====== Visualization of Astrophysical Data and Fitting Results ======
=== SextenFigures.ipynb ===
This file contains Python code for processing and visualizing astrophysical data, including manipulating photometry data, normalizing densities, creating color-magnitude diagrams, and plotting fitting results to identify best fits among model populations. It includes custom color schemes for visualization, use of matplotlib for plotting, and procedures for density normalization and scatter plot generation. The notebook likely serves as part of an analysis pipeline in astrophysical research, particularly focusing on the study of globular clusters or similar stellar systems.
```python
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
import pickle as pkl
from scipy.spatial.distance import cdist
from tqdm import tqdm
import numpy as np
from pathlib import Path
from scipy.interpolate import splrep, BSpline
from scipy.signal import savgol_filter
from pysep.atm.utils import load_new_style
from dataclasses import dataclass
from collections import namedtuple
import matplotlib
from scipy.signal import savgol_filter
from mplEasyAnimate import animation
```
```python
@dataclass
class colorscheme:
DartmouthGreen : str = "#00693E"
ForestGreen : str = "#12312B"
RichForestGreen : str = "#0D1E1C"
White : str = "#FFFFFF"
Black : str = "#000000"
AutumBrown : str = "#643C20"
BonfireRed : str = "#9D162E"
SpringGreen : str = "#C4DD88"
Violet : str = "#B607DE"
pallet = namedtuple("pallet", "C0 C1")
primary : namedtuple = pallet(DartmouthGreen, Black)
seconday : namedtuple = pallet(ForestGreen,AutumBrown)
tertiery : namedtuple = pallet(RichForestGreen,BonfireRed)
DGcmap =cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["#FFFFFF", colorscheme.DartmouthGreen])
```
```python
PHOTROOT = "/mnt/Astronomy/GraduateSchool/Thesis/GCConsistency/NGC2808/photometry/HUGS/ngc2808/photometry.pkl"
```
```python
def normalize_density(color, mag, density, n=5000):
normDensity = np.zeros(shape=color.shape[0])
for IDx, (c, m, d) in tqdm(enumerate(zip(color, mag, density)), total=len(density)):
distances = cdist(np.array([[c, m]]), np.array([color, mag]).T)[0]
closestIDX= np.argpartition(distances, n)
closestDensity = density[closestIDX[:n]]
meanNearDensity = np.mean(closestDensity)
normalizedDensity = d/meanNearDensity
normDensity[IDx] = normalizedDensity
return normDensity
def normalize_density_magBin(color, mag, density, binSize=0.1):
normDensity = np.zeros(shape=color.shape[0])
for IDx, (c, m, d) in tqdm(enumerate(zip(color, mag, density)), total=len(density)):
cut = (mag > m-binSize/2) & (mag <= m+binSize/2)
binDensity = density[cut]
meanBinDensity = np.mean(binDensity)
normalizedDensity = d/meanBinDensity
normDensity[IDx] = normalizedDensity
return normDensity
```
```python
densityCache = "/mnt/Astronomy/packages/localTests/fidanka/MC_1_Density.npz"
assert os.path.exists(densityCache), "Density Cache File not Found!"
```
```python
density = np.load(densityCache)['density']
with open(PHOTROOT, 'rb') as f:
HUGSPhotometry = pkl.load(f)[1]
color = HUGSPhotometry["F275W"] - HUGSPhotometry["F814W"]
mag = HUGSPhotometry["F814W"]
HUGSPhotometry['density'] = normalize_density_magBin(color, mag, density, binSize=0.3)
```
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```python
with plt.style.context(pubStyle):
fig, axs = plt.subplots(2,3, figsize=(15, 10))
ax1 = axs[0,0]
ax2 = axs[1,0]
ax3 = axs[0,1]
ax4 = axs[1,1]
ax5 = axs[0,2]
ax6 = axs[1,2]
f1 = "F275W"
f2 = "F814W"
f3 = f2
color = HUGSPhotometry[f1]-HUGSPhotometry[f2]
mag = HUGSPhotometry[f3]
density = HUGSPhotometry["density"]
condF = (color > 1.75) & (color < 5) & (mag < 22) & (mag > 15)
colorF = color[condF]
magF = mag[condF]
densityF = density[condF]
ax1.scatter(colorF, magF, s=1, c=colorscheme.DartmouthGreen)
ax1.invert_yaxis()
ax2.scatter(colorF, magF, s=1, c=densityF, cmap=DGcmap)
ax2.invert_yaxis()
condZ = (color > 1.75) & (color < 3.5) & (mag < 20) & (mag > 18.1)
colorZ = color[condZ]
magZ = mag[condZ]
densityZ = density[condZ]
ax3.scatter(colorZ, magZ, s=1, c=colorscheme.DartmouthGreen)
ax3.set_xticklabels([])
ax3.invert_yaxis()
# normDensityZ = normalize_density(colorZ, magZ, densityZ)
ax4.scatter(colorZ, magZ, s=1, c=densityZ, alpha=1, cmap=DGcmap)
ax4.invert_yaxis()
# IDs = ["A", "B", "C", "D", "E", "F"]
# Axs = [ax1, ax3, ax5, ax2, ax4, ax6]
# for ax, l in zip(Axs, IDs):
# ax.text(0.9, 0.5, l, transform=ax.transAxes)
plt.subplots_adjust(hspace=0)
ax1.set_ylabel(f"{f3}", fontsize=23)
ax2.set_ylabel(f"{f3}", fontsize=23)
# ax3.set_ylabel(f"{f3}", fontsize=23)
# ax4.set_ylabel(f"{f3}", fontsize=23)
ax2.set_xlabel(f"{f1}-{f2}")
ax4.set_xlabel(f"{f1}-{f2}")
ax6.set_xlabel(f"{f1}-{f2}")
condRGB = (color > 3) & (color < 5) & (mag < 17.5) & (mag > 15)
colorRGB = color[condRGB]
magRGB = mag[condRGB]
densityRGB = density[condRGB]
# normDensity = normalize_density(colorRGB, magRGB, densityRGB, n=10)
ax5.scatter(colorRGB, magRGB,s=1, c=colorscheme.DartmouthGreen)
ax5.invert_yaxis()
ax6.scatter(colorRGB, magRGB,s=1, c=densityRGB, cmap=DGcmap)
ax6.invert_yaxis()
fig.savefig("Figures/DensityMap.png", dpi=300)
```
```python
with open("ExampleFidOutput.pkl", 'rb') as f:
fiducial = pkl.load(f)
```
```python
with plt.style.context(pubStyle):
fig, ax = plt.subplots(1,1,figsize=(10,7))
fig.subplots_adjust(hspace=0)
ax.scatter(color, mag, s=1, c=HUGSPhotometry['density'], cmap=DGcmap)
ax.set_xlim(1.5, 5)
ax.set_ylim(15.5, 22)
ax.invert_yaxis()
ax.set_xlabel("F275W - F814W", fontsize=23)
ax.set_ylabel("F814W", fontsize=23)
ax.plot(fiducial['Acolor'], fiducial['mag'], linewidth = 5, color=colorscheme.BonfireRed, label="E")
ax.plot(fiducial['Bcolor'], fiducial['mag'], linewidth = 5, color=colorscheme.Violet, label="A", linestyle='dashed')
ax.legend(fontsize=23, frameon=False)
fig.savefig("Figures/NGC2808Fid.png", dpi=300)
# fig.savefig("../static/imgs/NGC2808Fid.png", dpi=200)
```
```python
with open("../../../../../../../../Astronomy/GraduateSchool/Thesis/GCConsistency/NGC2808/Analysis/fitting/ReducedResults.denseAlpha.pkl", 'rb') as f:
fitResults = pkl.load(f)
```
```python
with plt.style.context(pubStyle):
for n in tqdm(range(len(fitResults))):
fig, ax = plt.subplots(1,1,figsize=(10,7))
ax.plot(fiducial['Acolor'], fiducial['mag'], linewidth = 5, color=colorscheme.BonfireRed, label="E")
ax.plot(fiducial['Bcolor'], fiducial['mag'], linewidth = 5, color=colorscheme.Violet, label="A", linestyle='dashed')
ax.set_xlim(1.5, 5)
ax.set_ylim(15.5, 22)
# print(fitResults[n][0])
ax.set_title(f"n: {n}, chi2: {fitResults[n][0]:0.3f}")
A = fitResults[n][2][0]
E = fitResults[n][2][1]
Acolor = A['F275W'] - A["F814W"]
Amag = A["F814W"]
Ecolor = E["F275W"] - E["F814W"]
Emag = E["F814W"]
ax.plot(Acolor, Amag)
ax.plot(Ecolor, Emag)
ax.invert_yaxis()
plt.savefig(f"Figures/bfr-{n}.png")
plt.close(fig)
```
```python
nA = 0
nE = 4
with plt.style.context(pubStyle):
A = fitResults[nA][2][0]
E = fitResults[nE][2][1]
print(fitResults[nA][0])
print(fitResults[nE][0])
Acolor = A['WFC3_UVIS_F275W'] - A["WFC3_UVIS_F814W"]
Amag = A["WFC3_UVIS_F814W"]
Ecolor = E["WFC3_UVIS_F275W"] - E["WFC3_UVIS_F814W"]
Emag = E["WFC3_UVIS_F814W"]
fig, ax = plt.subplots(1,1,figsize=(10,7))
ASotedIDX = np.argsort(Amag)
ax.plot(fiducial['Acolor'], fiducial['mag'], color=colorscheme.BonfireRed, linestyle='dashed', alpha=0.5, label="Fiducial E")
ax.plot(savgol_filter(Acolor[ASotedIDX], 10, 2), Amag[ASotedIDX], color=colorscheme.BonfireRed, label="Best Fit E")
ax.plot(fiducial['Bcolor'], fiducial['mag'], color=colorscheme.Violet, linestyle='dashed', alpha=0.5, label="Fiducial A")
# ax.set_xlim(1.5, 5)
# ax.set_ylim(18, 21)
ax.plot(Ecolor, Emag, color=colorscheme.Violet, label="Best Fit A")
ax.invert_yaxis()
ax.legend(frameon=False)
# print(fitResults[nA][1][0][3].x)
# print(fitResults[nE][1][1][3].x)
# print(fitResults[nA][1][0][1])
# print(fitResults[nE][1][1][1])
# print(fitResults[nA][1][0][2])
# print(fitResults[nE][1][1][2])
ax.set_xlabel("F275W - F814W", fontsize=23)
ax.set_ylabel("F814W", fontsize=23)
fig.savefig("Figures/BestFit.png", dpi=300)
```
0.07313541839143663
0.132039504646009
```python
A
```
|
WFC3_UVIS_F200LP |
WFC3_UVIS_F218W |
WFC3_UVIS_F225W |
WFC3_UVIS_F275W |
WFC3_UVIS_F280N |
WFC3_UVIS_F300X |
WFC3_UVIS_F336W |
WFC3_UVIS_F343N |
WFC3_UVIS_F350LP |
WFC3_UVIS_F373N |
... |
WFC3_IR_F127M |
WFC3_IR_F128N |
WFC3_IR_F130N |
WFC3_IR_F132N |
WFC3_IR_F139M |
WFC3_IR_F140W |
WFC3_IR_F153M |
WFC3_IR_F160W |
WFC3_IR_F164N |
WFC3_IR_F167N |
| 0 |
25.978353 |
33.859351 |
33.659541 |
32.988767 |
30.306583 |
30.936987 |
29.587342 |
29.429675 |
25.782097 |
29.467213 |
... |
23.151844 |
23.073539 |
23.116001 |
23.090822 |
22.973418 |
22.967202 |
22.699599 |
22.698120 |
22.487449 |
22.519156 |
| 1 |
25.954552 |
33.831236 |
33.631871 |
32.952047 |
30.279008 |
30.905088 |
29.555683 |
29.398252 |
25.758344 |
29.434568 |
... |
23.131067 |
23.052768 |
23.095286 |
23.070104 |
22.952688 |
22.946525 |
22.679091 |
22.677667 |
22.467302 |
22.499040 |
| 2 |
25.930738 |
33.803047 |
33.604014 |
32.915213 |
30.251354 |
30.873142 |
29.524005 |
29.366815 |
25.734579 |
29.401911 |
... |
23.110263 |
23.031970 |
23.074543 |
23.049356 |
22.931943 |
22.925829 |
22.658564 |
22.657194 |
22.447123 |
22.478889 |
| 3 |
25.906920 |
33.774848 |
33.576146 |
32.878354 |
30.223691 |
30.841181 |
29.492311 |
29.335363 |
25.710810 |
29.369246 |
... |
23.089460 |
23.011173 |
23.053802 |
23.028610 |
22.911199 |
22.905135 |
22.638039 |
22.636723 |
22.426947 |
22.458742 |
| 4 |
25.883110 |
33.746654 |
33.548278 |
32.841488 |
30.196032 |
30.809220 |
29.460617 |
29.303914 |
25.687049 |
29.336588 |
... |
23.068670 |
22.990389 |
23.033073 |
23.007876 |
22.890467 |
22.884452 |
22.617526 |
22.616263 |
22.406783 |
22.438606 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
| 313 |
14.949465 |
21.859864 |
21.407965 |
19.522513 |
18.613011 |
18.281919 |
17.055310 |
16.928078 |
14.766674 |
16.485056 |
... |
12.671225 |
12.594520 |
12.638176 |
12.607687 |
12.484442 |
12.482223 |
12.211117 |
12.225296 |
12.052960 |
12.080236 |
| 314 |
14.916718 |
21.845558 |
21.411155 |
19.530729 |
18.592100 |
18.274660 |
17.044438 |
16.916905 |
14.733685 |
16.470239 |
... |
12.631767 |
12.554985 |
12.598540 |
12.567914 |
12.444282 |
12.441953 |
12.169769 |
12.183968 |
12.010663 |
12.037913 |
| 315 |
14.884109 |
21.831753 |
21.415111 |
19.539779 |
18.571538 |
18.267978 |
17.034075 |
16.906246 |
14.700830 |
16.455932 |
... |
12.592302 |
12.515440 |
12.558894 |
12.528127 |
12.404098 |
12.401656 |
12.128368 |
12.142587 |
11.968289 |
11.995514 |
| 316 |
14.851538 |
21.818069 |
21.419250 |
19.549028 |
18.551062 |
18.261435 |
17.023836 |
16.895713 |
14.668011 |
16.441746 |
... |
12.552842 |
12.475900 |
12.519251 |
12.488343 |
12.363915 |
12.361359 |
12.086961 |
12.101201 |
11.925905 |
11.953104 |
| 317 |
14.818997 |
21.804534 |
21.423317 |
19.559072 |
18.530665 |
18.255557 |
17.014354 |
16.885930 |
14.635219 |
16.428355 |
... |
12.513350 |
12.436329 |
12.479578 |
12.448531 |
12.323709 |
12.321042 |
12.045552 |
12.059809 |
11.883527 |
11.910705 |
318 rows × 57 columns
```python
print(type(fitResults))
```
```python
chi2 = list()
Ys = list()
alphas = list()
for pop in fitResults:
chi2.append(pop[0])
Ys.append((pop[1][0][1], pop[1][1][1]))
alphas.append((pop[1][0][2], pop[1][1][2]))
chi2 = np.array(chi2)
Ys = np.array(Ys)
alphas = np.array(alphas)
```
```python
YsA = Ys[:, 0]
sortedYsA = np.argsort(YsA)
plt.plot(YsA[sortedYsA], chi2[sortedYsA])
plt.xlim(
```
[]
```python
```
```python
Ys[:, 0]
```
array([0.24, 0.3 , 0.24, ..., 0.33, 0.3 , 0.24])
```python
sortedchi2
```
array([ 0, 1, 2, ..., 1079, 1080, 1081])
```python
```
array([0.05120079, 0.06275137, 0.07305412, ..., 0.99586754, 0.99739802,
0.99955145])
```python
```