853 lines
37 KiB
Plaintext
853 lines
37 KiB
Plaintext
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{
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"metadata": {
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"kernelspec": {
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"name": "python",
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"display_name": "Python (Pyodide)",
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"language": "python"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "python",
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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": "3.8"
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}
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},
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"nbformat_minor": 5,
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"nbformat": 4,
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"cells": [
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{
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"cell_type": "markdown",
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"source": "# Всё в Python является объектом",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"id": "917bfefc-582c-41b4-b3b2-df29da3c7200"
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},
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{
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"cell_type": "code",
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"source": "print(isinstance(\"add\", object))\nprint(isinstance(1_000, object))\nprint(isinstance(3.14, object))",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"execution_count": 1,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": "True\n\nTrue\n\nTrue\n"
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}
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],
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"id": "9aa513ac"
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},
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{
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"cell_type": "code",
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"source": "(3.14).as_integer_ratio()",
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"metadata": {},
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"execution_count": 3,
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"outputs": [
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{
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"execution_count": 3,
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"(7070651414971679, 2251799813685248)"
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]
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},
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"metadata": {}
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}
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],
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"id": "3ecb4677"
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},
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{
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"cell_type": "code",
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"source": "class Vector2D:\n x = 0\n y = 0\n \n def norm(self):\n return (self.x**2 + self.y**2)**0.5\n\nvec = Vector2D()",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"execution_count": 13,
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"outputs": [],
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"id": "b7025d1e-79cf-4ba7-a49d-7c1bf2fe063e"
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},
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{
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"cell_type": "code",
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"source": "isinstance(vec, object)",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"execution_count": 4,
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"outputs": [
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{
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"execution_count": 4,
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"True"
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]
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},
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"metadata": {}
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}
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],
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"id": "3371dfc5-234d-481c-8404-f1fd0fd68bb3"
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},
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{
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"cell_type": "code",
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"source": "isinstance(Vector2D, object)",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"execution_count": 5,
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"outputs": [
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{
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"execution_count": 5,
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"True"
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]
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},
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"metadata": {}
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}
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],
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"id": "88ccb376-5fbb-47ed-84b5-4773f26d3490"
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},
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{
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"cell_type": "code",
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"source": "isinstance(object, object)",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"execution_count": 4,
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"outputs": [
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{
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"execution_count": 4,
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"True"
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]
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},
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"metadata": {}
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}
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],
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"id": "4d77d081"
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},
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{
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"cell_type": "code",
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"source": "import math\nisinstance(math, object)",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"execution_count": 5,
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"outputs": [
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{
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"execution_count": 5,
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"True"
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]
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},
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"metadata": {}
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}
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],
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"id": "d4e0bb29-320c-4206-8fc3-4345eda4f73f"
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},
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{
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"cell_type": "code",
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"source": "def add(a,b): return a + b\nisinstance(add, object)",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"execution_count": 6,
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"outputs": [
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{
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"execution_count": 6,
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"True"
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]
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},
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"metadata": {}
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}
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],
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"id": "374f4bc2-7bb6-441d-96aa-392445856565"
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},
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{
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"cell_type": "code",
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"source": "add.x = 1",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"execution_count": 7,
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"outputs": [],
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"id": "0b0b8d31-917e-4fb0-8d41-3e49cc381494"
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},
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{
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"cell_type": "markdown",
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"source": "# Магические методы",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"id": "cce63a23"
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},
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{
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"cell_type": "code",
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"source": "dir(vec)",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"execution_count": 11,
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"outputs": [
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{
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"execution_count": 11,
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"['__class__',\n",
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" '__delattr__',\n",
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" '__dict__',\n",
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" '__dir__',\n",
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" '__doc__',\n",
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" '__eq__',\n",
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" '__format__',\n",
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" '__ge__',\n",
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" '__getattribute__',\n",
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" '__gt__',\n",
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" '__hash__',\n",
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" '__init__',\n",
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" '__init_subclass__',\n",
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" '__le__',\n",
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" '__lt__',\n",
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" '__module__',\n",
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" '__ne__',\n",
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" '__new__',\n",
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" '__reduce__',\n",
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" '__reduce_ex__',\n",
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" '__repr__',\n",
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" '__setattr__',\n",
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" '__sizeof__',\n",
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" '__str__',\n",
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" '__subclasshook__',\n",
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" '__weakref__',\n",
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" 'norm',\n",
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" 'x',\n",
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" 'y']"
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]
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},
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"metadata": {}
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}
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],
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"id": "f0cac210-b14c-4940-811f-2f7b982014f5"
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},
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{
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"cell_type": "code",
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"source": "dir(add)",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"execution_count": 18,
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"outputs": [
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{
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"execution_count": 18,
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"['__annotations__',\n",
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" '__call__',\n",
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" '__class__',\n",
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" '__closure__',\n",
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" '__code__',\n",
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" '__defaults__',\n",
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" '__delattr__',\n",
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" '__dict__',\n",
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" '__dir__',\n",
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" '__doc__',\n",
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" '__eq__',\n",
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" '__format__',\n",
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" '__ge__',\n",
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" '__get__',\n",
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" '__getattribute__',\n",
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" '__globals__',\n",
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" '__gt__',\n",
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" '__hash__',\n",
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" '__init__',\n",
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" '__init_subclass__',\n",
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|
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" '__kwdefaults__',\n",
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|
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" '__le__',\n",
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" '__lt__',\n",
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" '__module__',\n",
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" '__name__',\n",
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" '__ne__',\n",
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" '__new__',\n",
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" '__qualname__',\n",
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" '__reduce__',\n",
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" '__reduce_ex__',\n",
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|
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" '__repr__',\n",
|
|||
|
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" '__setattr__',\n",
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|
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" '__sizeof__',\n",
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|
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" '__str__',\n",
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" '__subclasshook__']"
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|
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]
|
|||
|
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},
|
|||
|
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"metadata": {}
|
|||
|
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}
|
|||
|
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],
|
|||
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"id": "1b5597dc-484a-43d3-b37a-cd30908d495d"
|
|||
|
|
},
|
|||
|
|
{
|
|||
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"cell_type": "markdown",
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"source": "* Управляют внутренней работой объектов\n* Хранят различную информацию объектов (которую можно получать в runtime)\n* Вызываются при использовании синтаксических конструкций\n* Вызываются встроенными (builtins) функциями\n* Область применения: перегрузка операторов, рефлексия и метапрограммирование",
|
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"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
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"name": "#%% md\n"
|
|||
|
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}
|
|||
|
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},
|
|||
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"id": "bc3cdb2f"
|
|||
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},
|
|||
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{
|
|||
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"cell_type": "code",
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"source": "class TenItemList:\n\n def __len__(self):\n return 10\n\n\nten_item_list = TenItemList()\nlen(ten_item_list)",
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"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
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|||
|
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}
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},
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"execution_count": 8,
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"outputs": [
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|
{
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|||
|
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"execution_count": 8,
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|||
|
|
"output_type": "execute_result",
|
|||
|
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"data": {
|
|||
|
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"text/plain": [
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|||
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"10"
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]
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|||
|
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},
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|||
|
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"metadata": {}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
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"id": "dabd01d8"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
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"cell_type": "markdown",
|
|||
|
|
"source": "# Всё в Python является объектом, а все синтаксические конструкции сводятся к вызовам магических методов",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%% md\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
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"id": "0effc8ba"
|
|||
|
|
},
|
|||
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{
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|||
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"cell_type": "markdown",
|
|||
|
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"source": "# Пример сложение\n",
|
|||
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"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%% md\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"id": "3841bda9"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": "class Vector2D:\n\n def __init__(self, x, y):\n self.x = x\n self.y = y\n\n def __add__(self, other):\n return Vector2D(self.x + other.y, self.x + other.y)\n\n def norm(self):\n return (self.x**2 + self.y**2)**0.5\n\nvec1 = Vector2D(1,2)\nvec2 = Vector2D(3,4)\nvec3 = vec1 + vec2\nvec3.x, vec3.y",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"(5, 5)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"id": "e3460fe5"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": "## Пример присваивание",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%% md\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"id": "538a0794"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": "class Vector2D:\n x = 0\n y = 0\n \n def norm(self):\n return (self.x**2 + self.y**2)**0.5\n\nvec = Vector2D()",
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"outputs": [],
|
|||
|
|
"id": "1acd0880"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": "vec = Vector2D()\nvec.__getattribute__(\"x\")",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"0"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"id": "1f2bfb38"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": "vec.__getattribute__(\"norm\")()",
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"0.0"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"id": "640d5fad"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": "vec.x = 5\nvec.__getattribute__(\"x\")",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"5"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"id": "21efb174"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": "vec.__setattr__(\"x\", 10)\ngetattr(vec, \"x\")",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"10"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"id": "dfbce5a2"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": "setattr(vec, \"x\", 20)\nvec.x",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"20"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"id": "1d6de7d3"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": "class Foo:\n def __setattr__(self, key, value):\n print(key, value)\n\nfoo = Foo()\nfoo.a = \"A\"",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": "a A\n"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"id": "40df4888"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": "foo.a",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"ename": "AttributeError",
|
|||
|
|
"evalue": "'Foo' object has no attribute 'a'",
|
|||
|
|
"output_type": "error",
|
|||
|
|
"traceback": [
|
|||
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|||
|
|
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
|
|||
|
|
"\u001b[0;32m/tmp/ipykernel_35789/2615815247.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfoo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
|||
|
|
"\u001b[0;31mAttributeError\u001b[0m: 'Foo' object has no attribute 'a'"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"id": "871b0b35"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": "",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%% md\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"id": "13c80f91"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": "# На самом деле все объекты реализованы как словари хранящие атрибуты объекта (однако есть возможности для оптимизаций)",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%% md\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"id": "266ca832"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": "class Vector2D:\n x = 0\n y = 0\n\n def norm(self):\n return (self.x**2 + self.y**2)**0.5\n\nvec = Vector2D()",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"execution_count": 23,
|
|||
|
|
"outputs": [],
|
|||
|
|
"id": "af1b83e9"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": "vec.__dict__",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"execution_count": 24,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"execution_count": 24,
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"{}"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"id": "acc6da9e"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": "Vector2D.__dict__",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"execution_count": 27,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"execution_count": 27,
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"mappingproxy({'__module__': '__main__',\n",
|
|||
|
|
" 'x': 0,\n",
|
|||
|
|
" 'y': 0,\n",
|
|||
|
|
" 'norm': <function __main__.Vector2D.norm(self)>,\n",
|
|||
|
|
" '__dict__': <attribute '__dict__' of 'Vector2D' objects>,\n",
|
|||
|
|
" '__weakref__': <attribute '__weakref__' of 'Vector2D' objects>,\n",
|
|||
|
|
" '__doc__': None})"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"id": "4cfbfaec"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": "vec.x = 5\nvec.__dict__",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"execution_count": 26,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"execution_count": 26,
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"{'x': 5}"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"id": "b5eb0e56"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": "# Модуль inspect --- информация об объектах в runtime\n\n* Не вся информация может быть доступна через магические методы\n* Недоступную информацию можно получить через модуль inspect",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%% md\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"id": "f4af619e"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": "import inspect\n\ndef add(a,b): return a + b\ninspect.isfunction(add)",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"execution_count": 28,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"execution_count": 28,
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"True"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"id": "cb6a08ad"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": "inspect.getsource(add)",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"execution_count": 29,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"execution_count": 29,
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"'def add(a,b): return a + b\\n'"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"id": "3bd00ea2"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": "from numpy import random\ninspect.getsource(random)",
|
|||
|
|
"metadata": {
|
|||
|
|
"pycharm": {
|
|||
|
|
"name": "#%%\n"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"execution_count": 30,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"execution_count": 30,
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
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"'\"\"\"\\n========================\\nRandom Number Generation\\n========================\\n\\nUse ``default_rng()`` to create a `Generator` and call its methods.\\n\\n=============== =========================================================\\nGenerator\\n--------------- ---------------------------------------------------------\\nGenerator Class implementing all of the random number distributions\\ndefault_rng Default constructor for ``Generator``\\n=============== =========================================================\\n\\n============================================= ===\\nBitGenerator Streams that work with Generator\\n--------------------------------------------- ---\\nMT19937\\nPCG64\\nPCG64DXSM\\nPhilox\\nSFC64\\n============================================= ===\\n\\n============================================= ===\\nGetting entropy to initialize a BitGenerator\\n--------------------------------------------- ---\\nSeedSequence\\n============================================= ===\\n\\n\\nLegacy\\n------\\n\\nFor backwards compatibility with previous versions of numpy before 1.17, the\\nvarious aliases to the global `RandomState` methods are left alone and do not\\nuse the new `Generator` API.\\n\\n==================== =========================================================\\nUtility functions\\n-------------------- ---------------------------------------------------------\\nrandom Uniformly distributed floats over ``[0, 1)``\\nbytes Uniformly distributed random bytes.\\npermutation Randomly permute a sequence / generate a random sequence.\\nshuffle Randomly permute a sequence in place.\\nchoice Random sample from 1-D array.\\n==================== =========================================================\\n\\n==================== =========================================================\\nCompatibility\\nfunctions - removed\\nin the new API\\n-------------------- ---------------------------------------------------------\\nrand Uniformly distributed values.\\nrandn Normally distributed values.\\nranf Uniformly distributed floating point numbers.\\nrandom_integers Uniformly distributed integers in a given range.\\n (deprecated, use ``integers(..., closed=True)`` instead)\\nrandom_sample Alias for `random_sample`\\nrandint Uniformly distributed integers in a given range\\nseed Seed the legacy random number generator.\\n==================== =========================================================\\n\\n==================== =========================================================\\nUnivariate\\ndistributions\\n-------------------- ---------------------------------------------------------\\nbeta Beta distribution over ``[0, 1]``.\\nbinomial Binomial distribution.\\nchisquare :math:`\\\\\\\\chi^2` distribution.\\nexponential Exponential distribution.\\nf F (Fisher-Snedecor) distribution.\\ngamma Gamma distribution.\\ngeometric Geometric distribution.\\ngumbel Gumbel distribution.\\nhypergeometric Hypergeometric distribution.\\nlaplace Laplace distribution.\\nlogistic Logistic distribution.\\nlognormal Log-normal distribution.\\nlogseries Logarithmic series distribution.\\nnegative_binomial Negative binomial distribution.\\nnoncentral_chisquare Non-central chi-square distribution.\\nnoncentral_f Non-central F distribution.\\nnormal Normal / Gaussian distribution.\\npareto Pareto distribution.\\npoisson Poisson distribution.\\npower Power distribution.\\nrayleigh Rayleigh distribution.\\ntriangular Triangular distribution.\\nuniform Uniform distribution.\\nvonmises Von Mises circular distribution.\\nwald Wald (inverse Gaussian) distribution.
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]
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"metadata": {}
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"id": "e2e6f3f7"
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},
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{
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"cell_type": "markdown",
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"source": "# Модуль inspect --- информация об объектах в runtime\n\n* Не вся информация может быть доступна через магические методы\n* Недоступную информацию можно получить через модуль inspect",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"id": "8c0cfc80"
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},
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{
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"cell_type": "code",
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"source": "import inspect\n\ndef add(a,b): return a + b\ninspect.isfunction(add)",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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"execution_count": 2,
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{
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"execution_count": 2,
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"True"
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"metadata": {}
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"id": "f49084f4"
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},
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{
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"cell_type": "code",
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"source": "inspect.getsource(add)",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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"execution_count": 12,
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"outputs": [
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{
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"execution_count": 12,
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"'def add(a,b): return a + b\\n'"
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"metadata": {}
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"id": "7de58194"
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},
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{
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"cell_type": "code",
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"source": "from numpy import random\ninspect.getsource(random)",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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"execution_count": 3,
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"outputs": [
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{
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"execution_count": 3,
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"'\"\"\"\\n========================\\nRandom Number Generation\\n========================\\n\\nUse ``default_rng()`` to create a `Generator` and call its methods.\\n\\n=============== =========================================================\\nGenerator\\n--------------- ---------------------------------------------------------\\nGenerator Class implementing all of the random number distributions\\ndefault_rng Default constructor for ``Generator``\\n=============== =========================================================\\n\\n============================================= ===\\nBitGenerator Streams that work with Generator\\n--------------------------------------------- ---\\nMT19937\\nPCG64\\nPCG64DXSM\\nPhilox\\nSFC64\\n============================================= ===\\n\\n============================================= ===\\nGetting entropy to initialize a BitGenerator\\n--------------------------------------------- ---\\nSeedSequence\\n============================================= ===\\n\\n\\nLegacy\\n------\\n\\nFor backwards compatibility with previous versions of numpy before 1.17, the\\nvarious aliases to the global `RandomState` methods are left alone and do not\\nuse the new `Generator` API.\\n\\n==================== =========================================================\\nUtility functions\\n-------------------- ---------------------------------------------------------\\nrandom Uniformly distributed floats over ``[0, 1)``\\nbytes Uniformly distributed random bytes.\\npermutation Randomly permute a sequence / generate a random sequence.\\nshuffle Randomly permute a sequence in place.\\nchoice Random sample from 1-D array.\\n==================== =========================================================\\n\\n==================== =========================================================\\nCompatibility\\nfunctions - removed\\nin the new API\\n-------------------- ---------------------------------------------------------\\nrand Uniformly distributed values.\\nrandn Normally distributed values.\\nranf Uniformly distributed floating point numbers.\\nrandom_integers Uniformly distributed integers in a given range.\\n (deprecated, use ``integers(..., closed=True)`` instead)\\nrandom_sample Alias for `random_sample`\\nrandint Uniformly distributed integers in a given range\\nseed Seed the legacy random number generator.\\n==================== =========================================================\\n\\n==================== =========================================================\\nUnivariate\\ndistributions\\n-------------------- ---------------------------------------------------------\\nbeta Beta distribution over ``[0, 1]``.\\nbinomial Binomial distribution.\\nchisquare :math:`\\\\\\\\chi^2` distribution.\\nexponential Exponential distribution.\\nf F (Fisher-Snedecor) distribution.\\ngamma Gamma distribution.\\ngeometric Geometric distribution.\\ngumbel Gumbel distribution.\\nhypergeometric Hypergeometric distribution.\\nlaplace Laplace distribution.\\nlogistic Logistic distribution.\\nlognormal Log-normal distribution.\\nlogseries Logarithmic series distribution.\\nnegative_binomial Negative binomial distribution.\\nnoncentral_chisquare Non-central chi-square distribution.\\nnoncentral_f Non-central F distribution.\\nnormal Normal / Gaussian distribution.\\npareto Pareto distribution.\\npoisson Poisson distribution.\\npower Power distribution.\\nrayleigh Rayleigh distribution.\\ntriangular Triangular distribution.\\nuniform Uniform distribution.\\nvonmises Von Mises circular distribution.\\nwald Wald (inverse Gaussian) distribution.
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]
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"metadata": {}
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}
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],
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"id": "8fd68968"
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{
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"cell_type": "code",
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"source": "",
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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"execution_count": null,
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"outputs": [],
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"id": "3d84fffb"
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}
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]
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}
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