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upload diagonal script
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‎get_diagonals.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Script to obtain diagonals of arrays, including rectangular arrays."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.random.seed(536)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"test_wide = np.random.randint(0, 10, (4,10))\n",
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"test_long = np.random.randint(0, 10, (10,4))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[5, 2, 3, 5, 5, 1, 5, 9, 4, 0],\n",
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" [9, 1, 4, 0, 6, 1, 4, 5, 0, 9],\n",
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" [9, 7, 2, 1, 0, 8, 8, 0, 3, 4],\n",
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" [6, 9, 0, 3, 7, 1, 5, 8, 8, 3]])"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"test_wide"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[4, 5, 0, 5],\n",
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" [0, 8, 2, 2],\n",
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" [4, 5, 7, 6],\n",
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" [3, 4, 4, 1],\n",
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" [1, 6, 8, 6],\n",
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" [2, 5, 4, 6],\n",
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" [1, 5, 3, 8],\n",
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" [8, 5, 4, 6],\n",
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" [9, 0, 5, 4],\n",
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" [4, 3, 1, 2]])"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"test_long"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([5, 2, 6])"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.diag(test_long, 1) # compare with numpy's inbuilt function"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1, 5])"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"test_wide[[0,3], [5,6]]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_diag(arr, k=0, from_element=None):\n",
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" '''from element = coordinates of an array element,\n",
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" use if want to calculate the offset from coords\n",
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" instead of giving the offset itself'''\n",
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" N,M = arr.shape\n",
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" if from_element != None:\n",
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" k = from_element[1] - from_element[0] # same as numpy\n",
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" if k > 0:\n",
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" row_lim = min(N, M-k)\n",
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" possible_rows = np.arange(0, row_lim)\n",
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" col_lim = min(M, k+N)\n",
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" possible_cols = np.arange(k, col_lim)\n",
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" elif k =< 0:\n",
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" row_lim = min(N, -k+M)\n",
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" possible_rows = np.arange(-k, row_lim)\n",
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" col_lim = min(M, N-(-k))\n",
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" possible_cols = np.arange(0, col_lim)\n",
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" return(arr[possible_rows, possible_cols])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([4, 8, 7, 1])"
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]
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},
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"get_diag(test_long, k=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([8, 0, 1])"
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]
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},
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"get_diag(test_long, k=-7)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([9, 0, 4])"
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]
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},
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"execution_count": 30,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"get_diag(test_wide, k=7)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.7.4 64-bit",
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"language": "python",
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"name": "python37464bitdff059f72f8b417fb86b0d43a0194990"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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