From d2f7b0c053e93b4d409d9661dbaff2eedcf391c1 Mon Sep 17 00:00:00 2001
From: Samo Penic <samo.penic@gmail.com>
Date: Sat, 14 Jan 2017 22:06:35 +0000
Subject: [PATCH] Added analyses collection file into trisurf package
---
nir_log | 92 +++---------------------------
trisurf/analyses.py | 72 ++++++++++++++++++++++++
2 files changed, 82 insertions(+), 82 deletions(-)
diff --git a/nir_log b/nir_log
index 9deaece..947c729 100644
--- a/nir_log
+++ b/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]])
diff --git a/trisurf/analyses.py b/trisurf/analyses.py
new file mode 100644
index 0000000..9a063d7
--- /dev/null
+++ b/trisurf/analyses.py
@@ -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
+
+
--
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