fix missing raman response flag during simulations without Ramman

README.md

@@ -2,11 +2,11 @@
 
 # gnlse-python
 
-gnlse-python is a Python set of scripts for solving 
-Generalized Nonlinear Schrodringer Equation. It is one of the WUST-FOG students 
+gnlse-python is a Python set of scripts for solving
+Generalized Nonlinear Schrodringer Equation. It is one of the WUST-FOG students
 projects developed by [Fiber Optics Group, WUST](http://www.fog.pwr.edu.pl/).
 
-Complete documentation is available at 
+Complete documentation is available at
 [https://gnlse.readthedocs.io](https://gnlse.readthedocs.io)
 
 ## Installation
@@ -15,7 +15,7 @@ Complete documentation is available at
 2. Activate it with `. gnlse/bin/activate`.
 3. Clone this repository `git clone https://github.com/WUST-FOG/gnlse-python.git`
 4. Install the requirements in this directory `pip install -r requirements.txt`.
-5. Install gnlse package `pip install .` or set `PYTHONPATH` enviroment variable
+5. Install gnlse package `pip install .` (or `pip install -v -e .` for develop mode) or set `PYTHONPATH` enviroment variable
 
 ```bash
 python -m venv gnlse
@@ -38,6 +38,7 @@ cd gnlse-python/examples
 
 python test_Dudley.py
 ```
+
 And you expect to visualise supercontinuum generation process in use of 3 types
  of pulses (simulation similar to Fig.3 of Dudley et. al, RMP 78 1135 (2006)):
 
@@ -47,12 +48,12 @@ And you expect to visualise supercontinuum generation process in use of 3 types
 
 - **Modular Design**
 
-  Main core of gnlse module is derived from the RK4IP matlab script 
-  written by J.C.Travers, H. Frosz and J.M. Dudley 
-  that is provided in "Supercontinuum Generation in Optical Fibers", 
-  edited by J. M. Dudley and J. R. Taylor (Cambridge 2010). 
+  Main core of gnlse module is derived from the RK4IP matlab script
+  written by J.C.Travers, H. Frosz and J.M. Dudley
+  that is provided in "Supercontinuum Generation in Optical Fibers",
+  edited by J. M. Dudley and J. R. Taylor (Cambridge 2010).
   The toolbox prepares integration using SCIPYs ode solvers (adaptive step size).
-  We decompose the solver framework into different components 
+  We decompose the solver framework into different components
   and one can easily construct a customized simulations
   by accounting different physical phenomena, ie. self stepening, Raman response.
 
@@ -61,7 +62,13 @@ And you expect to visualise supercontinuum generation process in use of 3 types
   We implement three different raman response functions:
     - 'blowwood':   Blow and D. Wood, IEEE J. of Quant. Elec., vol. 25, no. 12, pp. 2665–2673, Dec. 1989,
     - 'linagrawal': Lin and Agrawal, Opt. Lett., vol. 31, no. 21, pp. 3086–3088, Nov. 2006,
-    - 'hollenbeck': Hollenbeck and Cantrell J. Opt. Soc. Am. B / Vol. 19, No. 12 / December 2002.
+    - 'hollenbeck': Hollenbeck and Cantrell, J. Opt. Soc. Am. B, vol. 19, no. 12, Dec. 2002.
+
+- **Nonlinearity**
+
+  We implement the possibility to account effective mode area's dependence on frequency:
+    - provide float value for gamma (effective nonlinear coefficient)
+    - 'NonlinearityFromEffectiveArea': introduce effective mode area's dependence on frequency (J. Laegsgaard, Opt. Express, vol. 15, no. 24, pp. 16110-16123, Nov. 2007).
 
 - **Dispersion operator**
 
@@ -76,28 +83,33 @@ And you expect to visualise supercontinuum generation process in use of 3 types
     - plot_Raman_response.py: plots different Raman in temporal domain,
     - test_3rd_order_soliton.py: evolution of the spectral and temporal characteristics of the 3rd order soliton,
     - test_dispersion.py: example of supercontinuum generation using different dispersion operators,
+    - test_nonlinearity.py: example of supercontinuum generation using different GNLSE and M-GNLSE (take into account mode profile dispersion),
     - test_Dudley.py: example of supercontinuum generation with three types of input pulse,
     - test_gvd.py: example of impuls broadening due to group velocity dispersion,
     - test_import_export.py: example of saving file with `.mat` extension,
     - test_raman.py: example of soliton fision for diffrent raman response functions,
     - test_spm.py: example of self phase modulation,
     - test_spm+gvd.py: example of generation of 1st order soliton.
-    
+
 ## Release History
 
-v1.0.0 was released in 13/8/2020.
+v1.1.0 was released in 21/8/2021.
 The master branch works with **python 3.7**.
 
 * **1.0.0 -> Aug 13th, 2020**
     * The first proper release
     * CHANGE: Complete documentation and code
+* **1.1.0 -> Aug 21th, 2021**
+    * Modified-GNLSE extension
+    * CHANGE: Code refactor - relocate GNLSE's attribiutes setting into constructor
+    * ADD: Possibility to take into account the effective mode area's dependence on frequency
 
 ## Authors
 
 - [Adam Pawłowski](https://github.com/adampawl)
 - [Paweł Redman](https://redman.xyz/)
 - [Daniel Szulc](http://szulc.xyz/)
-- Magda Zatorska
+- [Magda Zatorska](https://github.com/magdazatorska)
 - [Sylwia Majchrowska](https://majsylw.netlify.app/)
 - [Karol Tarnowski](http://www.if.pwr.wroc.pl/~tarnowski/)
 
@@ -134,4 +146,4 @@ what you would like to change.
 Please make sure to update example tests as appropriate.
 
 ## License
-[MIT](https://choosealicense.com/licenses/mit/)
+[MIT](https://choosealicense.com/licenses/mit/)

gnlse/gnlse.py

@@ -183,6 +183,7 @@ def __init__(self, setup):
             self.scale = 1
 
         # Raman scattering
+        self.RW = None
         if setup.raman_model:
             self.fr, RT = setup.raman_model(self.t)
             if np.abs(self.fr) < np.finfo(float).eps: