Revert on Zach tabs and trailing spaces

2 files changed, 101 insertions, 101 deletions

diff --git a/wqflask/wqflask/marker_regression/marker_regression.py b/wqflask/wqflask/marker_regression/marker_regression.py
index f340e309..200f2207 100644
--- a/wqflask/wqflask/marker_regression/marker_regression.py
+++ b/wqflask/wqflask/marker_regression/marker_regression.py
@@ -201,14 +201,14 @@ class MarkerRegression(object):
             self.control_marker = start_vars['control_marker']
             self.do_control = start_vars['do_control']
             logger.info("Running qtlreaper")
-            results, self.json_data, self.perm_output, self.suggestive, self.significant, self.bootstrap_results = qtlreaper_mapping.gen_reaper_results(self.this_trait, 
-                                                                                                                                                        self.dataset, 
-                                                                                                                                                        self.samples, 
-                                                                                                                                                        self.json_data, 
-                                                                                                                                                        self.num_perm, 
-                                                                                                                                                        self.bootCheck, 
-                                                                                                                                                        self.num_bootstrap, 
-                                                                                                                                                        self.do_control, 
+            results, self.json_data, self.perm_output, self.suggestive, self.significant, self.bootstrap_results = qtlreaper_mapping.gen_reaper_results(self.this_trait,
+                                                                                                                                                        self.dataset,
+                                                                                                                                                        self.samples,
+                                                                                                                                                        self.json_data,
+                                                                                                                                                        self.num_perm,
+                                                                                                                                                        self.bootCheck,
+                                                                                                                                                        self.num_bootstrap,
+                                                                                                                                                        self.do_control,
                                                                                                                                                         self.control_marker,
                                                                                                                                                         self.manhattan_plot)
         elif self.mapping_method == "plink":
diff --git a/wqflask/wqflask/marker_regression/qtlreaper_mapping.py b/wqflask/wqflask/marker_regression/qtlreaper_mapping.py
index 568991f7..b072584c 100644
--- a/wqflask/wqflask/marker_regression/qtlreaper_mapping.py
+++ b/wqflask/wqflask/marker_regression/qtlreaper_mapping.py
@@ -1,93 +1,93 @@
-def gen_reaper_results(this_trait, dataset, samples_before, json_data, num_perm, bootCheck, num_bootstrap, do_control, control_marker, manhattan_plot):

-    genotype = dataset.group.read_genotype_file()

-    

-    if manhattan_plot != True:

-        genotype = genotype.addinterval()

-

-    samples, values, variances, sample_aliases = this_trait.export_informative()

-

-    trimmed_samples = []

-    trimmed_values = []

-    for i in range(0, len(samples)):

-        if this_trait.data[samples[i]].name in samples_before:

-            trimmed_samples.append(samples[i])

-            trimmed_values.append(values[i])

-

-    perm_output = []

-    bootstrap_results = []    

-            

-    if num_perm < 100:

-        suggestive = 0

-        significant = 0

-    else:

-        perm_output = genotype.permutation(strains = trimmed_samples, trait = trimmed_values, nperm=num_perm)

-        suggestive = perm_output[int(num_perm*0.37-1)]

-        significant = perm_output[int(num_perm*0.95-1)]

-        highly_significant = perm_output[int(num_perm*0.99-1)]

-

-    json_data['suggestive'] = suggestive

-    json_data['significant'] = significant

-

-    if control_marker != "" and do_control == "true":

-        reaper_results = genotype.regression(strains = trimmed_samples,

-                                             trait = trimmed_values,

-                                             control = str(control_marker))

-        if bootCheck:

-            control_geno = []

-            control_geno2 = []

-            _FIND = 0

-            for _chr in genotype:

-                for _locus in _chr:

-                    if _locus.name == control_marker:

-                        control_geno2 = _locus.genotype

-                        _FIND = 1

-                        break

-                if _FIND:

-                    break

-            if control_geno2:

-                _prgy = list(genotype.prgy)

-                for _strain in trimmed_samples:

-                    _idx = _prgy.index(_strain)

-                    control_geno.append(control_geno2[_idx])

-

-            bootstrap_results = genotype.bootstrap(strains = trimmed_samples,

-                                                        trait = trimmed_values,

-                                                        control = control_geno,

-                                                        nboot = num_bootstrap)

-    else:

-        reaper_results = genotype.regression(strains = trimmed_samples,

-                                             trait = trimmed_values)

-

-        if bootCheck:

-            bootstrap_results = genotype.bootstrap(strains = trimmed_samples,

-                                                        trait = trimmed_values,

-                                                        nboot = num_bootstrap)

-

-    json_data['chr'] = []

-    json_data['pos'] = []

-    json_data['lod.hk'] = []

-    json_data['markernames'] = []

-    #if self.additive:

-    #    self.json_data['additive'] = []

-

-    #Need to convert the QTL objects that qtl reaper returns into a json serializable dictionary

-    qtl_results = []

-    for qtl in reaper_results:

-        reaper_locus = qtl.locus

-        #ZS: Convert chr to int

-        converted_chr = reaper_locus.chr

-        if reaper_locus.chr != "X" and reaper_locus.chr != "X/Y":

-            converted_chr = int(reaper_locus.chr)

-        json_data['chr'].append(converted_chr)

-        json_data['pos'].append(reaper_locus.Mb)

-        json_data['lod.hk'].append(qtl.lrs)

-        json_data['markernames'].append(reaper_locus.name)

-        #if self.additive:

-        #    self.json_data['additive'].append(qtl.additive)

-        locus = {"name":reaper_locus.name, "chr":reaper_locus.chr, "cM":reaper_locus.cM, "Mb":reaper_locus.Mb}

-        qtl = {"lrs_value": qtl.lrs, "chr":converted_chr, "Mb":reaper_locus.Mb,

-               "cM":reaper_locus.cM, "name":reaper_locus.name, "additive":qtl.additive, "dominance":qtl.dominance}

-        qtl_results.append(qtl)

-

-

-    return qtl_results, json_data, perm_output, suggestive, significant, bootstrap_results

+def gen_reaper_results(this_trait, dataset, samples_before, json_data, num_perm, bootCheck, num_bootstrap, do_control, control_marker, manhattan_plot):
+    genotype = dataset.group.read_genotype_file()
+
+    if manhattan_plot != True:
+        genotype = genotype.addinterval()
+
+    samples, values, variances, sample_aliases = this_trait.export_informative()
+
+    trimmed_samples = []
+    trimmed_values = []
+    for i in range(0, len(samples)):
+        if this_trait.data[samples[i]].name in samples_before:
+            trimmed_samples.append(samples[i])
+            trimmed_values.append(values[i])
+
+    perm_output = []
+    bootstrap_results = []
+
+    if num_perm < 100:
+        suggestive = 0
+        significant = 0
+    else:
+        perm_output = genotype.permutation(strains = trimmed_samples, trait = trimmed_values, nperm=num_perm)
+        suggestive = perm_output[int(num_perm*0.37-1)]
+        significant = perm_output[int(num_perm*0.95-1)]
+        highly_significant = perm_output[int(num_perm*0.99-1)]
+
+    json_data['suggestive'] = suggestive
+    json_data['significant'] = significant
+
+    if control_marker != "" and do_control == "true":
+        reaper_results = genotype.regression(strains = trimmed_samples,
+                                             trait = trimmed_values,
+                                             control = str(control_marker))
+        if bootCheck:
+            control_geno = []
+            control_geno2 = []
+            _FIND = 0
+            for _chr in genotype:
+                for _locus in _chr:
+                    if _locus.name == control_marker:
+                        control_geno2 = _locus.genotype
+                        _FIND = 1
+                        break
+                if _FIND:
+                    break
+            if control_geno2:
+                _prgy = list(genotype.prgy)
+                for _strain in trimmed_samples:
+                    _idx = _prgy.index(_strain)
+                    control_geno.append(control_geno2[_idx])
+
+            bootstrap_results = genotype.bootstrap(strains = trimmed_samples,
+                                                        trait = trimmed_values,
+                                                        control = control_geno,
+                                                        nboot = num_bootstrap)
+    else:
+        reaper_results = genotype.regression(strains = trimmed_samples,
+                                             trait = trimmed_values)
+
+        if bootCheck:
+            bootstrap_results = genotype.bootstrap(strains = trimmed_samples,
+                                                        trait = trimmed_values,
+                                                        nboot = num_bootstrap)
+
+    json_data['chr'] = []
+    json_data['pos'] = []
+    json_data['lod.hk'] = []
+    json_data['markernames'] = []
+    #if self.additive:
+    #    self.json_data['additive'] = []
+
+    #Need to convert the QTL objects that qtl reaper returns into a json serializable dictionary
+    qtl_results = []
+    for qtl in reaper_results:
+        reaper_locus = qtl.locus
+        #ZS: Convert chr to int
+        converted_chr = reaper_locus.chr
+        if reaper_locus.chr != "X" and reaper_locus.chr != "X/Y":
+            converted_chr = int(reaper_locus.chr)
+        json_data['chr'].append(converted_chr)
+        json_data['pos'].append(reaper_locus.Mb)
+        json_data['lod.hk'].append(qtl.lrs)
+        json_data['markernames'].append(reaper_locus.name)
+        #if self.additive:
+        #    self.json_data['additive'].append(qtl.additive)
+        locus = {"name":reaper_locus.name, "chr":reaper_locus.chr, "cM":reaper_locus.cM, "Mb":reaper_locus.Mb}
+        qtl = {"lrs_value": qtl.lrs, "chr":converted_chr, "Mb":reaper_locus.Mb,
+               "cM":reaper_locus.cM, "name":reaper_locus.name, "additive":qtl.additive, "dominance":qtl.dominance}
+        qtl_results.append(qtl)
+
+
+    return qtl_results, json_data, perm_output, suggestive, significant, bootstrap_results