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Commit fd8a9da0 authored by Astrid Muennich's avatar Astrid Muennich
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added files for tutorial

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Installation and Configuration
=============================
1. log into max-exfl
2. install karabo in your home directory or under /gpfs/exfel/data/scratch/username:
wget http://exflserv05.desy.de/karabo/karaboFramework/tags/2.2.4/karabo-2.2.4-Release-CentOS-7-x86_64.sh
chmod +x karabo-2.2.4-Release-CentOS-7-x86_64.sh
./karabo-2.2.4-Release-CentOS-7-x86_64.sh
source karabo/activate
3. get pycalibration
git clone https://git.xfel.eu/gitlab/detectors/pycalibration.git
4. install requirements:
cd pycalibration
pip install -r requirements.txt .
5. adjust xfel_calibrate/settings.py:
change karabo_activate_path and ipcluster_path according to where you installed karabo
Create your own notebook
========================
1. Create a new notebook or re-arrange an existing on following the guidelines explained here: https://in.xfel.eu/readthedocs/docs/european-xfel-offline-calibration/en/latest/workflow.html
2. register you notebook:
add an entry to xfel_calibrate/notebooks.py
Note: use all capital letters for DETECTOR and TYPE
3. update:
pip install --upgrade .
Running the notebook
====================
1. make sure output folders exist
2. run it:
xfel-calibrate Tutorial TEST
3. Look at generated report in output folder
notebooks/Tutorial/calversion.ipynb 0 → 100644
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%% Cell type:markdown id: tags:
# Tutorial Calculation #
Author: Astrid Muennich, Version 0.1
A small example how to adapt a notebook to run with the offline calibration package "pycalibation".
The first cell contains all parameters that should be exposed to the command line.
%% Cell type:code id: tags:
``` python
out_folder = "/gpfs/exfel/data/scratch/amunnich/tutorial" # output folder
sensor_size = [10, 30] # defining the picture size
random_seed = 2345 # random seed for filling of fake data array. Change it to produce different results.
runs = 500 # how may iterations to fill histograms
cluster_profile = "tutorial"
```
%% Cell type:markdown id: tags:
First include what we need and set up the cluster profile. Everything that has a written response ina cell will show up in the report, e.g. prints but also return values or errors.
%% Cell type:code id: tags:
``` python
import matplotlib
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from ipyparallel import Client
print("Connecting to profile {}".format(cluster_profile))
view = Client(profile=cluster_profile)[:]
view.use_dill()
```
%% Output
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-75-f535e80204a3> in <module>()
5 import matplotlib.pyplot as plt
6
----> 7 from ipyparallel import Client
8
9 print("Connecting to profile {}".format(cluster_profile))
ImportError: No module named 'ipyparallel'
%% Cell type:markdown id: tags:
## Create some random data
%% Cell type:code id: tags:
``` python
def data_creation(random_seed):
np.random.seed = random_seed
return np.random.random((sensor_size))
```
%% Cell type:code id: tags:
``` python
fake_data = []
for i in range(runs):
fake_data.append(data_creation(random_seed+10*i))
```
%% Cell type:markdown id: tags:
Plot the random image. everything we write here in the markup cells will show up as text in the report.
%% Cell type:code id: tags:
``` python
plt.subplot(211)
plt.imshow(fake_data[0], interpolation="nearest")
plt.title('Random Image')
plt.ylabel('sensor height')
plt.subplot(212)
plt.imshow(fake_data[5], interpolation="nearest")
plt.xlabel('sensor width')
plt.ylabel('sensor height')
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.075, 0.9])
plt.colorbar(cax=cax).ax.set_ylabel("# counts")
plt.show()
```
%% Output
%% Cell type:markdown id: tags:
These plots show two randomly filled sensor images. We can use markup cells also as captions for images.
%% Cell type:markdown id: tags:
## Simple Analysis
%% Cell type:code id: tags:
``` python
mean = []
std = []
for im in fake_data:
mean.append(im.mean())
std.append(im.std())
```
%% Cell type:markdown id: tags:
We calculate the mean value of all images, as well as the standard deviation.
%% Cell type:code id: tags:
``` python
plt.hist(mean, 50)
plt.xlabel('mean')
plt.ylabel('counts')
plt.title('Mean value')
plt.show()
```
%% Output
%% Cell type:code id: tags:
``` python
plt.hist(std, 50)
plt.xlabel('mean')
plt.ylabel('counts')
plt.title('Mean value')
plt.show()
```
%% Output
notebooks/Tutorial/startversion.ipynb 0 → 100644
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%% Cell type:markdown id: tags:
Create a small simple notebook, without thinking about parameters or maxwell
%% Cell type:code id: tags:
``` python
import matplotlib
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
```
%% Cell type:code id: tags:
``` python
sensor_size = [10,20]
random_seed = 2345
np.random.seed = random_seed
fake_data = np.random.random((sensor_size))
```
%% Cell type:code id: tags:
``` python
plt.imshow(fake_data, interpolation="nearest")
plt.colorbar()
```
%% Output
<matplotlib.colorbar.Colorbar at 0x7f9f38551d30>
%% Cell type:markdown id: tags:
Calculate some stuff from the data
%% Cell type:code id: tags:
``` python
my_mean = fake_data.mean()
my_std = fake_data.std()
my_mean, my_std
```
%% Output
(0.5116986708989778, 0.29529469463486308)
%% Cell type:code id: tags:
``` python
```
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