try:
col_mapping[ ls[0] ] = ls[1]
except IndexError:
pass
return col_mapping
if __name__=="__main__":
parser = ArgumentParser()
parser.add_argument("-f", "--platereader_data", required=True, help="")
parser.add_argument("-m", "--col_mapping_file", required=True, help="")
parser.add_argument("-i", "--idx", type=int, help="")
parser.add_argument("-p", "--pts", default=5, type=int, help="last number of points taken")
opts = parser.parse_args()
df = parse_platereader_data.read_data( opts.platereader_data )
if opts.idx:
print "Time point taken:", df.Time[opts.idx]
df = df[:opts.idx]
data_Dict = np.mean( df.tail( opts.pts ) ).to_dict() # col:mP_value
#print data_Dict
col_mapping_dict = get_col_mapping( opts.col_mapping_file )
x = []
y = []
to_plot_Dict = {}
# get a dict, { conc:mp_value }
for col in col_mapping_dict.keys():
conc = float( col_mapping_dict[col] )
print ls
try:
col_mapping[ ls[0] ] = ls[1]
except IndexError:
pass
return col_mapping
if __name__=="__main__":
parser = ArgumentParser()
parser.add_argument("-f", "--table", required=True, help="")
parser.add_argument("-m", "--col_mapping_file", help="")
parser.add_argument("--sep", default="\t", help="")
opts = parser.parse_args()
data_frame = parse_platereader_data.read_data( opts.table )
print len(data_frame.columns), data_frame.columns
x = np.array( map( float, data_frame.Time ) )
colors = iter(cm.rainbow(np.linspace(0, 1, len(data_frame.columns))))
#inhib_x = []
inhib_y = []
if opts.col_mapping_file:
col_mapping_dict = get_col_mapping( opts.col_mapping_file )
for col, data in data_frame.iteritems():
if col=="Time":
continue
if col[0] in ["C", "D", "E", "F", "G"]:
continue
y = np.array( data_frame[col] )