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In [1]:

    
import matplotlib.pyplot as plt
import numpy as np
from copy import deepcopy
import pickle



In [4]:

    
#Read in Pannstars Data from file
data = {"ID":[], "g":[], "r":[], "g_err":[], "r_err":[], "l":[], "b":[]}
filenames = ["25L100-20B-10.csv", "25L10010B20.csv", "100L210-20B-10.csv", "100L21010B20.csv", "210L330-20B-10.csv", "210L33010B20.csv", "330L25allB.csv"]

for name in filenames:
    f = open("../../SummerREUDiskWork/public_html/SarahGonzalez2018/PANSTARRS DR1 Data/Data with Extinctions and Applied Color Cut/" + name, "r")
    f.readline()


    for line in f:
        ln = list(map(float, line.split(",")))
        data["ID"].append(ln[1])
        data["g"].append(ln[2])
        data["r"].append(ln[3])
        data["g_err"].append(ln[4])
        data["r_err"].append(ln[5])
        data["l"].append(ln[6])
        data["b"].append(ln[7])


    f.close()

print("Read in %d stars" % len(data["ID"]))









    



Read in 17300999 stars



In [3]:

    
import gc

gc.collect()









    Out[3]:





0



In [5]:

    
for i in np.arange(len(data["l"])):
    data["l"][i] = float(data["l"][i] > 180) * -(360 - data["l"][i]) +  float(data["l"][i] < 180) * (data["l"][i])



In [25]:

    
#Plot Pannstars data to make sure it was read in correctly
#Looks like there is a lot of dust
plt.figure(1, figsize=(16, 12))
mask = np.where((np.array(data["g"]) > 16.0) & (np.array(data["g"]) < 22.5)) 

plt.plot(np.array(data["l"])[mask],np.array(data["b"])[mask], 'o', ms=0.1, alpha=0.02)
plt.show()



In [6]:

    
#Slice the disk into l bins

for i in np.arange(0, 360, 2.5):
    temp = []
    mask = np.where((np.array(data["l"]) > i) & (np.array(data["l"]) < i+2.5)) 
    temp.append(np.array(data["l"])[mask])
    temp.append(np.array(data["b"])[mask])
    temp.append(np.array(data["g"])[mask])
    a = open("../Data/SlicedDisk/l_cuts/l-%d.pickle" % int((i+1.25)*100), "wb")
    pickle.dump(temp, a)
    a.close()



In [50]:

    
#Plot to check cuts
plt.plot(np.array(l_cut)[3, 0],np.array(l_cut)[3, 1], 'o', ms=0.1, alpha=0.5)
plt.show()



In [20]:

    
for i in np.arange(0, 360, 2.5):
    a = open("../Data/SlicedDisk/l_cuts/l-%d.pickle" % int((i+1.25)*100), "rb")
    loadedData = pickle.load(a)
    a.close()
    for j in np.arange(-20, 20, 2.5):
        temp = []
        mask = np.where((np.array(loadedData[1]) > j) & (np.array(loadedData[1]) < j+2.5)) 
        temp.append(np.array(loadedData[0])[mask])
        temp.append(np.array(loadedData[1])[mask])
        temp.append(np.array(loadedData[2])[mask])
        b = open("../Data/SlicedDisk/lb_cuts/l-%d-b-%d.pickle" % (int((i+1.25)*100), int((j+1.25)*100)), "wb")
        pickle.dump(temp, b)
        b.close()
    a.close()



In [25]:

    
b = open("../Data/SlicedDisk/lb_cuts/l-1125-b-1625.pickle", "rb")
temp = pickle.load(b)
b.close()


plt.hist(temp[2])
plt.show()



In [35]:

    
for i in np.arange(0, 360, 2.5):
    for j in np.arange(-20, 20, 2.5):
        a = open("../Data/SlicedDisk/lb_cuts/l-%d-b-%d.pickle" % (int((i+1.25)*100), int((j+1.25)*100)), "rb")
        loadedData = pickle.load(a)
        a.close()
        hist = np.histogram(loadedData[2], bins=13, range=(16.0, 22.5))
        b = open("../Data/SlicedDisk/lb_histograms/l-%d-b-%d.hist" % (int((i+1.25)*100), int((j+1.25)*100)), "w")
        b.write('0')
        for k in hist[0]:
            b.write(', ' + str(k))
        b.close()
    a.close()



In [ ]:

    
for i in np.arange(0, 360, 2.5):
    for j in np.arange(-20, 20, 2.5):
        a = open("../Data/SlicedDisk/lb_cuts/l-%d-b-%d.pickle" % (int((i+1.25)*100), int((j+1.25)*100)), "rb")
        loadedData = pickle.load(a)
        a.close()
        hist = np.histogram(loadedData[2], bins=13, range=(16.0, 22.5))
        b = open("../Data/SlicedDisk/lb_histograms/l-%d-b-%d.hist" % (int((i+1.25)*100), int((j+1.25)*100)), "w")
        b.write('0')
        for k in hist[0]:
            b.write(', ' + str(k))
        b.close()
    a.close()