Added analyses collection file into trisurf package

Samo Penic

2017-01-14 d2f7b0c053e93b4d409d9661dbaff2eedcf391c1

Added analyses collection file into trisurf package

 nir_log

@@ -2,6 +2,7 @@
from trisurf import tsmgr
from trisurf import trisurf
from trisurf import statistics
from trisurf import analyses

print("Running trisurf version "+ tsmgr.getTrisurfVersion())
Runs=[]
@@ -43,92 +44,19 @@
        run.setSubdir("run0")
        Runs.append(run)

#----------------------------

#kapa_list=[20,30]
#p=[10]
#
#N=5*Nshell**2+2
#Nc_list=[int(N*pp/100)  for pp in p]
#
#for kapa in kapa_list:
#    for Nc in Nc_list:
#        run=trisurf.Runner(snapshot='is_from_N25k'+str(kapa)+'V0_Nc312_c1.0.vtu')
#        run.setMaindir(("N", "k", "V", "_Nc", "_c","_w","_F"),  ("nshell","xk0","constvolswitch","number_of_vertices_with_c0","c0", "w","F"))
#        run.setSubdir("run0")
#
#        Runs.append(run)
#Here is how we wrap functions
def plotvolume(run, **kwargs):
    from trisurf import analyses
    analyses.plotColumnFromPostProcess(run,column='Volume',**kwargs)

#----------------------------
def plotbondrate(run, **kwargs):
    from trisurf import analyses
    analyses.plotColumnFromPostProcess(run,column='VertexMoveSucessRate',filename='statistics.csv',**kwargs)



#Nov format:
#hosts=({'name':'Hestia','address':'127.0.0.1', 'runs':Runs,  'username':'samo'},)

def analyze(run, **kwargs):
    host=kwargs.get('host', None)
    print("Demo analysis")
    print("Analysis on host "+host['name']+" for run "+run.Dir.fullpath()+" completed")
    print("here comes info on the run variable:")
    print(run)
    print("here comes info on the host variable:")
    print(host)
    print("here comes info on the args variable:")
    print(kwargs.get('args',None))

def plothbar(run, **kwargs):
    import matplotlib.pyplot as plt

    def smooth(y, box_pts):
        import numpy as np
        box = np.ones(box_pts)/box_pts
        y_smooth = np.convolve(y, box, mode='same')
        return y_smooth
    table=trisurf.Statistics(run.Dir.fullpath(),filename='data_tspoststat.csv').getTable()
    plt.plot(table['hbar'], '.')
    plt.title(run.Dir.fullpath())
    plt.xlabel('Iteration')
    plt.ylabel('hbar')
    smooth_window=10
    smoothed=smooth(table['hbar'],smooth_window)
    plt.plot(tuple(range(int(smooth_window/2),len(smoothed)-int(smooth_window/2))),smoothed[int(smooth_window/2):-int(smooth_window/2)])
    plt.show()
    print
    #if return False or no return statement, the analysis will continue with next running instance in the list. if return True, the analysis will stop after this run.
    return False

def plotrunningavginteractive(run, **kwargs):
    import matplotlib.pyplot as plt
    from trisurf import VTKRendering as vtk
    import math
    from multiprocessing import Process
    table=trisurf.Statistics(run.Dir.fullpath(),filename='data_tspoststat.csv').getTable()
    def running_avg(col):
        import numpy as np
        avg=[]	
        for i in range(0,len(col)):
            avg.append(np.average(col[:-i]))
        return avg
    def spawned_viewer(n):
        vtk.Renderer(kwargs.get('args', None),kwargs.get('host',None),run, n)

    fig=plt.figure(1)
    ra=running_avg(table['hbar'])
    l=len(table['hbar'])
    plt.plot(ra)
    plt.title('Running average')
    plt.ylabel('1/n sum_i=niter^n(hbar_i)')
    plt.xlabel('n')
    def onclick(event):
        #print('button=%d, x=%d, y=%d, xdata=%f, ydata=%f' % (event.button, event.x, event.y, event.xdata, event.ydata))
        p=Process(target=spawned_viewer, args=(l-math.floor(event.xdata)-1,))
        p.start()
    cid = fig.canvas.mpl_connect('button_press_event', onclick)
    plt.show()
    plt.close(1)
#start manager with configured runs
tsmgr.start(Runs, analyses={'analyze1':analyze, 'plotrunningavg':plotrunningavginteractive, 'plothbar':plothbar})
tsmgr.start(Runs, analyses={'demo':analyses.demo,'runningavg':analyses.plotrunningavginteractive, 'plothbar':analyses.plotColumnFromPostProcess, 'plotvol':plotvolume, 'plotbondrate':plotbondrate})

#statistics.combine(Runs)
#here is how we combine statistics of multiple runs
#statistics.combine([Runs[1],Runs[2]])

 trisurf/analyses.py

New file
@@ -0,0 +1,72 @@
from . import trisurf


def demo(run, **kwargs):
    host=kwargs.get('host', None)
    print("Demo analysis")
    print("Analysis on host "+host['name']+" for run "+run.Dir.fullpath()+" completed")
    print("here comes info on the run variable:")
    print(run)
    print("here comes info on the host variable:")
    print(host)
    print("here comes info on the args variable:")
    print(kwargs.get('args',None))


def plotrunningavginteractive(run, **kwargs):
    import matplotlib.pyplot as plt
    from trisurf import VTKRendering as vtk
    import math
    from multiprocessing import Process
    table=trisurf.Statistics(run.Dir.fullpath(),filename='data_tspoststat.csv').getTable()
    def running_avg(col):
        import numpy as np
        avg=[]	
        for i in range(0,len(col)):
            avg.append(np.average(col[:-i]))
        return avg
    def spawned_viewer(n):
        vtk.Renderer(kwargs.get('args', None),kwargs.get('host',None),run, n)

    fig=plt.figure(1)
    ra=running_avg(table['hbar'])
    l=len(table['hbar'])
    plt.plot(ra)
    plt.title('Running average')
    plt.ylabel('1/n sum_i=niter^n(hbar_i)')
    plt.xlabel('n')
    def onclick(event):
        #print('button=%d, x=%d, y=%d, xdata=%f, ydata=%f' % (event.button, event.x, event.y, event.xdata, event.ydata))
        p=Process(target=spawned_viewer, args=(l-math.floor(event.xdata)-1,))
        p.start()
    cid = fig.canvas.mpl_connect('button_press_event', onclick)
    plt.show()
    plt.close(1)


# -------------------------------
# these functions should be wrapped
# -------------------------------

def plotColumnFromPostProcess(run, filename='data_tspoststat.csv', column='hbar', **kwargs):
    import matplotlib.pyplot as plt

    def smooth(y, box_pts):
        import numpy as np
        box = np.ones(box_pts)/box_pts
        y_smooth = np.convolve(y, box, mode='same')
        return y_smooth
    table=trisurf.Statistics(run.Dir.fullpath(),filename=filename).getTable()
    plt.plot(table[column], '.')
    plt.title(run.Dir.fullpath())
    plt.xlabel('Iteration')
    plt.ylabel(column)
    smooth_window=10
    smoothed=smooth(table[column],smooth_window)
    plt.plot(tuple(range(int(smooth_window/2),len(smoothed)-int(smooth_window/2))),smoothed[int(smooth_window/2):-int(smooth_window/2)])
    plt.show()
    print
    #if return False or no return statement, the analysis will continue with next running instance in the list. if return True, the analysis will stop after this run.
    return False