi[negative_phi_indices] += 180.0
plot_phi(
axs[1],
tf.periods,
aurora_phi,
marker="o",
color=color_cyc[i_dec],
linestyle="None",
label=f"aurora {i_dec}",
markersize=markersize,
)
if aux_data:
# try:
decimation_levels = list(set(aux_data.decimation_levels))
shape_cyc = {0: "s", 1: "v", 2: "*", 3: "^"}
if xy_or_yx == "xy":
emtf_rho = aux_data.rxy
emtf_phi = aux_data.pxy
elif xy_or_yx == "yx":
emtf_rho = aux_data.ryx
emtf_phi = aux_data.pyx
axs[0].loglog(axs[0].get_xlim(), 100 * np.ones(2), color="k")
axs[1].semilogx(axs[1].get_xlim(), 45 * np.ones(2), color="k")
for i_dec in decimation_levels:
ndx = np.where(aux_data.decimation_levels == i_dec)[0]
axs[0].loglog(
aux_data.periods[ndx],
emtf_rho[ndx],
marker=shape_cyc[i_dec - 1],
color="k",
linestyle="None",
label=f"emtf " f"{int(i_dec-1)}",
markersize=markersize,
)
axs[1].semilogx(
aux_data.periods[ndx],
emtf_phi[ndx],
marker=shape_cyc[i_dec - 1],
color="k",
linestyle="None",
markersize=markersize,
label=f"emtf " f"{int(i_dec-1)}",
)
axs[0].legend(ncol=2)
axs[1].legend(ncol=2)
axs[1].set_xlabel("Period (s)", fontsize=x_axis_fontsize)
axs[0].set_ylabel(r"$\Omega$-m", fontsize=y_axis_fontsize)
axs[1].set_ylabel("Degrees", fontsize=y_axis_fontsize)
ttl_str = f"{tf.tf_header.local_station.id} {xy_or_yx} \n{ttl_str}"
axs[0].set_title(ttl_str, fontsize=ttl_fontsize)
if rho_ylims is not None:
axs[0].set_ylim(rho_ylims)
if phi_ylims is not None:
axs[1].set_ylim(phi_ylims)
if figures_path is None:
logger.info("figures path is not defined -- skipping saving figures")
return
else:
default_figure_basename = f"{self.local_station_id}_{xy_or_yx}.png"
figure_basename = kwargs.get("figure_basename", default_figure_basename)
out_file = figures_path.joinpath(figure_basename)
plt.savefig(out_file)
if show:
plt.show()
plt.close(fig)