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,
