Wb data iss1

.gitignore

@@ -63,4 +63,5 @@ target/
 
 
 # Misc
-*~
+*~
+*.idea

README.rst

@@ -6,101 +6,38 @@ systematically.
 
 .. contents::
 
+Disclaimer
+----------
+This is free software - use at your own risk and convenience.
+
+
+Usecase
+-------
+* You have some sort of computer model you want to do parameter studies with
+
+Features
+--------
+* Computes parallel
+* Works iteratively - *pymofa checks wheter you have already computed some task
+  and won't compute these again*
+
+Design
+------
+* With pymofa you write one python file to set up one computer experiment
+* This python file will contain a function (called the RUN_FUNC) that configures and exectues your model run
+* The parameters of the RUN_FUNC will be the parameters of the experiment
+
+This means:
+
+* Raw data will be stored with these parameters
+* You will need to give pymofa a list of parameter combination (i.e. a tuple
+  of parameter values of the same length as the parameters)
+* If you want to change the parameters, write a new experiment 
+
 Usage
 -----
-To use the modeling framework write a python script representing one
-"experiment" you want to perform with your model.
-
-There you have to specify the path where you want to save your data, the sample
-size you want to use, the parameter combinations with which you want to run
-your model and a function to set up and run your model and save resulting data
-::
-    # File: Experiment.py
-
-    # Imports
-    from ModelingFramwork import experiment_handling as eh
-    import YourModel
-    import cPickle
-    import itertools as it
-    import otherstuff
-
-    # For use on the cluster
-    SAVE_PATH = "path/where/to/store/the/ExperimentData"
-
-    def RUN_FUNC(pa, ra, me, ters, filename):
-       """
-       <Your Docs>
-
-       Parameters
-       ----------
-       ...
-       filename: string
-           the filename where to pickle - passed from "experiment_handling"
-           This needs to be the last parameter of the RUN_FUNC
-           Experiment handling makes sure that each model run gets an unique ID
-       """
-       # poss. do something with the parameters given above
-
-       # Running the model and obtian what you want to save
-       m = YourModel(mo, del, pa, ra, me, ters)
-       m.run()
-       result = m.get_result()
-
-       # Save Result
-       cPickle.dump(result, open(filename, "wb"))
-
-       # Determine some exit status and retun
-       # if exit_status != 1 MoFa will print a note
-       # of course, the data saving can be also dependend on exit status
-       exit_status = <determine exit status>
-       return exit_status
-
-    # Parameter combinations
-    pa = [1, 2, 3, 4]
-    ra = [1, 3, 5, 7, 9]
-    me = [0, 1]
-    ters = [42]
-    PARAM_COMBS = list(it.product(pa, ra, me, ters))
-
-    # Sample Size
-    SAMPLE_SIZE = 100
-
-    # Now start the computation
-    eh.compute(RUN_FUNC, PARAM_COMBS, SAMPLE_SIZE, SAVE_PATH)
-
-
-Additionally you can post-process the generated model data for evaluation. For
-this you need to provide an index, i.e. the subset of parameters that are the
-parameters you want to study in more detail, evaluation functions and a name of
-the resaved data file.
-::
-    # Continuation of Experiment.py
-
-    # index is a dict with int keys giving the position in RUN_FUN parameter
-    # list and string values giving their names
-    INDEX = {0: "pa", 1: "ra"}
-
-    # eva is a dict of evaluation functions computing a "macro" quantity from
-    # the "micro" data, stored by RUN_FUN. These function need to receive an
-    # interable of filenames as their parameter. The corresponding files were
-    # generated by RUN_FUNC and need tobe treated accordingly. The keys of the
-    # eva dict are the corresponding names of the "macro" quantity.
-    EVA = {"<macroQ1>": lambda fnames: <do_something_with>([np.load(f)
-                                                            for f in fnames]),
-          "<macroQ2>": lambda fnames: <do_something_different>([np.load(f)
-                                                                for f
-                                                                in fnames])}
-    # the name of the post processed data
-    NAME = "PostProcessedData_me0.pkl"
-
-    # another set of parameter combinations (optional) - you could also use
-    # PARAM_COMBS from above
-    PARAM_COMBS_me0 = list(it.product(pa, ra, [0], ters))
-
-    # This will now check for the largest available sample size and use it
-    eh.resave_data(SAVE_PATH, PARAM_COMBS_me0, INDEX, EVA, NAME)
-    # The file gets automatically saved at the sub-folder "./data/" which needs
-    # to exist
+Please have a look at the tutorials (either interactivly after downloading this
+repository or by starting `here <https://github.com/wbarfuss/pymofa/blob/master/tutorial/01_QuickStart.ipynb>`_)
 
 For further documentation, use the source!
 

pymofa/__init__.py

@@ -1 +1,4 @@
 """__init__.py of pymofa."""
+
+from .experiment_handling import experiment_handling
+from .safehdfstore import SafeHDFStore