Merge branch 'main' into accessor

pyproject.toml

@@ -24,12 +24,10 @@ readme = "README.rst"
 license = "BSD-3-Clause"
 requires-python = ">=3.9, <3.12"
 authors = [
-  {name = "Bas des Tombe", email = "[email protected]"},
-  {name = "Bart Schilperoort", email = "[email protected]"},
+  {name = "Bas des Tombe, Bart Schilperoort"},
 ]
 maintainers = [
-  {name = "Bas des Tombe", email = "[email protected]"},
-  {name = "Bart Schilperoort", email = "[email protected]"},
+  {name = "Bas des Tombe, Bart Schilperoort"},
 ]
 keywords = [
   "DTS",
@@ -56,15 +54,15 @@ dependencies = [
     "dask",
     "pandas",
     "xarray[parallel]",     # numbagg (llvmlite) is a pain to install with pip
-    "bottleneck",           # optional, speed up Xarray
-    "flox",                 # optional, speed up Xarray
+    "bottleneck",           # speeds up Xarray
+    "flox",                 # speeds up Xarray
     "pyyaml>=6.0.1",
     "xmltodict",
     "scipy",
     "statsmodels",
     "matplotlib",
     "netCDF4>=1.6.4",
-    "nc-time-axis>=1.4.1"  # optional plot dependency of xarray
+    "nc-time-axis>=1.4.1"  # plot dependency of xarray
 ]
 dynamic = ["version"]
 

src/dtscalibration/__init__.py

@@ -3,10 +3,10 @@
 from dtscalibration.datastore_utils import merge_double_ended
 from dtscalibration.datastore_utils import shift_double_ended
 from dtscalibration.datastore_utils import suggest_cable_shift_double_ended
-from dtscalibration.io import read_apsensing_files
-from dtscalibration.io import read_sensornet_files
-from dtscalibration.io import read_sensortran_files
-from dtscalibration.io import read_silixa_files
+from dtscalibration.io.apsensing import read_apsensing_files
+from dtscalibration.io.sensornet import read_sensornet_files
+from dtscalibration.io.sensortran import read_sensortran_files
+from dtscalibration.io.silixa import read_silixa_files
 from dtscalibration.plot import plot_accuracy
 from dtscalibration.plot import plot_location_residuals_double_ended
 from dtscalibration.plot import plot_residuals_reference_sections

src/dtscalibration/averaging_utils.py

@@ -7,12 +7,7 @@ def inverse_variance_weighted_mean(
     tmpw_store="tmpw",
     tmpw_var_store="tmpw_var",
 ):
-    """
-    Average two temperature datasets with the inverse of the variance as
-    weights. The two
-    temperature datasets `tmp1` and `tmp2` with their variances
-    `tmp1_var` and `tmp2_var`,
-    respectively. Are averaged and stored in the DataStore.
+    """Compute inverse variance weighted average, and add result in-place. 
 
     Parameters
     ----------

src/dtscalibration/calibrate_utils.py

@@ -242,7 +242,7 @@ def calibration_single_ended_solver(  # noqa: MC0001
 
     """
     # get ix_sec argsort so the sections are in order of increasing x
-    ix_sec = ds.dts.ufunc_per_section(sections=sections, x_indices=True, calc_per="all")
+    ix_sec = ds.ufunc_per_section(x_indices=True, calc_per="all")
     ds_sec = ds.isel(x=ix_sec)
 
     x_sec = ds_sec["x"].values
@@ -261,8 +261,8 @@ def calibration_single_ended_solver(  # noqa: MC0001
     p0_est_alpha = np.asarray([485.0] + no * [0.0] + nt * [1.4] + nta * nt * [0.0])
 
     # X \gamma  # Eq.34
-    cal_ref = ds.dts.ufunc_per_section(
-        sections=sections, label="st", ref_temp_broadcasted=True, calc_per="all"
+    cal_ref = ds.ufunc_per_section(
+        label="st", ref_temp_broadcasted=True, calc_per="all"
     )
     # cal_ref = cal_ref  # sort by increasing x
     data_gamma = 1 / (cal_ref.T.ravel() + 273.15)  # gamma
@@ -1195,7 +1195,7 @@ def calibration_double_ended_solver(  # noqa: MC0001
     -------
 
     """
-    ix_sec = ds.dts.ufunc_per_section(sections=sections, x_indices=True, calc_per="all")
+    ix_sec = ds.ufunc_per_section(x_indices=True, calc_per="all")
     ds_sec = ds.isel(x=ix_sec)
     ix_alpha_is_zero = ix_sec[0]  # per definition of E
 
@@ -1224,7 +1224,7 @@ def calibration_double_ended_solver(  # noqa: MC0001
         Zero_d,
         Z_TA_fw,
         Z_TA_bw,
-    ) = construct_submatrices(sections, nt, nx_sec, ds, trans_att, x_sec)
+    ) = construct_submatrices(nt, nx_sec, ds, ds.trans_att.values, x_sec)
 
     # y  # Eq.41--45
     y_F = np.log(ds_sec.st / ds_sec.ast).values.ravel()
@@ -1859,9 +1859,7 @@ def construct_submatrices(sections, nt, nx, ds, trans_att, x_sec):
 
     # Z \gamma  # Eq.47
     cal_ref = np.array(
-        ds.dts.ufunc_per_section(
-            sections=sections, label="st", ref_temp_broadcasted=True, calc_per="all"
-        )
+        ds.ufunc_per_section(label="st", ref_temp_broadcasted=True, calc_per="all")
     )
     data_gamma = 1 / (cal_ref.ravel() + 273.15)  # gamma
     coord_gamma_row = np.arange(nt * nx, dtype=int)
@@ -2242,10 +2240,10 @@ def calc_df_db_double_est(ds, sections, ix_alpha_is_zero, gamma_est):
     Ibwx0 = np.log(
         ds.rst.isel(x=ix_alpha_is_zero) / ds.rast.isel(x=ix_alpha_is_zero)
     ).values
-    ref_temps_refs = ds.dts.ufunc_per_section(
-        sections=sections, label="st", ref_temp_broadcasted=True, calc_per="all"
+    ref_temps_refs = ds.ufunc_per_section(
+        label="st", ref_temp_broadcasted=True, calc_per="all"
     )
-    ix_sec = ds.dts.ufunc_per_section(sections=sections, x_indices=True, calc_per="all")
+    ix_sec = ds.ufunc_per_section(x_indices=True, calc_per="all")
     ref_temps_x0 = (
         ref_temps_refs[ix_sec == ix_alpha_is_zero].flatten().compute() + 273.15
     )

src/dtscalibration/datastore.py

@@ -20,7 +20,7 @@
 from dtscalibration.datastore_utils import get_params_from_pval_double_ended
 from dtscalibration.datastore_utils import get_params_from_pval_single_ended
 from dtscalibration.datastore_utils import ufunc_per_section_helper
-from dtscalibration.io_utils import _dim_attrs
+from dtscalibration.io.utils import _dim_attrs
 from dtscalibration.variance_helpers import variance_stokes_constant_helper
 from dtscalibration.variance_helpers import variance_stokes_exponential_helper
 from dtscalibration.variance_helpers import variance_stokes_linear_helper
@@ -79,7 +79,13 @@ class DataStore(xr.Dataset):
     """
 
     def __init__(self, *args, autofill_dim_attrs=True, **kwargs):
-        super().__init__(*args, **kwargs)
+        with warnings.catch_warnings():
+            # Filter out nanosecond precision warning: no good way to avoid ATM.
+            warnings.filterwarnings(
+                "ignore",
+                message="Converting non-nanosecond precision timedelta values to nanosecond precision.",
+            )
+            super().__init__(*args, **kwargs)
 
         # check order of the dimensions of the data_vars
         # first 'x' (if in initiated DataStore), then 'time', then the rest
@@ -217,7 +223,7 @@ def sections(self, value):
             "Not possible anymore. Instead, pass the sections as an argument to \n"
             "ds.dts.calibrate_single_ended() or ds.dts.calibrate_double_ended()."
         )
-        raise NotImplementedError(msg)
+        raise DeprecationWarning(msg)
 
     def check_reference_section_values(self):
         """
@@ -2215,8 +2221,8 @@ def average_single_ended(
         ----------
         p_val : array-like, optional
             Define `p_val`, `p_var`, `p_cov` if you used an external function
-            for calibration. Has size 2 + `nt`. First value is :math:`\gamma`,
-            second is :math:`\Delta \\alpha`, others are :math:`C` for each
+            for calibration. Has size 2 + `nt`. First value is :math:`\\gamma`,
+            second is :math:`\\Delta \\alpha`, others are :math:`C` for each
             timestep.
             If set to False, no uncertainty in the parameters is propagated
             into the confidence intervals. Similar to the spec sheets of the DTS

src/dtscalibration/datastore_utils.py

@@ -1,3 +1,4 @@
+import warnings
 from typing import TYPE_CHECKING
 from typing import Optional
 from typing import Union
@@ -1108,8 +1109,13 @@ def suggest_cable_shift_double_ended(
             rast = ds["rast"].data[:nx2]
             x2 = ds["x"].data[i_shift:]
 
-        i_f = np.log(st / ast)
-        i_b = np.log(rst / rast)
+        with warnings.catch_warnings():
+            # Supress log(x/0) warnings. The data will result in NaNs values.
+            warnings.filterwarnings(
+                "ignore", message="invalid value encountered in log"
+            )
+            i_f = np.log(st / ast)
+            i_b = np.log(rst / rast)
 
         att = (i_b - i_f) / 2  # varianble E in article