Fix for #300

thouska · Feb 27, 2023 · 4533b7a · 4533b7a
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diff --git a/docs/Bayesian_uncertainty_analysis_with_DREAM.md b/docs/Bayesian_uncertainty_analysis_with_DREAM.md
@@ -125,4 +125,4 @@ Or if you want to check the posterior parameter distribution:
 
 *Figure 3: Posterior parameter distribution of HYMOD. Plotting the last 100 repetitions of the algorithm as a histogram.*
 
-The corresponding code is available for download [here](https://github.com/thouska/spotpy/blob/master/spotpy/examples/tutorial_dream_hymod.py).
+The corresponding code is available for download [here](https://github.com/thouska/spotpy/tree/master/tutorials/tutorial_dream_hymod.py).
diff --git a/docs/Calibration_with_SCE-UA.md b/docs/Calibration_with_SCE-UA.md
@@ -86,4 +86,4 @@ The best model run can be plotted by using basic Matplotlib skills:
 
 *Figure 2: Best model run of HYMOD, calibrated with SCE-UA, using RMSE as objective function.*
 
-The corresponding code is available for download [here](https://github.com/thouska/spotpy/blob/master/spotpy/examples/tutorial_sceua_hymod.py).
+The corresponding code is available for download [here](https://github.com/thouska/spotpy/tree/master/tutorials/tutorial_sceua_hymod.py).
diff --git a/docs/How_to_link_a_model_to_SPOTPY.md b/docs/How_to_link_a_model_to_SPOTPY.md
@@ -123,4 +123,4 @@ A value of 0 would be a perfect fit of the simulation and observation data, +inf
         return like
 Finally, save the file and check out the next example how to use the spotpy setup class with an algorithm.
 
-The shown example code is available [here](https://github.com/thouska/spotpy/blob/master/spotpy/examples/spot_setup_hymod_python.py)
+The shown example code is available [here](https://github.com/thouska/spotpy/tree/master/src/spotpy/examples/spot_setup_hymod_python.py)
diff --git a/docs/Sensitivity_analysis_with_FAST.md b/docs/Sensitivity_analysis_with_FAST.md
@@ -59,4 +59,4 @@ is available
 *Figure 1: Example output of a sensitivity analysis using the FAST (Fourier Amplitude Sensitivity Test). 
 Total sensitivity index is plotted for every model parameter. Sensitive parameters are plotted in blue, insensitive parameter in orange.*
 
-The corresponding code is available for download [here](https://github.com/thouska/spotpy/blob/master/spotpy/examples/tutorial_fast_hymod.py).
+The corresponding code is available for download [here](https://github.com/thouska/spotpy/blob/master/tutorials/tutorial_fast_hymod.py).
diff --git a/docs/index.md b/docs/index.md
@@ -106,18 +106,15 @@ Optional packages are:
 
 all packages are pre-installed e.g. in the following packages:
 
-* [Python xy](https://code.google.com/p/pythonxy/ "Python xy") version >2.7
-* [WinPython](http://winpython.sourceforge.net/ "WinPython) version >2.7
+* [Python xy](https://code.google.com/p/pythonxy/ "Python xy") version >3.5
+* [WinPython](http://winpython.sourceforge.net/ "WinPython") version >3.5
+* [Anaconda](https://www.anaconda.com/products/distribution "Anaconda") version >3.5
 
 ### Download
 SPOTPY is available on [PYPI](https://pypi.python.org/pypi/spotpy "spotpy") and [GitHub](https://github.com/thouska/spotpy "spotpy"). 
 This should make installation easy, as you just have to use a command window and type
 
 	pip install spotpy
-
-Alternatively, you can download the latest version of SPOTPY with a [SVN-Client](http://tortoisesvn.net/index.de.html "TortoiseSVN").
-
-With this software you just have to check out: `svn://fb09-pasig.umwelt.uni-giessen.de/spotpy/trunk`
 
 The recommended place for the SPOTPY package is in the site-packages folder in your Python Path, just the location of all other Python packages.
 
@@ -173,9 +170,9 @@ The results can be analysed with some pre-build statistics and plotting features
 		spotpy_setup_griewank.py    # Example SPOTPY setup to analyse the Griewank function
 		spotpy_setup_rosenbrock.py  # Example SPOTPY setup to analyse the Rosenbrock function
 		getting_started.py          # Recommended test file for starters
+
+	tutorials/
 		tutorial_rosenbrock.py      # Source code for the Rosenbrock example the Tutorial
 		tutorial_griewank.py        # Source code for the Rosenbrock example the Tutorial
 		tutorial_ackley.py          # Source code for the Rosenbrock example the Tutorial
 		tutorial_Parameterlist_iterator.py        # Example how to sample given parameter combinations
-		3dplot.py                   # Response surface plot of example files
-
Original file line number	Diff line number	Diff line change
Expand Up		@@ -125,4 +125,4 @@ Or if you want to check the posterior parameter distribution:

		Figure 3: Posterior parameter distribution of HYMOD. Plotting the last 100 repetitions of the algorithm as a histogram.

		The corresponding code is available for download [here](https://github.com/thouska/spotpy/blob/master/spotpy/examples/tutorial_dream_hymod.py).
		The corresponding code is available for download [here](https://github.com/thouska/spotpy/tree/master/tutorials/tutorial_dream_hymod.py).
Original file line number	Diff line number	Diff line change
Expand Up		@@ -86,4 +86,4 @@ The best model run can be plotted by using basic Matplotlib skills:

		Figure 2: Best model run of HYMOD, calibrated with SCE-UA, using RMSE as objective function.

		The corresponding code is available for download [here](https://github.com/thouska/spotpy/blob/master/spotpy/examples/tutorial_sceua_hymod.py).
		The corresponding code is available for download [here](https://github.com/thouska/spotpy/tree/master/tutorials/tutorial_sceua_hymod.py).