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b/coding/Untitled.qmd new file mode 100644 index 0000000..d5890ee --- /dev/null +++ b/coding/Untitled.qmd @@ -0,0 +1,398 @@ +--- +title: "第10次课" +editor: visual +--- + +```{r} +require(tidymodels) +taxi +set.seed(123) +taxi_split <- initial_split(taxi, prop = 0.8) +taxi_train <- training(taxi_split) +taxi_test <- testing(taxi_split) + +nrow(taxi_train) +``` +```{r} +tree_spec <- + decision_tree(cost_complexity = 0.002) %>% + set_mode("classification") + +tree_fit <- + workflow(tip ~ ., tree_spec) %>% + fit(data = taxi_train) +taxi_test |> + bind_cols( +predict(tree_fit, new_data = taxi_test)) + +``` + +```{r} +require(magrittr) +# %>%: magrittr +# |>: base +taxi_folds <- vfold_cv(taxi_train, v = 5) +tree_spec <- + decision_tree(cost_complexity = 0.002) |> + set_mode("classification") +taxi_wflow <- workflow() |> + add_formula(tip ~ .) |> + add_model(tree_spec) +taxi_res <- fit_resamples(taxi_wflow, taxi_folds) +taxi_res +taxi_metrics <- taxi_res |> + collect_metrics() + + +# Save the assessment set results +ctrl_taxi <- control_resamples(save_pred = TRUE) +taxi_res <- fit_resamples(taxi_wflow, taxi_folds, control = ctrl_taxi) + +taxi_res |> + collect_predictions() + +taxi_metrics <- metric_set(accuracy, specificity, sensitivity) + +taxi_preds |> + group_by(id) |> + taxi_metrics(truth = tip, estimate = .pred_class) + +``` + + +## 数据 + +```{r} +require(tidyverse) +sitedf <- readr::read_csv("https://www.epa.gov/sites/default/files/2014-01/nla2007_sampledlakeinformation_20091113.csv") |> + select(SITE_ID, + lon = LON_DD, + lat = LAT_DD, + name = LAKENAME, + area = LAKEAREA, + zmax = DEPTHMAX + ) |> + group_by(SITE_ID) |> + summarize(lon = mean(lon, na.rm = TRUE), + lat = mean(lat, na.rm = TRUE), + name = unique(name), + area = mean(area, na.rm = TRUE), + zmax = mean(zmax, na.rm = TRUE)) + + +visitdf <- readr::read_csv("https://www.epa.gov/sites/default/files/2013-09/nla2007_profile_20091008.csv") |> + select(SITE_ID, + date = DATE_PROFILE, + year = YEAR, + visit = VISIT_NO + ) |> + distinct() + + + +waterchemdf <- readr::read_csv("https://www.epa.gov/sites/default/files/2013-09/nla2007_profile_20091008.csv") |> + select(SITE_ID, + date = DATE_PROFILE, + depth = DEPTH, + temp = TEMP_FIELD, + do = DO_FIELD, + ph = PH_FIELD, + cond = COND_FIELD, + ) + +sddf <- readr::read_csv("https://www.epa.gov/sites/default/files/2014-10/nla2007_secchi_20091008.csv") |> + select(SITE_ID, + date = DATE_SECCHI, + sd = SECMEAN, + clear_to_bottom = CLEAR_TO_BOTTOM + ) + +trophicdf <- readr::read_csv("https://www.epa.gov/sites/default/files/2014-10/nla2007_trophic_conditionestimate_20091123.csv") |> + select(SITE_ID, + visit = VISIT_NO, + tp = PTL, + tn = NTL, + chla = CHLA) |> + left_join(visitdf, by = c("SITE_ID", "visit")) |> + select(-year, -visit) |> + group_by(SITE_ID, date) |> + summarize(tp = mean(tp, na.rm = TRUE), + tn = mean(tn, na.rm = TRUE), + chla = mean(chla, na.rm = TRUE) + ) + + + +phytodf <- readr::read_csv("https://www.epa.gov/sites/default/files/2014-10/nla2007_phytoplankton_softalgaecount_20091023.csv") |> + select(SITE_ID, + date = DATEPHYT, + depth = SAMPLE_DEPTH, + phyta = DIVISION, + genus = GENUS, + species = SPECIES, + tax = TAXANAME, + abund = ABUND) |> + mutate(phyta = gsub(" .*$", "", phyta)) |> + filter(!is.na(genus)) |> + group_by(SITE_ID, date, depth, phyta, genus) |> + summarize(abund = sum(abund, na.rm = TRUE)) |> + nest(phytodf = -c(SITE_ID, date)) + +envdf <- waterchemdf |> + filter(depth < 2) |> + select(-depth) |> + group_by(SITE_ID, date) |> + summarise_all(~mean(., na.rm = TRUE)) |> + ungroup() |> + left_join(sddf, by = c("SITE_ID", "date")) |> + left_join(trophicdf, by = c("SITE_ID", "date")) + +nla <- envdf |> + left_join(phytodf) |> + left_join(sitedf, by = "SITE_ID") |> + filter(!purrr::map_lgl(phytodf, is.null)) |> + mutate(cyanophyta = purrr::map(phytodf, ~ .x |> + dplyr::filter(phyta == "Cyanophyta") |> + summarize(cyanophyta = sum(abund, na.rm = TRUE)) + )) |> + unnest(cyanophyta) |> + select(-phyta) |> + mutate(clear_to_bottom = ifelse(is.na(clear_to_bottom), TRUE, FALSE)) + + +# library(rmdify) +# library(dwfun) +# dwfun::init() + +``` + + +```{r} +skimr::skim(nla) +``` +```{r} +nla |> + filter(tp > 1) |> + ggplot(aes(tn, tp)) + +geom_point() + +geom_smooth(method = "lm") + +scale_x_log10(breaks = scales::trans_breaks("log10", function(x) 10^x), + labels = scales::trans_format("log10", scales::math_format(10^.x))) + +scale_y_log10(breaks = scales::trans_breaks("log10", function(x) 10^x), + labels = scales::trans_format("log10", scales::math_format(10^.x))) + +m1 <- lm(log10(tp) ~ log10(tn), data = nla) + +summary(m1) +``` +```{r} +nla |> + filter(tp > 1) |> + ggplot(aes(tp, cyanophyta)) + +geom_point() + +geom_smooth(method = "lm") + +scale_x_log10(breaks = scales::trans_breaks("log10", function(x) 10^x), + labels = scales::trans_format("log10", scales::math_format(10^.x))) + +scale_y_log10(breaks = scales::trans_breaks("log10", function(x) 10^x), + labels = scales::trans_format("log10", scales::math_format(10^.x))) + + +m2 <- lm(log10(cyanophyta) ~ log10(tp), data = nla) + +summary(m2) + +m3 <- glm(log10(cyanophyta) ~ log10(tp), data = nla) +summary(m3) + +require(mgcv) +# 广义加性模型 General additive model +m4 <- gam(log10(cyanophyta) ~ s(log10(tp)) + s(log10(tn)) + s(log10(chla)), data = nla) +plot(m4) +summary(m4) +``` + + +```{r} +nla_split <- initial_split(nla, prop = 0.7, strata = zmax) +nla_train <- training(nla_split) +nla_test <- testing(nla_split) +``` + +```{r} +# xgboost, cyanophyta + +nla_formula <- as.formula("cyanophyta ~ temp + do + ph + cond + sd + tp + tn + chla + clear_to_bottom") +# nla_formula <- as.formula("cyanophyta ~ temp + do + ph + cond + sd + tp + tn") +nla_recipe <- recipes::recipe(nla_formula, data = nla_train) |> + recipes::step_string2factor(all_nominal()) |> + recipes::step_nzv(all_nominal()) |> + recipes::step_log(chla, cyanophyta, tn, tp, base = 10) |> + recipes::step_normalize(all_numeric_predictors()) |> + prep() +nla_recipe +``` + +```{r} +nla_cv <- recipes::bake( + nla_recipe, + new_data = training(nla_split) + ) |> + rsample::vfold_cv(v = 10) +nla_cv +``` + +```{r} +xgboost_model <- parsnip::boost_tree( + mode = "regression", + trees = 1000, + min_n = tune(), + tree_depth = tune(), + learn_rate = tune(), + loss_reduction = tune() +) |> + set_engine("xgboost", objective = "reg:squarederror") +xgboost_model +``` + +```{r} +# grid specification +xgboost_params <- dials::parameters( + min_n(), + tree_depth(), + learn_rate(), + loss_reduction() +) +xgboost_params +``` + +```{r} +xgboost_grid <- dials::grid_max_entropy( + xgboost_params, + size = 60 +) +knitr::kable(head(xgboost_grid)) +``` + +```{r} +xgboost_wf <- workflows::workflow() |> + add_model(xgboost_model) |> + add_formula(nla_formula) +xgboost_wf +``` + +```{r} +# hyperparameter tuning +if (FALSE) { + xgboost_tuned <- tune::tune_grid( + object = xgboost_wf, + resamples = nla_cv, + grid = xgboost_grid, + metrics = yardstick::metric_set(rmse, rsq, mae), + control = tune::control_grid(verbose = TRUE) + ) +saveRDS(xgboost_tuned, "./xgboost_tuned.RDS") +} +xgboost_tuned <- readRDS("./xgboost_tuned.RDS") +``` + +```{r} +xgboost_tuned |> + tune::show_best(metric = "rmse") |> + knitr::kable() +``` + +```{r} +xgboost_tuned |> + collect_metrics() +``` + +```{r} +xgboost_tuned |> + autoplot() +``` + +```{r} +xgboost_best_params <- xgboost_tuned |> + tune::select_best("rmse") + +knitr::kable(xgboost_best_params) +``` + +```{r} +xgboost_model_final <- xgboost_model |> + finalize_model(xgboost_best_params) +xgboost_model_final +``` + +```{r} +(train_processed <- bake(nla_recipe, new_data = nla_train)) +``` + +```{r} +train_prediction <- xgboost_model_final |> + # fit the model on all the training data + fit( + formula = nla_formula, + data = train_processed + ) |> + # predict the sale prices for the training data + predict(new_data = train_processed) |> + bind_cols(nla_train |> + mutate(.obs = log10(cyanophyta))) +xgboost_score_train <- + train_prediction |> + yardstick::metrics(.obs, .pred) |> + mutate(.estimate = format(round(.estimate, 2), big.mark = ",")) +knitr::kable(xgboost_score_train) +``` + + +```{r} +train_prediction |> + ggplot(aes(.pred, .obs)) + +geom_point() + +geom_smooth(method = "lm") +``` + +```{r} +test_processed <- bake(nla_recipe, new_data = nla_test) + +test_prediction <- xgboost_model_final |> + # fit the model on all the training data + fit( + formula = nla_formula, + data = train_processed + ) |> + # use the training model fit to predict the test data + predict(new_data = test_processed) |> + bind_cols(nla_test |> + mutate(.obs = log10(cyanophyta))) + +# measure the accuracy of our model using `yardstick` +xgboost_score <- test_prediction |> + yardstick::metrics(.obs, .pred) |> + mutate(.estimate = format(round(.estimate, 2), big.mark = ",")) + +knitr::kable(xgboost_score) +``` + +```{r} +cyanophyta_prediction_residual <- test_prediction |> + arrange(.pred) %>% + mutate(residual_pct = (.obs - .pred) / .pred) |> + select(.pred, residual_pct) + +cyanophyta_prediction_residual |> +ggplot(aes(x = .pred, y = residual_pct)) + + geom_point() + + xlab("Predicted Cyanophyta") + + ylab("Residual (%)") +``` + +```{r} +test_prediction |> + ggplot(aes(.pred, .obs)) + +geom_point() + +geom_smooth(method = "lm", colour = "black") +``` + diff --git a/coding/lesson10.qmd b/coding/lesson10.qmd new file mode 100644 index 0000000..c7c3af5 --- /dev/null +++ b/coding/lesson10.qmd @@ -0,0 +1,401 @@ +--- +title: "第10次课" +editor: visual +--- + +```{r} +require(tidymodels) +taxi +set.seed(123) +taxi_split <- initial_split(taxi, prop = 0.8) +taxi_train <- training(taxi_split) +taxi_test <- testing(taxi_split) + +nrow(taxi_train) +``` +```{r} +tree_spec <- + decision_tree(cost_complexity = 0.002) %>% + set_mode("classification") + +tree_fit <- + workflow(tip ~ ., tree_spec) %>% + fit(data = taxi_train) +taxi_test |> + bind_cols( +predict(tree_fit, new_data = taxi_test)) + +``` + +```{r} +require(magrittr) +# %>%: magrittr +# |>: base +taxi_folds <- vfold_cv(taxi_train, v = 5) +tree_spec <- + decision_tree(cost_complexity = 0.002) |> + set_mode("classification") +taxi_wflow <- workflow() |> + add_formula(tip ~ .) |> + add_model(tree_spec) +taxi_res <- fit_resamples(taxi_wflow, taxi_folds) +taxi_res +taxi_metrics <- taxi_res |> + collect_metrics() + + +# Save the assessment set results +ctrl_taxi <- control_resamples(save_pred = TRUE) +taxi_res <- fit_resamples(taxi_wflow, taxi_folds, control = ctrl_taxi) + +taxi_res |> + collect_predictions() + +taxi_metrics <- metric_set(accuracy, specificity, sensitivity) + +taxi_preds |> + group_by(id) |> + taxi_metrics(truth = tip, estimate = .pred_class) + +``` + + +## 数据 + +```{r} +require(tidyverse) +sitedf <- readr::read_csv("https://www.epa.gov/sites/default/files/2014-01/nla2007_sampledlakeinformation_20091113.csv") |> + select(SITE_ID, + lon = LON_DD, + lat = LAT_DD, + name = LAKENAME, + area = LAKEAREA, + zmax = DEPTHMAX + ) |> + group_by(SITE_ID) |> + summarize(lon = mean(lon, na.rm = TRUE), + lat = mean(lat, na.rm = TRUE), + name = unique(name), + area = mean(area, na.rm = TRUE), + zmax = mean(zmax, na.rm = TRUE)) + + +visitdf <- readr::read_csv("https://www.epa.gov/sites/default/files/2013-09/nla2007_profile_20091008.csv") |> + select(SITE_ID, + date = DATE_PROFILE, + year = YEAR, + visit = VISIT_NO + ) |> + distinct() + + + +waterchemdf <- readr::read_csv("https://www.epa.gov/sites/default/files/2013-09/nla2007_profile_20091008.csv") |> + select(SITE_ID, + date = DATE_PROFILE, + depth = DEPTH, + temp = TEMP_FIELD, + do = DO_FIELD, + ph = PH_FIELD, + cond = COND_FIELD, + ) + +sddf <- readr::read_csv("https://www.epa.gov/sites/default/files/2014-10/nla2007_secchi_20091008.csv") |> + select(SITE_ID, + date = DATE_SECCHI, + sd = SECMEAN, + clear_to_bottom = CLEAR_TO_BOTTOM + ) + +trophicdf <- readr::read_csv("https://www.epa.gov/sites/default/files/2014-10/nla2007_trophic_conditionestimate_20091123.csv") |> + select(SITE_ID, + visit = VISIT_NO, + tp = PTL, + tn = NTL, + chla = CHLA) |> + left_join(visitdf, by = c("SITE_ID", "visit")) |> + select(-year, -visit) |> + group_by(SITE_ID, date) |> + summarize(tp = mean(tp, na.rm = TRUE), + tn = mean(tn, na.rm = TRUE), + chla = mean(chla, na.rm = TRUE) + ) + + + +phytodf <- readr::read_csv("https://www.epa.gov/sites/default/files/2014-10/nla2007_phytoplankton_softalgaecount_20091023.csv") |> + select(SITE_ID, + date = DATEPHYT, + depth = SAMPLE_DEPTH, + phyta = DIVISION, + genus = GENUS, + species = SPECIES, + tax = TAXANAME, + abund = ABUND) |> + mutate(phyta = gsub(" .*$", "", phyta)) |> + filter(!is.na(genus)) |> + group_by(SITE_ID, date, depth, phyta, genus) |> + summarize(abund = sum(abund, na.rm = TRUE)) |> + nest(phytodf = -c(SITE_ID, date)) + +envdf <- waterchemdf |> + filter(depth < 2) |> + select(-depth) |> + group_by(SITE_ID, date) |> + summarise_all(~mean(., na.rm = TRUE)) |> + ungroup() |> + left_join(sddf, by = c("SITE_ID", "date")) |> + left_join(trophicdf, by = c("SITE_ID", "date")) + +nla <- envdf |> + left_join(phytodf) |> + left_join(sitedf, by = "SITE_ID") |> + filter(!purrr::map_lgl(phytodf, is.null)) |> + mutate(cyanophyta = purrr::map(phytodf, ~ .x |> + dplyr::filter(phyta == "Cyanophyta") |> + summarize(cyanophyta = sum(abund, na.rm = TRUE)) + )) |> + unnest(cyanophyta) |> + select(-phyta) |> + mutate(clear_to_bottom = ifelse(is.na(clear_to_bottom), TRUE, FALSE)) + + +# library(rmdify) +# library(dwfun) +# dwfun::init() + +``` + + +```{r} +skimr::skim(nla) +``` +```{r} +nla |> + filter(tp > 1) |> + ggplot(aes(tn, tp)) + +geom_point() + +geom_smooth(method = "lm") + +scale_x_log10(breaks = scales::trans_breaks("log10", function(x) 10^x), + labels = scales::trans_format("log10", scales::math_format(10^.x))) + +scale_y_log10(breaks = scales::trans_breaks("log10", function(x) 10^x), + labels = scales::trans_format("log10", scales::math_format(10^.x))) + +m1 <- lm(log10(tp) ~ log10(tn), data = nla) + +summary(m1) +``` +```{r} +nla |> + filter(tp > 1) |> + ggplot(aes(tp, cyanophyta)) + +geom_point() + +geom_smooth(method = "lm") + +scale_x_log10(breaks = scales::trans_breaks("log10", function(x) 10^x), + labels = scales::trans_format("log10", scales::math_format(10^.x))) + +scale_y_log10(breaks = scales::trans_breaks("log10", function(x) 10^x), + labels = scales::trans_format("log10", scales::math_format(10^.x))) + + +m2 <- lm(log10(cyanophyta) ~ log10(tp), data = nla) + +summary(m2) + +m3 <- glm(log10(cyanophyta) ~ log10(tp), data = nla) +summary(m3) + +require(mgcv) +# 广义加性模型 General additive model +m4 <- gam(log10(cyanophyta) ~ s(log10(tp)) + s(log10(tn)) + s(log10(chla)), data = nla) +plot(m4) +summary(m4) +``` + + +```{r} +nla_split <- initial_split(nla, prop = 0.7, strata = zmax) +nla_train <- training(nla_split) +nla_test <- testing(nla_split) +``` + +```{r} +# xgboost, cyanophyta + +nla_formula <- as.formula("cyanophyta ~ temp + do + ph + cond + sd + tp + tn + chla + clear_to_bottom") +# nla_formula <- as.formula("cyanophyta ~ temp + do + ph + cond + sd + tp + tn") +nla_recipe <- recipes::recipe(nla_formula, data = nla_train) |> + recipes::step_string2factor(all_nominal()) |> + recipes::step_nzv(all_nominal()) |> + recipes::step_log(chla, cyanophyta, tn, tp, base = 10) |> + recipes::step_normalize(all_numeric_predictors()) |> + prep() +nla_recipe +``` + +```{r} +nla_cv <- recipes::bake( + nla_recipe, + new_data = nla_train + ) |> + rsample::vfold_cv(v = 10) +nla_cv +``` + +```{r} +xgboost_model <- parsnip::boost_tree( + mode = "regression", + trees = 1000, + min_n = tune(), + tree_depth = tune(), + learn_rate = tune(), + loss_reduction = tune() +) |> + set_engine("xgboost", objective = "reg:squarederror") +xgboost_model +``` + +```{r} +# grid specification +xgboost_params <- dials::parameters( + min_n(), + tree_depth(), + learn_rate(), + loss_reduction() +) +xgboost_params +``` + +```{r} +xgboost_grid <- dials::grid_max_entropy( + xgboost_params, + size = 60 +) +knitr::kable(head(xgboost_grid)) +``` + +```{r} +xgboost_wf <- workflows::workflow() |> + add_model(xgboost_model) |> + add_formula(nla_formula) +xgboost_wf +``` + +```{r} + +install.packages("xgboost") + +# hyperparameter tuning +if (FALSE) { + xgboost_tuned <- tune::tune_grid( + object = xgboost_wf, + resamples = nla_cv, + grid = xgboost_grid, + metrics = yardstick::metric_set(rmse, rsq, mae), + control = tune::control_grid(verbose = TRUE) + ) +saveRDS(xgboost_tuned, "./xgboost_tuned.RDS") +} +xgboost_tuned <- readRDS("./xgboost_tuned.RDS") +``` + +```{r} +xgboost_tuned |> + tune::show_best(metric = "rmse") |> + knitr::kable() +``` + +```{r} +xgboost_tuned |> + collect_metrics() +``` + +```{r} +xgboost_tuned |> + autoplot() +``` + +```{r} +xgboost_best_params <- xgboost_tuned |> + tune::select_best("rmse") + +knitr::kable(xgboost_best_params) +``` + +```{r} +xgboost_model_final <- xgboost_model |> + finalize_model(xgboost_best_params) +xgboost_model_final +``` + +```{r} +(train_processed <- bake(nla_recipe, new_data = nla_train)) +``` + +```{r} +train_prediction <- xgboost_model_final |> + # fit the model on all the training data + fit( + formula = nla_formula, + data = train_processed + ) |> + # predict the sale prices for the training data + predict(new_data = train_processed) |> + bind_cols(nla_train |> + mutate(.obs = log10(cyanophyta))) +xgboost_score_train <- + train_prediction |> + yardstick::metrics(.obs, .pred) |> + mutate(.estimate = format(round(.estimate, 2), big.mark = ",")) +knitr::kable(xgboost_score_train) +``` + + +```{r} +train_prediction |> + ggplot(aes(.pred, .obs)) + +geom_point() + +geom_smooth(method = "lm") +``` + +```{r} +test_processed <- bake(nla_recipe, new_data = nla_test) + +test_prediction <- xgboost_model_final |> + # fit the model on all the training data + fit( + formula = nla_formula, + data = train_processed + ) |> + # use the training model fit to predict the test data + predict(new_data = test_processed) |> + bind_cols(nla_test |> + mutate(.obs = log10(cyanophyta))) + +# measure the accuracy of our model using `yardstick` +xgboost_score <- test_prediction |> + yardstick::metrics(.obs, .pred) |> + mutate(.estimate = format(round(.estimate, 2), big.mark = ",")) + +knitr::kable(xgboost_score) +``` + +```{r} +cyanophyta_prediction_residual <- test_prediction |> + arrange(.pred) %>% + mutate(residual_pct = (.obs - .pred) / .pred) |> + select(.pred, residual_pct) + +cyanophyta_prediction_residual |> +ggplot(aes(x = .pred, y = residual_pct)) + + geom_point() + + xlab("Predicted Cyanophyta") + + ylab("Residual (%)") +``` + +```{r} +test_prediction |> + ggplot(aes(.pred, .obs)) + +geom_point() + +geom_smooth(method = "lm", colour = "black") +``` + diff --git a/coding/xgboost_tuned.RDS b/coding/xgboost_tuned.RDS new file mode 100644 index 0000000..408ef18 Binary files /dev/null and b/coding/xgboost_tuned.RDS differ diff --git a/data/heights.RDS b/data/heights.RDS new file mode 100644 index 0000000..d0df4bf Binary files /dev/null and b/data/heights.RDS differ diff --git a/data/licordata.RDS b/data/licordata.RDS new file mode 100644 index 0000000..362490b Binary files /dev/null and b/data/licordata.RDS differ diff --git a/data/metadfdemo.RDS b/data/metadfdemo.RDS new file mode 100644 index 0000000..9984d61 Binary files /dev/null and b/data/metadfdemo.RDS differ diff --git a/data/writexldemo.xlsx b/data/writexldemo.xlsx index d3402fe..9faa336 100644 Binary files a/data/writexldemo.xlsx and b/data/writexldemo.xlsx differ diff --git a/index.qmd b/index.qmd index bc80839..d82047a 100644 --- a/index.qmd +++ b/index.qmd @@ -19,4 +19,7 @@ subtitle: "课程简介" - 网页公开：[https://drwater.rcees.ac.cn/course/public/RWEP/\@PUB/index.html](https://drwater.rcees.ac.cn/course/public/RWEP/@PUB/index.html) - 课件代码：[https://drwater.rcees.ac.cn/git/course/RWEP.git](https://drwater.rcees.ac.cn/git/course/RWEP.git) -- 代码web界面：[https://on.tty-share.com/s/2YElKFjzniilfg3vH-d1PUqqmz_MQzNki5dyxWWL7QONCX_77GzsdP9EEAgBQu3ONwM/](https://on.tty-share.com/s/2YElKFjzniilfg3vH-d1PUqqmz_MQzNki5dyxWWL7QONCX_77GzsdP9EEAgBQu3ONwM/) +- 代码web界面：[https://on.tty-share.com/s/4hC-HxrUnzWmL4JnbopI4W-qrYU1oQgeYuvyJNaGLREcxl0_wbNAJ5UYRf8oEa0ayp4/](https://on.tty-share.com/s/4hC-HxrUnzWmL4JnbopI4W-qrYU1oQgeYuvyJNaGLREcxl0_wbNAJ5UYRf8oEa0ayp4/) + + +