From a23cab03e10f99cef08386e44d5393f907f004f6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20Gliwny?= <p.gliwny@gmail.com>
Date: Wed, 31 Aug 2022 15:23:31 +0200
Subject: [PATCH 01/27] calcurate absorption using cubepy

---
 agnpy/absorption/absorption.py | 111 +++++++++++++++++++++++++++++++++
 1 file changed, 111 insertions(+)

diff --git a/agnpy/absorption/absorption.py b/agnpy/absorption/absorption.py
index 3251c813..6b566d45 100644
--- a/agnpy/absorption/absorption.py
+++ b/agnpy/absorption/absorption.py
@@ -27,6 +27,8 @@
 from ..emission_regions import Blob
 from ..synchrotron import nu_synch_peak, Synchrotron
 
+import cubepy as cp
+
 
 __all__ = ["sigma", "Absorption", "ebl_files_dict", "EBL"]
 
@@ -442,6 +444,96 @@ def evaluate_tau_blr_mu_s(
         )
         return (prefactor * integral).to_value("")
 
+    @staticmethod
+    def evaluate_tau_blr_cubepy(
+        nu,
+        z,
+        mu_s,
+        L_disk,
+        xi_line,
+        epsilon_line,
+        R_line,
+        r,
+        l_size=50,
+        mu=mu_to_integrate,
+        phi=phi_to_integrate,
+    ):
+        """Evaluates the gamma-gamma absorption produced by a spherical shell
+        BLR for a general set of model parameters using cubepy
+
+        Parameters
+        ----------
+        nu : :class:`~astropy.units.Quantity`
+            array of frequencies, in Hz, to compute the tau
+            **note** these are observed frequencies (observer frame)
+        z : float
+            redshift of the source
+        mu_s : float
+            cosine of the angle between the blob motion and the jet axis
+        L_disk : :class:`~astropy.units.Quantity`
+            Luminosity of the disk whose radiation is being reprocessed by the BLR
+        xi_line : float
+            fraction of the disk radiation reprocessed by the BLR
+        epsilon_line : string
+            dimensionless energy of the emitted line
+        R_line : :class:`~astropy.units.Quantity`
+            radius of the BLR spherical shell
+        r : :class:`~astropy.units.Quantity`
+            distance between the Broad Line Region and the blob
+        l_size : int
+            size of the array of distances from the BH to integrate over
+        mu, phi : :class:`~numpy.ndarray`
+            arrays of cosine of zenith and azimuth angles to integrate over
+
+        Returns
+        -------
+        :class:`~astropy.units.Quantity`
+            array of the tau values corresponding to each frequency
+        """
+        # conversions
+        epsilon_1 = nu_to_epsilon_prime(nu, z)
+
+        def f(x):
+            # mu -> x[0], phi -> x[1], log_l -> x[2]
+            mu = x[0]
+            phi = x[1]
+            log_l = x[2]
+            l = np.exp(log_l)
+            mu_star = mu_star_shell(mu, R_line.to_value('cm'), l)
+            _cos_psi = cos_psi(mu_s, mu_star, phi)
+            x = x_re_shell(mu, R_line.to_value('cm'), l)
+            s = epsilon_1 * epsilon_line * (1 - _cos_psi) / 2
+            integrand = (1 - _cos_psi)*l / x ** 2 * sigma(s).to_value("cm**2")
+            return integrand
+
+        # set boundary for integration
+        low = np.array(
+            [
+                [-1.0],
+                [0.0],
+                [np.log(r.to_value('cm'))]
+            ]
+        )
+
+        high = np.array(
+            [
+                [1.0],
+                [2.0*np.pi],
+                [np.log(1e5*r.to_value('cm'))]
+            ]
+        )
+
+        eps = 1.e-3 /1e45
+        value, error = cp.integrate(f, low, high, abstol=eps)
+        integral = value[0]*u.cm
+        prefactor = (L_disk * xi_line) / (
+            (4 * np.pi) ** 2 * epsilon_line * m_e * c ** 3
+        )
+
+        return (prefactor * integral[0]).to_value("")
+
+
+
     def tau_blr(self, nu):
         """Evaluates the gamma-gamma absorption produced by a spherical shell
         BLR for a general set of model parameters
@@ -478,6 +570,25 @@ def tau_blr_mu_s(self, nu):
             phi=self.phi,
         )
 
+    def tau_blr_cubepy(self, nu):
+        """Evaluates the gamma-gamma absorption produced by a spherical shell
+        BLR for a general set of model parameters with cubepy integration
+        method
+        """
+        return self.evaluate_tau_blr_cubepy(
+            nu,
+            self.z,
+            self.mu_s,
+            self.target.L_disk,
+            self.target.xi_line,
+            self.target.epsilon_line,
+            self.target.R_line,
+            self.r,
+            l_size=self.l_size,
+            mu=self.mu,
+            phi=self.phi,
+    )
+
     @staticmethod
     def evaluate_tau_dt(
         nu,

From 0ca6f70e1bff2107e3b74cfad99b60675575e41d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20Gliwny?= <p.gliwny@gmail.com>
Date: Fri, 9 Sep 2022 16:44:04 +0200
Subject: [PATCH 02/27] add set up error to integrate

---
 agnpy/absorption/absorption.py | 21 +++++++++++++--------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/agnpy/absorption/absorption.py b/agnpy/absorption/absorption.py
index 6b566d45..9dddfb42 100644
--- a/agnpy/absorption/absorption.py
+++ b/agnpy/absorption/absorption.py
@@ -447,6 +447,7 @@ def evaluate_tau_blr_mu_s(
     @staticmethod
     def evaluate_tau_blr_cubepy(
         nu,
+        eps_abs,
         z,
         mu_s,
         L_disk,
@@ -509,27 +510,29 @@ def f(x):
         # set boundary for integration
         low = np.array(
             [
-                [-1.0],
-                [0.0],
+                [min(mu_to_integrate)],
+                [min(phi_to_integrate)],
                 [np.log(r.to_value('cm'))]
             ]
         )
 
         high = np.array(
             [
-                [1.0],
-                [2.0*np.pi],
+                [max(mu_to_integrate)],
+                [max(phi_to_integrate)],
                 [np.log(1e5*r.to_value('cm'))]
             ]
         )
 
-        eps = 1.e-3 /1e45
-        value, error = cp.integrate(f, low, high, abstol=eps)
-        integral = value[0]*u.cm
+
         prefactor = (L_disk * xi_line) / (
             (4 * np.pi) ** 2 * epsilon_line * m_e * c ** 3
         )
 
+        prefactor = prefactor.to("cm-1")
+        eps = eps_abs / prefactor.to_value("cm-1")
+        value, error = cp.integrate(f, low, high, abstol=eps)
+        integral = value[0]*u.cm
         return (prefactor * integral[0]).to_value("")
 
 
@@ -558,6 +561,7 @@ def tau_blr_mu_s(self, nu):
         """
         return self.evaluate_tau_blr_mu_s(
             nu,
+            eps_abs,
             self.z,
             self.mu_s,
             self.target.L_disk,
@@ -570,13 +574,14 @@ def tau_blr_mu_s(self, nu):
             phi=self.phi,
         )
 
-    def tau_blr_cubepy(self, nu):
+    def tau_blr_cubepy(self, nu, eps_abs):
         """Evaluates the gamma-gamma absorption produced by a spherical shell
         BLR for a general set of model parameters with cubepy integration
         method
         """
         return self.evaluate_tau_blr_cubepy(
             nu,
+            eps_abs,
             self.z,
             self.mu_s,
             self.target.L_disk,

From 4b9f0e387b3e96848cb5866faffc5bb0bc5242a1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20Gliwny?= <p.gliwny@gmail.com>
Date: Tue, 20 Sep 2022 18:51:32 +0200
Subject: [PATCH 03/27] iterate freq to compute tau_blr

---
 agnpy/absorption/absorption.py | 35 +++++++++++++++++++---------------
 1 file changed, 20 insertions(+), 15 deletions(-)

diff --git a/agnpy/absorption/absorption.py b/agnpy/absorption/absorption.py
index 9dddfb42..b01ff6e5 100644
--- a/agnpy/absorption/absorption.py
+++ b/agnpy/absorption/absorption.py
@@ -574,25 +574,30 @@ def tau_blr_mu_s(self, nu):
             phi=self.phi,
         )
 
-    def tau_blr_cubepy(self, nu, eps_abs):
+    def tau_blr_cubepy(self, nu, eps_abs=1e-6):
         """Evaluates the gamma-gamma absorption produced by a spherical shell
         BLR for a general set of model parameters with cubepy integration
         method
         """
-        return self.evaluate_tau_blr_cubepy(
-            nu,
-            eps_abs,
-            self.z,
-            self.mu_s,
-            self.target.L_disk,
-            self.target.xi_line,
-            self.target.epsilon_line,
-            self.target.R_line,
-            self.r,
-            l_size=self.l_size,
-            mu=self.mu,
-            phi=self.phi,
-    )
+
+        tau_blr_list = []
+        for freq in nu:
+            tau_blr_list.append(
+            self.evaluate_tau_blr_cubepy(
+                freq,
+                eps_abs,
+                self.z,
+                self.mu_s,
+                self.target.L_disk,
+                self.target.xi_line,
+                self.target.epsilon_line,
+                self.target.R_line,
+                self.r,
+                l_size=self.l_size,
+                mu=self.mu,
+                phi=self.phi,
+                            ))
+        return tau_blr_list
 
     @staticmethod
     def evaluate_tau_dt(

From bcd1888529fc998a59c5c73907e106e86e03d62e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20Gliwny?= <p.gliwny@gmail.com>
Date: Tue, 10 Jan 2023 18:55:46 +0100
Subject: [PATCH 04/27] add notebook with absorption in BLR with cubepy

---
 .../absorption_in_BLR_with_cubepy.ipynb       | 175 ++++++++++++++++++
 1 file changed, 175 insertions(+)
 create mode 100644 docs/tutorials/absorption_in_BLR_with_cubepy.ipynb

diff --git a/docs/tutorials/absorption_in_BLR_with_cubepy.ipynb b/docs/tutorials/absorption_in_BLR_with_cubepy.ipynb
new file mode 100644
index 00000000..42cfd131
--- /dev/null
+++ b/docs/tutorials/absorption_in_BLR_with_cubepy.ipynb
@@ -0,0 +1,175 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import astropy.units as u\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from agnpy.absorption import Absorption\n",
+    "from agnpy.targets import SphericalShellBLR, lines_dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SUM_RATIO_LINES_BLR = 30.652\n",
+    "\n",
+    "def get_absor_blrs(E, L_disk, R_line, r_blob_bh, z):\n",
+    "    \n",
+    "    \"\"\"\"\n",
+    "    Function to calculate absorption in BLR shells.\n",
+    "    Radius and luminosity are normalized to Hbeta\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    E: numpy array \n",
+    "            Energy array with unit\n",
+    "    L_disk : astropy.units.Quantity\n",
+    "            Luminosity of the disk whose radiation is being reprocessed by the BLR\n",
+    "    R_line astropy.units.Quantity\n",
+    "            radius of the BLR spherical shell\n",
+    "    r_blob_bh : astropy.units.Quantity\n",
+    "            distance between the Broad Line Region and the blob\n",
+    "    z : float\n",
+    "            redshift of the source\n",
+    "    \"\"\"\n",
+    "    nu = E.to(\"Hz\", equivalencies=u.spectral())\n",
+    "    XI_LINE = 0.1\n",
+    "    blrs=dict()\n",
+    "    ec_blrs=dict()\n",
+    "    tau_z_all = np.zeros(nu.shape)\n",
+    "    for line in list(lines_dictionary.keys()):\n",
+    "        xi_line_this=lines_dictionary[line]['L_Hbeta_ratio']\n",
+    "        xi_line = (xi_line_this*XI_LINE)/SUM_RATIO_LINES_BLR\n",
+    "        R_line_this=R_line*lines_dictionary[line]['R_Hbeta_ratio']\n",
+    "        blrs[line] = SphericalShellBLR(L_disk, xi_line, line, R_line_this)\n",
+    "        ec_blrs[line] = Absorption(blrs[line], r=r_blob_bh, z=z)\n",
+    "        tau_z_this = ec_blrs[line].tau_blr_cubepy(nu, eps_abs=1e-6)\n",
+    "        tau_z_all+=tau_z_this\n",
+    "    A_blr = get_absorbtion_tau(np.array(tau_z_all))\n",
+    "    return A_blr\n",
+    "\n",
+    "def get_absorbtion_tau(tau):\n",
+    "    \"Function return absorption\"\n",
+    "    return np.exp(-tau)\n",
+    "\n",
+    "def log_parabola(energy, amplitude, reference, alpha, beta):\n",
+    "    \"Log Parabola model from gammapy\"\n",
+    "    xx = energy / reference\n",
+    "    exponent = -alpha - beta * np.log(xx)\n",
+    "    return amplitude * np.power(xx, exponent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "E = np.logspace(-1, 1, 20)*u.TeV\n",
+    "lp = log_parabola(\n",
+    "    E,\n",
+    "    amplitude=1e-12/((u.cm**2)*(u.s)*(u.TeV)),\n",
+    "    reference=1 * u.TeV,\n",
+    "    alpha=2.3,\n",
+    "    beta=0.5\n",
+    "    )\n",
+    "sed = E*E*lp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pawel1/anaconda3/envs/agn/lib/python3.7/site-packages/astropy/units/quantity.py:486: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 9.62 s, sys: 68 ms, total: 9.69 s\n",
+      "Wall time: 13.1 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# Set BLR parameters\n",
+    "L_disk=6e45*u.Unit(\"erg s-1\")\n",
+    "R_line = 2.45*1e17*u.cm\n",
+    "r_blob_bh = 1e18*u.cm\n",
+    "z = 1\n",
+    "A = get_absor_blrs(E, L_disk, R_line, r_blob_bh, z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7, 7))\n",
+    "plt.loglog(E, sed, 'k-')\n",
+    "plt.loglog(E, A*sed, 'r--')\n",
+    "plt.xlabel(\"E [TeV]\")\n",
+    "plt.ylabel(\"1/ cm$^2$ s TeV)\")\n",
+    "plt.grid(which='both')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 403a0bb516538ea449c8caa74b5fffcb83e0eee4 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Thu, 6 Apr 2023 14:43:31 +0200
Subject: [PATCH 05/27] remove l_size

---
 agnpy/absorption/absorption.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/agnpy/absorption/absorption.py b/agnpy/absorption/absorption.py
index 980631c9..4cc2e352 100644
--- a/agnpy/absorption/absorption.py
+++ b/agnpy/absorption/absorption.py
@@ -456,7 +456,6 @@ def evaluate_tau_blr_cubepy(
         epsilon_line,
         R_line,
         r,
-        l_size=50,
         mu=mu_to_integrate,
         phi=phi_to_integrate,
     ):
@@ -482,8 +481,6 @@ def evaluate_tau_blr_cubepy(
             radius of the BLR spherical shell
         r : :class:`~astropy.units.Quantity`
             distance between the Broad Line Region and the blob
-        l_size : int
-            size of the array of distances from the BH to integrate over
         mu, phi : :class:`~numpy.ndarray`
             arrays of cosine of zenith and azimuth angles to integrate over
 
@@ -594,7 +591,6 @@ def tau_blr_cubepy(self, nu, eps_abs=1e-6):
                 self.target.epsilon_line,
                 self.target.R_line,
                 self.r,
-                l_size=self.l_size,
                 mu=self.mu,
                 phi=self.phi,
                             ))

From c0b6cfa8096583c1bc9ca1d3dc093c6d3678857d Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Thu, 6 Apr 2023 15:45:40 +0200
Subject: [PATCH 06/27] start develop funtion to calcurate absorption over all
 line for BLR

---
 agnpy/absorption/absorption.py | 29 ++++++++++++++++++++++++++++-
 1 file changed, 28 insertions(+), 1 deletion(-)

diff --git a/agnpy/absorption/absorption.py b/agnpy/absorption/absorption.py
index 4cc2e352..ae026989 100644
--- a/agnpy/absorption/absorption.py
+++ b/agnpy/absorption/absorption.py
@@ -23,7 +23,7 @@
     x_re_shell_mu_s,
 )
 from ..utils.conversion import nu_to_epsilon_prime, to_R_g_units
-from ..targets import PointSourceBehindJet, SSDisk, SphericalShellBLR, RingDustTorus
+from ..targets import PointSourceBehindJet, SSDisk, SphericalShellBLR, RingDustTorus, lines_dictionary
 from ..emission_regions import Blob
 from ..synchrotron import nu_synch_peak, Synchrotron
 
@@ -595,6 +595,33 @@ def tau_blr_cubepy(self, nu, eps_abs=1e-6):
                 phi=self.phi,
                             ))
         return tau_blr_list
+    # TO DO
+    def tau_blr_all_lines_cubepy(self, 
+                                nu, 
+                                eps_abs=1e-6, 
+                                 E, 
+                                 L_disk, 
+                                 R_line, 
+                                 r_blob_bh, 
+                                 z):
+          """"
+        Function to calculate absorption in BLR shells.
+        Radius and luminosity are normalized to Hbeta
+        Parameters
+        ----------
+        E: numpy array 
+                Energy array with unit
+        L_disk : astropy.units.Quantity
+                Luminosity of the disk whose radiation is being reprocessed by the BLR
+        R_line astropy.units.Quantity
+                radius of the BLR spherical shell
+        r_blob_bh : astropy.units.Quantity
+                distance between the Broad Line Region and the blob
+        z : float
+                redshift of the source
+        """
+        for line in list(lines_dictionary.keys()):
+            xi_line_this=lines_dictionary[line]['L_Hbeta_ratio']
 
     @staticmethod
     def evaluate_tau_dt(

From 8e21ba8cef00c67f33eca3e724beb8199ed1c212 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Tue, 16 May 2023 16:15:33 +0200
Subject: [PATCH 07/27] added fun to calculate absorption in all BLR lines

---
 agnpy/absorption/absorption.py | 54 ++++++++++++++++++----------------
 1 file changed, 29 insertions(+), 25 deletions(-)

diff --git a/agnpy/absorption/absorption.py b/agnpy/absorption/absorption.py
index ae026989..b7089e08 100644
--- a/agnpy/absorption/absorption.py
+++ b/agnpy/absorption/absorption.py
@@ -559,7 +559,6 @@ def tau_blr_mu_s(self, nu):
         """
         return self.evaluate_tau_blr_mu_s(
             nu,
-            eps_abs,
             self.z,
             self.mu_s,
             self.target.L_disk,
@@ -595,33 +594,38 @@ def tau_blr_cubepy(self, nu, eps_abs=1e-6):
                 phi=self.phi,
                             ))
         return tau_blr_list
-    # TO DO
-    def tau_blr_all_lines_cubepy(self, 
-                                nu, 
-                                eps_abs=1e-6, 
-                                 E, 
-                                 L_disk, 
-                                 R_line, 
-                                 r_blob_bh, 
-                                 z):
-          """"
-        Function to calculate absorption in BLR shells.
-        Radius and luminosity are normalized to Hbeta
+
+    def tau_blr_all_lines_cubepy(self, nu, eps_abs=1e-6):
+        """
+        Calculate the absorption in all available BLR (Broad Line Region) shells.
+        The radius and luminosity are normalized to Hbeta.
+
         Parameters
         ----------
-        E: numpy array 
-                Energy array with unit
-        L_disk : astropy.units.Quantity
-                Luminosity of the disk whose radiation is being reprocessed by the BLR
-        R_line astropy.units.Quantity
-                radius of the BLR spherical shell
-        r_blob_bh : astropy.units.Quantity
-                distance between the Broad Line Region and the blob
-        z : float
-                redshift of the source
+        nu: numpy array
+            Frequency array with unit
+        eps_abs: float, optional
+            Absolute precision for the calculation, default is 1e-6
         """
-        for line in list(lines_dictionary.keys()):
-            xi_line_this=lines_dictionary[line]['L_Hbeta_ratio']
+        SUM_RATIO_LINES_BLR = 30.652
+        XI_TARGET_LINE = self.target.xi_line
+        tau_z_all = np.zeros(nu.shape)
+
+        blr_shells = dict()
+        absorption_shells = dict()
+
+        for line_key in lines_dictionary.keys():
+            xi_line_current = lines_dictionary[line_key]['L_Hbeta_ratio']
+            xi_line = (xi_line_current * XI_TARGET_LINE) / SUM_RATIO_LINES_BLR
+            R_line_current = self.target.R_line * lines_dictionary[line_key]['R_Hbeta_ratio']
+
+            blr_shells[line_key] = SphericalShellBLR(self.target.L_disk, xi_line, line_key, R_line_current)
+            absorption_shells[line_key] = Absorption(blr_shells[line_key], r=self.r, z=self.z)
+
+            tau_z_current = absorption_shells[line_key].tau_blr_cubepy(nu, eps_abs=eps_abs)
+            tau_z_all += tau_z_current
+
+        return tau_z_all
 
     @staticmethod
     def evaluate_tau_dt(

From 57d67bccfd29aa166056ad7f104cca9807819981 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Tue, 16 May 2023 16:27:00 +0200
Subject: [PATCH 08/27] nodified notebook to new function

---
 .../absorption_in_BLR_with_cubepy.ipynb       | 144 +++++++++++++++---
 1 file changed, 122 insertions(+), 22 deletions(-)

diff --git a/docs/tutorials/absorption_in_BLR_with_cubepy.ipynb b/docs/tutorials/absorption_in_BLR_with_cubepy.ipynb
index 42cfd131..c7b50fc3 100644
--- a/docs/tutorials/absorption_in_BLR_with_cubepy.ipynb
+++ b/docs/tutorials/absorption_in_BLR_with_cubepy.ipynb
@@ -2,10 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
     "import numpy as np\n",
     "import astropy.units as u\n",
     "import matplotlib.pyplot as plt\n",
@@ -16,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,23 +89,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/pawel1/anaconda3/envs/agn/lib/python3.7/site-packages/astropy/units/quantity.py:486: RuntimeWarning: invalid value encountered in sqrt\n",
-      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 9.62 s, sys: 68 ms, total: 9.69 s\n",
-      "Wall time: 13.1 s\n"
+      "CPU times: user 8.7 s, sys: 331 ms, total: 9.03 s\n",
+      "Wall time: 8.61 s\n"
      ]
     }
    ],
@@ -118,19 +113,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 504x504 with 1 Axes>"
+       "<Figure size 700x700 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -143,6 +136,113 @@
     "plt.grid(which='both')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Function inside agnpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set BLR parameters\n",
+    "L_disk=6e45*u.Unit(\"erg s-1\")\n",
+    "R_line = 2.45*1e17*u.cm\n",
+    "r_blob_bh = 1e18*u.cm\n",
+    "z = 1\n",
+    "xi_line = 0.1\n",
+    "\n",
+    "E = np.logspace(-1, 1, 20)*u.TeV\n",
+    "nu = E.to(\"Hz\", equivalencies=u.spectral())\n",
+    "\n",
+    "blr = SphericalShellBLR(L_disk, xi_line, \"Hbeta\", R_line)\n",
+    "abs_blr = Absorption(blr, r=r_blob_bh, z=z)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "E = np.logspace(-1, 1, 20)*u.TeV\n",
+    "lp = log_parabola(\n",
+    "    E,\n",
+    "    amplitude=1e-12/((u.cm**2)*(u.s)*(u.TeV)),\n",
+    "    reference=1 * u.TeV,\n",
+    "    alpha=2.3,\n",
+    "    beta=0.5\n",
+    "    )\n",
+    "sed = E*E*lp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pgliwny/anaconda3/envs/gammapy-0.20/lib/python3.8/site-packages/astropy/units/quantity.py:613: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set BLR parameters\n",
+    "L_disk=6e45*u.Unit(\"erg s-1\")\n",
+    "R_line = 2.45*1e17*u.cm\n",
+    "r_blob_bh = 1e18*u.cm\n",
+    "z = 1\n",
+    "xi_line = 0.1\n",
+    "\n",
+    "E = np.logspace(-1, 1, 20)*u.TeV\n",
+    "nu = E.to(\"Hz\", equivalencies=u.spectral())\n",
+    "\n",
+    "blr = SphericalShellBLR(L_disk, xi_line, \"Hbeta\", R_line)\n",
+    "abs_blr = Absorption(blr, r=r_blob_bh, z=z)\n",
+    "tau_all_lines = abs_blr.tau_blr_all_lines_cubepy(nu)\n",
+    "A_blr = get_absorbtion_tau(np.array(tau_all_lines))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7, 7))\n",
+    "plt.loglog(E, sed, 'k-')\n",
+    "plt.loglog(E, A_blr*sed, 'r--')\n",
+    "plt.xlabel(\"E [TeV]\")\n",
+    "plt.ylabel(\"1/ cm$^2$ s TeV)\")\n",
+    "plt.grid(which='both')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -153,7 +253,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -167,7 +267,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,

From 7039b9851517d5ad4b219d1ade93a1f559646b9a Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Fri, 26 Apr 2024 16:07:00 +0200
Subject: [PATCH 09/27] add test for absorption in BLR with cubepy

---
 agnpy/tests/test_absorption.py | 27 ++++++++++++++++++++++++++-
 1 file changed, 26 insertions(+), 1 deletion(-)

diff --git a/agnpy/tests/test_absorption.py b/agnpy/tests/test_absorption.py
index e01d4239..972a0ebf 100644
--- a/agnpy/tests/test_absorption.py
+++ b/agnpy/tests/test_absorption.py
@@ -8,7 +8,7 @@
 from agnpy.spectra import PowerLaw, BrokenPowerLaw
 from agnpy.emission_regions import Blob
 from agnpy.synchrotron import Synchrotron
-from agnpy.targets import PointSourceBehindJet, SSDisk, SphericalShellBLR, RingDustTorus
+from agnpy.targets import PointSourceBehindJet, SSDisk, SphericalShellBLR, RingDustTorus, lines_dictionary
 from agnpy.absorption import Absorption, EBL
 from agnpy.utils.math import axes_reshaper
 from .utils import (
@@ -228,6 +228,31 @@ def test_abs_dt_vs_point_source(self):
         # requires a 10% deviation from the two SED points
         assert check_deviation(nu, tau_dt, tau_ps_dt, 0.1)
 
+    def test_abs_blr_cubepy(self):
+        """Checks for any errors that may occur during the execution of the function that
+        calculates absorption in BLR with cubepy."""
+        SUM_RATIO_LINES_BLR = 30.652
+        E = np.logspace(-1, 1, 20) * u.TeV
+        nu = E.to("Hz", equivalencies=u.spectral())
+        L_disk = 6e45 * u.Unit("erg s-1")
+        R_line = 2.45 * 1e17 * u.cm
+        r_blob_bh = 1e18 * u.cm
+        z = 1
+        XI_LINE = 0.1
+        blr_dict = dict()
+        ec_blr_dict = dict()
+        tau_z_all = np.zeros(nu.shape)
+        for line in list(lines_dictionary.keys()):
+            xi_line_this = lines_dictionary[line]['L_Hbeta_ratio']
+            xi_line = (xi_line_this * XI_LINE) / SUM_RATIO_LINES_BLR
+            R_line_this = R_line * lines_dictionary[line]['R_Hbeta_ratio']
+            blr_dict[line] = SphericalShellBLR(L_disk, xi_line, line, R_line_this)
+            ec_blr_dict[line] = Absorption(blr_dict[line], r=r_blob_bh, z=z)
+            tau_z_this = ec_blr_dict[line].tau_blr_cubepy(nu, eps_abs=1e-3)
+            tau_z_all += tau_z_this
+        assert True
+
+
 
 def sigma_pp(b):
     """pair production cross section"""

From dd28724acb9e294a881059816c156ac4b2696924 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Fri, 26 Apr 2024 16:19:11 +0200
Subject: [PATCH 10/27] remove not used parameters

---
 agnpy/absorption/absorption.py | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/agnpy/absorption/absorption.py b/agnpy/absorption/absorption.py
index b7089e08..7c78bda6 100644
--- a/agnpy/absorption/absorption.py
+++ b/agnpy/absorption/absorption.py
@@ -455,9 +455,7 @@ def evaluate_tau_blr_cubepy(
         xi_line,
         epsilon_line,
         R_line,
-        r,
-        mu=mu_to_integrate,
-        phi=phi_to_integrate,
+        r
     ):
         """Evaluates the gamma-gamma absorption produced by a spherical shell
         BLR for a general set of model parameters using cubepy
@@ -589,10 +587,9 @@ def tau_blr_cubepy(self, nu, eps_abs=1e-6):
                 self.target.xi_line,
                 self.target.epsilon_line,
                 self.target.R_line,
-                self.r,
-                mu=self.mu,
-                phi=self.phi,
-                            ))
+                self.r
+            )
+            )
         return tau_blr_list
 
     def tau_blr_all_lines_cubepy(self, nu, eps_abs=1e-6):

From 92eb17bc2b47a7d49438c14cf26094810363b3f2 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Mon, 3 Jun 2024 17:18:26 +0200
Subject: [PATCH 11/27] Add more clear explanations for docs string about calc
 tau in BLR

---
 agnpy/absorption/absorption.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/agnpy/absorption/absorption.py b/agnpy/absorption/absorption.py
index 7c78bda6..473ea72b 100644
--- a/agnpy/absorption/absorption.py
+++ b/agnpy/absorption/absorption.py
@@ -595,7 +595,9 @@ def tau_blr_cubepy(self, nu, eps_abs=1e-6):
     def tau_blr_all_lines_cubepy(self, nu, eps_abs=1e-6):
         """
         Calculate the absorption in all available BLR (Broad Line Region) shells.
-        The radius and luminosity are normalized to Hbeta.
+        This function scales the radius and luminosity to the Hbeta line
+        and applies the computation to all the available BLR lines (shells) using
+        their respective ratios.
 
         Parameters
         ----------

From e75d6d845f088bee3beb73f2c5b6f6517af6f323 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Mon, 3 Jun 2024 18:20:53 +0200
Subject: [PATCH 12/27] Add function to compute inegrand for cubepy

---
 agnpy/absorption/absorption.py | 59 ++++++++++++++++++++++------------
 1 file changed, 39 insertions(+), 20 deletions(-)

diff --git a/agnpy/absorption/absorption.py b/agnpy/absorption/absorption.py
index 473ea72b..71fa8184 100644
--- a/agnpy/absorption/absorption.py
+++ b/agnpy/absorption/absorption.py
@@ -446,6 +446,40 @@ def evaluate_tau_blr_mu_s(
         return (prefactor * integral).to_value("")
 
     @staticmethod
+    def compute_integrand(cubepy_params_array, R_line, mu_s, epsilon_1, epsilon_line):
+        """
+        Computes the integrand for the gamma-gamma absorption model with
+        a spherical shell Broad Line Region (BLR).
+
+        Parameters
+        ----------
+        cubepy_params_array : array-like
+            Array containing integration parameters [mu, phi, log_l].
+        R_line : :class:`~astropy.units.Quantity`
+            Radius of the BLR spherical shell.
+        mu_s : float
+            Cosine of the angle between the blob motion and the jet axis.
+        epsilon_1 : float
+            Dimensionless energy parameter for the incoming photon.
+        epsilon_line : float
+            Dimensionless energy of the emitted line.
+
+        Returns
+        -------
+        float
+            The value of the integrand for the given parameters.
+        """
+        mu = cubepy_params_array[0]
+        phi = cubepy_params_array[1]
+        log_l = cubepy_params_array[2]
+        l = np.exp(log_l)
+        mu_star = mu_star_shell(mu, R_line.to_value('cm'), l)
+        _cos_psi = cos_psi(mu_s, mu_star, phi)
+        x = x_re_shell(mu, R_line.to_value('cm'), l)
+        s = epsilon_1 * epsilon_line * (1 - _cos_psi) / 2
+        integrand = (1 - _cos_psi) * l / x ** 2 * sigma(s).to_value("cm**2")
+        return integrand
+    @staticmethod
     def evaluate_tau_blr_cubepy(
         nu,
         eps_abs,
@@ -489,20 +523,6 @@ def evaluate_tau_blr_cubepy(
         """
         # conversions
         epsilon_1 = nu_to_epsilon_prime(nu, z)
-
-        def f(x):
-            # mu -> x[0], phi -> x[1], log_l -> x[2]
-            mu = x[0]
-            phi = x[1]
-            log_l = x[2]
-            l = np.exp(log_l)
-            mu_star = mu_star_shell(mu, R_line.to_value('cm'), l)
-            _cos_psi = cos_psi(mu_s, mu_star, phi)
-            x = x_re_shell(mu, R_line.to_value('cm'), l)
-            s = epsilon_1 * epsilon_line * (1 - _cos_psi) / 2
-            integrand = (1 - _cos_psi)*l / x ** 2 * sigma(s).to_value("cm**2")
-            return integrand
-
         # set boundary for integration
         low = np.array(
             [
@@ -511,7 +531,6 @@ def f(x):
                 [np.log(r.to_value('cm'))]
             ]
         )
-
         high = np.array(
             [
                 [max(mu_to_integrate)],
@@ -519,20 +538,20 @@ def f(x):
                 [np.log(1e5*r.to_value('cm'))]
             ]
         )
-
-
         prefactor = (L_disk * xi_line) / (
             (4 * np.pi) ** 2 * epsilon_line * m_e * c ** 3
         )
 
         prefactor = prefactor.to("cm-1")
         eps = eps_abs / prefactor.to_value("cm-1")
-        value, error = cp.integrate(f, low, high, abstol=eps)
+        value, error = cp.integrate(Absorption.compute_integrand,
+                                    low,
+                                    high,
+                                    abstol=eps,
+                                    args=(R_line, mu_s, epsilon_1, epsilon_line))
         integral = value[0]*u.cm
         return (prefactor * integral[0]).to_value("")
 
-
-
     def tau_blr(self, nu):
         """Evaluates the gamma-gamma absorption produced by a spherical shell
         BLR for a general set of model parameters

From a81e789ab46f59a4e2132fdc26254d6eac4745ac Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Mon, 3 Jun 2024 18:52:33 +0200
Subject: [PATCH 13/27] Add environment dependencies for cubepy

---
 environment.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/environment.yml b/environment.yml
index f2bfd433..a2653e7e 100644
--- a/environment.yml
+++ b/environment.yml
@@ -13,6 +13,7 @@ dependencies:
   - pyyaml # needed to read astropy ecsv file
   - matplotlib>=3.4
   - sherpa
+  - cubepy=1.0.2
   - pre-commit
   - pip:
     - agnpy

From eeed0a991832d79e4a4b3d15d222f7ad0afdc7dd Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Mon, 3 Jun 2024 18:58:32 +0200
Subject: [PATCH 14/27] Fix environment dependencies

---
 environment.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/environment.yml b/environment.yml
index a2653e7e..2ea66c2e 100644
--- a/environment.yml
+++ b/environment.yml
@@ -13,7 +13,7 @@ dependencies:
   - pyyaml # needed to read astropy ecsv file
   - matplotlib>=3.4
   - sherpa
-  - cubepy=1.0.2
+  - cubepy==1.0.2
   - pre-commit
   - pip:
     - agnpy

From 3aabb7c972ff06a880f3ae675b00bb18d61f25d9 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Mon, 3 Jun 2024 19:10:37 +0200
Subject: [PATCH 15/27] Check target name during calc tau

---
 agnpy/absorption/absorption.py | 4 ++++
 environment.yml                | 2 +-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/agnpy/absorption/absorption.py b/agnpy/absorption/absorption.py
index 71fa8184..bbfd9b2f 100644
--- a/agnpy/absorption/absorption.py
+++ b/agnpy/absorption/absorption.py
@@ -632,6 +632,10 @@ def tau_blr_all_lines_cubepy(self, nu, eps_abs=1e-6):
         blr_shells = dict()
         absorption_shells = dict()
 
+        # TODO write test
+        if self.target.line != "Hbeta":
+            raise KeyError("Please use Hbeta as reference")
+
         for line_key in lines_dictionary.keys():
             xi_line_current = lines_dictionary[line_key]['L_Hbeta_ratio']
             xi_line = (xi_line_current * XI_TARGET_LINE) / SUM_RATIO_LINES_BLR
diff --git a/environment.yml b/environment.yml
index 2ea66c2e..6ced4958 100644
--- a/environment.yml
+++ b/environment.yml
@@ -13,7 +13,7 @@ dependencies:
   - pyyaml # needed to read astropy ecsv file
   - matplotlib>=3.4
   - sherpa
-  - cubepy==1.0.2
+  - cubepy=<1.0.2
   - pre-commit
   - pip:
     - agnpy

From e6b73dffd0f183f436229b32641c04c93987dfcc Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Fri, 7 Jun 2024 15:26:14 +0200
Subject: [PATCH 16/27] Fix problem with cubepy instalion

---
 environment.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/environment.yml b/environment.yml
index 6ced4958..43afe191 100644
--- a/environment.yml
+++ b/environment.yml
@@ -13,8 +13,8 @@ dependencies:
   - pyyaml # needed to read astropy ecsv file
   - matplotlib>=3.4
   - sherpa
-  - cubepy=<1.0.2
   - pre-commit
   - pip:
     - agnpy
     - gammapy
+    - cubepy<=1.0.2

From 269eec2e2e2d840de9f48d03213582cc05c78d48 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Fri, 7 Jun 2024 15:57:26 +0200
Subject: [PATCH 17/27] add cubepy requires to setup.py

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 683badfb..82dc14ba 100644
--- a/setup.py
+++ b/setup.py
@@ -20,6 +20,6 @@
         "License :: OSI Approved :: BSD License",
         "Operating System :: OS Independent",
     ],
-    install_requires=["astropy>=5.0, <6.0", "numpy>=1.21", "scipy>=1.5,!=1.10", "matplotlib>=3.4"],
+    install_requires=["astropy>=5.0, <6.0", "numpy>=1.21", "scipy>=1.5,!=1.10", "matplotlib>=3.4", "cubepy<=1.0.2"],
     python_requires=">=3.8",
 )

From 70b6ef87215e9314149ff6af310c2c60e23048d0 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Fri, 19 Jul 2024 17:05:27 +0200
Subject: [PATCH 18/27] work on exmpale notebook

---
 .../absorption_in_BLR_with_cubepy.ipynb       | 208 +++++++++++++++---
 1 file changed, 178 insertions(+), 30 deletions(-)

diff --git a/docs/tutorials/absorption_in_BLR_with_cubepy.ipynb b/docs/tutorials/absorption_in_BLR_with_cubepy.ipynb
index c7b50fc3..cabf7f78 100644
--- a/docs/tutorials/absorption_in_BLR_with_cubepy.ipynb
+++ b/docs/tutorials/absorption_in_BLR_with_cubepy.ipynb
@@ -4,7 +4,16 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pgliwny/anaconda3/envs/agn/lib/python3.8/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.3)\n",
+      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n",
@@ -17,9 +26,16 @@
     "from agnpy.targets import SphericalShellBLR, lines_dictionary"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook presents how to use the multi-layer BLR model, which considers 27 BLR lines."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,15 +105,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pgliwny/anaconda3/envs/agn/lib/python3.8/site-packages/astropy/units/quantity.py:611: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 8.7 s, sys: 331 ms, total: 9.03 s\n",
-      "Wall time: 8.61 s\n"
+      "CPU times: user 20.9 s, sys: 904 ms, total: 21.8 s\n",
+      "Wall time: 20.7 s\n"
      ]
     }
    ],
@@ -113,17 +137,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 700x700 with 1 Axes>"
+       "<Figure size 504x504 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -143,11 +169,58 @@
     "# Function inside agnpy"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook demostrate how to use Absorption class to calcuare tau in BLR, which 26 shells following the Finke paper from 2016"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "def get_absorbtion_tau(tau):\n",
+    "    \"Function return absorption\"\n",
+    "    return np.exp(-tau)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set BLR parameters\n",
+    "L_disk=6e45*u.Unit(\"erg s-1\")\n",
+    "R_line = 2.45*1e17*u.cm\n",
+    "r_blob_bh = 1e18*u.cm\n",
+    "z = 1\n",
+    "xi_line = 0.1\n",
+    "\n",
+    "E = np.logspace(-1, 1, 20)*u.TeV\n",
+    "nu = E.to(\"Hz\", equivalencies=u.spectral())\n",
+    "\n",
+    "blr_one_line = SphericalShellBLR(L_disk, xi_line, \"Hbeta\", R_line)\n",
+    "abs_blr_one_line = Absorption(blr, r=r_blob_bh, z=z)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/pgliwny/anaconda3/envs/agn/lib/python3.8/site-packages/astropy/units/quantity.py:611: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
+     ]
+    }
+   ],
    "source": [
     "# Set BLR parameters\n",
     "L_disk=6e45*u.Unit(\"erg s-1\")\n",
@@ -160,12 +233,94 @@
     "nu = E.to(\"Hz\", equivalencies=u.spectral())\n",
     "\n",
     "blr = SphericalShellBLR(L_disk, xi_line, \"Hbeta\", R_line)\n",
-    "abs_blr = Absorption(blr, r=r_blob_bh, z=z)\n"
+    "abs_blr = Absorption(blr, r=r_blob_bh, z=z)\n",
+    "tau_all_lines = abs_blr.tau_blr_all_lines_cubepy(nu)\n",
+    "A_blr = get_absorbtion_tau(np.array(tau_all_lines))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f4222518ee0>]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.loglog(E, A_blr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "abs_nu_agnpy = abs_blr.absorption(nu)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f42225d47f0>]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(nu, abs_nu_agnpy, 'bo-')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,18 +337,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/pgliwny/anaconda3/envs/gammapy-0.20/lib/python3.8/site-packages/astropy/units/quantity.py:613: RuntimeWarning: invalid value encountered in sqrt\n",
-      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Set BLR parameters\n",
     "L_disk=6e45*u.Unit(\"erg s-1\")\n",
@@ -213,17 +359,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 700x700 with 1 Axes>"
+       "<Figure size 504x504 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -253,7 +401,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -267,7 +415,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,

From 246a7d39a6d6025cfe75cbe5a9d1545fa8cfc8e6 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Wed, 2 Oct 2024 15:28:19 +0200
Subject: [PATCH 19/27] remove notebook from docs

---
 .../absorption_in_BLR_with_cubepy.ipynb       | 423 ------------------
 1 file changed, 423 deletions(-)
 delete mode 100644 docs/tutorials/absorption_in_BLR_with_cubepy.ipynb

diff --git a/docs/tutorials/absorption_in_BLR_with_cubepy.ipynb b/docs/tutorials/absorption_in_BLR_with_cubepy.ipynb
deleted file mode 100644
index cabf7f78..00000000
--- a/docs/tutorials/absorption_in_BLR_with_cubepy.ipynb
+++ /dev/null
@@ -1,423 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/pgliwny/anaconda3/envs/agn/lib/python3.8/site-packages/scipy/__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.3)\n",
-      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import numpy as np\n",
-    "import astropy.units as u\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from agnpy.absorption import Absorption\n",
-    "from agnpy.targets import SphericalShellBLR, lines_dictionary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook presents how to use the multi-layer BLR model, which considers 27 BLR lines."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "SUM_RATIO_LINES_BLR = 30.652\n",
-    "\n",
-    "def get_absor_blrs(E, L_disk, R_line, r_blob_bh, z):\n",
-    "    \n",
-    "    \"\"\"\"\n",
-    "    Function to calculate absorption in BLR shells.\n",
-    "    Radius and luminosity are normalized to Hbeta\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    E: numpy array \n",
-    "            Energy array with unit\n",
-    "    L_disk : astropy.units.Quantity\n",
-    "            Luminosity of the disk whose radiation is being reprocessed by the BLR\n",
-    "    R_line astropy.units.Quantity\n",
-    "            radius of the BLR spherical shell\n",
-    "    r_blob_bh : astropy.units.Quantity\n",
-    "            distance between the Broad Line Region and the blob\n",
-    "    z : float\n",
-    "            redshift of the source\n",
-    "    \"\"\"\n",
-    "    nu = E.to(\"Hz\", equivalencies=u.spectral())\n",
-    "    XI_LINE = 0.1\n",
-    "    blrs=dict()\n",
-    "    ec_blrs=dict()\n",
-    "    tau_z_all = np.zeros(nu.shape)\n",
-    "    for line in list(lines_dictionary.keys()):\n",
-    "        xi_line_this=lines_dictionary[line]['L_Hbeta_ratio']\n",
-    "        xi_line = (xi_line_this*XI_LINE)/SUM_RATIO_LINES_BLR\n",
-    "        R_line_this=R_line*lines_dictionary[line]['R_Hbeta_ratio']\n",
-    "        blrs[line] = SphericalShellBLR(L_disk, xi_line, line, R_line_this)\n",
-    "        ec_blrs[line] = Absorption(blrs[line], r=r_blob_bh, z=z)\n",
-    "        tau_z_this = ec_blrs[line].tau_blr_cubepy(nu, eps_abs=1e-6)\n",
-    "        tau_z_all+=tau_z_this\n",
-    "    A_blr = get_absorbtion_tau(np.array(tau_z_all))\n",
-    "    return A_blr\n",
-    "\n",
-    "def get_absorbtion_tau(tau):\n",
-    "    \"Function return absorption\"\n",
-    "    return np.exp(-tau)\n",
-    "\n",
-    "def log_parabola(energy, amplitude, reference, alpha, beta):\n",
-    "    \"Log Parabola model from gammapy\"\n",
-    "    xx = energy / reference\n",
-    "    exponent = -alpha - beta * np.log(xx)\n",
-    "    return amplitude * np.power(xx, exponent)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "E = np.logspace(-1, 1, 20)*u.TeV\n",
-    "lp = log_parabola(\n",
-    "    E,\n",
-    "    amplitude=1e-12/((u.cm**2)*(u.s)*(u.TeV)),\n",
-    "    reference=1 * u.TeV,\n",
-    "    alpha=2.3,\n",
-    "    beta=0.5\n",
-    "    )\n",
-    "sed = E*E*lp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/pgliwny/anaconda3/envs/agn/lib/python3.8/site-packages/astropy/units/quantity.py:611: RuntimeWarning: invalid value encountered in sqrt\n",
-      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 20.9 s, sys: 904 ms, total: 21.8 s\n",
-      "Wall time: 20.7 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# Set BLR parameters\n",
-    "L_disk=6e45*u.Unit(\"erg s-1\")\n",
-    "R_line = 2.45*1e17*u.cm\n",
-    "r_blob_bh = 1e18*u.cm\n",
-    "z = 1\n",
-    "A = get_absor_blrs(E, L_disk, R_line, r_blob_bh, z)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(7, 7))\n",
-    "plt.loglog(E, sed, 'k-')\n",
-    "plt.loglog(E, A*sed, 'r--')\n",
-    "plt.xlabel(\"E [TeV]\")\n",
-    "plt.ylabel(\"1/ cm$^2$ s TeV)\")\n",
-    "plt.grid(which='both')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Function inside agnpy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook demostrate how to use Absorption class to calcuare tau in BLR, which 26 shells following the Finke paper from 2016"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_absorbtion_tau(tau):\n",
-    "    \"Function return absorption\"\n",
-    "    return np.exp(-tau)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set BLR parameters\n",
-    "L_disk=6e45*u.Unit(\"erg s-1\")\n",
-    "R_line = 2.45*1e17*u.cm\n",
-    "r_blob_bh = 1e18*u.cm\n",
-    "z = 1\n",
-    "xi_line = 0.1\n",
-    "\n",
-    "E = np.logspace(-1, 1, 20)*u.TeV\n",
-    "nu = E.to(\"Hz\", equivalencies=u.spectral())\n",
-    "\n",
-    "blr_one_line = SphericalShellBLR(L_disk, xi_line, \"Hbeta\", R_line)\n",
-    "abs_blr_one_line = Absorption(blr, r=r_blob_bh, z=z)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/pgliwny/anaconda3/envs/agn/lib/python3.8/site-packages/astropy/units/quantity.py:611: RuntimeWarning: invalid value encountered in sqrt\n",
-      "  result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Set BLR parameters\n",
-    "L_disk=6e45*u.Unit(\"erg s-1\")\n",
-    "R_line = 2.45*1e17*u.cm\n",
-    "r_blob_bh = 1e18*u.cm\n",
-    "z = 1\n",
-    "xi_line = 0.1\n",
-    "\n",
-    "E = np.logspace(-1, 1, 20)*u.TeV\n",
-    "nu = E.to(\"Hz\", equivalencies=u.spectral())\n",
-    "\n",
-    "blr = SphericalShellBLR(L_disk, xi_line, \"Hbeta\", R_line)\n",
-    "abs_blr = Absorption(blr, r=r_blob_bh, z=z)\n",
-    "tau_all_lines = abs_blr.tau_blr_all_lines_cubepy(nu)\n",
-    "A_blr = get_absorbtion_tau(np.array(tau_all_lines))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f4222518ee0>]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.loglog(E, A_blr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "abs_nu_agnpy = abs_blr.absorption(nu)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f42225d47f0>]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(nu, abs_nu_agnpy, 'bo-')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "E = np.logspace(-1, 1, 20)*u.TeV\n",
-    "lp = log_parabola(\n",
-    "    E,\n",
-    "    amplitude=1e-12/((u.cm**2)*(u.s)*(u.TeV)),\n",
-    "    reference=1 * u.TeV,\n",
-    "    alpha=2.3,\n",
-    "    beta=0.5\n",
-    "    )\n",
-    "sed = E*E*lp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set BLR parameters\n",
-    "L_disk=6e45*u.Unit(\"erg s-1\")\n",
-    "R_line = 2.45*1e17*u.cm\n",
-    "r_blob_bh = 1e18*u.cm\n",
-    "z = 1\n",
-    "xi_line = 0.1\n",
-    "\n",
-    "E = np.logspace(-1, 1, 20)*u.TeV\n",
-    "nu = E.to(\"Hz\", equivalencies=u.spectral())\n",
-    "\n",
-    "blr = SphericalShellBLR(L_disk, xi_line, \"Hbeta\", R_line)\n",
-    "abs_blr = Absorption(blr, r=r_blob_bh, z=z)\n",
-    "tau_all_lines = abs_blr.tau_blr_all_lines_cubepy(nu)\n",
-    "A_blr = get_absorbtion_tau(np.array(tau_all_lines))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(7, 7))\n",
-    "plt.loglog(E, sed, 'k-')\n",
-    "plt.loglog(E, A_blr*sed, 'r--')\n",
-    "plt.xlabel(\"E [TeV]\")\n",
-    "plt.ylabel(\"1/ cm$^2$ s TeV)\")\n",
-    "plt.grid(which='both')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From ede481898705eeed2a434f5d45a1a654350f6477 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Wed, 9 Oct 2024 16:58:31 +0200
Subject: [PATCH 20/27] make code more concise and renamed function

---
 agnpy/absorption/absorption.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/agnpy/absorption/absorption.py b/agnpy/absorption/absorption.py
index bbfd9b2f..8c422bd3 100644
--- a/agnpy/absorption/absorption.py
+++ b/agnpy/absorption/absorption.py
@@ -446,7 +446,7 @@ def evaluate_tau_blr_mu_s(
         return (prefactor * integral).to_value("")
 
     @staticmethod
-    def compute_integrand(cubepy_params_array, R_line, mu_s, epsilon_1, epsilon_line):
+    def compute_integrand_for_blr(cubepy_params_array, R_line, mu_s, epsilon_1, epsilon_line):
         """
         Computes the integrand for the gamma-gamma absorption model with
         a spherical shell Broad Line Region (BLR).
@@ -469,9 +469,7 @@ def compute_integrand(cubepy_params_array, R_line, mu_s, epsilon_1, epsilon_line
         float
             The value of the integrand for the given parameters.
         """
-        mu = cubepy_params_array[0]
-        phi = cubepy_params_array[1]
-        log_l = cubepy_params_array[2]
+        (mu, phi, log_l) = cubepy_params_array
         l = np.exp(log_l)
         mu_star = mu_star_shell(mu, R_line.to_value('cm'), l)
         _cos_psi = cos_psi(mu_s, mu_star, phi)
@@ -518,7 +516,7 @@ def evaluate_tau_blr_cubepy(
 
         Returns
         -------
-        :class:`~astropy.units.Quantity`
+        :class:float
             array of the tau values corresponding to each frequency
         """
         # conversions
@@ -544,7 +542,7 @@ def evaluate_tau_blr_cubepy(
 
         prefactor = prefactor.to("cm-1")
         eps = eps_abs / prefactor.to_value("cm-1")
-        value, error = cp.integrate(Absorption.compute_integrand,
+        value, error = cp.integrate(Absorption.compute_integrand_for_blr,
                                     low,
                                     high,
                                     abstol=eps,

From 6c00ba1356ad1e797690a0da2f1d0bb33950bf54 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Wed, 18 Dec 2024 15:20:09 +0100
Subject: [PATCH 21/27] add unit in doc and add tau_blr_lines_cubepy_function

---
 agnpy/absorption/absorption.py | 51 +++++++++++++++++++++++++++++++++-
 1 file changed, 50 insertions(+), 1 deletion(-)

diff --git a/agnpy/absorption/absorption.py b/agnpy/absorption/absorption.py
index 8c422bd3..0c6e74ea 100644
--- a/agnpy/absorption/absorption.py
+++ b/agnpy/absorption/absorption.py
@@ -467,7 +467,7 @@ def compute_integrand_for_blr(cubepy_params_array, R_line, mu_s, epsilon_1, epsi
         Returns
         -------
         float
-            The value of the integrand for the given parameters.
+            The value of the integrand for the given parameters in cm^2.
         """
         (mu, phi, log_l) = cubepy_params_array
         l = np.exp(log_l)
@@ -647,6 +647,55 @@ def tau_blr_all_lines_cubepy(self, nu, eps_abs=1e-6):
 
         return tau_z_all
 
+    def tau_blr_lines_cubepy(self, nu, lines_list, eps_abs=1e-6):
+        """
+        Calculate the absorption in all available BLR (Broad Line Region) shells.
+        This function scales the radius and luminosity to the Hβ (H-beta) line
+        and computes the absorption for all available BLR lines (shells) using
+        their respective ratios.
+
+        Parameters
+        ----------
+        nu : numpy.ndarray
+            Frequency array for which the absorption will be calculated.
+        lines_list : list of str
+            List of BLR emission line names (e.g., 'Hbeta', 'MgII') for which the
+             absorption will be calculated. Each name corresponds to a predefined
+            line with associated properties (e.g., wavelength, radius ratio, and
+            luminosity ratio) that are stored as lines_dictionary.
+        eps_abs : float, optional
+            Absolute precision for the calculation (default is 1e-6).
+
+        Returns
+        -------
+        tau : numpy.ndarray
+             Optical depth values corresponding to the input frequencies.
+        """
+
+        sum_ratio_lines = sum(lines_dictionary[line]['L_Hbeta_ratio'] for line in lines_list)
+        XI_TARGET_LINE = self.target.xi_line
+        tau_z_lines = np.zeros(nu.shape)
+
+        blr_shells = dict()
+        absorption_shells = dict()
+
+        # TODO write test
+        if self.target.line != "Hbeta":
+            raise KeyError("Please use Hbeta as reference")
+
+        for line_key in lines_list:
+            xi_line_current = lines_dictionary[line_key]['L_Hbeta_ratio']
+            xi_line = (xi_line_current * XI_TARGET_LINE) / sum_ratio_lines
+            R_line_current = self.target.R_line * lines_dictionary[line_key]['R_Hbeta_ratio']
+
+            blr_shells[line_key] = SphericalShellBLR(self.target.L_disk, xi_line, line_key, R_line_current)
+            absorption_shells[line_key] = Absorption(blr_shells[line_key], r=self.r, z=self.z)
+
+            tau_z_current = absorption_shells[line_key].tau_blr_cubepy(nu, eps_abs=eps_abs)
+            tau_z_lines += tau_z_current
+
+        return tau_z_lines
+
     @staticmethod
     def evaluate_tau_dt(
         nu,

From af96f66eac948c7aaba31e7499867b2d817a4509 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Tue, 21 Jan 2025 16:04:35 +0100
Subject: [PATCH 22/27] improved docs description

---
 agnpy/absorption/absorption.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/agnpy/absorption/absorption.py b/agnpy/absorption/absorption.py
index 0c6e74ea..f758fd88 100644
--- a/agnpy/absorption/absorption.py
+++ b/agnpy/absorption/absorption.py
@@ -611,7 +611,7 @@ def tau_blr_cubepy(self, nu, eps_abs=1e-6):
 
     def tau_blr_all_lines_cubepy(self, nu, eps_abs=1e-6):
         """
-        Calculate the absorption in all available BLR (Broad Line Region) shells.
+        Calculate absorption in multi-available BLR shells.
         This function scales the radius and luminosity to the Hbeta line
         and applies the computation to all the available BLR lines (shells) using
         their respective ratios.

From 33f96df92ae2d0901991db22d3b33005fbd83028 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Wed, 22 Jan 2025 17:25:58 +0100
Subject: [PATCH 23/27] start write test for compare tau blr standard with
 cubepy

---
 agnpy/absorption/tests/test_absorption.py | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/agnpy/absorption/tests/test_absorption.py b/agnpy/absorption/tests/test_absorption.py
index 649a058c..5ed5c6ed 100644
--- a/agnpy/absorption/tests/test_absorption.py
+++ b/agnpy/absorption/tests/test_absorption.py
@@ -252,6 +252,27 @@ def test_abs_blr_cubepy(self):
             tau_z_all += tau_z_this
         assert True
 
+    def test_abs_compare_tau_blr_cubepy_with_tau_bls(self):
+        """
+        Test standard tau blr with tau blr with cubepy
+        """
+        L_disk = 2e46 * u.Unit("erg s-1")
+        xi_line = 0.024
+        R_line = 1e17 * u.cm
+        blr = SphericalShellBLR(L_disk, xi_line, "Lyalpha", R_line)
+        r = 1e20 * u.cm
+
+        # absorption, consider a small viewing angle for this case
+        z = 0.859
+        theta_s = np.deg2rad(10)
+        abs_blr = Absorption(blr, r, z, mu_s=np.cos(theta_s))
+        # taus
+        E = np.logspace(2, 6) * u.GeV
+        nu = E.to("Hz", equivalencies=u.spectral())
+        tau_blr = abs_blr.tau(nu)
+        print(tau_blr)
+
+
 
 
 def sigma_pp(b):

From 1ca2ef9bb19a9e4d1f56cb0f083f28cf0a3701fa Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Wed, 22 Jan 2025 17:29:45 +0100
Subject: [PATCH 24/27] test

---
 agnpy/absorption/tests/test_absorption.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/agnpy/absorption/tests/test_absorption.py b/agnpy/absorption/tests/test_absorption.py
index 5ed5c6ed..cc2b73c4 100644
--- a/agnpy/absorption/tests/test_absorption.py
+++ b/agnpy/absorption/tests/test_absorption.py
@@ -270,7 +270,7 @@ def test_abs_compare_tau_blr_cubepy_with_tau_bls(self):
         E = np.logspace(2, 6) * u.GeV
         nu = E.to("Hz", equivalencies=u.spectral())
         tau_blr = abs_blr.tau(nu)
-        print(tau_blr)
+        # print(tau_blr)
 
 
 

From fe80a75d8df4a476405e51c7840e805c0a8e7598 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Thu, 23 Jan 2025 15:50:22 +0100
Subject: [PATCH 25/27] add test for tau calcuration with standard and cubepy
 method

---
 agnpy/absorption/tests/test_absorption.py | 21 +++++++++------------
 1 file changed, 9 insertions(+), 12 deletions(-)

diff --git a/agnpy/absorption/tests/test_absorption.py b/agnpy/absorption/tests/test_absorption.py
index cc2b73c4..8ea24c70 100644
--- a/agnpy/absorption/tests/test_absorption.py
+++ b/agnpy/absorption/tests/test_absorption.py
@@ -252,27 +252,24 @@ def test_abs_blr_cubepy(self):
             tau_z_all += tau_z_this
         assert True
 
-    def test_abs_compare_tau_blr_cubepy_with_tau_bls(self):
+    def test_tau_accuracy_standard_vs_cubepy(self):
         """
-        Test standard tau blr with tau blr with cubepy
+        This test aims to compare the calculation of tau using a standard method against
+        the implementation provided by the `cubepy` library.
+        The goal is to ensure that both methods yield consistent results within an acceptable tolerance.
         """
-        L_disk = 2e46 * u.Unit("erg s-1")
+        L_disk = 2e45 * u.Unit("erg s-1")
         xi_line = 0.024
         R_line = 1e17 * u.cm
         blr = SphericalShellBLR(L_disk, xi_line, "Lyalpha", R_line)
-        r = 1e20 * u.cm
-
-        # absorption, consider a small viewing angle for this case
+        r = 2e17 * u.cm
         z = 0.859
-        theta_s = np.deg2rad(10)
-        abs_blr = Absorption(blr, r, z, mu_s=np.cos(theta_s))
-        # taus
+        abs_blr = Absorption(blr, r, z)
         E = np.logspace(2, 6) * u.GeV
         nu = E.to("Hz", equivalencies=u.spectral())
         tau_blr = abs_blr.tau(nu)
-        # print(tau_blr)
-
-
+        tau_blr_cubepy = abs_blr.tau_blr_cubepy(nu, eps_abs=1e-3)
+        assert np.allclose(tau_blr, tau_blr_cubepy, rtol=1e-1, atol=1e-1)
 
 
 def sigma_pp(b):

From d9584a89311e122b34aa6245e045f051fa560782 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Mon, 27 Jan 2025 18:01:15 +0100
Subject: [PATCH 26/27] remove function for all lines and remove unnecessary
 objects

---
 agnpy/absorption/absorption.py | 48 +++-------------------------------
 1 file changed, 4 insertions(+), 44 deletions(-)

diff --git a/agnpy/absorption/absorption.py b/agnpy/absorption/absorption.py
index f758fd88..8faea48d 100644
--- a/agnpy/absorption/absorption.py
+++ b/agnpy/absorption/absorption.py
@@ -477,6 +477,7 @@ def compute_integrand_for_blr(cubepy_params_array, R_line, mu_s, epsilon_1, epsi
         s = epsilon_1 * epsilon_line * (1 - _cos_psi) / 2
         integrand = (1 - _cos_psi) * l / x ** 2 * sigma(s).to_value("cm**2")
         return integrand
+
     @staticmethod
     def evaluate_tau_blr_cubepy(
         nu,
@@ -609,44 +610,6 @@ def tau_blr_cubepy(self, nu, eps_abs=1e-6):
             )
         return tau_blr_list
 
-    def tau_blr_all_lines_cubepy(self, nu, eps_abs=1e-6):
-        """
-        Calculate absorption in multi-available BLR shells.
-        This function scales the radius and luminosity to the Hbeta line
-        and applies the computation to all the available BLR lines (shells) using
-        their respective ratios.
-
-        Parameters
-        ----------
-        nu: numpy array
-            Frequency array with unit
-        eps_abs: float, optional
-            Absolute precision for the calculation, default is 1e-6
-        """
-        SUM_RATIO_LINES_BLR = 30.652
-        XI_TARGET_LINE = self.target.xi_line
-        tau_z_all = np.zeros(nu.shape)
-
-        blr_shells = dict()
-        absorption_shells = dict()
-
-        # TODO write test
-        if self.target.line != "Hbeta":
-            raise KeyError("Please use Hbeta as reference")
-
-        for line_key in lines_dictionary.keys():
-            xi_line_current = lines_dictionary[line_key]['L_Hbeta_ratio']
-            xi_line = (xi_line_current * XI_TARGET_LINE) / SUM_RATIO_LINES_BLR
-            R_line_current = self.target.R_line * lines_dictionary[line_key]['R_Hbeta_ratio']
-
-            blr_shells[line_key] = SphericalShellBLR(self.target.L_disk, xi_line, line_key, R_line_current)
-            absorption_shells[line_key] = Absorption(blr_shells[line_key], r=self.r, z=self.z)
-
-            tau_z_current = absorption_shells[line_key].tau_blr_cubepy(nu, eps_abs=eps_abs)
-            tau_z_all += tau_z_current
-
-        return tau_z_all
-
     def tau_blr_lines_cubepy(self, nu, lines_list, eps_abs=1e-6):
         """
         Calculate the absorption in all available BLR (Broad Line Region) shells.
@@ -676,9 +639,6 @@ def tau_blr_lines_cubepy(self, nu, lines_list, eps_abs=1e-6):
         XI_TARGET_LINE = self.target.xi_line
         tau_z_lines = np.zeros(nu.shape)
 
-        blr_shells = dict()
-        absorption_shells = dict()
-
         # TODO write test
         if self.target.line != "Hbeta":
             raise KeyError("Please use Hbeta as reference")
@@ -688,10 +648,10 @@ def tau_blr_lines_cubepy(self, nu, lines_list, eps_abs=1e-6):
             xi_line = (xi_line_current * XI_TARGET_LINE) / sum_ratio_lines
             R_line_current = self.target.R_line * lines_dictionary[line_key]['R_Hbeta_ratio']
 
-            blr_shells[line_key] = SphericalShellBLR(self.target.L_disk, xi_line, line_key, R_line_current)
-            absorption_shells[line_key] = Absorption(blr_shells[line_key], r=self.r, z=self.z)
+            blr_shells = SphericalShellBLR(self.target.L_disk, xi_line, line_key, R_line_current)
+            absorption_shells = Absorption(blr_shells, r=self.r, z=self.z)
 
-            tau_z_current = absorption_shells[line_key].tau_blr_cubepy(nu, eps_abs=eps_abs)
+            tau_z_current = absorption_shells.tau_blr_cubepy(nu, eps_abs=eps_abs)
             tau_z_lines += tau_z_current
 
         return tau_z_lines

From a2b1aaf2406d8e7d9a9fdf890c5313f9191ecbe1 Mon Sep 17 00:00:00 2001
From: pawel21 <p.gliwny@gmail.com>
Date: Mon, 27 Jan 2025 18:24:35 +0100
Subject: [PATCH 27/27] pass line_dict.keys() as default argument

---
 agnpy/absorption/absorption.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/agnpy/absorption/absorption.py b/agnpy/absorption/absorption.py
index 8faea48d..702bf797 100644
--- a/agnpy/absorption/absorption.py
+++ b/agnpy/absorption/absorption.py
@@ -610,7 +610,7 @@ def tau_blr_cubepy(self, nu, eps_abs=1e-6):
             )
         return tau_blr_list
 
-    def tau_blr_lines_cubepy(self, nu, lines_list, eps_abs=1e-6):
+    def tau_blr_lines_cubepy(self, nu, lines_list=lines_dictionary.keys(), eps_abs=1e-6):
         """
         Calculate the absorption in all available BLR (Broad Line Region) shells.
         This function scales the radius and luminosity to the Hβ (H-beta) line