# HG changeset patch
# User bgruening
# Date 1537703547 14400
# Node ID 7b2455348edfbc354869f128f35b5d1096472896
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_parallel_coordinates_plot commit 93fcfe0fa6a64246d13e0fb7e35a357985b02465
diff -r 000000000000 -r 7b2455348edf paracords_plot.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/paracords_plot.py Sun Sep 23 07:52:27 2018 -0400
@@ -0,0 +1,83 @@
+import sys
+import argparse
+import plotly
+import plotly.graph_objs as go
+import pandas as pd
+
+def main(infile, col_dimensions, col_color):
+ """
+ Produce an interactive paracords plotting html
+ Args:
+ infile: str, tabular file
+ col_dimensions: str, comma separated index numbers. For example: "3,4,5"
+ col_color: str, index number
+ """
+ df = pd.read_csv(infile, sep='\t', parse_dates=True)
+
+ dimensions = []
+ col_dimensions = [int(x)-1 for x in col_dimensions.split(',')]
+ for col in col_dimensions:
+ values = df[df.columns[col]]
+ if all(type(e) is int for e in values ):
+ dimensions.append(
+ dict( values = values,
+ tickformat = ",.2r",
+ label = df.columns[col])
+ )
+ elif all(type(e) is float for e in values ):
+ dimensions.append(
+ dict( values = values,
+ tickformat = "g",
+ label = df.columns[col])
+ )
+ else:
+ unique_values = list(set(values))
+ dimensions.append(
+ dict( range = [0, len(unique_values)-1],
+ tickvals = list(range(len(unique_values))),
+ ticktext = [str(e) for e in unique_values],
+ values = list(map(lambda e: unique_values.index(e), values )),
+ label = df.columns[col])
+ )
+
+ col_color = int(col_color) - 1
+ colors = df[df.columns[col_color]]
+ if all(type(e) is int for e in colors ):
+ tickformat = ",.2r"
+ elif all(type(e) is float for e in colors ):
+ tickformat = "g"
+ else:
+ sys.exit("Error: the column for coloring must contain all numerical values!")
+
+ dimensions.append(
+ dict(
+ values = colors,
+ tickformat = tickformat,
+ label = df.columns[col_color]
+ )
+ )
+
+ line = dict(
+ color = colors,
+ colorscale = 'Jet',
+ showscale = True,
+ reversescale = True
+ )
+
+ data = [
+ go.Parcoords(
+ line = line,
+ dimensions = dimensions
+ )
+ ]
+
+ plotly.offline.plot(data, filename = "output.html", auto_open=False)
+
+if __name__ == "__main__":
+ aparser = argparse.ArgumentParser()
+ aparser.add_argument( "-i", "--input", dest="infile", required=True)
+ aparser.add_argument( "-d", "--col_dimensions", dest="col_dimensions")
+ aparser.add_argument( "-c", "--col_color", dest="col_color")
+ args = aparser.parse_args()
+
+ main(args.infile, args.col_dimensions, args.col_color)
\ No newline at end of file
diff -r 000000000000 -r 7b2455348edf paracords_plot.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/paracords_plot.xml Sun Sep 23 07:52:27 2018 -0400
@@ -0,0 +1,50 @@
+
+ of tabular data
+
+ python
+ pandas
+ plotly
+
+ echo $version
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ `_ from a tabular file.
+Multiple columns are chosen for dimensions and a single column for coloring. The plot is buried in a html file which
+provides rich interactive features. Image can be saved in various format, such as 'png', 'svg', 'jpeg' and so on.
+
+
+ ]]>
+
+
diff -r 000000000000 -r 7b2455348edf test-data/parcoords01.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/parcoords01.tabular Sun Sep 23 07:52:27 2018 -0400
@@ -0,0 +1,21 @@
+mean_fit_time mean_score_time mean_test_score param_estimator__C param_preprocessing_2__k params rank_test_score split0_test_score split1_test_score split2_test_score std_fit_time std_score_time std_test_score
+0.0008893807729085287 0.0 0.0 1 -1 {u'estimator__C': 1, u'preprocessing_2__k': -1} 17 0.0 0.0 0.0 0.0002001698111627413 0.0 0.0
+0.005897998809814453 0.0006273587544759115 0.7104173862063515 1 3 {u'estimator__C': 1, u'preprocessing_2__k': 3} 13 0.7343996871246798 0.7417527475529206 0.655099723941454 0.0011778246691045516 0.000130497581592697 0.0392305127271727
+0.0043866634368896484 0.0007084210713704427 0.7727810607699421 1 5 {u'estimator__C': 1, u'preprocessing_2__k': 5} 9 0.771515656506971 0.7527052831977578 0.7941222426050976 0.0002401647992627138 0.00024091292632273708 0.01693206165038913
+0.0042876402537028 0.000507354736328125 0.7884200773575789 1 7 {u'estimator__C': 1, u'preprocessing_2__k': 7} 3 0.8337293212274764 0.7482926039856558 0.7832383068596044 0.0002709806131418131 2.436617660189741e-05 0.03507131992939495
+0.004957675933837891 0.0005903244018554688 0.7938837807353147 1 9 {u'estimator__C': 1, u'preprocessing_2__k': 9} 1 0.8426657572705988 0.7542580823607264 0.784727502574619 0.00020288510531872486 2.4974464706042437e-05 0.03666839982018526
+0.0009076595306396484 0.0 0.0 10 -1 {u'estimator__C': 10, u'preprocessing_2__k': -1} 17 0.0 0.0 0.0 8.050432012007906e-05 0.0 0.0
+0.008490721384684244 0.0007415612538655599 0.6964967631489266 10 3 {u'estimator__C': 10, u'preprocessing_2__k': 3} 14 0.7446910527620116 0.6904780454148434 0.6543211912699249 0.001563277277825898 0.0001715582888325998 0.03713800110306298
+0.005631049474080403 0.000587622324625651 0.7548120191889218 10 5 {u'estimator__C': 10, u'preprocessing_2__k': 5} 10 0.7716860817215373 0.6864563697215776 0.8062936061236508 0.0003878558746535774 0.0001247907126998644 0.05035733178586511
+0.004897356033325195 0.0005120436350504557 0.7816262883430569 10 7 {u'estimator__C': 10, u'preprocessing_2__k': 7} 6 0.8502810525482809 0.695034968349346 0.7995628441315439 0.00010891963452719922 4.032274655021451e-05 0.06463552526766699
+0.006504058837890625 0.0006016095479329427 0.7892707238057951 10 9 {u'estimator__C': 10, u'preprocessing_2__k': 9} 2 0.8566443610793398 0.7030451244270151 0.8081226859110302 0.000614871577463233 4.3557942685511835e-05 0.06410787123516704
+0.0016646385192871094 0.0 0.0 100 -1 {u'estimator__C': 100, u'preprocessing_2__k': -1} 17 0.0 0.0 0.0 0.0007425448564300234 0.0 0.0
+0.01219940185546875 0.0010093053181966145 0.6936822719445683 100 3 {u'estimator__C': 100, u'preprocessing_2__k': 3} 15 0.7477066644894652 0.6812918848407985 0.6520482665034413 0.005464220817015395 0.00042272585996138215 0.04002310620753206
+0.011892954508463541 0.0010009606679280598 0.751406681938562 100 5 {u'estimator__C': 100, u'preprocessing_2__k': 5} 12 0.768624772882289 0.6797852691816515 0.8058100037517455 0.0007243971607516297 0.00022965706673197417 0.052870315695895634
+0.015247027079264322 0.001065651575724284 0.780353600621928 100 7 {u'estimator__C': 100, u'preprocessing_2__k': 7} 7 0.8501332206882686 0.6869532193186081 0.8039743618589076 0.0016621871966479448 0.00041067567761326383 0.0686798538282826
+0.024774789810180664 0.000926971435546875 0.7862118205161561 100 9 {u'estimator__C': 100, u'preprocessing_2__k': 9} 4 0.8575095848829983 0.693096967262038 0.8080289094034317 0.007803944173115239 0.00019574615459401132 0.06887121427650549
+0.0009613037109375 0.0 0.0 1000 -1 {u'estimator__C': 1000, u'preprocessing_2__k': -1} 17 0.0 0.0 0.0 0.0002434119417931474 0.0 0.0
+0.025811036427815754 0.0009682973225911459 0.6926668993618743 1000 3 {u'estimator__C': 1000, u'preprocessing_2__k': 3} 16 0.7494954690157997 0.677124152164965 0.6513810769048582 0.006503047963414654 0.0002558118647454582 0.041535456561393944
+0.08626842498779297 0.0014065901438395183 0.7514165183827944 1000 5 {u'estimator__C': 1000, u'preprocessing_2__k': 5} 11 0.7684665422059135 0.6797430247352857 0.806039988207184 0.035840170189150804 0.00041956478276977655 0.052951287235421717
+0.21045462290445963 0.0009483496348063151 0.7800159337722933 1000 7 {u'estimator__C': 1000, u'preprocessing_2__k': 7} 8 0.8495071574714655 0.6864874567307644 0.8040531871146499 0.17032759330924754 0.0004962102711314027 0.06868865927055896
+0.18308266003926596 0.0009226799011230469 0.7854772046965829 1000 9 {u'estimator__C': 1000, u'preprocessing_2__k': 9} 5 0.8569025232106273 0.6903587533500763 0.8091703375290451 0.12143508018264616 0.00022723913124792145 0.07002490141563245
diff -r 000000000000 -r 7b2455348edf test-data/parcoords02.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/parcoords02.tabular Sun Sep 23 07:52:27 2018 -0400
@@ -0,0 +1,3 @@
+mean_fit_time mean_score_time mean_test_score param_estimator__dual param_estimator__penalty params rank_test_score split0_test_score split1_test_score split2_test_score std_fit_time std_score_time std_test_score
+0.6255233287811279 0.0008620421091715494 0.968186638388123 False l1 {u'estimator__dual': False, u'estimator__penalty': 'l1'} 2 0.9697933227344993 0.9697933227344993 0.964968152866242 0.011841861534965003 0.00024772262760403203 0.0022740031457860075
+0.05705531438191732 0.0006122589111328125 0.9684517497348887 False l2 {u'estimator__dual': False, u'estimator__penalty': 'l2'} 1 0.9697933227344993 0.9705882352941176 0.964968152866242 0.008102407793155115 2.7493704443217997e-05 0.00248262907456837
diff -r 000000000000 -r 7b2455348edf test-data/parcoords_plot01.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/parcoords_plot01.html Sun Sep 23 07:52:27 2018 -0400
@@ -0,0 +1,7 @@
+
\ No newline at end of file
diff -r 000000000000 -r 7b2455348edf test-data/parcoords_plot02.html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/parcoords_plot02.html Sun Sep 23 07:52:27 2018 -0400
@@ -0,0 +1,7 @@
+
\ No newline at end of file