Etude d'une CCD¶

Jeu de données¶

  • images acquises en février 2023
  • CCD ATIK 460 EX () bin1
  • Températures: +5°C à -15°C pas de -5 °C
  • Poses: 0 (bias),1,2,5,10,20,30,60,120,240,300 sec
  • 16 images par série

darkb1(10)-0004-60.fit : "darkb1" + T +"-" + i +"-" + exp + ".fit" avec: T = temperature , notée (T) pour les temperatures négatives et i = n° pose unitaire au format ###0

Initialisations¶

In [1]:
import glob
from astropy.io import fits as fits
from matplotlib import pyplot as plt
from matplotlib.colors import LogNorm
import numpy as np
import pandas as pd
import scipy
import scipy.stats
In [2]:
path = r'D:\Temp\\'
display_infos = 0

Fonctions¶

Optimisation nombre de bins

  • Référence : Shimazaki and Shinomoto. Neural Comput, 2007, 19(6), 1503-1527
  • Source : https://www.neuralengine.org//res/histogram.html

Ouverture et visualisation d'une image fit : un bias¶

In [17]:
# choix image
temp = "0"
exptime = "0"
no = "0001"
#list_images = glob.glob(path + "dark_b1_" + temp + "-" + no+ "-" + exptime + ".fit")
filename = path + "dark_b1_" + temp + "-" + no+ "-" + exptime + ".fit"

hdu=fits.open(filename)
hdr=hdu[0].header
image=hdu[0].data
if display_infos:
    print(repr(hdr))
x_size = hdr["NAXIS1"]
y_size = hdr["NAXIS2"]
df = pd.DataFrame(image)

fig = plt.figure()
plt.imshow(image, cmap='gray',norm=LogNorm())
plt.grid(False)
plt.text(20,50,"bias T = 0°C",color = "white", fontsize = 12)


print('Statistics values')
print('Min:', np.min(image))
print('Max:', np.max(image))
print('Mean:', np.round(np.mean(image),0))
print('Stdev:', np.round(np.std(image),1))



NameFig = "F:\_120aa.eps"
plt.savefig(NameFig, format='eps',bbox_inches='tight', dpi = 75)
NameFig = "F:\_120aa.png"
plt.savefig(NameFig, format='png',bbox_inches='tight', dpi = 75)

Statistics values
Min: 195
Max: 583
Mean: 344.0
Stdev: 29.3

Coupes horizontales et verticales¶

In [20]:
x = int(x_size/2)
y = int(y_size/2)
w = 30
df = pd.DataFrame(image)

df_slice_horizontal=df.iloc[:][y-w//2:y+w//2]
df_slice_vertical=df_slice_horizontal=df.iloc[x-w//2:x+w//2][:]

fig,(ax1,ax2) = plt.subplots(2,figsize=(3,2))
fig.subplots_adjust(hspace=0.35)
ax1.plot(df_slice_horizontal.median(),"o",markersize=2)

ax2.plot(df_slice_horizontal.std(),"ro",markersize=2)
ax1.set_title("Bias - Coupe horizontale");
In [37]:
fig = plt.figure()
plt.imshow(image, cmap='gray',norm=LogNorm())
plt.grid(False)
plt.text(20,50,"bias T = 0°C",color = "white", fontsize = 12)

from scipy.interpolate import CubicSpline
x = np.arange(x_size)
y = df_slice_horizontal.median()
f = CubicSpline(x, y)
plt.plot(x,(f(x)-350)*10+350,"y-",markersize=2)
Out[37]:
[<matplotlib.lines.Line2D at 0x22a37351250>]

Visualisation d'une partie de l'image¶

In [5]:
x = int(x_size/2)
y = int(y_size/2)

x = 100
y = 100
ws = 10

df = pd.DataFrame(image)
df_cut_square=df.iloc[10:20,10:20]
df_cut_square=df.iloc[x:x+ws,y:y+ws]
#display(df_cut_square)

fig = plt.figure()
ax = plt.axes()
plt.imshow(df_cut_square, cmap='gray',norm=LogNorm())
plt.grid(False)
plt.tick_params(left = False,bottom=False)
ax.set_xticks(np.arange(0,10))
ax.set_yticks(np.arange(0,10))
for i in np.arange(10):
    for j in np.arange(10):
        s= str(df_cut_square.iloc[i,j])
        plt.text(i-0.25,j,s,color = "w",fontsize = 8,fontweight="bold")
plt.title = ""

NameFig = "F:\_120a.eps"
plt.savefig(NameFig, format='eps',bbox_inches='tight', dpi = 75)
NameFig = "F:\_120a.png"
plt.savefig(NameFig, format='png',bbox_inches='tight', dpi = 75)