This is for the first Homework

Homework_1_Hatam.ipynb

@@ -0,0 +1,189 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "52bbea1d-5c01-4cba-a0ad-fd7f61f5786c",
+   "metadata": {},
+   "source": [
+    "a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "811d25bd-911d-47d5-a09d-81937dbbf7aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Schwarzschild radius of the Sun is 2953.85 meter\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pint\n",
+    "\n",
+    "units = pint.UnitRegistry()\n",
+    "\n",
+    "G = 6.674e-11 * units.meter**3 / (units.kilogram * units.second**2)\n",
+    "c = 2.998e8 * units.meter / units.second\n",
+    "M_sun = 1.989e30 * units.kilogram\n",
+    "\n",
+    "def Schwarzschild_Radius(M):\n",
+    "    R_s = (2 * G * M) / c**2\n",
+    "    return R_s\n",
+    "\n",
+    "print(f\"The Schwarzschild radius of the Sun is {Schwarzschild_Radius(M_sun).to(units.meter):.2f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c774bdbc-0c90-47ab-8bf7-90abd035c262",
+   "metadata": {},
+   "source": [
+    "b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "44ad1650-f8dd-48c0-be28-aa43d763274d",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[9, 63, 54], [8, 40, 72], [21, 74, 84]] \n",
+      " [[9, 63, 54], [8, 40, 72], [21, 74, 84]] \n",
+      " [[ 9 63 54]\n",
+      " [ 8 40 72]\n",
+      " [21 74 84]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "#Nested loop\n",
+    "def nested(A,B):\n",
+    "    rows_A,cols_A=np.shape(A)\n",
+    "    rows_B,cols_B=np.shape(B)\n",
+    "    \n",
+    "    result=[]\n",
+    "    for i in range(rows_A):\n",
+    "        result.append([0]*cols_B)\n",
+    "    \n",
+    "    for i in range(rows_A):\n",
+    "        for j in range(cols_B):\n",
+    "            for k in range(cols_A):\n",
+    "                result[i][j]+=A[i][k]*B[k][j]\n",
+    "    return result\n",
+    "\n",
+    "\n",
+    "#list comprehension\n",
+    "def list_comp(A, B):\n",
+    "    transB=list(zip(*B))\n",
+    "    return [[sum(x*y for x,y in zip(i,j)) for j in transB] for i in A]\n",
+    "\n",
+    "\n",
+    "#numpy\n",
+    "def npy(A, B):\n",
+    "    A_np = np.array(A)\n",
+    "    B_np = np.array(B)\n",
+    "    return np.dot(A_np, B_np)\n",
+    "\n",
+    "matrix1 = np.random.randint(0, 10, size=(3, 3))\n",
+    "matrix2 = np.random.randint(0, 10, size=(3, 3))\n",
+    "\n",
+    "print(nested(matrix1,matrix2),'\\n',list_comp(matrix1,matrix2),'\\n',npy(matrix1,matrix2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb2cec3f-0bec-4fc8-b2a5-829f8f0b6c2f",
+   "metadata": {},
+   "source": [
+    "c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c6e7431a-a995-4437-9f86-afbf027c0076",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nested loop - Mean time: 0.844403 s, Standard Deviation: 0.045546\n",
+      "List comprehension - Mean time: 0.423700 s, Standard Deviation: 0.026283\n",
+      "NumPy - Mean time: 0.000099 s, Standard Deviation: 0.000296\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "def benchmark(multiply_func, runs=10, size=100):\n",
+    "    times = []\n",
+    "\n",
+    "    for _ in range(runs):\n",
+    "        matrix1 = np.random.randint(0, 10, size=(size, size))\n",
+    "        matrix2 = np.linalg.inv(matrix1)\n",
+    "\n",
+    "        start = time.time()\n",
+    "        multiply_func(matrix1, matrix2)\n",
+    "        end = time.time()\n",
+    "        times.append(end - start)\n",
+    "\n",
+    "    mean_time = np.mean(times)\n",
+    "    std_time = np.std(times)\n",
+    "\n",
+    "    return mean_time, std_time\n",
+    "\n",
+    "mean_nested, std_nested = benchmark(nested, runs=10, size=100)\n",
+    "mean_list_comp, std_list_comp = benchmark(list_comp, runs=10, size=100)\n",
+    "mean_npy, std_npy = benchmark(npy, runs=10, size=100)\n",
+    "\n",
+    "print(f\"Nested loop - Mean time: {mean_nested:.6f} s, Standard Deviation: {std_nested:.6f}\")\n",
+    "print(f\"List comprehension - Mean time: {mean_list_comp:.6f} s, Standard Deviation: {std_list_comp:.6f}\")\n",
+    "print(f\"NumPy - Mean time: {mean_npy:.6f} s, Standard Deviation: {std_npy:.6f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5eaccdc-f517-4e47-92fd-faae4de90586",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

pbhmergers.py

@@ -63,7 +63,7 @@ def rhocrit(self):
         hubz2 = (self.overden.omega_matter0/aa**3 + self.overden.omega_lambda0) * hubble**2
         #Critical density at redshift in units of kg m^-3
         rhocrit = 3 * hubz2 / (8*math.pi* self.ureg.newtonian_constant_of_gravitation)
-        print "rhocrit = ", rhocrit
+        print("rhocrit = ", rhocrit)
         return rhocrit.to_base_units()
 
     def R200(self, mass):
@@ -224,7 +224,7 @@ def mergerhalflife(self,mass,threefac=True, bhmass=None):
             threefac = self.threebodyratio(mass)
             threefac = np.max([threefac, np.ones_like(threefac)],axis=0)
             rate *= threefac
-        return 0.5*(mass/bhmass)/rat
+        return 0.5*(mass/bhmass)/rate
 
     def bias(self,mass):
         """The formula for halo bias in EPS theory (Mo & White 1996), eq. 13"""

pbhmergers.toml

@@ -0,0 +1,2 @@
+[tool.ruff]
+select = ["E", "F", "W"]