def import_to_csv():
person = read_csv('personImport.txt')
post = read_csv('postImport.txt')
friend = read_csv('friendImport.txt')
write_csv('person.csv',person)
write_csv('post.csv',post)
write_csv('friend.csv',friend)
def lookup(title):
articles = read_csv('article.csv')
for a in articles:
for b in range(len(a)):
if b == 1 and a[b] == title:
print("Title: " + a[b] + " \nBody: " + a[b + 1])
return articles
def test_all_users_from(path):
#if(path.exists):
users = read_csv(path)
for i in users:
fname = i[0]
lname = i[1]
email = i[2]
newUser = create_user(fname, lname, email)
test_user(newUser)
def change_body(title, new_body):
articles = read_csv('article.csv')
for a in articles:
for b in range(len(a)):
if b == 1 and a[b] == title:
a[b + 1] = new_body
write_csv('article.csv', articles)
print(articles)
return articles
def select_articles():
articles = read_csv('article.csv')
arts = ""
for a in articles:
for b in range(len(a)):
if b == 1:
arts = arts + a[b] + ", "
print("Available Articles: " + arts)
return arts
def display_author_list():
authors = read_csv('author.csv')
z = ""
for a in authors:
for b in a:
if '@' not in b:
z = z + b + ', '
print(z)
return z
def print_author_email():
authors = read_csv('author.csv')
z = ""
for a in authors:
for b in a:
if '@' in b:
z = z + b + ', '
print(z)
return z
def change_email(old_email, new_email):
authors = read_csv('author.csv')
for a in authors:
for b in range(len(a)):
if a[b] == old_email:
a[b] = new_email
print(authors)
write_csv('author.csv', authors)
return (authors)
def delete_author(name):
authors = read_csv('author.csv')
for a in authors:
for b in range(len(a)):
if a[b] == name:
a[b] = ""
a[b + 1] = ""
print(authors)
write_csv('author.csv', authors)
return authors
def author_exists(name):
#Failing Test
#return read_csv('author.csv')
#Passing Test
authors = read_csv('author.csv')
#print (authors)
z = ""
for a in authors:
for b in a:
if b == name:
z = name + " is in the list!"
if z == "":
z = "Name not in list"
print(z)
return z
]
inner_cut = [0.0170, 0.0800, 0.0500, 0.0, 0.057, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
outer_cut = [
0.0700, 0.2500, 0.1400, 0.3000, 0.180, 0.2100, 0.2400, 0.1350, 0.1900,
0.1400, 0.0870
]
background_cut = [
0.0800, 0.3000, 0.1700, 0.4000, 0.2500, 0.2800, 0.3300, 0.2000, 0.2500,
0.1800, 0.1200
]
outer_back_cut = 10.0
for i in range(len(datafile)):
if i == 7: continue
if skip[i] == 1: continue
data = f.read_csv(datafile[i])
clus_data = gce.make_cut(data, center_ra[i], center_dec[i], inner_cut[i],
outer_cut[i])
back_data = gce.make_cut(data, center_ra[i], center_dec[i],
background_cut[i], outer_back_cut)
clus_out = datafile[i][:-4] + '_cluster.csv'
back_out = datafile[i][:-4] + '_background.csv'
if (f.write_csv(clus_data, clus_out)) == 1:
print '#-cluster file', datafile[
i], 'successfully cut and saved as', clus_out
else:
print '!!!AN ERROR OCCURED - FILE NOT CUT CORRECTLY!!!'
if (f.write_csv(back_data, back_out)) == 1:
print '#-data file', datafile[
i], 'successfully cut and saved as', back_out
else:
#! /usr/bin/env python #'Bang' line - modify as needed
import files as f
import GCplot_enc as gcp
"""This script quickly loads data and calls plotting functions.
Matthew Newby (RPI), Jan 11, 2011
"""
main_data = f.read_csv('HR_NGC_5053_cluster.csv')
back_data = f.read_csv('HR_NGC_5053_background.csv')
gcp.plot_infiles(main_data, back_data, GCname='5053', to_file=1)
def read_dist(csv_file):
rows = read_csv(csv_file, int)
dist = defaultdict(int)
for degree, count in rows:
dist[degree] = count
return dist
def user_list():
return read_csv('user.csv')
userList = read_csv('user.csv')
print(userList)
plot_file = 1
for cluster in range(len(NAME)):
if (SKIP[cluster] == 1): continue
for run in range(len(CONVOLVE[cluster])):
gc_name = NAME[cluster] +'_'+ str(CONVOLVE[cluster][run])
con_dist = CONVOLVE[cluster][run]
real_dist = DISTANCE[cluster]
in_area = AREA_IN[cluster]
back_area = AREA_BACK[cluster]
if (HIGH_CUTS != []): high_cut = HIGH_CUTS[run]
print '***** Starting run', gc_name, '*****'
main_data = f.read_csv(datafile[cluster])
back_data = f.read_csv(backfile[cluster])
con_clus = gce.convolve(main_data, real_dist, con_dist)
#don't convolve background! Background is static!!!
#Actually, DO convolve background! need to accurately remove it from cluster!
# If cluster moves before subtraction, background needs to move, too!
con_back = gce.convolve(back_data, real_dist, con_dist)
con_clus_f = gce.select_stars(con_clus, low_limit = 0.1)
con_back_f = gce.select_stars(back_data, low_limit = 0.1)
lc,wc = con_clus_f.shape
lb,wb = con_back_f.shape
cluster_x = sc.zeros(lc)
background_x = sc.zeros(lb)
r[i] = data_in[i][3]
m = sc.zeros((length,4))
for i in range(length):
m[i,0] = data_in[i][0]
m[i,1] = data_in[i][1]
m[i,2] = g[i]
m[i,3] = r[i]
return m
data_out = []
for cluster in range(len(NAME)):
if (SKIP[cluster] == 1): continue
con_dist = DISTANCE[cluster]
real_dist = DISTANCE[cluster]
gc_name = NAME[cluster]
main_data = f.read_csv(datafile[cluster])
l, w = main_data.shape
print '#-Starting run', gc_name
print '#-Total number of stars in cluster:', l
''' Sort the Data '''
blue, yellow, red = [], [], []
for i in range(l):
"""Remove stars from outside interesting range"""
if ( (main_data[i,2] - 5.*(m.log10(real_dist*1000) - 1.) ) < low_cut ): continue
if ( (main_data[i,2] - 5.*(m.log10(real_dist*1000) - 1.) ) > high_cut ): continue
g_minus_r = (main_data[i,2] - main_data[i,3])
if g_minus_r < BLUE_LIMIT:
blue.append(main_data[i,:])
elif g_minus_r > RED_LIMIT:
red.append(main_data[i,:])
else:
subs = []
"""Plot Initializations"""
fig = plt.figure()
plt.subplots_adjust(hspace=0.001, wspace=0.001)
#plt.title(save_name)
#plt.xlabel('g-r')
#plt.ylabel('Mg')
for i in range(len(gc_names)):
name = gc_names[i]
distance = gc_distances[i]
#metal = gc_metals[i]
HB_limit = cutoff[i]
"""Get Data"""
if (i == 10):
data_iso = f.read_data(('Iso_new_Pal5.dat'))
data_clus = f.read_csv('noU_Pal5_cluster.csv') #'HR_Pal_5_cluster.csv'
data_fid = f.read_data(('Pal5_2_fiducial_out.txt'))
else:
data_iso = f.read_data(('Iso_new_' + name + '.dat'))
#data_iso = f.read_data( ('Iso_series_'+name+'_A.dat') )
data_clus = f.read_csv('noU_NGC_' + name +
'_cluster.csv') #'HR_NGC_'+name+'_cluster.csv'
data_fid = f.read_data(
('NGC_' + name + '_' + str(fid_res[i]) + '_fiducial_out.txt'))
"""Setup Data for Plotting"""
mm = -0.01463023
bb = 0.08928602
iso_x_list, iso_y_list = [], []
for j in range(len(data_iso[:, 8])):
if (data_iso[j, 8] > HB_limit):
iso_x_list.append((data_iso[j, 8] - data_iso[j, 9]) +
def person_list():
return read_csv('person.csv')
import math as m
import numpy as np
import scipy as sc
import files as f
'''python script for finding SEGUE plate ra, dec.
Matthew Newby, Aug 16, 2010'''
data = f.read_csv('SEGUE_t2_mnewby.csv')
#plate,fiberid,ra,dec,l,b,feha,teffa,logga,alphafe,elodierv
length, width = data.shape
#finding number and ids of unique plates
plate_id = []
for i in range(length):
if (plate_id.count(data[i,0])==0):
plate_id.append(data[i,0])
print len(plate_id), plate_id
#finding min and max ra and dec for each plate
plate_values = []
for i in range(len(plate_id)):
field = [1000, -1000, 1000, -1000]
for j in range(length):
if (data[j,0]==plate_id[i]):
if (data[j,2] < field[0]): field[0]=int(data[j,2])
if (data[j,2] > field[1]): field[1]=(int(data[j,2])+1)
if (data[j,3] < field[2]): field[2]=int(data[j,3])
if (data[j,3] > field[3]): field[3]=(int(data[j,3])+1)
plate_values.append(field)
#print '(( ra BETWEEN', field[0], 'AND', field[1], \
#') AND ( dec BETWEEN', field[2], 'AND', field[3], ')) OR'
print 'All plates searched'
def author_list():
return read_csv('author.csv')
def read_friendships():
read_csv('user_friends.csv')
def friend_list():
return read_csv('friend.csv')
def read_user_posts(first_name, last_name):
for p in read_csv(first_name + '_' + last_name + '_postlogger.csv'):
return a[0, 1]
#HIGH_CUTS = [6.3, 6.3, 6.5, 6.5, 7.1, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0] #7089, short series
plot_file = 1
for cluster in range(len(NAME)):
if (SKIP[cluster] == 1): continue
for run in range(len(CONVOLVE[cluster])):
gc_name = NAME[cluster] + '_' + str(CONVOLVE[cluster][run])
con_dist = CONVOLVE[cluster][run]
real_dist = DISTANCE[cluster]
in_area = AREA_IN[cluster]
back_area = AREA_BACK[cluster]
if (HIGH_CUTS != []): high_cut = HIGH_CUTS[run]
print '***** Starting run', gc_name, '*****'
main_data = f.read_csv(datafile[cluster])
back_data = f.read_csv(backfile[cluster])
con_clus = gce.convolve(main_data, real_dist, con_dist)
#don't convolve background! Background is static!!!
#Actually, DO convolve background! need to accurately remove it from cluster!
# If cluster moves before subtraction, background needs to move, too!
con_back = gce.convolve(back_data, real_dist, con_dist)
con_clus_f = gce.select_stars(con_clus, low_limit=0.1)
con_back_f = gce.select_stars(back_data, low_limit=0.1)
lc, wc = con_clus_f.shape
lb, wb = con_back_f.shape
cluster_x = sc.zeros(lc)
background_x = sc.zeros(lb)
def read_user_single_post(post_num):
posts = read_csv(first_name + '_' + last_name + '_postlogger.csv')
for i in posts[i]:
if posts[0] == post_num:
return posts[1]
datafile = 'HR_NGC_5904_cluster.csv'
backfile = 'HR_NGC_5904_background.csv'
real_dist = 7.3
in_area = 0.076
back_area = 1.8537
cut_hist = 1
low_cut = 2.0
high_cut = 7.0
plot_file = 1
for run in range(len(NAME)):
gc_name = NAME[run]
con_dist = CONVOLVE[run]
print '***** Starting run', gc_name, '*****'
main_data = f.read_csv(datafile)
back_data = f.read_csv(backfile)
con_clus = gce.convolve(main_data, real_dist, con_dist)
con_back = gce.convolve(back_data, real_dist, con_dist)
con_clus_f = gce.select_stars(con_clus)
con_back_f = gce.select_stars(con_back)
lc,wc = con_clus_f.shape
lb,wb = con_back_f.shape
cluster_x = sc.zeros(lc)
background_x = sc.zeros(lb)
for i in range(lc):
cluster_x[i] = ( con_clus_f[i,2] - 5.*(m.log10(real_dist*1000) - 1.) )
for i in range(lb):
background_x[i] = ( con_back_f[i,2] - 5.*(m.log10(real_dist*1000) - 1.) )
def delete_post(post_num):
posts = read_csv(first_name + '_' + last_name + '_postlogger.csv')
for i in posts[i]:
if posts[0] == post_num:
del posts[1]
def user_list():
return read_csv('user.csv')
def read_users():
read_csv('users.csv')
st_dev = 0.0
for j in range(len(in_list)):
st_dev = st_dev + ((in_list[j] - mu)**2)
return np.sqrt(st_dev/float(len(in_list)))
#initial parameters; this would be a good point for a wrapper.
data_file = 'HR_Pal_5_cluster.csv'
cluster_name = 'Pal5_2'
distance = 21.0 #starting distance
step_size = 0.2 #smaller gives more points, but larger errors
save_data = 1 #saves isocrone files as output if set to 1
#initializations
plot_title = cluster_name + ', fiducial fit(green)'
file_str = 'HR_fiducial_' + cluster_name + '.ps' #plot file string
#load in and prepare SDSS data
gc_data = f.read_csv(data_file)
gc_l, gc_w = gc_data.shape
gc_array = sc.zeros((gc_l,2), float)
gc_array[:,0] = gc_data[:,2] - 5.*(m.log10(distance*1000) - 1.0) #'g' values
gc_array[:,1] = (gc_data[:,2] - gc_data[:,3]) #'g-r' values
bounds = sc.arange(1.0, 7.0, step_size)
#break up data CMD into strips in Mg
#reduce Mg strips - reject outliers, get mean, st_dev for each strip
#Note - if distances are fitted, then this part needs to be done every iteration.
fit_array = sc.zeros((len(bounds), 3),float) #average g, g-r mean, g-r st_dev
for i in range(1, len(bounds)):
star_list, g_list = [], []
for j in range(gc_l):
if ( (gc_array[j,0] < bounds[i]) and (gc_array[j,0] >= bounds[i-1])):
#Do standard deviation in g, too?
g_list.append(gc_array[j,0])
def create_post(post_content):
posts = read_csv(first_name + '_' + last_name + '_postlogger.csv')
for i in posts:
num = i
posts.append([num, post_content])
write_csv(first_name + '_' + last_name + '_postlogger.csv', posts)
def post_list():
return read_csv('post.csv')
"""Plot Initializations"""
fig = plt.figure()
plt.subplots_adjust(hspace=0.001, wspace=0.001)
#plt.title(save_name)
#plt.xlabel('g-r')
#plt.ylabel('Mg')
for i in range(len(gc_names)):
name = gc_names[i]
distance = gc_distances[i]
#metal = gc_metals[i]
HB_limit = cutoff[i]
"""Get Data"""
if (i == 10):
data_iso = f.read_data( ('Iso_new_Pal5.dat') )
data_clus = f.read_csv('noU_Pal5_cluster.csv') #'HR_Pal_5_cluster.csv'
data_fid = f.read_data( ('Pal5_2_fiducial_out.txt') )
else:
data_iso = f.read_data( ('Iso_new_'+name+'.dat') )
#data_iso = f.read_data( ('Iso_series_'+name+'_A.dat') )
data_clus = f.read_csv('noU_NGC_'+name+'_cluster.csv') #'HR_NGC_'+name+'_cluster.csv'
data_fid = f.read_data( ('NGC_'+name+'_'+str(fid_res[i])+'_fiducial_out.txt') )
"""Setup Data for Plotting"""
mm = -0.01463023
bb = 0.08928602
iso_x_list, iso_y_list = [], []
for j in range(len(data_iso[:,8])):
if (data_iso[j,8] > HB_limit):
iso_x_list.append((data_iso[j,8] - data_iso[j,9]) + mm*data_iso[j,8] + bb)
iso_y_list.append( data_iso[j,8] )
"""
# Get Data
#Data is: ra, dec, g, r, u, flags
filename = 'noU_NGC_6205.csv'
name = '6205_test'
center_ra, center_dec = 250.423, 36.460 #NGC 6205
#center_ra, center_dec = 229.013, -0.123 #Pal 5
radii_step = 0.04
steps = 25
suffix = filename[-4:]
if suffix == '.txt':
data = f.read_data(filename)
elif suffix == '.csv':
data = f.read_csv(filename)
'''Initial Skyplot'''
plt.figure(1)
data_1 = data[:, 0]
data_2 = data[:, 1]
#Strange indices are due to phantom liast point - not in data, but added by code somehow...
plt.scatter(data_1[:-1], data_2[:-1], 1, 'k', 'o')
plt.xlabel('ra')
plt.ylabel('dec')
plt.axis('scaled')
y_limits = plt.ylim()
x_limits = plt.xlim()
plot_file = name + '_initial_skyplot.ps'
plt.savefig(plot_file, papertype='letter')
plt.close('all')
'wide_7089.csv',
'wide_Pal5.csv'
]
center_ra = [182.525, 198.228, 199.109, 205.545, 211.36, 229.641, 250.423, 259.1680,
322.493, 323.362, 229.013]
center_dec = [18.530, 18.164, 17.697, 28.376, 28.53, 2.083, 36.460, 43.1033,
12.167, -0.826, -0.123]
inner_cut = [0.0170, 0.0800, 0.0500, 0.0, 0.057, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
outer_cut = [0.0700, 0.2500, 0.1400, 0.3000, 0.180, 0.2100, 0.2400, 0.1350,
0.1900, 0.1400, 0.0870]
background_cut = [0.0800, 0.3000, 0.1700, 0.4000, 0.2500, 0.2800, 0.3300, 0.2000,
0.2500, 0.1800, 0.1200]
outer_back_cut = 10.0
for i in range(len(datafile)):
if i == 7: continue
if skip[i] == 1: continue
data = f.read_csv(datafile[i])
clus_data = gce.make_cut(data, center_ra[i], center_dec[i], inner_cut[i], outer_cut[i])
back_data = gce.make_cut(data, center_ra[i], center_dec[i], background_cut[i], outer_back_cut)
clus_out = datafile[i][:-4]+'_cluster.csv'
back_out = datafile[i][:-4]+'_background.csv'
if (f.write_csv(clus_data, clus_out)) == 1:
print '#-cluster file', datafile[i], 'successfully cut and saved as', clus_out
else:
print '!!!AN ERROR OCCURED - FILE NOT CUT CORRECTLY!!!'
if (f.write_csv(back_data, back_out)) == 1:
print '#-data file', datafile[i], 'successfully cut and saved as', back_out
else:
print '!!!AN ERROR OCCURED - FILE NOT CUT CORRECTLY!!!'
print '#---All Done'
plt.close('all')
return 1
if __name__ == '__main__':
print '#- number of arguments:', (len(sys.argv)-1)
# Read arguments
filename = sys.argv[1]
column = int(sys.argv[2])
size = float(sys.argv[3])
if len(sys.argv) > 4:
spread = [float(sys.argv[4]), float(sys.argv[5])]
else: spread = []
if len(sys.argv) > 6:
name = sys.argv[6]
else: name = 'quick'
# Load Data
suffix = filename[-4:]
if suffix == '.txt':
data = f.read_data(filename)
elif suffix == '.csv':
data = f.read_csv(filename)
# Run scripts
to_bin = data[:,column]
reg_hist = make_hist(to_bin, size, spread)
plot_histogram(reg_hist, size, name=(name+'_normal') )
f.write_data(reg_hist, fileout=(name+'_normal.txt'), header='# Centers, Counts')
cum_hist = cumulative_hist(to_bin, size, spread)
plot_histogram(cum_hist, size, name=(name+'_cumulative') )
f.write_data(cum_hist, fileout=(name+'_cumulative.txt'), header='# Centers, Counts')
print '#done with quick histograms'
#! /usr/bin/env python #'Bang' line - modify as needed
import files as f
import GCplot_enc as gcp
"""This script quickly loads data and calls plotting functions.
Matthew Newby (RPI), Jan 11, 2011
"""
main_data = f.read_csv('HR_NGC_5053_cluster.csv')
back_data = f.read_csv('HR_NGC_5053_background.csv')
gcp.plot_infiles(main_data, back_data, GCname='5053', to_file=1)
for j in range(len(in_list)):
st_dev = st_dev + ((in_list[j] - mu)**2)
return np.sqrt(st_dev / float(len(in_list)))
#initial parameters; this would be a good point for a wrapper.
iso_file = '6205_isocrone_12.3gyr_1.41feH.dat'
data_file = 'HR_NGC_6205_cluster.csv'
cluster_name = 'NGC_6205_CG97_clean2'
distance = 7.7 #starting distance
save_data = 0 #saves isocrone files as output if set to 1
#initializations
#plot_title = cluster_name + ', isocrone(blue) and fiducial fit(green)'
file_str = 'HRISO_compare_' + cluster_name #plot file string
#load in and prepare SDSS data
gc_data = f.read_csv(data_file)
gc_l, gc_w = gc_data.shape
gc_array = sc.zeros((gc_l, 2), float)
gc_array[:,
0] = gc_data[:,
2] - 5. * (m.log10(distance * 1000) - 1.0) #'g' values
gc_array[:, 1] = (gc_data[:, 2] - gc_data[:, 3]) #'g-r' values
#load in and prepare isocrone data
iso_data = f.read_data(iso_file) #<--might fail due to leading whitespace?
iso_l, iso_w = iso_data.shape
iso_in = sc.zeros((iso_l, 2), float)
iso_in[:, 0] = iso_data[:, 8] #'Mg' values
iso_in[:, 1] = (iso_data[:, 8] - iso_data[:, 9]) #'g-r' values
#Chop up isocrone so that the bounds are equal
gminusr = 0.6
for i in range(1, iso_l):
import math as m
import numpy as np
import scipy as sc
import files as f
import gctools_enc as gce
import gauss_fit_enc as gfe
import GCplot_enc as gcp
'''python script for running convolve analysis scripts
Matthew Newby, June 6, 2010'''
datafile = 'HR_Pal_5_cluster.csv'
NAME = ['Pal5_ah23', 'Pal5_ah24', 'Pal5_ah25', 'Pal5_ah26', 'Pal5_ah27', 'Pal5_ah28']
CONVOLVE = [23.2, 24.2, 25.2, 26.2, 27.2, 28.2]
real_dist = 23.2
repeat_runs = 3
print '#', CONVOLVE
print '#-Total iterations:', repeat_runs
for i in range(repeat_runs):
for run in range(len(NAME)):
gc_name = NAME[run]
con_dist = CONVOLVE[run]
print '#***** Starting run', gc_name, 'from distace', real_dist, \
'to distance:', con_dist ,'*****'
main_data = f.read_csv(datafile)
con_clus = gce.convolve(main_data, real_dist, con_dist)
if gce.con_analysis(main_data, con_clus, right_limit=0.3, left_limit=0.1) == 1:
print '#*****', gc_name, '-process complete*****'
print '#---ALL RUNS COMPLETE---'
