heart, models.geodetic: introduce GeodeticDataset id for handleing da…

…taset names and components for GNSS correctly

beat/apps/beat.py

@@ -2184,7 +2184,7 @@ def setup(parser):
  except (UserIOWarning, KeyError):
  raise ImportError("Full resolution data could not be loaded!")
  elif isinstance(dataset, heart.GNSSCompoundComponent):
- logger.info("Found GNSS Compound %s, importing to kite..." % dataset.name)
+ logger.info("Found GNSS Compound %s, importing to kite..." % dataset.id)
  scene = dataset.to_kite_scene()
  # scene.spool()
  sandbox.setReferenceScene(scene)

beat/covariance.py

@@ -187,7 +187,7 @@ def do_import(self, dataset):
  return dataset.covariance.data
  else:
  raise ValueError(
- "Data covariance for dataset %s needs to be defined!" % dataset.name
+ "Data covariance for dataset %s needs to be defined!" % dataset.id
  )
 
  def do_non_toeplitz(self, dataset, result):
@@ -204,7 +204,7 @@ def do_non_toeplitz(self, dataset, result):
  if num.isnan(scaling).any():
  raise ValueError(
  "Estimated Non-Toeplitz covariance matrix for dataset %s contains Nan! "
- "Please increase 'max_dist_perc'!" % dataset.name
+ "Please increase 'max_dist_perc'!" % dataset.id
  )
 
  return scaling

beat/heart.py

@@ -1074,6 +1074,10 @@ def __init__(self, **kwargs):
  self.corrections = None
  super(GeodeticDataset, self).__init__(**kwargs)
 
+ @property
+ def id(self):
+ return self.name
+
  def get_corrections(self, hierarchicals, point=None):
  """
  Needs to be specified on inherited dataset classes.
@@ -1177,6 +1181,10 @@ def __init__(self, **kwargs):
  self._station2index = None
  super(GNSSCompoundComponent, self).__init__(**kwargs)
 
+ @property
+ def id(self):
+ return "%s_%s" % (self.name, self.component)
+
  def update_los_vector(self):
  if self.component == "east":
  c = num.array([0, 1, 0])

beat/models/geodetic.py

@@ -131,24 +131,11 @@ def init_weights(self):
  def n_t(self):
  return len(self.datasets)
 
- def get_all_dataset_names(self, hp_name):
+ def get_all_dataset_ids(self, hp_name):
  """
  Return unique GNSS stations and radar acquisitions.
  """
- names = []
- for dataset in self.datasets:
- if dataset.typ == hp_name.split("_")[1]:
- if isinstance(dataset, heart.DiffIFG):
- names.append(dataset.name)
- elif isinstance(dataset, heart.GNSSCompoundComponent):
- names.append(dataset.component)
- else:
- TypeError(
- 'Geodetic Dataset of class "%s" not '
- "supported" % dataset.__class__.__name__
- )
-
- return names
+ return [dataset.id for dataset in self.datasets]
 
  def analyse_noise(self, tpoint=None):
  """
@@ -166,7 +153,7 @@ def analyse_noise(self, tpoint=None):
  for dataset, result in zip(self.datasets, results):
  logger.info(
  'Retrieving geodetic data-covariances with structure "%s" '
- "for %s ..." % (self.config.noise_estimator.structure, dataset.name)
+ "for %s ..." % (self.config.noise_estimator.structure, dataset.id)
  )
 
  cov_d_geodetic = self.noise_analyser.get_data_covariance(
@@ -196,7 +183,7 @@ def get_hypersize(self, hp_name=""):
  -------
  int
  """
- n_datasets = len(self.get_all_dataset_names(hp_name))
+ n_datasets = len(self.get_all_dataset_ids(hp_name))
  if n_datasets == 0:
  raise ConfigInconsistentError(
  'Found no data for hyperparameter "%s". Please either load'
@@ -276,7 +263,7 @@ def get_filename(attr, ending="csv"):
  return os.path.join(
  results_path,
  "{}_{}_{}.{}".format(
- os.path.splitext(dataset.name)[0], attr, stage_number, ending
+ os.path.splitext(dataset.id)[0], attr, stage_number, ending
  ),
  )
 
@@ -506,7 +493,7 @@ def get_variance_reductions(self, point, results=None, weights=None):
  logger.debug("nom %f, denom %f" % (float(nom), float(denom)))
  var_red = 1 - (nom / denom)
 
- logger.debug("Variance reduction for %s is %f" % (dataset.name, var_red))
+ logger.debug("Variance reduction for %s is %f" % (dataset.id, var_red))
 
  if 0:
  from matplotlib import pyplot as plt
@@ -516,7 +503,7 @@ def get_variance_reductions(self, point, results=None, weights=None):
  plt.colorbar(im)
  plt.show()
 
- var_reds[dataset.name] = var_red
+ var_reds[dataset.id] = var_red
 
  return var_reds
 
@@ -548,7 +535,7 @@ def get_standardized_residuals(self, point, results=None, weights=None):
  point, dataset, counter, hp_specific=hp_specific
  )
  choli = num.linalg.inv(dataset.covariance.chol(num.exp(hp * 2.0)))
- stdz_residuals[dataset.name] = choli.dot(result.processed_res)
+ stdz_residuals[dataset.id] = choli.dot(result.processed_res)
 
  return stdz_residuals
 

beat/plotting/common.py

@@ -881,7 +881,7 @@ def plot_covariances(datasets, covariances):
  if i_l == 0:
  ax.set_ylabel("Sample idx")
  ax.set_xlabel("Sample idx")
- ax.set_title(dataset.name)
+ ax.set_title(dataset.id)
 
  cbaxes = fig.add_axes([cbl, cbb, cbw, cbh])
  cblabel = "Covariance [m²]"
@@ -896,7 +896,7 @@ def plot_covariances(datasets, covariances):
  cbs.set_label(cblabel, fontsize=fontsize)
  else:
  logger.info(
- 'Did not find "%s" covariance component for %s', attr, dataset.name
+ 'Did not find "%s" covariance component for %s', attr, dataset.id
  )
  fig.delaxes(ax)
 

beat/plotting/geodetic.py

@@ -218,11 +218,11 @@ def gnss_fits(problem, stage, plot_options):
  bvar_reductions_comp = {}
  for dataset in dataset_to_result.keys():
  target_var_reds = []
- target_bvar_red = bvar_reductions[dataset.name]
+ target_bvar_red = bvar_reductions[dataset.id]
  target_var_reds.append(target_bvar_red)
  bvar_reductions_comp[dataset.component] = target_bvar_red * 100.0
  for var_reds in ens_var_reductions:
- target_var_reds.append(var_reds[dataset.name])
+ target_var_reds.append(var_reds[dataset.id])
 
  all_var_reductions[dataset.component] = num.array(target_var_reds) * 100.0
 
@@ -387,7 +387,7 @@ def gnss_fits(problem, stage, plot_options):
  Z = 0
 
  out_filename = "/tmp/histbounds.txt"
- in_rows = num.atleast_2d(all_var_reductions[dataset.component]).T
+ in_rows = num.atleast_2d(var_reductions_ens).T
 
  m.gmt.pshistogram(
  in_rows=in_rows,
@@ -543,7 +543,7 @@ def scene_fits(problem, stage, plot_options):
 
  if po.plot_projection == "individual":
  for result, dataset in zip(bresults_tmp, composite.datasets):
- result.processed_res = stdz_residuals[dataset.name]
+ result.processed_res = stdz_residuals[dataset.id]
 
  bvar_reductions = composite.get_variance_reductions(
  bpoint, weights=composite.weights, results=bresults_tmp
@@ -599,11 +599,11 @@ def scene_fits(problem, stage, plot_options):
  all_var_reductions = {}
  for dataset in dataset_to_result.keys():
  target_var_reds = []
- target_var_reds.append(bvar_reductions[dataset.name])
+ target_var_reds.append(bvar_reductions[dataset.id])
  for var_reds in ens_var_reductions:
- target_var_reds.append(var_reds[dataset.name])
+ target_var_reds.append(var_reds[dataset.id])
 
- all_var_reductions[dataset.name] = num.array(target_var_reds) * 100.0
+ all_var_reductions[dataset.id] = num.array(target_var_reds) * 100.0
 
  figures = []
  axes = []
@@ -892,7 +892,7 @@ def draw_sources(ax, sources, scene, po, event, **kwargs):
  vmin = -dcolims[tidx]
  vmax = dcolims[tidx]
  logger.debug(
- "Variance of residual for %s is: %f", dataset.name, datavec.var()
+ "Variance of residual for %s is: %f", dataset.id, datavec.var()
  )
  else:
  vmin = -colims[tidx]
@@ -956,8 +956,8 @@ def draw_sources(ax, sources, scene, po, event, **kwargs):
  if po.nensemble > 1:
  in_ax = plot_inset_hist(
  axs[2],
- data=num.atleast_2d(all_var_reductions[dataset.name]),
- best_data=bvar_reductions[dataset.name] * 100.0,
+ data=num.atleast_2d(all_var_reductions[dataset.id]),
+ best_data=bvar_reductions[dataset.id] * 100.0,
  linewidth=1.0,
  bbox_to_anchor=(0.75, 0.775, 0.25, 0.225),
  labelsize=6,