956 lines
37 KiB
Python
956 lines
37 KiB
Python
"""
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modules/planarian_metrics.py
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Intégration des métriques EthoVision XT + comportementales dans PlanarianScanner.
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Métriques par frame :
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Mobilité : velocity, distance, moving, mobility_state
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Thigmo : dist_to_wall_mm, near_wall
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Photo : dist_to_light_mm, heading_to_light_deg, fleeing_light
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Chemo : dist_to_food_mm, heading_to_food_deg, approaching_food, in_food_zone
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Social : nearest_neighbour_mm, in_avoid_zone, in_aggreg_zone, chem_repulsion_level
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Métriques résumé (summary) :
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Mobilité : movedCenter_pointTotal_mm, velocity_mean_mm_s, durations par état
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Thigmo : thigmotaxis_pct_time_near_wall
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Photo : photo_pct_time_fleeing, photo_mean_dist_mm, photo_latency_s
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Chemo : chemo_pct_time_approaching, chemo_pct_time_in_zone,
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chemo_latency_s, chemo_mean_dist_mm
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Social : social_pct_time_avoiding, social_pct_time_aggregating,
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social_mean_nn_mm, social_contact_events
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Created on 25 avr. 2026
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@author: denis
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"""
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import csv
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import io
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import json
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import logging
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import math
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import os
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import time
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from datetime import datetime, timezone
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from typing import Optional
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Constantes EthoVision (seuils de mobilité par défaut)
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# ---------------------------------------------------------------------------
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THRESH_IMMOBILE_DEFAULT = 0.2 # en-dessous : Immobile (mm/s)
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THRESH_MOBILE_DEFAULT = 1.5 # entre les deux : Mobile, au-delà : Highly mobile
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STATE_IMMOBILE = "Immobile"
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STATE_MOBILE = "Mobile"
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STATE_HIGH_MOBILE = "Highly mobile"
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# Paramètres comportementaux (défauts)
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BEHAVIOUR_DEFAULTS = {
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# Thigmotactisme
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"thigmotaxis_wall_dist_mm": 1.0,
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# Phototactisme
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"photo_mode": "none",
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"photo_strength": 0.0,
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"photo_x": 0.5,
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"photo_y": 0.5,
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"photo_flee_angle_deg": 90.0, # angle max tête/source pour considérer "fuite"
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# Chimiotactisme
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"chemo_strength": 0.0,
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"chemo_x": 0.5,
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"chemo_y": 0.5,
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"chemo_radius_mm": 2.0,
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"chemo_approach_angle_deg": 90.0, # angle max tête/nourriture pour considérer "approche"
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# Interactions inter-individus
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"avoid_radius_mm": 3.0,
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"aggreg_radius_mm": 6.0,
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}
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# ---------------------------------------------------------------------------
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# Helpers géométriques
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# ---------------------------------------------------------------------------
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def _angle_between_deg(vx1: float, vy1: float, vx2: float, vy2: float) -> float:
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"""
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Calcule l'angle en degrés entre deux vecteurs 2D.
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Retourne 0.0 si l'un des vecteurs est nul.
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Args:
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vx1, vy1 : premier vecteur
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vx2, vy2 : second vecteur
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Returns:
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angle en degrés [0, 180]
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"""
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n1 = math.sqrt(vx1**2 + vy1**2)
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n2 = math.sqrt(vx2**2 + vy2**2)
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if n1 < 1e-9 or n2 < 1e-9:
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return 0.0
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cos_a = max(-1.0, min(1.0, (vx1 * vx2 + vy1 * vy2) / (n1 * n2)))
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return math.degrees(math.acos(cos_a))
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def _heading_to_target_deg(
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cx: float, cy: float,
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tx: float, ty: float,
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dx: float, dy: float,
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) -> float:
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"""
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Calcule l'angle entre la direction de déplacement et le vecteur vers une cible.
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Args:
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cx, cy : position courante
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tx, ty : position cible
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dx, dy : vecteur de déplacement (cx - prev_cx, cy - prev_cy)
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Returns:
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angle en degrés [0, 180] — 0 = va droit vers la cible, 180 = fuit
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"""
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to_target_x = tx - cx
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to_target_y = ty - cy
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return _angle_between_deg(dx, dy, to_target_x, to_target_y)
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# ---------------------------------------------------------------------------
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# Classe EthoVisionMetrics
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# ---------------------------------------------------------------------------
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class EthoVisionMetrics:
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"""
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Calcule et accumule toutes les métriques comportementales compatibles
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EthoVision XT à partir des données brutes de PlanarianTracker.
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Métriques calculées :
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- Mobilité EthoVision (distance, vitesse, états Immobile/Mobile/Très mobile)
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- Thigmotactisme (distance paroi, % temps près du bord)
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- Phototactisme (distance source, orientation, % fuite, latence)
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- Chimiotactisme (distance nourriture, % approche, % zone, latence)
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- Interactions inter-individus (voisin le plus proche, évitement,
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agrégation, répulsion chimique, événements de contact)
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Une instance par planaire suivi.
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Usage :
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metrics = EthoVisionMetrics(px_per_mm=26.25, fps=10, behaviour={...})
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for frame in capture:
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raw = tracker.process(frame, ts)
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record = metrics.update(
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raw,
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well_radius_mm = 8.0,
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arena_center_px = (250, 250),
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photo_source_px = (100, 100),
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others_pos_mm = [(x1,y1), (x2,y2)],
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chem_level = 0.3,
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)
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await client.store_metric(record, ...)
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summary = metrics.summary()
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"""
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def __init__(
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self,
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px_per_mm: float,
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fps: float,
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thresh_immobile: float = THRESH_IMMOBILE_DEFAULT,
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thresh_mobile: float = THRESH_MOBILE_DEFAULT,
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behaviour: Optional[dict] = None,
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):
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"""
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Args:
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px_per_mm : facteur de conversion pixels → mm
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fps : fréquence de capture (images/s)
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thresh_immobile : seuil vitesse Immobile/Mobile (mm/s)
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thresh_mobile : seuil vitesse Mobile/Très mobile (mm/s)
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behaviour : dict de paramètres comportementaux (cf. BEHAVIOUR_DEFAULTS)
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"""
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self.px_per_mm = px_per_mm
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self.fps = fps
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self.dt = 1.0 / fps
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self.thresh_immobile = thresh_immobile
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self.thresh_mobile = thresh_mobile
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self.beh = {**BEHAVIOUR_DEFAULTS, **(behaviour or {})}
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# --- Accumulateurs mobilité ---
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self.total_distance_mm = 0.0
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self.duration_moving_s = 0.0
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self.duration_stopped_s = 0.0
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self.frame_count = 0
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self._mob_counts = {STATE_IMMOBILE: 0, STATE_MOBILE: 0, STATE_HIGH_MOBILE: 0}
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self._mob_durations = {STATE_IMMOBILE: 0.0, STATE_MOBILE: 0.0, STATE_HIGH_MOBILE: 0.0}
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self._current_state = None
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# --- Accumulateurs thigmotactisme ---
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self._near_wall_frames = 0
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# --- Accumulateurs phototactisme ---
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self._flee_frames = 0 # frames en fuite
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self._photo_dist_sum = 0.0 # somme distances source
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self._photo_dist_count = 0
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self._photo_latency_s = None # temps avant 1ère fuite (s)
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# --- Accumulateurs chimiotactisme ---
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self._approach_frames = 0 # frames en approche nourriture
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self._in_zone_frames = 0 # frames dans la zone nourriture
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self._chemo_dist_sum = 0.0
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self._chemo_dist_count = 0
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self._chemo_latency_s = None # temps avant 1ère entrée zone (s)
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# --- Accumulateurs interactions inter-individus ---
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self._avoid_frames = 0 # frames en zone d'évitement
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self._aggreg_frames = 0 # frames en zone d'agrégation
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self._nn_sum = 0.0 # somme distances voisin le plus proche
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self._nn_count = 0
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self._contact_events = 0 # transitions False→True de in_avoid_zone
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self._prev_in_avoid = False
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# --- Position précédente (vecteur de déplacement) ---
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self._prev_cx_mm = None
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self._prev_cy_mm = None
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self._prev_ts = None
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# ------------------------------------------------------------------ #
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# Helpers internes
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# ------------------------------------------------------------------ #
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def _px_to_mm(self, px: float) -> float:
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"""Convertit des pixels en millimètres."""
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return px / self.px_per_mm
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def _classify(self, v: float) -> str:
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"""Classifie la vitesse en état de mobilité EthoVision."""
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if v <= self.thresh_immobile:
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return STATE_IMMOBILE
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elif v <= self.thresh_mobile:
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return STATE_MOBILE
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return STATE_HIGH_MOBILE
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def _elapsed_s(self) -> float:
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"""Temps écoulé depuis le début de la session (s)."""
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return self.frame_count * self.dt
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# ------------------------------------------------------------------ #
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# Méthode principale
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# ------------------------------------------------------------------ #
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def update(
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self,
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raw: dict,
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well_radius_mm: float = 8.0,
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arena_center_px: tuple = (250, 250),
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photo_source_px: Optional[tuple] = None,
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others_pos_mm: Optional[list] = None,
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chem_level: float = 0.0,
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) -> dict:
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"""
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Calcule toutes les métriques comportementales pour une frame.
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Args:
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raw : dict brut de PlanarianTracker.process()
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clés : detected, cx, cy, speed_px_s, ts
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well_radius_mm : rayon du puits en mm
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arena_center_px : centre de l'arène en pixels (cx, cy)
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photo_source_px : position de la source lumineuse en pixels (ou None)
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others_pos_mm : liste de (x_mm, y_mm) des autres planaires
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chem_level : concentration chimique locale [0-1] (depuis ChemicalMap)
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Returns:
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dict complet prêt pour ReductStore
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"""
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self.frame_count += 1
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ts = raw.get("timestamp", time.time())
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if not raw.get("detected", False):
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self.duration_stopped_s += self.dt
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state = self._current_state or STATE_IMMOBILE
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self._mob_durations[state] += self.dt
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return {"timestamp": ts, "detected": False}
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# --- Position en mm (relative au centre de l'arène) ---
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cx_px = raw["cx"] - arena_center_px[0]
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cy_px = raw["cy"] - arena_center_px[1]
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cx_mm = self._px_to_mm(cx_px)
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cy_mm = self._px_to_mm(cy_px)
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# --- Vitesse / distance ---
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speed_px_s = raw.get("speed_px_s", 0.0)
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velocity_mm_s = self._px_to_mm(speed_px_s)
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dist_mm = velocity_mm_s * self.dt
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self.total_distance_mm += dist_mm
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# Vecteur de déplacement (pour calculs d'angle)
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if self._prev_cx_mm is not None:
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move_dx = cx_mm - self._prev_cx_mm
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move_dy = cy_mm - self._prev_cy_mm
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else:
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move_dx, move_dy = 0.0, 0.0
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# --- Mobilité ---
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is_moving = velocity_mm_s > self.thresh_immobile
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if is_moving:
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self.duration_moving_s += self.dt
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else:
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self.duration_stopped_s += self.dt
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new_state = self._classify(velocity_mm_s)
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if new_state != self._current_state:
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self._mob_counts[new_state] += 1
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self._current_state = new_state
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self._mob_durations[new_state] += self.dt
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# ================================================================
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# THIGMOTACTISME
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# ================================================================
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well_radius_px = well_radius_mm * self.px_per_mm
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dist_center_px = math.sqrt(cx_px**2 + cy_px**2)
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dist_wall_mm = self._px_to_mm(well_radius_px - dist_center_px)
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near_wall_thr = self.beh.get("thigmotaxis_wall_dist_mm", 1.0)
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is_near_wall = dist_wall_mm < near_wall_thr
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if is_near_wall:
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self._near_wall_frames += 1
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# ================================================================
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# PHOTOTACTISME
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# ================================================================
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photo_mode = self.beh.get("photo_mode", "none")
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dist_light_mm = 0.0
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heading_light_deg = 0.0
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fleeing_light = False
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if photo_mode != "none" and photo_source_px is not None:
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lx_px = photo_source_px[0] - arena_center_px[0]
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ly_px = photo_source_px[1] - arena_center_px[1]
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lx_mm = self._px_to_mm(lx_px)
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ly_mm = self._px_to_mm(ly_px)
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dl = math.sqrt((cx_mm - lx_mm)**2 + (cy_mm - ly_mm)**2)
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dist_light_mm = dl
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self._photo_dist_sum += dl
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self._photo_dist_count += 1
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# Angle entre déplacement et direction vers la source
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heading_light_deg = _heading_to_target_deg(
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cx_mm, cy_mm, lx_mm, ly_mm, move_dx, move_dy
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)
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# Fuite = planaire s'éloigne de la source (angle > seuil)
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flee_thr = self.beh.get("photo_flee_angle_deg", 90.0)
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fleeing_light = (heading_light_deg > flee_thr) and is_moving
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if fleeing_light:
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self._flee_frames += 1
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if self._photo_latency_s is None:
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self._photo_latency_s = self._elapsed_s()
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# ================================================================
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# CHIMIOTACTISME
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# ================================================================
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chemo_x_frac = self.beh.get("chemo_x", 0.5)
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chemo_y_frac = self.beh.get("chemo_y", 0.5)
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chemo_r_mm = self.beh.get("chemo_radius_mm", 2.0)
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chemo_strength= self.beh.get("chemo_strength", 0.0)
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dist_food_mm = 0.0
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heading_food_deg = 0.0
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approaching_food = False
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in_food_zone = False
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if chemo_strength > 0.0:
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# Position nourriture en mm relative au centre
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fx_mm = (chemo_x_frac - 0.5) * 2.0 * well_radius_mm
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fy_mm = (chemo_y_frac - 0.5) * 2.0 * well_radius_mm
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df = math.sqrt((cx_mm - fx_mm)**2 + (cy_mm - fy_mm)**2)
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dist_food_mm = df
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self._chemo_dist_sum += df
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self._chemo_dist_count += 1
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in_food_zone = df <= chemo_r_mm
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if in_food_zone:
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self._in_zone_frames += 1
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if self._chemo_latency_s is None:
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self._chemo_latency_s = self._elapsed_s()
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heading_food_deg = _heading_to_target_deg(
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cx_mm, cy_mm, fx_mm, fy_mm, move_dx, move_dy
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)
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approach_thr = self.beh.get("chemo_approach_angle_deg", 90.0)
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approaching_food = (heading_food_deg < approach_thr) and is_moving
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if approaching_food:
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self._approach_frames += 1
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# ================================================================
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# INTERACTIONS INTER-INDIVIDUS
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# ================================================================
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avoid_r_mm = self.beh.get("avoid_radius_mm", 3.0)
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aggreg_r_mm = self.beh.get("aggreg_radius_mm", 6.0)
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nearest_nn_mm = float("inf")
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in_avoid_zone = False
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in_aggreg_zone = False
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if others_pos_mm:
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for ox_mm, oy_mm in others_pos_mm:
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d = math.sqrt((cx_mm - ox_mm)**2 + (cy_mm - oy_mm)**2)
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if d < nearest_nn_mm:
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nearest_nn_mm = d
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if nearest_nn_mm < avoid_r_mm:
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in_avoid_zone = True
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self._avoid_frames += 1
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elif nearest_nn_mm < aggreg_r_mm:
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in_aggreg_zone = True
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self._aggreg_frames += 1
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self._nn_sum += nearest_nn_mm
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self._nn_count += 1
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# Événement de contact : transition vers zone d'évitement
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if in_avoid_zone and not self._prev_in_avoid:
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self._contact_events += 1
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else:
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nearest_nn_mm = 0.0
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self._prev_in_avoid = in_avoid_zone
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# --- Mise à jour position précédente ---
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self._prev_cx_mm = cx_mm
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self._prev_cy_mm = cy_mm
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self._prev_ts = ts
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# ================================================================
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# RECORD COMPLET
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# ================================================================
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return {
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# Identification
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"timestamp": ts,
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"detected": True,
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# Position (mm, relative au centre)
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"x_mm": round(cx_mm, 4),
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"y_mm": round(cy_mm, 4),
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# Position brute pixels
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"cx_px": raw["cx"],
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"cy_px": raw["cy"],
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# Mobilité EthoVision
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"velocity_mm_s": round(velocity_mm_s, 4),
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"distance_mm": round(dist_mm, 4),
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"total_distance_mm": round(self.total_distance_mm, 4),
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"moving": int(is_moving),
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"duration_moving_s": round(self.duration_moving_s, 3),
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"duration_stopped_s": round(self.duration_stopped_s, 3),
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"mobility_state": new_state,
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"mobility_immobile_freq": self._mob_counts[STATE_IMMOBILE],
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"mobility_immobile_duration_s": round(self._mob_durations[STATE_IMMOBILE], 3),
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"mobility_mobile_freq": self._mob_counts[STATE_MOBILE],
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"mobility_mobile_duration_s": round(self._mob_durations[STATE_MOBILE], 3),
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"mobility_high_mobile_freq": self._mob_counts[STATE_HIGH_MOBILE],
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"mobility_high_mobile_duration_s": round(self._mob_durations[STATE_HIGH_MOBILE], 3),
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# Thigmotactisme
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"dist_to_wall_mm": round(dist_wall_mm, 4),
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"near_wall": int(is_near_wall),
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# Phototactisme
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"dist_to_light_mm": round(dist_light_mm, 4),
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"heading_to_light_deg": round(heading_light_deg, 2),
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"fleeing_light": int(fleeing_light),
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# Chimiotactisme
|
|
"dist_to_food_mm": round(dist_food_mm, 4),
|
|
"heading_to_food_deg": round(heading_food_deg, 2),
|
|
"approaching_food": int(approaching_food),
|
|
"in_food_zone": int(in_food_zone),
|
|
# Interactions inter-individus
|
|
"nearest_neighbour_mm": round(nearest_nn_mm, 4) if nearest_nn_mm != float("inf") else 0.0,
|
|
"in_avoid_zone": int(in_avoid_zone),
|
|
"in_aggreg_zone": int(in_aggreg_zone),
|
|
"chem_repulsion_level": round(chem_level, 4),
|
|
# Passthrough tracker
|
|
"area_px": raw.get("area_px", 0),
|
|
"axial_pos": raw.get("axial_pos", 0.0),
|
|
"axial_speed": raw.get("axial_speed", 0.0),
|
|
}
|
|
|
|
# ------------------------------------------------------------------ #
|
|
# Résumé de session
|
|
# ------------------------------------------------------------------ #
|
|
|
|
def summary(self) -> dict:
|
|
"""
|
|
Retourne le résumé global de la session.
|
|
Nomenclature EthoVision XT + métriques comportementales.
|
|
À appeler en fin d'expérience.
|
|
|
|
Returns:
|
|
dict avec toutes les métriques agrégées
|
|
"""
|
|
total_s = self.frame_count * self.dt
|
|
det = max(self._photo_dist_count, 1) # frames avec détection
|
|
|
|
return {
|
|
# Identification session
|
|
"total_frames": self.frame_count,
|
|
"total_duration_s": round(total_s, 3),
|
|
# --- Mobilité EthoVision ---
|
|
"movedCenter_pointTotal_mm": round(self.total_distance_mm, 4),
|
|
"velocity_mean_mm_s": round(
|
|
self.total_distance_mm / total_s if total_s > 0 else 0.0, 4),
|
|
"movement_moving_duration_s": round(self.duration_moving_s, 3),
|
|
"movement_not_moving_duration_s": round(self.duration_stopped_s, 3),
|
|
"mobility_immobile_frequency": self._mob_counts[STATE_IMMOBILE],
|
|
"mobility_immobile_duration_s": round(self._mob_durations[STATE_IMMOBILE], 3),
|
|
"mobility_mobile_frequency": self._mob_counts[STATE_MOBILE],
|
|
"mobility_mobile_duration_s": round(self._mob_durations[STATE_MOBILE], 3),
|
|
"mobility_highly_mobile_frequency": self._mob_counts[STATE_HIGH_MOBILE],
|
|
"mobility_highly_mobile_duration_s": round(self._mob_durations[STATE_HIGH_MOBILE], 3),
|
|
# --- Thigmotactisme ---
|
|
"thigmotaxis_pct_time_near_wall": round(
|
|
100.0 * self._near_wall_frames / max(self.frame_count, 1), 2),
|
|
# --- Phototactisme ---
|
|
"photo_pct_time_fleeing": round(
|
|
100.0 * self._flee_frames / max(self.frame_count, 1), 2),
|
|
"photo_mean_dist_mm": round(
|
|
self._photo_dist_sum / max(self._photo_dist_count, 1), 4),
|
|
"photo_latency_s": round(self._photo_latency_s, 3)
|
|
if self._photo_latency_s is not None else None,
|
|
# --- Chimiotactisme ---
|
|
"chemo_pct_time_approaching": round(
|
|
100.0 * self._approach_frames / max(self.frame_count, 1), 2),
|
|
"chemo_pct_time_in_zone": round(
|
|
100.0 * self._in_zone_frames / max(self.frame_count, 1), 2),
|
|
"chemo_latency_s": round(self._chemo_latency_s, 3)
|
|
if self._chemo_latency_s is not None else None,
|
|
"chemo_mean_dist_mm": round(
|
|
self._chemo_dist_sum / max(self._chemo_dist_count, 1), 4),
|
|
# --- Interactions inter-individus ---
|
|
"social_pct_time_avoiding": round(
|
|
100.0 * self._avoid_frames / max(self.frame_count, 1), 2),
|
|
"social_pct_time_aggregating": round(
|
|
100.0 * self._aggreg_frames / max(self.frame_count, 1), 2),
|
|
"social_mean_nn_mm": round(
|
|
self._nn_sum / max(self._nn_count, 1), 4),
|
|
"social_contact_events": self._contact_events,
|
|
}
|
|
|
|
def reset(self):
|
|
"""Réinitialise tous les accumulateurs (changement de puits ou planaire)."""
|
|
self.__init__(
|
|
self.px_per_mm, self.fps,
|
|
self.thresh_immobile, self.thresh_mobile, self.beh,
|
|
)
|
|
|
|
@staticmethod
|
|
def _empty_record(ts: float) -> dict:
|
|
"""Enregistrement vide (planaire non détecté)."""
|
|
return {"timestamp": ts, "detected": False}
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Paramètres expérimentaux
|
|
# ---------------------------------------------------------------------------
|
|
|
|
class ExperimentParams:
|
|
"""
|
|
Conteneur des paramètres d'une expérience.
|
|
Instanciable depuis un dict, un fichier CSV ou un modèle Django.
|
|
"""
|
|
|
|
REQUIRED = {"experiment", "well", "px_per_mm", "fps"}
|
|
|
|
DEFAULTS = {
|
|
"well_radius_mm": 8.0,
|
|
"thresh_immobile": THRESH_IMMOBILE_DEFAULT,
|
|
"thresh_mobile": THRESH_MOBILE_DEFAULT,
|
|
"planarian_count": 1,
|
|
"tube_axis": "vertical",
|
|
"min_area_px": 20,
|
|
"max_area_ratio": 0.10,
|
|
**BEHAVIOUR_DEFAULTS,
|
|
}
|
|
|
|
def __init__(self, data: dict):
|
|
missing = self.REQUIRED - set(data.keys())
|
|
if missing:
|
|
raise ValueError(f"Paramètres manquants : {missing}")
|
|
merged = {**self.DEFAULTS, **data}
|
|
for k, v in merged.items():
|
|
setattr(self, k, self._cast(k, v))
|
|
|
|
@staticmethod
|
|
def _cast(key: str, value):
|
|
"""Cast automatique des valeurs CSV (toutes en string) vers le bon type."""
|
|
float_keys = {
|
|
"px_per_mm", "fps", "well_radius_mm", "thresh_immobile", "thresh_mobile",
|
|
"photo_strength", "photo_x", "photo_y", "photo_flee_angle_deg",
|
|
"chemo_strength", "chemo_x", "chemo_y", "chemo_radius_mm",
|
|
"chemo_approach_angle_deg", "thigmotaxis_wall_dist_mm",
|
|
"avoid_radius_mm", "aggreg_radius_mm", "max_area_ratio",
|
|
}
|
|
int_keys = {"planarian_count", "min_area_px"}
|
|
if key in float_keys:
|
|
return float(value)
|
|
if key in int_keys:
|
|
return int(value)
|
|
if isinstance(value, str) and value.lower() in ("true", "false"):
|
|
return value.lower() == "true"
|
|
return value
|
|
|
|
@classmethod
|
|
def from_csv_row(cls, row: dict) -> "ExperimentParams":
|
|
"""Instancie depuis une ligne de csv.DictReader."""
|
|
return cls(row)
|
|
|
|
@classmethod
|
|
def from_csv_file(cls, filepath: str) -> list:
|
|
"""Charge toutes les expériences d'un fichier CSV."""
|
|
results = []
|
|
with open(filepath, newline="", encoding="utf-8") as f:
|
|
for row in csv.DictReader(f):
|
|
try:
|
|
results.append(cls.from_csv_row(row))
|
|
except ValueError as e:
|
|
logger.warning(f"Ligne ignorée : {e} — {row}")
|
|
return results
|
|
|
|
def to_dict(self) -> dict:
|
|
"""Sérialise en dict."""
|
|
return {k: getattr(self, k)
|
|
for k in {**self.DEFAULTS, **{r: None for r in self.REQUIRED}}}
|
|
|
|
def build_metrics(self) -> EthoVisionMetrics:
|
|
"""Construit l'instance EthoVisionMetrics pour ces paramètres."""
|
|
behaviour = {k: getattr(self, k) for k in BEHAVIOUR_DEFAULTS if hasattr(self, k)}
|
|
return EthoVisionMetrics(
|
|
px_per_mm = self.px_per_mm,
|
|
fps = self.fps,
|
|
thresh_immobile = self.thresh_immobile,
|
|
thresh_mobile = self.thresh_mobile,
|
|
behaviour = behaviour,
|
|
)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Client ReductStore
|
|
# ---------------------------------------------------------------------------
|
|
|
|
class ReductStoreClient:
|
|
"""
|
|
Interface asynchrone avec ReductStore pour PlanarianScanner.
|
|
|
|
Labels : experiment | well | planarian | record_type (frame|summary)
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
url: str = "http://localhost:8383",
|
|
token: str = "",
|
|
bucket: str = "planarian_metrics",
|
|
quota_type=None,
|
|
quota_size=1000_000_000
|
|
):
|
|
self.url = url
|
|
self.token = token
|
|
self.bucket_name = bucket
|
|
self.quota_type = quota_type
|
|
self.quota_size = quota_size
|
|
self._client = None
|
|
self._bucket = None
|
|
|
|
|
|
async def _create_bucket(self):
|
|
from reduct import Client, BucketSettings
|
|
self._client = Client(self.url, api_token=self.token)
|
|
settings = BucketSettings(
|
|
quota_type=self.quota_type,
|
|
quota_size=self.quota_size,
|
|
exist_ok=True,
|
|
)
|
|
return await self._client.create_bucket(self.bucket_name, settings, exist_ok=True)
|
|
|
|
async def connect(self):
|
|
"""Initialise la connexion et crée le bucket si nécessaire."""
|
|
self._bucket = await self._create_bucket()
|
|
logger.info(f"ReductStore connecté : {self.url} / {self.bucket_name}")
|
|
|
|
'''
|
|
async def connect(self):
|
|
"""Initialise la connexion et crée le bucket si nécessaire."""
|
|
from reduct import Client, BucketSettings, QuotaType
|
|
self._client = Client(self.url, api_token=self.token)
|
|
self._bucket = await self._client.create_bucket(
|
|
self.bucket_name,
|
|
BucketSettings(quota_type=QuotaType.NONE),
|
|
exist_ok=True,
|
|
)
|
|
logger.info(f"ReductStore connecté : {self.url} / {self.bucket_name}")
|
|
'''
|
|
|
|
async def store_metric(self, record: dict, experiment: str, well: str, uuid: str,
|
|
planarian: int = 0, record_type: str = "frame", ts_us: Optional[int] = None, ):
|
|
"""
|
|
Stocke un enregistrement dans ReductStore.
|
|
|
|
Le timestamp est rendu unique par planaire en ajoutant l'index
|
|
du planaire comme offset sub-microseconde — évite le 409 Conflict
|
|
quand plusieurs planaires du même puits écrivent dans la même frame.
|
|
|
|
Args:
|
|
uuid : identifiant unique de session (permet de filtrer
|
|
plusieurs sessions d'un même puits/expérience)
|
|
record : dict de métriques (issu de EthoVisionMetrics.update())
|
|
experiment : identifiant de l'expérience
|
|
well : identifiant du puits
|
|
planarian : index du planaire (défaut 0)
|
|
record_type : "frame" ou "summary"
|
|
ts_us : timestamp en microsecondes (défaut : maintenant)
|
|
"""
|
|
if self._bucket is None:
|
|
await self.connect()
|
|
|
|
# ts_us de base + offset planaire (0, 1, 2…) pour unicité garantie
|
|
base_ts = ts_us or int(time.time() * 1_000_000)
|
|
unique_ts = base_ts + planarian
|
|
labels = {
|
|
"experiment": experiment,
|
|
"well": well,
|
|
"planarian": str(planarian),
|
|
"record_type": record_type,
|
|
}
|
|
"""
|
|
if uuid:
|
|
labels["uuid"] = uuid"""
|
|
|
|
await self._bucket.write(
|
|
#entry_name = "metrics",
|
|
entry_name = uuid,
|
|
data = json.dumps(record).encode("utf-8"),
|
|
timestamp = unique_ts,
|
|
labels = labels,
|
|
content_type = "application/json",
|
|
)
|
|
|
|
async def store_summary(
|
|
self,
|
|
summary: dict,
|
|
experiment: str,
|
|
well: str,
|
|
uuid: str,
|
|
planarian: int = 0,
|
|
record_type: str = "summary",
|
|
):
|
|
"""
|
|
Stocke le résumé de fin de session dans ReductStore.
|
|
|
|
Args:
|
|
summary : dict issu de EthoVisionMetrics.summary()
|
|
experiment : identifiant de l'expérience
|
|
well : identifiant du puits
|
|
uuid : identifiant unique de session (même valeur que
|
|
celle utilisée dans store_metric pour cette session)
|
|
planarian : index du planaire
|
|
"""
|
|
await self.store_metric(record=summary,experiment=experiment,
|
|
well=well, uuid=uuid, planarian=planarian, record_type=record_type)
|
|
|
|
async def get_tracking_data(
|
|
self,
|
|
experiment: str,
|
|
well: str,
|
|
uuid: str,
|
|
planarian: int = 0,
|
|
record_type: str = "frame",
|
|
start: Optional[datetime] = None,
|
|
stop: Optional[datetime] = None,
|
|
) -> list:
|
|
"""
|
|
Récupère les enregistrements filtrés par labels.
|
|
|
|
Args:
|
|
experiment : identifiant de l'expérience
|
|
well : identifiant du puits
|
|
uuid : filtre sur une session spécifique (optionnel —
|
|
si vide, retourne toutes les sessions)
|
|
planarian : index du planaire
|
|
record_type : "frame" | "summary"
|
|
start, stop : plage temporelle (datetime UTC, optionnel)
|
|
"""
|
|
if self._bucket is None:
|
|
await self.connect()
|
|
|
|
when = {
|
|
"$and": [
|
|
#{"&experiment": {"$contains": experiment}},
|
|
#{"&well": {"$contains": well}},
|
|
{"&planarian": {"$contains": str(planarian)}},
|
|
{"&record_type": {"$contains": record_type}},
|
|
]
|
|
}
|
|
kwargs = {'when': when}
|
|
if start:
|
|
kwargs["start"] = int(start.timestamp() * 1_000_000)
|
|
if stop:
|
|
kwargs["stop"] = int(stop.timestamp() * 1_000_000)
|
|
|
|
records = []
|
|
async for rec in self._bucket.query(uuid, **kwargs):
|
|
try:
|
|
records.append(json.loads(await rec.read_all()))
|
|
except Exception as e:
|
|
logger.warning(f"Entrée illisible ignorée : {e}")
|
|
return records
|
|
|
|
@staticmethod
|
|
def _convert_timestamps(records: list) -> list:
|
|
"""
|
|
Convertit le champ 'timestamp' (epoch float secondes) en ISO 8601 UTC
|
|
dans chaque enregistrement.
|
|
|
|
Args:
|
|
records : liste de dicts issus de ReductStore
|
|
|
|
Returns:
|
|
nouvelle liste avec timestamp converti (originaux non modifiés)
|
|
"""
|
|
converted = []
|
|
for r in records:
|
|
row = dict(r)
|
|
ts = row.get("timestamp")
|
|
if ts is not None:
|
|
try:
|
|
row["timestamp"] = (
|
|
datetime.fromtimestamp(float(ts), tz=timezone.utc)
|
|
.strftime("%Y-%m-%dT%H:%M:%S.%f") + "Z"
|
|
)
|
|
except (ValueError, TypeError, OSError):
|
|
pass
|
|
converted.append(row)
|
|
return converted
|
|
|
|
@staticmethod
|
|
def _build_filepath(output_dir: str, experiment: str,
|
|
well: str, planarian: int, record_type: str) -> str:
|
|
"""
|
|
Construit le chemin du fichier CSV de sortie.
|
|
Nom : <experiment>_<well>_planaire<NN>_<record_type>.csv
|
|
|
|
Args:
|
|
output_dir : répertoire de sortie (créé si absent)
|
|
experiment : identifiant de l'expérience
|
|
well : identifiant du puits
|
|
planarian : index du planaire
|
|
record_type : "frame" ou "summary"
|
|
|
|
Returns:
|
|
chemin absolu du fichier CSV
|
|
"""
|
|
dirpath = os.path.abspath(output_dir)
|
|
os.makedirs(dirpath, exist_ok=True)
|
|
filename = f"{experiment}_{well}_planaire-{planarian}-{record_type}.csv"
|
|
return os.path.join(dirpath, filename)
|
|
|
|
async def export_csv(
|
|
self,
|
|
experiment: str,
|
|
well: str,
|
|
uuid: str,
|
|
planarian: int = 0,
|
|
record_type: str = "frame",
|
|
output_dir: str = ".",
|
|
start: Optional[datetime] = None,
|
|
stop: Optional[datetime] = None,
|
|
) -> tuple:
|
|
"""
|
|
Exporte les données depuis ReductStore vers un fichier CSV.
|
|
Le répertoire de sortie est choisi via output_dir.
|
|
Le champ timestamp est converti en ISO 8601 UTC.
|
|
|
|
Args:
|
|
experiment : identifiant de l'expérience
|
|
well : identifiant du puits
|
|
uuid : filtre sur une session spécifique (optionnel —
|
|
si vide, retourne toutes les sessions)
|
|
planarian : index du planaire
|
|
record_type : "frame" | "summary"
|
|
output_dir : répertoire de sortie (défaut : répertoire courant)
|
|
start, stop : plage temporelle (datetime UTC, optionnel)
|
|
|
|
Returns:
|
|
tuple (filepath, nb_lignes)
|
|
"""
|
|
|
|
# record, experiment, well, uuid,
|
|
records = await self.get_tracking_data(
|
|
experiment,
|
|
well,
|
|
uuid,
|
|
planarian=planarian,
|
|
record_type=record_type,
|
|
start=start,
|
|
stop=stop,
|
|
)
|
|
|
|
if not records:
|
|
logger.warning(f"Aucune donnée pour {experiment}/{well}/{planarian}")
|
|
return "", 0
|
|
|
|
records = self._convert_timestamps(records)
|
|
filepath = self._build_filepath(output_dir, experiment, well, planarian, record_type)
|
|
fieldnames = list(dict.fromkeys(k for r in records for k in r.keys()))
|
|
|
|
with open(filepath, "w", newline="", encoding="utf-8") as f:
|
|
writer = csv.DictWriter(f, fieldnames=fieldnames, extrasaction="ignore")
|
|
writer.writeheader()
|
|
writer.writerows(records)
|
|
|
|
logger.info(f"Export CSV : {len(records)} lignes → {filepath}")
|
|
return filepath, len(records)
|
|
|
|
async def export_csv_response(
|
|
self,
|
|
experiment: str,
|
|
well: str,
|
|
uuid: str,
|
|
planarian: int = 0,
|
|
record_type: str = "frame",
|
|
start: Optional[datetime] = None,
|
|
stop: Optional[datetime] = None,
|
|
) -> tuple:
|
|
"""
|
|
Génère le contenu CSV en mémoire (pour réponse HTTP Django).
|
|
Le champ timestamp est converti en ISO 8601 UTC.
|
|
|
|
Returns:
|
|
tuple (contenu_csv_str, nb_lignes)
|
|
"""
|
|
records = await self.get_tracking_data(
|
|
experiment,
|
|
well,
|
|
uuid,
|
|
planarian=planarian,
|
|
record_type=record_type,
|
|
start=start,
|
|
stop=stop,
|
|
)
|
|
|
|
if not records:
|
|
return "", 0
|
|
records = self._convert_timestamps(records)
|
|
fieldnames = list(dict.fromkeys(k for r in records for k in r.keys()))
|
|
out = io.StringIO()
|
|
writer = csv.DictWriter(out, fieldnames=fieldnames, extrasaction="ignore")
|
|
writer.writeheader()
|
|
writer.writerows(records)
|
|
return out.getvalue(), len(records)
|
|
|
|
async def close(self):
|
|
"""
|
|
Ferme la connexion ReductStore.
|
|
Note : reduct-py >= 1.x ne nécessite pas de fermeture explicite —
|
|
la méthode est conservée pour compatibilité d'interface.
|
|
"""
|
|
self._client = None
|
|
self._bucket = None
|
|
logger.info("ReductStore déconnecté")
|