import os, sys

def get_immediate_subdirectories(dir):

return [name for name in os.listdir(dir) if os.path.isdir(os.path.join(dir, name))]

sys.path += get_immediate_subdirectories("./")

from datetime import datetime

from DataStore import *

from ReadData import *

from JobManager import *

from Network import *

from Generate_Grid import *

from AnalyzeResults import *

from Helpers import *

from mcz import *

from dfg4grn import *

from ReadConfig import *

from clr import *

from genie3 import *

from tlclr import *

from inferelator import *

def get_golden_genes(filename):

return gold_genes

golden_genes = get_golden_genes("datasets/RootArrayData/NitrogenGenes.txt")

# Instantsiate settings file

settings = {}

settings = ReadConfig(settings)

settings["global"]["working_dir"] = os.getcwd() + '/'

settings["global"]["experiment_name"] = "Compare_Dex_And_DFG"

# Create date string to append to output_dir

t = datetime.now().strftime("%Y-%m-%d_%H.%M.%S")

settings["global"]["output_dir"] = settings["global"]["output_dir"] + "/" + \

settings["global"]["experiment_name"] + "-" + t + "/"

os.mkdir(settings["global"]["output_dir"])

# Read in the gold standard network

# Read in the gold standard network

#goldnet.read_goldstd(settings["global"]["large_network_goldnet_file"])

#ko_file, kd_file, ts_file, wt_file, mf_file, goldnet = get_example_data_files(sys.argv[1], settings)

# Read data into program

# Where the format is "FILENAME" "DATATYPE"

dex_storage = ReadData("datasets/RootArrayData/DexRatios.csv", "dex")

dexcombined = ReadData("datasets/RootArrayData/DexRatios.csv", "dex")

dex_storage2 = ReadData("datasets/RootArrayData/HHO3_DEX_ratios.csv", "dex")

cnlo_storage = ReadData("datasets/RootArrayData/Root_CNLO_Krouk.txt", "dex")

cnlo_no3_storage = ReadData("datasets/RootArrayData/Root_CNLO_Krouk.txt", "dex")

no3_1_storage = ReadData("datasets/RootArrayData/Root_NO3_Wang03.txt", "dex")

no3_2_storage = ReadData("datasets/RootArrayData/Root_NO3_Wang04.txt", "dex")

no3_3_storage = ReadData("datasets/RootArrayData/Root_NO3_Wang07.txt", "dex")

#ts_storage = ReadData("datasets/RootArrayData/Root_WT_Krouk11.txt", "dex")

tfs_file = open("datasets/RootArrayData/tfs.csv", 'r')

line = tfs_file.readlines()[0]

tfs = line.strip().split(',')

tfs = [x.upper() for x in tfs]

kno3_1 = ReadData("datasets/RootArrayData/KNO3norm1.csv", "dex")

kno3_2 = ReadData("datasets/RootArrayData/KNO3norm2.csv", "dex")

kno3_3 = ReadData("datasets/RootArrayData/KNO3norm3.csv", "dex")

kno3_4 = ReadData("datasets/RootArrayData/KNO3norm4.csv", "dex")

settings["global"]["time_series_delta_t"] = "3 3 3 3 3 5"

dex_storage.filter(kno3_1.gene_list)

dexcombined.filter(kno3_1.gene_list)

dex_storage2.filter(kno3_1.gene_list)

cnlo_storage.filter(kno3_1.gene_list)

cnlo_no3_storage.filter(kno3_1.gene_list)

no3_1_storage.filter(kno3_1.gene_list)

no3_2_storage.filter(kno3_1.gene_list)

no3_3_storage.filter(kno3_1.gene_list)

dexcombined.combine(dex_storage2)

no3_storage = no3_1_storage

no3_storage.combine(no3_2_storage)

no3_storage.combine(no3_3_storage)

cnlo_no3_storage.combine(no3_storage)

#all_storage.combine(cnlo_no3_storage)

#dex_storage.combine(cnlo_storage)

#dex_storage.combine(no3_storage)

#dex_storage.normalize()

no3_storage.normalize()

cnlo_storage.normalize()

cnlo_no3_storage.normalize()

#all_storage.normalize()

ts_storage = [kno3_1, kno3_2, kno3_3, kno3_4]

#for s in ts_storage:

#s.normalize()

# Setup job manager

jobman = JobManager(settings)

# Figure out list of dex targets and put them into a network so we can

# compare

net = Network()

net.gene_list = dex_storage.gene_list

target = sys.argv[1]

if target == "At1g25550":

dex_storage = dex_storage2

for gene1 in dex_storage.gene_list:

net.network[gene1] = {}

for gene2 in dex_storage.gene_list:

net.network[gene1][gene2] = 0

for gene in dex_storage.gene_list:

print dex_storage.experiments[0].ratios[gene]

if dex_storage.experiments[0].ratios[gene] >= 2.0:

print dex_storage.experiments[0].ratios[gene]

net.network[target][gene] = 1

elif dex_storage.experiments[0].ratios[gene] <= 0.5:

net.network[target][gene] = -1

else:

net.network[target][gene] = 0

# Run a fresh DFG

#dfg = DFG4GRN()

#settings["dfg4grn"]["eta_z"] = 0.1

#settings["dfg4grn"]["lambda_w"] = 0.1

#settings["dfg4grn"]["tau"] = 3

#settings["dfg4grn"]["delta_t"] = "3 3 3 3 3 5"

#dfg.setup(ts_storage, TFList(tfs), settings, "DFG-NO_WT_LambdaW-{0}_Dex".format(0.1), 1)

#jobman.queueJob(dfg)

#jobman.runQueue()

#jobman.waitToClear()

import pickle

dfg = pickle.load(open("output/Dex-BothDexTest-20models-2012-06-24_00.18.28/DFG-NO_WT_LambdaW-0.1_Dex-False/DFG-NO_WT_LambdaW-0.1_Dex-False.pickle",'r'))

dfg.read_output(dfg.settings)

#tprs, fprs, rocs = GenerateMultiROC(jobman.finished, goldnet, False, settings["global"]["output_dir"] + "/OverallROC.pdf")

#ps, rs, precs = GenerateMultiPR(jobman.finished, goldnet, False, settings["global"]["output_dir"] + "/OverallPR.pdf")

SaveResults(jobman.finished, [], settings)

# Compare the network targets

#results = []

#for job in jobman.finished:

#if job.alg.alg_name == "dfg4grn":

#results.append([job.alg.name, job.alg.best_sign, job.alg.best_sign_all])

#out = open(settings["global"]["output_dir"] + "/results.txt",'w')

#for r in results:

#out.write(r[0] + "\t" + str(r[1]) + "\t" + str(r[2]) + "\n")

#out = open(settings["global"]["output_dir"] + "/results-sorted.txt",'w')

#results.sort(key=lambda x: x[0])

#for r in results:

#out.write(r[0] + "\t" + str(r[1]) + "\t" + str(r[2]) + "\n")

out_file = open(target + " to DFG targets.txt", 'w')

correct = 0

targets = 0

for gene1 in net.gene_list:

if net.network[target + ""][gene1] > 0 and dfg.network.network[target + ""][gene1] > 0:

correct += 1

targets += 1

out_file.write(target + " -> " + gene1 + ": Correct +\n")

elif net.network[target + ""][gene1] < 0 and dfg.network.network[target + ""][gene1] < 0:

correct += 1

targets += 1

out_file.write( target + " -> " + gene1 + ": Correct -\n")

elif net.network[target + ""][gene1] > 0:

targets += 1

out_file.write(target + " -> " + gene1 + ": Missed +\n")

elif net.network[target + ""][gene1] < 0:

targets += 1

out_file.write(target + " -> " + gene1 + ": Missed -\n")

out_file.write("\nNumber correct: {0}\nTotal targets: {1}\nPercent obtained: {2}".format(correct, targets, float(correct) / targets))

out_file.close()

# Now loop over each gene and find whether or not it is a target from

# dex and a target from DFG, print each list and the intersection

dfg_induct = []

dex_induct = []

dfg_rep = []

dex_rep = []

for gene in net.gene_list:

if net.network[target][gene] > 0:

dex_induct.append(gene)

if net.network[target][gene] < 0:

dex_rep.append(gene)

if dfg.network.network[target][gene] > 0:

dfg_induct.append(gene)

if dfg.network.network[target][gene] < 0:

dfg_rep.append(gene)

induct_intersect = []

rep_intersect = []

for gene1 in dfg_induct:

for gene2 in dex_induct:

if gene1 == gene2:

induct_intersect.append(gene1)

for gene1 in dfg_rep:

for gene2 in dex_rep:

if gene1 == gene2:

rep_intersect.append(gene1)

print dfg_rep

print dex_rep

out_file = open(target + " target list.txt", 'w')

out_file.write("Total DEX induction targets: " + str(len(dex_induct)) + "\n")

out_file.write("Total DEX repression targets: " + str(len(dex_rep)) + "\n\n")

out_file.write("DFG Induction Targets\n")

for gene in dfg_induct:

out_file.write(gene + '\n')

out_file.write("\nDEX Induction Targets\n")

for gene in dex_induct:

out_file.write(gene + '\n')

out_file.write("\nDFG Repression Targets\n")

for gene in dfg_rep:

out_file.write(gene + '\n')

out_file.write("\nDEX Repression Targets\n")

for gene in dex_rep:

out_file.write(gene + '\n')

out_file.write("\nInduction Intersection\n")

for gene in induct_intersect:

out_file.write(gene + '\n')

out_file.write("\nRepression Intersection\n")

for gene in rep_intersect:

out_file.write(gene + '\n')

out_file.close()

print dfg.weights[0]

print dfg.network.network[target]