def PTC(file_name, ax1, ax2, cutoff=1):
hdul, vbb, wideint, wwideint = load_hdul(file_name)
diffvr_els = 0
for C in CHANNEL_5:
mean_el, diffvr_el, corr, gain = load_data(hdul, C, cutoff=cutoff)
mean_el, diffvr_el = mean_el/1000, diffvr_el/1000
ax1.plot(mean_el, diffvr_el, 'o', label=f'Channel {str(C)}')
ax2.plot(mean_el, diffvr_el - mean_el, 'o', label=f'Channel {str(C)}')
diffvr_els += diffvr_el/4
lin = np.linspace(0, 70 * cutoff, 5000)
ax1.plot(lin, lin, '--')
ax1.legend(loc='upper left')
ax1.set_xlabel('Mean (kel)')
ax1.set_ylabel('Variance ($kel^2$)')
ax1.annotate('Possion Noise: $\sigma_{S}^{2}=\mu$', xy=(60, 60), xytext=(40, 69),
arrowprops=dict(width=2, headlength=4, facecolor='black'), fontsize=14)
ax1.set_xlim(-2, 72 * cutoff)
lin = np.linspace(0, 70 * cutoff, 5000)
ax2.plot(lin, np.zeros(5000), '--')
ax2.legend(loc='lower left')
ax2.set_xlabel('Mean (kel)')
ax2.set_ylabel('Variance ($kel^2$)')
ax2.set_xlim(-2, 72 * cutoff)
return diffvr_els
def raw_PTC(file_name, ax):
hdul, vbb, wideint, wwideint = load_hdul(file_name)
for C in CHANNEL_5:
channel_data = hdul[1].data[hdul[1].data['chans'] == C]
mean, diffvr = channel_data['mn1db'], channel_data['diffvr']
mean, diffvr = dip_remove(mean, diffvr)
mean, diffvr = mean/1000, diffvr/1000
ax.plot(mean, diffvr, 'o', label=f'Channel {str(C)}')
ax.legend(loc='upper left')
ax.set_xlabel('Mean (kADU)')
ax.set_ylabel('Variance ($kADU^2$)')
def PTC_fit(file_name, deg, ax3):
hdul, vbb, wideint, wwideint = load_hdul(file_name)
data_len = len(load_data(hdul, 5)[0])
mean_el_ave, diffvr_el_ave = np.zeros(np.shape(data_len)), np.zeros(np.shape(data_len))
for idx, C in enumerate(CHANNEL_5):
mean_el, diffvr_el, corr, gain = load_data(hdul, C)
mean_el, diffvr_el = mean_el/1000, diffvr_el/1000
mean_el_ave, diffvr_el_ave = mean_el_ave + mean_el/4, diffvr_el_ave + diffvr_el/4
diffvr_el_1, diffvr_el_n = poly_fit(mean_el_ave, diffvr_el_ave, deg)
ax3.plot(mean_el_ave, diffvr_el_ave - diffvr_el_1, 'x', label=f'deg=1')
ax3.plot(mean_el_ave, diffvr_el_ave - diffvr_el_n, 'x', label=f'deg={deg}')
ax3.set_xlabel('Mean (kel)')
ax3.set_ylabel('Residual ($kel^2$)')
ax3.legend()
def correlation_map(file_name, C, limit, ax):
hdul, vbb, wideint, wwideint = load_hdul(file_name)
mean_el, diffvr_el, corr, gain = load_data(hdul, C)
X, Y = np.meshgrid(np.arange(limit), np.arange(limit))
Z = np.zeros([limit, limit])
ij_pairs = [(i, j) for i in np.arange(limit) for j in np.arange(limit) if i + j != 0]
for ij_pair in ij_pairs:
i, j = ij_pair[0], ij_pair[1]
reg = LinearRegression(fit_intercept=True).fit(np.linspace(0, 1, np.shape(corr)[0]).reshape(-1, 1), corr[:, i, j])
Z[i, j] = reg.coef_[0]
ax.view_init(ax.elev+15, ax.azim + 102)
ax.set_xlabel('Serial direction (plx)')
ax.set_ylabel('Parallel direction (plx)')
ax.set_zlabel('Coefficient (frac)')
make_bars(ax, X, Y, Z, width=0.23)
def covariance(file_name, fit, ax):
hdul, vbb, wideint, wwideint = load_hdul(file_name)
data_len = len(load_data(hdul, 5)[0])
mean_el_ave, cov_el_ave = np.zeros(np.shape(data_len)), np.zeros(
np.shape(data_len))
ijs = [(i, j) for i in range(0, 10) for j in range(0, 10) if i + j != 0]
for C in CHANNEL_5:
mean_el, cov_el, corr, gain = load_data(hdul, C)
mean_el, cov_el = mean_el / 1000, cov_el / 1000
for ij in ijs:
i, j = ij[0], ij[1]
if fit is True:
reg = LinearRegression(fit_intercept=False).fit(
mean_el.reshape(-1, 1), corr[:, i, j])
corr_ij = reg.predict(mean_el.reshape(-1, 1))
# ransac = RANSACRegressor(residual_threshold=0.01, stop_probability=0.99)
# ransac.fit(mean_el.reshape(-1, 1), corr[:, i, j])
# corr_ij = ransac.predict(mean_el.reshape(-1, 1))
else:
corr_ij = corr[:, i, j]
cov_el = cov_el + corr_ij * mean_el
"""
if C == 10:
cov_el = cov_el + corr_ij * mean_el * 1.3
else:
cov_el = cov_el + corr_ij * mean_el * 5.2
"""
ax.plot(mean_el, cov_el, 'o', label=f'Channel {str(C)}')
mean_el_ave, cov_el_ave = mean_el_ave + mean_el / 4, cov_el_ave + cov_el / 4
lin = np.linspace(0, 72, 5000)
ax.plot(lin, lin, '--')
ax.set_xlabel('Mean (kel)')
ax.set_ylabel('Variance + Covariance ($kel^2$)')
ax.legend(loc='upper left')
ax.set_xlim(-2, 74)
return mean_el_ave, cov_el_ave
def photometry(file_name, C, inlier_mask):
hdul, vbb, wideint, wwideint = load_hdul(file_name)
outlier_mask = np.logical_not(inlier_mask)
mean_el, diffvr_el, corr, gain = load_data(hdul, C)
channel_data = hdul[1].data[hdul[1].data['chans'] == C]
foo = np.shape(mean_el)[0]
mean1_el, mean2_el = channel_data['mn1db'][:foo] * gain, channel_data['mn2db'][:foo] * gain
mean1_ph, mean2_ph = channel_data['photons1'][:foo], channel_data['photons2'][:foo]
mean_el_diff = (mean1_el - mean2_el) / max(mean1_el - mean2_el)
mean_ph_diff = (mean1_ph - mean2_ph) / max(mean1_ph - mean2_ph)
print(np.shape(mean_el_diff[inlier_mask]))
print(np.shape(mean_el_diff[outlier_mask]))
plt.figure(figsize=(8, 6))
plt.plot(mean_ph_diff[inlier_mask], mean_el_diff[inlier_mask], '.', label='inlier')
plt.plot(mean_ph_diff[outlier_mask], mean_el_diff[outlier_mask], '.', label='outlier')
plt.legend(loc='upper left')
plt.xlabel('Normalized Photon Difference (frac)')
plt.ylabel('Normalized Electron Difference (frac)')
def correlation(file_name, C, i, j, ax):
hdul, vbb, wideint, wwideint = load_hdul(file_name)
mean_el, diffvr_el, corr, gain = load_data(hdul, C)
mean_el = mean_el / 1000
corr_ij = corr[:, i, j]
# if i == 0 and j == 1:
# corr_ij -= 0.0008
reg = LinearRegression(fit_intercept=True).fit((mean_el).reshape(-1, 1), corr_ij)
prediction = reg.predict((mean_el).reshape(-1, 1))
# ransac = linear_model.RANSACRegressor(residual_threshold=0.01, stop_probability=0.99)
# ransac.fit(mean_el.reshape(-1, 1), corr_ij)
# inlier_mask = ransac.inlier_mask_
# prediction = ransac.predict(mean_el.reshape(-1, 1))
# outlier_mask = np.logical_not(inlier_mask)
# ax.plot(mean_el[outlier_mask], corr_ij[outlier_mask], 'x', label='Outlier')
p = ax.scatter(mean_el[5:], corr_ij[5:], edgecolors='k', label=f'Channel average, $R_{{{str(i)+str(j)}}}$')
ax.plot(mean_el, prediction, '-', color=p.get_facecolor()[0], linewidth=6)
ax.set_xlabel('Mean (kel)')
ax.set_ylabel('Correlation (frac)')
ax.legend(loc='upper left')
ax.set_ylim(-0.002, 0.011)