From 1f5f22af6a4a9e31d3f837341f55b05a058d9667 Mon Sep 17 00:00:00 2001
From: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de>
Date: Wed, 21 Dec 2022 12:43:44 +0100
Subject: [PATCH] Using all channels by default.
---
pes_to_spec/model.py | 15 ++++++---------
pes_to_spec/test/offline_analysis.py | 16 +++++++++-------
2 files changed, 15 insertions(+), 16 deletions(-)
diff --git a/pes_to_spec/model.py b/pes_to_spec/model.py
index aac55a5..4395b0c 100644
--- a/pes_to_spec/model.py
+++ b/pes_to_spec/model.py
@@ -8,6 +8,7 @@ from scipy.optimize import fmin_l_bfgs_b
from sklearn.decomposition import PCA, IncrementalPCA
from sklearn.model_selection import train_test_split
from sklearn.base import TransformerMixin, BaseEstimator
+from itertools import product
import matplotlib.pyplot as plt
@@ -124,17 +125,13 @@ class Model(TransformerMixin, BaseEstimator):
"""
def __init__(self,
- channels:List[str]=["channel_1_D",
- "channel_2_B",
- "channel_3_A",
- "channel_3_B",
- "channel_4_C",
- "channel_4_D"],
- n_pca_lr: int=400,
+ channels:List[str]=[f"channel_{j}_{k}"
+ for j, k in product(range(1, 5), ["A", "B", "C", "D"])],
+ n_pca_lr: int=600,
n_pca_hr: int=20,
high_res_sigma: float=0.2,
- tof_start: Optional[int]=31445,
- delta_tof: Optional[int]=200,
+ tof_start: Optional[int]=None,
+ delta_tof: Optional[int]=300,
validation_size: float=0.05):
self.channels = channels
self.n_pca_lr = n_pca_lr
diff --git a/pes_to_spec/test/offline_analysis.py b/pes_to_spec/test/offline_analysis.py
index 62fb6b3..c3a5668 100755
--- a/pes_to_spec/test/offline_analysis.py
+++ b/pes_to_spec/test/offline_analysis.py
@@ -109,17 +109,19 @@ def main():
#retvol_raw = run["SA3_XTD10_PES/MDL/DAQ_MPOD", "u212.value"].select_trains(by_id[tids]).ndarray()
#retvol_raw_timestamp = run["SA3_XTD10_PES/MDL/DAQ_MPOD", "u212.timestamp"].select_trains(by_id[tids]).ndarray()
- model = Model(channels=["channel_1_D",
- "channel_2_B",
- "channel_3_A",
- "channel_3_B",
- "channel_4_C",
- "channel_4_D"],
+ # these have been manually selected:
+ #useful_channels = ["channel_1_D",
+ # "channel_2_B",
+ # "channel_3_A",
+ # "channel_3_B",
+ # "channel_4_C",
+ # "channel_4_D"]
+ model = Model(channels=channels,
n_pca_lr=400,
n_pca_hr=20,
high_res_sigma=0.2,
tof_start=None,
- delta_tof=200,
+ delta_tof=300,
validation_size=0.05)
train_idx = np.isin(tids, train_tids)
--
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