From 5d0c0fa5af7aa90d318e0eceb4e0c4a60885a7e1 Mon Sep 17 00:00:00 2001
From: Thomas Kluyver <thomas.kluyver@xfel.eu>
Date: Mon, 9 Dec 2024 11:00:21 +0100
Subject: [PATCH] Apply suggestions from review
---
src/cal_tools/jungfrau/jungfrau_ff.py | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/src/cal_tools/jungfrau/jungfrau_ff.py b/src/cal_tools/jungfrau/jungfrau_ff.py
index 8af48745f..c4f6d33b7 100644
--- a/src/cal_tools/jungfrau/jungfrau_ff.py
+++ b/src/cal_tools/jungfrau/jungfrau_ff.py
@@ -68,8 +68,8 @@ def chunk_multi(data, block_size):
for j in range(0, cols, chunk_cols):
chunk = data[
...,
- i:min(i+chunk_rows, rows),
- j:min(j+chunk_cols, cols)
+ i: i+chunk_rows,
+ j: j+chunk_cols
]
chunks.append(chunk)
@@ -96,7 +96,7 @@ def fill_histogram(image_data, histogram_bins):
if not isinstance(image_data, np.ndarray):
raise TypeError("Expected imgs numpy ndarray type.")
- if image_data.ndim < 4:
+ if image_data.ndim != 4:
raise ValueError("Expected 4D imgs array.")
n_cells, n_rows, n_cols = image_data.shape[1:]
@@ -218,7 +218,7 @@ def rebin_histo(hist, bin_centers, rebin_factor):
x_out = bin_centers[::rebin_factor]
h_out = np.sum(
- hist[:len(hist) - (len(hist) % rebin_factor)].reshape(-1, rebin_factor), # noqa
+ hist.reshape(-1, rebin_factor),
axis=1,
)
x_out = bin_centers[::rebin_factor]
--
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