def plotMappingArray(self): myData = aD.ArangeData() data_array = myData.readAnyFormat( config=False, mydtype=np.str_, mypath=self.mypath_handler['MAP_PLOT_FILE'], data_shape='shaped', skiprow=0, delim=' ') if np.prod(data_array.shape) == 1 or np.prod(data_array.shape) == 5: data_array = np.expand_dims(data_array, axis=0) #print data_array self.plot_map_array = {} j = 0 self.plot_map_array['run_nr'] = data_array[j, 1] while j < data_array[:, 0].size: self.plot_map_array['plot_map_id' + str(j)] = data_array[j, 0] self.plot_map_array[data_array[j, 0] + '_config'] = data_array[j, 1] self.plot_map_array['nbins_' + data_array[j, 0]] = data_array[j, 2] self.plot_map_array['data_offset_' + data_array[j, 0]] = data_array[j, 3] self.plot_map_array['load_obs_' + data_array[j, 0]] = data_array[j, 4] j += 1 self.plot_map_array['nr_plot_keys'] = j
def plotKeywords(self, myconfig_datafile='myconfig.txt'): self.plot_config_array = {} myData = aD.ArangeData() data_array = myData.readAnyFormat(config=False, mydtype=np.str_, mypath=myconfig_datafile, data_shape='shaped', comment='#', delim='= ') i = 0 while i < data_array[:, 0].size: self.plot_config_array[data_array[i, 0]] = data_array[i, 1] i += 1
def __init__(self): myData = aD.ArangeData() data_array = myData.readAnyFormat( config=False, mydtype=np.str_, mypath=mycomp + 'anaconda/pro/myRun/run_config/mypath_handler.txt', data_shape='shaped', comment='#') self.mypath_handler = {} j = 0 while j < data_array[:, 0].size: self.mypath_handler[str(data_array[j, 0])] = data_array[j, 1] j += 1
def SAMScaleFactorMapping(self): self.SAM_scale_factor_map = {} myData = aD.ArangeData() data_array = myData.readAnyFormat( config=False, mydtype=np.str_, mypath=self.mypath_handler['SAM_SCALE_FACTOR_MAPPING'], data_shape='shaped', comment='#', delim=' ') #print data_array def check_data(data_array): try: if data_array.size == 4: data_array = np.expand_dims(data_array, axis=0) if data_array[0, 2] != 'None': i = 0 while i < data_array[:, 0].size: self.SAM_scale_factor_map[data_array[i, 0] + '_redshift' + str(i)] = float( data_array[i, 2]) self.SAM_scale_factor_map[data_array[i, 0] + '_snapid' + str(i)] = data_array[i, 1] self.SAM_scale_factor_map[data_array[i, 0] + '_scale_factor' + str(i)] = float( data_array[i, 3]) i += 1 except: print 'data_array=None' print 'HERE:', data_array check_data(data_array) #print self.SAM_scale_factor_map return self.SAM_scale_factor_map
def filterDataConfig(self): self.mycond_config_array = {} myData = aD.ArangeData() data_array = myData.readAnyFormat( config=False, mydtype=np.str_, mypath=self.mypath_handler['MYCUT_VALUES_CONFIGFILE'], data_shape='shaped', comment='#', delim=' ') if data_array.size == 10: data_array = np.expand_dims(data_array, axis=0) #print data_array i = 0 while i < data_array[:, 0].size: self.mycond_config_array['name' + str(i)] = data_array[i, 0] self.mycond_config_array[data_array[i, 0] + '_min'] = data_array[i, 1] self.mycond_config_array[data_array[i, 0] + '_max'] = data_array[i, 2] self.mycond_config_array[data_array[i, 0] + '_unit'] = data_array[i, 3] self.mycond_config_array[data_array[i, 0] + '_data_type'] = data_array[i, 4] self.mycond_config_array[data_array[i, 0] + '_format'] = data_array[i, 5] self.mycond_config_array[data_array[i, 0] + '_col_id'] = data_array[i, 6] self.mycond_config_array[data_array[i, 6] + '_name'] = data_array[i, 0] self.mycond_config_array[data_array[i, 0] + '_name_in_plot'] = data_array[i, 7] self.mycond_config_array[data_array[i, 0] + '_exclude'] = data_array[i, 8] self.mycond_config_array[data_array[i, 0] + '_output_col_name'] = data_array[i, 9] i += 1 self.mycond_config_array['nr_entries'] = i #print self.mycond_config_array return self.mycond_config_array
def histoConfigs(self): self.histo_config_array = {} myData = aD.ArangeData() data_array = myData.readAnyFormat( config=False, mydtype=np.str_, mypath=self.mypath_handler['HISTO_CONFIGFILE'], data_shape='shaped', comment='#', delim=': ') if data_array.size == 2: data_array = np.expand_dims(data_array, axis=0) i = 0 while i < data_array[:, 0].size: self.histo_config_array[data_array[i, 0]] = data_array[i, 1] i += 1
def physicsSpecs(self): self.physics_specs = {} myData = aD.ArangeData() data_array = myData.readAnyFormat( config=False, mydtype=np.str_, mypath=self.mypath_handler['MY_PHYSICS_SPECS'], data_shape='shaped', comment='#', delim='= ') if data_array.size == 2: data_array = np.expand_dims(data_array, axis=0) i = 0 while i < data_array[:, 0].size: self.physics_specs[data_array[i, 0]] = data_array[i, 1] i += 1
def nameConvMap(self): self.name_conv_map = {} myData = aD.ArangeData() data_array = myData.readAnyFormat( config=False, mydtype=np.str_, mypath=self.mypath_handler['MY_NAME_CONV_MAP_FILE'], data_shape='shaped', comment='#', delim=' ') if data_array.size == 2: data_array = np.expand_dims(data_array, axis=0) i = 0 while i < data_array[:, 0].size: #print 'i:', i, data_array[i,0], '-->', data_array[i,1] self.name_conv_map[data_array[i, 0]] = data_array[i, 1] i += 1
def getCUTEParameterFile(self): self.CUTE_params = {} myData = aD.ArangeData() data_array = myData.readAnyFormat( config=False, mydtype=np.str_, mypath=self.mypath_handler['MYCUTE_PARAMFILE'], data_shape='shaped', comment='#', delim='= ') i = 0 while i < data_array[:, 0].size: self.CUTE_params['name' + str(i)] = data_array[i, 0] self.CUTE_params[data_array[i, 0] + '_param'] = data_array[i, 1] i += 1 self.CUTE_params['nr_entries'] = i return self.CUTE_params
def plotMarkerColorKewords(self): myData = aD.ArangeData() data_array = myData.readAnyFormat( config=False, mydtype=np.str_, mypath=self.mypath_handler['MARKER_COLOR_MAP_FILE'], data_shape='shaped', skiprow=0, delim=' ') if data_array.size == 6: data_array = np.expand_dims(data_array, axis=0) self.plot_marker_color_map_array = {} j = 0 while j < data_array[:, 0].size: self.plot_marker_color_map_array['catname' + str(j)] = data_array[j, 0] self.plot_marker_color_map_array[data_array[j, 0] + '_marker_code'] = data_array[j, 1] if len(data_array[j, 2]) == 1: self.plot_marker_color_map_array[data_array[j, 0] + '_color_code'] = data_array[j, 2] else: self.plot_marker_color_map_array[ data_array[j, 0] + '_color_code'] = '#' + data_array[j, 2] self.plot_marker_color_map_array[data_array[j, 0] + '_linestyle_code'] = data_array[j, 3] self.plot_marker_color_map_array[data_array[j, 0] + '_color_map'] = data_array[j, 4] self.plot_marker_color_map_array[ data_array[j, 0] + '_marker_facecolor_code'] = data_array[j, 5] j += 1
def snapIdzRed(self, data, myconfig_datafile, catkey, inputfilename_part1, snapid, inputfilename_part2, file_format, use_snapidz_mapping, config_array): #print 'HERE!', use_snapidz_mapping if use_snapidz_mapping == 'True': mySnap = aD.ArangeData() if catkey.startswith('sussing') == -1: snap_array = mySnap.readAnyFormat(config=False, mypath=myconfig_datafile, data_shape='unshaped', nr_col=6, mydat_off=0, nr_rows=120, skiprow=1) else: snap_array = mySnap.readAnyFormat(config=False, mypath=myconfig_datafile, data_shape='shaped', comment='#', delim='\t') #print 'snap_array' i = 0 while i < snap_array[:, 0].size: if int(snap_array[i, 0]) < 10: if len(snapid) == 3: #061 name_correction = '00' else: name_correction = '0' elif int(snap_array[i, 0]) >= 100: name_correction = '' else: if len(snapid) == 3: name_correction = '0' else: name_correction = '' if config_array[catkey + '_data_format'] == 'HDF5': key = inputfilename_part1 + '_' + name_correction + str( int(snap_array[i, 0])) else: key = inputfilename_part1 + name_correction + str( int(snap_array[i, 0])) + inputfilename_part2 + file_format a = 0 while a < config_array[catkey + '_nr_zs_count']: if catkey.startswith('sussing') == -1: self.snapid_array[catkey + '_' + key + '_snapid' + str(a)] = { 'id': key, 'a': snap_array[i, 1], 'z': snap_array[i, 2], 't(t0)': snap_array[i, 3], 't(year)': snap_array[i, 4] } else: self.snapid_array[catkey + '_' + catkey + '_snapid' + str(a)] = { 'id': key, 'a': snap_array[i, 2], 'z': snap_array[i, 1], 't(year)': snap_array[i, 3] } a += 1 i += 1 else: i = 0 while i < config_array[catkey + '_nr_zs_count']: #print 'key_filename part1:', inputfilename_part1, 'snapid:', snapid, 'part2:', inputfilename_part2 if config_array[catkey + '_create_subcat'] == 'True': #print 'create subcat!' #print 'scale_factor_map:', self.SAM_scale_factor_map my_z = self.SAM_scale_factor_map[catkey + '_redshift' + str(i)] elif config_array[ catkey + '_use_snapidz_mapping'] == 'False' and config_array[ catkey + '_load_from_file'] == 'False' and config_array[ catkey + '_load_subcat'] != 'True': #print 'snapidz_mapping False!' my_z = config_array[catkey + '_snapid' + str(i)] elif config_array[catkey + '_load_from_file'] == 'True' or config_array[ catkey + '_load_subcat'] == 'True': #print 'manual z:', config_array[catkey+'_manual_input_redshift'] my_z = config_array[catkey + '_manual_input_redshift'] if inputfilename_part1 == '': self.snapid_array[catkey + '_snapid' + str(i)] = { 'z': my_z } else: self.snapid_array[catkey + '_' + inputfilename_part1 + '_snapid' + str(i)] = { 'z': my_z } i += 1 print 'self.snapid_array:', self.snapid_array
def load_matplot_stylefiles(self, catname, ncolours=0): color = {} linestyle = {} marker = {} markercol = {} colormap = {} colorbar = {} plottype = {} alpha = {} plotlegend = {} add_xaxis = {} add_yaxis = {} myData = aD.ArangeData() mypaths = { '0': self.mypath_handler['MATPLOT_LINESTYLE'], 'dic0': linestyle, '1': self.mypath_handler['MATPLOT_COL'], 'dic1': color, '2': self.mypath_handler['MATPLOT_MARKERSTYLE'], 'dic2': marker, '3': self.mypath_handler['MATPLOT_MARKERCOL'], 'dic3': markercol, '4': self.mypath_handler['MATPLOT_COLORMAP'], 'dic4': colormap, '5': self.mypath_handler['MATPLOT_COLORBAR'], 'dic5': colorbar, '6': self.mypath_handler['MATPLOT_PLOTTYPE'], 'dic6': plottype, '7': self.mypath_handler['MATPLOT_ALPHA'], 'dic7': alpha, '8': self.mypath_handler['MATPLOT_PLOTLEGEND'], 'dic8': plotlegend, '9': self.mypath_handler['MATPLOT_ADD_XAXIS'], 'dic9': add_xaxis, '10': self.mypath_handler['MATPLOT_ADD_YAXIS'], 'dic10': add_yaxis } a = 0 while a <= 10: data_array = myData.readAnyFormat(config=False, mydtype=np.str_, mypath=mypaths[str(a)], data_shape='shaped', comment='', delim='= ') #print 'mypath:', mypaths[str(a)] #print 'data_array:', data_array if data_array.size == 2: data_array = np.expand_dims(data_array, axis=0) i = 0 while i < data_array[:, 0].size: mypaths['dic' + str(a)].update( {data_array[i, 0]: data_array[i, 1]}) i += 1 a += 1 if ncolours != 0: import distinct_colours as cb_col #print 'use color-blind friendly colors!' # print catname cb_colours = cb_col.get_distinct(ncolours) if ncolours == 1: if catname.find('Galacticus') != -1: #print 'Gal' color['0'] = '#225588' elif catname.find('SAG_') != -1 or str(catname).find( 'SAG_1Gpc_v2') != -1: #print 'SAG' color['0'] = '#CC6677' elif catname.find('SAGE') != -1: #print 'SAGE' color['0'] = '#DDCC77' else: #print 'else' color['0'] = cb_colours[0] else: i = 0 while i < ncolours: color[str(i)] = cb_colours[i] i += 1 return linestyle, color, marker, markercol, colormap, colorbar, plottype, alpha, plotlegend, add_xaxis, add_yaxis
def readMyRunConfig(self, read_filenr_sequence=False, myfile_format='.txt'): data = {} self.snapid_array = {} myData = aD.ArangeData() data_array = myData.readAnyFormat( config=False, mydtype=np.str_, mypath=self.mypath_handler['RUNFILE'], data_shape='shaped', comment='#') my_count = np.char.count(data_array, ':') data['nr_cats'] = np.sum(my_count) j = 0 count = 0 while j < np.sum(my_count): i = 0 # i=redshift a = 0 # a=filenr #print 'j:', j, 'i:', i, 'count:', count start = data_array[i + count].find(':') key_catname = 'catname' + str(j) data[key_catname] = data_array[i + count][start + 1::] i += 1 key = data[key_catname] + '_mysoftlink_dir' data[key] = data_array[i + count] #print 'data[key]:', data[key] i += 1 key = data[key_catname] + '_mydir' data[key] = data_array[i + count] #print 'data[key]:', data[key] i += 1 cat_test = -1 data = self.myConfigArray( data, data[key] + data[key_catname] + '_config.txt', data[key_catname]) self.snapIdzRed(self.snapid_array, data[key] + 'snapidzred.txt', data[key_catname], data[data[key_catname] + '_inputfilename_part1'], data[data[key_catname] + '_snapid0'], data[data[key_catname] + '_inputfilename_part2'], myfile_format, data[data[key_catname] + '_use_snapidz_mapping'], data) if read_filenr_sequence == True: read_filenr_from = int( data_array[i + count][len(data[data[key_catname] + '_inputfilename_part1'])::]) read_filenr_until = int( data_array[i + count + 1][len(data[data[key_catname] + '_inputfilename_part1'])::]) data['read_filenr_from'] = read_filenr_from data['read_filenr_until'] = read_filenr_until #print 'from:', read_filenr_from, 'until:', read_filenr_until while cat_test <= -1 and i + count != data_array.size: #print 'CAT_test! i:', i, 'a:', a if read_filenr_sequence == True: b = read_filenr_from while b < read_filenr_until + 1: key = data[key_catname] + '_filename' + str(b) data[key] = data[key_catname] + '_' + data[ data[key_catname] + '_inputfilename_part1'] + str(b) b += 1 else: #print 'key:', key key = data[key_catname] + '_filename' + str(a) data[key] = data[key_catname] + '_' + data_array[i + count] #print 'data[key]:', data[key] if key.find('DIV') != -1: self.snapIdzRed( self.snapid_array, data[key] + 'snapidzred.txt', data[key_catname], data[data[key_catname] + '_inputfilename_part1'], data[data[key_catname] + '_snapid'], data[data[key_catname] + '_inputfilename_part2'], myfile_format, data[data[key_catname] + '_use_snapidz_mapping'], key_filename=data[key]) i += 1 a += 1 if i + count != data_array.size: #print 'i+count:', i+count, 'mydata size', data_array.size cat_test = data_array[i + count].find(':') #print 'cat_test:', cat_test count = count + i j += 1 #Count nr of Redshifts of data['nr_zs'] = a #print 'snapid_array:', self.snapid_array self.config_array = data
def myConfigArray(self, data, myconfig_datafile, catkey): data_array = mL.myMultiColDetector(myconfig_datafile) myData = aD.ArangeData() unit_corr_array = myData.readAnyFormat( config=False, mydtype=np.str_, mypath=self.mypath_handler['MY_UNIT_CORRECT_FILE'], data_shape='shaped', skiprow=0, delim=' ') if np.prod(unit_corr_array.shape) == 3: unit_corr_array = np.expand_dims(unit_corr_array, axis=0) # analyse_tarsel_array= myData.readAnyFormat(config=False, mydtype=np.str_, mypath=self.mypath_handler['ANALYSE_TARSEL_CONFIGFILE'], data_shape='shaped', skiprow=0, delim=' ') # # if np.prod(unit_corr_array.shape)==2: # analyse_tarsel_array = np.expand_dims(analyse_tarsel_array, axis=0) unit_map_array = {} i = 0 while i < unit_corr_array[:, 0].size: multicol_test = mL.multicolTestAlgorithm(unit_corr_array[i, 1]) c = 0 while c < multicol_test.size: unit_map_array[catkey + '_unit_corr_' + unit_corr_array[i, 0] + str(c)] = multicol_test[c] unit_map_array[catkey + '_data_type_' + unit_corr_array[i, 0]] = unit_corr_array[i, 2] unit_map_array[catkey + '_unit_corr_' + unit_corr_array[i, 0] + '_nr_corrs'] = c + 1 #print 'i:', i, 'name:', unit_corr_array[i,0], 'c:', c,'multicol_test[c]:', multicol_test[c], '[i,1]:', unit_corr_array[i,1],'[i,2]:', unit_corr_array[i,2], unit_map_array[catkey+'_unit_corr_'+unit_corr_array[i,0]+str(c)] #print 'data_type:', unit_map_array[catkey+'_data_type_'+unit_corr_array[i,0]], 'unit_corr:', unit_map_array[catkey+'_unit_corr_'+unit_corr_array[i,0]+str(c)], 'nr_corrs:', unit_map_array[catkey+'_unit_corr_'+unit_corr_array[i,0]+'_nr_corrs'] #print '------------' #print ' ' c += 1 i += 1 #unit_map_array[catkey+'_unit_corr_'+unit_corr_array[i,0]] = unit_corr_array[i+c,1] #unit_map_array[catkey+'_data_type_'+unit_corr_array[i,0]] = unit_corr_array[i+c,2] #i+=1 data[catkey + '_id_col_array'] = {} data[catkey + '_halo_id_col_array'] = {} data[catkey + '_analyse_tarsel_id_col_array'] = {} i = 0 x = 0 y = 0 z = 0 nr_cols2read_col = 0 nr_cols2read_halo_col = 0 nr_cols2read_tarsel_col = 0 while i < data_array[:, 0].size: #print 'i:', i, 'a:', data_array[i,1], data_array[i,:] #print 'name', data_array[i,2], 'value:', data_array[i,0] #print data_array[i,2] data[catkey + '_' + data_array[i, 2]] = mL.check_datatype( data_array[i, 0]) if data_array[i, 2].startswith('col_'): #print 'here: assembly id_col_array!' if data_array[i, 0] != str(99): nr_cols2read_col += 1 p = 0 while p < unit_map_array[catkey + '_unit_corr_' + data_array[i, 2][4::] + '_nr_corrs']: #print 'x:', x, data_array[i,2][4::], 'p:', 'nr_corrs:', unit_map_array[catkey+'_unit_corr_'+data_array[i,2][4::]+'_nr_corrs'] #print unit_map_array[catkey+'_unit_corr_'+data_array[i,2][4::]+str(p)] #print data[catkey+'_unit_corr_'+unit_map_array[catkey+'_unit_corr_'+data_array[i,2][4::]+str(p)]] data[catkey + '_id_col_array'].update({ 'name' + str(x): data_array[i, 2][4::], data_array[i, 2][4::] + '_col_id': data[catkey + '_' + data_array[int(data_array[i, 1]), 2]], str(data[catkey + '_' + data_array[int(data_array[i, 1]), 2]]) + '_name' + str(x): data_array[i, 2][4::], 'data_type' + str(x): unit_map_array[catkey + '_data_type_' + data_array[i, 2][4::]], 'col_id' + str(x): data[catkey + '_' + data_array[int(data_array[i, 1]), 2]], 'corr_type' + str(x): unit_map_array[catkey + '_unit_corr_' + data_array[i, 2][4::] + str(p)], 'unit_corr' + str(x): data[catkey + '_unit_corr_' + unit_map_array[catkey + '_unit_corr_' + data_array[i, 2][4::] + str(p)]], 'conv_to_AB_mag' + str(x): data[catkey + '_conv_to_AB_mag'] }) p += 1 x += 1 if data_array[i, 2].startswith('halo_col_'): #print 'here: assembly id_col_array!' nr_cols2read_halo_col += 1 if data_array[i, 0] != str(99): #print 'y:', y, data_array[i,2][9::] #print unit_map_array[catkey+'_unit_corr_'+data_array[i,2][9::]] #print data[catkey+'_unit_corr_'+unit_map_array[catkey+'_unit_corr_'+data_array[i,2][9::]]] data[catkey + '_halo_id_col_array'].update({ 'name' + str(y): data_array[i, 2][4::], data_array[i, 2][9::] + '_col_id': data[catkey + '_' + data_array[int(data_array[i, 1]), 2]], 'data_type' + str(y): unit_map_array[catkey + '_data_type_' + data_array[i, 2][9::]], 'col_id' + str(y): data[catkey + '_' + data_array[int(data_array[i, 1]), 2]], 'corr_type' + str(y): unit_map_array[catkey + '_unit_corr_' + data_array[i, 2][9::]], 'unit_corr' + str(y): data[catkey + '_unit_corr_' + unit_map_array[catkey + '_unit_corr_' + data_array[i, 2][9::]]], 'dummy' + str(y): 'dummy' }) y += 1 if data_array[i, 2].startswith('analyse_tarsel_'): #print 'here: assembly id_col_array!' nr_cols2read_tarsel_col += 1 name_map = {} k = 1 find_name_before = len('analyse_tarsel_') if data_array[i, 2].find('hist') != -1: #print 'analyse_tarsel_HIST!!!' name_map.update({ 'col_name1': data[catkey + '_id_col_array']['name0'], 'id_col_1': 0, 'col_name2': data[catkey + '_id_col_array']['name0'], 'id_col_2': 0 }) else: while k < 3: #print 'name', data_array[i,2], 'value:', data_array[i,0], 'k:', k, 'find_name_before:', find_name_before, 'len of name to find:', len(data_array[i,2]) find_name = data_array[i, 2][find_name_before::].find('-') if find_name != -1: try: #print data_array[i,2][find_name_before+find_name-1:find_name_before+find_name+2] find_band_name = data_array[ i, 2][find_name_before + find_name - 1:find_name_before + find_name + 2] find_minus_sign = find_band_name.find('-') #correct the minus sign #print 'minus:', find_minus_sign col_name = 'mAB_dA_total_cut_' + find_band_name[ find_minus_sign - 1:find_minus_sign] + '_' + find_band_name[ find_minus_sign + 1::] #print 'col_name:', col_name try: id_col = data[catkey + '_id_col_array'][col_name + '_col_id'] except: try: #print '-: try2:', col_name = 'mAB_dA_total_' + find_band_name[ 0] id_col = data[catkey + '_id_col_array'][ col_name + '_col_id'] #print 'col_name:', col_name except: col_name = 'MAB_dA_total_' + find_band_name[ 0] id_col = data[catkey + '_id_col_array'][ col_name + '_col_id'] #print 'col_name:', col_name except: id_col = 99 data[catkey + '_id_col_array'].update({ 'name': col_name, col_name + '_col_id': id_col }) find_name = 3 else: #print 'find_name_before:', find_name_before find_name = data_array[ i, 2][find_name_before::].find('_') #print 'find_name else:', find_name if find_name == 0: find_name_before += 1 find_name = len( data_array[i, 2]) - find_name_before #print 'find_name+1:', find_name_before, 'find_name:', find_name try: try: #print 'col name --> mAB!' col_name = 'mAB_dA_total_' + data_array[ i, 2][find_name_before:find_name_before + find_name] print 'try:', data_array[i, 2][ find_name_before:find_name_before + find_name], 'col_name:', col_name, data[ catkey + '_id_col_array'][col_name + '_col_id'] except: try: print 'try2:', col_name = 'mAB_dA_total_' + find_band_name[ 0] id_col = data[catkey + '_id_col_array'][ col_name + '_col_id'] #print 'col_name:', col_name except: col_name = 'MAB_dA_total_' + find_band_name[ 0] id_col = data[catkey + '_id_col_array'][ col_name + '_col_id'] #print 'col_name:', col_name except: try: if data_array[i, 2][ find_name_before:find_name_before + find_name].find('I') != -1: #print 'col name --> MAB!' col_name = 'MAB_total_' + data_array[ i, 2][find_name_before + 1:find_name_before + 1 + find_name - 1] else: col_name = 'mAB_total_' + data_array[ i, 2][find_name_before: find_name_before + find_name] print 'try3:', data[ catkey + '_id_col_array'][col_name + '_col_id'] except: try: col_name = 'mAB_dA_total_cut_' + data_array[ i, 2][find_name_before: find_name_before + find_name] print data[catkey + '_id_col_array'][col_name + '_col_id'] except: if col_name.find('rhalfmass') != -1: col_name = 'rhalf_mass' elif col_name.find('rhalfd') != -1: col_name = 'rhalf_disk' elif col_name.find('rhalfb') != -1: col_name = 'rhalf_bulge' elif col_name.find('NFW') != -1: col_name = 'NFW_con' elif col_name.find('mhalo200c') != -1: col_name = 'mhalo_200c' elif col_name.find('mbasic200c') != -1: col_name = 'mbasic_200c' elif col_name.find('angMdisk') != -1: col_name = 'angM_disk' elif col_name.find( 'angMspheroid') != -1: col_name = 'angM_spheroid' elif col_name.find('mbar') != -1: col_name = 'mstar' elif col_name.find('bdisk') != -1: col_name = 'angM_disk' elif col_name.find('bbulge') != -1: col_name = 'angM_spheroid' else: col_name = data_array[ i, 2][find_name_before: find_name_before + find_name] print 'except:', col_name try: #print 'except2:', col_name, if catkey == 'SAGE_1Gpc' and col_name == 'mcold': col_name = 'mcold_disk' #if catkey=='Galacticus_1Gpc' and col_name=='Mzgas': col_name='zgas_spheroid' #print data[catkey+'_id_col_array'][col_name+'_col_id'] except: #print 'col_name:', col_name, 'total name:', data_array[i,2][find_name_before::], find_name = data_array[ i, 2][find_name_before + len(col_name) + 1::].find('_') #print 'except3:', 'find name:', find_name, col_name = data_array[ i, 2][find_name_before: find_name_before + len(col_name) + 1 + find_name] find_name = len(col_name) #print 'new col_name:', col_name, 'new find_name_before:', find_name_before, #print data[catkey+'_id_col_array'][col_name+'_col_id'] find_name_before += find_name #name_map.update({'col_name'+str(k): col_name, 'id_col_'+str(k): id_col}) name_map.update({'col_name' + str(k): col_name}) k += 1 print 'name_map:', name_map #data[catkey+'_analyse_tarsel_id_col_array'].update({'name'+str(z): data_array[i,2], data_array[i,2]+'_col_name1': name_map['col_name1'], data_array[i,2]+'_col_name_id1': data[catkey+'_id_col_array'][name_map['col_name1']+'_col_id'], data_array[i,2]+'_col_name2': name_map['col_name2'], data_array[i,2]+'_col_name_id2': data[catkey+'_id_col_array'][name_map['col_name2']+'_col_id']}) data[catkey + '_analyse_tarsel_id_col_array'].update({ 'name' + str(z): data_array[i, 2], data_array[i, 2] + '_col_name1': name_map['col_name1'], data_array[i, 2] + '_col_name2': name_map['col_name2'] }) z += 1 if data_array[i, 2].startswith('snapid'): snapidID = int(data_array[i, 1]) data[catkey + '_snapid0'] = data_array[i, 0] #print 'i:', i, 'snapid'+str(0), data[catkey+'_snapid'+str(0)] a = i + 1 count = 1 while a < data_array[:, 0].size: if data_array[a, 1] == str(snapidID): data[catkey + '_snapid' + str(count)] = data_array[a, 0] #print 'i:', i, 'a:', a, data_array[a,:], 'snapid'+str(count), data[catkey+'_snapid'+str(count)] count += 1 a += 1 i += count - 1 data[catkey + '_nr_zs_count'] = count i += 1 data[catkey + '_id_col_array'].update({'nr_entries': x}) data[catkey + '_id_col_array'].update( {'nr_cols2read': nr_cols2read_col}) data[catkey + '_halo_id_col_array'].update({'nr_entries': y}) data[catkey + '_halo_id_col_array'].update( {'nr_cols2read': nr_cols2read_halo_col}) data[catkey + '_analyse_tarsel_id_col_array'].update({'nr_entries': z}) return data
# Load packages import config as conf myConfig = conf.Configuration() import scipy as scipy import arangeData as aD myData = aD.ArangeData() import myLib as mL import numpy as np #from astropy.cosmology import Planck13 as cosmo #import astropy.constants as const #import cosmolopy as cosfunc import os system_info = os.getcwd() start = system_info.find('anaconda') mycomp = system_info[:start] import outputData as oD myOutput = oD.OutputData(config=False) from time import time class MyFunctions: def normaliseHisto(self, histo,