From c9ce7a1a005fc0900b21ddd9435ffb8e1816adca Mon Sep 17 00:00:00 2001
From: AdityaSavara <39929571+AdityaSavara@users.noreply.github.com>
Date: Tue, 10 Nov 2020 00:48:05 -0500
Subject: [PATCH] improved automatic abscissa generation

---
 CheKiPEUQ/InverseProblem.py                   |  5 +-
 ...mple3c_abscissa_automatically_generated.py | 60 +++++++++++++++++++
 setup.py                                      |  2 +-
 3 files changed, 65 insertions(+), 2 deletions(-)
 create mode 100644 Examples/Example03/runfile_Example3c_abscissa_automatically_generated.py

diff --git a/CheKiPEUQ/InverseProblem.py b/CheKiPEUQ/InverseProblem.py
index bff54575..412d3496 100644
--- a/CheKiPEUQ/InverseProblem.py
+++ b/CheKiPEUQ/InverseProblem.py
@@ -147,7 +147,10 @@ def __init__(self, UserInput = None):
         else:
             self.UserInput.num_response_dimensions = UserInput.responses['num_responses']
         if len(UserInput.responses['responses_abscissa']) == 0: #This means it has not been provided and we will make one.
-            UserInput.responses_abscissa = np.linspace(0, self.UserInput.num_response_dimensions, self.UserInput.num_response_dimensions)
+            UserInput.responses_abscissa = [] #the one from input should already be a list, but we start a fresh one.
+            for responseIndex in range(0,self.UserInput.num_response_dimensions):
+                numPoints = len(UserInput.responses_observed[responseIndex])
+                UserInput.responses_abscissa.append(np.linspace(0, numPoints,numPoints))
         else:
             UserInput.responses_abscissa = UserInput.responses['responses_abscissa']
         UserInput.responses_abscissa = np.array(nestedObjectsFunctions.makeAtLeast_2dNested(UserInput.responses_abscissa))        
diff --git a/Examples/Example03/runfile_Example3c_abscissa_automatically_generated.py b/Examples/Example03/runfile_Example3c_abscissa_automatically_generated.py
new file mode 100644
index 00000000..6b09884b
--- /dev/null
+++ b/Examples/Example03/runfile_Example3c_abscissa_automatically_generated.py
@@ -0,0 +1,60 @@
+import sys; sys.path.append('../../');  import CheKiPEUQ as CKPQ
+import CheKiPEUQ.UserInput as UserInput
+
+if __name__ == "__main__":
+    import processing_functions_tpd_odeint
+    observed_data_Filename = 'ExperimentalDataAcetaldehydeTPDCeO2111MullinsTruncatedConstantErrors.csv'
+    times, responses_observed, observedResponses_uncertainties = processing_functions_tpd_odeint.import_integrals_settings(observed_data_Filename)
+    #experiments_datarame = pd.read_csv(observed_data_Filename)    
+    
+    
+    #UserInput.responses['responses_abscissa'] = times
+    UserInput.responses['responses_observed'] = responses_observed
+    UserInput.responses['responses_observed_uncertainties'] = observedResponses_uncertainties*1
+
+    
+    #UserInput.simulated_response_plot_settings['x_label'] = 'time (s)'
+    UserInput.simulated_response_plot_settings['y_label'] = r'$rate (s^{-1})$'
+    #UserInput.simulated_response_plot_settings['y_range'] = [0.00, 0.025] #optional.
+    UserInput.simulated_response_plot_settings['figure_name'] = 'Posterior_Example1' #This creates the filename, also.
+
+    UserInput.model['parameterNamesAndMathTypeExpressionsDict'] = {'Ea_1':r'$E_{a1}$','Ea_2':r'$E_{a2}$','log_A1':r'$log(A_{1})$','log_A2':r'$log(A_{2})$','gamma1':r'$\gamma_{1}$','gamma2':r'$\gamma_{2}$'}
+    UserInput.model['InputParameterPriorValues'] = [41.5, 41.5, 13.0, 13.0, 0.1, 0.1] # Ea1_mean, Ea2_mean, log_A1_mean, log_A2_mean, gamma_1_mean, gamma_2_mean 
+    UserInput.model['InputParametersPriorValuesUncertainties'] = [20, 20, 2, 2, 0.1, 0.1] #If user wants to use a prior with covariance, then this must be a 2D array/ list. To assume no covariance, a 1D
+    UserInput.model['InputParameterInitialGuess'] = [40, 20, 13, 13.0, 0.1, 0.2]#[39.08033543, 25.85946199, 10.02022149, 14.67430303, -0.41925677,  0.62673177] #This is where the mcmc chain will start.
+    #InputParameterInitialValues = [41.5, 41.5, 13.0, 13.0, 0.1, 0.1] # Ea1_mean, Ea2_mean, log_A1_mean, log_A2_mean, gamma_1_mean, gamma_2_mean 
+    
+    UserInput.model['reducedParameterSpace']=[0,1,2,3]
+    
+    #InputParametersInitialValuesUncertainties = [200, 200, 13, 13, 0.1, 0.1] #If user wants to use a prior with covariance, then this must be a 2D array/ list. To assume no covariance, a 1D array can be used.
+    UserInput.model['simulateByInputParametersOnlyFunction'] = processing_functions_tpd_odeint.TPR_integerated_simulationFunctionWrapper #This must simulate with *only* the parameters listed above, and no other arguments.
+    UserInput.model['simulationOutputProcessingFunction'] = None  #Optional: a function to process what comes out of the simulation Function and then return an observable vector.
+        
+    UserInput.parameter_estimation_settings['verbose'] = False 
+    UserInput.parameter_estimation_settings['checkPointFrequency'] = 100
+     
+    UserInput.parameter_estimation_settings['mcmc_mode'] = 'unbiased'
+    UserInput.parameter_estimation_settings['mcmc_random_seed'] = 0 #Normally set to None so that mcmc is set to be random. To get the same results repeatedly, such as for testing purposes, set the random seed to 0 or another integer for testing purposes.
+    UserInput.parameter_estimation_settings['mcmc_burn_in'] = 0
+    UserInput.parameter_estimation_settings['mcmc_length'] = 100
+    UserInput.parameter_estimation_settings['mcmc_relative_step_length'] = 0.1
+    UserInput.parameter_estimation_settings['mcmc_modulate_accept_probability']  = 0 #Default value of 0. Changing this value sharpens or flattens the posterior. A value greater than 1 flattens the posterior by accepting low values more often. It can be useful when greater sampling is more important than accuracy. One way of using this feature is to try with a value of 0, then with the value equal to the number of priors for comparison, and then to gradually decrease this number as low as is useful (to minimize distortion of the result). A downside of changing changing this variable to greater than 1 is that it slows the the ascent to the maximum of the prior, so there is a balance in using it. In contrast, numbers increasingly less than one (such as 0.90 or 0.10) will speed up the ascent to the maximum of the posterior, but will also result in fewer points being retained.
+
+    UserInput.contour_settings_custom['contours_normalized'] = True
+
+    #After making the UserInput, now we make a 'parameter_estimation' object from it.
+    PE_object = CKPQ.parameter_estimation(UserInput)
+    
+#    #Now we do parameter estimation.
+#    PE_object.doMetropolisHastings()
+    PE_object.doOptimizeNegLogP(method="BFGS", printOptimum=True, verbose=True)
+    print(PE_object.map_parameter_set, PE_object.map_logP)
+    #PE_object.doGridSearch('doMetropolisHastings')
+    #PE_object.doGridSearch('doOptimizeNegLogP', verbose = True,gridSamplingNumOfIntervals = [1,1,1,1,0,0], passThroughArgs={'method':'BFGS'})
+    #PE_object.doGridSearch('getLogP', verbose = True)
+    
+#    PE_object.createAllPlots() #This function calls each of the below functions.
+#    PE_object.makeHistogramsForEachParameter()    
+#    PE_object.makeSamplingScatterMatrixPlot()
+    PE_object.createSimulatedResponsesPlots()
+    #TODO: call the mum_pce plotting objects, which will be PE_object.createContourGraphs() or something like that.
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 1b3b7616..b4f4d3f3 100644
--- a/setup.py
+++ b/setup.py
@@ -21,7 +21,7 @@
 EMAIL = 'AditySavara2008@u.northwestern.edu'  #Eric A. Walker is a co-author and can be reached at WALKER, ERIC <walke299@email.sc.edu>
 AUTHOR = 'Aditya Savara'
 REQUIRES_PYTHON = '>=3.5.0'
-VERSION = '0.3.1'
+VERSION = '0.3.2'
 LICENSE = 'BSD-3-Clause'
 
 # What packages are required for this module to be executed?