From bf102cc178f84bbdf31d0f42723332bfd01d35e9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Leon=20Michel=20Gori=C3=9Fen?=
<leon.gorissen@llt.rwth-aachen.de>
Date: Fri, 26 Jul 2024 16:43:14 +0200
Subject: [PATCH] delete some unnecessary logging info
---
.../dynamics_learning/testing/__init__.py | 12 ++----------
1 file changed, 2 insertions(+), 10 deletions(-)
diff --git a/dynamics_learning/dynamics_learning/testing/__init__.py b/dynamics_learning/dynamics_learning/testing/__init__.py
index d8f99de..a498370 100644
--- a/dynamics_learning/dynamics_learning/testing/__init__.py
+++ b/dynamics_learning/dynamics_learning/testing/__init__.py
@@ -137,18 +137,13 @@ class Dataset:
if date == "20240719_141438":
continue
dates.append(date)
-
- logger.info(dates)
- self.most_recent_file = max(
- dates
- ) # FIXME this is not returning the most recent file
+ self.most_recent_file = max(dates)
logger.info(f"Most recent file is set to {self.most_recent_file}.")
return self.most_recent_file
def _delete_files(self, files, most_recent_file: None):
logger.info(f"Most recent file is {most_recent_file}.")
for file in files:
- logger.info(f"Checking {file.name}.")
if most_recent_file:
if most_recent_file not in file.name:
local_file_path = Path(self.local_path + "/" + file.name)
@@ -190,7 +185,6 @@ class Dataset:
for file in files
if file.metadata_form()["Robot UUID"][0] == ROBOT_UUID
]
- logger.info(f"Filtered files: {filtered_files}")
if filtered_files == []:
logger.critical("No files found with given robot_uuid.")
sys.exit(1)
@@ -201,7 +195,6 @@ class Dataset:
for file in files
if file.metadata_form()["Robot UUID"][0] != ROBOT_UUID
]
- logger.info(f"Files to delete: {delete_files}")
if filtered_files:
# check for the most recent file
@@ -211,7 +204,6 @@ class Dataset:
# delete all files with wrong robot_uuid
if delete_files:
- logger.log("I think this needs fixing")
self._delete_files(delete_files, None)
return self.analysis_file
@@ -442,7 +434,7 @@ LSTM Implementation Output: Concluded
color=rwth_style.green,
)
- axs[joint_number // num_col, joint_number % num_col].scatter( # FIXME
+ axs[joint_number // num_col, joint_number % num_col].scatter(
t_meas[window_size - 1 :],
y_pred[:, joint_number],
label="Prediction based on command trajectory (NN)",
--
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