| Next changeset 1:7b21a9b5922f (2018-10-10) |
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Commit message:
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/plotly_parallel_coordinates_plot commit 93fcfe0fa6a64246d13e0fb7e35a357985b02465 |
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added:
paracords_plot.py paracords_plot.xml test-data/parcoords01.tabular test-data/parcoords02.tabular test-data/parcoords_plot01.html test-data/parcoords_plot02.html |
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| 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 |
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| @@ -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 |
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| 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 |
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| @@ -0,0 +1,50 @@ +<tool id="plotly_parallel_coordinates_plot" name="Parallel Coordinates Plot" version="0.1"> + <description>of tabular data</description> + <requirements> + <requirement type="package" version="3.6">python</requirement> + <requirement type="package" version="0.22.0">pandas</requirement> + <requirement type="package" version="3.1.1">plotly</requirement> + </requirements> + <version_command>echo $version</version_command> + <command detect_errors="aggressive"><![CDATA[ + python '$__tool_directory__/paracords_plot.py' + -i '$infile' + -d '$col_dimensions' + -c '$col_color' +]]> + </command> + <inputs> + <param name="infile" type="data" format="tabular" label="Select data file :"/> + <param name="col_dimensions" multiple="true" type="data_column" data_ref="infile" use_header_names="true" display="checkboxes" label="Select the columns for dimentions:"/> + <param name="col_color" type="data_column" data_ref="infile" use_header_names="true" label="Select a column containg the values for coloring:" help="e.g. mean_test_score"/> + </inputs> + + <outputs> + <data name="output" format="html" from_work_dir="output.html" label="Plot visualization of tabular data on ${on_string}"/> + </outputs> + <tests> + <test> + <param name="infile" value="parcoords01.tabular" ftype="tabular"/> + <param name="col_dimensions" value="4,5"/> + <param name="col_color" value="3"/> + <output name="output" file="parcoords_plot01.html" compare="sim_size"/> + </test> + <test> + <param name="infile" value="parcoords02.tabular" ftype="tabular"/> + <param name="col_dimensions" value="4,5"/> + <param name="col_color" value="3"/> + <output name="output" file="parcoords_plot02.html" compare="sim_size"/> + </test> + </tests> + <help><![CDATA[ +**What it does** + + +Produce a `parallel coordinates plot <https://plot.ly/python/parallel-coordinates-plot/>`_ 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. + + + ]]> + </help> +</tool> |
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| 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 |
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| @@ -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 |
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| 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 |
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| @@ -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 |
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| 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 |
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| 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 |
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