Progress bar is now completely working

Still need to figure out the problem that occurred
with negative p-values after I refactored the LMM
code

6 files changed, 159 insertions, 121 deletions

diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py
index c6d67e68..d474302c 100755
--- a/wqflask/base/data_set.py
+++ b/wqflask/base/data_set.py
@@ -75,6 +75,7 @@ class Markers(object):
     def add_pvalues(self, p_values):
         for marker, p_value in itertools.izip(self.markers, p_values):
             marker['p_value'] = p_value
+            print("p_value is:", marker['p_value'])
             marker['lod_score'] = -math.log10(marker['p_value'])
             #Using -log(p) for the LRS; need to ask Rob how he wants to get LRS from p-values
             marker['lrs_value'] = -math.log10(marker['p_value']) * 4.61
diff --git a/wqflask/wqflask/marker_regression/marker_regression.py b/wqflask/wqflask/marker_regression/marker_regression.py
index 412d9e35..4ddc89c6 100755
--- a/wqflask/wqflask/marker_regression/marker_regression.py
+++ b/wqflask/wqflask/marker_regression/marker_regression.py
@@ -497,7 +497,8 @@ class MarkerRegression(object):
                                          genotype_matrix,
                                          kinship_matrix,
                                          REML=True,
-                                         refit=False)
+                                         refit=False,
+                                         temp_data=self.temp_data)
             
         Bench().report()
 
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
index cc2e32a7..12f7c2ea 100644
--- a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
+++ b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
@@ -95,53 +95,53 @@ def calculate_kinship(genotype_matrix, temp_data):
     kinship_matrix = np.dot(genotype_matrix,genotype_matrix.T) * 1.0/float(m)
     return kinship_matrix
 
-def GWAS(Y, X, K, Kva=[], Kve=[], X0=None, REML=True, refit=False):
-      """
-        Performs a basic GWAS scan using the LMM.  This function
-        uses the LMM module to assess association at each SNP and 
-        does some simple cleanup, such as removing missing individuals 
-        per SNP and re-computing the eigen-decomp
-
-        Y - n x 1 phenotype vector 
-        X - n x m SNP matrix
-        K - n x n kinship matrix
-        Kva,Kve = linalg.eigh(K) - or the eigen vectors and values for K
-        X0 - n x q covariate matrix
-        REML - use restricted maximum likelihood 
-        refit - refit the variance component for each SNP
-      """
-      n = X.shape[0]
-      m = X.shape[1]
+def GWAS(Y, X, K, Kva=[], Kve=[], X0=None, REML=True, refit=False, temp_data=None):
+    """
+      Performs a basic GWAS scan using the LMM.  This function
+      uses the LMM module to assess association at each SNP and
+      does some simple cleanup, such as removing missing individuals
+      per SNP and re-computing the eigen-decomp
+
+      Y - n x 1 phenotype vector
+      X - n x m SNP matrix
+      K - n x n kinship matrix
+      Kva,Kve = linalg.eigh(K) - or the eigen vectors and values for K
+      X0 - n x q covariate matrix
+      REML - use restricted maximum likelihood
+      refit - refit the variance component for each SNP
+    """
+    n = X.shape[0]
+    m = X.shape[1]
 
-      if X0 == None: X0 = np.ones((n,1))
-      
-      # Remove missing values in Y and adjust associated parameters
-      v = np.isnan(Y)
-      if v.sum():
-         keep = True - v
-         Y = Y[keep]
-         X = X[keep,:]
-         X0 = X0[keep,:]
-         K = K[keep,:][:,keep]
-         Kva = []
-         Kve = []
+    if X0 == None: X0 = np.ones((n,1))
 
-      L = LMM(Y,K,Kva,Kve,X0)
-      if not refit: L.fit()
+    # Remove missing values in Y and adjust associated parameters
+    v = np.isnan(Y)
+    if v.sum():
+        keep = True - v
+        Y = Y[keep]
+        X = X[keep,:]
+        X0 = X0[keep,:]
+        K = K[keep,:][:,keep]
+        Kva = []
+        Kve = []
 
-      PS = []
-      TS = []
+    L = LMM(Y,K,Kva,Kve,X0)
+    if not refit: L.fit()
 
-      for i in range(m):
-         x = X[:,i].reshape((n,1))
-         v = np.isnan(x).reshape((-1,))
-         if v.sum():
+    PS = []
+    TS = []
+
+    for counter in range(m):
+        x = X[:,counter].reshape((n,1))
+        v = np.isnan(x).reshape((-1,))
+        if v.sum():
             keep = True - v
             xs = x[keep,:]
-            if xs.var() == 0: 
-               PS.append(np.nan) 
-               TS.append(np.nan) 
-               continue
+            if xs.var() == 0:
+                PS.append(np.nan)
+                TS.append(np.nan)
+                continue
 
             Ys = Y[keep]
             X0s = X0[keep,:]
@@ -150,19 +150,124 @@ def GWAS(Y, X, K, Kva=[], Kve=[], X0=None, REML=True, refit=False):
             if refit: Ls.fit(X=xs)
             else: Ls.fit()
             ts,ps = Ls.association(xs,REML=REML)
-         else:
-            if x.var() == 0: 
-               PS.append(np.nan) 
-               TS.append(np.nan) 
-               continue
+        else:
+            if x.var() == 0:
+                PS.append(np.nan)
+                TS.append(np.nan)
+                continue
 
             if refit: L.fit(X=x)
             ts,ps = L.association(x,REML=REML)
             
-         PS.append(ps)
-         TS.append(ts)
+        percent_complete = 45 + int(round((counter/m)*55))
+        print("Percent complete: ", percent_complete)
+        temp_data.store("percent_complete", percent_complete)
 
-      return TS,PS
+        PS.append(ps)
+        TS.append(ts)
+
+    return TS,PS
+
+#def GWAS(pheno_vector,
+#        genotype_matrix,
+#        kinship_matrix,
+#        kinship_eigenvals=None,
+#        kinship_eigenvectors=None,
+#        covariate_matrix=None,
+#        restricted_max_likelihood=True,
+#        refit=False,
+#        temp_data=None):
+#    """
+#    Performs a basic GWAS scan using the LMM.  This function
+#    uses the LMM module to assess association at each SNP and 
+#    does some simple cleanup, such as removing missing individuals 
+#    per SNP and re-computing the eigen-decomp
+#
+#    Y - n x 1 phenotype vector 
+#    X - n x m SNP matrix
+#    K - n x n kinship matrix
+#    Kva,Kve = linalg.eigh(K) - or the eigen vectors and values for K
+#    X0 - n x q covariate matrix
+#    REML - use restricted maximum likelihood 
+#    refit - refit the variance component for each SNP
+#        
+#    """
+#
+#    assert temp_data, "You forgot to pass in temp_data"
+#    
+#    if kinship_eigenvals == None:
+#        kinship_eigenvals = []
+#    if kinship_eigenvectors == None:
+#        kinship_eigenvectors = []
+#        
+#    n = genotype_matrix.shape[0]
+#    m = genotype_matrix.shape[1]
+#
+#    if covariate_matrix == None:
+#        covariate_matrix = np.ones((n,1))
+#    
+#    # Remove missing values in Y and adjust associated parameters
+#    pheno_not_number = np.isnan(pheno_vector)
+#    if pheno_not_number.sum():
+#        keep = True - pheno_not_number
+#        pheno_vector = pheno_vector[keep]
+#        genotype_matrix = genotype_matrix[keep,:]
+#        covariate_matrix = covariate_matrix[keep,:]
+#        kinship_matrix = kinship_matrix[keep,:][:,keep]
+#        kinship_eigenvals = []
+#        kinship_eigenvectors = []
+#
+#    lmm_ob = LMM(pheno_vector,
+#                 kinship_matrix,
+#                 kinship_eigenvals,
+#                 kinship_eigenvectors,
+#                 covariate_matrix)
+#    if not refit:
+#        lmm_ob.fit()
+#
+#    p_value_matrix = []
+#    t_stats_matrix = []
+#
+#    for counter in range(m):
+#        #pheno_vector_2 = geno_vector[:, counter]
+#        #x = pheno_vector_2.reshape((n,1))
+#        x = genotype_matrix[:,counter].reshape((n,1))
+#        v = np.isnan(x).reshape((-1,))
+#        if v.sum():
+#            keep = True - v
+#            xs = x[keep,:]
+#            if xs.var() == 0: 
+#                p_value_matrix.append(np.nan) 
+#                t_stats_matrix.append(np.nan) 
+#                continue
+# 
+#            pheno_vector_2 = pheno_vector[keep]
+#            covariate_matrix_2 = covariate_matrix[keep,:]
+#            kinship_matrix_2 = kinship_matrix[keep,:][:,keep]
+#            lmm_ob_2 = LMM(pheno_vector, kinship_matrix, covariate_matrix=covariate_matrix)
+#            if refit:
+#                lmm_ob_2.fit(X=xs)
+#            else:
+#                lmm_ob_2.fit()
+#            t_stats, p_values = lmm_ob_2.association(xs, REML=restricted_max_likelihood)
+#        else:
+#            if x.var() == 0: 
+#                p_value_matrix.append(np.nan) 
+#                t_stats_matrix.append(np.nan) 
+#                continue
+# 
+#            if refit:
+#                lmm_ob.fit(X=x)
+#            t_stats,p_values = lmm_ob.association(x, REML=restricted_max_likelihood)
+#           
+#        p_value_matrix.append(p_values)
+#        t_stats_matrix.append(t_stats)
+#        
+#        percent_complete = 45 + int(round((counter/m)*55))
+#        print("Percent complete: ", percent_complete)
+#        temp_data.store("percent_complete", percent_complete)
+#
+#    return p_value_matrix, t_stats_matrix
 
 class LMM:
 
diff --git a/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.coffee b/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.coffee
index 35572f67..157f56a9 100644
--- a/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.coffee
+++ b/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.coffee
@@ -30,7 +30,6 @@ $ ->
 
     $("#marker_regression").click(() =>
         $("#progress_bar_container").modal()
-
         url = "/marker_regression"
         form_data = $('#trait_data_form').serialize()
         console.log("form_data is:", form_data)
diff --git a/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.js b/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.js
index 78459692..c8b0aa7b 100644
--- a/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.js
+++ b/wqflask/wqflask/static/new/javascript/show_trait_mapping_tools.js
@@ -1,74 +1,6 @@
 // Generated by CoffeeScript 1.4.0
 (function() {
 
-  $(function() {
-    var composite_mapping_fields, get_progress, submit_special, toggle_enable_disable,
-      _this = this;
-    submit_special = function() {
-      var url;
-      console.log("In submit_special");
-      console.log("this is:", this);
-      console.log("$(this) is:", $(this));
-      url = $(this).data("url");
-      console.log("url is:", url);
-      $("#trait_data_form").attr("action", url);
-      return $("#trait_data_form").submit();
-    };
-    get_progress = function() {
-      var params, params_str, temp_uuid, url,
-        _this = this;
-      console.log("temp_uuid:", $("#temp_uuid").val());
-      temp_uuid = $("#temp_uuid").val();
-      params = {
-        key: temp_uuid
-      };
-      params_str = $.param(params);
-      url = "/get_temp_data?" + params_str;
-      console.log("url:", url);
-      $.ajax({
-        type: "GET",
-        url: url,
-        success: function(progress_data) {
-          console.log("in get_progress data:", progress_data);
-          console.log(progress_data['percent_complete'] + "%");
-          return $('#marker_regression_progress').css("width", progress_data['percent_complete'] + "%");
-        }
-      });
-      return false;
-    };
-    $("#marker_regression").click(function() {
-      var form_data, url;
-      $("#progress_bar_container").modal();
-      url = "/marker_regression";
-      form_data = $('#trait_data_form').serialize();
-      console.log("form_data is:", form_data);
-      $.ajax({
-        type: "POST",
-        url: url,
-        data: form_data,
-        success: function(data) {
-          clearInterval(_this.my_timer);
-          $('#progress_bar_container').modal('hide');
-          return $("body").html(data);
-        }
-      });
-      console.log("settingInterval");
-      _this.my_timer = setInterval(get_progress, 1000);
-      return false;
-    });
-    composite_mapping_fields = function() {
-      return $(".composite_fields").toggle();
-    };
-    $("#use_composite_choice").change(composite_mapping_fields);
-    toggle_enable_disable = function(elem) {
-      return $(elem).prop("disabled", !$(elem).prop("disabled"));
-    };
-    $("#choose_closet_control").change(function() {
-      return toggle_enable_disable("#control_locus");
-    });
-    return $("#display_all_lrs").change(function() {
-      return toggle_enable_disable("#suggestive_lrs");
-    });
-  });
+
 
 }).call(this);
diff --git a/wqflask/wqflask/templates/show_trait_progress_bar.html b/wqflask/wqflask/templates/show_trait_progress_bar.html
index 2984cc02..eff5c391 100644
--- a/wqflask/wqflask/templates/show_trait_progress_bar.html
+++ b/wqflask/wqflask/templates/show_trait_progress_bar.html
@@ -3,7 +3,7 @@
     <h3 id="progress_bar">Loading...</h3>
   </div>
     <div class="modal-body">
-        <div class="progress progress-striped active">
+        <div class="progress active">
             <div id="marker_regression_progress" class="bar"></div>
         </div>
     </div>