Merge pull request #99 from bdestombe/master

Renaming ST to st, REV-AST to rast, etc. Reduced flexibility in defining labels.

Author: bdestombe

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.9.0
+current_version = 0.9.2
 commit = True
 tag = True
 

CHANGELOG.rst

@@ -1,6 +1,14 @@
 
 Changelog
 =========
+0.9.2 (2020-03-17)
+------------------
+* Reduced flexibility in defining Stokes labels
+
+0.9.1 (2020-03-17)
+------------------
+* Same as 0.9.2
+
 0.9.0 (2020-03-16)
 ------------------
 * Increased precision of least squares solver, as this was limiting theprecision of the parameter estimation
@@ -28,7 +36,7 @@ Changelog
 0.7.3 (2020-01-24)
 ------------------
 * Solved xlim in subplots of plot_residuals_reference_sections funciton
-* Solved YAML depreciation related problems
+* Solved YAML deprecation related problems
 * Introduced new approach for double ended calibration, with a different Cfor the forward and backward channel
 * First code added for time variant asymmetric attenuation, such as connectors.
 * First code added for matching sections

CITATION.cff

@@ -22,5 +22,5 @@ doi: "10.5281/zenodo.1410097"
 license: "BSD-3-Clause"
 repository-code: "https://github.com/dtscalibration/python-dts-calibration"
 title: "Python distributed temperature sensing calibration"
-version: "v0.9.0"
+version: "v0.9.2"
 url: "https://python-dts-calibration.readthedocs.io"

README.rst

@@ -37,9 +37,9 @@ Overview
     :alt: PyPI Package latest release
     :target: https://pypi.python.org/pypi/dtscalibration
 
-.. |commits-since| image:: https://img.shields.io/github/commits-since/dtscalibration/python-dts-calibration/v0.9.0.svg
+.. |commits-since| image:: https://img.shields.io/github/commits-since/dtscalibration/python-dts-calibration/v0.9.2.svg
     :alt: Commits since latest release
-    :target: https://github.com/dtscalibration/python-dts-calibration/compare/v0.9.0...master
+    :target: https://github.com/dtscalibration/python-dts-calibration/compare/v0.9.2...master
 
 .. |wheel| image:: https://img.shields.io/pypi/wheel/dtscalibration.svg
     :alt: PyPI Wheel

docs/conf.py

@@ -36,7 +36,7 @@
 year = str(date.today().year)
 author = 'Bas des Tombe'
 copyright = '{0}, {1}'.format(year, author)
-version = release = '0.9.0'
+version = release = '0.9.2'
 
 pygments_style = 'trac'
 templates_path = ['.']

docs/examples/notebooks/02Common_DataStore_functions_slice_mean_max_std_resample.ipynb.rst

@@ -12,7 +12,7 @@ Examples of how to do some of the more commonly used functions:
 .. code:: ipython3
 
     import os
-    
+
     from dtscalibration import read_silixa_files
 
 
@@ -28,7 +28,7 @@ learned from the previous notebook.
 .. code:: ipython3
 
     filepath = os.path.join('..', '..', 'tests', 'data', 'single_ended')
-    
+
     ds = read_silixa_files(
         directory=filepath,
         timezone_netcdf='UTC',
@@ -55,7 +55,7 @@ http://xarray.pydata.org/en/stable/indexing.html
 
 .. code:: ipython3
 
-    ds['ST']  # is the data stored, presented as a DataArray
+    ds['st']  # is the data stored, presented as a DataArray
 
 
 
@@ -87,7 +87,7 @@ http://xarray.pydata.org/en/stable/indexing.html
 
 .. code:: ipython3
 
-    ds['TMP'].plot(figsize=(12, 8));
+    ds['tmp'].plot(figsize=(12, 8));
 
 1 mean, min, max
 ----------------

docs/examples/notebooks/04Calculate_variance_Stokes.ipynb.rst

@@ -10,10 +10,10 @@ estimate the variance of the noise to: - Perform a weighted calibration
 .. code:: ipython3
 
     import os
-    
+
     from dtscalibration import read_silixa_files
     from matplotlib import pyplot as plt
-    
+
     %matplotlib inline
 
 
@@ -26,7 +26,7 @@ estimate the variance of the noise to: - Perform a weighted calibration
 .. code:: ipython3
 
     filepath = os.path.join('..', '..', 'tests', 'data', 'double_ended2')
-    
+
     ds = read_silixa_files(
         directory=filepath,
         timezone_netcdf='UTC',
@@ -58,25 +58,25 @@ method.
 
 .. code:: ipython3
 
-    print(ds.variance_stokes.__doc__) 
+    print(ds.variance_stokes.__doc__)
 
 
 .. parsed-literal::
 
     Backwards compatibility
-    
+
             Use:
             - `variance_stokes_constant` for small setups with small variations in
             intensity
             - `variance_stokes_exponential` for small setups with very few time
             steps
             - `variance_stokes_linear` for larger setups with more time steps
-            
+
 
 
 .. code:: ipython3
 
-    I_var, residuals = ds.variance_stokes(st_label='ST')
+    I_var, residuals = ds.variance_stokes(st_label='st')
     print("The variance of the Stokes signal along the reference sections "
           "is approximately {} on a {} sec acquisition time".format(I_var, ds.userAcquisitionTimeFW.data[0]))
 
@@ -89,7 +89,7 @@ method.
 .. code:: ipython3
 
     from dtscalibration import plot
-    
+
     fig_handle = plot.plot_residuals_reference_sections(
             residuals,
             sections,
@@ -127,7 +127,7 @@ by coils/sharp bends in cable - Attenuation caused by a splice
 
     import scipy
     import numpy as np
-    
+
     sigma = residuals.std()
     mean = residuals.mean()
     x = np.linspace(mean - 3*sigma, mean + 3*sigma, 100)

docs/examples/notebooks/05Calibrate_single_ols.ipynb.rst

@@ -15,10 +15,10 @@ anymore.
 .. code:: ipython3
 
     import os
-    
+
     from dtscalibration import read_silixa_files
     import matplotlib.pyplot as plt
-    
+
     %matplotlib inline
 
 
@@ -31,12 +31,12 @@ anymore.
 .. code:: ipython3
 
     filepath = os.path.join('..', '..', 'tests', 'data', 'single_ended')
-    
+
     ds = read_silixa_files(
         directory=filepath,
         timezone_netcdf='UTC',
         file_ext='*.xml')
-    
+
     ds100 = ds.sel(x=slice(-30, 101))  # only calibrate parts of the fiber, in meters
     sections = {
                 'probe1Temperature':    [slice(20, 25.5)],  # warm bath
@@ -61,8 +61,8 @@ anymore.
 
 .. parsed-literal::
 
-    
-    
+
+
             Parameters
             ----------
             store_p_cov : str
@@ -119,17 +119,17 @@ anymore.
                 variance of the estimate of dalpha.
                 Covariances between alpha and other parameters are not accounted
                 for.
-    
+
             Returns
             -------
-    
-            
+
+
 
 
 .. code:: ipython3
 
-    ds100.calibration_single_ended(st_label='ST',
-                                   ast_label='AST',
+    ds100.calibration_single_ended(st_label='st',
+                                   ast_label='ast',
                                    method='ols')
 
 
@@ -144,9 +144,9 @@ Lets compare our calibrated values with the device calibration
 .. code:: ipython3
 
     ds1 = ds100.isel(time=0)  # take only the first timestep
-    
-    ds1.TMPF.plot(linewidth=1, figsize=(12, 8), label='User calibrated')  # plot the temperature calibrated by us
-    ds1.TMP.plot(linewidth=1, label='Device calibrated')  # plot the temperature calibrated by the device
+
+    ds1.tmpf.plot(linewidth=1, figsize=(12, 8), label='User calibrated')  # plot the temperature calibrated by us
+    ds1.tmp.plot(linewidth=1, label='Device calibrated')  # plot the temperature calibrated by the device
     plt.title('Temperature at the first time step')
     plt.legend();
 

docs/examples/notebooks/06Calibrate_double_ols.ipynb.rst

@@ -13,7 +13,7 @@ channel should be aligned.
 .. code:: ipython3
 
     import os
-    
+
     from dtscalibration import read_silixa_files
     import matplotlib.pyplot as plt
     %matplotlib inline
@@ -28,12 +28,12 @@ channel should be aligned.
 .. code:: ipython3
 
     filepath = os.path.join('..', '..', 'tests', 'data', 'double_ended2')
-    
+
     ds = read_silixa_files(
         directory=filepath,
         timezone_netcdf='UTC',
         file_ext='*.xml')
-    
+
     ds100 = ds.sel(x=slice(0, 100))  # only calibrate parts of the fiber
     sections = {
         'probe1Temperature': [slice(7.5, 17.), slice(70., 80.)],  # cold bath
@@ -57,8 +57,8 @@ channel should be aligned.
 
 .. parsed-literal::
 
-    
-    
+
+
             Parameters
             ----------
             store_p_cov : str
@@ -149,20 +149,20 @@ channel should be aligned.
                 has three items. The first two items are the slices of the sections
                 that are matched. The third item is a boolean and is True if the two
                 sections have a reverse direction ("J-configuration").
-    
-    
+
+
             Returns
             -------
-    
-            
+
+
 
 
 .. code:: ipython3
 
-    st_label = 'ST'
-    ast_label = 'AST'
-    rst_label = 'REV-ST'
-    rast_label = 'REV-AST'
+    st_label = 'st'
+    ast_label = 'ast'
+    rst_label = 'rst'
+    rast_label = 'rast'
     ds100.calibration_double_ended(sections=sections,
                                    st_label=st_label,
                                    ast_label=ast_label,
@@ -171,8 +171,8 @@ channel should be aligned.
                                    method='ols')
 
 After calibration, two data variables are added to the ``DataStore``
-object: - ``TMPF``, temperature calculated along the forward direction -
-``TMPB``, temperature calculated along the backward direction
+object: - ``tmpf``, temperature calculated along the forward direction -
+``tmpb``, temperature calculated along the backward direction
 
 A better estimate, with a lower expected variance, of the temperature
 along the fiber is the average of the two. We cannot weigh on more than
@@ -181,10 +181,10 @@ the other, as we do not have more information about the weighing.
 .. code:: ipython3
 
     ds1 = ds100.isel(time=0)  # take only the first timestep
-    
-    ds1.TMPF.plot(linewidth=1, label='User cali. Forward', figsize=(12, 8))  # plot the temperature calibrated by us
-    ds1.TMPB.plot(linewidth=1, label='User cali. Backward')  # plot the temperature calibrated by us
-    ds1.TMP.plot(linewidth=1, label='Device calibrated')  # plot the temperature calibrated by the device
+
+    ds1.tmpf.plot(linewidth=1, label='User cali. Forward', figsize=(12, 8))  # plot the temperature calibrated by us
+    ds1.tmpb.plot(linewidth=1, label='User cali. Backward')  # plot the temperature calibrated by us
+    ds1.tmp.plot(linewidth=1, label='Device calibrated')  # plot the temperature calibrated by the device
     plt.legend();
 
 
@@ -198,9 +198,9 @@ first.
 
 .. code:: ipython3
 
-    ds1['TMPAVG'] = (ds1.TMPF + ds1.TMPB) / 2
+    ds1['TMPAVG'] = (ds1.tmpf + ds1.tmpb) / 2
     ds1_diff = ds1.TMP - ds1.TMPAVG
-    
+
     ds1_diff.plot(figsize=(12, 8));