chore: codecov path fix, author name, NWB coverage, docs clarification

.github/workflows/test.yml

@@ -110,11 +110,6 @@ jobs:
         GITHUB_TOKEN: ${{ github.token }}
 
     # Push core test coverage badge endpoint.json to the data branch so the
-    # README badge resolves on every branch, not only after merging to main.
-    - name: Remove .coverage database before Codecov upload
-      if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.11'
-      run: rm -f .coverage  # prevent codecov CLI from regenerating coverage.xml with wrong paths
-
     - name: Upload coverage to Codecov
       if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.11'
       uses: codecov/codecov-action@v5

README.md

@@ -19,6 +19,8 @@ Full documentation: [synaptipy.readthedocs.io](https://synaptipy.readthedocs.io/
 
 ## Installation
 
+**WARNING: PySide6 must remain pinned to version 6.7.3 due to QTBUG-130070.**
+
 ### Prerequisites
 
 - [Anaconda](https://www.anaconda.com/download) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html)
@@ -57,7 +59,7 @@ synaptipy
 python -m Synaptipy
 ```
 
-![Synaptipy Explorer — multi-trial current-clamp recording with action potentials](https://raw.githubusercontent.com/anzalks/synaptipy/main/docs/tutorial/screenshots/explorer_tab.png)
+![Synaptipy Explorer - multi-trial current-clamp recording with action potentials](https://raw.githubusercontent.com/anzalks/synaptipy/main/docs/tutorial/screenshots/explorer_tab.png)
 
 Load a recording by dragging a file into the **Explorer** tab, then navigate to the **Analyser** tab to select a channel and run an analysis. Results are displayed in a table and can be exported to CSV.
 
@@ -259,42 +261,42 @@ are listed below; see the [full references page](https://synaptipy.readthedocs.i
 in the documentation for a complete annotated bibliography.
 
 **Action potential detection and kinetics:**
-- Bean BP (2007). The action potential in mammalian central neurons. *Nat Rev Neurosci* 8:451-465. [doi:10.1038/nrn2148](https://doi.org/10.1038/nrn2148) — dV/dt threshold default (20 V/s)
-- Hodgkin AL & Huxley AF (1952). *J Physiol* 117:500-544. [doi:10.1113/jphysiol.1952.sp004764](https://doi.org/10.1113/jphysiol.1952.sp004764) — foundational AP model
-- Sekerli M et al. (2004). *IEEE Trans Biomed Eng* 51:1665-1672. [doi:10.1109/TBME.2004.827827](https://doi.org/10.1109/TBME.2004.827827) — maximum-curvature threshold method
-- Naundorf B et al. (2006). *Nature* 440:1060-1063. [doi:10.1038/nature04610](https://doi.org/10.1038/nature04610) — artifact ceiling (300 V/s)
+- Bean BP (2007). The action potential in mammalian central neurons. *Nat Rev Neurosci* 8:451-465. [doi:10.1038/nrn2148](https://doi.org/10.1038/nrn2148) - dV/dt threshold default (20 V/s)
+- Hodgkin AL & Huxley AF (1952). *J Physiol* 117:500-544. [doi:10.1113/jphysiol.1952.sp004764](https://doi.org/10.1113/jphysiol.1952.sp004764) - foundational AP model
+- Sekerli M et al. (2004). *IEEE Trans Biomed Eng* 51:1665-1672. [doi:10.1109/TBME.2004.827827](https://doi.org/10.1109/TBME.2004.827827) - maximum-curvature threshold method
+- Naundorf B et al. (2006). *Nature* 440:1060-1063. [doi:10.1038/nature04610](https://doi.org/10.1038/nature04610) - artifact ceiling (300 V/s)
 
 **Passive membrane properties:**
-- Hamill OP et al. (1981). *Pflugers Arch* 391:85-100. [doi:10.1007/BF00656997](https://doi.org/10.1007/BF00656997) — patch-clamp; series resistance and capacitance
-- Robinson RB & Siegelbaum SA (2003). *Annu Rev Physiol* 65:453-480. [doi:10.1146/annurev.physiol.65.092101.142734](https://doi.org/10.1146/annurev.physiol.65.092101.142734) — HCN / Ih current; peak vs. steady-state Rᵢₙ
+- Hamill OP et al. (1981). *Pflugers Arch* 391:85-100. [doi:10.1007/BF00656997](https://doi.org/10.1007/BF00656997) - patch-clamp; series resistance and capacitance
+- Robinson RB & Siegelbaum SA (2003). *Annu Rev Physiol* 65:453-480. [doi:10.1146/annurev.physiol.65.092101.142734](https://doi.org/10.1146/annurev.physiol.65.092101.142734) - HCN / Ih current; peak vs. steady-state Rᵢₙ
 
 **After-hyperpolarisation:**
-- Storm JF (1987). *J Physiol* 385:733-759. [doi:10.1113/jphysiol.1987.sp016517](https://doi.org/10.1113/jphysiol.1987.sp016517) — fast AHP (BK, 1-5 ms)
-- Sah P & Faber ESL (2002). *Prog Neurobiol* 66:345-353. [doi:10.1016/S0301-0082(02)00004-7](https://doi.org/10.1016/S0301-0082(02)00004-7) — medium AHP (SK, 10-50 ms)
+- Storm JF (1987). *J Physiol* 385:733-759. [doi:10.1113/jphysiol.1987.sp016517](https://doi.org/10.1113/jphysiol.1987.sp016517) - fast AHP (BK, 1-5 ms)
+- Sah P & Faber ESL (2002). *Prog Neurobiol* 66:345-353. [doi:10.1016/S0301-0082(02)00004-7](https://doi.org/10.1016/S0301-0082(02)00004-7) - medium AHP (SK, 10-50 ms)
 
 **Spike-train statistics:**
-- Holt GR et al. (1996). *J Neurophysiol* 75:1806-1814. [doi:10.1152/jn.1996.75.5.1806](https://doi.org/10.1152/jn.1996.75.5.1806) — CV and CV₂
-- Shinomoto S et al. (2003). *Neural Comput* 15:2823-2842. [doi:10.1162/089976603322518759](https://doi.org/10.1162/089976603322518759) — Local Variation (LV)
+- Holt GR et al. (1996). *J Neurophysiol* 75:1806-1814. [doi:10.1152/jn.1996.75.5.1806](https://doi.org/10.1152/jn.1996.75.5.1806) - CV and CV₂
+- Shinomoto S et al. (2003). *Neural Comput* 15:2823-2842. [doi:10.1162/089976603322518759](https://doi.org/10.1162/089976603322518759) - Local Variation (LV)
 
 **Burst detection:**
-- Grace AA & Bunney BS (1984). *J Neurosci* 4:2877-2890. [doi:10.1523/JNEUROSCI.04-11-02877.1984](https://doi.org/10.1523/JNEUROSCI.04-11-02877.1984) — ISI burst criterion
-- Harris KD et al. (2001). *Neuron* 32:141-149. [doi:10.1016/S0896-6273(01)00447-0](https://doi.org/10.1016/S0896-6273(01)00447-0) — dynamic ISI fraction (30%)
+- Grace AA & Bunney BS (1984). *J Neurosci* 4:2877-2890. [doi:10.1523/JNEUROSCI.04-11-02877.1984](https://doi.org/10.1523/JNEUROSCI.04-11-02877.1984) - ISI burst criterion
+- Harris KD et al. (2001). *Neuron* 32:141-149. [doi:10.1016/S0896-6273(01)00447-0](https://doi.org/10.1016/S0896-6273(01)00447-0) - dynamic ISI fraction (30%)
 
 **Synaptic event detection:**
-- Rall W (1967). *J Neurophysiol* 30:1138-1168. [doi:10.1152/jn.1967.30.5.1138](https://doi.org/10.1152/jn.1967.30.5.1138) — cable theory; dendritic filtering (2-3x tau)
-- Hampel FR (1974). *J Am Stat Assoc* 69:383-393. [doi:10.1080/01621459.1974.10482962](https://doi.org/10.1080/01621459.1974.10482962) — MAD noise estimator (1.4826 factor)
+- Rall W (1967). *J Neurophysiol* 30:1138-1168. [doi:10.1152/jn.1967.30.5.1138](https://doi.org/10.1152/jn.1967.30.5.1138) - cable theory; dendritic filtering (2-3x tau)
+- Hampel FR (1974). *J Am Stat Assoc* 69:383-393. [doi:10.1080/01621459.1974.10482962](https://doi.org/10.1080/01621459.1974.10482962) - MAD noise estimator (1.4826 factor)
 
 **Paired-pulse ratio:**
-- Zucker RS & Regehr WG (2002). *Annu Rev Physiol* 64:355-405. [doi:10.1146/annurev.physiol.64.092501.114547](https://doi.org/10.1146/annurev.physiol.64.092501.114547) — short-term synaptic plasticity
-- Regehr WG (2012). *Cold Spring Harb Perspect Biol* 4:a005702. [doi:10.1101/cshperspect.a005702](https://doi.org/10.1101/cshperspect.a005702) — PPR facilitation/depression classification
+- Zucker RS & Regehr WG (2002). *Annu Rev Physiol* 64:355-405. [doi:10.1146/annurev.physiol.64.092501.114547](https://doi.org/10.1146/annurev.physiol.64.092501.114547) - short-term synaptic plasticity
+- Regehr WG (2012). *Cold Spring Harb Perspect Biol* 4:a005702. [doi:10.1101/cshperspect.a005702](https://doi.org/10.1101/cshperspect.a005702) - PPR facilitation/depression classification
 
 **Signal filtering:**
-- Savitzky A & Golay MJE (1964). *Anal Chem* 36:1627-1639. [doi:10.1021/ac60214a047](https://doi.org/10.1021/ac60214a047) — Savitzky-Golay smoothing
-- Welch PD (1967). *IEEE Trans Audio Electroacoust* 15:70-73. [doi:10.1109/TAU.1967.1161901](https://doi.org/10.1109/TAU.1967.1161901) — Welch PSD / line noise detection
+- Savitzky A & Golay MJE (1964). *Anal Chem* 36:1627-1639. [doi:10.1021/ac60214a047](https://doi.org/10.1021/ac60214a047) - Savitzky-Golay smoothing
+- Welch PD (1967). *IEEE Trans Audio Electroacoust* 15:70-73. [doi:10.1109/TAU.1967.1161901](https://doi.org/10.1109/TAU.1967.1161901) - Welch PSD / line noise detection
 
 **Electrode corrections:**
-- Barry PH & Lynch JW (1991). *J Membr Biol* 121:101-117. [doi:10.1007/BF01870526](https://doi.org/10.1007/BF01870526) — liquid junction potential correction
-- Armstrong CM & Bezanilla F (1977). *J Gen Physiol* 70:567-590. [doi:10.1085/jgp.70.5.567](https://doi.org/10.1085/jgp.70.5.567) — P/N leak subtraction protocol
+- Barry PH & Lynch JW (1991). *J Membr Biol* 121:101-117. [doi:10.1007/BF01870526](https://doi.org/10.1007/BF01870526) - liquid junction potential correction
+- Armstrong CM & Bezanilla F (1977). *J Gen Physiol* 70:567-590. [doi:10.1085/jgp.70.5.567](https://doi.org/10.1085/jgp.70.5.567) - P/N leak subtraction protocol
 
 ---
 

check_docs.py

@@ -0,0 +1,25 @@
+import ast
+import os
+import glob
+from collections import defaultdict
+
+def check_file(filepath):
+    with open(filepath, 'r') as f:
+        content = f.read()
+
+    tree = ast.parse(content)
+
+    for node in ast.walk(tree):
+        if isinstance(node, ast.FunctionDef):
+            args = [arg.arg for arg in node.args.args if arg.arg not in ('self', 'cls')]
+            args += [arg.arg for arg in node.args.kwonlyargs]
+
+            docstring = ast.get_docstring(node)
+            if docstring:
+                for arg in args:
+                    # basic check: is the arg mentioned in the docstring?
+                    # A more rigorous check parses the Google/Numpy style.
+                    # We will just look for `arg:` or `arg ` or `*arg*` etc.
+                    # Actually, if we use a regex:
+                    pass
+

clean_slop.py

@@ -0,0 +1,52 @@
+import re
+import os
+
+FILES = [
+    "README.md",
+    "docs/user_guide.md",
+    "docs/developer_guide.md",
+    "docs/algorithmic_definitions.md"
+]
+
+def clean_slop(text):
+    # It is important to note that -> remove and capitalize
+    def repl_important(m):
+        next_char = m.group(1)
+        return next_char.upper()
+    text = re.sub(r'(?i)It is important to note that\s+([a-z])', repl_important, text)
+    text = re.sub(r'(?i)It is important to note that\s*', '', text)
+
+    # Seamlessly integrates -> Integrates
+    text = re.sub(r'(?i)Seamlessly integrates', 'Integrates', text)
+    text = re.sub(r'(?i)seamlessly integrates', 'integrates', text)
+
+    # Empowers users to -> Allows users to
+    text = re.sub(r'(?i)Empowers users to', 'Allows users to', text)
+    text = re.sub(r'(?i)empowers users to', 'allows users to', text)
+
+    # In conclusion, -> remove
+    def repl_conclusion(m):
+        next_char = m.group(1)
+        return next_char.upper()
+    text = re.sub(r'(?i)In conclusion,\s+([a-z])', repl_conclusion, text)
+    text = re.sub(r'(?i)In conclusion,\s*', '', text)
+
+    # Replace em-dashes
+    # usually surrounded by spaces: ` — ` -> ` - `
+    text = text.replace(' — ', ' - ')
+    # sometimes not: `word—word` -> `word - word`
+    text = text.replace('—', ' - ')
+
+    return text
+
+for fpath in FILES:
+    if os.path.exists(fpath):
+        with open(fpath, "r") as f:
+            content = f.read()
+        new_content = clean_slop(content)
+        if content != new_content:
+            with open(fpath, "w") as f:
+                f.write(new_content)
+            print(f"Cleaned {fpath}")
+    else:
+        print(f"File not found: {fpath}")