diff --git a/py/picca/delta_extraction/masks/dla_mask.py b/py/picca/delta_extraction/masks/dla_mask.py index 4e80288fb..4ec8766a5 100644 --- a/py/picca/delta_extraction/masks/dla_mask.py +++ b/py/picca/delta_extraction/masks/dla_mask.py @@ -5,7 +5,8 @@ from astropy.table import Table import fitsio import numpy as np -from numba import njit +from scipy.constants import speed_of_light as SPEED_LIGHT +from scipy.special import voigt_profile from picca.delta_extraction.astronomical_objects.forest import Forest from picca.delta_extraction.errors import MaskError @@ -23,12 +24,13 @@ "los_id name": "THING_ID", }) -np.random.seed(0) -NUM_POINTS = 10000 -GAUSSIAN_DIST = np.random.normal(size=NUM_POINTS) * np.sqrt(2) - +LAMBDA_LYA = float(ABSORBER_IGM["LYA"]) ## Lya wavelength [A] +LAMBDA_LYB = float(ABSORBER_IGM["LYB"]) ## Lyb wavelength [A] +OSCILLATOR_STRENGTH_LYA = 0.41641 +OSCILLATOR_STRENGTH_LYB = 0.079142 +GAMMA_LYA = 6.2648e08 # s^-1 damping constant +GAMMA_LYB = 1.6725e8 # s^-1 damping constant -@njit def dla_profile(lambda_, z_abs, nhi): """Compute DLA profile @@ -44,61 +46,55 @@ def dla_profile(lambda_, z_abs, nhi): DLA column density in log10(cm^-2) """ transmission = np.exp( - -tau_lya(lambda_, z_abs, nhi) - -tau_lyb(lambda_, z_abs, nhi)) + -compute_tau(lambda_, z_abs, nhi, LAMBDA_LYA, OSCILLATOR_STRENGTH_LYA, GAMMA_LYA) + -compute_tau(lambda_, z_abs, nhi, LAMBDA_LYB, OSCILLATOR_STRENGTH_LYB, GAMMA_LYB) + ) return transmission -### Implementation of Pasquier code, -### also in Rutten 2003 at 3.3.3 -LAMBDA_LYA = float(ABSORBER_IGM["LYA"]) ## Lya wavelength [A] -@njit -def tau_lya(lambda_, z_abs, nhi): - """Compute the optical depth for Lyman-alpha absorption. +# constants to compute the optical depth of the DLA absoprtion +ELEMENTARY_CHARGE = 1.6021e-19 # C +EPSILON_0 = 8.8541e-12 # C^2.s^2.kg^-1.m^-3 +PROTON_MASS = 1.6726e-27 # kg +ELECTRON_MASS = 9.109e-31 # kg +BOLTZMAN_CONSTANT_K = 1.3806e-23 # m^2.kg.s^-2.K-1 +GAS_TEMP = 5 * 1e4 # K - Arguments - --------- - lambda_: array of floats - Wavelength (in Angs) +# precomputed factors to save time +# compared to equation 36 of Garnett et al 2017 there is a sqrt(2) missing +# this is because otherwise we need to divide this quantity by sqrt(2) +# when calling scipy.special.voigt +GAUSSIAN_BROADENING_B = np.sqrt(BOLTZMAN_CONSTANT_K * GAS_TEMP / PROTON_MASS) +# the 1e-10 appears as the wavelengths are given in Angstroms +LORENTZIAN_BROADENING_GAMMA_PREFACTOR = 1e-10 / (4 * np.pi) +TAU_PREFACTOR = ( + ELEMENTARY_CHARGE**2 * 1e-10 / ELECTRON_MASS / SPEED_LIGHT / 4 / EPSILON_0) - z_abs: float - Redshift of the absorption +def compute_tau(lambda_, z_abs, log_nhi, lambda_t, oscillator_strength_f, gamma): + r"""Compute the optical depth for DLA absorption. - nhi: float - DLA column density in log10(cm^-2) + Tau is computed using equations 34 to 36 of Garnett et al. 2017. We add + a factor 4pi\epsion_0 in the denominator of their equation 34 so that + dimensions match. The equations we use are: - Return - ------ - tau: array of float - The optical depth. - """ - gamma = 6.625e8 ## damping constant of the transition [s^-1] - osc_strength = 0.4164 ## oscillator strength of the atomic transition - speed_light = 3e8 ## speed of light [m/s] - thermal_velocity = 30000. ## sqrt(2*k*T/m_proton) with - ## T = 5*10^4 ## [m.s^-1] - nhi_cm2 = 10**nhi ## column density [cm^-2] - lambda_rest_frame = lambda_ / (1 + z_abs) - ## wavelength at DLA restframe [A] - - u_voight = ((speed_light / thermal_velocity) * - (LAMBDA_LYA / lambda_rest_frame - 1)) - ## dimensionless frequency offset in Doppler widths. - a_voight = LAMBDA_LYA * 1e-10 * gamma / (4 * np.pi * thermal_velocity) - ## Voigt damping parameter - voigt_profile = voigt(a_voight, u_voight) - thermal_velocity /= 1000. - ## 1.497e-16 = e**2/(4*sqrt(pi)*epsilon0*m_electron*c)*1e-10 - ## [m^2.s^-1.m/] - ## we have b/1000 & 1.497e-15 to convert - ## 1.497e-15*osc_strength*lambda_rest_frame*h/n to cm^2 - tau = (1.497e-15 * nhi_cm2 * osc_strength * lambda_rest_frame * voigt_profile / - thermal_velocity) - return tau + \tau(\lambda, z_{abs}, N_{HI}) = N_{HI} \frac {e^{2} f\lambda_{t} } + {4 \epsion_0 m_{e} c } \phi{\nu, b, \gamma} + + where e is the elementary charge, \lambda_{t} is the transition wavelength + and f is the oscillator strength of the transition. The line profile + function \phi is a Voigt profile, where \nu is ther elative velocity -LAMBDA_LYB = float(ABSORBER_IGM["LYB"]) -@njit -def tau_lyb(lambda_, z_abs, nhi): - """Compute the optical depth for Lyman-beta absorption. + \nu = c ( \frac{ \lambda } { \lambda_{t}* (1+z_{DLA}) } ) , + + b / \sqrt{2} is the standard deviation of the Gaussian (Maxwellian) + broadening contribution: + + b = \sqrt{ \frac{ 2kT }{ m_{p} } } + + and \gamma is the width of the Lorenztian broadening contribution: + + \gamma = \frac { \Gamma \lambda_{t} } { 4\pi } + + where \Gamma is a damping constant Arguments --------- @@ -108,55 +104,36 @@ def tau_lyb(lambda_, z_abs, nhi): z_abs: float Redshift of the absorption - nhi: float + log_nhi: float DLA column density in log10(cm^-2) + lambda_t: float + Transition wavelength, in Angstroms, e.g. for Lya 1215.67 + + oscillator_strength_f: float + Oscillator strength, e.g. f = 0.41611 for Lya + + gamma: float + Damping constant (in s^-1) + Return ------ tau: array of float The optical depth. """ - gamma = 0.079120 - osc_strength = 1.897e8 - speed_light = 3e8 ## speed of light m/s - thermal_velocity = 30000. - nhi_cm2 = 10**nhi - lambda_rest_frame = lambda_ / (1 + z_abs) - - u_voight = ((speed_light / thermal_velocity) * - (LAMBDA_LYB / lambda_rest_frame - 1)) - a_voight = LAMBDA_LYB * 1e-10 * gamma / (4 * np.pi * thermal_velocity) - voigt_profile = voigt(a_voight, u_voight) - thermal_velocity /= 1000. - tau = (1.497e-15 * nhi_cm2 * osc_strength * lambda_rest_frame * voigt_profile / - thermal_velocity) - return tau - -# maybe we should replace this by scipy.special.voigt_profile? -# if it is in numba -@njit -def voigt(a_voight, u_voight): - """Compute the classical Voigt function + # compute broadenings for the voight profile + relative_velocity_nu = SPEED_LIGHT * (lambda_ / (1 + z_abs) / lambda_t - 1) + lorentzian_broadening_gamma = ( + LORENTZIAN_BROADENING_GAMMA_PREFACTOR * gamma * lambda_t) - Arguments - --------- - a_voight: float - Voigt damping parameter. + # convert column density to m^2 + nhi = 10**log_nhi * 1e4 - u_voight: array of floats - Dimensionless frequency offset in Doppler widths. + # the 1e-10 converts the wavelength from Angstroms to meters + tau = TAU_PREFACTOR * nhi * oscillator_strength_f * lambda_t * voigt_profile( + relative_velocity_nu, GAUSSIAN_BROADENING_B, lorentzian_broadening_gamma) - Return - ------ - voigt: array of float - The Voigt function for each element in a, u - """ - unnormalized_voigt = np.zeros_like(u_voight) - for index in range(unnormalized_voigt.shape[0]): - unnormalized_voigt[index] = np.mean( - 1.0 / (a_voight**2 + (GAUSSIAN_DIST - u_voight[index])**2) - ) - return unnormalized_voigt * a_voight / np.sqrt(np.pi) + return tau class DlaMask(Mask): """Class to mask DLAs diff --git a/py/picca/delta_extraction/utils.py b/py/picca/delta_extraction/utils.py index ad8ebdd6e..e22bf5bef 100644 --- a/py/picca/delta_extraction/utils.py +++ b/py/picca/delta_extraction/utils.py @@ -11,10 +11,10 @@ from picca.delta_extraction.errors import DeltaExtractionError -module_logger = logging.getLogger(__name__) - SPEED_LIGHT = speed_light / 1000. # [km/s] +module_logger = logging.getLogger(__name__) + ABSORBER_IGM = { "Halpha": 6562.8, "Hbeta": 4862.68, diff --git a/py/picca/dla.py b/py/picca/dla.py index ea3d426c5..4c1a35fc2 100644 --- a/py/picca/dla.py +++ b/py/picca/dla.py @@ -7,6 +7,7 @@ import numpy as np from . import constants +from scipy.special import voigt_profile np.random.seed(0) num_points = 10000 @@ -64,8 +65,7 @@ def profile_lya_absorption(lambda_, z_abs, nhi): """ return np.exp(-DLA.tau_lya(lambda_, z_abs, nhi)) - ### Implementation of Pasquier code, - ### also in Rutten 2003 at 3.3.3 + ### Implementation based on Garnett2018 @staticmethod def tau_lya(lambda_, z_abs, nhi): """Computes the optical depth for Lyman-alpha absorption. @@ -81,29 +81,29 @@ def tau_lya(lambda_, z_abs, nhi): Returns: The optical depth. """ - lambda_lya = constants.ABSORBER_IGM["LYA"] ## Lya wavelength [A] - gamma = 6.625e8 ## damping constant of the transition [s^-1] - osc_strength = 0.4164 ## oscillator strength of the atomic transition - speed_light = 3e8 ## speed of light [m/s] - thermal_velocity = 30000. ## sqrt(2*k*T/m_proton) with - ## T = 5*10^4 ## [m.s^-1] - nhi_cm2 = 10**nhi ## column density [cm^-2] - lambda_rest_frame = lambda_ / (1 + z_abs) - ## wavelength at DLA restframe [A] - - u_voight = ((speed_light / thermal_velocity) * - (lambda_lya / lambda_rest_frame - 1)) - ## dimensionless frequency offset in Doppler widths. - a_voight = lambda_lya * 1e-10 * gamma / (4 * np.pi * thermal_velocity) - ## Voigt damping parameter - voigt = DLA.voigt(a_voight, u_voight) - thermal_velocity /= 1000. - ## 1.497e-16 = e**2/(4*sqrt(pi)*epsilon0*m_electron*c)*1e-10 - ## [m^2.s^-1.m/] - ## we have b/1000 & 1.497e-15 to convert - ## 1.497e-15*osc_strength*lambda_rest_frame*h/n to cm^2 - tau = (1.497e-15 * nhi_cm2 * osc_strength * lambda_rest_frame * voigt / - thermal_velocity) + e = 1.6021e-19 #C + epsilon0 = 8.8541e-12 #C^2.s^2.kg^-1.m^-3 + f = 0.4164 + mp = 1.6726e-27 #kg + me = 9.109e-31 #kg + c = 2.9979e8 #m.s^-1 + k = 1.3806e-23 #m^2.kg.s^-2.K-1 + T = 5*1e4 #K + gamma = 6.2648e+08 #s^-1 + lam_lya = constants.ABSORBER_IGM["LYA"] #A + + lambda_rest_frame = lambda_/(1+z_abs) + + v = c *(lambda_rest_frame/lam_lya-1) + b = np.sqrt(2*k*T/mp) + small_gamma = gamma*lam_lya/(4*np.pi)*1e-10 + + nhi_m2 = 10**nhi*1e4 + + tau = nhi_m2*np.pi*e**2*f*lam_lya*1e-10 + tau /= 4*np.pi*epsilon0*me*c + tau *= voigt_profile(v, b/np.sqrt(2), small_gamma) + return tau @staticmethod @@ -138,37 +138,27 @@ def tau_lyb(lambda_, z_abs, nhi): Returns: The optical depth. """ - lam_lyb = constants.ABSORBER_IGM["LYB"] - gamma = 0.079120 - osc_strength = 1.897e8 - speed_light = 3e8 ## speed of light m/s - thermal_velocity = 30000. - nhi_cm2 = 10**nhi - lambda_rest_frame = lambda_ / (1 + z_abs) - - u_voight = ((speed_light / thermal_velocity) * - (lam_lyb / lambda_rest_frame - 1)) - a_voight = lam_lyb * 1e-10 * gamma / (4 * np.pi * thermal_velocity) - voigt = DLA.voigt(a_voight, u_voight) - thermal_velocity /= 1000. - tau = (1.497e-15 * nhi_cm2 * osc_strength * lambda_rest_frame * voigt / - thermal_velocity) - return tau - - @staticmethod - def voigt(a_voight, u_voight): - """Computes the classical Voigt function - - Args: - a_voight: array of floats - Voigt damping parameter. - - u_voight: array of floats - Dimensionless frequency offset in Doppler widths. - - Returns: - The Voigt function for each element in a, u - """ - unnormalized_voigt = np.mean( - 1 / (a_voight**2 + (gaussian_dist[:, None] - u_voight)**2), axis=0) - return unnormalized_voigt * a_voight / np.sqrt(np.pi) + e = 1.6021e-19 # C + epsilon0 = 8.8541e-12 # C^2.s^2.kg^-1.m^-3 + f = 0.079142 + mp = 1.6726e-27 # kg + me = 9.109e-31 # kg + c = 2.9979e8 # m.s^-1 + k = 1.3806e-23 # m^2.kg.s^-2.K-1 + T = 5 * 1e4 # K + gamma = 1.6725e8 # s^-1 + lam_lyb = constants.ABSORBER_IGM["LYB"] #A + + lambda_rest_frame = lambda_/(1+z_abs) + + v = c *(lambda_rest_frame/lam_lyb-1) + b = np.sqrt(2*k*T/mp) + small_gamma = gamma*lam_lyb/(4*np.pi)*1e-10 + + nhi_m2 = 10**nhi*1e4 + + tau = nhi_m2*np.pi*e**2*f*lam_lyb*1e-10 + tau /= 4*np.pi*epsilon0*me*c + tau *= voigt_profile(v, b/np.sqrt(2), small_gamma) + + return tau \ No newline at end of file diff --git a/py/picca/tests/delta_extraction/data/dla_mask_forest1_remove.txt b/py/picca/tests/delta_extraction/data/dla_mask_forest1_remove.txt index 91b27bc3a..64bffeba5 100644 --- a/py/picca/tests/delta_extraction/data/dla_mask_forest1_remove.txt +++ b/py/picca/tests/delta_extraction/data/dla_mask_forest1_remove.txt @@ -1,180 +1,181 @@ 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4.0 0.9895058193736713 +3.6184025007673264 1.0 4.0 0.9899105888735864 +3.6187025007673266 1.0 4.0 0.9902887668240218 +3.6190025007673268 1.0 4.0 0.9906427272198688 +3.619302500767327 1.0 4.0 0.990974582669223 +3.619602500767327 1.0 4.0 0.9912862182177022 +3.6199025007673273 1.0 4.0 0.9915793201776922 diff --git a/py/picca/tests/delta_extraction/data/dla_mask_forest2_set_ivar.txt b/py/picca/tests/delta_extraction/data/dla_mask_forest2_set_ivar.txt index 7e533903a..cf9de3102 100644 --- a/py/picca/tests/delta_extraction/data/dla_mask_forest2_set_ivar.txt +++ b/py/picca/tests/delta_extraction/data/dla_mask_forest2_set_ivar.txt @@ -1,196 +1,196 @@ -log_lambda flux ivar transmission_correction -3.5617025007672907 1.0 0.0 0.0 +# log_lambda flux ivar transmission_correction +3.5617025007672907 1.0 0.0 4.02566259993509e-81 3.562002500767291 1.0 0.0 0.0 -3.562302500767291 1.0 0.0 2.0716346415298674e-37 -3.5626025007672912 1.0 0.0 4.5738846427922414e-11 -3.5629025007672914 1.0 0.0 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b/py/picca/tests/delta_extraction/mask_tests.py index 48f9f7b41..914914d36 100644 --- a/py/picca/tests/delta_extraction/mask_tests.py +++ b/py/picca/tests/delta_extraction/mask_tests.py @@ -468,6 +468,8 @@ def test_dla_mask_remove(self): """ in_file = f"{THIS_DIR}/data/dummy_absorbers_cat.fits.gz" out_file = f"{THIS_DIR}/results/dla_mask_print.txt" + out_file_forest1 = f"{THIS_DIR}/results/dla_mask_forest1_remove.txt" + out_file_forest2 = f"{THIS_DIR}/results/dla_mask_forest2_remove.txt" test_file = f"{THIS_DIR}/data/dla_mask_print.txt" test_file_forest1 = f"{THIS_DIR}/data/dla_mask_forest1_remove.txt" test_file_forest2 = f"{THIS_DIR}/data/dla_mask_forest2_remove.txt" @@ -490,6 +492,16 @@ def test_dla_mask_remove(self): # apply mask to forest with 1 DLA forest = copy.deepcopy(forest1) mask.apply_mask(forest) + + # save the results + f = open(out_file_forest1, "w") + f.write("# log_lambda flux ivar transmission_correction\n") + for log_lambda, flux, ivar, transmission_correction in zip( + forest.log_lambda, forest.flux, forest.ivar, forest.transmission_correction): + f.write(f"{log_lambda} {flux} {ivar} {transmission_correction}\n") + f.close() + + # load expected values and compare forest_masked = np.genfromtxt(test_file_forest1, names=True) self.assertEqual(forest.flux.size, forest_masked["flux"].size) self.assertTrue(np.allclose(forest.flux, forest_masked["flux"])) @@ -501,23 +513,17 @@ def test_dla_mask_remove(self): # apply mask to forest with 2 DLAs forest = copy.deepcopy(forest2) - """ - print("Forest2, remove, before masking ") - print("------------------------") - print(forest.flux.size) - for log_lambda, flux, ivar, transmission_correction in zip(forest.log_lambda, forest.flux, forest.ivar, forest.transmission_correction): - print(f"{log_lambda} {flux} {ivar} {transmission_correction}") - print("------------------------") - """ mask.apply_mask(forest) - """ - print("Forest2, remove, after masking ") - print("------------------------") - print(forest.flux.size) - for log_lambda, flux, ivar, transmission_correction in zip(forest.log_lambda, forest.flux, forest.ivar, forest.transmission_correction): - print(f"{log_lambda} {flux} {ivar} {transmission_correction}") - print("------------------------") - """ + + # save the results + f = open(out_file_forest2, "w") + f.write("# log_lambda flux ivar transmission_correction\n") + for log_lambda, flux, ivar, transmission_correction in zip( + forest.log_lambda, forest.flux, forest.ivar, forest.transmission_correction): + f.write(f"{log_lambda} {flux} {ivar} {transmission_correction}\n") + f.close() + + # load expected values and compare forest_masked = np.genfromtxt(test_file_forest2, names=True) self.assertEqual(forest.flux.size, forest_masked["flux"].size) self.assertTrue(np.allclose(forest.flux, forest_masked["flux"])) @@ -547,6 +553,8 @@ def test_dla_mask_set_ivar(self): """ in_file = f"{THIS_DIR}/data/dummy_absorbers_cat.fits.gz" out_file = f"{THIS_DIR}/results/dla_mask_print.txt" + out_file_forest1 = f"{THIS_DIR}/results/dla_mask_forest1_set_ivar.txt" + out_file_forest2 = f"{THIS_DIR}/results/dla_mask_forest2_set_ivar.txt" test_file = f"{THIS_DIR}/data/dla_mask_print.txt" test_file_forest1 = f"{THIS_DIR}/data/dla_mask_forest1_set_ivar.txt" test_file_forest2 = f"{THIS_DIR}/data/dla_mask_forest2_set_ivar.txt" @@ -568,23 +576,17 @@ def test_dla_mask_set_ivar(self): # apply mask to forest with 1 DLA forest = copy.deepcopy(forest1) - """ - print("Forest1, set ivar, before masking ") - print("------------------------") - print(forest1.flux.size) - for log_lambda, flux, ivar, transmission_correction in zip(forest.log_lambda, forest.flux, forest.ivar, forest.transmission_correction): - print(f"{log_lambda} {flux} {ivar} {transmission_correction}") - print("------------------------") - """ mask.apply_mask(forest) - """ - print("Forest1, set ivar, after masking ") - print("------------------------") - print(forest.flux.size) - for log_lambda, flux, ivar, transmission_correction in zip(forest.log_lambda, forest.flux, forest.ivar, forest.transmission_correction): - print(f"{log_lambda} {flux} {ivar} {transmission_correction}") - print("------------------------") - """ + + # save the results + f = open(out_file_forest1, "w") + f.write("# log_lambda flux ivar transmission_correction\n") + for log_lambda, flux, ivar, transmission_correction in zip( + forest.log_lambda, forest.flux, forest.ivar, forest.transmission_correction): + f.write(f"{log_lambda} {flux} {ivar} {transmission_correction}\n") + f.close() + + # load expected values and compare forest_masked = np.genfromtxt(test_file_forest1, names=True) self.assertEqual(forest.flux.size, forest_masked["flux"].size) self.assertTrue(np.allclose(forest.flux, forest_masked["flux"])) @@ -596,23 +598,18 @@ def test_dla_mask_set_ivar(self): # apply mask to forest with 2 DLAs forest = copy.deepcopy(forest2) - """ - print("Forest2, set ivar, before masking ") - print("------------------------") - print(forest.flux.size) - for log_lambda, flux, ivar, transmission_correction in zip(forest.log_lambda, forest.flux, forest.ivar, forest.transmission_correction): - print(f"{log_lambda} {flux} {ivar} {transmission_correction}") - print("------------------------") - """ mask.apply_mask(forest) - """ - print("Forest2, set ivar, after masking ") - print("------------------------") - print(forest.flux.size) - for log_lambda, flux, ivar, transmission_correction in zip(forest.log_lambda, forest.flux, forest.ivar, forest.transmission_correction): - print(f"{log_lambda} {flux} {ivar} {transmission_correction}") - print("------------------------") - """ + + # save the results + f = open(out_file_forest2, "w") + f.write("# log_lambda flux ivar transmission_correction\n") + for log_lambda, flux, ivar, transmission_correction in zip( + forest.log_lambda, forest.flux, forest.ivar, forest.transmission_correction): + f.write(f"{log_lambda} {flux} {ivar} {transmission_correction}\n") + f.close() + + # load expected values and compare + forest_masked = np.genfromtxt(test_file_forest2, names=True) self.assertEqual(forest.flux.size, forest_masked["flux"].size) self.assertTrue(np.allclose(forest.flux, forest_masked["flux"])) diff --git a/pylintrc b/pylintrc index 2e3f6e7e0..7e550d1b6 100644 --- a/pylintrc +++ b/pylintrc @@ -3,7 +3,7 @@ # A comma-separated list of package or module names from where C extensions may # be loaded. Extensions are loading into the active Python interpreter and may # run arbitrary code. -extension-pkg-whitelist= +extension-pkg-whitelist=scipy.special # Add files or directories to the blacklist. They should be base names, not # paths.