Adding initial file to perform CTL analysis

2 files changed, 87 insertions, 0 deletions

diff --git a/wqflask/wqflask/ctl/__init__.py b/wqflask/wqflask/ctl/__init__.py
new file mode 100755
index 00000000..e69de29b
--- /dev/null
+++ b/wqflask/wqflask/ctl/__init__.py
diff --git a/wqflask/wqflask/ctl/ctl_analysis.py b/wqflask/wqflask/ctl/ctl_analysis.py
new file mode 100644
index 00000000..13f08a0f
--- /dev/null
+++ b/wqflask/wqflask/ctl/ctl_analysis.py
@@ -0,0 +1,87 @@
+# CTL analysis for GN2
+# Author / Maintainer: Danny Arends <Danny.Arends@gmail.com>
+import sys
+from numpy import *
+import scipy as sp                            # SciPy
+import rpy2.robjects as ro                    # R Objects
+import rpy2.rinterface as ri
+
+from base.webqtlConfig import GENERATED_IMAGE_DIR
+from utility import webqtlUtil                # Random number for the image
+
+import base64
+import array
+
+from utility import helper_functions
+
+from rpy2.robjects.packages import importr
+utils = importr("utils")
+
+## Get pointers to some common R functions
+r_library       = ro.r["library"]             # Map the library function
+r_options       = ro.r["options"]             # Map the options function
+r_read_csv      = ro.r["read.csv"]            # Map the read.csv function
+r_dim           = ro.r["dim"]                 # Map the dim function
+r_c             = ro.r["c"]                   # Map the c function
+r_cat           = ro.r["cat"]                 # Map the cat function
+r_paste         = ro.r["paste"]               # Map the paste function
+r_unlist        = ro.r["unlist"]              # Map the unlist function
+r_unique        = ro.r["unique"]              # Map the unique function
+r_length        = ro.r["length"]              # Map the length function
+r_unlist        = ro.r["unlist"]              # Map the unlist function
+r_list          = ro.r.list                   # Map the list function
+r_matrix        = ro.r.matrix                 # Map the matrix function
+r_seq           = ro.r["seq"]                 # Map the seq function
+r_table         = ro.r["table"]               # Map the table function
+r_names         = ro.r["names"]               # Map the names function
+r_sink          = ro.r["sink"]                # Map the sink function
+r_is_NA         = ro.r["is.na"]               # Map the is.na function
+r_file          = ro.r["file"]                # Map the file function
+r_png           = ro.r["png"]                 # Map the png function for plotting
+r_dev_off       = ro.r["dev.off"]             # Map the dev.off function
+
+class CTL(object):
+    def __init__(self):
+        print("Initialization of CTL")
+        #log = r_file("/tmp/genenetwork_wcgna.log", open = "wt")
+        #r_sink(log)                                  # Uncomment the r_sink() commands to log output from stdout/stderr to a file
+        #r_sink(log, type = "message")
+        r_library("ctl")                            # Load WGCNA - Should only be done once, since it is quite expensive
+        r_options(stringsAsFactors = False)
+        print("Initialization of CTL done, package loaded in R session")
+        self.r_CTLscan    = ro.r["CTLscan"]                              # Map the CTLscan function
+        print("Obtained pointers to CTL functions")
+
+    def run_analysis(self, requestform):
+        print("Starting CTL analysis on dataset")
+
+        self.trait_db_list = [trait.strip() for trait in requestform['trait_list'].split(',')]
+        print("Retrieved phenotype data from database", requestform['trait_list'])
+        helper_functions.get_trait_db_obs(self, self.trait_db_list)
+
+        self.input = {}           # self.input contains the phenotype values we need to send to R
+        strains = []              # All the strains we have data for (contains duplicates)
+        traits  = []              # All the traits we have data for (should not contain duplicates)
+        for trait in self.trait_list:
+            traits.append(trait[0].name)
+            self.input[trait[0].name] = {}
+            for strain in trait[0].data:
+                strains.append(strain)
+                self.input[trait[0].name][strain]  = trait[0].data[strain].value
+        sys.stdout.flush()
+
+    def render_image(self, results):
+        print("pre-loading imgage results:", self.results['imgloc'])
+        imgfile = open(self.results['imgloc'], 'rb')
+        imgdata = imgfile.read()
+        imgB64 = imgdata.encode("base64")
+        bytesarray = array.array('B', imgB64)
+        self.results['imgdata'] = bytesarray
+
+    def process_results(self, results):
+        print("Processing WGCNA output")
+        template_vars = {}
+        template_vars["input"] = self.input
+        sys.stdout.flush()
+        return(dict(template_vars))
+