analysis

Author: FedeClaudi

.gitignore

@@ -1,3 +1,4 @@
+.vscode/settings.json
 proj/secrets.py
 
 .vscode/*

.vscode/settings.json

@@ -1,4 +1,4 @@
 {
-    "python.pythonPath": "/Users/federicoclaudi/miniconda3/envs/dev/bin/python",
+    "python.pythonPath": "C:\\Users\\Federico\\.conda\\envs\\dev\\python.exe",
     "cSpell.enabled": true
 }

analysis/__init__py

@@ -0,0 +1,6 @@
+def check_two_conf_equal(c1, c2):
+    for key, val in c1.items():
+        if c2[key] != val:
+            return False
+
+    return True

analysis/utils.py

@@ -26,7 +26,7 @@ def make_figure(self):
 
         self.f = plt.figure(figsize=(16, 8))
 
-        gs = self.f.add_gridspec(2, 6)
+        gs = self.f.add_gridspec(2, 3)  # 6)
         self.xy_ax = self.f.add_subplot(gs[:, :2])
         self.xy_ax.axis("equal")
         self.xy_ax.axis("off")
@@ -35,11 +35,11 @@ def make_figure(self):
 
         self.sax = self.f.add_subplot(gs[1, 2:4])
 
-        self.accel_ax = self.f.add_subplot(gs[0, 4])
+        # self.accel_ax = self.f.add_subplot(gs[0, 4])
 
-        self.goal_ax = self.f.add_subplot(gs[0, 5])
+        # self.goal_ax = self.f.add_subplot(gs[0, 5])
 
-        self.cost_ax = self.f.add_subplot(gs[1, 4:])
+        # self.cost_ax = self.f.add_subplot(gs[1, 4:])
 
         clean_axes(self.f)
 
@@ -316,17 +316,17 @@ def visualize_world_live(self, curr_goals, elapsed=None):
         # plot control
         self._plot_control()
 
-        # plot current waypoint
+        # plot sped
         self._plot_current_variables()
 
-        # plot accelerations
-        self._plot_accelerations()
+        # # plot accelerations
+        # self._plot_accelerations()
 
-        # plot cost
-        self._plot_cost()
+        # # plot cost
+        # self._plot_cost()
 
-        # plot goal
-        self._plot_goal(curr_goals[0, :])
+        # # plot goal
+        # self._plot_goal(curr_goals[0, :])
 
         # display plot
         self.f.canvas.draw()

proj/environment/plotter.py

@@ -43,7 +43,12 @@ def complete_given_xy(x, y, params, planning_params):
 
 
 def compute_trajectory_stats(
-    trajectory, duration, params, planning_params, min_dist_travelled=150
+    trajectory,
+    duration,
+    params,
+    planning_params,
+    min_dist_travelled=150,
+    mute=False,
 ):
     # Compute stats
     n_points = len(trajectory)
@@ -85,46 +90,59 @@ def compute_trajectory_stats(
     )
 
     # log stuff
-    logger.info(
-        f"""
-        Trajectory metadata 
-
-
-        n_points = {n_points}
-        distance_travelled = {round(distance_travelled, 2)}
-        waypoint_density = {round(waypoint_density, 2)}
-        start_goal_distance = {round(start_goal_distance, 2)}
-        duration = {round(duration, 2)}
-        lookahead = {lookahead}
-        perc_lookahead = {perc_lookahead}
-    """
-    )
-    print(table)
-
-    if waypoint_density < 2 or waypoint_density > 3:
+    if not mute:
         logger.info(
-            f"Waypoint density of {round(waypoint_density, 3)} out of range, it should be 2 < wp < 3!",
-            extra={"markdown": True},
-        )
-        print(
-            f"[bold red]Waypoint density of {round(waypoint_density, 3)} out of range, it should be 2 < wp < 3!"
+            f"""
+            Trajectory metadata 
+
+
+            n_points = {n_points}
+            distance_travelled = {round(distance_travelled, 2)}
+            waypoint_density = {round(waypoint_density, 2)}
+            start_goal_distance = {round(start_goal_distance, 2)}
+            duration = {round(duration, 2)}
+            lookahead = {lookahead}
+            perc_lookahead = {perc_lookahead}
+        """
         )
 
-    if perc_lookahead < 0.05:
-        logger.info(
-            f"Lookahead of {lookahead} is {perc_lookahead} of the # of waypoints, that might be too low. Values closer to 5% are advised.",
-            extra={"markdown": True},
-        )
-        print(
-            f"[bold red]Lookahead of {lookahead} is {perc_lookahead} of the # of waypoints, that might be too low. Values closer to 5% are advised."
-        )
-    if distance_travelled < min_dist_travelled * params["px_to_cm"]:
-        logger.warning(
-            "Distance travelled below minimal requirement, erroring"
-        )
-        return None, None
+        print(table)
 
-    return trajectory, duration
+    metadata = dict(
+        n_points=n_points,
+        distance_travelled=round(distance_travelled, 2),
+        waypoint_density=round(waypoint_density, 2),
+        start_goal_distance=round(start_goal_distance, 2),
+        duration=round(duration, 2),
+        lookahead=lookahead,
+        perc_lookahead=perc_lookahead,
+    )
+
+    if not mute:
+        if waypoint_density < 2 or waypoint_density > 3:
+            logger.info(
+                f"Waypoint density of {round(waypoint_density, 3)} out of range, it should be 2 < wp < 3!",
+                extra={"markdown": True},
+            )
+            print(
+                f"[bold red]Waypoint density of {round(waypoint_density, 3)} out of range, it should be 2 < wp < 3!"
+            )
+
+        if perc_lookahead < 0.05:
+            logger.info(
+                f"Lookahead of {lookahead} is {perc_lookahead} of the # of waypoints, that might be too low. Values closer to 5% are advised.",
+                extra={"markdown": True},
+            )
+            print(
+                f"[bold red]Lookahead of {lookahead} is {perc_lookahead} of the # of waypoints, that might be too low. Values closer to 5% are advised."
+            )
+        if distance_travelled < min_dist_travelled * params["px_to_cm"]:
+            logger.warning(
+                "Distance travelled below minimal requirement, erroring"
+            )
+            return None, None
+
+    return trajectory, duration, metadata
 
 
 # ---------------------------------- Curves ---------------------------------- #
@@ -253,6 +271,6 @@ def from_tracking(n_steps, params, planning_params, cache_fld, *args):
     return (
         compute_trajectory_stats(
             trajectory, len(x[start:]) / fps, params, planning_params
-        ),
+        )[:2],
         trials,
     )

proj/environment/trajectories.py

@@ -52,7 +52,7 @@ class Config:
 
     USE_FAST = True  # if true use cumba's methods
     SPAWN_TYPE = "trajectory"
-    LIVE_PLOT = False
+    LIVE_PLOT = True
 
     mouse_type = "easy"
     model_type = "cart"

proj/model/config.py

@@ -26,6 +26,9 @@
 
     db_app = "D:\\Dropbox (UCL - SWC)\\Apps\\loco_upload"
 
+
+analysis_fld = str(Path(main_fld) / "analysis")
+
 # winstor paths
 winstor_main = Path("/nfs/winstor/branco/Federico/Locomotion/control/data")
 winstor_trial_cache = (

proj/paths.py

@@ -1,13 +1,93 @@
-from proj import Model
-from rich import print
-from sympy import latex
-from pylatexenc.latex2text import LatexNodes2Text
+# %%
+from pathlib import Path
+from rich.progress import track
+import numpy as np
+import pyinspect as pi
+import matplotlib.pyplot as plt
+import shutil
 
-model = Model()
-model.get_combined_dynamics_kinematics()
+from fcutils.maths.geometry import calc_distance_between_points_in_a_vector_2d
 
-g, inp, M = model.matrixes["g"], model.matrixes["inp"], model.matrixes["M"]
+from proj.paths import analysis_fld, db_app
+from proj.utils.misc import load_results_from_folder
+from proj.environment.trajectories import compute_trajectory_stats
 
-mod = g + M * inp
+from analysis.utils import check_two_conf_equal
 
-print(LatexNodes2Text().latex_to_text((latex(inp))))
+# %%
+# Load all data in DB upload folder
+flds = [f for f in Path(db_app).glob("*") if f.is_dir()]
+
+
+loaded = dict(trajectory=[], history=[], cost_history=[],)
+
+config = None
+
+for fld in track(flds):
+    _config, trajectory, history, cost_history = load_results_from_folder(fld)
+
+    # Check that all data have the same config
+    if config is None:
+        config = _config
+    else:
+        if not check_two_conf_equal(config, _config):
+            raise ValueError("Not all simulations have the same config")
+
+    # stash
+    loaded["trajectory"].append(trajectory)
+    loaded["history"].append(history)
+    loaded["cost_history"].append(cost_history)
+
+
+pi.ok("Data loaded")
+
+# %%
+# Copy all outcome plots in same folder
+outcomes_fld = Path(analysis_fld) / "outcomes"
+outcomes_fld.mkdir(exist_ok=True)
+
+for fld in track(flds):
+    src = list(fld.glob("outcome.png"))[0]
+    dst = outcomes_fld / (src.parent.name + "_outcome.png")
+
+    shutil.copy(src, dst)
+
+pi.ok("All outcome images copied", str(outcomes_fld))
+
+# %%
+"""
+    Look at trajectory length vs distance travelled
+"""
+
+traj_dist = []
+actual_dist = []
+
+for traj, hist in track(
+    zip(loaded["trajectory"], loaded["history"]),
+    total=len(loaded["trajectory"]),
+):
+    metad = compute_trajectory_stats(
+        traj, 1, config["trajectory"], config["planning"], mute=True
+    )[-1]
+    traj_dist.append(metad["distance_travelled"])
+
+    actual_dist.append(
+        np.sum(calc_distance_between_points_in_a_vector_2d(hist.x, hist.y))
+        + config["trajectory"]["dist_th"]
+    )
+
+# %%
+f, ax = plt.subplots(figsize=(12, 12))
+ax.scatter(
+    traj_dist,
+    actual_dist,
+    s=100,
+    color="g",
+    alpha=0.4,
+    lw=1,
+    ec=[0.3, 0.3, 0.3],
+)
+ax.plot([400, 1000], [400, 1000], lw=2, color=[0.6, 0.6, 0.6], zorder=-1)
+
+ax.set(xlabel="Trajectory length", ylabel="Distance travelled")
+# %%