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Update examples to use std::string. (#182)

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In examples and tests, use std::string.

In addtion:
1. Address follow-up from:
https://github.com/ArthurSonzogni/FTXUI/pull/179
2. Fix a bug when Input is used with std::string.
This commit is contained in:
Arthur Sonzogni
2021-08-09 00:27:37 +02:00
committed by GitHub
parent 3b4ab618a3
commit 9a54528bca
60 changed files with 817 additions and 836 deletions
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17
examples/dom/paragraph.cpp
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@@ -1,16 +1,15 @@
#include <stdio.h> // for getchar
#include <ftxui/dom/elements.hpp> // for operator|, hflow, border, Element, hbox, flex, vbox
#include <ftxui/screen/screen.hpp> // for Dimension, Screen
#include <string> // for allocator, wstring
#include <stdio.h> // for getchar
#include <ftxui/dom/elements.hpp> // for operator|, hflow, paragraph, border, Element, hbox, flex, vbox
#include <ftxui/screen/screen.hpp> // for Full, Screen
#include <string> // for allocator, string
#include "ftxui/dom/deprecated.hpp" // for paragraph
#include "ftxui/dom/node.hpp" // for Render
#include "ftxui/screen/box.hpp" // for ftxui
#include "ftxui/dom/node.hpp" // for Render
#include "ftxui/screen/box.hpp" // for ftxui
int main(int argc, const char* argv[]) {
using namespace ftxui;
std::wstring p =
LR"(In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule) describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if cancer is related to age, then, using Bayes' theorem, a person's age can be used to more accurately assess the probability that they have cancer, compared to the assessment of the probability of cancer made without knowledge of the person's age. One of the many applications of Bayes' theorem is Bayesian inference, a particular approach to statistical inference. When applied, the probabilities involved in Bayes' theorem may have different probability interpretations. With the Bayesian probability interpretation the theorem expresses how a subjective degree of belief should rationally change to account for availability of related evidence. Bayesian inference is fundamental to Bayesian statistics.)";
std::string p =
R"(In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule) describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if cancer is related to age, then, using Bayes' theorem, a person's age can be used to more accurately assess the probability that they have cancer, compared to the assessment of the probability of cancer made without knowledge of the person's age. One of the many applications of Bayes' theorem is Bayesian inference, a particular approach to statistical inference. When applied, the probabilities involved in Bayes' theorem may have different probability interpretations. With the Bayesian probability interpretation the theorem expresses how a subjective degree of belief should rationally change to account for availability of related evidence. Bayesian inference is fundamental to Bayesian statistics.)";
auto document = vbox({
hbox({