from . import trisurf
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def analysis(analysis_name='unnamed_analysis'):
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"""Decorator for adding the analysis functions to function lists"""
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def analysis_decorator(analysis_function):
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trisurf._analysis_list[analysis_name]=analysis_function
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def wrapper(*args, **kwargs):
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analysis_function(*args,**kwargs)
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return wrapper
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return analysis_decorator
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@analysis('demo')
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def demo(run, **kwargs):
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host=kwargs.get('host', None)
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print("Demo analysis")
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print("Analysis on host "+host['name']+" for run "+run.Dir.fullpath()+" completed")
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print("here comes info on the run variable:")
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print(run)
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print("here comes info on the host variable:")
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print(host)
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print("here comes info on the args variable:")
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print(kwargs.get('args',None))
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# can be wrapped to specify scalar_field)
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@analysis('plotrunningavginteractive')
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def plotrunningavginteractive(run, scalar_field='vertices_idx', **kwargs):
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import matplotlib.pyplot as plt
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from trisurf import VTKRendering as vtk
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import math
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from multiprocessing import Process
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table=trisurf.Statistics(run.Dir.fullpath(),filename='data_tspoststat.csv').getTable()
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def running_avg(col):
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import numpy as np
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avg=[]
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for i in range(0,len(col)):
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avg.append(np.average(col[:-i]))
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return avg
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def spawned_viewer(n):
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vtk.Renderer(kwargs.get('args', None),kwargs.get('host',None),run, timestep=n,scalar_field=scalar_field)
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fig=plt.figure(1)
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ra=running_avg(table['hbar'])
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l=len(table['hbar'])
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plt.plot(ra)
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plt.title('Running average')
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plt.ylabel('1/n sum_i=niter^n(hbar_i)')
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plt.xlabel('n')
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def onclick(event):
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#print('button=%d, x=%d, y=%d, xdata=%f, ydata=%f' % (event.button, event.x, event.y, event.xdata, event.ydata))
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p=Process(target=spawned_viewer, args=(l-math.floor(event.xdata)-1,))
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p.start()
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cid = fig.canvas.mpl_connect('button_press_event', onclick)
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plt.show()
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plt.close(1)
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# -------------------------------
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# these functions should be wrapped
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# -------------------------------
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@analysis('plotColumnFromPostProcess')
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def plotColumnFromPostProcess(run, filename='data_tspoststat.csv', column='hbar', **kwargs):
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import matplotlib.pyplot as plt
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def smooth(y, box_pts):
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import numpy as np
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box = np.ones(box_pts)/box_pts
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y_smooth = np.convolve(y, box, mode='same')
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return y_smooth
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table=trisurf.Statistics(run.Dir.fullpath(),filename=filename).getTable()
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plt.plot(table[column], '.')
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plt.title(run.Dir.fullpath())
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plt.xlabel('Iteration')
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plt.ylabel(column)
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smooth_window=10
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smoothed=smooth(table[column],smooth_window)
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plt.plot(tuple(range(int(smooth_window/2),len(smoothed)-int(smooth_window/2))),smoothed[int(smooth_window/2):-int(smooth_window/2)])
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plt.show()
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print
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#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.
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return False
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