def _Flip_X_Axis_fired(self):
global files_selected
for ifile in files_selected.file_list:
this_plot = jpl.Plotting(plot_info={'save_file': ifile})
if not os.path.isfile(this_plot.PickleFile):
print('FNF:', this_plot.PickleFile)
pass
this_plot.LoadPickle(DefWipe=False)
this_plot.Flip_X_Axis()
this_plot.PlotAll()
this_plot.close_fig()
def UpdateFileList(plot_info, rc_params, file_list, window_size=None):
for ifile in file_list:
this_plot = jpl.Plotting(plot_info={'save_file': ifile})
if not os.path.isfile(this_plot.PickleFile):
print('FNF:', this_plot.PickleFile)
pass
else:
print('Updating:', this_plot.PickleFile)
if window_size is not None:
this_plot.LoadPickle(DefWipe=False, ForceWindowSize=window_size)
else:
this_plot.LoadPickle(DefWipe=False)
this_plot.UpdateInfo(plot_info)
pl.rcParams.update(rc_params)
this_plot.PlotAll()
this_plot.close_fig()
def TestAuto():
'''
testing function for standard autocorrelation analysis
'''
def thisFun(*x):
return x[0]
def thisDer(*x):
return [1]
const = 100
this_size = 20000
values = np.random.uniform(size=this_size)
values2 = np.arange(this_size)/this_size
values3 = np.random.normal(loc=0.5,scale=0.25,size=this_size)
val_df = pa.DataFrame()
# tuple_list = []
# for ii in range(100):
# tuple_list.append(('-1-',ii))
# for ii in range(400):
# tuple_list.append(('-2-',ii))
# for ii in range(1000):
# tuple_list.append(('-3-',ii))
# for ii in range(500):
# tuple_list.append(('-4-',ii))
tuple_list = []
for ii in range(this_size//2):
tuple_list.append(('-1-',ii))
for ii in range(this_size//2):
tuple_list.append(('-2-',ii))
#
# tuple_list = []
# for ii in range(this_size):
# tuple_list.append(('-1-',ii))
indicies = pa.MultiIndex.from_tuples(tuple_list,names=['stream','configs'])
# indicies = range(this_size)
# val_df = pa.DataFrame()
# val_df['one'] = pa.Series(values,index=indicies)
# val_df['two'] = pa.Series(values2,index=indicies)
# val_df['three'] = pa.Series(values3,index=indicies)
# def RatFun(one,two,three):
# return const*one/(two*three)
#
# def RatFunDer(one,two,three):
# return [const/(two*three),-const*one/(three*two**2),-const*one/(two*three**2)]
val_df = pa.DataFrame()
val_df['one'] = pa.Series(values,index=indicies)
val_df['two'] = pa.Series(values2,index=indicies)
val_df['three'] = pa.Series(values3,index=indicies)
def RatFun(one,two):
return const*one*two
def RatFunDer(one,two):
return [const*two,const*one]
testdata = AutoCorrelate(Fun=[thisFun,thisDer],name='test_bootstrap_uniform',data=val_df[['one']])
testdata2 = AutoCorrelate(Fun=[thisFun,thisDer],name='test_bootstrap_arange',data=val_df[['two']])
testdata3 = AutoCorrelate(Fun=[thisFun,thisDer],name='test_bootstrap_normal',data=val_df[['three']])
testdatarat = AutoCorrelate(Fun=[RatFun,RatFunDer],name='test_auto_ratio',data=val_df[['one','two']])
this_info = pa.Series()
this_info['save_file'] = this_dir+'/TestGraphs/test_Wopt.pdf'
this_info['title'] = 'Test Auto Graph'
# this_info['xlims'] = [0,10]
# this_info['ylims'] = [0,15]
import PlotData as jpl
data_plot = jpl.Plotting(plot_info=this_info)
data_plot = testdata.PlotWopt(data_plot)
data_plot = testdata2.PlotWopt(data_plot)
data_plot = testdata3.PlotWopt(data_plot)
data_plot = testdatarat.PlotWopt(data_plot)
# data_plot.LoadPickle(DefWipe=False)
data_plot.PrintData()
data_plot.PlotAll()
this_info = pa.Series()
this_info['save_file'] = this_dir+'/TestGraphs/test_Auto.pdf'
this_info['title'] = 'Test Auto Graph'
# this_info['xlims'] = [0,10]
# this_info['ylims'] = [0,15]
import PlotData as jpl
data_plot = jpl.Plotting(plot_info=this_info)
data_plot = testdata.PlotTauInt(data_plot)
data_plot = testdata2.PlotTauInt(data_plot)
data_plot = testdata3.PlotTauInt(data_plot)
data_plot = testdatarat.PlotTauInt(data_plot)
# data_plot.LoadPickle(DefWipe=False)
data_plot.PrintData()
data_plot.PlotAll()
return testdata,testdata2,testdata3,testdatarat
\hoffset -1.5cm
\headsep 1.5cm
\parindent 1.2em
\baselineskip 16pt plus 2pt minus 2pt
\begin{document}
\tiny
'''
this_info = pa.Series()
mat_graph_folder = data_dir + 'MatHack/'
mkdir_p(mat_graph_folder)
this_info['save_file'] = mat_graph_folder + 'FitrComp.pdf'
this_info['title'] = r'fitr comp'
this_info['xlabel'] = r'$\sqrt{8t}/\sqrt{8t_{0}}$'
this_info['ylabel'] = r'ratio'
data_plot = jpl.Plotting(plot_info=this_info)
table_out = {}
for iens, ifile in zip(master_ens_list, this_filelist):
for iblock in block_flags:
this_file = ifile.replace('.py3p', iblock + '.py3p')
if os.path.isfile(this_file):
print('Reading: ', this_file)
with open(this_file, 'rb') as f:
fit_data, dump = pik.load(f)
data_plot = fit_data.PlotVaryFitr(data_plot)
fit_data.SortChi()
table_out[fit_data.name] = fit_data.Get_Formatted_Table(
fmt_latex=True).to_latex(escape=False).replace(
'{}', fit_data.name.replace('_', r'\_'))
else:
print('FNF: ', this_file)
