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jpm-behavior/read_data.m
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function [X, yc, pc, ynan, p, text_vars] = read_data(beh_type, lag,... | |
beh, dirstring, pats) | |
% This function was made to read in the data. The data function | |
% reads in the data as a table which reads in blanks as NaNs. It | |
% renames the variables as necessary and selects a subset of variables | |
% to analyze. | |
% It runs the cleaning and lagging data to provide an X and y dataset. | |
% Parameters: | |
% beh_type: 'AGG','SIB','BOTH' - the type of behavior | |
% lag: number of lagged days to examine; positive integer | |
% beh: whether or not to include prior behavior as a lagged variable | |
% (true or false) | |
% dirstring: a string to save out the directory | |
% pats: a string array of variable patterns, can be prefix to include | |
% all variables of that type or can be specific to the variable name. | |
T = readtable(strcat(beh_type,'.csv'),... | |
'PreserveVariableNames',true); | |
T = renamevars(T,["BM_Menses"],["Menses"]); | |
if ~exist('beh','var') | |
beh = false; | |
end | |
A = {'Weather_TMAX',... | |
'Weather_TMIN',... | |
'Weather_PRCP',... | |
'Weather_SNOW',... | |
'Weather_SNWD',... | |
'Moon Rise',... | |
'Moon Culmination',... | |
'Moon Set',... | |
'Moon Altitude',... | |
'Moon Azimuth',... | |
'Moon Shadow Length',... | |
'Moon Distance',... | |
'Moon Phase',... | |
'Moon Age',... | |
'Moon Next New',... | |
'Moon Next Full',... | |
'Allergen_Trees',... | |
'Allergen_Weeds',... | |
'Allergen_Grasses',... | |
'Allergen_Total',... | |
'Sleep_Hours',... | |
'Sleep_Interruptions',... | |
'BM_Count',... | |
'BM_Bristol_Extreme',... | |
'BM_Bristol_Max',... | |
'BM_Bristol_Min',... | |
'BM_Bristol',... | |
'BM_Intervention',... | |
'BM_Time',... | |
'BM_Abnormal',... | |
'BM_Constipation',... | |
'BM_Diarrhea',... | |
'Menses'}; | |
if beh | |
A{end+1} = 'Behavior_Factor'; | |
pats = [pats, "Behavior_Factor"]; | |
end | |
B = string(A); | |
T_X = table2array(T(:,B)); | |
T_y = table2array(T(:,'Behavior_Factor')); | |
T_person = table2array(T(:,'Person_ID')); | |
% females = load('females.csv'); | |
% non_females = setdiff(unique(T_person), females); | |
% %females = non_females; | |
% for i=1:length(females) | |
% subplot(round(length(females)/2),2,i) | |
% menses = T_X(T_person==females(i),end); | |
% plot(menses); | |
% hold on; | |
% unique(menses(~isnan(menses))) | |
% end | |
% error() | |
dataset = 'complete'; | |
[Xc,yc,pc, ynan,p] = CleanUpData_lag(T_X,... | |
T_y,dataset,T_person,lag); | |
%X = zscore(Xc); | |
X = Xc; | |
vars = zeros(length(B),1); | |
for k=1:length(pats) | |
for i=1:length(B) | |
if startsWith(B(i), pats(k)) | |
vars(i) = 1; | |
end | |
end | |
end | |
vars = logical(vars); | |
lagged_vars = []; | |
for i=1:lag | |
lagged_vars = [lagged_vars; vars]; | |
end | |
lagged_vars = logical(lagged_vars); | |
text_vars = B(lagged_vars); | |
log_output(dirstring,string(text_vars)); | |
size(lagged_vars) | |
writematrix(lagged_vars, strcat(dirstring,'/variables.txt')) | |
pause(3) | |
size(X) | |
X = X(:,lagged_vars); | |
size(X) | |
pause(2) | |
end | |