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)