Week 09

.ipynb_checkpoints/CE302-Week08-checkpoint.py

@@ -0,0 +1,59 @@
+##############################################
+# 2021_0414-AAD: Multhithreading Example #
+##############################################
+# There are two approaches in code efficiency: 
+# 1. Parallel process : splitting the cpus
+# 2. Creating paralle threads
+###############################
+
+#%% IMPORTS
+
+import os, pandas as pd , time , datetime, numpy as np 
+
+import threading # module for multithreading
+
+#%% 
+exitFlag = 0
+
+class MyThread( threading.Thread) :
+    def __init__( self , threatID , name , my_delay) :
+        threading.Thread.__init__(self)
+        self.threatID = threatID
+        self.name  = name 
+        self.delay = my_delay
+
+    def run(self):
+        print( f"Starting : {self.name}" )
+
+        print_time( self.name , self.delay , 5 )
+
+        print( f"Exiting : {self.name}")
+
+def print_time( threadName ,  my_delay , counter ):
+
+    while counter :
+        if exitFlag : 
+            threadName.exit()
+        time.sleep( my_delay )
+
+        print( f"{threadName} : time is {time.ctime(time.time())}")
+
+        counter -= 1
+
+#%%
+# Crate the threats
+
+thread_1 = MyThread( 1 , "Thread name 1" , 1)
+
+thread_2 = MyThread( 2 , "Thread name 2" , 5)
+
+# Start the threads
+
+#%%
+
+thread_1.start()
+thread_2.start()
+
+print( f"The file is ok")
+
+# %%

.ipynb_checkpoints/CE302-Week09-checkpoint.ipynb

@@ -6,6 +6,295 @@
    "source": [
     "# CE302 ADVANCED PROGRAMMING TECHNIQUES FOR ENGINEERS"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create the vectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vect  = np.array([1,2,3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vect.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3,)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vect.T.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a matrix from the vectors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vec1 = np.array([1,2,3])\n",
+    "vec2 = np.array([3,4,5])\n",
+    "vec3 = np.array([4,5,6])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Variable   Type       Data/Info\n",
+      "-------------------------------\n",
+      "np         module     <module 'numpy' from '/Us<...>kages/numpy/__init__.py'>\n",
+      "os         module     <module 'os' from '/Users<...>da3/lib/python3.8/os.py'>\n",
+      "sys        module     <module 'sys' (built-in)>\n",
+      "vec1       ndarray    3: 3 elems, type `int64`, 24 bytes\n",
+      "vec2       ndarray    3: 3 elems, type `int64`, 24 bytes\n",
+      "vec3       ndarray    3: 3 elems, type `int64`, 24 bytes\n",
+      "vect       ndarray    3: 3 elems, type `int64`, 24 bytes\n"
+     ]
+    }
+   ],
+   "source": [
+    "whos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "myMatrice = np.vstack([vec1 , vec2 , vec3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [3, 4, 5],\n",
+       "       [4, 5, 6]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "myMatrice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 4, 6])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "myMatrice.diagonal()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 3, 4],\n",
+       "       [2, 4, 5],\n",
+       "       [3, 5, 6]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "myMatrice.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 3)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "myMatrice.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  4,  6],\n",
+       "       [ 6,  8, 10],\n",
+       "       [ 8, 10, 12]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "myMatrice + myMatrice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  4,  6],\n",
+       "       [ 6,  8, 10],\n",
+       "       [ 8, 10, 12]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.add( myMatrice , myMatrice)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Subtraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0],\n",
+       "       [0, 0, 0],\n",
+       "       [0, 0, 0]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "myMatrice - myMatrice\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {