def getDAMICy(file='data/DAMIC_siyield_allerr.txt'):
#units of E should be eV
#get the data
damic_data = dp.getXYdata_wXYerr(file)
#print(damic_data.keys())
#convert to numpy arrays
damic_data['xx'] = np.asarray(damic_data['xx']) * 1000 #make units eV
damic_data['yy'] = np.asarray(damic_data['yy']) * 1000 #make units eV
damic_data['ex'] = np.asarray(damic_data['ex']) * 1000 #make units eV
damic_data['ey'] = np.asarray(damic_data['ey']) * 1000 #make units eV
#get the yield stuff
damic_data['yy_yield'] = damic_data['yy'] / damic_data['xx']
damic_data['ey_yield'] = damic_data['yy_yield'] * np.sqrt((damic_data['ey']/damic_data['yy'])**2 + \
(damic_data['ex']/damic_data['xx'])**2)
#spline fit
damic_y = inter.UnivariateSpline(damic_data['xx'],
damic_data['yy_yield'],
s=1.5)
return damic_y
import sys
#sys.path.append('../python/')
import NGC5533_functions as nf
import noordermeer as noord # Traced data of Galaxy NGC 5533
import numpy as np
import matplotlib.pyplot as plt
import lmfit as lm
import dataPython as dp
import scipy.interpolate as inter
################################
##### Measured data points #####
################################
data = dp.getXYdata_wXYerr('../data/100kpc_data.txt')
#data = dp.getXYdata_wXYerr('../galactic-spin-binder/NGC_5533/data/final/100kpc_data.txt')
r_dat = np.asarray(data['xx'])
v_dat = np.asarray(data['yy'])
v_err0 = np.asarray(data['ex'])
v_err1 = np.asarray(data['ey'])
###########################################
###### Uncertainty Band and Weights #######
###########################################
# Uncertainty band, using Noordermeer's function with a guessed delta_i
delta_i = 3 # guessed value
v_i = (v_dat / np.tan(52*(np.pi/180)) * delta_i *(np.pi/180))
# Traced uncertainty band
import numpy as np
import sys
sys.path.append('../../python/')
import dataPython as dp
import scipy.integrate as si
##############################
### Import data/text files ###
##############################
# Note: there's no need to import the radius for each component as everything has the same r array
# (the r array of the raw data)
# Datapoints:
data = dp.getXYdata_wXYerr('../testing/7814/ngc7814data')
radius = np.asarray(data['xx'])
v_dat = np.asarray(data['yy'])
v_err1 = np.asarray(data['ey'])
# Disk:
disk_data = dp.getXYZdata('../testing/7814/7814reallydisk.dat')
disk = np.asarray(disk_data['zz'])
# Bulge:
bulge_data = dp.getXYZdata('../testing/7814/7814reallybulge.dat')
bulge = np.asarray(bulge_data['zz'])
# Gas:
gas_data = dp.getXYZdata('../testing/7814/7814gascomp.dat')
gas = np.asarray(gas_data['zz'])
def f(arraysize, Mbh):
#fractions: now changing the number of dots plottd within each bracket, which changes as the slider
#variable ("arraysize") changes
#and trim to the first x elements of the pre-caclated radius arrays for each bracket
arraysize = int(arraysize) #units: dot
re = wi.rrr[:arraysize]
anglee = wi.angle[:arraysize]
#plot for each bracket, two equations for each bracket
y = wi.c_y + re * np.sin(anglee) #y coordinates
x = wi.c_x + re * np.cos(anglee) #x coordinates
#below: changing the physical size of the plotted dots as the bh mass slider changes. the if else statement is fancy
#but not necessary
Mbh = 1.5 * Mbh #judging by eye
if .1 <= Mbh < 1:
Mbh = Mbh**1.5
elif 1 <= Mbh < 2:
Mbh = Mbh**2
else:
Mbh = Mbh**3 #display size of each marker
f, (ax1, ax2) = plt.subplots(1, 2, sharey=False)
f.set_figheight(15)
f.set_figwidth(35)
# Define r, for some reason this isn't working in code7814.py, so put directly here:
data = dp.getXYdata_wXYerr('../../testing/7814/ngc7814data')
r = np.asarray(data['xx'])
ax1.scatter(x, y, color="r", marker='o', s=Mbh)
ax1.imshow(img)
ax1.axis('off')
ax2.plot(r, h(r, Mbh, arraysize), label=("halo"), color='green')
ax2.errorbar(r, wi.v_dat, yerr=wi.v_err1, fmt='bo', label='Data')
ax2.plot(r, wi.b, label=("Bulge"), color='orange')
ax2.plot(r, wi.d, label=("Disk"), color='purple')
ax2.plot(r, wi.g, label=("Gas"), color='blue')
ax2.plot(r, t(r, Mbh, arraysize), label=("Total Curve"), color='red')
ax2.set_xlim([wi.minkpc, 19])
ax2.set_ylim([0, 400])
ax2.set_title('NGC 7814', fontsize=40)
ax2.set_ylabel('Velocity [km/s]', fontsize=30)
ax2.set_xlabel('radius [kpc]', fontsize=30)
ax2.legend(loc='upper right', prop={'size': 23}, ncol=3)
residuals = wi.v_dat - t(wi.r, Mbh, arraysize)
# Determining errors
errors = wi.v_err1**2 #second term is inclination uncertainty
# Chi squared
chisquared = np.sum(residuals**2 / errors**2)
#chisquared = stats.chisquare(v_dat,totalcurve(r,M,bpref,dpref,rc,rho00,gpref))
reducedchisquared = chisquared * (1 / (len(wi.r) - 6))
props = dict(boxstyle='round', facecolor='white', alpha=0.5)
ax2.text(0.4,
360,
r"Reduced $\chi^2$:" + '\n' + "{:.2f}".format(reducedchisquared),
bbox=props,
size=30)
import matplotlib.pyplot as plt
import scipy.optimize as opt
import lmfit as lm
import sys
sys.path.append('../python')
import dataPython as dp
import NGC5533_functions as nf
# In[2]:
#**********************importing text files******************************
#there's no need to import the radius for each component as everything has the same r array (the r array of the raw data)
#data:
data = dp.getXYdata_wXYerr('../testing/891/891_data')
r = np.asarray(data['xx'])
v_dat = np.asarray(data['yy'])
v_err1 = np.asarray(data['ey'])
#disk:
disk = dp.getXYdata('../testing/891/891_dtDisk.dat')
d = np.asarray(disk['yy'])
#bulge:
bulge = dp.getXYdata('../testing/891/891_dtBulge.dat')
b = np.asarray(bulge['yy'])
#gas:
gas = dp.getXYdata('../testing/891/891_dtGas.dat')
g = np.asarray(gas['yy'])
#***************************define total curve
#D=9.25 #disk M-L ratio provided in [1]
from ipywidgets import interactive, fixed, FloatSlider, HBox, Layout, Button, Label, Output, VBox
from IPython.display import display, clear_output
from IPython.display import Javascript
import scipy.stats as stats
import warnings
warnings.filterwarnings("ignore") #ignore warnings
# In[2]:
#import data files:
#TRACING:**************************************
#data points:
data = dp.getXYdata_wXYerr('../NGC_5005/traced_data/ngc5005_data.txt')
r_dat = np.asarray(data['xx'])
v_dat = np.asarray(data['yy'])
v_err0 = np.asarray(data['ex'])
v_err1 = np.asarray(data['ey'])
#v_err1=v_err1[:len(v_err1)-1]
#r_dat=r_dat[:len(r_dat)-1]
#v_dat=v_dat[:len(v_dat)-1]
#gas rotmod:
gas_rdata = dp.getXYZdata('../testing/aygas.dat') #rotmod_gas.dat
rgasr = gas_rdata['xx']
rgasv = gas_rdata['zz']
rgasv = np.asarray(rgasv)
rgasv_spline = inter.InterpolatedUnivariateSpline(rgasr, rgasv, k=5)
rgasv_fit = rgasv_spline(r_dat)