Removed pylmm references and related functions

4 files changed, 4 insertions, 211 deletions

diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py
index 79f72390..ca6621e9 100644
--- a/wqflask/base/data_set.py
+++ b/wqflask/base/data_set.py
@@ -317,7 +317,7 @@ class DatasetGroup(object):
 
         mapping_id = g.db.execute("select MappingMethodId from InbredSet where Name= '%s'" % self.name).fetchone()[0]
         if mapping_id == "1":
-            mapping_names = ["QTLReaper", "PYLMM", "R/qtl"]
+            mapping_names = ["QTLReaper", "R/qtl"]
         elif mapping_id == "2":
             mapping_names = ["GEMMA"]
         elif mapping_id == "4":
diff --git a/wqflask/utility/tools.py b/wqflask/utility/tools.py
index ea216a35..86ef2e1e 100644
--- a/wqflask/utility/tools.py
+++ b/wqflask/utility/tools.py
@@ -107,9 +107,6 @@ def js_path(module=None):
         return try_guix
     raise "No JS path found for "+module+" (if not in Guix check JS_GN_PATH)"
 
-def pylmm_command(guess=None):
-    return assert_bin(get_setting("PYLMM_COMMAND",guess))
-
 def gemma_command(guess=None):
     return assert_bin(get_setting("GEMMA_COMMAND",guess))
 
@@ -276,7 +273,6 @@ SMTP_CONNECT = get_setting('SMTP_CONNECT')
 SMTP_USERNAME = get_setting('SMTP_USERNAME')
 SMTP_PASSWORD = get_setting('SMTP_PASSWORD')
 
-PYLMM_COMMAND      = app_set("PYLMM_COMMAND",pylmm_command())
 GEMMA_COMMAND      = app_set("GEMMA_COMMAND",gemma_command())
 assert(GEMMA_COMMAND is not None)
 PLINK_COMMAND      = app_set("PLINK_COMMAND",plink_command())
diff --git a/wqflask/wqflask/heatmap/heatmap.py b/wqflask/wqflask/heatmap/heatmap.py
index ff589693..1bdf252b 100644
--- a/wqflask/wqflask/heatmap/heatmap.py
+++ b/wqflask/wqflask/heatmap/heatmap.py
@@ -24,12 +24,9 @@ import reaper
 from base.trait import GeneralTrait
 from base import data_set
 from base import species
-# from wqflask.my_pylmm.pyLMM import lmm
-# from wqflask.my_pylmm.pyLMM import input
 from utility import helper_functions
 from utility import Plot, Bunch
 from utility import temp_data
-from utility.tools import PYLMM_COMMAND
 
 from MySQLdb import escape_string as escape
 
@@ -144,4 +141,4 @@ class Heatmap(object):
                 if qtl.additive > 0:
                     self.trait_results[this_trait.name].append(-float(qtl.lrs))
                 else:
-                    self.trait_results[this_trait.name].append(float(qtl.lrs))
\ No newline at end of file
+                    self.trait_results[this_trait.name].append(float(qtl.lrs))
diff --git a/wqflask/wqflask/marker_regression/run_mapping.py b/wqflask/wqflask/marker_regression/run_mapping.py
index 3057e340..73d985b8 100644
--- a/wqflask/wqflask/marker_regression/run_mapping.py
+++ b/wqflask/wqflask/marker_regression/run_mapping.py
@@ -38,7 +38,7 @@ from utility import temp_data
 from utility.benchmark import Bench
 from wqflask.marker_regression import gemma_mapping, rqtl_mapping, qtlreaper_mapping, plink_mapping
 
-from utility.tools import locate, locate_ignore_error, PYLMM_COMMAND, GEMMA_COMMAND, PLINK_COMMAND, TEMPDIR
+from utility.tools import locate, locate_ignore_error, GEMMA_COMMAND, PLINK_COMMAND, TEMPDIR
 from utility.external import shell
 from base.webqtlConfig import TMPDIR, GENERATED_TEXT_DIR
 
@@ -239,11 +239,6 @@ class RunMapping(object):
             self.manhattan_plot = True
             results = plink_mapping.run_plink(self.this_trait, self.dataset, self.species, self.vals, self.maf)
             #results = self.run_plink()
-        elif self.mapping_method == "pylmm":
-            logger.debug("RUNNING PYLMM")
-            if self.num_perm > 0:
-                self.run_permutations(str(temp_uuid))
-            results = self.gen_data(str(temp_uuid))
         else:
             logger.debug("RUNNING NOTHING")
 
@@ -354,201 +349,6 @@ class RunMapping(object):
 
         count, p_values = self.parse_rqtl_output(plink_output_filename)
 
-    def run_permutations(self, temp_uuid):
-        """Runs permutations and gets significant and suggestive LOD scores"""
-
-        top_lod_scores = []
-
-        #logger.debug("self.num_perm:", self.num_perm)
-
-        for permutation in range(self.num_perm):
-
-            pheno_vector = np.array([val == "x" and np.nan or float(val) for val in self.vals])
-            np.random.shuffle(pheno_vector)
-
-            key = "pylmm:input:" + temp_uuid
-
-            if self.dataset.group.species == "human":
-                p_values, t_stats = self.gen_human_results(pheno_vector, key, temp_uuid)
-            else:
-                genotype_data = [marker['genotypes'] for marker in self.dataset.group.markers.markers]
-
-                no_val_samples = self.identify_empty_samples()
-                trimmed_genotype_data = self.trim_genotypes(genotype_data, no_val_samples)
-
-                genotype_matrix = np.array(trimmed_genotype_data).T
-
-                params = dict(pheno_vector = pheno_vector.tolist(),
-                            genotype_matrix = genotype_matrix.tolist(),
-                            restricted_max_likelihood = True,
-                            refit = False,
-                            temp_uuid = temp_uuid,
-
-                            # meta data
-                            timestamp = datetime.datetime.now().isoformat(),
-                            )
-
-                json_params = json.dumps(params)
-                Redis.set(key, json_params)
-                Redis.expire(key, 60*60)
-
-                command = PYLMM_COMMAND+' --key {} --species {}'.format(key,"other")
-                shell(command)
-
-                json_results = Redis.blpop("pylmm:results:" + temp_uuid, 45*60)
-                results = json.loads(json_results[1])
-                p_values = [float(result) for result in results['p_values']]
-
-                lowest_p_value = 1
-                for p_value in p_values:
-                    if p_value < lowest_p_value:
-                        lowest_p_value = p_value
-
-                #logger.debug("lowest_p_value:", lowest_p_value)
-                top_lod_scores.append(-math.log10(lowest_p_value))
-
-        #logger.debug("top_lod_scores:", top_lod_scores)
-
-        self.suggestive = np.percentile(top_lod_scores, 67)
-        self.significant = np.percentile(top_lod_scores, 95)
-
-    def gen_data(self, temp_uuid):
-        """Generates p-values for each marker"""
-
-        logger.debug("self.vals is:", self.vals)
-        pheno_vector = np.array([(val == "x" or val == "") and np.nan or float(val) for val in self.vals])
-
-        #lmm_uuid = str(uuid.uuid4())
-
-        key = "pylmm:input:" + temp_uuid
-        logger.debug("key is:", pf(key))
-        #with Bench("Loading cache"):
-        #    result = Redis.get(key)
-
-        if self.dataset.group.species == "human":
-            p_values, t_stats = self.gen_human_results(pheno_vector, key, temp_uuid)
-
-        else:
-            logger.debug("NOW CWD IS:", os.getcwd())
-            genotype_data = [marker['genotypes'] for marker in self.dataset.group.markers.markers]
-
-            no_val_samples = self.identify_empty_samples()
-            trimmed_genotype_data = self.trim_genotypes(genotype_data, no_val_samples)
-
-            genotype_matrix = np.array(genotype_data).T
-
-            #logger.debug("pheno_vector: ", pf(pheno_vector))
-            #logger.debug("genotype_matrix: ", pf(genotype_matrix))
-            #logger.debug("genotype_matrix.shape: ", pf(genotype_matrix.shape))
-
-            #params = {"pheno_vector": pheno_vector,
-            #            "genotype_matrix": genotype_matrix,
-            #            "restricted_max_likelihood": True,
-            #            "refit": False,
-            #            "temp_data": tempdata}
-
-            # logger.debug("genotype_matrix:", str(genotype_matrix.tolist()))
-            # logger.debug("pheno_vector:", str(pheno_vector.tolist()))
-
-            params = dict(pheno_vector = pheno_vector.tolist(),
-                        genotype_matrix = genotype_matrix.tolist(),
-                        restricted_max_likelihood = True,
-                        refit = False,
-                        temp_uuid = temp_uuid,
-
-                        # meta data
-                        timestamp = datetime.datetime.now().isoformat(),
-                        )
-
-            json_params = json.dumps(params)
-            #logger.debug("json_params:", json_params)
-            Redis.set(key, json_params)
-            Redis.expire(key, 60*60)
-            logger.debug("before printing command")
-
-            command = PYLMM_COMMAND + ' --key {} --species {}'.format(key, "other")
-            logger.debug("command is:", command)
-            logger.debug("after printing command")
-
-            shell(command)
-
-            #t_stats, p_values = lmm.run(key)
-            #lmm.run(key)
-
-            json_results = Redis.blpop("pylmm:results:" + temp_uuid, 45*60)
-            results = json.loads(json_results[1])
-            p_values = [float(result) for result in results['p_values']]
-            t_stats = results['t_stats']
-
-            #t_stats, p_values = lmm.run(
-            #    pheno_vector,
-            #    genotype_matrix,
-            #    restricted_max_likelihood=True,
-            #    refit=False,
-            #    temp_data=tempdata
-            #)
-            #logger.debug("p_values:", p_values)
-
-        self.dataset.group.markers.add_pvalues(p_values)
-
-        return self.dataset.group.markers.markers
-
-    def gen_human_results(self, pheno_vector, key, temp_uuid):
-        file_base = locate(self.dataset.group.name,"mapping")
-
-        plink_input = input.plink(file_base, type='b')
-        input_file_name = os.path.join(webqtlConfig.SNP_PATH, self.dataset.group.name + ".snps.gz")
-
-        pheno_vector = pheno_vector.reshape((len(pheno_vector), 1))
-        covariate_matrix = np.ones((pheno_vector.shape[0],1))
-        kinship_matrix = np.fromfile(open(file_base + '.kin','r'),sep=" ")
-        kinship_matrix.resize((len(plink_input.indivs),len(plink_input.indivs)))
-
-        logger.debug("Before creating params")
-
-        params = dict(pheno_vector = pheno_vector.tolist(),
-                    covariate_matrix = covariate_matrix.tolist(),
-                    input_file_name = input_file_name,
-                    kinship_matrix = kinship_matrix.tolist(),
-                    refit = False,
-                    temp_uuid = temp_uuid,
-
-                    # meta data
-                    timestamp = datetime.datetime.now().isoformat(),
-                    )
-
-        logger.debug("After creating params")
-
-        json_params = json.dumps(params)
-        Redis.set(key, json_params)
-        Redis.expire(key, 60*60)
-
-        logger.debug("Before creating the command")
-
-        command = PYLMM_COMMAND+' --key {} --species {}'.format(key, "human")
-
-        logger.debug("command is:", command)
-
-        os.system(command)
-
-        json_results = Redis.blpop("pylmm:results:" + temp_uuid, 45*60)
-        results = json.loads(json_results[1])
-        t_stats = results['t_stats']
-        p_values = results['p_values']
-
-
-        #p_values, t_stats = lmm.run_human(key)
-
-        #p_values, t_stats = lmm.run_human(
-        #        pheno_vector,
-        #        covariate_matrix,
-        #        input_file_name,
-        #        kinship_matrix,
-        #        loading_progress=tempdata
-        #    )
-
-        return p_values, t_stats
-
     def identify_empty_samples(self):
         no_val_samples = []
         for sample_count, val in enumerate(self.vals):
@@ -657,4 +457,4 @@ def trim_markers_for_table(markers):
         trimmed_sorted_markers = sorted_markers[:2000]
         return trimmed_sorted_markers
     else:
-        return sorted_markers
\ No newline at end of file
+        return sorted_markers