From 0cfdb6392bde05af789dca19eae6ad1846a776d2 Mon Sep 17 00:00:00 2001
From: Andi Gerken <andi.gerken@gmail.com>
Date: Mon, 14 Feb 2022 15:15:59 +0100
Subject: [PATCH] Fixed follow_iid plots. Fixed validation to allow np.nan
---
src/robofish/evaluate/evaluate.py | 24 +++++++++++++++++-------
src/robofish/io/validation.py | 9 ++++-----
2 files changed, 21 insertions(+), 12 deletions(-)
diff --git a/src/robofish/evaluate/evaluate.py b/src/robofish/evaluate/evaluate.py
index 7195278..7b83ee1 100644
--- a/src/robofish/evaluate/evaluate.py
+++ b/src/robofish/evaluate/evaluate.py
@@ -504,12 +504,20 @@ def evaluate_follow_iid(
calculate_follow(poses[i, :, 0:2], all_poses[j, :, 0:2])
)
file.close()
- follow.append(path_follow)
- iid.append(path_iid)
+ follow.append(np.array(path_follow))
+ iid.append(np.array(path_iid))
grids = []
worldBoundsX, worldBoundsY = max(worldBoundsX), max(worldBoundsY)
- maxDist = (worldBoundsX ** 2 + worldBoundsY ** 2) ** (1 / 2)
+
+ follow_flat = np.concatenate([f.flatten() for f in follow])
+ iid_flat = np.concatenate([i.flatten() for i in iid])
+
+ # Find the 0.5%/ 99.5% quantiles as min and max for the axis
+ follow_range = np.max(
+ [-1 * np.quantile(follow_flat, 0.005), np.quantile(follow_flat, 0.995)]
+ )
+ max_iid = np.quantile(iid_flat, 0.995)
for i in range(len(follow)):
follow_iid_data = pd.DataFrame(
@@ -524,10 +532,12 @@ def evaluate_follow_iid(
x="IID [cm]",
y="Follow",
data=follow_iid_data,
- linewidth=0,
- kind="scatter",
- xlim=(0, maxDist),
- ylim=(-5, 5),
+ kind="hist",
+ xlim=(0, max_iid),
+ ylim=(-follow_range, follow_range),
+ cbar=True,
+ legend=True,
+ joint_kws={"bins": 50},
)
grid.fig.set_figwidth(9)
grid.fig.set_figheight(6)
diff --git a/src/robofish/io/validation.py b/src/robofish/io/validation.py
index cd27713..d3013e3 100644
--- a/src/robofish/io/validation.py
+++ b/src/robofish/io/validation.py
@@ -304,6 +304,8 @@ def validate_positions_range(world_size, positions, e_name):
# positions which are just a bit over the world edge are fine
error_allowance = 1.01
+ positions = np.array([p for p in positions if not any(np.isnan(p))])
+
allowed_x = [
-1 * world_size[0] * error_allowance / 2,
world_size[0] * error_allowance / 2,
@@ -332,12 +334,9 @@ def validate_positions_range(world_size, positions, e_name):
def validate_orientations_length(orientations, e_name):
- ori_lengths = np.linalg.norm(orientations, axis=1)
+ orientations = np.array([o for o in orientations if not any(np.isnan(o))])
- # import matplotlib.pyplot as plt
- #
- # plt.plot(ori_lengths)
- # plt.show()
+ ori_lengths = np.linalg.norm(orientations, axis=1)
# Check if all orientation lengths are all 1. Different lengths cause warnings.
assert_validate(
--
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