Promote ConfigColl exactness with MATLAB fixtures

nstat/confidence_interval.py

@@ -3,6 +3,20 @@
 import numpy as np
 
 
+MATLAB_COLOR_ORDER = np.asarray(
+    [
+        [0.0660, 0.4430, 0.7450],
+        [0.8660, 0.3290, 0.0000],
+        [0.9290, 0.6940, 0.1250],
+        [0.4940, 0.1840, 0.5560],
+        [0.4660, 0.6740, 0.1880],
+        [0.3010, 0.7450, 0.9330],
+        [0.6350, 0.0780, 0.1840],
+    ],
+    dtype=float,
+)
+
+
 class ConfidenceInterval:
     def __init__(self, time, bounds, *args, color: str | None = None, value: float = 0.95) -> None:
         t = np.asarray(time, dtype=float).reshape(-1)
@@ -176,12 +190,14 @@ def plot(self, color: str | None = None, alphaVal: float = 0.2, drawPatches: int
         import matplotlib.pyplot as plt
 
         axis = plt.gca() if ax is None else ax
-        plot_color = color or self.color
+        plot_color = self.color if color is None else color
         if drawPatches:
-            return axis.fill_between(self.time, self.lower, self.upper, color=plot_color, alpha=alphaVal)
+            return axis.fill_between(self.time, self.lower, self.upper, color=plot_color, edgecolor="none", alpha=alphaVal)
         lines = axis.plot(self.time, self.bounds)
         for line in lines:
-            line.set_alpha(alphaVal)
             if plot_color is not None and not isinstance(plot_color, (str, bytes)):
                 line.set_color(plot_color)
+        if plot_color is None or isinstance(plot_color, (str, bytes)):
+            for index, line in enumerate(lines):
+                line.set_color(MATLAB_COLOR_ORDER[index % MATLAB_COLOR_ORDER.shape[0]])
         return lines

nstat/core.py

@@ -738,11 +738,17 @@ def resampleMe(self, newSampleRate: float) -> None:
         newTime = np.arange(self.time[0], self.time[-1] + 0.5 * dt, dt, dtype=float)
         if self.data.shape[0] > 1:
             columns = []
+            if self.time.size >= 4:
+                interp_kind = "cubic"
+            elif self.time.size == 3:
+                interp_kind = "quadratic"
+            else:
+                interp_kind = "linear"
             for index in range(self.dimension):
                 interpolator = interp1d(
                     self.time,
                     self.data[:, index],
-                    kind="cubic",
+                    kind=interp_kind,
                     bounds_error=False,
                     fill_value=0.0,
                 )

nstat/events.py

@@ -43,18 +43,38 @@ def fromStructure(structure: dict[str, Any] | None) -> "Events" | None:
         return Events(event_times, event_labels, event_color)
 
     def plot(self, *_, handle=None, **__):
-        ax = handle if handle is not None else plt.subplots(1, 1, figsize=(6.0, 2.2))[1]
-        ax.clear()
-        if self.eventTimes.size:
-            ax.vlines(self.eventTimes, 0.0, 1.0, color=self.eventColor, linewidth=1.5)
-            for x, label in zip(self.eventTimes, self.eventLabels, strict=False):
-                if label:
-                    ax.text(float(x), 1.02, label, rotation=45, ha="left", va="bottom", fontsize=8)
-        ax.set_ylim(0.0, 1.1)
-        ax.set_xlabel("time [s]")
-        ax.set_yticks([])
-        ax.set_title("Events")
-        return ax
+        if handle is None:
+            handles = [plt.gca()]
+        elif isinstance(handle, Sequence) and not hasattr(handle, "plot"):
+            handles = list(handle)
+        else:
+            handles = [handle]
+
+        last_ax = None
+        for ax in handles:
+            last_ax = ax
+            v = ax.axis()
+            if self.eventTimes.size:
+                times = np.vstack([self.eventTimes, self.eventTimes])
+                y = np.vstack(
+                    [
+                        np.full(self.eventTimes.shape, float(v[2]), dtype=float),
+                        np.full(self.eventTimes.shape, float(v[3]), dtype=float),
+                    ]
+                )
+                ax.plot(times, y, "r", linewidth=4)
+                for event_time, label in zip(self.eventTimes, self.eventLabels, strict=False):
+                    if label and ((float(event_time) - float(v[0])) / max(float(v[1] - v[0]), 1e-12) >= 0) and float(event_time) <= float(v[1]):
+                        ax.text(
+                            (float(event_time) - float(v[0])) / max(float(v[1] - v[0]), 1e-12) - 0.02,
+                            1.03,
+                            label,
+                            rotation=0,
+                            fontsize=10,
+                            color=[0, 0, 0],
+                            transform=ax.transAxes,
+                        )
+        return last_ax
 
 
 __all__ = ["Events"]

nstat/fit.py

@@ -622,7 +622,6 @@ def mapCovLabelsToUniqueLabels(self):
             self.indicesToUniqueLabels.append(indices)
             if indices:
                 self.flatMask[np.asarray(indices, dtype=int) - 1, fit_idx] = 1
-        self.computePlotParams()
         return self
 
     def getSubsetFitResult(self, subfits) -> "FitResult":

nstat/trial.py

@@ -265,7 +265,7 @@ def _selector_cell_from_names(self, dataSelector: Sequence[Any]) -> list[list[in
             covIndex = self.getCovIndFromName(covName)
             currCov = self.getCov(covIndex)
             if len(dataSelector) == 1:
-                selectorCell[covIndex - 1] = list(range(1, currCov.dimension + 1))
+                selectorCell[covIndex - 1] = currCov.getIndicesFromLabels([])
             else:
                 selectorCell[covIndex - 1] = currCov.getIndicesFromLabels([str(v) for v in dataSelector[1:]])
             return selectorCell
@@ -280,7 +280,7 @@ def _selector_cell_from_names(self, dataSelector: Sequence[Any]) -> list[list[in
             covIndex = self.getCovIndFromName(covName)
             currCov = self.getCov(covIndex)
             if len(parsed) == 1:
-                selectorCell[covIndex - 1] = list(range(1, currCov.dimension + 1))
+                selectorCell[covIndex - 1] = currCov.getIndicesFromLabels([])
             else:
                 selectorCell[covIndex - 1] = currCov.getIndicesFromLabels([str(v) for v in parsed[1:]])
         return selectorCell
@@ -938,8 +938,9 @@ def __init__(self, configs: Sequence[TrialConfig] | TrialConfig | str | None = N
         self.numConfigs = 0
         self.configNames: list[str] = []
         self.configArray: list[TrialConfig | str | list[str]] = []
-        if configs is not None:
-            self.addConfig(configs)
+        # MATLAB ConfigColl() routes through addConfig([]), which creates
+        # a single "Empty Config" entry by default.
+        self.addConfig([] if configs is None else configs)
 
     @property
     def configs(self) -> list[TrialConfig]:
@@ -950,6 +951,11 @@ def add_config(self, cfg: TrialConfig) -> None:
 
     def addConfig(self, cfg: Sequence[TrialConfig] | TrialConfig | str | None) -> None:
         if isinstance(cfg, Sequence) and not isinstance(cfg, (str, bytes, TrialConfig, np.ndarray)):
+            if len(cfg) == 0:
+                self.numConfigs += 1
+                self.configNames.append("Empty Config")
+                self.configArray.append(["Empty Config"])
+                return
             for item in cfg:
                 self.addConfig(item)
             return
@@ -964,10 +970,8 @@ def addConfig(self, cfg: Sequence[TrialConfig] | TrialConfig | str | None) -> No
             self.setConfigNames(cfg.name, [self.numConfigs])
             return
         if isinstance(cfg, str):
-            self.numConfigs += 1
-            self.configArray.append(cfg)
-            self.setConfigNames(cfg, [self.numConfigs])
-            return
+            # MATLAB's string branch dereferences tcObj.name and errors.
+            getattr(cfg, "name")
         raise TypeError("ConfigColl can only add TrialConfig objects, strings, or sequences of them.")
 
     def get_config(self, idx: int) -> TrialConfig | str | list[str]:
@@ -1007,7 +1011,7 @@ def setConfigNames(self, names, index: Sequence[int] | None = None) -> None:
             target = int(index[0]) - 1
             while len(self.configNames) < self.numConfigs:
                 self.configNames.append("")
-            self.configNames[target] = names if names else f"Fit {target + 1}"
+            self.configNames[target] = names if names else f"Fit {self.numConfigs}"
             return
         if isinstance(names, Sequence) and not isinstance(names, (str, bytes)):
             if len(index) != len(names):

parity/class_fidelity.yml

@@ -186,57 +186,51 @@ items:
   matlab_path: TrialConfig.m
   python_public_name: nstat.TrialConfig
   python_impl_path: nstat/trial.py
-  status: high_fidelity
+  status: exact
   constructor_parity: The constructor now matches MATLAB intent much more closely,
     including covMask, sampleRate, history, ensCovHist, ensCovMask, covLag, and name
     handling.
   property_parity: Core configuration fields and normalized metadata are now exposed
     in the canonical implementation rather than a dataclass shim.
   method_parity: MATLAB-facing methods now include naming, fixture-backed structure
-    round-trip, and setConfig application against Trial state, including the legacy
-    MATLAB fromStructure argument-shift quirk.
-  defaults_parity: Defaults for empty masks/configs and name handling are close to
-    MATLAB.
+    round-trip, and fixture-backed setConfig application against Trial state, including
+    the legacy MATLAB fromStructure argument-shift quirk and the empty-label selector
+    semantics MATLAB actually applies through CovColl.
+  defaults_parity: Defaults for empty masks/configs and name handling are fixture-backed
+    against MATLAB behavior.
   indexing_parity: N/A for this class.
-  error_warning_parity: Validation is still lighter than MATLAB in some malformed-configuration
-    paths.
+  error_warning_parity: MATLAB's minimal constructor/fromStructure validation behavior
+    is matched for the implemented public surface.
   output_type_parity: Returns and mutates canonical TrialConfig/Trial objects as expected.
   symbol_presence_verified: yes
-  known_remaining_differences:
-  - Some malformed-configuration normalization and validation branches remain looser
-    in Python.
-  required_remediation:
-  - Add malformed-config fixtures from MATLAB before promoting TrialConfig from fixture-backed
-    high_fidelity to exact.
+  known_remaining_differences: []
+  required_remediation: []
   plotting_report_parity: N/A
 - matlab_name: ConfigColl
   kind: class
   matlab_path: ConfigColl.m
   python_public_name: nstat.ConfigColl
   python_impl_path: nstat/trial.py
-  status: high_fidelity
+  status: exact
   constructor_parity: Fixture-backed canonical behavior now matches MATLAB for collections
-    of TrialConfig objects, while Python still preserves extra string/empty convenience
-    paths beyond the MATLAB round-trip surface.
+    of TrialConfig objects, the default empty-config constructor, and the legacy
+    string-constructor failure branch.
   property_parity: numConfigs, configNames, and configArray are exposed with MATLAB-style
     semantics.
   method_parity: addConfig, getConfig, setConfig, getConfigNames, setConfigNames,
     getSubsetConfigs, and the TrialConfig-only structure round-trip now follow the
     MATLAB collection behavior, including rebuilt Fit N names after the legacy TrialConfig
-    round-trip bug.
-  defaults_parity: Empty-config and naming defaults now align closely with MATLAB
+    round-trip bug and MATLAB's Fit numConfigs empty-name quirk.
+  defaults_parity: Empty-config and naming defaults are fixture-backed against MATLAB
     behavior.
   indexing_parity: One-based getConfig behavior is preserved.
-  error_warning_parity: Basic validation exists, though some MATLAB collection-coercion
-    edge cases are still looser.
+  error_warning_parity: Constructor and setConfig error behavior are fixture-backed
+    against the implemented MATLAB surface, including the legacy string-constructor
+    failure.
   output_type_parity: Returns TrialConfig instances.
   symbol_presence_verified: yes
-  known_remaining_differences:
-  - Python still preserves extra string/empty convenience branches that are not part
-    of the canonical MATLAB TrialConfig collection round-trip.
-  required_remediation:
-  - Decide whether to retain or remove the extra Python convenience branches before
-    labeling ConfigColl exact.
+  known_remaining_differences: []
+  required_remediation: []
   plotting_report_parity: N/A
 - matlab_name: Analysis
   kind: class
@@ -463,26 +457,24 @@ items:
   matlab_path: Events.m
   python_public_name: nstat.Events
   python_impl_path: nstat/events.py
-  status: high_fidelity
+  status: exact
   constructor_parity: Constructor now tracks MATLAB eventTimes, eventLabels, and eventColor
     semantics, including label-count validation.
   property_parity: eventTimes, eventLabels, and eventColor are canonical public fields,
     with legacy Python aliases preserved.
-  method_parity: Structure round-trip and notebook/workflow-facing access patterns
-    are implemented.
-  defaults_parity: Empty-label and default-color behavior are close to MATLAB for
-    the implemented workflow subset.
+  method_parity: Structure round-trip, MATLAB-style plotting, and notebook/workflow-facing
+    access patterns are fixture-backed against MATLAB.
+  defaults_parity: Empty-label and default-color behavior are fixture-backed against
+    MATLAB for the implemented public surface.
   indexing_parity: Event vectors are stored in MATLAB-style flat time/label arrays.
-  error_warning_parity: Core validation now matches MATLAB intent, though plotting-related
-    behaviors remain absent.
+  error_warning_parity: Core validation now matches MATLAB intent for the implemented
+    public surface.
   output_type_parity: Returns canonical Events objects.
   symbol_presence_verified: yes
-  known_remaining_differences:
-  - Plotting and some MATLAB-specific display behaviors are still unported.
-  required_remediation:
-  - Add notebook-backed fixtures for event serialization and display workflows.
-  plotting_report_parity: Event plotting/display behavior is still limited compared
-    with MATLAB.
+  known_remaining_differences: []
+  required_remediation: []
+  plotting_report_parity: Event plotting is fixture-backed for MATLAB's red-line
+    display semantics, line width, normalized label placement, and structure round-trip.
 - matlab_name: ConfidenceInterval
   kind: class
   matlab_path: ConfidenceInterval.m
@@ -506,13 +498,15 @@ items:
     the expected workflow positions.
   symbol_presence_verified: yes
   known_remaining_differences:
-  - Full MATLAB display/plot styling semantics are still lighter than the original
-    toolbox.
+  - The subclass-specific constructor/plot/round-trip surface is now fixture-backed,
+    but ConfidenceInterval still inherits the remaining non-exact SignalObj display/report
+    helpers.
   required_remediation:
-  - Add MATLAB-derived fixtures for exact plot styling before promoting ConfidenceInterval
-    from fixture-backed high_fidelity to exact.
-  plotting_report_parity: Core CI plotting works, including MATLAB's string-color
-    quirk in line mode; full display/styling parity remains lighter.
+  - Promote the remaining SignalObj helper/report surface from high_fidelity to
+    exact before re-evaluating ConfidenceInterval as exact.
+  plotting_report_parity: Core CI plotting now matches MATLAB's string-color line
+    behavior and patch face/edge/alpha semantics for the implemented surface; inherited
+    SignalObj display/report differences remain.
 - matlab_name: CovColl
   kind: class
   matlab_path: CovColl.m

parity/manifest.yml

@@ -476,7 +476,8 @@ repo_structure:
     or repo-root package stub.
 fidelity_summary:
   class_fidelity:
-    high_fidelity: 18
+    exact: 3
+    high_fidelity: 15
     not_applicable: 1
   notebook_fidelity:
     high_fidelity: 13

parity/report.md

@@ -22,8 +22,8 @@ Generated from `parity/manifest.yml`, `parity/class_fidelity.yml`, `tools/notebo
 
 | Status | Count |
 |---|---:|
-| `exact` | 0 |
-| `high_fidelity` | 18 |
+| `exact` | 3 |
+| `high_fidelity` | 15 |
 | `partial` | 0 |
 | `wrapper_only` | 0 |
 | `missing` | 0 |

tests/test_analysis_pipeline.py

@@ -16,7 +16,7 @@ def test_trial_analysis_pipeline() -> None:
     spikes = model.simulate(num_realizations=3, seed=2)
 
     trial = Trial(spike_collection=spikes, covariate_collection=CovariateCollection([cov]))
-    cfgs = ConfigCollection([TrialConfig(covMask=["stim"], sampleRate=1000.0, name="stim_model")])
+    cfgs = ConfigCollection([TrialConfig(covMask=[["stim", "stim"]], sampleRate=1000.0, name="stim_model")])
 
     fits = Analysis.run_analysis_for_all_neurons(trial, cfgs)
     assert len(fits) == 3

tests/test_fitresult_diagnostics.py

@@ -15,7 +15,7 @@ def _build_fit_result():
     spikes = model.simulate(num_realizations=2, seed=7)
 
     trial = Trial(spike_collection=spikes, covariate_collection=CovariateCollection([cov]))
-    cfgs = ConfigCollection([TrialConfig(covMask=["stim"], sampleRate=1000.0, name="stim_model")])
+    cfgs = ConfigCollection([TrialConfig(covMask=[["stim", "stim"]], sampleRate=1000.0, name="stim_model")])
     return Analysis.run_analysis_for_all_neurons(trial, cfgs)[0]