initial mismatch notebook added

notebooks/deflection_angle.ipynb

@@ -209,7 +209,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,

notebooks/lens_class.ipynb

@@ -61,50 +61,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "8bad1244-8a25-42dc-8348-37ac4d090762",
    "metadata": {},
    "outputs": [],
    "source": [
     "from astropy.cosmology import FlatLambdaCDM\n",
     "import astropy.units as u\n",
-    "from astropy.constants import c, G, eps0, e, M_sun"
+    "from astropy.constants import c, G, eps0, e, M_sun\n",
+    "import lal"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "af540765-17f0-434c-ba3d-de37640e3fcc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1080784.208277988 kg(3/2) m(1/2) / (A2 s2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "fac = (128 * eps0 * c * np.sqrt(G))\n",
-    "fac = 3/fac.si\n",
-    "print(fac)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e3087b17-73e2-4213-9c47-18aa5cff8254",
+   "execution_count": 4,
+   "id": "cf623760-221e-4dd1-ab2b-b9f15065ba47",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.012690446026682e-13 1 / m(1/2)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cosmo = FlatLambdaCDM(H0=70, Om0=0.3)\n",
     "zs = 2.0\n",
@@ -112,26 +85,37 @@
     "D_s = cosmo.angular_diameter_distance(zs)\n",
     "D_l = cosmo.angular_diameter_distance(zl)\n",
     "D_ls = cosmo.angular_diameter_distance_z1z2(zl, zs)\n",
-    "dist_fac = np.sqrt(D_s/D_l/D_ls).si\n",
-    "print(dist_fac)"
+    "Ml = 100 * lal.MRSUN_SI * u.m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88c5ba85-2906-44b9-acab-f1616fdbaf34",
+   "metadata": {},
+   "source": [
+    "We compute the factor \n",
+    "$$\n",
+    "    \\left(\\frac{Q_{\\text{eff}}}{M_L}\\right)^2\\sqrt{\\frac{D_s/M_L}{(D_l/M_L) (D_{ls}/M_L)}}\n",
+    "$$"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "f1d9ccba-6000-43b7-9337-b19e85eacffc",
+   "execution_count": 6,
+   "id": "9cb726d1-57e6-41ca-b58b-1506bbeabe14",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.175284050217618e-07 kg(3/2) / (A2 s2)\n"
+      "7.734141245310819e-11\n"
      ]
     }
    ],
    "source": [
-    "print(fac * dist_fac)"
+    "fac = (np.sqrt(D_s.si * Ml / D_l.si / D_ls.si))\n",
+    "print(fac)"
    ]
   },
   {
@@ -646,9 +630,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "igwn-py39",
    "language": "python",
-   "name": "python3"
+   "name": "igwn-py39"
   },
   "language_info": {
    "codemirror_mode": {
@@ -660,7 +644,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.14"
+   "version": "3.9.19"
   }
  },
  "nbformat": 4,

scripts/generate_condor_submit_file.py

@@ -2,8 +2,14 @@
 import os
 import numpy as np
 
-y_arr = [0.5, 1.0, 2.0]
-q_arr = [-0.5, 0.1, 0.5]
+## The following config. is used to generate F(t), F(w), h_U, h_L sample waveforms
+# y_arr = [0.5, 1.0, 2.0]
+# q_arr = [-0.5, 0.1, 0.5]
+# t_max = 200
+
+## The following config. is used to generate data for Q=0 mismatch checks
+y_arr = np.geomspace(0.1, 3, 50)
+Ml_arr = np.geomspace(1, 1e5, 50)
 t_max = 200
 
 # y_arr = np.logspace(-1, np.log10(2.0), 3000)
@@ -28,6 +34,7 @@ vars = {
     "Log": dir_path+"logs/log.log.$(Cluster).$(Process).txt",
     "notify_user": "uddeepta.deka@icts.res.in",
     "Notification": "Never",
+    "requirements" : '(Machine != "node72.alice.icts.res.in")',
     "request_cpus": 4,
     "request_memory": 1024,
 }
@@ -36,5 +43,6 @@ with open(dir_path + condor_filename, 'w') as fp:
     for key, val in vars.items():
         fp.write(f"{key}\t = {val}\n")
     for y in y_arr:
-        for q in q_arr:
-            fp.write(f"\narguments = y {y} q {q} t_max {t_max} folder_path {dir_path}results/\nQueue\n")
\ No newline at end of file
+        # for q in q_arr:
+        for Ml in Ml_arr:
+            fp.write(f"\narguments = y {y} Ml {Ml} t_max {t_max} folder_path {dir_path}results/\nQueue\n")
\ No newline at end of file

scripts/isolated_lens_ampfac.py

@@ -47,15 +47,15 @@ def lens_ampl_function(w, y_l, intrp_hyp1f1=None):
         w_2 = w[np.where(w > w_max)[0]]
         
         if len(w_1) == 0:
-            F_f = lens_ampl_function_geom_optics(w, y_l)
+            F_f = lens_ampl_function_geom_optics(w_2, y_l)
         
         else:
             if len(w_2) != 0:
-                F_w = lens_ampl_function_wave_optics(w, y_l, intrp_hyp1f1=intrp_hyp1f1)
-                F_g = lens_ampl_function_geom_optics(w, y_l)
+                F_w = lens_ampl_function_wave_optics(w_1, y_l, intrp_hyp1f1=intrp_hyp1f1)
+                F_g = lens_ampl_function_geom_optics(w_2, y_l)
                 F_f = np.concatenate((F_w,F_g))
 
             else:
-                F_f = lens_ampl_function_wave_optics(w, y_l, intrp_hyp1f1=intrp_hyp1f1)
+                F_f = lens_ampl_function_wave_optics(w_1, y_l, intrp_hyp1f1=intrp_hyp1f1)
 
     return F_f