In [1]:
from IPython.display import Image, display
Consider the following completely made-up scenario:
There's a country that has many different companies working at many different cities within it.
We also have information of each employee working at each of these companies. All kinds of things like height, names, hair length, tenure, and so on. Because these companies are located in different cities, they each adopt a separate (unknown) cost-of-living adjustment that they apply uniformly to all of their employees. And, for each company, each employee gets the same salary.
We have a method of getting the salary from the employees but, for contrived reasons, we get a measured value drawn from a Normal distribution centered on their true salary and an estimate of the error (which will be different for each salary). Again, we get 2 numbers when we get the salary information: the salary and an estimate error. The salary data is heteroskedastic (but we have a good idea of the errors for each number).
And this data was taken over the past 4 decades.
There is a linear relationship between an employee's height and the relative salary at each of these companies.
If this turns out to be the case, then, this is a huge deal (Nobel prize winning deal). Let's just add that at this point, the relationship has to be a straight line in salary vs height space, in that, if this hypothesis were correct, it would appear as a straight line (of any slope/intercept) in this plot (assuming no other systematic errors).
So, a group of researchers takes the salary data for all companies in the Northern part of the country, and the employee's height data for each company, and fits a straight line to the data. They report the slope of each line and an estimated error on the fit, and they find significant non-zero slopes across the ensemble of these measurements.
Here's an example of the kind of fit being done:
In [166]:
plot_example_company(company=19, daa=2.5)
The above plot was generated for company number 19, with a slope of 2.5, and a best fit line is clearly a decent fit to the data. They made 140 measurements across the Northern half of the country. Reporting the slopes and errors for each company. The data looks like:
In [195]:
keck[['delta_alpha', 'error_delta_alpha']].rename(columns={'delta_alpha':'slope', 'error_delta_alpha':'error'}).head()
Out[195]:
A bunch of other information was recorded at the same time (when the measurement was made, and so on). So they summarized the results across 140 companies by plotting the slope value vs when measurement was made (because if there were some height-bias in the different salaries, it might have changed over time). Remember the expected relationship for this plot should be a bunch of (statistical) zeros.
In [196]:
plot_example_telescope_results()
In [217]:
# np.average(keck.delta_alpha, weights=1.0 / keck.extra_error_delta_alpha**2.0)
# np.sqrt(1.0 / np.sum(1.0 / keck.extra_error_delta_alpha**2.0))
Taking a weighted mean of all slope measurements, they found a non-zero result of -5.7 $\pm$ 1.1 ppm. To look for a relationship with time, they took a weighted mean of measurements in time, binned so that each bin would contain roughly an equal number of companies. The next 2-panel plot shows the individual measurements in the upper panel colored to correspond with the color their weighted bin on the lower panel. (The horizontal lines on the upper panel denote the limits on the y-axis for the lower panel).
In [216]:
plot_example_telescope_bins(nbins=12, dataframe=keck)
This looks very interesting! But we only looked at the Northern half of the country. Another team of researchers went off to measure the Southern half. And they found the following:
In [224]:
plot_example_telescope_bins(nbins=13, dataframe=vlt)
A surprising result! The weighted bins are no longer uniformly negative, but appear to switch from negative to positive around 1.5 decades ago. Because the data include the lat/longitude of each of these companies, they went to see if they could fit a model that took into account spatial differences. Basically, if you want to think of this semi-accurately, the country is around an entire planet, with companies appearing at specific locations. And there is a gradient across the whole planet that can be thought of in terms of Latitude alone. The fitting model is technically called a dipole, so I will call it a dipole fit.
In [241]:
plot_a_v_theta(vlt, color=0, nbins=13, label='VLT')
plot_a_v_theta(keck, color=2, nbins=13, label='Keck')
In [242]:
plot_a_v_theta(all_systems, color=1, nbins=13, label='Combined')
Another group of scientists discovers a systematic error in the way the salary measurements are being made. Not only do we have height, salary, and location data, we also have each employee's hair length. It turns out that there is no relationship between height and hair length (just like in the real world).
However, for even further contrived reasons, the systematic error between an employee's hair length and their measured salary! Just to provide a mental model of what happens, try to stomach the following example. For the Southern companies, they use 2 rulers to measure the length of an employee's hair, one for if their hair is less than say... 5 cm, and another one for if their hair is longer than 5 cm. The Northern companies use 1 ruler to measure their employee's hair.
The thing is, for all of these rulers, on a per-company basis, they might introduce a systematic error in the recorded . The good news is that if there's an error, it's reasonably well-behaved. So, for the 2-ruler system, you'd have something that might look like:
In [278]:
plot_example_systematic(run17)
Where the two rulers show up clearly as two lines w/ roughly the same slope. The y-intercept of this is meaningless, as we only can ever know the relative salary differences per company.
In [ ]:
In [340]:
def generate_sigmas(vshifts):
return np.random.rand(len(vshifts)) * 30.0 + 50.0
def create_blank_transitions_dataset():
df = pd.DataFrame(columns=['system',
'J2000',
'source',
'wavelength',
'vshift',
'sigma',
'qval',
'rest_wave',
])
count = 0
abs_count = 0
for index, row in all_systems.iterrows():
waves = []
rest_waves = []
vshifts = []
qvals_list = []
for tran in row['transitions'].split():
vshift = 0.0
rest_wave = qvals.loc[codes.loc[tran].trans].wave
measured_wave = rest_wave * (1 + row.z_absorption)
qval = qvals.loc[codes.loc[tran].trans].qval
waves.append(measured_wave)
rest_waves.append(rest_wave)
vshifts.append(vshift)
qvals_list.append(qval)
vshifts = np.array(vshifts)
errors = generate_sigmas(vshifts)
# vshifts += errors * np.random.randn(len(vshifts))
# vshifts = vshifts - vshifts[0]
for single in range(len(vshifts)):
abs_count += 1
df.loc[abs_count] = [int(index), #system
row['J2000'],
row.source,
waves[single], #wavelength
vshifts[single],
errors[single],
qvals_list[single], # qvalues
rest_waves[single],
]
df['system'] = df.system.astype(int)
# For numerical stability during fitting, dividing the x-values by 1.0e14
df['x'] = -2.0 * c * df['qval'] * df['rest_wave'] / 1.0e14
return df
In [366]:
def fit_alpha(dataframe):
"""Takes a dataframe of transitions and returns a systems dataframe w/ best fit sim_fit_alpha."""
new_df = all_systems.copy()
new_df['sim_fit_alpha'] = -1.0
for index, dfg in dataframe.groupby('system'):
design_matrix = sm.add_constant(dfg.x)
results = sm.WLS(dfg.vshift, design_matrix, weights=1.0/dfg.sigma).fit()
chisq = np.sum((dfg.vshift - results.fittedvalues)**2.0 / (dfg.sigma) ** 2.0)
const, slope = results.params
new_df['sim_fit_alpha'].iloc[index] = slope
return new_df
In [386]:
distortion_1 = run17.copy()
In [400]:
for trans in vlt[vlt.J2000.str.startswith('J043037')]['transitions']:
trans = trans.split()
for tran in trans:
print(tran, codes.loc[tran].trans)
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temp2.plot()
In [527]:
def plot_distortion(distortions, xlims=(3000, 10000)):
fig, ax = plt.subplots(figsize=(12, 8))
wave = np.arange(xlims[0], xlims[1], 2)
vshift = np.ones_like(wave)
temp3 = pd.DataFrame(columns=['wavelength', 'vshift'],
data=np.array([wave, vshift]).T)
temp3['vshift'] = temp3.apply(lambda x:offset(x, distortions), axis=1)
temp3.plot(x='wavelength', y='vshift', ax=ax)
plot_distortion(temp)
In [531]:
plot_distortion(temp4)
In [485]:
vlt_ccds.columns
Out[485]:
In [491]:
def list_of_distortion(chip='B390', slope=0.0, offset=0.0, chip_info=vlt_ccds):
wav_min = chip_info.loc[chip].wav_min
midpoint = chip_info.loc[chip].midpoint
wav_max = chip_info.loc[chip].wav_max
intercept = -slope * (midpoint + offset)
return list([chip, wav_min, midpoint, wav_max, slope, intercept])
In [495]:
vlt_ccds
Out[495]:
In [ ]:
columns = ['chip', 'wav_min', 'midpoint', 'wav_max', 'slope', 'intercept']
chips=['B346', 'B437', 'R580', 'R860']
distortion_1 = [list_of_distortion(chip=chip, slope=0.2) for chip in chips]
temp = pd.DataFrame(columns=columns, data=distortion_1)
In [544]:
def create_distortion_from_ccd(chips=['B390', 'R580'],
slopes=[0.0, 20.5],
offsets=[0.0, 15.5],
columns=['chip', 'wav_min', 'midpoint', 'wav_max', 'slope', 'intercept'],
chip_info=vlt_ccds,):
chips=['B390', 'R580', ]
temp_list = []
for (chip, slope, offset) in zip(chips, slopes, offsets):
temp_list.append(list_of_distortion(chip=chip, slope=slope, offset=offset))
return pd.DataFrame(columns=columns, data=temp_list)
create_distortion_from_ccd()
Out[544]:
In [518]:
def offset(row, distortions):
add_vshift = []
for index, distortion in distortions.iterrows():
if (row.wavelength > distortion.wav_min) & (row.wavelength < distortion.wav_max):
add_vshift.append(row.wavelength * distortion.slope + distortion.intercept)
if len(add_vshift) == 0:
add_vshift.append(0.0)
return np.average(add_vshift)
In [521]:
temp2['vshift'] += temp2.apply(lambda x:offset(x, temp), axis=1)
In [519]:
temp2
Out[519]:
In [555]:
Out[555]:
In [598]:
fig, ax = plt.subplots(figsize=(12, 8))
ax.scatter(vlt_ccds.wav_min, np.zeros_like(vlt_ccds.wav_min), c=sns.color_palette()[0])
ax.scatter(vlt_ccds.wav_max, np.zeros_like(vlt_ccds.wav_min), c=sns.color_palette()[0], label='CCD wavelength edges')
# ax.scatter()
# temp_transitions.wavelength.hist(bins=50, ax=ax, alpha=0.5, color=sns.color_palette()[0])
sns.distplot(temp_transitions.wavelength, ax=ax, hist=True)
knot_positions = [3024.5,
3259.1,
np.average([3732.4, 3884.0, ]),
4518.8,
4999.4,
5655.8,
np.average([6650.6, 6834.9]),
9463.6,
10605.7,
]
ax.scatter(knot_positions,
np.zeros_like(knot_positions)-0.00002,
c='k',
s=100,
marker='x',
label='Knot positions'
)
ax.set_ylim(-0.00005, 0.0005)
ax.set_yticklabels([])
ax.set_ylabel("Fraction of transitions measured")
ax.set_xlabel("Wavelength [$\AA$]")
ax.legend(loc='best')
fig.tight_layout()
In [599]:
import ipywidgets
In [600]:
ipywidgets.__version__
Out[600]:
In [529]:
vlt_ccds
Out[529]:
In [536]:
np.random.choice()
Out[536]:
In [376]:
def velocity_shift(wavelength, distortion=run17):
for segment in distortion:
if (wavelength > distortion[segment]['waves_start']) and (wavelength <= distortion[segment]['waves_end']):
slope = distortion[segment]['slope']
offset = distortion[segment]['offset']
return -(wavelength * slope + offset)
In [378]:
# temp_transitions['vshift'] += temp_transitions.wavelength.apply(lambda x: velocity_shift(x))
In [537]:
temp_transitions = create_blank_transitions_dataset()
In [538]:
temp_transitions['vshift'] = temp_transitions.apply(lambda x:offset(x, temp4), axis=1)
In [539]:
temp_systems2 = fit_alpha(temp_transitions)
In [379]:
temp_systems = fit_alpha(temp_transitions)
In [381]:
plot_example_telescope_bins(nbins=13,
alphacol='sim_fit_alpha',
dataframe=temp_systems[temp_systems.source == 'VLT'])
In [535]:
plot_example_telescope_bins(nbins=13,
alphacol='sim_fit_alpha',
dataframe=temp_systems2[temp_systems2.source == 'VLT'])
In [540]:
plot_example_telescope_bins(nbins=13,
alphacol='sim_fit_alpha',
dataframe=temp_systems2[temp_systems2.source == 'VLT'])
In [ ]:
In [ ]:
row = all_systems.iloc[company]
df = df_a[df_a.system==company]
heights = df.x
design_matrix = sm.add_constant(heights)
results = sm.WLS(vshifts, design_matrix, weights=1.0/sigmas).fit()
chisq = np.sum((vshifts - results.fittedvalues)**2.0 / (sigmas) ** 2.0)
const, slope = results.params
ax.scatter(heights, vshifts, color=sns.color_palette()[color_index], label='')
ax.errorbar(heights, vshifts, yerr=sigmas, c=sns.color_palette()[color_index], ls='none', label='')
ax.plot(heights, results.fittedvalues, color='k', label='Fit slope: ' + str(round(slope, 2)) + " ppm")
ax.legend(loc='best')
ax.set_xlabel("Height")
ax.set_ylabel("Salary")
ax.set_title("Company: " + str(company) + " Generating slope: " + str(daa))
fig.tight_layout()
In [215]:
def plot_example_telescope_bins(nbins = 12,
alphacol = 'delta_alpha',
errorcol = 'error_delta_alpha',
binned_lim = 25.0,
dataframe=keck,
):
fig, (ax, ax2) = plt.subplots(figsize=(12, 10),
nrows=2,
sharex=True,
gridspec_kw={'height_ratios':[1.5,1]},
)
for index, df in enumerate(np.array_split(dataframe.sort_values('z_absorption'), nbins)):
color = sns.color_palette(n_colors=13)[index]
x = df.z_absorption
y = df[alphacol]
e = df[errorcol]
ax.scatter(x, (y), c=color, label='', s=40)
ax.errorbar(x, (y), yerr=e, c=color, ls='none', label='')
x = np.average(df.z_absorption)
y = np.average(df[alphacol], weights=(1.0 / (df[errorcol] ** 2.0)))
e = np.sqrt(1.0 / np.sum(1.0 / (df[errorcol] ** 2.0)))
label=''
if index == 0:
label=label
else:
label=''
ax2.scatter(x, y, c=color, label=label)
ax2.errorbar(x, y, yerr=e, c=color)
ax.hlines(0, -2, 6, linestyles=':', lw=1.5, color='k')
ax.hlines(binned_lim, -2, 6, linestyles=':', lw=.5, color='k')
ax.hlines(-binned_lim, -2, 6, linestyles=':', lw=.5, color='k')
ax2.hlines(0, -2, 6, linestyles=':', lw=1.5, color='k')
ax.set_ylabel(r"Slopes per Company ($\Delta \alpha/\alpha$)")
ax2.set_ylabel(r"Weighted Binneds")
ax2.set_xlabel(r"Decades Ago (Redshift [z])")
ax.legend(loc='best')
ax.set_xlim(0.3, 3.9)
ax.set_ylim(-150, 150)
ax2.set_ylim(-binned_lim, binned_lim)
fig.tight_layout()
In [117]:
def fit_hypothesis(system=0, dataframe1=df_a, hypothesis='x'):
"""Return the chisq and the fit model object for a given dataframe and hypothesis."""
plotdf1 = dataframe1[dataframe1.system == system]
assert(hypothesis in ['x', 'w'])
if hypothesis == 'x':
X = sm.add_constant(plotdf1.x)
elif hypothesis == 'w':
X = sm.add_constant(plotdf1.wavelength)
results = sm.WLS(plotdf1.vshift, X, weights=1.0/plotdf1.sigma).fit()
chisq = np.sum((plotdf1.vshift - results.fittedvalues)**2.0 / (plotdf1.sigma) ** 2.0)
return chisq, results
In [164]:
def plot_example_company(company=19, # which system to use
daa=2.5, # ppm of generated slope
):
row = all_systems.iloc[company]
df = df_a[df_a.system==company]
heights = df.x
color_index = 0
waves = []
rest_waves = []
vshifts = []
qvals_list = []
for tran in row['transitions'].split():
vshift = 0.0
rest_wave = qvals.loc[codes.loc[tran].trans].wave
measured_wave = rest_wave * (1 + row.z_absorption)
qval = qvals.loc[codes.loc[tran].trans].qval
vshift += shifted_velocity(daa,
qval,
rest_wave)
waves.append(measured_wave)
rest_waves.append(rest_wave)
vshifts.append(vshift)
qvals_list.append(qval)
waves = np.array(waves)
rest_waves = np.array(rest_waves)
vshifts = np.array(vshifts)
qvals_list = np.array(qvals_list)
sigmas = np.ones_like(waves) * 5.0
vshifts += sigmas * np.random.randn(len(vshifts))
fig, ax = plt.subplots(figsize=(12, 8))
design_matrix = sm.add_constant(heights)
results = sm.WLS(vshifts, design_matrix, weights=1.0/sigmas).fit()
chisq = np.sum((vshifts - results.fittedvalues)**2.0 / (sigmas) ** 2.0)
const, slope = results.params
ax.scatter(heights, vshifts, color=sns.color_palette()[color_index], label='')
ax.errorbar(heights, vshifts, yerr=sigmas, c=sns.color_palette()[color_index], ls='none', label='')
ax.plot(heights, results.fittedvalues, color='k', label='Fit slope: ' + str(round(slope, 2)) + " ppm")
ax.legend(loc='best')
ax.set_xlabel("Height")
ax.set_ylabel("Salary")
ax.set_title("Company: " + str(company) + " Generating slope: " + str(daa))
fig.tight_layout()
In [ ]:
def plot_example_telescope_results(dataframe=keck):
fig, ax = plt.subplots(figsize=(12, 8))
x = dataframe.z_absorption
y = dataframe.delta_alpha
e = dataframe.error_delta_alpha
color=0
label='Slopes'
ax.scatter(x, (y), c=sns.color_palette()[color], label=label)
ax.errorbar(x, (y), yerr=e, c=sns.color_palette()[color], ls='none', label='')
ax.hlines(0, -2, 6, linestyles=':', lw=1.5, color='k')
ax.set_ylabel(r"Slopes per Company ($\Delta \alpha/\alpha$)")
ax.set_xlabel(r"Decades Ago (Keck redshift [z])")
ax.legend(loc='best')
ax.set_xlim(0.3, 3.7)
ax.set_ylim(-200, 200)
fig.tight_layout()
In [ ]:
def generate_dataset(gen_dipole_alpha=True,
wavelength_distortion=False,
seed=228,
):
df = pd.DataFrame(columns=['system',
'J2000',
'source',
'wavelength',
'vshift',
'sigma',
'qval',
'rest_wave'
])
count = 0
abs_count = 0
for index, row in all_systems.iterrows():
waves = []
rest_waves = []
vshifts = []
qvals_list = []
for tran in row['transitions'].split():
vshift = 0.0
rest_wave = qvals.loc[codes.loc[tran].trans].wave
measured_wave = rest_wave * (1 + row.z_absorption)
qval = qvals.loc[codes.loc[tran].trans].qval
if gen_dipole_alpha:
vshift += shifted_velocity(row['dipole_delta_alpha'],
qval,
rest_wave)
if wavelength_distortion:
vshift += distorted_velocity(row, measured_wave)
waves.append(measured_wave)
rest_waves.append(rest_wave)
vshifts.append(vshift)
qvals_list.append(qval)
vshifts = np.array(vshifts)
errors = sigmas(vshifts)
vshifts += errors * np.random.randn(len(vshifts))
vshifts = vshifts - vshifts[0]
for single in range(len(vshifts)):
abs_count += 1
df.loc[abs_count] = [int(index), #system
row['J2000'],
row.source,
waves[single], #wavelength
vshifts[single],
errors[single],
qvals_list[single], # qvalues
rest_waves[single],
]
df['system'] = df.system.astype(int)
# For numerical stability during fitting, dividing the x-values by 1.0e14
df['x'] = -2.0 * c * df['qval'] * df['rest_wave'] / 1.0e14
return df
In [114]:
fig, ax = plt.subplots(figsize=(12, 8))
for company in keck.index:
chisq, results = fit_hypothesis(system=company, dataframe1=df_a, hypothesis='x')
results.params.x
ax.scatter(keck.iloc[company].delta_alpha, results.params.x)
In [277]:
def read_systematic(infile='../data/run.17.json'):
"""Read systematic error file."""
# with open('../data/run.17.json', 'w') as fp:
# json.dump(run17, fp, indent=4, sort_keys=True)
with open(infile, 'r') as fp:
temp = dict(json.load(fp))
return temp
run17 = {int(k):v for k, v in temp.items()}
def plot_example_systematic(region_dictionary=run17):
color = "black"
linewidth = 5.0
fig, ax = plt.subplots(figsize=(12, 8))
for index in region_dictionary:
begin = region_dictionary[index]['waves_start']
end = region_dictionary[index]['waves_end']
wavelength = np.linspace(begin, end, 50)
quad = region_dictionary[index]['quad']
slope = region_dictionary[index]['slope']
offset = region_dictionary[index]['offset']
if index == 1:
color = "blue"
linestyle = "-"
elif index == 3:
color = "red"
linestyle = '-'
else:
color = "black"
linestyle = '--'
ax.plot(wavelength, quad * wavelength ** 2.0 + wavelength * slope + offset,
color=color,
linewidth=linewidth,
linestyle=linestyle)
ax.set_xlim(2900, 7500)
ax.set_xlabel("Hairlength")
ax.set_ylabel("Systematic Error of Salary")
fig.tight_layout()
In [89]:
keck[keck.J2000.eq("J000149-015940")]
Out[89]:
In [60]:
fig, ax = plt.subplots(figsize=(12, 8))
x = keck.z_absorption
y = keck.delta_alpha
e = keck.error_delta_alpha
color=0
label='measurement'
ax.scatter(x, (y), c=sns.color_palette()[color], label=label)
ax.errorbar(x, (y), yerr=e, c=sns.color_palette()[color], ls='none', label='')
ax.hlines(0, -2, 6, linestyles=':', lw=1.5, color='k')
ax.set_ylabel(r"$\Delta \alpha/\alpha$")
ax.set_xlabel(r"Decades Ago (Keck redshift [z]")
ax.legend(loc='best')
ax.set_xlim(0.3, 3.7)
ax.set_ylim(-200, 200)
fig.tight_layout()
In [70]:
fig, (ax, ax2) = plt.subplots(nrows=2)
x = keck.z_absorption
y = keck.delta_alpha
e = keck.error_delta_alpha
color=0
label='measurement'
ax.scatter(x, (y), c=sns.color_palette()[color], label='', s=40)
ax.errorbar(x, (y), yerr=e, c=sns.color_palette()[color], ls='none', label='')
ax.hlines(0, -2, 6, linestyles=':', lw=1.5, color='k')
ax.set_ylabel(r"$\Delta \alpha/\alpha$")
ax.set_xlabel(r"Decades Ago (Keck redshift [z])")
ax.legend(loc='best')
ax.set_xlim(0.3, 3.7)
ax.set_ylim(-200, 200)
plot_a_v_z(keck, nbins=13, ylims=200, ax=ax2)
ax2.set_ylim(-20, 20)
fig.tight_layout()
In [ ]:
plot_a_v_z(keck)
It's important to note that none of the measurements by themselves is enough to establish a measurement at a 5-$\sigma$ level, but taken as an ensemble, it approaches ~4.7$\sigma$.
In [ ]:
I'm going to give you a set of data. This set of data, the y-values will be generated in one of two ways (or a combination).
Here is a single system, plotted as though it was generated in either $\alpha$ or w processes (and transitions smoothly between them to highlight the fact that that y-values are the same. It is merely the x-axis that gets changed:
Given a dataset, can you devise a test that quantifies the relative likelihood of either process $\alpha$ or w generating it?
One extra piece of information that is important: if $\alpha$ is generating a signal, it must be a straight line in the $\alpha$ plot. If it is a w-process, it can be more flexible.
In [2]:
%matplotlib inline
%config InlineBackend.figure_format='retina'
import warnings
warnings.filterwarnings('ignore')
In [3]:
from __future__ import absolute_import, division, print_function
import matplotlib as mpl
from matplotlib import pyplot as plt
from matplotlib.patches import Rectangle
from matplotlib.pyplot import GridSpec
from itertools import combinations, islice, takewhile
import seaborn as sns
import json
import mpld3
import numpy as np
import pandas as pd
import scipy.sparse
import os, sys
import warnings
from astropy import units as u
from astropy.coordinates import SkyCoord
import statsmodels.api as sm
sns.set_context('poster', font_scale=1.3)
from ipywidgets import interact, FloatSlider, SelectMultiple, interactive
# "constants"
from scipy.constants import c # speed of light in m/s
In [ ]:
DIP_RA = 17.3
DIP_RA_ERR = 1.0
DIP_DEC = -61.0
DIP_DEC_ERR = 10.0
DIP_AMPLITUDE = 0.97e-5
DIP_AMPLITUDE_ERR = 0.21e-5 # average of asymmetric errors
DIP_MONOPOLE = -0.178e-5
DIP_MONOPOLE_ERR = 0.084e-5
dipole = SkyCoord(DIP_RA, DIP_DEC, unit=(u.hourangle, u.deg))
# Data
vltqs = pd.read_csv('../data/vlt-transitions-new.tsv', sep='\t')
vltqs['trans'] = vltqs['transition'] + vltqs.four.astype(str)
keckqs = pd.read_csv("../data/keck-transitions-new.tsv", sep='\t')
keckqs['trans'] = keckqs.transition + keckqs.four.astype(str)
qvals = pd.read_csv("../data/qvalues.txt", sep=' ', index_col=0)
codes = pd.concat([keckqs.set_index('code')[['trans', 'wavelength']],
vltqs.set_index('code')[['trans', 'wavelength']]])
all_systems = pd.read_csv("../data/full-parse-new.tsv", sep='\t')
vlt_ccds = pd.read_csv("../data/vlt-ccd.csv", index_col='chip')
In [343]:
vlt = all_systems[all_systems.source.eq('VLT')].copy()
keck = all_systems[all_systems.source.eq('Keck')].copy()
In [62]:
def plot_a_v_z(dataframe, alphacol='delta_alpha',
errorcol='extra_error_delta_alpha',
nbins=13,
color=0,
ylims=(20),
label='',
fig=None,
ax=None,
):
if fig == None:
fig = plt.gcf()
if ax == None:
ax = plt.gca()
for index, df in enumerate(np.array_split(dataframe.sort_values('z_absorption'), nbins)):
x = np.average(df.z_absorption)
y = np.average(df[alphacol], weights=(1.0 / (df[errorcol] ** 2.0)))
e = np.sqrt(1.0 / np.sum(1.0 / (df[errorcol] ** 2.0)))
if index == 0:
label=label
else:
label=''
ax.scatter(x, y, c=sns.color_palette()[color], label=label)
ax.errorbar(x, y, yerr=e, c=sns.color_palette()[color])
ax.hlines(0, -2, 6, linestyles=':', lw=1.5, color='k')
ax.set_ylabel(r"$\Delta \alpha/\alpha$")
ax.set_xlabel(r"Redshift [z]")
ax.legend(loc='best')
ax.set_xlim(0.3, 3.7)
ax.set_ylim(-ylims, ylims)
fig.tight_layout()
In [32]:
def plot_a_v_zresid(dataframe,
dataframe2,
alphacol='delta_alpha',
alphacol2='dipole_delta_alpha',
errorcol='extra_error_delta_alpha', color=0, label=''):
"""Measured - model"""
fig = plt.gcf()
ax = plt.gca()
nbins = 13
for index in range(nbins):
df = np.array_split(dataframe.sort_values('z_absorption'), nbins)[index]
x = np.average(df.z_absorption)
y = np.average(df[alphacol], weights=(1.0 / (df[errorcol] ** 2.0)))
e = np.sqrt(1.0 / np.sum(1.0 / (df[errorcol] ** 2.0)))
df2 = np.array_split(dataframe2.sort_values('z_absorption'), nbins)[index]
x2 = np.average(df2.z_absorption)
y2 = np.average(df2[alphacol2], weights=(1.0 / (df2[errorcol] ** 2.0)))
e2 = np.sqrt(1.0 / np.sum(1.0 / (df2[errorcol] ** 2.0)))
if index == 0:
label=label
else:
label=''
ax.scatter(x, (y - y2), c=sns.color_palette()[color], label=label)
ax.errorbar(x, (y - y2), yerr=e, c=sns.color_palette()[color])
ax.hlines(0, -2, 6, linestyles=':', lw=1.5, color='k')
ax.set_ylabel(r"Residual $\Delta \alpha/\alpha$")
ax.set_xlabel(r"Redshift [z]")
ax.legend(loc='best')
ax.set_xlim(0.3, 3.7)
ax.set_ylim(-20, 20)
fig.tight_layout()
In [33]:
all_systems.head()
Out[33]:
In [34]:
plot_a_v_zresid(vlt, vlt)
In [231]:
def plot_a_v_theta(dataframe,
alphacol='delta_alpha',
alphacol2='dipole_delta_alpha',
nbins=13,
errorcol='extra_error_delta_alpha', color=0, label=''):
"""Measured - model"""
fig = plt.gcf()
ax = plt.gca()
for index in range(nbins):
df = np.array_split(dataframe.sort_values('dipole_angle'), nbins)[index]
x = np.average(df.dipole_angle)
y = np.average(df[alphacol], weights=(1.0 / (df[errorcol] ** 2.0)))
e = np.sqrt(1.0 / np.sum(1.0 / (df[errorcol] ** 2.0)))
if index == 0:
label=label
else:
label=''
ax.scatter(x, (y), c=sns.color_palette()[color], label=label)
ax.errorbar(x, (y), yerr=e, c=sns.color_palette()[color])
ax.hlines(0, -2, 200, linestyles=':', lw=1.5, color='k')
ax.vlines(90, -30, 30, linestyles=':', lw=1.5, color='k')
ax.set_ylabel(r"Residual $\Delta \alpha/\alpha$")
ax.set_xlabel(r"$\Theta$, angle from best-fitting dipole [degrees]")
ax.legend(loc='best')
ax.set_xlim(0.0, 180.0)
ax.set_ylim(-25, 25)
fig.tight_layout()
In [226]:
plot_a_v_theta(vlt, color=0)
plot_a_v_theta(keck, color=2)
In [37]:
plot_a_v_theta(keck, color=2)
In [229]:
first = keck[keck.sigflag == 1]
second = keck[keck.sigflag == 2]
In [230]:
plt.scatter(first.z_absorption, first.delta_alpha)
plt.scatter(second.z_absorption, second.delta_alpha)
Out[230]:
In [38]:
fig, ax = plt.subplots(figsize=(12, 8))
plot_a_v_z(vlt, color=2, label='VLT (measured)')
plot_a_v_zresid(vlt, vlt)
In [40]:
fig, ax = plt.subplots(figsize=(12, 8))
ax.scatter(vlt.z_absorption, vlt.dipole_delta_alpha)
ax.scatter(vlt.z_absorption, vlt.delta_alpha)
plot_a_v_z(vlt, alphacol='dipole_delta_alpha', color=3, label='VLT (fill with dipole-fit values)')
In [41]:
fig, ax = plt.subplots(figsize=(12, 8))
plot_a_v_z(vlt, alphacol='dipole_delta_alpha', color=3, label='VLT (fill with dipole-fit values)')
plot_a_v_z(vlt, color=2, label='VLT (measured)')
In [43]:
fig, ax = plt.subplots(figsize=(12, 8))
plot_a_v_z(keck, color=2, label='Keck (measured)')
In [44]:
fig, ax = plt.subplots(figsize=(12, 8))
plot_a_v_z(keck, alphacol='dipole_delta_alpha', color=3, label='Keck (fill with dipole-fit values)')
In [190]:
fig, ax = plt.subplots(figsize=(12, 8))
plot_a_v_z(keck, color=2, label='Keck (measured)')
plot_a_v_z(keck, alphacol='dipole_delta_alpha', color=3, label='Keck (fill with dipole-fit values)')
In [151]:
def observed_shifts(telescope='VLT'):
waves = []
shifts = []
for index, row in all_systems[all_systems.source.eq(telescope)].iterrows():
for tran in row['transitions'].split():
rest_wave = qvals.loc[codes.loc[tran].trans].wave
measured_wave = rest_wave * (1 + row.z_absorption)
qval = qvals.loc[codes.loc[tran].trans].qval
waves.append(measured_wave)
shifts.append(shifted_velocity(row.delta_alpha, qval, rest_wave))
return np.array(waves), np.array(shifts)
def parse_j2000(name):
"""Takes the J2000 name stored in the results and returns it in a format astropy can understand."""
return ' '.join([name[1:3], name[3:5], name[5:7], name[7:10], name[10:12], name[12:]])
def j2000_to_theta(name):
"""Returns the angle (degrees) between the position on the sky from
a given `name` and the position of the dipole model from 2012, King."""
c = SkyCoord(parse_j2000(name), unit=(u.hourangle, u.deg))
return float(c.separation(dipole).to_string(decimal=True))
def dipole_alpha(name):
"""Returns the value of Delta alpha/alpha as given by the best fit 2012 King model for
the given name (position).
"""
theta = j2000_to_theta(name)
return (DIP_AMPLITUDE * np.cos(np.deg2rad(theta)) + DIP_MONOPOLE) * 1e6
def plot_system(system, dataframe):
plotdf = dataframe[dataframe.system == system]
fig, (ax1, ax2) = plt.subplots(figsize=(12, 10), nrows=2)
sns.regplot('wavelength', 'vshift', data=plotdf, ax=ax1)
sns.regplot('x', 'vshift', data=plotdf, ax=ax2)
fig.tight_layout()
def shifted_velocity(del_alpha, q, lamb):
# vj =v0 + ∆α xj, xj =−2cqjλ0j,
x = -2 * c * q * lamb
return del_alpha * x * 1e-14
def VLT_distortion(measured_wave,
cutoff=10000.,
slope1=0.06,
intercept1 = -100.0,
slope2 =0.160,
intercept2=-1500.0,
):
"""Telescope dependent distortion function for the VLT sample."""
if measured_wave < cutoff:
return measured_wave * slope1 + intercept1
else:
return measured_wave * slope2 + intercept2
def Keck_distortion(measured_wave, cutoff=10000.):
"""Telescope dependent distortion function for the Keck sample."""
slope1 = .0600
intercept1 = -100
slope2 = .160
intercept2 = -1500
if measured_wave < cutoff:
return measured_wave * slope1 + intercept1
else:
return measured_wave * slope2 + intercept2
def distorted_velocity(row, measured_wave):
"""Telescope dependent distortion function for the VLT sample."""
if row.source == "VLT":
return VLT_distortion(measured_wave)
elif row.source == "Keck":
return Keck_distortion(measured_wave)
In [152]:
df_a = generate_dataset(gen_dipole_alpha=True, wavelength_distortion=False)
df_w = generate_dataset(gen_dipole_alpha=False, wavelength_distortion=True)
In [104]:
def plot_hypotheses(system, dataframe1, dataframe2):
plotdf1 = dataframe1[dataframe1.system == system]
plotdf2 = dataframe2[dataframe2.system == system]
fig, ((ax2, ax4), (ax3, ax1)) = plt.subplots(figsize=(14, 10), nrows=2, ncols=2, )
chi_one, mod_one = fit_hypothesis(system=system, dataframe1=dataframe1, hypothesis='w')
ax1.errorbar(plotdf1.wavelength, plotdf1.vshift, yerr=plotdf1.sigma, ls='none',
label=r'$\chi^2$ = ' + str(np.round(chi_one, 2)), color=sns.color_palette()[2])
ax1.scatter(plotdf1.wavelength, plotdf1.vshift, color=sns.color_palette()[2], label='')
ax1.plot(plotdf1.wavelength, mod_one.fittedvalues, color='k')
chi_one, mod_one = fit_hypothesis(system=system, dataframe1=dataframe1, hypothesis='x')
ax3.errorbar(plotdf1.x, plotdf1.vshift, yerr=plotdf1.sigma, ls='none',
label=r'$\chi^2$ = ' + str(np.round(chi_one, 2)), color=sns.color_palette()[0])
ax3.scatter(plotdf1.x, plotdf1.vshift, color=sns.color_palette()[0], label='')
ax3.plot(plotdf1.x, mod_one.fittedvalues, color='k')
chi_one, mod_one = fit_hypothesis(system=system, dataframe1=dataframe2, hypothesis='w')
ax2.errorbar(plotdf2.wavelength, plotdf2.vshift, yerr=plotdf2.sigma, ls='none',
label=r'$\chi^2$ = ' + str(np.round(chi_one, 2)), color=sns.color_palette()[0])
ax2.scatter(plotdf2.wavelength, plotdf2.vshift, color=sns.color_palette()[0], label='')
ax2.plot(plotdf2.wavelength, mod_one.fittedvalues, color='k')
chi_one, mod_one = fit_hypothesis(system=system, dataframe1=dataframe2, hypothesis='x')
ax4.errorbar(plotdf2.x, plotdf2.vshift, yerr=plotdf2.sigma, ls='none',
label=r'$\chi^2$ = ' + str(np.round(chi_one, 2)), color=sns.color_palette()[2])
ax4.scatter(plotdf2.x, plotdf2.vshift, color=sns.color_palette()[2], label='')
ax4.plot(plotdf2.x, mod_one.fittedvalues, color='k')
autoAxis = ax2.axis()
rec = Rectangle((autoAxis[0],
autoAxis[2]),
(autoAxis[1]-autoAxis[0]),
(autoAxis[3]-autoAxis[2]),
fill=False,lw=2)
rec = ax2.add_patch(rec)
rec.set_clip_on(False)
autoAxis = ax3.axis()
rec = Rectangle((autoAxis[0],
autoAxis[2]),
(autoAxis[1]-autoAxis[0]),
(autoAxis[3]-autoAxis[2]),
fill=False,lw=2)
rec = ax3.add_patch(rec)
rec.set_clip_on(False)
ax3.set_title(r"$\alpha$ process $\alpha$ fit")
ax2.set_title(r"W process W fit")
ax1.set_title(r"$\alpha$ process W fit")
ax4.set_title(r"W process $\alpha$ fit")
ax2.set_ylabel("vshift [m/s]")
ax3.set_ylabel("vshift [m/s]")
ax1.set_xlabel("wavelength")
ax2.set_xlabel("wavelength")
ax3.set_xlabel("x")
ax4.set_xlabel("x")
leg = ax1.legend(handlelength=0, handletextpad=0, fancybox=True, frameon=True, facecolor='white', loc='best')
for item in leg.legendHandles:
item.set_visible(False)
leg = ax2.legend(handlelength=0, handletextpad=0, fancybox=True, frameon=True, facecolor='white', loc='best')
for item in leg.legendHandles:
item.set_visible(False)
leg = ax3.legend(handlelength=0, handletextpad=0, fancybox=True, frameon=True, facecolor='white', loc='best')
for item in leg.legendHandles:
item.set_visible(False)
leg = ax4.legend(handlelength=0, handletextpad=0, fancybox=True, frameon=True, facecolor='white', loc='best')
for item in leg.legendHandles:
item.set_visible(False)
fig.tight_layout()
In [15]:
plot_hypotheses(231, df_a, df_w)
In [70]:
plot_hypotheses(264, df_a, df_w)
In [17]:
df_a[df_a.system==264][['wavelength', 'x', 'vshift', 'sigma']]
Out[17]:
In [230]:
chisqs = {# data_hypo
'x_x':[],
'x_w':[],
'w_x':[],
'w_w':[]}
data = {'x':df_a, 'w':df_w}
for system in sorted(df_w.system.unique()):
for df in ['x', 'w']:
for hypo in ['x', 'w']:
chisq, results = fit_hypothesis(system=system, dataframe1=data[df], hypothesis=hypo)
chisqs['_'.join([df, hypo])].append(chisq)
for item in chisqs:
chisqs[item] = np.array(chisqs[item])
In [245]:
np.sum(chisqs['w_x']/chisqs['w_w'] < 1.0) / len(chisqs['w_x'])
Out[245]:
In [253]:
np.sum(chisqs['x_x']/chisqs['x_w'] < 1.0) / len(chisqs['w_x'])
Out[253]:
In [247]:
all_systems.head()
Out[247]:
In [251]:
angles = []
dipole_alphas = []
measured_alphas = []
for index, row in all_systems.iterrows():
name = row['J2000']
angles.append(j2000_to_theta(name))
dipole_alphas.append(dipole_alpha(name))
measured_alphas.append(row.delta_alpha)
In [264]:
plt.scatter(angles, dipole_alphas)
plt.scatter(angles, measured_alphas, alpha=0.5)
Out[264]:
In [263]:
plt.scatter(angles, measured_alphas)
Out[263]:
In [ ]:
# Todo: ipywidgets minimization of spline + offsets for vshift
In [329]:
alpha = []
distortion = []
for (index, g) in df2.groupby('system'):
X = sm.add_constant(g.x)
model = sm.WLS(g.vshift, X, weights=1.0/g.sigma)
results = model.fit()
chisq = np.sum((g.vshift - results.fittedvalues)**2.0 / (g.sigma) ** 2.0)
if chisq < 1e7:
alpha.append(chisq)
X = sm.add_constant(g.wavelength)
model = sm.WLS(g.vshift, X, weights=1.0/g.sigma)
results = model.fit()
chisq = np.sum((g.vshift - results.fittedvalues)**2.0 / (g.sigma) ** 2.0)
if chisq < 1e10:
distortion.append(chisq)
alpha = np.array(alpha)
distortion = np.array(distortion)
fig, ax = plt.subplots(figsize=(12, 8))
ax.hist(alpha, alpha=0.5,
bins=np.linspace(0, 5, 50),
label='Alpha hypothesis')
ax.hist(distortion, alpha=0.5,
bins=np.linspace(0, 5, 50),
label='Distortion hypothesis')
# ax.hist(alpha / distortion,
# bins=np.linspace(0, 5, 50),
# label='Wavelength',
# alpha=0.5)
# ax.set_xlim(0, 5.0, )
ax.legend()
fig.tight_layout()
We have many measurements of $\alpha$ on both the Keck and VLT telescopes. Measurements with spectrographs both telescopes reveal statistically significant (though small in absolute terms) differences between the expected positions of absorption lines and the measured positions.
There are many possible hypotheses that could explain these differences. To list just two:
We can consider any velocity shift measurements as a hypothesis test. Previous studies started with the assumption that any shift in absorption lines would come as a result of a change in $\alpha$. We have the reported $\frac{\Delta \alpha}{\alpha}$ values for both the Keck and VLT samples. If we invert the hypothesis: use the measured alpha values as a system of velocity shifts, we can effectively discriminate which hypothesis best fits the data.
The goal will be to simulate what the velocity shifts look like if the dipole model is correct, and compare that to what we currently see.
The end goal of this analysis is ultimately a change in how $\frac{\Delta \alpha}{\alpha}$ is measured. The proposed recipe is:
vpfit already allows for this).This will likely have rather low power for any single absorption system, but for ensembles, I think that it's likely the only case
Impact of instrumental systematic errors on fine-structure constant measurements with quasar spectra (2014) by Jonathan B. Whitmore, Michael T. Murphy
Bayesian Approach Overview (pdf) by Ewan Cameron, Tony Pettitt
Bayesian Approach 2 by Ewan Cameron, Tony Pettitt
Spatial variation in the fine-structure constant -- new results from VLT/UVES (2012) by Julian A. King, John K. Webb, Michael T. Murphy, Victor V. Flambaum, Robert F. Carswell, Matthew B. Bainbridge, Michael R. Wilczynska, F. Elliot Koch
In [5]:
species = set([spec[0] for spec in qvals.index.str.split('I')])
colors = sns.color_palette(n_colors=len(species))
plot_colors = {}
for index, specie in enumerate(sorted(species)):
plot_colors[specie] = colors[index]
In [11]:
def plot_absorption(specie=['Al', 'Mg'],
z=1.3,
daa=0.05,
):
fig, ax = plt.subplots(figsize=(12, 6))
ax.hlines(1, 0, 10000)
for tran, row in qvals[qvals.index.str.startswith(tuple(specie))].iterrows():
ax.vlines((1.0 + z) * row.wave + row.qval * ((daa + 1)**2.0-1.0),
0, 1, color=plot_colors[tran[:2]])
ax.vlines((1.0 + z) * row.wave,
0, 1, color=plot_colors[tran[:2]], alpha=0.2)
ax.set_xlim(3000, 8e3)
ax.set_ylim(0, 1.1)
ax.set_ylabel("Normalized Flux")
ax.set_xlabel(r"Wavelength $\AA$")
for spec in specie:
ax.vlines(-1, -1, 0, color=plot_colors[spec], label=spec)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.tight_layout()
plot_absorption(specie=['Al', 'Mg', 'Fe', 'Ni'], )
In [51]:
def plot_shift(specie=['Al', 'Mg'],
z=1.3,
daa=0.05,
):
fig, ax = plt.subplots(figsize=(12, 6))
ax.hlines(0, 0, 10000)
for tran, row in qvals[qvals.index.str.startswith(tuple(specie))].iterrows():
ax.scatter((1.0 + z) * row.wave,
# row.qval * ((daa + 1)**2.0-1.0),
shifted_velocity(daa, row.qval, (1.0 + z) * row.wave),
color=plot_colors[tran[:2]], zorder=3)
ax.set_xlim(3000, 8e3)
ax.set_ylim(-200, 200)
ax.set_ylabel("Velocity Shift [m/s]")
ax.set_xlabel(r"Observed Wavelength $\AA$")
for spec in specie:
ax.vlines(-1, -1, 0, color=plot_colors[spec], label=spec, zorder=-3)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
fig.tight_layout()
plot_shift(specie=['Al', 'Mg', 'Fe', 'Ni'], )
In [52]:
redshift_slider = FloatSlider(value=2.1, min=0.0, max=3.5)
alpha_slider = FloatSlider(value=0.0, min=-5.0, max=5.0, step=0.1)
species_select = SelectMultiple(options=['Al', 'Cr', 'Fe', 'Mg', 'Mn', 'Ni', 'Si'],
description="Species",
value=['Al', 'Fe']
)
# w = interactive(plot_absorption,
# specie=species_select,
# z=redshift_slider,
# daa=alpha_slider,
# )
shift_interactive = interactive(plot_shift,
specie=species_select,
z=redshift_slider,
daa=alpha_slider,
)
In [53]:
shift_interactive
In [42]:
all_systems.head()
Out[42]:
In [15]:
def plot_shifts(telescope='VLT'):
fig, ax = plt.subplots(figsize=(12, 6))
w, s = observed_shifts(telescope=telescope)
sns.regplot(w, s, lowess=True, scatter_kws={'alpha':0.2}, ax=ax)
ax.set_ylim(-200, 200)
ax.set_xlabel(r"Observed Wavelength [$\AA$]")
ax.set_ylabel("Velocity [m/s]")
ax.set_title(telescope)
fig.tight_layout()
In [16]:
plot_shifts(telescope='VLT')
In [17]:
plot_shifts(telescope='Keck')
In [59]:
def plot_all(title='Keck + VLT'):
fig, ax = plt.subplots(figsize=(12, 8))
telescope='VLT'
w, s = observed_shifts(telescope=telescope)
sns.regplot(w, s, lowess=True, scatter_kws={'alpha':0.5}, ax=ax, label=telescope)
telescope='Keck'
w, s = observed_shifts(telescope=telescope)
sns.regplot(w, s, lowess=True, scatter_kws={'alpha':0.5}, ax=ax, label=telescope)
ax.set_xlim(2000, 10000)
ax.set_ylim(-200, 200)
ax.legend()
ax.set_xlabel(r"Observed Wavelength [$\AA$]")
ax.set_ylabel("Velocity [m/s]")
ax.set_title(title)
fig.tight_layout()
In [60]:
plot_all()
In [61]:
def observed_shifts_by_sample(sample='A'):
waves = []
shifts = []
for index, row in all_systems[all_systems['sample'] == sample].iterrows():
for tran in row['transitions'].split():
rest_wave = qvals.loc[codes.loc[tran].trans].wave
measured_wave = rest_wave * (1 + row.z_absorption)
qval = qvals.loc[codes.loc[tran].trans].qval
waves.append(measured_wave)
shifts.append(shifted_velocity(row.delta_alpha, qval, rest_wave))
return np.array(waves), np.array(shifts)
def plot_samples():
fig, ax = plt.subplots(figsize=(12, 8))
telescope='VLT'
for sample in sorted(all_systems['sample'].unique()):
w, s = observed_shifts_by_sample(sample=sample)
sns.regplot(w, s, lowess=True, scatter_kws={'alpha':0.5}, ax=ax, label=sample)
ax.set_ylim(-400, 400)
ax.set_xlim(2000, 10000)
ax.legend()
# frame = ax.get_frame()
# frame.set_alpha(1.0)
ax.set_xlabel(r"Observed Wavelength [$\AA$]")
ax.set_ylabel("Velocity [m/s]")
ax.set_title('Samples')
fig.tight_layout()
In [62]:
plot_samples()
In [54]:
w
In [74]:
def plot_sim(telescope='VLT', use_dipole=True):
fig, ax = plt.subplots(figsize=(12, 6))
w, s = observed_shifts(telescope=telescope)
sns.regplot(w, s, lowess=True, scatter_kws={'alpha':0.05}, ax=ax, label='Implied by Measured VLT')
w, s = simulated_shifts(telescope=telescope, use_dipole=use_dipole)
sns.regplot(w, s, lowess=True, scatter_kws={'alpha':0.05}, ax=ax, label='Implied by Dipole VLT')
ax.set_ylim(-200, 200)
ax.legend()
ax.set_xlabel(r"Observed Wavelength [$\AA$]")
ax.set_ylabel("Velocity [m/s]")
ax.set_title('Simulated velocity shifts for ' + telescope)
fig.tight_layout()
In [75]:
plot_sim()
In [69]:
def simulated_shifts(telescope='VLT', use_dipole=True):
waves = []
shifts = []
for index, row in all_systems[all_systems.source.eq(telescope)].iterrows():
for tran in row['transitions'].split():
rest_wave = qvals.loc[codes.loc[tran].trans].wave
measured_wave = rest_wave * (1 + row.z_absorption)
qval = qvals.loc[codes.loc[tran].trans].qval
waves.append(measured_wave)
# da = row.delta_alpha
if use_dipole:
da = row['dipole_delta_alpha']
else:
da = -3.0
shifts.append(shifted_velocity(da, qval, rest_wave))
return np.array(waves), np.array(shifts)
plot_sim(use_dipole=False)
In [ ]:
In [ ]:
In [80]:
Out[80]:
In [99]:
telescope = 'VLT'
row = all_systems[all_systems.source.eq(telescope)].sample(1,
random_state=2
).iloc[0]
waves = []
shifts = []
for tran in row['transitions'].split():
rest_wave = qvals.loc[codes.loc[tran].trans].wave
measured_wave = rest_wave * (1 + row.z_absorption)
qval = qvals.loc[codes.loc[tran].trans].qval
waves.append(measured_wave)
# da = row.delta_alpha
# if use_dipole:
da = row['dipole_delta_alpha']
# else:
# da = -3.0
shifts.append(shifted_velocity(da, qval, rest_wave))
wav, shift = np.array(waves), np.array(shifts)
fig, ax = plt.subplots(figsize=(12, 6))
ax.scatter(wav, shift)
ax.set_ylim(-200, 200)
# ax.legend()
ax.set_xlabel(r"Observed Wavelength [$\AA$]")
ax.set_ylabel("Velocity [m/s]")
ax.set_title('Simulated velocity shifts for ' + telescope)
fig.tight_layout()
In [ ]:
telescope = 'VLT'
row = all_systems[all_systems.source.eq(telescope)].sample(1,
random_state=2
).iloc[0]
waves = []
shifts = []
for tran in row['transitions'].split():
rest_wave = qvals.loc[codes.loc[tran].trans].wave
measured_wave = rest_wave * (1 + row.z_absorption)
qval = qvals.loc[codes.loc[tran].trans].qval
waves.append(measured_wave)
# da = row.delta_alpha
# if use_dipole:
da = row['dipole_delta_alpha']
# else:
# da = -3.0
shifts.append(shifted_velocity(da, qval, rest_wave))
wav, shift = np.array(waves), np.array(shifts)
fig, ax = plt.subplots(figsize=(12, 6))
ax.scatter(wav, shift)
ax.set_ylim(-200, 200)
# ax.legend()
ax.set_xlabel(r"Observed Wavelength [$\AA$]")
ax.set_ylabel("Velocity [m/s]")
ax.set_title('Simulated velocity shifts for ' + telescope + " " + str(np.round(da, 2)))
fig.tight_layout()
In [113]:
telescope = 'VLT'
row = all_systems[all_systems.source.eq(telescope)].sample(1,
random_state=3
).iloc[0]
waves = []
shifts = []
for tran in row['transitions'].split():
rest_wave = qvals.loc[codes.loc[tran].trans].wave
measured_wave = rest_wave * (1 + row.z_absorption)
qval = qvals.loc[codes.loc[tran].trans].qval
waves.append(measured_wave)
# da = row.delta_alpha
# if use_dipole:
da = row['dipole_delta_alpha']
# else:
# da = -3.0
shifts.append(shifted_velocity(da, qval, rest_wave))
wav, shift = np.array(waves), np.array(shifts)
fig, ax = plt.subplots(figsize=(12, 6))
ax.scatter(wav, shift)
ax.set_ylim(-200, 200)
# ax.legend()
ax.set_xlabel(r"Observed Wavelength [$\AA$]")
ax.set_ylabel("Velocity [m/s]")
ax.set_title('Simulated velocity shifts for ' + telescope + " " + str(da))
fig.tight_layout()
In [112]:
telescope = 'VLT'
row = all_systems[all_systems.source.eq(telescope)].sample(1,
random_state=8
).iloc[0]
waves = []
shifts = []
for tran in row['transitions'].split():
rest_wave = qvals.loc[codes.loc[tran].trans].wave
measured_wave = rest_wave * (1 + row.z_absorption)
qval = qvals.loc[codes.loc[tran].trans].qval
waves.append(measured_wave)
da = row['dipole_delta_alpha']
shifts.append(shifted_velocity(da, qval, rest_wave))
wav, shift = np.array(waves), np.array(shifts)
fig, ax = plt.subplots(figsize=(12, 6))
ax.scatter(wav, shift)
waves = []
shifts = []
for tran in row['transitions'].split():
rest_wave = qvals.loc[codes.loc[tran].trans].wave
measured_wave = rest_wave * (1 + row.z_absorption)
qval = qvals.loc[codes.loc[tran].trans].qval
waves.append(measured_wave)
da = row.delta_alpha
shifts.append(shifted_velocity(da, qval, rest_wave))
wav, shift = np.array(waves), np.array(shifts)
ax.scatter(wav, shift)
ax.set_xlabel(r"Observed Wavelength [$\AA$]")
ax.set_ylabel("Velocity [m/s]")
ax.set_title('Simulated velocity shifts for ' + telescope + " " + str(da))
fig.tight_layout()
In [ ]:
In [ ]:
In [ ]:
In [222]:
# Todo consider including the outliers stripped via the LTS method
# # Outliers http://astronomy.swin.edu.au/~mmurphy/files/KingJ_12a_VLT+Keck.dat
# 127 J194454+770552 3.02 2.8433 -4.959 1.334 C Keck 1 1
# http://astronomy.swin.edu.au/~mmurphy/MurphyM_09a.dat
# - - 2.8433 dgl -4.959 1.334 0.000 0.149 0.000 C
# 145 J000448-415728 2.76 1.5419 -5.270 0.906 D VLT 3 1
# TableA1
# b1b2j4j5j6j7j8