From d84f7578631fff7f10f170aca85960b8fd3cd62a Mon Sep 17 00:00:00 2001
From: mschwoerer <82171591+mschwoer@users.noreply.github.com>
Date: Wed, 7 Aug 2024 12:58:51 +0200
Subject: [PATCH] #296: set tqdm log minimum to 5 seconds

---
 alphadia/fdrexperimental.py        | 4 ++--
 alphadia/outputaccumulator.py      | 3 ++-
 alphadia/transferlearning/train.py | 2 +-
 3 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/alphadia/fdrexperimental.py b/alphadia/fdrexperimental.py
index c8d4cc20..901258eb 100644
--- a/alphadia/fdrexperimental.py
+++ b/alphadia/fdrexperimental.py
@@ -368,7 +368,7 @@ def fit(self, x: np.ndarray, y: np.ndarray):
         y_test = torch.from_numpy(y_test).float().to(self.device)
 
         batch_count = 0
-        for epoch in tqdm(range(self.epochs)):
+        for epoch in tqdm(range(self.epochs), mininterval=5):
             train_loss_sum = 0.0
             test_loss_sum = 0.0
 
@@ -1122,7 +1122,7 @@ def fit(self, x: np.ndarray, y: np.ndarray):
 
         batch_count = 0
 
-        for epoch in tqdm(range(self.epochs)):
+        for epoch in tqdm(range(self.epochs), mininterval=5):
             # shuffle batches
             order = np.random.permutation(num_batches)
             batch_start_list = batch_start_list[order]
diff --git a/alphadia/outputaccumulator.py b/alphadia/outputaccumulator.py
index 4d0ef135..63010c63 100644
--- a/alphadia/outputaccumulator.py
+++ b/alphadia/outputaccumulator.py
@@ -553,7 +553,8 @@ def ms2_quality_control(
                 precursor_df["frag_stop_idx"],
                 strict=True,
             )
-        )
+        ),
+        mininterval=5,
     ):
         # get XIC correlations and intensities for the precursor
         fragment_correlation_view = fragment_correlation_df.iloc[start_idx:stop_idx]
diff --git a/alphadia/transferlearning/train.py b/alphadia/transferlearning/train.py
index 69eaeed7..343be3a2 100644
--- a/alphadia/transferlearning/train.py
+++ b/alphadia/transferlearning/train.py
@@ -287,7 +287,7 @@ def _order_intensities(
             The reordered fragment intensity dataframe.
         """
         reordered = unordered_frag_df.copy()
-        for i in tqdm(range(len(reordered_precursor_df))):
+        for i in tqdm(range(len(reordered_precursor_df)), mininterval=5):
             new_start_idx = reordered_precursor_df.iloc[i]["frag_start_idx"]
             new_end_idx = reordered_precursor_df.iloc[i]["frag_stop_idx"]