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from . import trisurf |
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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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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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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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