Adding small readme.

flasc/model_tuning/floris_tuning.py

@@ -12,11 +12,11 @@
 # See https://floris.readthedocs.io for documentation
 
 # This is a preliminary implementation of tuning methods for FLORIS to SCADA. 
-# The code is focused on methods for the empirical guassian wake model and is 
+# The code is focused on methods for the Empirical Guassian wake model and is 
 # based on contributions from Elizabeth Eyeson, Paul Fleming (paul.fleming@nrel.gov)
-# Misha Sinner (Michael.Sinner@nrel.gov) and Eric Simley  at NREL, 
-# and Bart Doekemeijer of Shell.  If interested to contribute to this work please
-# reach out via github or email
+# Misha Sinner (michael.sinner@nrel.gov) and Eric Simley at NREL, and Bart
+# Doekemeijer at Shell, as well as discussions with Diederik van Binsbergen at 
+# NTNU. Please see readme.txt for more information.
 
 
 import numpy as np

flasc/model_tuning/readme.txt

@@ -0,0 +1,32 @@
+Copyright 2023 NREL
+Licensed under the Apache License, Version 2.0 (the "License"); you may not
+use this file except in compliance with the License. You may obtain a copy of
+the License at http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+License for the specific language governing permissions and limitations under
+the License.
+
+See https://floris.readthedocs.io for documentation
+
+____ model_tuning ____
+
+This is a preliminary implementation of tuning methods for FLORIS to SCADA. 
+The code is focused on methods for the Empirical Guassian wake model and is 
+based on contributions from Elizabeth Eyeson, Paul Fleming (paul.fleming@nrel.gov)
+Misha Sinner (michael.sinner@nrel.gov) and Eric Simley at NREL, and Bart
+Doekemeijer at Shell, as well as discussions with Diederik van Binsbergen at 
+NTNU. 
+
+Please treat this module as a beta implementation.
+
+We are planning to extend the capabilities of the model_tuning module in coming
+version releases. If you are interested in contributing to this effort, please
+reach out to Paul or Misha via email. Planned improvements include:
+- Streamlining of processes and code
+- Added flexibility for implementing other loss functions
+- Cosine power loss exponent fitting
+- Possible accelerated model fitting by refinement of swept parameters
+- Methods for fitting multiple parameters simultaneously