Merge branch '0.10.x' of github.com:stonerlab/Stoner-PythonCode into 0.10.x

Author: gb119

.github/workflows/build_conda.yaml

@@ -33,3 +33,11 @@ jobs:
           user: phygbu
           label: main
           token: ${{ secrets.ANACONDA }} # Replace with the right name of your secret
+      - name: Upload wheel
+        env:
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
+        run: |
+          python -m pip install twine
+          python setup.py sdist bdist_wheel
+          twine upload dist/*

Stoner/__init__.py

@@ -36,7 +36,7 @@
 Options = _Options()
 
 
-__version_info__ = ("0", "10", "12")
+__version_info__ = ("0", "10", "13")
 __version__ = ".".join(__version_info__)
 
 __homepath__ = pathlib.Path(__file__).parent.resolve()

scripts/PCAR-New.ini

@@ -1,19 +1,25 @@
 [Options]
+# This is the model used to fit the data - do not change!
 model: Stoner.analysis.fitting.models.superconductivity.Strijkers
 # Controls whether the data and fit are plotted or not.
 show_plot: True
 # Sets whether to print a nice report to the console
 print_report: True
 # Save the final result ?
-save_fit: False
-#
-# These are optional options that turn on various (experimental) bits of code
-#
+save_fit: True
+# #####################################################################################################################
+# These are optional options that turn on various (experimental) bits of code - only use once manual fitting is done
+# #####################################################################################################################
 #This turns on the normalisation
 normalise: False
 # This turns on the fitting a quadratic to the outer 10% to determine the normalisation
 # Don't use it unless you know why this might be sensible for your data !
 fancy_normaliser: False
+# This allows us to customise the normaliser used - default is just a quadratic - either a function in the global
+# namespace, or a fully dotted package.module.function name.
+#normaliser_function = quadratic_abs
+# This is the name of a function to use as a preprocessor - function must be in global namespace.
+#preprocessor = down_only
 # Turns on rescaling the V axis into mV
 # Only use if data not already correctly scaled.
 rescale_v: False
@@ -26,7 +32,7 @@ decompose: True
 fancy_result: True
 #
 # Can switch between a least-squares fitting algorithm based on the lmfit module, or othogonal distance regression "odr"
-method=differential_evolution
+method=odr
 
 # These settings will read old style files directly
 [Data]
@@ -41,6 +47,8 @@ separator: ,
 v_scale:1000
 # Only need this if normalise is True and fancy_normaliser is False
 Normal_conductance: 0.0
+# Used to set the fraction of the x-range used to fit the background
+background_fraction=0.75
 # Set true to discard data at high bias
 discard: False
 # Bias limit to discard at

scripts/PCAR-New.py

@@ -7,6 +7,7 @@
 import configparser as ConfigParser
 import pathlib
 import inspect
+from importlib import import_module
 
 import numpy as np
 from Stoner import Data
@@ -29,7 +30,7 @@ def load_config(self):
                 f"Could not find the fitting ini file {inifile}!"
             )
 
-        tmp = cfg_data_from_ini(inifile, filename=False)
+        tmp = cfg_data_from_ini(inifile)
         self.setas = tmp.setas.clone
         self.column_headers = tmp.column_headers
         self.metadata = tmp.metadata
@@ -77,7 +78,22 @@ def RescaleV(self):
             vscale = self.config.getfloat("Data", "v_scale")
             self.data[:, self.vcol] *= vscale
             print(f"Rescaled voltage data by {vscale}")
-            return self
+        return self
+
+    def Preprocess(self):
+        """Run an arbitart function over the data if the option is specified."""
+        preprocessor_name = self.config.get(
+            "Options", "preprocessor", fallback="_pass"
+        )
+        if "." in preprocessor_name:
+            module = ".".join(preprocessor_name.split(".")[:-1])
+            preprocessor = preprocessor_name.split(".")[-1]
+            module = import_module(module)
+            preprocessor = getattr(module, preprocessor)
+        else:
+            preprocessor = globals()[preprocessor_name]
+
+        return preprocessor(self)
 
     def Normalise(self):
         """Normalise the data if the relevant options are turned on in the config file.
@@ -92,24 +108,39 @@ def Normalise(self):
             if self.config.has_option(
                 "Options", "fancy_normaliser"
             ) and self.config.getboolean("Options", "fancy_normaliser"):
+                fraction = 1 - self.config.getfloat(
+                    "Data", "background_fraction", fallback=0.1
+                )
+                normaliser_name = self.config.get(
+                    "Options", "normaliser_function", fallback="quadratic"
+                )
+                if "." in normaliser_name:
+                    module = ".".join(normaliser_name.split(".")[:-1])
+                    normaliser = normaliser_name.split(".")[-1]
+                    module = import_module(module)
+                    normaliser = getattr(module, normaliser)
+                else:
+                    normaliser = globals()[normaliser_name]
                 vmax, _ = self.max(self.vcol)
                 vmin, _ = self.min(self.vcol)
                 p, pv = self.curve_fit(
-                    quadratic,
-                    bounds=lambda x, y: (x > 0.9 * vmax) or (x < 0.9 * vmin),
+                    normaliser,
+                    bounds=lambda x, y: (x > fraction * vmax)
+                    or (x < fraction * vmin),
                 )
+                normaliser_repr = getattr(
+                    normaliser,
+                    "representation",
+                    f"{normaliser_name}(V,{','.join(p)})",
+                ).format(p)
                 print(
-                    "Fitted normal conductance background of G="
-                    + str(p[0])
-                    + "V^2 +"
-                    + str(p[1])
-                    + "V+"
-                    + str(p[2])
+                    f"Fitted normal conductance background of G={normaliser_repr}"
                 )
                 self["normalise.coeffs"] = p
                 self["normalise.coeffs_err"] = np.sqrt(np.diag(pv))
+                self["normaliser_name"] = normaliser_name
                 self.apply(
-                    lambda x: x[self.gcol] / quadratic(x[self.vcol], *p),
+                    lambda x: x[self.gcol] / normaliser(x[self.vcol], *p),
                     self.gcol,
                     header=self.column_headers[self.gcol],
                 )
@@ -192,7 +223,7 @@ def plot_results(self):
     def Fit(self):
         """Run the fitting code."""
         # Run a pre-fitting data munge chain
-        self.RescaleV().Discard().offset_correct().Decompose().Normalise()
+        self.Preprocess().RescaleV().Discard().offset_correct().Decompose().Normalise()
         chi2 = self.p0.shape[0] > 1
 
         method = getattr(self, self.method)
@@ -227,6 +258,29 @@ def Fit(self):
             fit.save(False)
 
 
+def quadratic_abs(x, a, b, c):
+    """A function that returns a quadratic expression, but in terms of abs(x)."""
+    x = np.abs(x)
+    return c + x * (b + a * x)
+
+
+def _pass(x):
+    """A do nothing preprocess function."""
+    return x
+
+
+def down_only(data):
+    """Selects only the down sweep data - this is a hack that works becuase Set V is not noisy"""
+    ix = np.where(np.diff(np.sign(np.diff(data["Set V"]))) != 0)[:2][0]
+    dix = np.diff(ix) > 10
+    dix = np.append(dix, True)
+    ix = ix[dix]
+    ix = ix[:2]
+
+    data.data = data.data[slice(*ix)]
+    return data
+
+
 if __name__ == "__main__":
     d = working()
     d.load_config()

scripts/PCAR-chi^2.ini

@@ -6,9 +6,9 @@ show_plot: True
 print_report: True
 # Save the final result ?
 save_fit: False
-#
+# ################################################################################
 # These are optional options that turn on various (experimental) bits of code
-#
+# #################################################################################
 #This turns on the normalisation
 normalise: False
 # This turns on the fitting a quadratic to the outer 10% to determine the normalisation