From e3cacdf8ea6c35024a2b5e25544e0c42e14dfadb Mon Sep 17 00:00:00 2001
From: Karim Ahmed <karim.ahmed@xfel.eu>
Date: Tue, 7 Jan 2020 16:05:42 +0100
Subject: [PATCH] revert unrelated changes
---
cal_tools/cal_tools/agipdlib.py | 73 +++++++++++++++------------------
1 file changed, 34 insertions(+), 39 deletions(-)
diff --git a/cal_tools/cal_tools/agipdlib.py b/cal_tools/cal_tools/agipdlib.py
index 47198bc14..f5445ec0d 100644
--- a/cal_tools/cal_tools/agipdlib.py
+++ b/cal_tools/cal_tools/agipdlib.py
@@ -1106,11 +1106,11 @@ class AgipdCorrections:
def get_valid_image_idx(self):
""" Return the indices of valid data
"""
- idx_base = self.idx_base
+ agipd_base = self.idx_base
if self.index_v == 2:
- count = np.squeeze(self.infile[idx_base + "image/count"])
- first = np.squeeze(self.infile[idx_base + "image/first"])
+ count = np.squeeze(self.infile[agipd_base + "image/count"])
+ first = np.squeeze(self.infile[agipd_base + "image/first"])
if np.count_nonzero(count != 0) == 0:
raise IOError("File has no valid counts")
valid = count != 0
@@ -1137,12 +1137,12 @@ class AgipdCorrections:
self.valid_indices = np.squeeze(valid_indices).astype(np.int32)
elif self.index_v == 1:
- status = np.squeeze(self.infile[idx_base + "image/status"])
+ status = np.squeeze(self.infile[agipd_base + "image/status"])
if np.count_nonzero(status != 0) == 0:
raise IOError("File {} has no valid counts".format(
self.infile))
- last = np.squeeze(self.infile[idx_base + "image/last"])
- first = np.squeeze(self.infile[idx_base + "image/first"])
+ last = np.squeeze(self.infile[agipd_base + "image/last"])
+ first = np.squeeze(self.infile[agipd_base + "image/first"])
valid = status != 0
last_index = int(last[status != 0][-1]) + 1
first_index = int(first[status != 0][0])
@@ -1281,47 +1281,42 @@ class AgipdCorrections:
# sanitize indices
for do in ["image", ]:
- # uq: INDEX/trainID
- # fidxv: INDEX/.../image/first idx values
- # cntsv: INDEX/.../image/counts values
- # Extract parameters through identifying
- # unique trains, index and numbers.
uq, fidxv, cntsv = np.unique(alltrains[firange - firange[0]],
return_index=True,
return_counts=True)
- # Validate calculated CORR INDEX contents by checking difference between
- # trainId stored in RAW data and trains from
- train_diff = np.isin(np.array(self.infile["/INDEX/trainId"]), uq, invert=True)
-
- # Insert zeros for missing trains.
- # fidxv and cntsv should have same length as
- # raw INDEX/.../image/first and INDEX/.../image/count,
- # respectively
- for i, diff in enumerate(train_diff):
- if diff:
- if i < len(cntsv):
- cntsv = np.insert(cntsv, i, 0)
- if i == 0:
- fidxv = np.insert(fidxv, i, 0)
- else:
- fidxv = np.insert(fidxv, i, fidxv[i-1])
- else:
- # append if at the end of the array
- cntsv = np.append(cntsv, 0)
- fidxv = np.append(fidxv, 0)
-
- # save INDEX contents (first, count) in CORR files
+ duq = (uq[1:] - uq[:-1]).astype(np.int64)
+
+ cfidxv = [fidxv[0], ]
+ ccntsv = [cntsv[0], ]
+ for i, du in enumerate(duq.tolist()):
+ if du > 1000:
+ du = 1
+ cntsv[i] = 0
+ cfidxv += [0] * (du - 1) + [fidxv[i + 1], ]
+ ccntsv += [0] * (du - 1) + [cntsv[i + 1], ]
+
+ mv = len(cfidxv)
+ fidx = np.zeros(len(cfidxv), fidxv.dtype)
+ fidx[self.valid[:mv]] = np.array(cfidxv)[self.valid[:mv]]
+
+ for i in range(len(fidx) - 1, 2, -1):
+ if fidx[i - 1] == 0 and fidx[i] != 0:
+ fidx[i - 1] = fidx[i]
+
+ cnts = np.zeros(len(cfidxv), cntsv.dtype)
+ cnts[self.valid[:mv]] = np.array(ccntsv)[self.valid[:mv]]
+
self.outfile.create_dataset(idx_base + "{}/first".format(do),
- fidxv.shape,
- dtype=fidxv.dtype,
- data=fidxv,
+ fidx.shape,
+ dtype=fidx.dtype,
+ data=fidx,
fletcher32=True)
self.outfile.create_dataset(idx_base + "{}/count".format(do),
- cntsv.shape,
- dtype=cntsv.dtype,
- data=cntsv,
+ cnts.shape,
+ dtype=cnts.dtype,
+ data=cnts,
fletcher32=True)
def create_output_datasets(self):
--
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