import pandas as pd

import numpy as np

from cruzdb import Genome

#import PyMySQL

#PyMySQL.install_as_MySQLdb()

#import MySQLdb

'''

To install cruzdb into anaconda, go to pkgs dir within anaconda

wget https://conda.anaconda.org/travis/linux-64/cruzdb-0.5.4-py27_0.tar.bz2

conda install cruzdb-0.5.4-py27_0.tar.bz2

# cruzdb relies on MySQLdb:

sudo pip install MySQL-python

# On conda:

conda install -c https://conda.anaconda.org/chuongdo mysql-python

# fix .dylib error from mysql.so by fixing path variables

lib /usr/local/mysql/lib/libmysqlclient.18.dylib /Library/Python/2.7/site-packages/_mysql.so

'''

# Test file

input_vcf = "Practice_Files/VariantCallFile10prc.vcf"

# Read in as pandas df

vcf_ex = pd.read_csv(input_vcf, sep = r'\t', engine = 'python', skiprows = 55)

vcf_ex = pd.DataFrame(vcf_ex)

print vcf_ex.head()

# Rename columns to fix the syntax in chromosome number

names = vcf_ex.columns.values

new_names = ['CHROM']

new_names.extend(names[1:])

print "\n", new_names

vcf_ex.columns = new_names

# If QUAL > 0.5, sample passes

vcf_ex_sub = vcf_ex.loc[vcf_ex.QUAL > 0.5, ['CHROM', 'POS']].copy()

print vcf_ex_sub.head()

# Get the Genome object from cruzdb

# connects to MySQL genome browser at UCSC

g = Genome('hg38')

# Convert table 'refGene' to pandas dataframe

# columns of interest 'chrom' (chrX, %s), 'txStart' (number, %s), 'txEnd' (number , %s)

print "Extracting reference genome table (HG38) from UCSC Genome Browser"

df = g.dataframe('refGene')

df[['txStart', 'txEnd']] = df[['txStart', 'txEnd']].astype(int)

genes = pd.Series(np.zeros(vcf_ex_sub.shape[0]))

#gene = hg19.bin_query('refGene', vcf_ex_sub.CHROM[1], vcf_ex_sub.POS[1], vcf_ex_sub.POS[1])

#print vcf_ex_sub.POS.iloc[0]

for i in range(0, vcf_ex_sub.shape[0]):

#genes[i] = df[[df.chrom == str(vcf_ex_sub.CHROM.iloc[i]) and df.txStart >= str(vcf_ex_sub.POS.iloc[i])]].bool() #and df.txStart >= str(vcf_ex_sub.POS.iloc[i]) and df.txEnd <= vcf_ex_sub.POS.iloc[i]].bool(),

chrom = vcf_ex_sub['CHROM'].iloc[i]

location = vcf_ex_sub['POS'].iloc[i]

#print i, chrom, location

tmp = df.copy()

tmp = tmp[tmp.chrom == chrom]

tmp = tmp[tmp['txStart'] <= location]

tmp = tmp[tmp['txEnd'] >= location]

if tmp.empty:

geneName = '0'

else :

geneName = tmp['name2'].iloc[0]

print geneName

#vcf_ex_sub['Hugo_Symbol'] = genes

#hg19.bin_query('refGene', vcf_ex_sub.CHROM, vcf_ex_sub.POS, vcf_ex_sub.POS)

#print vcf_ex_sub.head()

#print genes[0:5]

#vcf_ex['Gene'] = hg19.query('refGene', vcf_ex.C)

'''

for i, line in enumerate(open('dat.txt')):

toks = line.split()

if i == 0:

print "\t".join(['gene'] + toks)

else:

chrom, posns = toks[0].split(":")

start, end = map(int, posns.rstrip("|").split("-"))

genes = hg19.bin_query('refGene', chrom, start, end)

print "\t".join(["|".join(set(g.name2 for g in genes))] + toks)

'''