3 Commits
1.0.0 ... 1.0.1

Author SHA1 Message Date
Jakob Lass
c70d7759d0 Update of tutorial
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2025-08-21 13:23:53 +02:00
Jakob Lass
21fa774c95 Bump version 2025-08-21 11:43:49 +02:00
Jakob Lass
4f6e75915a Correct initialization of convergence loop 2025-08-21 11:41:15 +02:00
4 changed files with 48 additions and 44 deletions

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@@ -1,5 +1,5 @@
[build-system]
requires = ["setuptools >= 77.0.3"]
requires = ["setuptools"]# >= 77.0.3"]
build-backend = "setuptools.build_meta"
[tool.setuptools]
@@ -8,7 +8,7 @@ package-dir = {"" = "src"}
[project]
name = "AMBER-ds4ms"
version = "1.0.0"
version = "1.0.1"
dependencies = [
"numpy>=1.14",
"scipy>=1.7",
@@ -27,7 +27,7 @@ maintainers = [
]
description = "AMBER: Algorithm for Multiplexing spectrometer Background Estimation with Rotation-independence"
readme = "README.md"
license-files = ["LICENSE"]
#license-files = ["LICENSE"]
keywords = ["Machine Learning", "Signal Segmentation", "Background Determination"]
classifiers = [
"Development Status :: 4 - Beta",

View File

@@ -9,14 +9,15 @@
project = 'AMBER'
copyright = '2025, J. Lass, V. Cohen, B. B. Haro, & D. G. Mazzone'
author = 'J. Lass, V. Cohen, B. B. Haro, & D. G. Mazzone'
release = '1.0.0'
release = '1.0.1'
# -- General configuration ---------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
extensions = [
'nbsphinx', # allows notebooks
'myst_parser' # allows markdown
'myst_parser', # allows markdown
'IPython.sphinxext.ipython_console_highlighting',
]
source_suffix = {
'.rst': 'restructuredtext',
@@ -24,7 +25,7 @@ source_suffix = {
'.md': 'markdown',
}
templates_path = ['_templates']
exclude_patterns = []
exclude_patterns = ['**.ipynb_checkpoints']

File diff suppressed because one or more lines are too long

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@@ -613,11 +613,10 @@ class background():
# Loss function
loss = []#np.zeros(n_epochs, dtype=self.dtype)
old_loss = 2000000
new_loss = 1000000
old_loss = 0
new_loss = 0
k = 0
while (old_loss - new_loss > 1e-3) and (k < n_epochs):
while ((old_loss - new_loss > 1e-3) and (k < n_epochs)) or k < 2:
# Compute A = Y - B by filling the nans with 0s
A = np.where(np.isnan(Y_r - b_tmp) == True, 0.0, Y_r - b_tmp)