{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Query" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "This notebook should demonstrate how the functions of the module :mod:`fluiddyn.io.query` can be used. \n", "\n", "This functions are useful to query things to the user with a simple interaction through text. So for this notebook, we need to interact manually." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from fluiddyn.io.query import query, query_number, query_yes_no" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Would you like to continue? [Y/n] " ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query_yes_no('Would you like to continue?')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Would you like to continue? [y/N] " ] }, { "data": { "text/plain": [ "False" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query_yes_no('Would you like to continue?', default='no')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Would you like to continue? [Y/n] " ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query_yes_no('Would you like to continue?')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Would you like to continue? [Y/n] Please respond with 'yes' or 'no' (or 'y' or 'n').\n", "Would you like to continue? [Y/n] " ] }, { "data": { "text/plain": [ "False" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query_yes_no('Would you like to continue?')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Which number do you like? " ] }, { "data": { "text/plain": [ "1.2" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query_number('Which number do you like?')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello, what is your name?" ] }, { "data": { "text/plain": [ "'Boule'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query('Hello, what is your name?')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "..." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }