From e7608ced12aafbd53173c7db693adf200ab8eaa7 Mon Sep 17 00:00:00 2001
From: Ivan Raikov <ivan.g.raikov@gmail.com>
Date: Sat, 19 Sep 2026 08:37:41 -0700
Subject: [PATCH 1/3] adapting network clamp to support multiple input spike
 namespaces; various fixes to online spike generation features

---
 src/miv_simulator/clamps/network.py           | 290 +++++++++-------
 src/miv_simulator/input_features.py           | 321 ++++++++++--------
 .../interface/legacy/network_clamp/go.py      |   6 +-
 .../legacy/network_clamp/optimize.py          |   6 +-
 .../interface/legacy/network_clamp/show.py    |   6 +-
 src/miv_simulator/plotting.py                 |  44 ++-
 .../scripts/analysis/network_clamp.py         |  18 +-
 .../scripts/generate_input_features.py        | 127 ++++---
 8 files changed, 487 insertions(+), 331 deletions(-)

diff --git a/src/miv_simulator/clamps/network.py b/src/miv_simulator/clamps/network.py
index 0ffc87da..00f3bdea 100644
--- a/src/miv_simulator/clamps/network.py
+++ b/src/miv_simulator/clamps/network.py
@@ -26,13 +26,15 @@
 from miv_simulator.utils.neuron import configure_hoc_env, h
 from miv_simulator.stimulus import (
     oscillation_phase_mod_config,
-    rate_maps_from_features,
-    get_2D_arena_spatial_mesh,
-    generate_linear_trajectory,
+    global_oscillation_initial_phases,
     get_equilibration,
-    generate_input_spike_trains,
-    read_stimulus,
 )
+from miv_simulator.input_features import (
+    FEATURE_TYPE_NAMES,
+    constant_rate_vector,
+    generate_constant_rate_spike_train,
+)
+from spike_encoder import EncoderTimeConfig
 from miv_simulator.utils import (
     Context,
     config_logging,
@@ -158,62 +160,89 @@ def init_inputs_from_spikes(
     presyn_sources,
     time_range,
     spike_events_path,
-    spike_events_namespace,
+    spike_events_namespaces,
     arena_id,
     stimulus_id,
     spike_train_attr_name="t",
     n_trials=1,
 ):
-    """Initializes presynaptic spike sources from a file with spikes times."""
+    """Initializes presynaptic spike sources from a file with spike times.
+
+    Spike trains are read from each of the given namespaces and, when a
+    presynaptic cell is present in more than one namespace, the train for
+    that cell is the union of all its spike times.
+    """
+    if isinstance(spike_events_namespaces, str):
+        spike_events_namespaces = [spike_events_namespaces]
     populations = sorted(presyn_sources.keys())
 
     equilibration_duration = float(
         env.stimulus_config.get("Equilibration Duration", 0.0)
     )
+    spkdata_time_range = None
     if time_range is not None:
         spkdata_time_range = (
             time_range[0] - equilibration_duration,
             time_range[1],
         )
 
-    if arena_id and stimulus_id:
-        this_spike_events_namespace = (
-            f"{spike_events_namespace} {arena_id} {stimulus_id}"
+    ## Load spike times of presynaptic cells
+    spkindlst = defaultdict(list)
+    spktlst = defaultdict(list)
+    spkpoplst = []
+
+    for spike_events_namespace in spike_events_namespaces:
+        if arena_id and stimulus_id:
+            this_spike_events_namespace = (
+                f"{spike_events_namespace} {arena_id} {stimulus_id}"
+            )
+        elif arena_id:
+            this_spike_events_namespace = f"{spike_events_namespace} {arena_id}"
+        else:
+            this_spike_events_namespace = spike_events_namespace
+
+        spkdata = spikedata.read_spike_events(
+            spike_events_path,
+            populations,
+            this_spike_events_namespace,
+            spike_train_attr_name=spike_train_attr_name,
+            time_range=spkdata_time_range,
+            n_trials=n_trials,
+            merge_trials=True,
+            comm=env.comm,
+            io_size=env.io_size,
         )
-    elif arena_id:
-        this_spike_events_namespace = f"{spike_events_namespace} {arena_id}"
-    else:
-        this_spike_events_namespace = spike_events_namespace
 
-    ## Load spike times of presynaptic cells
-    spkdata = spikedata.read_spike_events(
-        spike_events_path,
-        populations,
-        this_spike_events_namespace,
-        spike_train_attr_name=spike_train_attr_name,
-        time_range=spkdata_time_range,
-        n_trials=n_trials,
-        merge_trials=True,
-        comm=env.comm,
-        io_size=env.io_size,
-    )
+        this_spkindlst = spkdata["spkindlst"]
+        this_spktlst = spkdata["spktlst"]
+        this_spkpoplst = spkdata["spkpoplst"]
 
-    spkindlst = spkdata["spkindlst"]
-    spktlst = spkdata["spktlst"]
-    spkpoplst = spkdata["spkpoplst"]
+        ## Accumulate spike times of each presynaptic population across namespaces
+        for population in populations:
+            if population not in spkpoplst:
+                spkpoplst.append(population)
+            pop_index = list_index(population, this_spkpoplst)
+            if pop_index is None:
+                continue
+            spkindlst[population].append(this_spkindlst[pop_index])
+            spktlst[population].append(this_spktlst[pop_index])
 
     ## Organize spike times by index of presynaptic population and gid
     input_source_dict = {}
     for population in populations:
         pop_index = int(env.Populations[population])
-        spk_pop_index = list_index(population, spkpoplst)
-        if spk_pop_index is None:
+        if population not in spkindlst:
             logger.warning(
                 f"No spikes found for population {population} in file {spike_events_path}"
             )
             continue
-        spk_inds = spkindlst[spk_pop_index]
-        spk_ts = spktlst[spk_pop_index]
+
+        ## Combine the spike times collected from all namespaces
+        spk_inds = np.concatenate(spkindlst[population])
+        spk_ts = np.concatenate(spktlst[population])
+        sort_inds = np.argsort(spk_ts)
+        spk_inds = spk_inds[sort_inds]
+        spk_ts = spk_ts[sort_inds]
 
         spikes_attr_dict = {}
         gid_range = presyn_sources[population]
@@ -246,18 +275,24 @@ def init_inputs_from_features(
     n_trials=1,
     seed=None,
 ):
-    """Initializes presynaptic spike sources from a file with input selectivity features represented as firing rates."""
+    """Initializes presynaptic spike sources from a file with input selectivity
+    features represented as firing rates, using the input_features/spike_encoder
+    on-the-fly generation pipeline."""
 
     logger.info(f"init_inputs_from_features: seed = {seed}")
     populations = sorted(presyn_sources.keys())
 
-    if time_range is not None:
-        if time_range[0] is None:
-            time_range[0] = 0.0
+    if time_range is None:
+        time_range = [0.0, env.tstop]
+    elif time_range[0] is None:
+        time_range[0] = 0.0
 
     equilibration_duration = float(env.stimulus_config["Equilibration Duration"])
-    spatial_resolution = float(env.stimulus_config["Spatial Resolution"])
     temporal_resolution = float(env.stimulus_config["Temporal Resolution"])
+    trial_duration = (time_range[1] - time_range[0]) + equilibration_duration
+    time_config = EncoderTimeConfig(
+        duration_ms=trial_duration, dt_ms=temporal_resolution
+    )
 
     this_input_features_namespaces = [
         f"{input_features_namespace} {arena_id}"
@@ -265,41 +300,10 @@ def init_inputs_from_features(
     ]
 
     input_features_attr_names = [
-        "Selectivity Type",
-        "Num Fields",
-        "Field Width",
-        "Peak Rate",
-        "Module ID",
-        "Grid Spacing",
-        "Grid Orientation",
-        "Field Width Concentration Factor",
-        "X Offset",
-        "Y Offset",
+        "Feature Type",
+        "peak_rate",
     ]
 
-    selectivity_type_names = {i: n for n, i in env.selectivity_types.items()}
-
-    arena = env.stimulus_config["Arena"][arena_id]
-    arena_x, arena_y = get_2D_arena_spatial_mesh(
-        arena=arena, spatial_resolution=spatial_resolution
-    )
-
-    stimulus_spec = arena.trajectories[stimulus_id]
-    t, x, y, d = generate_linear_trajectory(
-        stimulus_spec,
-        temporal_resolution=temporal_resolution,
-        equilibration_duration=equilibration_duration,
-    )
-    if time_range is not None:
-        t_range_inds = np.where(
-            (t <= time_range[1]) & (t >= time_range[0] - equilibration_duration)
-        )[0]
-        t = t[t_range_inds]
-        x = x[t_range_inds]
-        y = y[t_range_inds]
-        d = d[t_range_inds]
-    stimulus = t, x, y, d
-
     equilibrate = get_equilibration(env)
 
     input_source_dict = {}
@@ -331,28 +335,54 @@ def init_inputs_from_features(
                 comm=env.comm,
             )
             for gid, selectivity_attr_dict in input_features_iter:
+                this_feature_type = int(selectivity_attr_dict["Feature Type"][0])
+                this_feature_type_name = FEATURE_TYPE_NAMES[this_feature_type]
+                if this_feature_type_name != "linear_rate":
+                    raise RuntimeError(
+                        "init_inputs_from_features: feature type "
+                        f"{this_feature_type_name} is not supported; "
+                        "only 'linear_rate' features are currently implemented"
+                    )
+                peak_rate = float(selectivity_attr_dict["peak_rate"][0])
+
                 phase_mod_config = None
                 if phase_mod_config_dict is not None:
                     phase_mod_config = phase_mod_config_dict[gid]
 
-                spikes_attr_dict[gid] = generate_input_spike_trains(
-                    env,
-                    population,
-                    selectivity_type_names,
-                    stimulus,
-                    gid,
-                    selectivity_attr_dict,
-                    equilibrate=equilibrate,
-                    phase_mod_config=phase_mod_config,
-                    spike_train_attr_name=spike_train_attr_name,
-                    n_trials=n_trials,
-                    return_selectivity_features=False,
-                    merge_trials=True,
-                    time_range=time_range,
-                    comm=env.comm,
-                    seed=seed,
+                local_random = np.random.RandomState()
+                gid_seed = (
+                    int(seed)
+                    if seed is not None
+                    else int(env.model_config["Random Seeds"]["Input Spiketrains"])
+                    + gid
                 )
-                spikes_attr_dict[gid][spike_train_attr_name] += equilibration_duration
+                local_random.seed(gid_seed)
+
+                initial_phases = None
+                if (phase_mod_config is not None) and (n_trials > 1):
+                    initial_phases = global_oscillation_initial_phases(env, n_trials)
+
+                trial_spike_times = []
+                for i in range(n_trials):
+                    initial_phase = (
+                        initial_phases[i] if initial_phases is not None else 0.0
+                    )
+                    times = generate_constant_rate_spike_train(
+                        peak_rate,
+                        time_config,
+                        phase_mod_config=phase_mod_config,
+                        initial_phase=initial_phase,
+                        equilibrate=equilibrate,
+                        local_random=local_random,
+                    )
+                    trial_spike_times.append(times + float(i) * trial_duration)
+
+                spike_times = np.asarray(
+                    np.concatenate(trial_spike_times), dtype=np.float32
+                )
+                spike_times += time_range[0]
+
+                spikes_attr_dict[gid] = {spike_train_attr_name: spike_times}
 
         input_source_dict[pop_index] = {"spiketrains": spikes_attr_dict}
 
@@ -367,7 +397,7 @@ def init(
     stimulus_id=None,
     n_trials=1,
     spike_events_path=None,
-    spike_events_namespace="Spike Events",
+    spike_events_namespaces=("Spike Events",),
     spike_train_attr_name="t",
     input_features_path=None,
     input_features_namespaces=None,
@@ -395,6 +425,9 @@ def init(
 
     compile_and_load(directory=env.mechanisms_path)
 
+    if isinstance(spike_events_namespaces, str):
+        spike_events_namespaces = [spike_events_namespaces]
+
     if phase_mod and coords_path is None:
         raise RuntimeError(
             "network_clamp.init: when phase_mod is True, coords_path must be provided"
@@ -415,8 +448,8 @@ def init(
         else:
             t_range = [t_min, t_max]
 
-    ## Attribute namespace that contains recorded spike events
-    namespace_id = spike_events_namespace
+    ## Attribute namespaces that contain recorded spike events
+    namespace_id = list(spike_events_namespaces)
 
     my_cell_index_list = []
     for i, gid in enumerate(cell_index_set):
@@ -523,7 +556,7 @@ def init(
             soma_positions_dict[population] = abs_positions
             del distances
 
-    if env.opsin_config is not None:
+    if hasattr(env, "opsin_config") and env.opsin_config is not None:
         opsin_pop_dict = {
             pop_name: set(env.cells[pop_name].keys()).difference(
                 set(env.artificial_cells[pop_name].keys())
@@ -552,7 +585,7 @@ def init(
             presyn_sources,
             t_range,
             spike_events_path,
-            spike_events_namespace,
+            spike_events_namespaces,
             arena_id,
             stimulus_id,
             spike_train_attr_name,
@@ -871,7 +904,7 @@ def init_state_objfun(
     dataset_prefix,
     results_path,
     spike_events_path,
-    spike_events_namespace,
+    spike_events_namespaces,
     spike_events_t,
     input_features_path,
     input_features_namespaces,
@@ -906,7 +939,7 @@ def init_state_objfun(
         stimulus_id,
         n_trials,
         spike_events_path,
-        spike_events_namespace=spike_events_namespace,
+        spike_events_namespaces=spike_events_namespaces,
         spike_train_attr_name=spike_events_t,
         coords_path=coords_path,
         distances_namespace=distances_namespace,
@@ -1029,7 +1062,7 @@ def init_rate_objfun(
     dataset_prefix,
     results_path,
     spike_events_path,
-    spike_events_namespace,
+    spike_events_namespaces,
     spike_events_t,
     coords_path,
     distances_namespace,
@@ -1059,7 +1092,7 @@ def init_rate_objfun(
         stimulus_id,
         n_trials,
         spike_events_path=spike_events_path,
-        spike_events_namespace=spike_events_namespace,
+        spike_events_namespaces=spike_events_namespaces,
         spike_train_attr_name=spike_events_t,
         coords_path=coords_path,
         distances_namespace=distances_namespace,
@@ -1269,7 +1302,7 @@ def init_rate_dist_objfun(
     dataset_prefix,
     results_path,
     spike_events_path,
-    spike_events_namespace,
+    spike_events_namespaces,
     spike_events_t,
     coords_path,
     distances_namespace,
@@ -1302,7 +1335,7 @@ def init_rate_dist_objfun(
         stimulus_id,
         n_trials,
         spike_events_path,
-        spike_events_namespace=spike_events_namespace,
+        spike_events_namespaces=spike_events_namespaces,
         spike_train_attr_name=spike_events_t,
         coords_path=coords_path,
         distances_namespace=distances_namespace,
@@ -1317,28 +1350,41 @@ def init_rate_dist_objfun(
 
     time_step = env.stimulus_config["Temporal Resolution"]
 
-    target_rate_vector_dict = rate_maps_from_features(
-        env,
+    time_range = (0.0, t_max)
+    time_bins = np.arange(time_range[0], time_range[1] + time_step, time_step)
+
+    target_time_config = EncoderTimeConfig(
+        duration_ms=len(time_bins) * time_step, dt_ms=time_step
+    )
+    target_features_ns = f"{target_features_namespace} {target_features_arena}"
+    target_features_iter = scatter_read_cell_attribute_selection(
+        (input_features_path if input_features_path is not None else spike_events_path),
         population,
-        cell_index_set=my_cell_index_set,
-        input_features_path=target_features_path,
-        input_features_namespace=target_features_namespace,
-        time_range=None,
-        arena_id=arena_id,
+        selection=list(my_cell_index_set),
+        namespace=target_features_ns,
+        mask={"Feature Type", "peak_rate"},
+        comm=env.comm,
     )
+    target_rate_vector_dict = {}
+    for gid, selectivity_attr_dict in target_features_iter:
+        this_feature_type = int(selectivity_attr_dict["Feature Type"][0])
+        this_feature_type_name = FEATURE_TYPE_NAMES[this_feature_type]
+        if this_feature_type_name != "linear_rate":
+            raise RuntimeError(
+                "network_clamp.optimize: target feature type "
+                f"{this_feature_type_name} is not supported; "
+                "only 'linear_rate' features are currently implemented"
+            )
+        peak_rate = float(selectivity_attr_dict["peak_rate"][0])
+        target_rate_vector_dict[gid] = constant_rate_vector(
+            peak_rate, target_time_config
+        )
+
     for gid, target_rate_vector in target_rate_vector_dict.items():
         target_rate_vector[
             np.isclose(target_rate_vector, 0.0, atol=1e-3, rtol=1e-3)
         ] = 0.0
 
-    trj_d, trj_t = read_stimulus(
-        (input_features_path if input_features_path is not None else spike_events_path),
-        target_features_arena,
-        target_features_stimulus,
-    )
-    time_range = (0.0, min(np.max(trj_t), t_max))
-    time_bins = np.arange(time_range[0], time_range[1] + time_step, time_step)
-
     opt_param_config = optimization_params(
         env.netclamp_config.optimize_parameters,
         [population],
@@ -1671,7 +1717,7 @@ def dist_run(
     arena_id = init_params["arena_id"]
     stimulus_id = init_params["stimulus_id"]
     spike_events_path = init_params["spike_events_path"]
-    spike_events_namespace = init_params["spike_events_namespace"]
+    spike_events_namespaces = init_params["spike_events_namespaces"]
     spike_events_t = init_params["spike_events_t"]
     coords_path = init_params["coords_path"]
     distances_namespace = init_params["distances_namespace"]
@@ -1692,7 +1738,7 @@ def dist_run(
         stimulus_id,
         n_trials,
         spike_events_path,
-        spike_events_namespace=spike_events_namespace,
+        spike_events_namespaces=spike_events_namespaces,
         coords_path=coords_path,
         distances_namespace=distances_namespace,
         phase_mod=phase_mod,
@@ -1785,7 +1831,7 @@ def show(
     dataset_prefix,
     results_path,
     spike_events_path,
-    spike_events_namespace,
+    spike_events_namespaces,
     spike_events_t,
     input_features_path,
     input_features_namespaces,
@@ -1820,7 +1866,7 @@ def show(
             arena_id,
             stimulus_id,
             spike_events_path=spike_events_path,
-            spike_events_namespace=spike_events_namespace,
+            spike_events_namespaces=spike_events_namespaces,
             spike_train_attr_name=spike_events_t,
             input_features_path=input_features_path,
             input_features_namespaces=input_features_namespaces,
@@ -1853,7 +1899,7 @@ def go(
     mechanisms_path,
     dataset_prefix,
     spike_events_path,
-    spike_events_namespace,
+    spike_events_namespaces,
     spike_events_t,
     coords_path,
     distances_namespace,
@@ -2002,7 +2048,7 @@ def go(
             stimulus_id,
             n_trials,
             spike_events_path,
-            spike_events_namespace=spike_events_namespace,
+            spike_events_namespaces=spike_events_namespaces,
             spike_train_attr_name=spike_events_t,
             coords_path=coords_path,
             distances_namespace=distances_namespace,
@@ -2060,7 +2106,7 @@ def optimize(
     results_file,
     results_path,
     spike_events_path,
-    spike_events_namespace,
+    spike_events_namespaces,
     spike_events_t,
     coords_path,
     distances_namespace,
@@ -2154,7 +2200,7 @@ def optimize(
             stimulus_id,
             n_trials,
             spike_events_path,
-            spike_events_namespace=spike_events_namespace,
+            spike_events_namespaces=spike_events_namespaces,
             spike_train_attr_name=spike_events_t,
             coords_path=coords_path,
             distances_namespace=distances_namespace,
diff --git a/src/miv_simulator/input_features.py b/src/miv_simulator/input_features.py
index 407e05ae..366c2c35 100644
--- a/src/miv_simulator/input_features.py
+++ b/src/miv_simulator/input_features.py
@@ -5,7 +5,6 @@
 import time
 import h5py
 from typing import Dict, List, Optional, Tuple, Any, Callable
-from collections import defaultdict
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
 import numpy as np
@@ -14,7 +13,6 @@
 from miv_simulator.stimulus import stationary_phase_mod
 from mpi4py import MPI
 from neuroh5.io import (
-    NeuroH5CellAttrGen,
     append_cell_attributes,
     read_population_ranges,
 )
@@ -28,6 +26,95 @@
 
 logger = get_script_logger(os.path.basename(__file__))
 
+
+def constant_rate_signal_array(
+    time_config: EncoderTimeConfig,
+    phase_mod_config: Optional[Any] = None,
+    initial_phase: float = 0.0,
+    equilibrate: Optional[Tuple[np.ndarray, int]] = None,
+) -> np.ndarray:
+    """
+    Builds a modulation envelope for a constant-rate
+    ("linear_rate" / "constant" selectivity) input: 1.0 at every time bin of
+    `time_config` by default, multiplied by an oscillatory phase-modulation
+    envelope (renormalized so its mean stays at 1.0, so that the average
+    output rate is unaffected by modulation) and/or a ramp that suppresses
+    the response during an initial equilibration period.
+    """
+    num_steps = time_config.num_steps
+    if phase_mod_config is not None:
+        t = time_config.get_time_vector_ms()
+        envelope = stationary_phase_mod(
+            t,
+            phase_mod_config.phase_range,
+            phase_mod_config.phase_pref,
+            phase_mod_config.phase_offset + initial_phase,
+            phase_mod_config.mod_depth,
+            phase_mod_config.frequency,
+        )
+        mean_envelope = np.mean(envelope)
+        if mean_envelope > 0.0:
+            envelope = envelope / mean_envelope
+    else:
+        envelope = np.ones(num_steps, dtype=np.float64)
+
+    if equilibrate is not None:
+        equilibrate_filter, equilibrate_len = equilibrate
+        envelope[:equilibrate_len] = np.multiply(
+            envelope[:equilibrate_len], equilibrate_filter
+        )
+
+    return envelope
+
+
+def constant_rate_vector(
+    peak_rate: float,
+    time_config: EncoderTimeConfig,
+    phase_mod_config: Optional[Any] = None,
+    initial_phase: float = 0.0,
+    equilibrate: Optional[Tuple[np.ndarray, int]] = None,
+) -> np.ndarray:
+    """
+    Returns the instantaneous firing rate (Hz) at each time bin of
+    `time_config` for a constant-selectivity input.
+    """
+    envelope = constant_rate_signal_array(
+        time_config, phase_mod_config, initial_phase, equilibrate
+    )
+    return envelope * peak_rate
+
+
+def generate_constant_rate_spike_train(
+    peak_rate: float,
+    time_config: EncoderTimeConfig,
+    phase_mod_config: Optional[Any] = None,
+    initial_phase: float = 0.0,
+    equilibrate: Optional[Tuple[np.ndarray, int]] = None,
+    local_random: Optional[np.random.RandomState] = None,
+) -> np.ndarray:
+    """
+    Generates spike times (ms, relative to the start of `time_config`) for a
+    homogeneous or phase-modulated Poisson process with the given peak rate,
+    using the neural_spike_encoding PoissonSpikeGenerator.
+    """
+    rates_hz = constant_rate_vector(
+        peak_rate, time_config, phase_mod_config, initial_phase, equilibrate
+    )
+    bin_time_config = EncoderTimeConfig(
+        duration_ms=time_config.dt_ms, dt_ms=time_config.dt_ms
+    )
+    generator = PoissonSpikeGenerator(
+        time_config=bin_time_config, random_seed=local_random
+    )
+    spike_times, _ = generator.encode(
+        rates_hz.reshape(-1, 1), start_time_ms=0.0, return_times=True
+    )
+    times = spike_times[0]
+    if len(times) == 0:
+        return np.asarray([], dtype=np.float32)
+    return np.asarray(np.concatenate(times), dtype=np.float32)
+
+
 sys_excepthook = sys.excepthook
 
 
@@ -85,6 +172,32 @@ def from_feature_coordinates(self, feature_coordinates: np.ndarray) -> np.ndarra
         pass
 
 
+class ConstantModality(InputModality):
+    """
+    Modality for inputs whose response does not depend on any external
+    signal or feature-space position (e.g. a "constant" selectivity input
+    that fires at a fixed peak rate for the whole population). Carries a
+    single dimensionless coordinate for bookkeeping only.
+    """
+
+    def __init__(self, name: str = "constant"):
+        feature_coordinate_system = CoordinateSystemConfig(
+            dimensions=1,
+            bounds=[(0.0, 1.0)],
+            units=["dimensionless"],
+        )
+        super().__init__(name, "constant", feature_coordinate_system)
+
+    def preprocess_signal(self, stimulus: np.ndarray) -> np.ndarray:
+        return stimulus
+
+    def to_feature_coordinates(self, modality_coordinates: np.ndarray) -> np.ndarray:
+        return modality_coordinates
+
+    def from_feature_coordinates(self, feature_coordinates: np.ndarray) -> np.ndarray:
+        return feature_coordinates
+
+
 class LinearRateInput:
     def __init__(
         self,
@@ -206,6 +319,10 @@ def get_response(
         return self.encoder.encode(signal)
 
 
+FEATURE_TYPE_CODES = {"linear_rate": 0, "receptive_field": 1}
+FEATURE_TYPE_NAMES = {code: name for name, code in FEATURE_TYPE_CODES.items()}
+
+
 class FeatureEncoding:
     """Specification for a feature encoder."""
 
@@ -296,7 +413,9 @@ def get_response(self, signal: np.ndarray, **kwargs) -> np.ndarray:
     def to_attribute_dict(self) -> Dict[str, np.ndarray]:
         """Convert to attribute dictionary for storage."""
         attr_dict = {
-            "Feature Type": np.array([self.encoding.feature_type], dtype=np.uint8),
+            "Feature Type": np.array(
+                [FEATURE_TYPE_CODES[self.encoding.feature_type]], dtype=np.uint8
+            ),
             "Position": self.position.astype(np.float32),
         }
         # Add encoder-specific attributes
@@ -314,13 +433,13 @@ def to_attribute_dict(self) -> Dict[str, np.ndarray]:
                 if isinstance(v, (int, float))
             }
         )
-        # Add metadata
+        # Add any extra metadata supplied at construction time
         attr_dict.update(
             {
                 k: np.array(
                     [v], dtype=np.float32 if isinstance(v, (int, float)) else object
                 )
-                for k, v in self.metadata.items()
+                for k, v in self.kwargs.items()
                 if isinstance(v, (int, float, str))
             }
         )
@@ -463,8 +582,8 @@ def generate_features(
     def _generate_position(self, local_random: np.random.RandomState) -> np.ndarray:
         """Generate a position in feature space according to the density function."""
         # TODO: use rejection sampling based on the provided density function
-        dimensions = self.modality.coordinate_system.dimensions
-        bounds = self.modality.coordinate_system.bounds
+        dimensions = self.modality.feature_coordinate_system.dimensions
+        bounds = self.modality.feature_coordinate_system.bounds
 
         position = np.zeros(dimensions)
         for d in range(dimensions):
@@ -1106,9 +1225,15 @@ def generate_input_features(
     debug_count: int,
 ):
     """
+    Writes the already-generated features of `population` (e.g. built via
+    `FeatureSpace.create_population(..., rank=..., size=...)`, which
+    distributes feature generation round-robin across MPI ranks) to a
+    NeuroH5 cell attributes namespace.
+
     :param env: env.Env
-    :param population: InputFeaturePopulation
-    :param coords_path: str (path to file)
+    :param population: InputFeaturePopulation with features already generated
+    :param coords_path: str (path to file); used only to validate that the
+        population's cell count matches the coordinates file
     :param distances_namespace: str
     :param output_path: str
     :param io_size: int
@@ -1145,7 +1270,6 @@ def generate_input_features(
     population_ranges = read_population_ranges(coords_path, comm)[0]
     population_name = population.name
 
-    reference_u_arc_distance_bounds_dict = {}
     if rank == 0:
         if population_name not in population_ranges:
             raise RuntimeError(
@@ -1166,145 +1290,74 @@ def generate_input_features(
                     f"generate_input_features: only {unique_gid_count}/{pop_size} unique cell indexes found "
                     f"for specified population: {population_name} in provided coords_path: {coords_path}"
                 )
-            try:
-                reference_u_arc_distance_bounds_dict[population_name] = (
-                    coords_f["Populations"][population_name][distances_namespace].attrs[
-                        "Reference U Min"
-                    ],
-                    coords_f["Populations"][population_name][distances_namespace].attrs[
-                        "Reference U Max"
-                    ],
-                )
-            except Exception:
-                raise RuntimeError(
-                    f"generate_input_features: problem locating attributes "
-                    f"containing reference bounds in namespace: "
-                    f"{distances_namespace} for population: {population_name} from "
-                    f"coords_path: {coords_path}"
-                )
     comm.barrier()
-    reference_u_arc_distance_bounds_dict = comm.bcast(
-        reference_u_arc_distance_bounds_dict, root=0
-    )
-
-    local_random = np.random.RandomState()
 
-    pop_norm_distances = {}
-
-    if rank == 0:
-        logger.info(f"Generating normalized distances for population {population}...")
+    start_time = time.time()
+    features = list(population.features.values())
+    if debug:
+        features = features[:debug_count]
 
-    reference_u_arc_distance_bounds = reference_u_arc_distance_bounds_dict[population]
+    write_every = max(1, int(math.floor(write_size / comm.size)))
 
-    this_pop_norm_distances = {}
+    feature_attr_dict = {}
+    gid_count = 0
+    for i, feature in enumerate(features):
+        feature_attr_dict[feature.gid] = feature.to_attribute_dict()
+        gid_count += 1
 
-    start_time = time.time()
-    gid_count = defaultdict(lambda: 0)
-    distances_attr_gen = NeuroH5CellAttrGen(
-        coords_path,
-        population,
-        namespace=distances_namespace,
-        comm=comm,
-        io_size=io_size,
-        cache_size=cache_size,
-    )
-
-    ## Normalize population distances
-    for iter_count, (gid, distances_attr_dict) in enumerate(distances_attr_gen):
-        req = comm.Ibarrier()
-        if gid is not None:
+        if i > 0 and i % write_every == 0:
+            req = comm.Ibarrier()
+            total_gid_count = comm.reduce(gid_count, root=0, op=MPI.SUM)
+            req.wait()
             if rank == 0:
                 logger.info(
-                    f"Rank {rank} generating selectivity features for gid {gid}..."
+                    f"generated {total_gid_count} features in population {population_name} in "
+                    f"{(time.time() - start_time):.2f} s"
                 )
-            u_arc_distance = distances_attr_dict["U Distance"][0]
-            # v_arc_distance = distances_attr_dict["V Distance"][0]
-            norm_u_arc_distance = (
-                u_arc_distance - reference_u_arc_distance_bounds[0]
-            ) / (
-                reference_u_arc_distance_bounds[1] - reference_u_arc_distance_bounds[0]
-            )
 
-            this_pop_norm_distances[gid] = norm_u_arc_distance
-
-        features = population.generate_features(
-            start_gid=rank, rank=rank, size=comm.size, local_random=local_random
-        )
-
-        write_every = max(1, int(math.floor(write_size / comm.size)))
-
-        feature_attr_dict = {}
-        n_iter = comm.allreduce(len(features), op=MPI.MAX)
-        gid_count = 0
-        for i in range(n_iter):
-            feature = None
-            if i < len(features):
-                feature = features[i]
-                feature_attr_dict[feature.gid] = feature.to_attribute_dict()
-                gid_count += 1
-            if (i > 0 and i % write_every == 0) or (debug and i == debug_count):
+            if not dry_run:
                 req = comm.Ibarrier()
-                total_gid_count = comm.reduce(gid_count, root=0, op=MPI.SUM)
-                req.wait()
                 if rank == 0:
                     logger.info(
-                        f"generated {gid_count} features in population {population_name} in "
-                        f"{(time.time() - start_time):.2f} s"
+                        f"writing selectivity features for {population_name}..."
                     )
+                append_cell_attributes(
+                    output_path,
+                    population_name,
+                    feature_attr_dict,
+                    namespace=output_feature_namespace,
+                    comm=comm,
+                    io_size=io_size,
+                    chunk_size=chunk_size,
+                    value_chunk_size=value_chunk_size,
+                )
+                req.wait()
+                del feature_attr_dict
+                feature_attr_dict = {}
+                gc.collect()
 
-                if not dry_run:
-                    req = comm.Ibarrier()
-                    if rank == 0:
-                        logger.info(
-                            f"writing selectivity features for {population_name}..."
-                        )
-                    append_cell_attributes(
-                        output_path,
-                        population_name,
-                        feature_attr_dict,
-                        namespace=output_feature_namespace,
-                        comm=comm,
-                        io_size=io_size,
-                        chunk_size=chunk_size,
-                        value_chunk_size=value_chunk_size,
-                    )
-                    req.wait()
-                    del feature_attr_dict
-                    feature_attr_dict = {}
-                    gc.collect()
-
-            if debug and iter_count >= debug_count:
-                break
-
-        pop_norm_distances[population] = this_pop_norm_distances
+    total_gid_count = comm.reduce(gid_count, root=0, op=MPI.SUM)
+    if rank == 0:
+        logger.info(
+            f"generated selectivity features for {total_gid_count} {population_name} cells in "
+            f"{(time.time() - start_time):.2f} s"
+        )
 
-        total_gid_count = 0
+    if not dry_run:
         req = comm.Ibarrier()
-        total_gid_count = comm.reduce(gid_count, root=0, op=MPI.SUM)
-        req.wait()
-
         if rank == 0:
-            logger.info(
-                f"generated selectivity features for {total_gid_count} {population_name} cells in "
-                f"{(time.time() - start_time):.2f} s"
-            )
-
-        if not dry_run:
-            req = comm.Ibarrier()
-            if rank == 0:
-                logger.info(f"writing selectivity features for {population}...")
-            append_cell_attributes(
-                output_path,
-                population,
-                feature_attr_dict,
-                namespace=output_feature_namespace,
-                comm=comm,
-                io_size=io_size,
-                chunk_size=chunk_size,
-                value_chunk_size=value_chunk_size,
-            )
-            req.wait()
-            del feature_attr_dict
-            gc.collect()
-        req = comm.Ibarrier()
+            logger.info(f"writing selectivity features for {population_name}...")
+        append_cell_attributes(
+            output_path,
+            population_name,
+            feature_attr_dict,
+            namespace=output_feature_namespace,
+            comm=comm,
+            io_size=io_size,
+            chunk_size=chunk_size,
+            value_chunk_size=value_chunk_size,
+        )
         req.wait()
+        del feature_attr_dict
+        gc.collect()
+    comm.barrier()
diff --git a/src/miv_simulator/interface/legacy/network_clamp/go.py b/src/miv_simulator/interface/legacy/network_clamp/go.py
index d622b3bc..4fbf851b 100644
--- a/src/miv_simulator/interface/legacy/network_clamp/go.py
+++ b/src/miv_simulator/interface/legacy/network_clamp/go.py
@@ -25,7 +25,9 @@ class Config:
         mechanisms_path: str = "."
         dataset_path: Optional[str] = None
         spike_events_path: Optional[str] = None
-        spike_events_namespace: str = "Spike Events"
+        spike_events_namespaces: List[str] = Field(
+            default_factory=lambda: ["Spike Events"]
+        )
         spike_events_t: str = "t"
         coordinates: str = Field("???")
         distances_namespace: str = ("Arc Distances",)
@@ -62,7 +64,7 @@ def __call__(self):
             mechanisms_path=self.config.mechanisms_path,
             dataset_prefix=self.config.dataset_path,
             spike_events_path=self.config.spike_events_path,
-            spike_events_namespace=self.config.spike_events_namespace,
+            spike_events_namespaces=self.config.spike_events_namespaces,
             spike_events_t=self.config.spike_events_t,
             coords_path=self.config.coordinates,
             distances_namespace=self.config.distances_namespace,
diff --git a/src/miv_simulator/interface/legacy/network_clamp/optimize.py b/src/miv_simulator/interface/legacy/network_clamp/optimize.py
index 2df6133f..ef964194 100644
--- a/src/miv_simulator/interface/legacy/network_clamp/optimize.py
+++ b/src/miv_simulator/interface/legacy/network_clamp/optimize.py
@@ -31,7 +31,9 @@ class Config:
         param_config_name: Optional[str] = None
         param_type: str = "synaptic"
         spike_events_path: Optional[str] = None
-        spike_events_namespace: str = "Spike Events"
+        spike_events_namespaces: List[str] = Field(
+            default_factory=lambda: ["Spike Events"]
+        )
         spike_events_t: str = "t"
         coordinates: str = Field("???")
         distances_namespace: str = ("Arc Distances",)
@@ -76,7 +78,7 @@ def __call__(self):
             results_file=None,
             results_path=self.local_directory("data/results", create=True),
             spike_events_path=self.config.spike_events_path,
-            spike_events_namespace=self.config.spike_events_namespace,
+            spike_events_namespaces=self.config.spike_events_namespaces,
             spike_events_t=self.config.spike_events_t,
             coords_path=self.config.coordinates,
             distances_namespace=self.config.distances_namespace,
diff --git a/src/miv_simulator/interface/legacy/network_clamp/show.py b/src/miv_simulator/interface/legacy/network_clamp/show.py
index c63f5c79..63fbc012 100644
--- a/src/miv_simulator/interface/legacy/network_clamp/show.py
+++ b/src/miv_simulator/interface/legacy/network_clamp/show.py
@@ -20,7 +20,9 @@ class Config:
         mechanisms_path: str = "."
         dataset_path: Optional[str] = None
         spike_events_path: Optional[str] = None
-        spike_events_namespace: str = "Spike Events"
+        spike_events_namespaces: List[str] = Field(
+            default_factory=lambda: ["Spike Events"]
+        )
         spike_events_t: str = "t"
         input_features_path: Optional[str] = None
         input_features_namespaces: List[str] = Field(
@@ -45,7 +47,7 @@ def __call__(self):
             dataset_prefix=self.config.dataset_path,
             results_path=self.local_directory("data/results", create=True),
             spike_events_path=self.config.spike_events_path,
-            spike_events_namespace=self.config.spike_events_namespace,
+            spike_events_namespaces=self.config.spike_events_namespaces,
             spike_events_t=self.config.spike_events_t,
             input_features_path=self.config.input_features_path,
             input_features_namespaces=self.config.input_features_namespaces,
diff --git a/src/miv_simulator/plotting.py b/src/miv_simulator/plotting.py
index f4ea81be..3a62f8d9 100644
--- a/src/miv_simulator/plotting.py
+++ b/src/miv_simulator/plotting.py
@@ -13,7 +13,7 @@
 from matplotlib.offsetbox import AnchoredText
 from matplotlib.ticker import MaxNLocator
 from matplotlib.animation import FuncAnimation, writers
-from miv_simulator import cells, spikedata, statedata, stimulus, synapses
+from miv_simulator import cells, spikedata, statedata, synapses
 from miv_simulator.volume import network_volume
 from miv_simulator.env import Env
 from miv_simulator.utils import (
@@ -39,6 +39,8 @@
     NeuroH5ProjectionGen,
     bcast_cell_attributes,
 )
+from spike_encoder import EncoderTimeConfig
+from miv_simulator.input_features import FEATURE_TYPE_NAMES, constant_rate_vector
 from scipy import interpolate, ndimage, signal
 
 if hasattr(h, "nrnmpi_init"):
@@ -2511,25 +2513,43 @@ def plot_network_clamp(
                 "plot_network_clamp: config_file must be provided with target_input_features_path."
             )
         env = Env(
-            config_file=config_file,
-            arena_id=target_input_features_arena_id,
-            trajectory_id=target_input_features_trajectory_id,
+            config=config_file,
             config_prefix=config_prefix,
         )
 
         if env.analysis_config is not None:
             baks_config.update(env.analysis_config["Firing Rate Inference"])
 
-        target_trj_rate_maps = stimulus.rate_maps_from_features(
-            env,
+        target_time_config = EncoderTimeConfig(
+            duration_ms=time_range[1] - time_range[0],
+            dt_ms=float(env.stimulus_config["Temporal Resolution"]),
+        )
+        target_features_ns = (
+            f"{target_input_features_namespace} {target_input_features_arena_id}"
+        )
+        target_features_attr_iter = read_cell_attributes(
+            target_input_features_path,
             state_pop_name,
-            cell_index_set=[gid],
-            input_features_path=target_input_features_path,
-            input_features_namespace=target_input_features_namespace,
-            time_range=time_range,
-            include_time=True,
+            namespace=target_features_ns,
+            mask={"Feature Type", "peak_rate"},
         )
-        target_rate_time, target_rate = target_trj_rate_maps[gid]
+        target_selectivity_attr_dict = dict(target_features_attr_iter).get(gid, None)
+        if target_selectivity_attr_dict is None:
+            raise RuntimeError(
+                f"plot_network_clamp: no target input features found for gid {gid} "
+                f"in namespace {target_features_ns}"
+            )
+        this_feature_type = int(target_selectivity_attr_dict["Feature Type"][0])
+        this_feature_type_name = FEATURE_TYPE_NAMES[this_feature_type]
+        if this_feature_type_name != "linear_rate":
+            raise RuntimeError(
+                "plot_network_clamp: target feature type "
+                f"{this_feature_type_name} is not supported; "
+                "only 'linear_rate' features are currently implemented"
+            )
+        peak_rate = float(target_selectivity_attr_dict["peak_rate"][0])
+        target_rate = constant_rate_vector(peak_rate, target_time_config)
+        target_rate_time = target_time_config.get_time_vector_ms() + time_range[0]
         target_rate_ip = interpolate.Akima1DInterpolator(target_rate_time, target_rate)
 
     maxN = 0
diff --git a/src/miv_simulator/scripts/analysis/network_clamp.py b/src/miv_simulator/scripts/analysis/network_clamp.py
index b8fb964a..be55c01c 100644
--- a/src/miv_simulator/scripts/analysis/network_clamp.py
+++ b/src/miv_simulator/scripts/analysis/network_clamp.py
@@ -73,14 +73,14 @@ def cli():
 )
 @click.option(
     "--spike-events-path",
-    "-s",
     type=click.Path(exists=True, dir_okay=False, file_okay=True),
     help="path to neuroh5 file containing spike times",
 )
 @click.option(
     "--spike-events-namespace",
+    multiple=True,
     type=str,
-    default="Spike Events",
+    default=("Spike Events",),
     help="namespace containing spike times",
 )
 @click.option(
@@ -101,7 +101,7 @@ def cli():
     type=str,
     multiple=True,
     required=False,
-    default=["Place Selectivity", "Grid Selectivity"],
+    default=("Place Selectivity", "Grid Selectivity"),
     help="namespace containing input selectivity features",
 )
 @click.option("--use-coreneuron", is_flag=True, help="enable use of CoreNEURON")
@@ -247,14 +247,14 @@ def show(
 )
 @click.option(
     "--spike-events-path",
-    "-s",
     type=click.Path(),
     help="path to neuroh5 file containing spike times",
 )
 @click.option(
     "--spike-events-namespace",
+    multiple=True,
     type=str,
-    default="Spike Events",
+    default=("Spike Events",),
     help="namespace containing spike times",
 )
 @click.option(
@@ -287,7 +287,7 @@ def show(
     type=str,
     multiple=True,
     required=False,
-    default=["Place Selectivity", "Grid Selectivity"],
+    default=("Place Selectivity", "Grid Selectivity"),
     help="namespace containing input selectivity features",
 )
 @click.option(
@@ -535,9 +535,9 @@ def go(
 )
 @click.option(
     "--spike-events-namespace",
+    multiple=True,
     type=str,
-    required=False,
-    default="Spike Events",
+    default=("Spike Events",),
     help="namespace containing input spike times",
 )
 @click.option(
@@ -570,7 +570,7 @@ def go(
     type=str,
     multiple=True,
     required=False,
-    default=["Place Selectivity", "Grid Selectivity"],
+    default=("Place Selectivity", "Grid Selectivity"),
     help="namespace containing input selectivity features",
 )
 @click.option(
diff --git a/src/miv_simulator/scripts/generate_input_features.py b/src/miv_simulator/scripts/generate_input_features.py
index 1107c1b6..b7192eb5 100644
--- a/src/miv_simulator/scripts/generate_input_features.py
+++ b/src/miv_simulator/scripts/generate_input_features.py
@@ -3,7 +3,16 @@
 import sys
 
 import click
-from miv_simulator.input_features import generate_input_features
+import numpy as np
+from mpi4py import MPI
+from neuroh5.io import read_population_ranges
+
+from miv_simulator.env import Env
+from miv_simulator.input_features import (
+    ConstantModality,
+    FeatureSpace,
+    generate_input_features,
+)
 from miv_simulator.utils import list_find
 
 
@@ -27,27 +36,15 @@
     default=None,
 )
 @click.option("--arena-id", type=str, default="A")
-@click.option("--populations", "-p", type=str, multiple=True)
+@click.option("--populations", "-p", type=str, multiple=True, required=True)
 @click.option("--io-size", type=int, default=-1)
 @click.option("--chunk-size", type=int, default=1000)
 @click.option("--value-chunk-size", type=int, default=1000)
 @click.option("--cache-size", type=int, default=50)
 @click.option("--write-size", type=int, default=10000)
 @click.option("--verbose", "-v", is_flag=True)
-@click.option("--gather", is_flag=True)
-@click.option("--interactive", is_flag=True)
 @click.option("--debug", is_flag=True)
 @click.option("--debug-count", type=int, default=10)
-@click.option("--plot", is_flag=True)
-@click.option("--show-fig", is_flag=True)
-@click.option("--save-fig", required=False, type=str, default=None)
-@click.option(
-    "--save-fig-dir",
-    type=click.Path(exists=True, file_okay=False, dir_okay=True),
-    default=None,
-)
-@click.option("--font-size", type=float, default=14)
-@click.option("--fig-format", required=False, type=str, default="svg")
 @click.option("--dry-run", is_flag=True)
 def main(
     config,
@@ -63,44 +60,78 @@ def main(
     cache_size,
     write_size,
     verbose,
-    gather,
-    interactive,
     debug,
     debug_count,
-    plot,
-    show_fig,
-    save_fig,
-    save_fig_dir,
-    font_size,
-    fig_format,
     dry_run,
 ):
-    generate_input_features(
-        config,
-        coords_path,
-        distances_namespace,
-        output_path,
-        arena_id,
-        populations,
-        io_size,
-        chunk_size,
-        value_chunk_size,
-        cache_size,
-        write_size,
-        verbose,
-        gather,
-        interactive,
-        debug,
-        debug_count,
-        plot,
-        show_fig,
-        save_fig,
-        save_fig_dir,
-        font_size,
-        fig_format,
-        dry_run,
-        config_prefix=config_prefix,
-    )
+    env = Env(config=config, config_prefix=config_prefix)
+    comm = MPI.COMM_WORLD
+
+    population_ranges = read_population_ranges(coords_path, comm)[0]
+
+    selectivity_type = env.selectivity_types.get("constant")
+    if selectivity_type is None:
+        raise RuntimeError(
+            "generate-input-features: 'constant' selectivity type is not defined "
+            "in the 'Input Selectivity Types' section of the model definitions"
+        )
+
+    feature_space = FeatureSpace(name="Input Features")
+    modality = ConstantModality()
+    feature_space.register_modality(modality)
+
+    local_random = np.random.RandomState()
+
+    for population_name in populations:
+        if population_name not in population_ranges:
+            raise RuntimeError(
+                f"generate-input-features: population {population_name} not found "
+                f"in coords_path {coords_path}"
+            )
+        pop_start, pop_size = population_ranges[population_name]
+
+        try:
+            peak_rate = float(
+                env.stimulus_config["Peak Rate"][population_name][selectivity_type]
+            )
+        except KeyError as e:
+            raise RuntimeError(
+                "generate-input-features: no 'Peak Rate' configured for population "
+                f"'{population_name}', selectivity type 'constant', in the Stimulus "
+                "configuration"
+            ) from e
+
+        population = feature_space.create_population(
+            name=population_name,
+            modality_name=modality.name,
+            n_features=pop_size,
+            encoding_distribution={
+                "feature_type": "linear_rate",
+                "peak_rate": peak_rate,
+            },
+            start_gid=pop_start,
+            local_random=local_random,
+            rank=comm.rank,
+            size=comm.size,
+        )
+
+        generate_input_features(
+            env,
+            population,
+            coords_path,
+            distances_namespace,
+            output_path,
+            f"Constant Selectivity {arena_id}",
+            io_size,
+            chunk_size,
+            value_chunk_size,
+            cache_size,
+            write_size,
+            dry_run,
+            verbose,
+            debug,
+            debug_count,
+        )
 
 
 if __name__ == "__main__":

From 6d74d05de65c7fe6724b4c2632d98c77ff63d6cd Mon Sep 17 00:00:00 2001
From: Ivan Raikov <ivan.g.raikov@gmail.com>
Date: Sat, 19 Sep 2026 08:38:58 -0700
Subject: [PATCH 2/3] version set to 0.4.1

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 38cbd4f8..ff1ee2a3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "miv-simulator"
-version = "0.4.0"
+version = "0.4.1"
 description = "Mind-In-Vitro simulator"
 authors = []
 dependencies = [

From f417900d0e2bf65edda3ede4cfef81e593b31226 Mon Sep 17 00:00:00 2001
From: Ivan Raikov <ivan.g.raikov@gmail.com>
Date: Sat, 19 Sep 2026 09:25:57 -0700
Subject: [PATCH 3/3] fixes to unit tests and optogenetic module

---
 src/miv_simulator/opto/models.py    |   96 +-
 src/miv_simulator/opto/protocols.py |   35 +-
 tests/test_optimize_network_api.py  |  105 +-
 uv.lock                             | 3343 ++++++++++++++++-----------
 4 files changed, 2061 insertions(+), 1518 deletions(-)

diff --git a/src/miv_simulator/opto/models.py b/src/miv_simulator/opto/models.py
index ca18f9ae..f41884c6 100644
--- a/src/miv_simulator/opto/models.py
+++ b/src/miv_simulator/opto/models.py
@@ -1,8 +1,8 @@
 """
 Rhodopsin model class and functions
-    * Three-state model :math:`\{C, O, D\}`
-    * Four-state model :math:`\{C_1, O_1, O_2, C_2\}`
-    * Six-state model :math:`\{C_1, I_1, O_1, O_2, I_2, C_2\}`
+    * Three-state model :math:`\\{C, O, D\\}`
+    * Four-state model :math:`\\{C_1, O_1, O_2, C_2\\}`
+    * Six-state model :math:`\\{C_1, I_1, O_1, O_2, I_2, C_2\\}`
 Based on code from the PyRhO: A Multiscale Optogenetics Simulation Platform
 https://github.com/ProjectPyRhO/PyRhO.git
 """
@@ -192,6 +192,8 @@ def calcSoln(self, t, s0=None):
     def plotActivation(
         self, actFunc, label=None, phis=np.logspace(12, 21, 1001), ax=None
     ):
+        import matplotlib.pyplot as plt
+
         if ax is None:
             ax = plt.gca()
         else:
@@ -203,8 +205,8 @@ def plotActivation(
         else:
             ax.plot(phis, actFunc(phis))
         ax.set_xscale("log")
-        ax.set_xlabel(r"$\phi \ \mathrm{[photons \cdot mm^{-2} \cdot s^{-1}]}$")
-        ax.set_ylabel(r"$\mathrm{Transition\ rate \ [ms^{-1}]}$")
+        ax.set_xlabel(r"$\\phi \ \\mathrm{[photons \\cdot mm^{-2} \\cdot s^{-1}]}$")
+        ax.set_ylabel(r"$\\mathrm{Transition\\ rate \\ [ms^{-1}]}$")
         return
 
 
@@ -249,32 +251,33 @@ class RhO_3states(RhodopsinModel):
 
     # TODO: Try using \begin{align*} now this is a raw string, or leave a comment if it does not work
     equations = r"""
-                $$ \dot{C} = G_{r}(\phi)D - G_{a}(\phi)C $$
-                $$ \dot{O} = G_{a}(\phi)C - G_{d}O $$
-                $$ \dot{D} = G_{d}O - G_{r}(\phi)D $$
+                $$ \dot{C} = G_{r}(\\phi)D - G_{a}(\\phi)C $$
+                $$ \dot{O} = G_{a}(\\phi)C - G_{d}O $$
+                $$ \dot{D} = G_{d}O - G_{r}(\\phi)D $$
                 $$ C + O + D = 1 $$
                 $$ $$
-                $$ G_a(\phi) = k_a\frac{\phi^p}{\phi^p + \phi_m^p} $$
-                $$ G_r(\phi) = k_r\frac{\phi^q}{\phi^q + \phi_m^q} + G_{r0} $$
+                $$ G_a(\\phi) = k_a\frac{\\phi^p}{\\phi^p + \\phi_m^p} $$
+                $$ G_r(\\phi) = k_r\frac{\\phi^q}{\\phi^q + \\phi_m^q} + G_{r0} $$
                 $$ $$
-                $$ f_{\phi}(\phi) = O \qquad \qquad $$
+                $$ f_{\\phi}(\\phi) = O \qquad \qquad $$
                 $$ f_v(v) = v_1\frac{1-e^{-(v-E)/v_0}}{(v-E)} $$
-                $$ I_{\phi} = g_0 \cdot f_{\phi}(\phi) \cdot f_v(v) \cdot (v-E) $$
+                $$ I_{\\phi} = g_0 \\cdot f_{\\phi}(\\phi) \\cdot f_v(v) \\cdot (v-E) $$
                 """
 
     _latex = [
-        r"$\dot{C} = G_{r}(\phi)D - G_{a}(\phi)C$",
-        r"$\dot{O} = G_{a}(\phi)C - G_{d}O$",
-        r"$\dot{D} = G_{d}O - G_{r}(\phi)D$",
+        r"$\dot{C} = G_{r}(\\phi)D - G_{a}(\\phi)C$",
+        r"$\dot{O} = G_{a}(\\phi)C - G_{d}O$",
+        r"$\dot{D} = G_{d}O - G_{r}(\\phi)D$",
         r"$C + O + D = 1$",
-        r"$G_a(\phi) = k\\frac{\phi^p}{\phi^p + \phi_m^p}$",
-        r"$G_r(\phi) = \mathcal{H}(\phi) \cdot G_{r1} + G_{r0}$",
-        r"$f_{\phi}(\phi) = O$" r"$f_v(v) = \\frac{1-\\exp({-(v-E)/v_0})}{(v-E)/v_1}$",
-        r"$I_{\phi} = g_0 \cdot f_{\phi}(\phi) \cdot f_v(v) \cdot (v-E)$",
+        r"$G_a(\\phi) = k\\frac{\\phi^p}{\\phi^p + \\phi_m^p}$",
+        r"$G_r(\\phi) = \mathcal{H}(\\phi) \\cdot G_{r1} + G_{r0}$",
+        r"$f_{\\phi}(\\phi) = O$"
+        r"$f_v(v) = \\frac{1-\\exp({-(v-E)/v_0})}{(v-E)/v_1}$",
+        r"$I_{\\phi} = g_0 \\cdot f_{\\phi}(\\phi) \\cdot f_v(v) \\cdot (v-E)$",
     ]
 
-    eqIss = r"""$I_{SS} = \bar{g_0} \cdot \frac{G_a \cdot G_r}{G_d \cdot (G_r + G_a) + G_a \cdot G_r} \cdot (v-E)
-    = \bar{g_0} \cdot \frac{\tau_d}{\tau_d + \tau_r + \tau_\phi} \cdot (v-E)$"""
+    eqIss = r"""$I_{SS} = \bar{g_0} \\cdot \frac{G_a \\cdot G_r}{G_d \\cdot (G_r + G_a) + G_a \\cdot G_r} \\cdot (v-E)
+    = \bar{g_0} \\cdot \frac{\tau_d}{\tau_d + \tau_r + \tau_\\phi} \\cdot (v-E)$"""
 
     def _calcGa(self, phi):
         return self.k_a * phi**self.p / (phi**self.p + self.phi_m**self.p)
@@ -310,7 +313,8 @@ def solveStates(self, s_0, t, phi_t=None):
 
         if phi_t is not None:
             self.setLight(float(phi_t(t)))
-        C, O, D = s_0  # Split state vector into individual variables
+        # Split state vector into individual variables
+        C, O, D = s_0  # noqa: E741
         dCdt = -self.Ga * C + self.Gr * D  # C'
         dOdt = self.Ga * C - self.Gd * O  # O'
         dDdt = self.Gd * O - self.Gr * D  # D'
@@ -345,7 +349,7 @@ def hessian(self, s_0, t):
     def calcfphi(self, states=None):
         if states is None:
             states = self.states
-        C, O, D = states.T
+        C, O, D = states.T  # noqa: E741
         return O
 
     # def calcOn(self,t):
@@ -403,7 +407,7 @@ def calcSoln(self, t, s0=None):
             - Z_2 * lambda_1 * (lambda_2 - Gd - Gr) * Exp_2
             + (RSD * Gd**2 * Gr * (C_0 + D_0 + O_0))
         ) / (Gd * SP * RSD)
-        O = (
+        O = (  # noqa: E741
             -Z_1 * lambda_2 * (lambda_1 - Gr) * Exp_1
             + Z_2 * lambda_1 * (lambda_2 - Gr) * Exp_2
             + (RSD * Ga * Gd * Gr * (C_0 + D_0 + O_0))
@@ -470,20 +474,20 @@ class RhO_4states(RhodopsinModel):
     connect = [[0, 1, 0, 0], [1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0]]
 
     equations = r"""
-                $$ \dot{C_1} = G_{d1}O_1 + G_{r0}C_2 - G_{a1}(\phi)C_1 $$
-                $$ \dot{O_1} = G_{a1}(\phi)C_1 + G_{b}(\phi)O_2 - (G_{d1} + G_{f}(\phi))O_1 $$
-                $$ \dot{O_2} = G_{a2}(\phi)C_2 + G_{f}(\phi)O_1 - (G_{d2} + G_{b}(\phi))O_2 $$
-                $$ \dot{C_2} = G_{d2}O_2 - (G_{r0} + G_{a2}(\phi))C_2 $$
+                $$ \dot{C_1} = G_{d1}O_1 + G_{r0}C_2 - G_{a1}(\\phi)C_1 $$
+                $$ \dot{O_1} = G_{a1}(\\phi)C_1 + G_{b}(\\phi)O_2 - (G_{d1} + G_{f}(\\phi))O_1 $$
+                $$ \dot{O_2} = G_{a2}(\\phi)C_2 + G_{f}(\\phi)O_1 - (G_{d2} + G_{b}(\\phi))O_2 $$
+                $$ \dot{C_2} = G_{d2}O_2 - (G_{r0} + G_{a2}(\\phi))C_2 $$
                 $$ C_1 + O_1 + O_2 + C_2 = 1 $$
                 $$$$
-                $$ G_{a1}(\phi) = k_1 \frac{\phi^p}{\phi^p + \phi_m^p} $$
-                $$ G_{f}(\phi)  = k_{f} \frac{\phi^q}{\phi^q + \phi_m^q} + G_{f0} $$
-                $$ G_{b}(\phi)  = k_{b} \frac{\phi^q}{\phi^q + \phi_m^q} + G_{b0} $$
-                $$ G_{a2}(\phi) = k_2 \frac{\phi^p}{\phi^p + \phi_m^p} $$
+                $$ G_{a1}(\\phi) = k_1 \frac{\\phi^p}{\\phi^p + \\phi_m^p} $$
+                $$ G_{f}(\\phi)  = k_{f} \frac{\\phi^q}{\\phi^q + \\phi_m^q} + G_{f0} $$
+                $$ G_{b}(\\phi)  = k_{b} \frac{\\phi^q}{\\phi^q + \\phi_m^q} + G_{b0} $$
+                $$ G_{a2}(\\phi) = k_2 \frac{\\phi^p}{\\phi^p + \\phi_m^p} $$
                 $$$$
-                $$ f_{\phi}(\phi) = O_1+\gamma O_2 $$
+                $$ f_{\\phi}(\\phi) = O_1+\gamma O_2 $$
                 $$ f_v(v) = v_1\frac{1-e^{-(v-E)/v_0}}{(v-E)} $$
-                $$ I_{\phi} = g_0 \cdot f_{\phi}(\phi) \cdot f_v(v) \cdot (v-E) $$
+                $$ I_{\\phi} = g_0 \\cdot f_{\\phi}(\\phi) \\cdot f_v(v) \\cdot (v-E) $$
                 """
 
     def _calcGa1(self, phi):
@@ -673,22 +677,22 @@ class RhO_6states(RhodopsinModel):
     ]
 
     equations = r"""
-                $$ \dot{C_1} = G_{d1}O_1 + G_{r0}C_2 - G_{a1}(\phi)C_1 $$
-                $$ \dot{I_1} = G_{a1}(\phi)C_1 - G_{o1}I_1 $$
-                $$ \dot{O_1} = G_{o1}I_1 + G_{b}(\phi)O_2 - (G_{d1} + G_{f}(\phi))O_1 $$
-                $$ \dot{O_2} = G_{o2}I_2 + G_{f}(\phi)O_1 - (G_{d2} + G_{b}(\phi))O_2 $$
-                $$ \dot{I_2} = G_{a2}(\phi)C_2 - G_{o2}I_2 $$
-                $$ \dot{C_2} = G_{d2}O_2 - (G_{r0} + G_{a2}(\phi))C_2 $$
+                $$ \dot{C_1} = G_{d1}O_1 + G_{r0}C_2 - G_{a1}(\\phi)C_1 $$
+                $$ \dot{I_1} = G_{a1}(\\phi)C_1 - G_{o1}I_1 $$
+                $$ \dot{O_1} = G_{o1}I_1 + G_{b}(\\phi)O_2 - (G_{d1} + G_{f}(\\phi))O_1 $$
+                $$ \dot{O_2} = G_{o2}I_2 + G_{f}(\\phi)O_1 - (G_{d2} + G_{b}(\\phi))O_2 $$
+                $$ \dot{I_2} = G_{a2}(\\phi)C_2 - G_{o2}I_2 $$
+                $$ \dot{C_2} = G_{d2}O_2 - (G_{r0} + G_{a2}(\\phi))C_2 $$
                 $$ C_1 + I_1 + O_1 + O_2 + I_2 + C_2 = 1 $$
                 $$$$
-                $$ G_{a1}(\phi) = k_{1} \frac{\phi^p}{\phi^p + \phi_m^p} $$
-                $$ G_{f}(\phi)  = k_{f} \frac{\phi^q}{\phi^q + \phi_m^q} + G_{f0} $$
-                $$ G_{b}(\phi)  = k_{b} \frac{\phi^q}{\phi^q + \phi_m^q} + G_{b0} $$
-                $$ G_{a2}(\phi) = k_{2} \frac{\phi^p}{\phi^p + \phi_m^p} $$
+                $$ G_{a1}(\\phi) = k_{1} \frac{\\phi^p}{\\phi^p + \\phi_m^p} $$
+                $$ G_{f}(\\phi)  = k_{f} \frac{\\phi^q}{\\phi^q + \\phi_m^q} + G_{f0} $$
+                $$ G_{b}(\\phi)  = k_{b} \frac{\\phi^q}{\\phi^q + \\phi_m^q} + G_{b0} $$
+                $$ G_{a2}(\\phi) = k_{2} \frac{\\phi^p}{\\phi^p + \\phi_m^p} $$
                 $$$$
-                $$ f_{\phi}(\phi) = O_1+\gamma O_2 $$
+                $$ f_{\\phi}(\\phi) = O_1+\gamma O_2 $$
                 $$ f_v(v) = v_1\frac{1-e^{-(v-E)/v_0}}{(v-E)} $$
-                $$ I_{\phi} = g_0 \cdot f_{\phi}(\phi) \cdot f_v(v) \cdot (v-E) $$
+                $$ I_{\\phi} = g_0 \\cdot f_{\\phi}(\\phi) \\cdot f_v(v) \\cdot (v-E) $$
                 """
 
     def _calcGa1(self, phi):
diff --git a/src/miv_simulator/opto/protocols.py b/src/miv_simulator/opto/protocols.py
index 82e380e3..889f0900 100644
--- a/src/miv_simulator/opto/protocols.py
+++ b/src/miv_simulator/opto/protocols.py
@@ -246,9 +246,7 @@ def extraPrep(self):
 
         self.t_start, self.t_end = 0, self.Dt_total
         self.phi_ts = self.genPulseSet()
-        self.runLabels = [
-            r"$f={}\mathrm{{Hz}}$ ".format(round_sig(f, 3)) for f in self.fs
-        ]
+        self.runLabels = [r"$f={}\mathrm{{Hz}}$ ".format(round(f, 3)) for f in self.fs]
 
     def getShortestPeriod(self):
         return 1000 / self.sr  # dt [ms]
@@ -348,7 +346,6 @@ def prepare(self):
         self.phis.sort(reverse=True)
         self.nPhis = len(self.phis)
 
-        self.addStimulus = config.addStimulus
         self.extraPrep()
         return
 
@@ -485,7 +482,7 @@ def make_param_dict(*xs):
         0,
         None,
         pq.mole * pq.mm**-2 * pq.second**-1,
-        "\mathbf{\phi}",
+        "\\mathbf{\\phi}",
         "List of flux values",
     ),  #'photons/s/mm^2'
     (
@@ -515,7 +512,7 @@ def make_param_dict(*xs):
         0,
         None,
         pq.mole * pq.mm**-2 * pq.second**-1,
-        "\mathbf{\phi}",
+        "\\mathbf{\\phi}",
         "List of flux values",
     ),  #'photons/s/mm^2'
     (
@@ -545,7 +542,7 @@ def make_param_dict(*xs):
         0,
         None,
         pq.mole * pq.mm**-2 * pq.second**-1,
-        "\mathbf{\phi}",
+        "\\mathbf{\\phi}",
         "List of flux values",
     ),  #'photons/s/mm^2'
     (
@@ -554,7 +551,7 @@ def make_param_dict(*xs):
         None,
         None,
         pq.mole * pq.mm**-2 * pq.second**-1,
-        "\phi_0",
+        "\\phi_0",
         "Constant offset for flux",
     ),  #'photons/s/mm^2'
     (
@@ -563,7 +560,7 @@ def make_param_dict(*xs):
         False,
         True,
         1,
-        "\phi_{t=0}>0",
+        "\\phi_{t=0}>0",
         "Start at maximum flux (else minimum)",
     ),
     (
@@ -572,7 +569,7 @@ def make_param_dict(*xs):
         0,
         None,
         pq.Hz,
-        "\mathbf{f}",
+        "\\mathbf{f}",
         "List of modulation frequencies",
     ),  #'pq.Hz' #50, 100, 500, 1000
     (
@@ -602,7 +599,7 @@ def make_param_dict(*xs):
         None,
         None,
         pq.mole * pq.mm**-2 * pq.second**-1,
-        "\mathbf{\phi}",
+        "\\mathbf{\\phi}",
         "List of flux values",
     ),  # 'photons/s/mm^2'
     (
@@ -611,7 +608,7 @@ def make_param_dict(*xs):
         None,
         None,
         pq.mole * pq.mm**-2 * pq.second**-1,
-        "\phi_0",
+        "\\phi_0",
         "Constant offset for flux",
     ),  # 'photons/s/mm^2'
     (
@@ -629,7 +626,7 @@ def make_param_dict(*xs):
         False,
         True,
         1,
-        "\phi_{t=0}>0",
+        "\\phi_{t=0}>0",
         "Start at maximum flux (else minimum)",
     ),
     (
@@ -661,7 +658,7 @@ def make_param_dict(*xs):
         None,
         None,
         pq.mole * pq.mm**-2 * pq.second**-1,
-        "\mathbf{\phi}",
+        "\\mathbf{\\phi}",
         "List of flux values",
     ),  # 'photons/s/mm^2' #1e12,1e13,1e14,1e15,
     (
@@ -670,7 +667,7 @@ def make_param_dict(*xs):
         None,
         None,
         pq.mole * pq.mm**-2 * pq.second**-1,
-        "\phi_0",
+        "\\phi_0",
         "Constant offset for flux",
     ),  # 'photons/s/mm^2'
     (
@@ -700,7 +697,7 @@ def make_param_dict(*xs):
         None,
         None,
         pq.mole * pq.mm**-2 * pq.second**-1,
-        "\mathbf{\phi}",
+        "\\mathbf{\\phi}",
         "List of flux values",
     ),  # 'photons/s/mm^2'
     (
@@ -739,7 +736,7 @@ def make_param_dict(*xs):
         None,
         None,
         pq.mole * pq.mm**-2 * pq.second**-1,
-        "\mathbf{\phi}",
+        "\\mathbf{\\phi}",
         "List of flux values",
     ),  # 'photons/s/mm^2' # Change to 1e17?
     (
@@ -769,7 +766,7 @@ def make_param_dict(*xs):
         None,
         None,
         pq.mole * pq.mm**-2 * pq.second**-1,
-        "\mathbf{\phi}",
+        "\\mathbf{\\phi}",
         "List of flux values",
     ),  # 'photons/s/mm^2' #1e12
     (
@@ -808,7 +805,7 @@ def make_param_dict(*xs):
         None,
         None,
         pq.mole * pq.mm**-2 * pq.second**-1,
-        "\mathbf{\phi}",
+        "\\mathbf{\\phi}",
         "List of flux values",
     ),  # 'photons/s/mm^2'
     (
diff --git a/tests/test_optimize_network_api.py b/tests/test_optimize_network_api.py
index ccb24d3c..91dce8ee 100644
--- a/tests/test_optimize_network_api.py
+++ b/tests/test_optimize_network_api.py
@@ -1,19 +1,23 @@
 """
 Integration tests for optimize_network module using the new
-_network_objectives API.  These tests do not import miv_simulator.
+_network_objectives API.
+
+The real optimize_network and network_objectives modules are imported
+canonically, with only the dmosopt package stubbed, since dmosopt is
+not a miv_simulator dependency and its import requires spawning a
+distributed worker pool.
 """
 
 import sys
 import types
+
 import numpy as np
-import importlib.util
 
 # ---------------------------------------------------------------------------
-# Build a fake ``dmosopt`` package with a real ``MOASMO`` subpackage so
-# ``from dmosopt.MOASMO import get_best`` succeeds.
+# Stub the ``dmosopt`` package so that importing optimize_network does not
+# require a distributed worker environment.
 # ---------------------------------------------------------------------------
 
-# Fake dmosopt package
 dmosopt_pkg = types.ModuleType("dmosopt")
 dmosopt_pkg.__path__ = []  # makes it a package
 
@@ -28,91 +32,22 @@
 dmosopt_pkg.dmosopt = dmosopt_dmo
 dmosopt_pkg.MOASMO = dmosopt_moasmo
 
+stub_names = ["dmosopt", "dmosopt.dmosopt", "dmosopt.MOASMO"]
+saved_modules = {name: sys.modules.get(name) for name in stub_names}
+
 sys.modules["dmosopt"] = dmosopt_pkg
 sys.modules["dmosopt.dmosopt"] = dmosopt_dmo
 sys.modules["dmosopt.MOASMO"] = dmosopt_moasmo
 
-# Other external deps
-sys.modules["neuron"] = types.ModuleType("neuron")
-sys.modules["neuron"].h = None
-sys.modules["click"] = types.ModuleType("click")
-sys.modules["click"].get_current_context = lambda: None
-sys.modules["mpi4py"] = types.ModuleType("mpi4py")
-sys.modules["mpi4py"].MPI = types.ModuleType("mpi4py.MPI")
-sys.modules["mpi4py"].MPI.COMM_WORLD = types.SimpleNamespace(size=1)
-
-# ---------------------------------------------------------------------------
-# Build a fake ``miv_simulator`` package tree so the real optimize_network
-# module can execute its imports.
-# ---------------------------------------------------------------------------
+from miv_simulator import network_objectives as mod_no  # noqa: E402
+from miv_simulator import optimize_network as mod_on  # noqa: E402
 
-fake_miv = types.ModuleType("miv_simulator")
-
-# env
-fake_env_mod = types.ModuleType("miv_simulator.env")
-fake_env_mod.Env = lambda **kw: None  # placeholder
-fake_miv.env = fake_env_mod
-
-# network
-fake_network_mod = types.ModuleType("miv_simulator.network")
-fake_miv.network = fake_network_mod
-
-# mechanisms
-fake_mechanisms_mod = types.ModuleType("miv_simulator.mechanisms")
-fake_mechanisms_mod.compile_and_load = lambda **kw: None
-fake_miv.mechanisms = fake_mechanisms_mod
-
-# utils
-fake_utils_mod = types.ModuleType("miv_simulator.utils")
-fake_utils_mod.read_from_yaml = lambda x: {}
-fake_utils_mod.write_to_yaml = lambda p, d: None
-fake_utils_mod.get_module_logger = lambda name: types.SimpleNamespace(
-    info=lambda *a, **k: None
-)
-fake_miv.utils = fake_utils_mod
-
-# synapses
-fake_synapses_mod = types.ModuleType("miv_simulator.synapses")
-_syn = lambda **kw: None  # dummy SynParam-like object # noqa: E731
-_syn._asdict = lambda: {}
-fake_synapses_mod.syn_param_from_dict = lambda d: _syn
-fake_synapses_mod.SynParam = type("SynParam", (), {})
-fake_miv.synapses = fake_synapses_mod
-
-# optimization
-fake_optimization_mod = types.ModuleType("miv_simulator.optimization")
-fake_optimization_mod.optimization_params = lambda *a, **k: None
-fake_optimization_mod.update_network_params = lambda env, ptv: None
-fake_optimization_mod.network_features = lambda env, t1, t2, pops: {}
-fake_miv.optimization = fake_optimization_mod
-
-# Load the real network_objectives module
-
-spec_no = importlib.util.spec_from_file_location(
-    "miv_simulator.network_objectives",
-    "src/miv_simulator/network_objectives.py",
-)
-mod_no = importlib.util.module_from_spec(spec_no)
-spec_no.loader.exec_module(mod_no)
-fake_miv.network_objectives = mod_no
-
-# Register the package tree
-sys.modules["miv_simulator"] = fake_miv
-sys.modules["miv_simulator.env"] = fake_miv.env
-sys.modules["miv_simulator.network"] = fake_miv.network
-sys.modules["miv_simulator.mechanisms"] = fake_miv.mechanisms
-sys.modules["miv_simulator.utils"] = fake_miv.utils
-sys.modules["miv_simulator.synapses"] = fake_miv.synapses
-sys.modules["miv_simulator.optimization"] = fake_miv.optimization
-sys.modules["miv_simulator.network_objectives"] = fake_miv.network_objectives
-
-# Finally load the real optimize_network module
-spec2 = importlib.util.spec_from_file_location(
-    "miv_simulator.optimize_network",
-    "src/miv_simulator/optimize_network.py",
-)
-mod_on = importlib.util.module_from_spec(spec2)
-spec2.loader.exec_module(mod_on)
+# Restore the dmosopt modules that were shadowed by the stubs
+for name, module in saved_modules.items():
+    if module is None:
+        sys.modules.pop(name, None)
+    else:
+        sys.modules[name] = module
 
 
 # ---------------------------------------------------------------------------
diff --git a/uv.lock b/uv.lock
index 87d96c10..99777c0c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,6 +1,10 @@
 version = 1
-revision = 1
-requires-python = ">=3.11"
+revision = 3
+requires-python = ">=3.10.2"
+resolution-markers = [
+    "python_full_version >= '3.11'",
+    "python_full_version < '3.11'",
+]
 
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