Hi Bhargav ,
It sounds like you are trying to understanding data patterns, and making informed decisions based on these insights.To highlight specific regions in a stackplot where the value of a certain variable goes to zero, you can utilize additional plotting techniques to draw attention to those areas. One way to achieve this is by overlaying a scatter plot or line plot on top of your existing stacked plot to emphasize the points or regions where the variable 'Ftot' equals zero.Here's how you can modify your code to achieve this highlighting effect:
% Sample data for demonstration final_log = rand(100, 2); % Assuming final_log contains data for "TC2" and "Ftot"
% Define column indices for "TC2" and "Ftot" tc2_index = 1; ftot_index = 2;
% Create the stacked plot using numerical indices p1 = stackedplot(final_log(:, [tc2_index, ftot_index]));
% Find indices where Ftot is zero zero_indices = find(final_log(:, ftot_index) == 0);
% Highlight regions where Ftot is zero for i = 1:length(zero_indices) area([zero_indices(i)-0.5, zero_indices(i)+0.5], [0, 0], 'FaceColor', 'red', 'FaceAlpha', 0.5); end
In the above code snippet, random data is generated to simulate the dataset final_log, assuming it contains information for "TC2" and "Ftot". The indices for "TC2" and "Ftot" columns are specified as tc2_index and ftot_index, respectively. A stacked plot p1 is created using the numerical indices of the columns "TC2" and "Ftot". Afterwards, the code identifies the indices where the value of "Ftot" is zero using the find function and stores them in zero_indices and loop iterates over the zero indices found in the previous step. For each zero index, an area is plotted with a width of 1 unit centered at the zero index, highlighting the region where Ftot equals zero with a red color and 50% transparency.
By incorporating this modification into your existing code, you can effectively draw attention to specific areas in the stackplot where the value of 'Ftot' reaches zero. This approach will enhance the interpretability of your data visualization and allows for clearer insights into the behavior of the variable of interest. Please let me know if you have further questions.
