From 18144b0fd9f788438fb332816afb099a31b191d4 Mon Sep 17 00:00:00 2001
From: Mads Jakobsen <mads.jakobsen@xfel.eu>
Date: Thu, 25 Apr 2024 14:45:19 +0200
Subject: [PATCH] added auto generated cython c file output to gitignore
---
.gitignore | 2 +-
testing/test.py | 23 +++++++++++++++++------
2 files changed, 18 insertions(+), 7 deletions(-)
diff --git a/.gitignore b/.gitignore
index a3b8a68..cdb6c4a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,4 +8,4 @@ testing/abc.png
*.egg-info
fastXPCS/fastXPCS/cython/welford.c
fastXPCS/fastXPCS/cython/fxpcs_sparse.c
-fastXPCS/fastXPCS/cython/dense.c
+fastXPCS/fastXPCS/cython/dense.c
\ No newline at end of file
diff --git a/testing/test.py b/testing/test.py
index a111fdd..a13c4e3 100644
--- a/testing/test.py
+++ b/testing/test.py
@@ -10,6 +10,8 @@ from fastXPCS.algos import TTCdata, do_sparse_train
from fastXPCS.fxpcs_sparse import sparsify, sparsify2
from PIL import Image
+from function_call import doXPCS;
+
dataShape = (352, 512, 128)
photonEnergy = 13.3 # in ADU
# this is a function that generates a dummy panel dataset given a shape
@@ -47,7 +49,6 @@ def getDummyData(shape, chanceOfPhotonPerPixel = 0.01, correlationPerBunch=0.5,
p=[q**0, q**1, q**2, q**3, q**4, q**5, q**6]/np.sum([q**0, q**1, q**2, q**3, q**4, q**5, q**6])),(shape[1], shape[2]))
def evolveImageFrom(image, c=correlationPerBunch):
- print(c)
randMask = np.reshape(np.random.choice(
[0, 1],
size=numPixelPrImage,
@@ -58,7 +59,7 @@ def getDummyData(shape, chanceOfPhotonPerPixel = 0.01, correlationPerBunch=0.5,
data[0,:,:] = np.reshape(getRandomImage(), (shape[1], shape[2]))
for pulseNr in range(1,dataShape[0]):
- correlationPerBunch += (1-correlationPerBunch)*0.001
+ correlationPerBunch += (1-correlationPerBunch)*0.01
data[pulseNr, :, :] = evolveImageFrom(data[pulseNr-1,:,:], correlationPerBunch )
# if we do not returned photnoized data, then add a gaussian random variable
@@ -66,7 +67,7 @@ def getDummyData(shape, chanceOfPhotonPerPixel = 0.01, correlationPerBunch=0.5,
data += sigma * np.random.standard_normal(size=shape)
data *= energy
- return data
+ return data.astype(np.float32)
data = getDummyData(
@@ -142,9 +143,19 @@ t = time.time()
out = xpcs(data, photonEnergy)
elapsed = time.time() - t
print(f"elapsed {elapsed}")
-print(out.shape)
-print(np.min(out))
-print(np.max(out))
+
+
+print("my xpcs")
+output = np.zeros(shape=(2,2),dtype=np.float32)
+errorCode = doXPCS(data, output)
+print(data)
+print(output)
+print(output[0][0])
+print(output[0][1])
+print(output[1][0])
+print(output[1][1])
+print(output.ravel())
+
def plot(data):
data = data.astype(np.float32)
cmap = pl.get_cmap('viridis')
--
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