added small information to README, LICENSE

CONTRIBUTING.rst

@@ -23,6 +23,13 @@
    releases.
 
 
+
+============
+TODO
+============
+This is a general guide to contribute to python based projects and does not yet contain any ``cristallina`` specific information.
+
+
 ============
 Contributing
 ============

LICENSE.txt

@@ -1,21 +1 @@
-The MIT License (MIT)
-
-Copyright (c) 2022 Alexander Steppke
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+No explicit LICENSE given, all code belongs to the respective authors.

README.rst

@@ -37,13 +37,12 @@ cristallina
     Cristallina data analysis modules and plotting utilities.
 
 
-A longer description of your project goes here...
+Here we collect modules for data analysis, plotting and utility functions for the Cristallina endstation. 
+
+The data analysis is based on the common SwissFEL data architecture, with convienent access provided by [sf_datafiles](https://github.com/paulscherrerinstitute/sf_datafiles). 
+
+The test suite (based on pytest) requires access to some data only available on either the cristallina consoles or the RA cluster. 
 
 
-.. _pyscaffold-notes:
 
-Note
-====
 
-This project has been set up using PyScaffold 4.2.3. For details and usage
-information on PyScaffold see https://pyscaffold.org/.

src/cristallina/utils.py

@@ -134,7 +134,91 @@ def heuristic_extract_smalldata_path():
     small_data_path = f"/das/work/units/cristallina/p{p_number}/smalldata/"
     return small_data_path  
 
+######################## Little useful functions ########################
+
+ def find_nearest(array, value):
+    '''Finds an index in an array with a value that is nearest to given number'''
+    array = np.asarray(array)
+    idx = (np.abs(array - value)).argmin()
+    return idx
+
+def find_two_nearest(time_array,percentage):
+    '''Finds indeces of the two values that are the nearest to the given value in an array'''
+    array = np.asarray(time_array)
+    value = (np.max(array)-np.min(array))*percentage+np.min(array) 
+    idx = (np.abs(array - value)).argmin()
+    indices = np.sort([np.argsort(np.abs(array-value))[0], np.argsort(np.abs(array-value))[1]])
+    return indices   
+
+def gauss(x, H, A, x0, sigma):
+    """Returns gauss function value"""
+    return H + A * np.exp(-(x - x0) ** 2 / (2 * sigma ** 2))
+
+def gauss_fit(x, y, fit_details=None, plot=None:
+    '''Returns [baseline_offset, Amplitude, center, sigma, FWHM]'''
+
+    # Initial guesses
+    mean = sum(x * y) / sum(y)
+    sigma = np.sqrt(sum(y * (x - mean) ** 2) / sum(y))
+    FWHM = 2.35482 * sigma
+
+    # Fit
+    popt, pcov = curve_fit(gauss, x, y, p0=[min(y), max(y), mean, sigma])
+
+    # Add FWHM to the ouptuts
+    popt = np.append(popt,2.35482 * popt[3])
+
+    # Print results
+    if fit_details :
+        print('The baseline offset is', popt[0])
+        print('The center is', popt[2])
+        print('The sigma of the fit is', popt[3])
+        print('The maximum intensity is', popt[0] + popt[1])
+        print('The Amplitude is', popt[1])
+        print('The FWHM is', 2.35482 * popt[3])
+
+    # Show plot
+    if plot:
+        plt.figure()
+        plt.plot(x, y, '.k', label='data')
+        plt.plot(x, gauss(x, *gauss_fit(x, y)[0:4]), '--r', label='fit')
+
+        plt.legend()
+        plt.title('Gaussian fit')
+        plt.xlabel('X')
+        plt.ylabel('Y')
+        plt.show()
+
+    return popt
+
+######################## Unit conversions ########################
+
+def joules_to_eV(joules):
+    """Just a unit conversion"""
+    eV = joules * 6.241509e18
+    return eV
+
+def eV_to_joules(eV):
+    """Just a unit conversion"""
+    joules = eV * 1.602176565e-19
+    return joules    
     
- 
-    
-    
+def photon_energy_from_wavelength(wavelength):
+    '''Returns photon energy in eV from wavelength in meters. Source https://www.kmlabs.com/en/wavelength-to-photon-energy-calculator'''
+    Eph = 1239.8 / (wavelength*1e9)
+    return Eph
+
+def wavelength_from_photon_energy(Eph):
+    '''Returns wavelength in meters from photon energy in eV. Source https://www.kmlabs.com/en/wavelength-to-photon-energy-calculator'''
+    wavelength = 1239.8 / (Eph*1e9)
+    return wavelength
+
+def sigma_to_FWHM(sigma):
+    """Gaussian sigma to FWHM"""
+    FWHM = sigma * 2.355
+    return FWHM
+
+def FWHM_to_sigma(FWHM):
+    """FWHM to gaussian sigma"""
+    sigma = FWHM / 2.355
+    return sigma

tests/test_utils.py

@@ -1,7 +1,7 @@
 import pytest
 import os
 
-from cristallina.utils import ROI, print_run_info, heuristic_extract_pgroup
+from cristallina.utils import ROI, print_run_info, heuristic_extract_pgroup, gauss
 
 __author__ = "Alexander Steppke"
 
@@ -25,3 +25,12 @@ def test_ROI():
 def test_extract_pgroup():
     os.chdir("/sf/cristallina/data/p19739")
     assert heuristic_extract_pgroup() == '19739'
+
+def test_gauss():
+    # Test a random Gauss value and check whether the value didn't change in time
+    assert gauss(1.5,2.7,7,4.33,3) == 7.186044070987355
+
+def test_find_nearest():
+    # Make an array of 1 to 100 and check that the nearest index is the value
+    a = np.linspace(0,99,100)
+    assert find_nearest(a,10.1) == 10