enh: several improvements regarding metadata handling

CHANGELOG

@@ -1,3 +1,10 @@
+0.6.8
+ - setup: bump afmformats from 0.5.1 to 0.7.0 (improved metadata view
+   in info tab)
+ - setup: bump nanite from 1.4.1 to 1.5.1 (compatibility to afmformats)
+ - enh: fit results and parameter export dialog now supports new groups
+   in afmformats 0.7.0
+ - enh: autosave now only stores fit results and rating parameters
 0.6.7
  - setup: bump nanite from 1.4.0 to 1.4.1
    (baseline is now a free parameter by default)

docs/sec_interface.rst

@@ -171,16 +171,8 @@ Tab: Info
 The info tab shows metadata related to the currently shown curve.
 Unknown values are indicated as *nan*.
 
-Dataset:
-    All information related to the dataset on disk and of the curve
-    relative to this dataset.
-
-Experiment:
-    All information on the actual experiment.
-
-QMap:
-    If the curve is part of a quantiative map (2D FD scan), then
-    the scan grid properties and the curve position are listed.
+Acquisition:
+    Settings of the acquisition software
 
 Ancillaries:
     Ancillary parameters are computed for each model. Here, the
@@ -189,6 +181,20 @@ Ancillaries:
     the fitted curve has its maximum. Fit models may have their own
     specific ancillary parameters.
 
+Dataset:
+    Measurement parameters of the dataset
+
+QMap:
+    If the curve is part of a quantiative map (2D FD scan), then
+    the scan grid properties and the curve position are listed.
+
+Setup:
+    Information about the AFM setup used
+
+Storage:
+    Information about the data file, which are not particularly important
+    for the experiment, but allow to identify a dataset or curve
+
 .. _sec_ui_fd_tab_qmap:
 
 Tab: QMap

pyjibe/fd/dlg_export_vals.py

@@ -31,9 +31,11 @@ def done(self, r):
                 if not fname.endswith(".tsv"):
                     fname += ".tsv"
                 user_choices = {
+                    "acquisition": self.checkBox_acquisition,
                     "dataset": self.checkBox_dataset,
-                    "experiment": self.checkBox_experiment,
                     "qmap": self.checkBox_qmap,
+                    "setup": self.checkBox_setup,
+                    "storage": self.checkBox_storage,
                     "params_initial": self.checkBox_initial,
                     "params_fitted": self.checkBox_fitted,
                     "params_ancillary": self.checkBox_ancillary,

pyjibe/fd/dlg_export_vals.ui

@@ -26,11 +26,34 @@
      <property name="title">
       <string>Metadata</string>
      </property>
-     <layout class="QVBoxLayout" name="verticalLayout_3">
-      <item>
+     <layout class="QGridLayout" name="gridLayout">
+      <item row="2" column="0">
+       <widget class="QCheckBox" name="checkBox_qmap">
+        <property name="toolTip">
+         <string>Grid position, shape, etc. (if applicable)</string>
+        </property>
+        <property name="text">
+         <string>QMap</string>
+        </property>
+       </widget>
+      </item>
+      <item row="0" column="0">
+       <widget class="QCheckBox" name="checkBox_acquisition">
+        <property name="toolTip">
+         <string>AFM acquisition settings</string>
+        </property>
+        <property name="text">
+         <string>Acquisition</string>
+        </property>
+        <property name="checked">
+         <bool>true</bool>
+        </property>
+       </widget>
+      </item>
+      <item row="1" column="0">
        <widget class="QCheckBox" name="checkBox_dataset">
         <property name="toolTip">
-         <string>Path, identifier, etc.</string>
+         <string>Dataset parameters</string>
         </property>
         <property name="text">
          <string>Dataset</string>
@@ -40,23 +63,23 @@
         </property>
        </widget>
       </item>
-      <item>
-       <widget class="QCheckBox" name="checkBox_experiment">
+      <item row="0" column="1">
+       <widget class="QCheckBox" name="checkBox_setup">
         <property name="toolTip">
-         <string>Duration, spring constant, etc.</string>
+         <string>AFM setup</string>
         </property>
         <property name="text">
-         <string>Experiment</string>
+         <string>Setup</string>
         </property>
        </widget>
       </item>
-      <item>
-       <widget class="QCheckBox" name="checkBox_qmap">
+      <item row="1" column="1">
+       <widget class="QCheckBox" name="checkBox_storage">
         <property name="toolTip">
-         <string>Grid position, shape, etc. (if applicable)</string>
+         <string>File names, dates, ...</string>
         </property>
         <property name="text">
-         <string>QMap</string>
+         <string>Storage</string>
         </property>
        </widget>
       </item>

pyjibe/fd/export.py

@@ -10,13 +10,10 @@
 from .. import units
 
 #: Valid export choices in `save_tsv_metadata_results`
-EXPORT_CHOICES = [
-    "dataset",
-    "experiment",
+EXPORT_CHOICES = list(meta.META_FIELDS.keys()) + [
     "params_ancillary",
     "params_fitted",
     "params_initial",
-    "qmap",
     "rating"]
 
 
@@ -69,25 +66,28 @@ def save_tsv_metadata_results(filename, fdist_list, which=EXPORT_CHOICES):
                 if "params_initial" in fdist.fit_properties:
                     fp = fdist.fit_properties["params_initial"]
                     for ki in fp:
-                        label, hrvalue = get_unitname_value(
-                            name=nmodel.get_parm_name(model_key, ki),
-                            value=fp[ki].value,
-                            unit=nmodel.get_parm_unit(model_key, ki))
-                        if label not in columns:
-                            columns[label] = [np.nan] * len(fdist_list)
-                        columns[label][ii] = hrvalue
-
+                        if not fp[ki].vary:
+                            label, hrvalue = get_unitname_value(
+                                name=nmodel.get_parm_name(model_key, ki),
+                                value=fp[ki].value,
+                                unit=nmodel.get_parm_unit(model_key, ki))
+                            if label not in columns:
+                                columns[label] = [np.nan] * len(fdist_list)
+                            columns[label][ii] = hrvalue
+
+            # Fitted
             if "params_fitted" in which:
                 if "params_fitted" in fdist.fit_properties:
                     fp = fdist.fit_properties["params_fitted"]
                     for ki in fp:
-                        label, hrvalue = get_unitname_value(
-                            name=nmodel.get_parm_name(model_key, ki),
-                            value=fp[ki].value,
-                            unit=nmodel.get_parm_unit(model_key, ki))
-                        if label not in columns:
-                            columns[label] = [np.nan] * len(fdist_list)
-                        columns[label][ii] = hrvalue
+                        if fp[ki].vary:
+                            label, hrvalue = get_unitname_value(
+                                name=nmodel.get_parm_name(model_key, ki),
+                                value=fp[ki].value,
+                                unit=nmodel.get_parm_unit(model_key, ki))
+                            if label not in columns:
+                                columns[label] = [np.nan] * len(fdist_list)
+                            columns[label][ii] = hrvalue
 
                     # Additional fit parameters
                     props = {"xmin": ("Fit interval minimum", "m"),

pyjibe/fd/main.py

@@ -227,8 +227,7 @@ def autosave(self, fdist):
                         )
                         fname = os.path.join(adir, newbase)
                 # Export data
-                which = export.EXPORT_CHOICES.copy()
-                which.remove("params_ancillary")
+                which = ["params_fitted", "rating"]
                 export.save_tsv_metadata_results(filename=fname,
                                                  fdist_list=exp_curv,
                                                  which=which)
@@ -331,6 +330,8 @@ def on_curve_list(self):
         self.tab_qmap.mpl_qmap_update()
         # Display edelta
         self.tab_edelta.mpl_edelta_update()
+        # Autosave
+        self.autosave(fdist)
 
     def on_curve_list_item_changed(self, item):
         """An item in the curve list was changed

pyjibe/fd/tab_info.py

@@ -17,54 +17,40 @@ def __init__(self, *args, **kwargs):
         uic.loadUi(path_ui, self)
 
     def update_info(self, fdist):
-        msum = fdist.metadata.get_summary()
-
-        text = []
+        hr_info = {}
 
-        # Dataset
-        text.append("<b>Dataset</b>")
-        for kk in msum["dataset"]:
-            text.append(get_string_rep_meta(kk, msum["dataset"][kk]))
-
-        # Experiment
-        text.append("")
-        text.append("<b>Experiment:</b>")
-        texte = []
-        for kk in msum["experiment"]:
-            texte.append(get_string_rep_meta(kk, msum["experiment"][kk]))
-        text += sorted(texte)
-
-        # QMap
-        if msum["qmap"] and not has_all_nans(msum["qmap"]):
-            text.append("")
-            text.append("<b>QMap:</b>")
-            textq = []
-            for kk in msum["qmap"]:
-                textq.append(get_string_rep_meta(kk, msum["qmap"][kk]))
-            text += sorted(textq)
-
-        # Analysis
-        if msum["analysis"] and not has_all_nans(msum["analysis"]):
-            text.append("")
-            text.append("<b>Analysis:</b>")
-            texta = []
-            for kk in msum["analysis"]:
-                texta.append(get_string_rep_meta(kk, msum["analysis"][kk]))
-            text += sorted(texta)
+        # Metadata
+        msum = fdist.metadata.get_summary()
+        for sec in msum:
+            if sec == "qmap":
+                if msum["qmap"] and not has_all_nans(msum["qmap"]):
+                    textq = []
+                    for kk in msum["qmap"]:
+                        textq.append(get_string_rep_meta(kk, msum["qmap"][kk]))
+                    hr_info["QMap"] = textq
+            else:
+                atext = []
+                for kk in msum[sec]:
+                    atext.append(get_string_rep_meta(kk, msum[sec][kk]))
+                hr_info[sec.capitalize()] = atext
 
         # Ancillaries
         anc_dict = fdist.get_ancillary_parameters()
         if anc_dict:
-            text.append("")
-            text.append("<b>Ancillaries:</b>")
             text_meta = []
             model_key = fdist.fit_properties["model_key"]
             for kk in anc_dict:
                 text_meta.append(
                     get_string_rep(name=model.get_parm_name(model_key, kk),
                                    value=anc_dict[kk],
                                    unit=model.get_parm_unit(model_key, kk)))
-            text += sorted(text_meta)
+            hr_info["Ancillaries"] = text_meta
+
+        text = []
+        for sec in sorted(hr_info.keys()):
+            text.append("<b>{}:</b>".format(sec))
+            text += sorted(hr_info[sec])
+            text.append("")
 
         textstring = "<br>".join(text)
 

setup.py

@@ -24,7 +24,7 @@
     license="GPL v3",
     description=description,
     long_description=open('README.rst').read() if exists('README.rst') else '',
-    install_requires=["afmformats>=0.5.1",
+    install_requires=["afmformats>=0.7.0",
                       "appdirs",
                       "nanite>=1.4.1",
                       "matplotlib",