comparison pairwise_metrics.xml @ 19:49e5ce162e0e draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f54ff2ba2f8e7542d68966ce5a6b17d7f624ac48

18:ca5debf1fc88

import pandas
import numpy as np
from sklearn.metrics import pairwise
from sklearn.metrics import pairwise_distances_argmin
from scipy.io import mmread
from scipy.io import mmwrite

input_json_path = sys.argv[1]
params = json.load(open(input_json_path, "r"))

options = params["input_type"]["metric_functions"]["options"]
metric_function = params["input_type"]["metric_functions"]["selected_metric_function"]

input_iter = []
#for $i, $s in enumerate( $input_type.input_files )
input_index=$i
input_path="${s.input.file_name}"
#if $input_type.selected_input_type == "sparse":
input_iter.append(mmread(open(input_path, 'r')))
#else:
input_iter.append(pandas.read_csv(input_path, sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False ).values)
#end if
#end for

19:49e5ce162e0e

import pandas
import numpy as np
from sklearn.metrics import pairwise
from sklearn.metrics import pairwise_distances_argmin
from scipy.io import mmread

input_json_path = sys.argv[1]
with open(input_json_path, "r") as param_handler:
    params = json.load(param_handler)


options = params["input_type"]["metric_functions"]["options"]
metric_function = params["input_type"]["metric_functions"]["selected_metric_function"]

input_iter = []
#for $i, $s in enumerate( $input_type.input_files )
input_index=$i
input_path="${s.input.file_name}"
#if $input_type.selected_input_type == "sparse":
input_iter.append(mmread(input_path))
#else:
input_iter.append(pandas.read_csv(input_path, sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False ).values)
#end if
#end for