mt_remap_symmetric

Hi

"My code encountered some difficulties again, and it seems that the problem occurred during the mt_remap_symmetric step. My issue is that the origin is at the top-left corner of the screen, meaning that both the x and y coordinates increase as you move down and to the right, and there are no negative values. After performing the remapping with the default mt_remap_symmetric code, the result looks strange. So, what should I do to mirror the right-side trajectories to the left?"

Any advice would be greatly appreciated.

Below is my code and data structure

library(mousetrap)
library(readbulk)
> raw_data <- read_bulk(directory = "D:/networkanalysis/xsbdata", extension = ".csv")
mt_data <- mt_import_wide(raw_data,
+                           xpos_label = "x",
+                           ypos_label = "y",
+                           zpos_label = NULL,
+                           timestamps_label = "t",
+                           mt_id_label = NULL,
+                           pos_sep = "_",
+                           reset_timestamps = TRUE,
+                           verbose = TRUE
+ )
No mt_id_label provided. A new trial identifying variable called mt_id was created.
No pos_ids provided. The following variables were found using grep:
1240 variables found for timestamps.
1240 variables found for xpos.
1240 variables found for ypos.
 # 定义dimensions，假设你需要对齐 'xpos' 和 'ypos' 维度
> dimensions <- c('xpos', 'ypos')
> # 对齐轨迹数据
> mt_data <- mt_align_start(
+     mt_data,
+     start = NULL,  # 使用平均起始点对齐
+     verbose = TRUE
+ )
No start coordinates were provided. Aligning to: 681.921875,709.196354166667
> 
> mt_data <- mt_remap_symmetric(
+     mt_data,
+     use = "trajectories",
+     save_as = "trajectories",
+     dimensions = c("xpos", "ypos"),
+     remap_xpos = "left",
+     remap_ypos = "up"
+ )
> 
> mt_data <- mt_exclude_initiation(mt_data,
+                                  use="trajectories",              # 使用轨迹数据
+                                  save_as="trajectories",      # 保存修改后的结果
+                                  dimensions=c("xpos","ypos"),     # 指定位置维度，假设是 'xpos' 和 'ypos'
+                                  timestamps="timestamps",         # 时间戳字段名称
+                                  reset_timestamps=TRUE,           # 是否重置时间戳
+                                  verbose=TRUE                     # 显示进度信息
+ )
mt_data <- mt_exclude_finish(
+     mt_data,
+     use = "trajectories",
+     save_as = "trajectories",
+     dimensions = c("xpos", "ypos"),
+     timestamps = "timestamps",
+     verbose =TRUE 
+ )
 mt_data <- mt_length_normalize(
+     mt_data,
+     use = "trajectories",         # 原始轨迹数据
+     dimensions = c("xpos", "ypos"),
+     save_as = "ln_trajectories",  # 保存归一化的轨迹
+     n_points = 20                 # 归一化后的轨迹点数
+ )
> 
> mt_data <- mt_time_normalize(
+     mt_data,
+     use = "trajectories",
+     save_as = "tn_trajectories",
+     dimensions = c("xpos", "ypos"),
+     timestamps = "timestamps",
+     nsteps = 101,
+     verbose = FALSE
+ )
> 
> mt_data <- mt_measures(
+     mt_data,  # 使用整个mt_data对象，包含处理后的轨迹数据
+     use = "ln_trajectories",  # 使用长度归一化的轨迹
+     save_as = "measures",  # 将计算结果存储到"measures"数据框中
+     dimensions = c("xpos", "ypos"),  # 计算x和y位置
+     timestamps = "timestamps",  # 使用时间戳列
+     flip_threshold = 0,  # 设置 flip 阈值
+     verbose = TRUE  # 启用详细输出
+ )
 agg_data <- mt_aggregate_per_subject(
+     mt_data,                        # 你的数据对象
+     use_variables = "xpos_flips",           # 计算 AUC 值
+     use2_variables = "exp_type",    # 使用 exp_type 进行分组
+     subject_id ="subID"                # 不需要按 subID 分组
+ )
> 
> t.test(xpos_flips ~ exp_type, data = agg_data)
 agg_data <- mt_aggregate_per_subject(
+     mt_data,                        # 你的数据对象
+     use_variables = "AUC",           # 计算 AUC 值
+     use2_variables = "exp_type",    # 使用 exp_type 进行分组
+     subject_id ="subID"                # 不需要按 subID 分组
+ )
> 
> t.test(AUC ~ exp_type, data = agg_data)
mt_plot(mt_data, use="tn_trajectories", color="exp_type")

    

        
            
        
    

mt_plot_aggregate(mt_data, use="tn_trajectories", color="exp_type", subject_id="subID")