Mercurial > repos > iuc > rpy_statistics_collection
changeset 1:2e7bc1bb2dbe draft default tip
Uploaded
| author | iuc |
|---|---|
| date | Fri, 09 Jan 2015 12:56:07 -0500 |
| parents | ffcdde989859 |
| children | |
| files | cca.xml cor.xml gsummary.xml histogram.xml kcca.xml kpca.xml linear_regression.xml logistic_regression_vif.xml partialR_square.xml pca.xml readme.rst scatterplot.xml short_reads_figure_score.xml statistic_tools_macros.xml tool_dependencies.xml tool_test_output.html tool_test_output.json |
| diffstat | 17 files changed, 1363 insertions(+), 944 deletions(-) [+] |
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--- a/cca.xml Tue Jul 29 06:30:45 2014 -0400 +++ b/cca.xml Fri Jan 09 12:56:07 2015 -0500 @@ -1,97 +1,101 @@ -<tool id="cca1" name="Canonical Correlation Analysis" version="1.1.0"> - <description> </description> - <expand macro="requirements" /> - <macros> - <import>statistic_tools_macros.xml</import> - </macros> - <command interpreter="python"> - cca.py - $input1 - $x_cols - $y_cols - $x_scale - $y_scale - $std_scores - $out_file1 - $out_file2 - </command> - <inputs> - <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/> - <param name="x_cols" label="Select columns containing X variables " type="data_column" data_ref="input1" numerical="True" multiple="true" > - <validator type="no_options" message="Please select at least one column."/> - </param> - <param name="y_cols" label="Select columns containing Y variables " type="data_column" data_ref="input1" numerical="True" multiple="true" > - <validator type="no_options" message="Please select at least one column."/> - </param> - <param name="x_scale" type="select" label="Type of Scaling for X variables" help="Can be used to center and/or scale variables"> - <option value="none" selected="true">None</option> - <option value="center">Center only</option> - <option value="scale">Scale only</option> - <option value="both">Center and Scale</option> - </param> - <param name="y_scale" type="select" label="Type of Scaling for Y variables" help="Can be used to center and/or scale variables"> - <option value="none" selected="true">None</option> - <option value="center">Center only</option> - <option value="scale">Scale only</option> - <option value="both">Center and Scale</option> - </param> - <param name="std_scores" type="select" label="Report standardized scores?" help="Selecting 'Yes' will rescale scores (and coefficients) to produce scores of unit variance"> - <option value="no" selected="true">No</option> - <option value="yes">Yes</option> - </param> - </inputs> - <outputs> - <data format="input" name="out_file1" metadata_source="input1" /> - <data format="pdf" name="out_file2" /> - </outputs> - <tests> - <test> - <param name="input1" value="iris.tabular"/> - <param name="x_cols" value="3,4"/> - <param name="y_cols" value="1,2"/> - <param name="x_scale" value="both"/> - <param name="y_scale" value="scale"/> - <param name="std_scores" value="yes"/> - <output name="out_file1" file="cca_out1.tabular"/> - <output name="out_file2" file="cca_out2.pdf"/> - </test> - </tests> - <help> - - -.. class:: infomark - -**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters* - ------ - -.. class:: infomark - -**What it does** - -This tool uses functions from 'yacca' library from R statistical package to perform Canonical Correlation Analysis (CCA) on the input data. -It outputs two files, one containing the summary statistics of the performed CCA, and the other containing helioplots, which display structural loadings of X and Y variables on different canonical components. - -*Carter T. Butts (2009). yacca: Yet Another Canonical Correlation Analysis Package. R package version 1.1.* - ------ - -.. class:: warningmark - -**Note** - -- This tool currently treats all predictor and response variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. - -- Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis. - -- The summary statistics in the output are described below: - - - correlation: Canonical correlation between the canonical variates (i.e. transformed variables) - - F-statistic: F-value obtained from F Test for Canonical Correlations Using Rao's Approximation - - p-value: denotes significance of canonical correlations - - Coefficients: represent the coefficients of X and Y variables on each canonical variate - - Loadings: represent the correlations between the original variables in each set and their respective canonical variates - - CrossLoadings: represent the correlations between the original variables in each set and the opposite canonical variates - - </help> -</tool> +<tool id="cca1" name="Canonical Correlation Analysis" version="1.1.0"> + <description> </description> + <expand macro="requirements" /> + <macros> + <import>statistic_tools_macros.xml</import> + </macros> + <command interpreter="python"> +<![CDATA[ + cca.py + $input1 + $x_cols + $y_cols + $x_scale + $y_scale + $std_scores + $out_file1 + $out_file2 +]]> + </command> + <inputs> + <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/> + <param name="x_cols" label="Select columns containing X variables " type="data_column" data_ref="input1" numerical="True" multiple="true" > + <validator type="no_options" message="Please select at least one column."/> + </param> + <param name="y_cols" label="Select columns containing Y variables " type="data_column" data_ref="input1" numerical="True" multiple="true" > + <validator type="no_options" message="Please select at least one column."/> + </param> + <param name="x_scale" type="select" label="Type of Scaling for X variables" help="Can be used to center and/or scale variables"> + <option value="none" selected="true">None</option> + <option value="center">Center only</option> + <option value="scale">Scale only</option> + <option value="both">Center and Scale</option> + </param> + <param name="y_scale" type="select" label="Type of Scaling for Y variables" help="Can be used to center and/or scale variables"> + <option value="none" selected="true">None</option> + <option value="center">Center only</option> + <option value="scale">Scale only</option> + <option value="both">Center and Scale</option> + </param> + <param name="std_scores" type="select" label="Report standardized scores?" help="Selecting 'Yes' will rescale scores (and coefficients) to produce scores of unit variance"> + <option value="no" selected="true">No</option> + <option value="yes">Yes</option> + </param> + </inputs> + <outputs> + <data format="input" name="out_file1" metadata_source="input1" /> + <data format="pdf" name="out_file2" /> + </outputs> + <tests> + <test> + <param name="input1" value="iris.tabular"/> + <param name="x_cols" value="3,4"/> + <param name="y_cols" value="1,2"/> + <param name="x_scale" value="both"/> + <param name="y_scale" value="scale"/> + <param name="std_scores" value="yes"/> + <output name="out_file1" file="cca_out1.tabular"/> + <output name="out_file2" file="cca_out2.pdf"/> + </test> + </tests> + <help> +<![CDATA[ + + +.. class:: infomark + +**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters* + +----- + +.. class:: infomark + +**What it does** + +This tool uses functions from 'yacca' library from R statistical package to perform Canonical Correlation Analysis (CCA) on the input data. +It outputs two files, one containing the summary statistics of the performed CCA, and the other containing helioplots, which display structural loadings of X and Y variables on different canonical components. + +*Carter T. Butts (2009). yacca: Yet Another Canonical Correlation Analysis Package. R package version 1.1.* + +----- + +.. class:: warningmark + +**Note** + +- This tool currently treats all predictor and response variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. + +- Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis. + +- The summary statistics in the output are described below: + + - correlation: Canonical correlation between the canonical variates (i.e. transformed variables) + - F-statistic: F-value obtained from F Test for Canonical Correlations Using Rao's Approximation + - p-value: denotes significance of canonical correlations + - Coefficients: represent the coefficients of X and Y variables on each canonical variate + - Loadings: represent the correlations between the original variables in each set and their respective canonical variates + - CrossLoadings: represent the correlations between the original variables in each set and the opposite canonical variates + +]]> + </help> +</tool>
--- a/cor.xml Tue Jul 29 06:30:45 2014 -0400 +++ b/cor.xml Fri Jan 09 12:56:07 2015 -0500 @@ -1,102 +1,112 @@ -<tool id="cor2" name="Correlation" version="1.1.0"> - <description>for numeric columns</description> - <expand macro="requirements" /> - <macros> - <import>statistic_tools_macros.xml</import> - </macros> - <command interpreter="python">cor.py $input1 $out_file1 $numeric_columns $method</command> - <inputs> - <param format="tabular" name="input1" type="data" label="Dataset" help="Dataset missing? See TIP below"/> - <param name="numeric_columns" label="Numerical columns" type="data_column" numerical="True" multiple="True" data_ref="input1" help="Multi-select list - hold the appropriate key while clicking to select multiple columns" /> - <param name="method" type="select" label="Method"> - <option value="pearson">Pearson</option> - <option value="kendall">Kendall rank</option> - <option value="spearman">Spearman rank</option> - </param> - </inputs> - <outputs> - <data format="txt" name="out_file1" /> - </outputs> - <tests> - <!-- - Test a tabular input with the first line being a comment without a # character to start - --> - <test> - <param name="input1" value="cor.tabular" /> - <param name="numeric_columns" value="2,3" /> - <param name="method" value="pearson" /> - <output name="out_file1" file="cor_out.txt" /> - </test> - </tests> - <help> - -.. class:: infomark - -**TIP:** If your data is not TAB delimited, use *Text Manipulation->Convert* - -.. class:: warningmark - -Missing data ("nan") removed from each pairwise comparison - ------ - -**Syntax** - -This tool computes the matrix of correlation coefficients between numeric columns. - -- All invalid, blank and comment lines are skipped when performing computations. The number of skipped lines is displayed in the resulting history item. - -- **Pearson's Correlation** reflects the degree of linear relationship between two variables. It ranges from +1 to -1. A correlation of +1 means that there is a perfect positive linear relationship between variables. The formula for Pearson's correlation is: - - .. image:: $PATH_TO_IMAGES/pearson.png - - where n is the number of items - -- **Kendall's rank correlation** is used to measure the degree of correspondence between two rankings and assessing the significance of this correspondence. The formula for Kendall's rank correlation is: - - .. image:: $PATH_TO_IMAGES/kendall.png - - where n is the number of items, and P is the sum. - -- **Spearman's rank correlation** assesses how well an arbitrary monotonic function could describe the relationship between two variables, without making any assumptions about the frequency distribution of the variables. The formula for Spearman's rank correlation is - - .. image:: $PATH_TO_IMAGES/spearman.png - - where D is the difference between the ranks of corresponding values of X and Y, and N is the number of pairs of values. - ------ - -**Example** - -- Input file:: - - #Person Height Self Esteem - 1 68 4.1 - 2 71 4.6 - 3 62 3.8 - 4 75 4.4 - 5 58 3.2 - 6 60 3.1 - 7 67 3.8 - 8 68 4.1 - 9 71 4.3 - 10 69 3.7 - 11 68 3.5 - 12 67 3.2 - 13 63 3.7 - 14 62 3.3 - 15 60 3.4 - 16 63 4.0 - 17 65 4.1 - 18 67 3.8 - 19 63 3.4 - 20 61 3.6 - -- Computing the correlation coefficients between columns 2 and 3 of the above file (using Pearson's Correlation), the output is:: - - 1.0 0.730635686279 - 0.730635686279 1.0 - - So the correlation for our twenty cases is .73, which is a fairly strong positive relationship. - </help> -</tool> +<tool id="cor2" name="Correlation" version="1.1.0"> + <description>for numeric columns</description> + <expand macro="requirements" /> + <macros> + <import>statistic_tools_macros.xml</import> + </macros> + <command interpreter="python"> +<![CDATA[ + cor.py + $input1 + $out_file1 + $numeric_columns + $method +]]> + </command> + <inputs> + <param format="tabular" name="input1" type="data" label="Dataset" help="Dataset missing? See TIP below"/> + <param name="numeric_columns" label="Numerical columns" type="data_column" numerical="True" multiple="True" data_ref="input1" help="Multi-select list - hold the appropriate key while clicking to select multiple columns" /> + <param name="method" type="select" label="Method"> + <option value="pearson">Pearson</option> + <option value="kendall">Kendall rank</option> + <option value="spearman">Spearman rank</option> + </param> + </inputs> + <outputs> + <data format="txt" name="out_file1" /> + </outputs> + <tests> + <!-- + Test a tabular input with the first line being a comment without a # character to start + --> + <test> + <param name="input1" value="cor.tabular" /> + <param name="numeric_columns" value="2,3" /> + <param name="method" value="pearson" /> + <output name="out_file1" file="cor_out.txt" /> + </test> + </tests> + <help> +<![CDATA[ + +.. class:: infomark + +**TIP:** If your data is not TAB delimited, use *Text Manipulation->Convert* + +.. class:: warningmark + +Missing data ("nan") removed from each pairwise comparison + +----- + +**Syntax** + +This tool computes the matrix of correlation coefficients between numeric columns. + +- All invalid, blank and comment lines are skipped when performing computations. The number of skipped lines is displayed in the resulting history item. + +- **Pearson's Correlation** reflects the degree of linear relationship between two variables. It ranges from +1 to -1. A correlation of +1 means that there is a perfect positive linear relationship between variables. The formula for Pearson's correlation is: + + .. image:: $PATH_TO_IMAGES/pearson.png + + where n is the number of items + +- **Kendall's rank correlation** is used to measure the degree of correspondence between two rankings and assessing the significance of this correspondence. The formula for Kendall's rank correlation is: + + .. image:: $PATH_TO_IMAGES/kendall.png + + where n is the number of items, and P is the sum. + +- **Spearman's rank correlation** assesses how well an arbitrary monotonic function could describe the relationship between two variables, without making any assumptions about the frequency distribution of the variables. The formula for Spearman's rank correlation is + + .. image:: $PATH_TO_IMAGES/spearman.png + + where D is the difference between the ranks of corresponding values of X and Y, and N is the number of pairs of values. + +----- + +**Example** + +- Input file:: + + #Person Height Self Esteem + 1 68 4.1 + 2 71 4.6 + 3 62 3.8 + 4 75 4.4 + 5 58 3.2 + 6 60 3.1 + 7 67 3.8 + 8 68 4.1 + 9 71 4.3 + 10 69 3.7 + 11 68 3.5 + 12 67 3.2 + 13 63 3.7 + 14 62 3.3 + 15 60 3.4 + 16 63 4.0 + 17 65 4.1 + 18 67 3.8 + 19 63 3.4 + 20 61 3.6 + +- Computing the correlation coefficients between columns 2 and 3 of the above file (using Pearson's Correlation), the output is:: + + 1.0 0.730635686279 + 0.730635686279 1.0 + + So the correlation for our twenty cases is .73, which is a fairly strong positive relationship. +]]> + </help> +</tool>
--- a/gsummary.xml Tue Jul 29 06:30:45 2014 -0400 +++ b/gsummary.xml Fri Jan 09 12:56:07 2015 -0500 @@ -4,7 +4,14 @@ <macros> <import>statistic_tools_macros.xml</import> </macros> - <command interpreter="python">gsummary.py $input $out_file1 "$cond"</command> + <command interpreter="python"> +<![CDATA[ + gsummary.py + $input + $out_file1 + "$cond" +]]> + </command> <inputs> <param format="tabular" name="input" type="data" label="Summary statistics on" help="Dataset missing? See TIP below"/> <param name="cond" size="30" type="text" value="c5" label="Column or expression" help="See syntax below"> @@ -22,6 +29,7 @@ </test> </tests> <help> +<![CDATA[ .. class:: warningmark @@ -29,7 +37,7 @@ .. class:: infomark -**TIP:** If your data is not TAB delimited, use *Text Manipulation->Convert delimiters to TAB* +**TIP:** If your data is not TAB delimited, use *Text Manipulation->Convert delimiters to TAB* .. class:: infomark @@ -73,5 +81,6 @@ #sum mean stdev 0% 25% 50% 75% 100% 29250.000 7312.500 7198.636 1700.000 1895.000 5280.000 10697.500 16990.000 +]]> </help> </tool>
--- a/histogram.xml Tue Jul 29 06:30:45 2014 -0400 +++ b/histogram.xml Fri Jan 09 12:56:07 2015 -0500 @@ -1,77 +1,91 @@ -<tool id="histogram_rpy" name="Histogram" version="1.1.0"> - <description>of a numeric column</description> - <expand macro="requirements" /> - <macros> - <import>statistic_tools_macros.xml</import> - </macros> - <command interpreter="python">histogram.py $input $out_file1 $numerical_column "$title" "$xlab" $breaks $density $frequency</command> - <inputs> - <param name="input" type="data" format="tabular" label="Dataset" help="Dataset missing? See TIP below"/> - <param name="numerical_column" type="data_column" data_ref="input" numerical="True" label="Numerical column for x axis" /> - <param name="breaks" type="integer" size="4" value="0" label="Number of breaks (bars)"/> - <param name="title" type="text" size="30" value="Histogram" label="Plot title"/> - <param name="xlab" type="text" size="30" value="V1" label="Label for x axis"/> - <param name="density" type="boolean" checked="yes" label="Include smoothed density"/> - <param name="frequency" type="boolean" checked="no" label="Plot as frequency (counts)"/> - </inputs> - <outputs> - <data format="pdf" name="out_file1" /> - </outputs> - <tests> - <test> - <param name="input" value="histogram_in1.tabular" ftype="tabular"/> - <param name="numerical_column" value="2"/> - <param name="breaks" value="0"/> - <param name="title" value="Histogram"/> - <param name="xlab" value="V1"/> - <param name="density" value="true"/> - <param name="frequency" value="false"/> - <output name="out_file1" lines_diff="10" file="histogram_out1.pdf"/> - </test> - </tests> - <help> - -.. class:: infomark - -**TIP:** To remove comment lines that do not begin with a *#* character, use *Text Manipulation->Remove beginning* - - .. class:: infomark - -**TIP:** If your data is not TAB delimited, use *Text Manipulation->Convert* - ------ - -**Syntax** - -This tool computes a histogram of the numerical values in a column of a dataset. - -- All invalid, blank and comment lines in the dataset are skipped. The number of skipped lines is displayed in the resulting history item. -- **Column for x axis** - only numerical columns are possible. -- **Number of breaks(bars)** - breakpoints between histogram cells. Value of '0' will determine breaks automatically. -- **Plot title** - the histogram title. -- **Label for x axis** - the label of the x axis for the histogram. -- **Include smoothed density** - if checked, the resulting graph will join the given corresponding points with line segments. - ------ - -**Example** - -- Input file:: - - 1 68 4.1 - 2 71 4.6 - 3 62 3.8 - 4 75 4.4 - 5 58 3.2 - 6 60 3.1 - 7 67 3.8 - 8 68 4.1 - 9 71 4.3 - 10 69 3.7 - -- Create a histogram on column 2 of the above dataset. - -.. image:: $PATH_TO_IMAGES/histogram2.png - -</help> -</tool> +<tool id="histogram_rpy" name="Histogram" version="1.1.0"> + <description>of a numeric column</description> + <expand macro="requirements" /> + <macros> + <import>statistic_tools_macros.xml</import> + </macros> + <command interpreter="python"> +<![CDATA[ + histogram.py + $input + $out_file1 + $numerical_column + "$title" + "$xlab" + $breaks + $density + $frequency +]]> + </command> + <inputs> + <param name="input" type="data" format="tabular" label="Dataset" help="Dataset missing? See TIP below"/> + <param name="numerical_column" type="data_column" data_ref="input" numerical="True" label="Numerical column for x axis" /> + <param name="breaks" type="integer" size="4" value="0" label="Number of breaks (bars)"/> + <param name="title" type="text" size="30" value="Histogram" label="Plot title"/> + <param name="xlab" type="text" size="30" value="V1" label="Label for x axis"/> + <param name="density" type="boolean" checked="yes" label="Include smoothed density"/> + <param name="frequency" type="boolean" checked="no" label="Plot as frequency (counts)"/> + </inputs> + <outputs> + <data format="pdf" name="out_file1" /> + </outputs> + <tests> + <test> + <param name="input" value="histogram_in1.tabular" ftype="tabular"/> + <param name="numerical_column" value="2"/> + <param name="breaks" value="0"/> + <param name="title" value="Histogram"/> + <param name="xlab" value="V1"/> + <param name="density" value="true"/> + <param name="frequency" value="false"/> + <output name="out_file1" lines_diff="10" file="histogram_out1.pdf"/> + </test> + </tests> + <help> +<![CDATA[ + +.. class:: infomark + +**TIP:** To remove comment lines that do not begin with a *#* character, use *Text Manipulation->Remove beginning* + + .. class:: infomark + +**TIP:** If your data is not TAB delimited, use *Text Manipulation->Convert* + +----- + +**Syntax** + +This tool computes a histogram of the numerical values in a column of a dataset. + +- All invalid, blank and comment lines in the dataset are skipped. The number of skipped lines is displayed in the resulting history item. +- **Column for x axis** - only numerical columns are possible. +- **Number of breaks(bars)** - breakpoints between histogram cells. Value of '0' will determine breaks automatically. +- **Plot title** - the histogram title. +- **Label for x axis** - the label of the x axis for the histogram. +- **Include smoothed density** - if checked, the resulting graph will join the given corresponding points with line segments. + +----- + +**Example** + +- Input file:: + + 1 68 4.1 + 2 71 4.6 + 3 62 3.8 + 4 75 4.4 + 5 58 3.2 + 6 60 3.1 + 7 67 3.8 + 8 68 4.1 + 9 71 4.3 + 10 69 3.7 + +- Create a histogram on column 2 of the above dataset. + +.. image:: $PATH_TO_IMAGES/histogram2.png + +]]> + </help> +</tool>
--- a/kcca.xml Tue Jul 29 06:30:45 2014 -0400 +++ b/kcca.xml Fri Jan 09 12:56:07 2015 -0500 @@ -1,151 +1,155 @@ -<tool id="kcca1" name="Kernel Canonical Correlation Analysis" version="1.1.0"> - <description> </description> - <expand macro="requirements" /> - <macros> - <import>statistic_tools_macros.xml</import> - </macros> - <command interpreter="python"> - kcca.py - --input=$input1 - --output1=$out_file1 - --x_cols=$x_cols - --y_cols=$y_cols - --kernel=$kernelChoice.kernel - --features=$features - #if $kernelChoice.kernel == "rbfdot" or $kernelChoice.kernel == "anovadot": - --sigma=$kernelChoice.sigma - --degree="None" - --scale="None" - --offset="None" - --order="None" - #elif $kernelChoice.kernel == "polydot": - --sigma="None" - --degree=$kernelChoice.degree - --scale=$kernelChoice.scale - --offset=$kernelChoice.offset - --order="None" - #elif $kernelChoice.kernel == "tanhdot": - --sigma="None" - --degree="None" - --scale=$kernelChoice.scale - --offset=$kernelChoice.offset - --order="None" - #elif $kernelChoice.kernel == "besseldot": - --sigma=$kernelChoice.sigma - --degree=$kernelChoice.degree - --scale="None" - --offset="None" - --order=$kernelChoice.order - #elif $kernelChoice.kernel == "anovadot": - --sigma=$kernelChoice.sigma - --degree=$kernelChoice.degree - --scale="None" - --offset="None" - --order="None" - #else: - --sigma="None" - --degree="None" - --scale="None" - --offset="None" - --order="None" - #end if - </command> - <inputs> - <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/> - <param name="x_cols" label="Select columns containing X variables " type="data_column" data_ref="input1" numerical="True" multiple="true" > - <validator type="no_options" message="Please select at least one column."/> - </param> - <param name="y_cols" label="Select columns containing Y variables " type="data_column" data_ref="input1" numerical="True" multiple="true" > - <validator type="no_options" message="Please select at least one column."/> - </param> - <param name="features" size="10" type="integer" value="2" label="Number of canonical components to return" help="Enter an integer value greater than 0"/> - <conditional name="kernelChoice"> - <param name="kernel" type="select" label="Kernel function"> - <option value="rbfdot" selected="true">Gaussian Radial Basis Function</option> - <option value="polydot">Polynomial</option> - <option value="vanilladot">Linear</option> - <option value="tanhdot">Hyperbolic</option> - <option value="laplacedot">Laplacian</option> - <option value="besseldot">Bessel</option> - <option value="anovadot">ANOVA Radial Basis Function</option> - <option value="splinedot">Spline</option> - </param> - <when value="vanilladot" /> - <when value="splinedot" /> - <when value="rbfdot"> - <param name="sigma" size="10" type="float" value="1" label="sigma (inverse kernel width)" /> - </when> - <when value="laplacedot"> - <param name="sigma" size="10" type="float" value="1" label="sigma (inverse kernel width)" /> - </when> - <when value="polydot"> - <param name="degree" size="10" type="float" value="1" label="degree" /> - <param name="scale" size="10" type="float" value="1" label="scale" /> - <param name="offset" size="10" type="float" value="1" label="offset" /> - </when> - <when value="tanhdot"> - <param name="scale" size="10" type="float" value="1" label="scale" /> - <param name="offset" size="10" type="float" value="1" label="offset" /> - </when> - <when value="besseldot"> - <param name="sigma" size="10" type="float" value="1" label="sigma" /> - <param name="order" size="10" type="float" value="1" label="order" /> - <param name="degree" size="10" type="float" value="1" label="degree" /> - </when> - <when value="anovadot"> - <param name="sigma" size="10" type="float" value="1" label="sigma" /> - <param name="degree" size="10" type="float" value="1" label="degree" /> - </when> - </conditional> - </inputs> - <outputs> - <data format="input" name="out_file1" metadata_source="input1" /> - </outputs> - <tests> - <test> - <param name="input1" value="iris.tabular"/> - <param name="x_cols" value="1,2"/> - <param name="y_cols" value="3,4"/> - <param name="kernel" value="anovadot"/> - <param name="features" value="4"/> - <param name="sigma" value="0.1"/> - <param name="degree" value="2"/> - <output name="out_file1" file="kcca_out1.tabular" compare="re_match"/> - </test> - <test> - <param name="input1" value="iris.tabular"/> - <param name="x_cols" value="3,4"/> - <param name="y_cols" value="1,2"/> - <param name="kernel" value="rbfdot"/> - <param name="features" value="2"/> - <param name="sigma" value="0.5"/> - <output name="out_file1" file="kcca_out2.tabular" compare="re_match"/> - </test> - </tests> - <help> - - -.. class:: infomark - -**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters* - ------ - -.. class:: infomark - -**What it does** - -This tool uses functions from 'kernlab' library from R statistical package to perform Kernel Canonical Correlation Analysis (kCCA) on the input data. - -*Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis (2004). kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software 11(9), 1-20. URL http://www.jstatsoft.org/v11/i09/* - ------ - -.. class:: warningmark - -**Note** - -This tool currently treats all variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis. - - </help> -</tool> +<tool id="kcca1" name="Kernel Canonical Correlation Analysis" version="1.1.0"> + <description> </description> + <expand macro="requirements" /> + <macros> + <import>statistic_tools_macros.xml</import> + </macros> + <command interpreter="python"> +<![CDATA[ + kcca.py + --input=$input1 + --output1=$out_file1 + --x_cols=$x_cols + --y_cols=$y_cols + --kernel=$kernelChoice.kernel + --features=$features + #if $kernelChoice.kernel == "rbfdot" or $kernelChoice.kernel == "anovadot": + --sigma=$kernelChoice.sigma + --degree="None" + --scale="None" + --offset="None" + --order="None" + #elif $kernelChoice.kernel == "polydot": + --sigma="None" + --degree=$kernelChoice.degree + --scale=$kernelChoice.scale + --offset=$kernelChoice.offset + --order="None" + #elif $kernelChoice.kernel == "tanhdot": + --sigma="None" + --degree="None" + --scale=$kernelChoice.scale + --offset=$kernelChoice.offset + --order="None" + #elif $kernelChoice.kernel == "besseldot": + --sigma=$kernelChoice.sigma + --degree=$kernelChoice.degree + --scale="None" + --offset="None" + --order=$kernelChoice.order + #elif $kernelChoice.kernel == "anovadot": + --sigma=$kernelChoice.sigma + --degree=$kernelChoice.degree + --scale="None" + --offset="None" + --order="None" + #else: + --sigma="None" + --degree="None" + --scale="None" + --offset="None" + --order="None" + #end if +]]> + </command> + <inputs> + <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/> + <param name="x_cols" label="Select columns containing X variables " type="data_column" data_ref="input1" numerical="True" multiple="true" > + <validator type="no_options" message="Please select at least one column."/> + </param> + <param name="y_cols" label="Select columns containing Y variables " type="data_column" data_ref="input1" numerical="True" multiple="true" > + <validator type="no_options" message="Please select at least one column."/> + </param> + <param name="features" size="10" type="integer" value="2" label="Number of canonical components to return" help="Enter an integer value greater than 0"/> + <conditional name="kernelChoice"> + <param name="kernel" type="select" label="Kernel function"> + <option value="rbfdot" selected="true">Gaussian Radial Basis Function</option> + <option value="polydot">Polynomial</option> + <option value="vanilladot">Linear</option> + <option value="tanhdot">Hyperbolic</option> + <option value="laplacedot">Laplacian</option> + <option value="besseldot">Bessel</option> + <option value="anovadot">ANOVA Radial Basis Function</option> + <option value="splinedot">Spline</option> + </param> + <when value="vanilladot" /> + <when value="splinedot" /> + <when value="rbfdot"> + <param name="sigma" size="10" type="float" value="1" label="sigma (inverse kernel width)" /> + </when> + <when value="laplacedot"> + <param name="sigma" size="10" type="float" value="1" label="sigma (inverse kernel width)" /> + </when> + <when value="polydot"> + <param name="degree" size="10" type="float" value="1" label="degree" /> + <param name="scale" size="10" type="float" value="1" label="scale" /> + <param name="offset" size="10" type="float" value="1" label="offset" /> + </when> + <when value="tanhdot"> + <param name="scale" size="10" type="float" value="1" label="scale" /> + <param name="offset" size="10" type="float" value="1" label="offset" /> + </when> + <when value="besseldot"> + <param name="sigma" size="10" type="float" value="1" label="sigma" /> + <param name="order" size="10" type="float" value="1" label="order" /> + <param name="degree" size="10" type="float" value="1" label="degree" /> + </when> + <when value="anovadot"> + <param name="sigma" size="10" type="float" value="1" label="sigma" /> + <param name="degree" size="10" type="float" value="1" label="degree" /> + </when> + </conditional> + </inputs> + <outputs> + <data format="input" name="out_file1" metadata_source="input1" /> + </outputs> + <tests> + <test> + <param name="input1" value="iris.tabular"/> + <param name="x_cols" value="1,2"/> + <param name="y_cols" value="3,4"/> + <param name="kernel" value="anovadot"/> + <param name="features" value="4"/> + <param name="sigma" value="0.1"/> + <param name="degree" value="2"/> + <output name="out_file1" file="kcca_out1.tabular" compare="re_match"/> + </test> + <test> + <param name="input1" value="iris.tabular"/> + <param name="x_cols" value="3,4"/> + <param name="y_cols" value="1,2"/> + <param name="kernel" value="rbfdot"/> + <param name="features" value="2"/> + <param name="sigma" value="0.5"/> + <output name="out_file1" file="kcca_out2.tabular" compare="re_match"/> + </test> + </tests> + <help> +<![CDATA[ + + +.. class:: infomark + +**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters* + +----- + +.. class:: infomark + +**What it does** + +This tool uses functions from 'kernlab' library from R statistical package to perform Kernel Canonical Correlation Analysis (kCCA) on the input data. + +*Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis (2004). kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software 11(9), 1-20. URL http://www.jstatsoft.org/v11/i09/* + +----- + +.. class:: warningmark + +**Note** + +This tool currently treats all variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis. + +]]> + </help> +</tool>
--- a/kpca.xml Tue Jul 29 06:30:45 2014 -0400 +++ b/kpca.xml Fri Jan 09 12:56:07 2015 -0500 @@ -1,141 +1,145 @@ -<tool id="kpca1" name="Kernel Principal Component Analysis" version="1.1.0"> - <description> </description> - <expand macro="requirements" /> - <macros> - <import>statistic_tools_macros.xml</import> - </macros> - <command interpreter="python"> - kpca.py - --input=$input1 - --output1=$out_file1 - --output2=$out_file2 - --var_cols=$var_cols - --kernel=$kernelChoice.kernel - --features=$features - #if $kernelChoice.kernel == "rbfdot" or $kernelChoice.kernel == "anovadot": - --sigma=$kernelChoice.sigma - --degree="None" - --scale="None" - --offset="None" - --order="None" - #elif $kernelChoice.kernel == "polydot": - --sigma="None" - --degree=$kernelChoice.degree - --scale=$kernelChoice.scale - --offset=$kernelChoice.offset - --order="None" - #elif $kernelChoice.kernel == "tanhdot": - --sigma="None" - --degree="None" - --scale=$kernelChoice.scale - --offset=$kernelChoice.offset - --order="None" - #elif $kernelChoice.kernel == "besseldot": - --sigma=$kernelChoice.sigma - --degree=$kernelChoice.degree - --scale="None" - --offset="None" - --order=$kernelChoice.order - #elif $kernelChoice.kernel == "anovadot": - --sigma=$kernelChoice.sigma - --degree=$kernelChoice.degree - --scale="None" - --offset="None" - --order="None" - #else: - --sigma="None" - --degree="None" - --scale="None" - --offset="None" - --order="None" - #end if - </command> - <inputs> - <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/> - <param name="var_cols" label="Select columns containing input variables " type="data_column" data_ref="input1" numerical="True" multiple="true" > - <validator type="no_options" message="Please select at least one column."/> - </param> - <param name="features" size="10" type="integer" value="2" label="Number of principal components to return" help="To return all, enter 0"/> - <conditional name="kernelChoice"> - <param name="kernel" type="select" label="Kernel function"> - <option value="rbfdot" selected="true">Gaussian Radial Basis Function</option> - <option value="polydot">Polynomial</option> - <option value="vanilladot">Linear</option> - <option value="tanhdot">Hyperbolic</option> - <option value="laplacedot">Laplacian</option> - <option value="besseldot">Bessel</option> - <option value="anovadot">ANOVA Radial Basis Function</option> - <option value="splinedot">Spline</option> - </param> - <when value="vanilladot" /> - <when value="splinedot" /> - <when value="rbfdot"> - <param name="sigma" size="10" type="float" value="1" label="sigma (inverse kernel width)" /> - </when> - <when value="laplacedot"> - <param name="sigma" size="10" type="float" value="1" label="sigma (inverse kernel width)" /> - </when> - <when value="polydot"> - <param name="degree" size="10" type="integer" value="1" label="degree" /> - <param name="scale" size="10" type="integer" value="1" label="scale" /> - <param name="offset" size="10" type="integer" value="1" label="offset" /> - </when> - <when value="tanhdot"> - <param name="scale" size="10" type="integer" value="1" label="scale" /> - <param name="offset" size="10" type="integer" value="1" label="offset" /> - </when> - <when value="besseldot"> - <param name="sigma" size="10" type="integer" value="1" label="sigma" /> - <param name="order" size="10" type="integer" value="1" label="order" /> - <param name="degree" size="10" type="integer" value="1" label="degree" /> - </when> - <when value="anovadot"> - <param name="sigma" size="10" type="integer" value="1" label="sigma" /> - <param name="degree" size="10" type="integer" value="1" label="degree" /> - </when> - </conditional> - </inputs> - <outputs> - <data format="input" name="out_file1" metadata_source="input1" /> - <data format="pdf" name="out_file2" /> - </outputs> - <tests> - <test> - <param name="input1" value="iris.tabular"/> - <param name="var_cols" value="1,2,3,4"/> - <param name="kernel" value="polydot"/> - <param name="features" value="2"/> - <param name="offset" value="0"/> - <param name="scale" value="1"/> - <param name="degree" value="2"/> - <output name="out_file1" file="kpca_out1.tabular"/> - <output name="out_file2" file="kpca_out2.pdf"/> - </test> - </tests> - <help> - - -.. class:: infomark - -**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters* - ------ - -.. class:: infomark - -**What it does** - -This tool uses functions from 'kernlab' library from R statistical package to perform Kernel Principal Component Analysis (kPCA) on the input data. It outputs two files, one containing the summary statistics of the performed kPCA, and the other containing a scatterplot matrix of rotated values reported by kPCA. - -*Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis (2004). kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software 11(9), 1-20. URL http://www.jstatsoft.org/v11/i09/* - ------ - -.. class:: warningmark - -**Note** - -This tool currently treats all variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis. - - </help> -</tool> +<tool id="kpca1" name="Kernel Principal Component Analysis" version="1.1.0"> + <description> </description> + <expand macro="requirements" /> + <macros> + <import>statistic_tools_macros.xml</import> + </macros> + <command interpreter="python"> +<![CDATA[ + kpca.py + --input=$input1 + --output1=$out_file1 + --output2=$out_file2 + --var_cols=$var_cols + --kernel=$kernelChoice.kernel + --features=$features + #if $kernelChoice.kernel == "rbfdot" or $kernelChoice.kernel == "anovadot": + --sigma=$kernelChoice.sigma + --degree="None" + --scale="None" + --offset="None" + --order="None" + #elif $kernelChoice.kernel == "polydot": + --sigma="None" + --degree=$kernelChoice.degree + --scale=$kernelChoice.scale + --offset=$kernelChoice.offset + --order="None" + #elif $kernelChoice.kernel == "tanhdot": + --sigma="None" + --degree="None" + --scale=$kernelChoice.scale + --offset=$kernelChoice.offset + --order="None" + #elif $kernelChoice.kernel == "besseldot": + --sigma=$kernelChoice.sigma + --degree=$kernelChoice.degree + --scale="None" + --offset="None" + --order=$kernelChoice.order + #elif $kernelChoice.kernel == "anovadot": + --sigma=$kernelChoice.sigma + --degree=$kernelChoice.degree + --scale="None" + --offset="None" + --order="None" + #else: + --sigma="None" + --degree="None" + --scale="None" + --offset="None" + --order="None" + #end if +]]> + </command> + <inputs> + <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/> + <param name="var_cols" label="Select columns containing input variables " type="data_column" data_ref="input1" numerical="True" multiple="true" > + <validator type="no_options" message="Please select at least one column."/> + </param> + <param name="features" size="10" type="integer" value="2" label="Number of principal components to return" help="To return all, enter 0"/> + <conditional name="kernelChoice"> + <param name="kernel" type="select" label="Kernel function"> + <option value="rbfdot" selected="true">Gaussian Radial Basis Function</option> + <option value="polydot">Polynomial</option> + <option value="vanilladot">Linear</option> + <option value="tanhdot">Hyperbolic</option> + <option value="laplacedot">Laplacian</option> + <option value="besseldot">Bessel</option> + <option value="anovadot">ANOVA Radial Basis Function</option> + <option value="splinedot">Spline</option> + </param> + <when value="vanilladot" /> + <when value="splinedot" /> + <when value="rbfdot"> + <param name="sigma" size="10" type="float" value="1" label="sigma (inverse kernel width)" /> + </when> + <when value="laplacedot"> + <param name="sigma" size="10" type="float" value="1" label="sigma (inverse kernel width)" /> + </when> + <when value="polydot"> + <param name="degree" size="10" type="integer" value="1" label="degree" /> + <param name="scale" size="10" type="integer" value="1" label="scale" /> + <param name="offset" size="10" type="integer" value="1" label="offset" /> + </when> + <when value="tanhdot"> + <param name="scale" size="10" type="integer" value="1" label="scale" /> + <param name="offset" size="10" type="integer" value="1" label="offset" /> + </when> + <when value="besseldot"> + <param name="sigma" size="10" type="integer" value="1" label="sigma" /> + <param name="order" size="10" type="integer" value="1" label="order" /> + <param name="degree" size="10" type="integer" value="1" label="degree" /> + </when> + <when value="anovadot"> + <param name="sigma" size="10" type="integer" value="1" label="sigma" /> + <param name="degree" size="10" type="integer" value="1" label="degree" /> + </when> + </conditional> + </inputs> + <outputs> + <data format="input" name="out_file1" metadata_source="input1" /> + <data format="pdf" name="out_file2" /> + </outputs> + <tests> + <test> + <param name="input1" value="iris.tabular"/> + <param name="var_cols" value="1,2,3,4"/> + <param name="kernel" value="polydot"/> + <param name="features" value="2"/> + <param name="offset" value="0"/> + <param name="scale" value="1"/> + <param name="degree" value="2"/> + <output name="out_file1" file="kpca_out1.tabular"/> + <output name="out_file2" file="kpca_out2.pdf"/> + </test> + </tests> + <help> +<![CDATA[ + + +.. class:: infomark + +**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters* + +----- + +.. class:: infomark + +**What it does** + +This tool uses functions from 'kernlab' library from R statistical package to perform Kernel Principal Component Analysis (kPCA) on the input data. It outputs two files, one containing the summary statistics of the performed kPCA, and the other containing a scatterplot matrix of rotated values reported by kPCA. + +*Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis (2004). kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software 11(9), 1-20. URL http://www.jstatsoft.org/v11/i09/* + +----- + +.. class:: warningmark + +**Note** + +This tool currently treats all variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis. + +]]> + </help> +</tool>
--- a/linear_regression.xml Tue Jul 29 06:30:45 2014 -0400 +++ b/linear_regression.xml Fri Jan 09 12:56:07 2015 -0500 @@ -1,72 +1,76 @@ -<tool id="LinearRegression1" name="Perform Linear Regression" version="1.1.0"> - <description> </description> - <expand macro="requirements" /> - <macros> - <import>statistic_tools_macros.xml</import> - </macros> - <command interpreter="python"> - linear_regression.py - $input1 - $response_col - $predictor_cols - $out_file1 - $out_file2 - 1>/dev/null - </command> - <inputs> - <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/> - <param name="response_col" label="Response column (Y)" type="data_column" data_ref="input1" numerical="True"/> - <param name="predictor_cols" label="Predictor columns (X)" type="data_column" data_ref="input1" numerical="True" multiple="true" > - <validator type="no_options" message="Please select at least one column."/> - </param> - </inputs> - <outputs> - <data format="input" name="out_file1" metadata_source="input1" /> - <data format="pdf" name="out_file2" /> - </outputs> - <tests> - <test> - <param name="input1" value="regr_inp.tabular"/> - <param name="response_col" value="3"/> - <param name="predictor_cols" value="1,2"/> - <output name="out_file1" file="regr_out.tabular"/> - <output name="out_file2" file="regr_out.pdf"/> - </test> - </tests> - <help> - - -.. class:: infomark - -**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters* - ------ - -.. class:: infomark - -**What it does** - -This tool uses the 'lm' function from R statistical package to perform linear regression on the input data. It outputs two files, one containing the summary statistics of the performed regression, and the other containing diagnostic plots to check whether model assumptions are satisfied. - -*R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.* - ------ - -.. class:: warningmark - -**Note** - -- This tool currently treats all predictor and response variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. - -- Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis. - -- The summary statistics in the output are described below: - - - sigma: the square root of the estimated variance of the random error (standard error of the residiuals) - - R-squared: the fraction of variance explained by the model - - Adjusted R-squared: the above R-squared statistic adjusted, penalizing for the number of the predictors (p) - - p-value: p-value for the t-test of the null hypothesis that the corresponding slope is equal to zero against the two-sided alternative. - - - </help> -</tool> +<tool id="LinearRegression1" name="Perform Linear Regression" version="1.1.0"> + <description> </description> + <expand macro="requirements" /> + <macros> + <import>statistic_tools_macros.xml</import> + </macros> + <command interpreter="python"> +<![CDATA[ + linear_regression.py + $input1 + $response_col + $predictor_cols + $out_file1 + $out_file2 + 1>/dev/null +]]> + </command> + <inputs> + <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/> + <param name="response_col" label="Response column (Y)" type="data_column" data_ref="input1" numerical="True"/> + <param name="predictor_cols" label="Predictor columns (X)" type="data_column" data_ref="input1" numerical="True" multiple="true" > + <validator type="no_options" message="Please select at least one column."/> + </param> + </inputs> + <outputs> + <data format="input" name="out_file1" metadata_source="input1" /> + <data format="pdf" name="out_file2" /> + </outputs> + <tests> + <test> + <param name="input1" value="regr_inp.tabular"/> + <param name="response_col" value="3"/> + <param name="predictor_cols" value="1,2"/> + <output name="out_file1" file="regr_out.tabular"/> + <output name="out_file2" file="regr_out.pdf"/> + </test> + </tests> + <help> +<![CDATA[ + + +.. class:: infomark + +**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters* + +----- + +.. class:: infomark + +**What it does** + +This tool uses the 'lm' function from R statistical package to perform linear regression on the input data. It outputs two files, one containing the summary statistics of the performed regression, and the other containing diagnostic plots to check whether model assumptions are satisfied. + +*R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.* + +----- + +.. class:: warningmark + +**Note** + +- This tool currently treats all predictor and response variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. + +- Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis. + +- The summary statistics in the output are described below: + + - sigma: the square root of the estimated variance of the random error (standard error of the residiuals) + - R-squared: the fraction of variance explained by the model + - Adjusted R-squared: the above R-squared statistic adjusted, penalizing for the number of the predictors (p) + - p-value: p-value for the t-test of the null hypothesis that the corresponding slope is equal to zero against the two-sided alternative. + + +]]> + </help> +</tool>
--- a/logistic_regression_vif.xml Tue Jul 29 06:30:45 2014 -0400 +++ b/logistic_regression_vif.xml Fri Jan 09 12:56:07 2015 -0500 @@ -5,12 +5,14 @@ <import>statistic_tools_macros.xml</import> </macros> <command interpreter="python"> - logistic_regression_vif.py +<![CDATA[ + logistic_regression_vif.py $input1 $response_col $predictor_cols $out_file1 1>/dev/null +]]> </command> <inputs> <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/> @@ -33,11 +35,12 @@ </test> </tests> <help> +<![CDATA[ .. class:: infomark -**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters* +**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters* ----- @@ -67,9 +70,10 @@ - Coefficient indicates log ratio of (probability to be class 1 / probability to be class 0) - This tool also provides **Variance Inflation Factor or VIF** which quantifies the level of multicollinearity. The tool will automatic generate VIF if the model has more than one predictor. The higher the VIF, the higher is the multicollinearity. Multicollinearity will inflate standard error and reduce level of significance of the predictor. In the worst case, it can reverse direction of slope for highly correlated predictors if one of them is significant. A general thumb-rule is to use those predictors having VIF lower than 10 or 5. -- **vif** is calculated by +- **vif** is calculated by - First, regressing each predictor over all other predictors, and recording R-squared for each regression. - Second, computing vif as 1/(1- R_squared) +]]> </help> </tool>
--- a/partialR_square.xml Tue Jul 29 06:30:45 2014 -0400 +++ b/partialR_square.xml Fri Jan 09 12:56:07 2015 -0500 @@ -1,69 +1,73 @@ -<tool id="partialRsq" name="Compute partial R square" version="1.1.0"> - <description> </description> - <expand macro="requirements" /> - <macros> - <import>statistic_tools_macros.xml</import> - </macros> - <command interpreter="python"> - partialR_square.py - $input1 - $response_col - $predictor_cols - $out_file1 - 1>/dev/null - </command> - <inputs> - <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/> - <param name="response_col" label="Response column (Y)" type="data_column" data_ref="input1" /> - <param name="predictor_cols" label="Predictor columns (X)" type="data_column" data_ref="input1" multiple="true"> - <validator type="no_options" message="Please select at least one column."/> - </param> - </inputs> - <outputs> - <data format="input" name="out_file1" metadata_source="input1" /> - </outputs> - <tests> - <!-- Test data with vlid values --> - <test> - <param name="input1" value="regr_inp.tabular"/> - <param name="response_col" value="3"/> - <param name="predictor_cols" value="1,2"/> - <output name="out_file1" file="partialR_result.tabular"/> - </test> - - </tests> - <help> - -.. class:: infomark - -**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters* - ------ - -.. class:: infomark - -**What it does** - -This tool computes the Partial R squared for all possible variable subsets using the following formula: - -**Partial R squared = [SSE(without i: 1,2,...,p-1) - SSE (full: 1,2,..,i..,p-1) / SSE(without i: 1,2,...,p-1)]**, which denotes the case where the 'i'th predictor is dropped. - - - -In general, **Partial R squared = [SSE(without i: 1,2,...,p-1) - SSE (full: 1,2,..,i..,p-1) / SSE(without i: 1,2,...,p-1)]**, where, - -- SSE (full: 1,2,..,i..,p-1) = Sum of Squares left out by the full set of predictors SSE(X1, X2 … Xp) -- SSE (full: 1,2,..,i..,p-1) = Sum of Squares left out by the set of predictors excluding; for example, if we omit the first predictor, it will be SSE(X2 … Xp). - - -The 4 columns in the output are described below: - -- Column 1 (Model): denotes the variables present in the model -- Column 2 (R-sq): denotes the R-squared value corresponding to the model in Column 1 -- Column 3 (Partial R squared_Terms): denotes the variable/s for which Partial R squared is computed. These are the variables that are absent in the reduced model in Column 1. A '-' in this column indicates that the model in Column 1 is the Full model. -- Column 4 (Partial R squared): denotes the Partial R squared value corresponding to the variable/s in Column 3. A '-' in this column indicates that the model in Column 1 is the Full model. - -*R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.* - - </help> -</tool> +<tool id="partialRsq" name="Compute partial R square" version="1.1.0"> + <description> </description> + <expand macro="requirements" /> + <macros> + <import>statistic_tools_macros.xml</import> + </macros> + <command interpreter="python"> +<![CDATA[ + partialR_square.py + $input1 + $response_col + $predictor_cols + $out_file1 + 1>/dev/null +]]> + </command> + <inputs> + <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/> + <param name="response_col" label="Response column (Y)" type="data_column" data_ref="input1" /> + <param name="predictor_cols" label="Predictor columns (X)" type="data_column" data_ref="input1" multiple="true"> + <validator type="no_options" message="Please select at least one column."/> + </param> + </inputs> + <outputs> + <data format="input" name="out_file1" metadata_source="input1" /> + </outputs> + <tests> + <!-- Test data with vlid values --> + <test> + <param name="input1" value="regr_inp.tabular"/> + <param name="response_col" value="3"/> + <param name="predictor_cols" value="1,2"/> + <output name="out_file1" file="partialR_result.tabular"/> + </test> + + </tests> + <help> +<![CDATA[ + +.. class:: infomark + +**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters* + +----- + +.. class:: infomark + +**What it does** + +This tool computes the Partial R squared for all possible variable subsets using the following formula: + +**Partial R squared = [SSE(without i: 1,2,...,p-1) - SSE (full: 1,2,..,i..,p-1) / SSE(without i: 1,2,...,p-1)]**, which denotes the case where the 'i'th predictor is dropped. + + + +In general, **Partial R squared = [SSE(without i: 1,2,...,p-1) - SSE (full: 1,2,..,i..,p-1) / SSE(without i: 1,2,...,p-1)]**, where, + +- SSE (full: 1,2,..,i..,p-1) = Sum of Squares left out by the full set of predictors SSE(X1, X2 … Xp) +- SSE (full: 1,2,..,i..,p-1) = Sum of Squares left out by the set of predictors excluding; for example, if we omit the first predictor, it will be SSE(X2 … Xp). + + +The 4 columns in the output are described below: + +- Column 1 (Model): denotes the variables present in the model +- Column 2 (R-sq): denotes the R-squared value corresponding to the model in Column 1 +- Column 3 (Partial R squared_Terms): denotes the variable/s for which Partial R squared is computed. These are the variables that are absent in the reduced model in Column 1. A '-' in this column indicates that the model in Column 1 is the Full model. +- Column 4 (Partial R squared): denotes the Partial R squared value corresponding to the variable/s in Column 3. A '-' in this column indicates that the model in Column 1 is the Full model. + +*R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.* + +]]> + </help> +</tool>
--- a/pca.xml Tue Jul 29 06:30:45 2014 -0400 +++ b/pca.xml Fri Jan 09 12:56:07 2015 -0500 @@ -1,101 +1,105 @@ -<tool id="pca1" name="Principal Component Analysis" version="1.1.0"> - <description> </description> - <expand macro="requirements" /> - <macros> - <import>statistic_tools_macros.xml</import> - </macros> - <command interpreter="python"> - pca.py - $input1 - $var_cols - $methodChoice.method - $out_file1 - $out_file2 - #if $methodChoice.method == "svd": - $methodChoice.scale - #end if - </command> - <inputs> - <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/> - <param name="var_cols" label="Select columns containing input variables " type="data_column" data_ref="input1" numerical="True" multiple="true" > - <validator type="no_options" message="Please select at least one column."/> - </param> - <conditional name="methodChoice"> - <param name="method" type="select" label="Method" help="The correlation matrix can only be used if there are no constant variables"> - <option value="cor" selected="true">Eigenvectors of Correlation (princomp)</option> - <option value="cov">Eigenvectors of Covariance (princomp)</option> - <option value="svd">Singular Value Decomposition (prcomp)</option> - </param> - <when value="cor" /> - <when value="cov" /> - <when value="svd"> - <param name="scale" type="select" label="Centering and Scaling" help="Can be used to center and/or scale variables"> - <option value="none" selected="true">None</option> - <option value="center">Center only</option> - <option value="scale">Scale only</option> - <option value="both">Center and Scale</option> - </param> - </when> - </conditional> - </inputs> - <outputs> - <data format="input" name="out_file1" metadata_source="input1" /> - <data format="pdf" name="out_file2" /> - </outputs> - <tests> - <test> - <param name="input1" value="iris.tabular"/> - <param name="var_cols" value="1,2,3,4"/> - <param name="method" value="cor"/> - <output name="out_file1" file="pca_out1.tabular"/> - <output name="out_file2" file="pca_out2.pdf"/> - </test> - <test> - <param name="input1" value="iris.tabular"/> - <param name="var_cols" value="1,2,3,4"/> - <param name="method" value="cov"/> - <output name="out_file1" file="pca_out3.tabular"/> - <output name="out_file2" file="pca_out4.pdf"/> - </test> - <test> - <param name="input1" value="iris.tabular"/> - <param name="var_cols" value="1,2,3,4"/> - <param name="method" value="svd"/> - <param name="scale" value="both"/> - <output name="out_file1" file="pca_out5.tabular"/> - <output name="out_file2" file="pca_out6.pdf"/> - </test> - </tests> - <help> - - -.. class:: infomark - -**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters* - ------ - -.. class:: infomark - -**What it does** - -This tool performs Principal Component Analysis on the given numeric input data using functions from R statistical package - 'princomp' function (for Eigenvector based solution) and 'prcomp' function (for Singular value decomposition based solution). It outputs two files, one containing the summary statistics of PCA, and the other containing biplots of the observations and principal components. - -*R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.* - ------ - -.. class:: warningmark - -**Note** - -- This tool currently treats all variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis. - -- The summary statistics in the output are described below: - - - Std. deviation: Standard deviations of the principal components - - Loadings: a list of eigen-vectors/variable loadings - - Scores: Scores of the input data on the principal components - - </help> -</tool> +<tool id="pca1" name="Principal Component Analysis" version="1.1.0"> + <description> </description> + <expand macro="requirements" /> + <macros> + <import>statistic_tools_macros.xml</import> + </macros> + <command interpreter="python"> +<![CDATA[ + pca.py + $input1 + $var_cols + $methodChoice.method + $out_file1 + $out_file2 + #if $methodChoice.method == "svd": + $methodChoice.scale + #end if +]]> + </command> + <inputs> + <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/> + <param name="var_cols" label="Select columns containing input variables " type="data_column" data_ref="input1" numerical="True" multiple="true" > + <validator type="no_options" message="Please select at least one column."/> + </param> + <conditional name="methodChoice"> + <param name="method" type="select" label="Method" help="The correlation matrix can only be used if there are no constant variables"> + <option value="cor" selected="true">Eigenvectors of Correlation (princomp)</option> + <option value="cov">Eigenvectors of Covariance (princomp)</option> + <option value="svd">Singular Value Decomposition (prcomp)</option> + </param> + <when value="cor" /> + <when value="cov" /> + <when value="svd"> + <param name="scale" type="select" label="Centering and Scaling" help="Can be used to center and/or scale variables"> + <option value="none" selected="true">None</option> + <option value="center">Center only</option> + <option value="scale">Scale only</option> + <option value="both">Center and Scale</option> + </param> + </when> + </conditional> + </inputs> + <outputs> + <data format="input" name="out_file1" metadata_source="input1" /> + <data format="pdf" name="out_file2" /> + </outputs> + <tests> + <test> + <param name="input1" value="iris.tabular"/> + <param name="var_cols" value="1,2,3,4"/> + <param name="method" value="cor"/> + <output name="out_file1" file="pca_out1.tabular"/> + <output name="out_file2" file="pca_out2.pdf"/> + </test> + <test> + <param name="input1" value="iris.tabular"/> + <param name="var_cols" value="1,2,3,4"/> + <param name="method" value="cov"/> + <output name="out_file1" file="pca_out3.tabular"/> + <output name="out_file2" file="pca_out4.pdf"/> + </test> + <test> + <param name="input1" value="iris.tabular"/> + <param name="var_cols" value="1,2,3,4"/> + <param name="method" value="svd"/> + <param name="scale" value="both"/> + <output name="out_file1" file="pca_out5.tabular"/> + <output name="out_file2" file="pca_out6.pdf"/> + </test> + </tests> + <help> +<![CDATA[ + + +.. class:: infomark + +**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters* + +----- + +.. class:: infomark + +**What it does** + +This tool performs Principal Component Analysis on the given numeric input data using functions from R statistical package - 'princomp' function (for Eigenvector based solution) and 'prcomp' function (for Singular value decomposition based solution). It outputs two files, one containing the summary statistics of PCA, and the other containing biplots of the observations and principal components. + +*R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.* + +----- + +.. class:: warningmark + +**Note** + +- This tool currently treats all variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis. + +- The summary statistics in the output are described below: + + - Std. deviation: Standard deviations of the principal components + - Loadings: a list of eigen-vectors/variable loadings + - Scores: Scores of the input data on the principal components + +]]> + </help> +</tool>
--- a/readme.rst Tue Jul 29 06:30:45 2014 -0400 +++ b/readme.rst Fri Jan 09 12:56:07 2015 -0500 @@ -2,11 +2,12 @@ =============================== These wrappers are based on RPy1 versions included in Galaxy-Main and -ported by John Chilton to RPy2. Please see the following Mail from -galaxy-dev: +ported by John Chilton and Björn Grüning to RPy2 and Galaxy tool dependencies. +Please see the following Mail from galaxy-dev: http://lists.bx.psu.edu/pipermail/galaxy-dev/2013-May/014694.html + Missing ports to RPy2: - rgenetics/rgQC.py @@ -17,27 +18,17 @@ ToDo: -- add tool_dependencies.xml to RPy2 and R-3.0 - testing - porting of missing tools -============ -Installation -============ - - - ======= History ======= - - v0.1: no release yet - - Wrapper Licence (MIT/BSD style) ===============================
--- a/scatterplot.xml Tue Jul 29 06:30:45 2014 -0400 +++ b/scatterplot.xml Fri Jan 09 12:56:07 2015 -0500 @@ -1,70 +1,75 @@ -<tool id="scatterplot_rpy" name="Scatterplot" version="1.1.0"> - <description>of two numeric columns</description> - <expand macro="requirements" /> - <macros> - <import>statistic_tools_macros.xml</import> - </macros> - <command interpreter="python">scatterplot.py $input $out_file1 $col1 $col2 "$title" "$xlab" "$ylab"</command> - <inputs> - <param name="input" type="data" format="tabular" label="Dataset" help="Dataset missing? See TIP below"/> - <param name="col1" type="data_column" data_ref="input" numerical="True" label="Numerical column for x axis" /> - <param name="col2" type="data_column" data_ref="input" numerical="True" label="Numerical column for y axis" /> - <param name="title" size="30" type="text" value="Scatterplot" label="Plot title"/> - <param name="xlab" size="30" type="text" value="V1" label="Label for x axis"/> - <param name="ylab" size="30" type="text" value="V2" label="Label for y axis"/> - </inputs> - <outputs> - <data format="pdf" name="out_file1" /> - </outputs> - - <tests> - <test> - <param name="input" value="scatterplot_in1.tabular" ftype="tabular"/> - <param name="col1" value="2"/> - <param name="col2" value="3"/> - <param name="title" value="Scatterplot"/> - <param name="xlab" value="V1"/> - <param name="ylab" value="V2"/> - <output name="out_file1" file="scatterplot_out1.pdf" /> - </test> - </tests> - <help> - -.. class:: infomark - -**TIP:** If your data is not TAB delimited, use *Text Manipulation->Convert* - ------ - -**Syntax** - -This tool creates a simple scatter plot between two variables containing numeric values of a selected dataset. - -- All invalid, blank and comment lines in the dataset are skipped. The number of skipped lines is displayed in the resulting history item. - -- **Plot title** The scatterplot title -- **Label for x axis** and **Label for y axis** The labels for x and y axis of the scatterplot. - ------ - -**Example** - -- Input file:: - - 1 68 4.1 - 2 71 4.6 - 3 62 3.8 - 4 75 4.4 - 5 58 3.2 - 6 60 3.1 - 7 67 3.8 - 8 68 4.1 - 9 71 4.3 - 10 69 3.7 - -- Create a simple scatterplot between the variables in column 2 and column 3 of the above dataset. - -.. image:: $PATH_TO_IMAGES/images/scatterplot.png - -</help> -</tool> +<tool id="scatterplot_rpy" name="Scatterplot" version="1.1.0"> + <description>of two numeric columns</description> + <expand macro="requirements" /> + <macros> + <import>statistic_tools_macros.xml</import> + </macros> + <command interpreter="python"> +<![CDATA[ + scatterplot.py $input $out_file1 $col1 $col2 "$title" "$xlab" "$ylab" +]]> + </command> + <inputs> + <param name="input" type="data" format="tabular" label="Dataset" help="Dataset missing? See TIP below"/> + <param name="col1" type="data_column" data_ref="input" numerical="True" label="Numerical column for x axis" /> + <param name="col2" type="data_column" data_ref="input" numerical="True" label="Numerical column for y axis" /> + <param name="title" size="30" type="text" value="Scatterplot" label="Plot title"/> + <param name="xlab" size="30" type="text" value="V1" label="Label for x axis"/> + <param name="ylab" size="30" type="text" value="V2" label="Label for y axis"/> + </inputs> + <outputs> + <data format="pdf" name="out_file1" /> + </outputs> + <tests> + <test> + <param name="input" value="scatterplot_in1.tabular" ftype="tabular"/> + <param name="col1" value="2"/> + <param name="col2" value="3"/> + <param name="title" value="Scatterplot"/> + <param name="xlab" value="V1"/> + <param name="ylab" value="V2"/> + <output name="out_file1" file="scatterplot_out1.pdf" /> + </test> + </tests> + <help> +<![CDATA[ + +.. class:: infomark + +**TIP:** If your data is not TAB delimited, use *Text Manipulation->Convert* + +----- + +**Syntax** + +This tool creates a simple scatter plot between two variables containing numeric values of a selected dataset. + +- All invalid, blank and comment lines in the dataset are skipped. The number of skipped lines is displayed in the resulting history item. + +- **Plot title** The scatterplot title +- **Label for x axis** and **Label for y axis** The labels for x and y axis of the scatterplot. + +----- + +**Example** + +- Input file:: + + 1 68 4.1 + 2 71 4.6 + 3 62 3.8 + 4 75 4.4 + 5 58 3.2 + 6 60 3.1 + 7 67 3.8 + 8 68 4.1 + 9 71 4.3 + 10 69 3.7 + +- Create a simple scatterplot between the variables in column 2 and column 3 of the above dataset. + +.. image:: $PATH_TO_IMAGES/images/scatterplot.png + +]]> + </help> +</tool>
--- a/short_reads_figure_score.xml Tue Jul 29 06:30:45 2014 -0400 +++ b/short_reads_figure_score.xml Fri Jan 09 12:56:07 2015 -0500 @@ -1,31 +1,37 @@ <tool id="quality_score_distribution" name="Build base quality distribution" version="1.1.0"> -<description></description> -<expand macro="requirements" /> + <description></description> + <expand macro="requirements" /> <macros> <import>statistic_tools_macros.xml</import> </macros> -<command interpreter="python">short_reads_figure_score.py $input1 $output1 </command> - -<inputs> -<page> - <param name="input1" type="data" format="qualsolexa, qual454" label="Quality score file" help="No dataset? Read tip below"/> -</page> -</inputs> + <command interpreter="python"> + <![CDATA[ + short_reads_figure_score.py + $input1 + $output1 + ]]> + </command> + <inputs> + <page> + <param name="input1" type="data" format="qualsolexa, qual454" label="Quality score file" help="No dataset? Read tip below"/> + </page> + </inputs> -<outputs> - <data name="output1" format="png" /> -</outputs> -<tests> - <test> - <param name="input1" value="solexa.qual" ftype="qualsolexa" /> - <output name="output1" file="solexaScore.png" ftype="png" /> - </test> - <test> - <param name="input1" value="454.qual" ftype="qual454" /> - <output name="output1" file="454Score.png" ftype="png" /> - </test> -</tests> -<help> + <outputs> + <data name="output1" format="png" /> + </outputs> + <tests> + <test> + <param name="input1" value="solexa.qual" ftype="qualsolexa" /> + <output name="output1" file="solexaScore.png" ftype="png" /> + </test> + <test> + <param name="input1" value="454.qual" ftype="qual454" /> + <output name="output1" file="454Score.png" ftype="png" /> + </test> + </tests> + <help> +<![CDATA[ .. class:: warningmark @@ -43,13 +49,13 @@ Roche (454) or ABI SOLiD data:: - >seq1 - 23 33 34 25 28 28 28 32 23 34 27 4 28 28 31 21 28 + >seq1 + 23 33 34 25 28 28 28 32 23 34 27 4 28 28 31 21 28 Illumina (Solexa) data:: - -40 -40 40 -40 -40 -40 -40 40 - + -40 -40 40 -40 -40 -40 -40 40 + ----- **Output example** @@ -62,26 +68,25 @@ - For Roche (454) X-axis (shown above) indicates **relative** position (in %) within reads as this technology produces reads of different lengths; - For Illumina (Solexa) and ABI SOLiD X-axis shows **absolute** position in nucleotides within reads. - + Every box on the plot shows the following values:: - o <---- Outliers + o <---- Outliers o - -+- <---- Upper Extreme Value that is no more - | than box length away from the box + -+- <---- Upper Extreme Value that is no more + | than box length away from the box | - +--+--+ <---- Upper Quartile + +--+--+ <---- Upper Quartile | | - +-----+ <---- Median + +-----+ <---- Median | | - +--+--+ <---- Lower Quartile + +--+--+ <---- Lower Quartile | | - -+- <---- Lower Extreme Value that is no more + -+- <---- Lower Extreme Value that is no more than box length away from the box - o <---- Outlier - - - -</help> + o <---- Outlier + +]]> + </help> </tool>
--- a/statistic_tools_macros.xml Tue Jul 29 06:30:45 2014 -0400 +++ b/statistic_tools_macros.xml Fri Jan 09 12:56:07 2015 -0500 @@ -1,7 +1,7 @@ <macros> <xml name="requirements"> <requirements> - <requirement type="package" version="3.0.1">R_3_0_1</requirement> + <requirement type="package" version="3.1.2">R</requirement> <requirement type="package" version="2.2.3.6">rpy2</requirement> <requirement type="package" version="1.7.1">numpy</requirement> <requirement type="package" version="5.9">ncurses</requirement>
--- a/tool_dependencies.xml Tue Jul 29 06:30:45 2014 -0400 +++ b/tool_dependencies.xml Fri Jan 09 12:56:07 2015 -0500 @@ -4,13 +4,13 @@ <repository changeset_revision="c0735727b530" name="package_ncurses_5_9" owner="iuc" toolshed="https://testtoolshed.g2.bx.psu.edu" /> </package> <package name="readline" version="6.2"> - <repository changeset_revision="246f31787ca1" name="package_readline_6_2" owner="iuc" toolshed="https://testtoolshed.g2.bx.psu.edu" /> + <repository changeset_revision="4e4f3dc94efa" name="package_readline_6_2" owner="iuc" toolshed="https://testtoolshed.g2.bx.psu.edu" /> </package> - <package name="R_3_0_1" version="3.0.1"> - <repository changeset_revision="1c0e86a44f73" name="package_r_3_0_1" owner="iuc" toolshed="https://testtoolshed.g2.bx.psu.edu" /> + <package name="R" version="3.1.2"> + <repository changeset_revision="f0626dac6765" name="package_r_3_1_2" owner="iuc" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" /> </package> <package name="rpy2" version="2.2.3.6"> - <repository changeset_revision="191967e6496b" name="package_rpy2_2_3_6" owner="bgruening" toolshed="https://testtoolshed.g2.bx.psu.edu" /> + <repository changeset_revision="5afc30651412" name="package_rpy2_2_3_6" owner="bgruening" toolshed="https://testtoolshed.g2.bx.psu.edu" /> </package> <package name="numpy" version="1.7.1"> <repository changeset_revision="84125ffacb90" name="package_numpy_1_7" owner="iuc" toolshed="https://testtoolshed.g2.bx.psu.edu" /> @@ -19,8 +19,8 @@ <install version="1.0"> <actions> <action type="setup_r_environment"> - <repository changeset_revision="1c0e86a44f73" name="package_r_3_0_1" owner="iuc" toolshed="https://testtoolshed.g2.bx.psu.edu"> - <package name="R_3_0_1" version="3.0.1" /> + <repository changeset_revision="f0626dac6765" name="package_r_3_1_2" owner="iuc" toolshed="https://testtoolshed.g2.bx.psu.edu"> + <package name="R" version="3.1.2" /> </repository> <package>https://github.com/bgruening/download_store/raw/master/r_statistic_tools/kernlab_0.9-19.tar.gz</package> <package>https://github.com/bgruening/download_store/raw/master/r_statistic_tools/yacca_1.1.tar.gz</package>
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+ <div class="navbar-header"> + + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false" aria-controls="navbar"> + <span class="sr-only">Toggle navigation</span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <a class="navbar-brand" href="#">Tool Test Results (powered by Planemo)</a> + </div> + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav navbar-right"> + + <li><a href="https://galaxyproject.org">Galaxy</a></li> + <li><a href="https://planemo.readthedocs.com">Planemo</a></li> + + </ul> + <div class="navbar-form navbar-right"> + </div> + </div> + </div> + </nav> + + <div class="container-fluid"> + <div class="row"> + <div class="col-sm-3 col-md-2 sidebar"> + <ul class="nav nav-sidebar"> + <li><a href="#overview" class="text-success"><strong>Overview</strong></a></li> + </ul> + <ul class="nav nav-sidebar" 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0) { + var $problemsLabel = $('<div>').text("problems: "); + var $problemsDiv = $('<div style="margin-left:10px;">'); + var $problemsUl = $('<ul>'); + for(var problemIndex in testResult.problems) { + $problemsUl.append($('<li>').append($('<code>').text(testResult.problems[problemIndex]))); + } + $problemsDiv.append($problemsUl); + $panelBody.append($problemsLabel).append($problemsDiv); + } + var $commandLabel = $('<div>command:</div>'); + var $stdoutLabel = $('<div>job standard output:</div>'); + var $stderrLabel = $('<div>job standard error:</div>'); + var $command; + if(testResult.command !== null) { + $command = $('<pre class="pre-scrollable" style="margin-left:10px;">').text(testResult.command); + } else { + $command = $('<div class="alert alert-warning" style="margin-left:10px;">').text("No command recorded."); + } + var $stdout; + if(testResult.stdout !== null) { + $stdout = $('<pre class="pre-scrollable" style="margin-left:10px;">').text(testResult.stdout); + } else { + $stdout = $('<div class="alert alert-warning" style="margin-left:10px;">').text("No standard output recorded."); + } + var $stderr; + if(testResult.stderr !== null) { + $stderr = $('<pre class="pre-scrollable" style="margin-left:10px;">').text(testResult.stderr); + } else { + $stderr = $('<div class="alert alert-warning" style="margin-left:10px;">').text("No standard error recorded."); + } + $panelBody + .append($commandLabel) + .append($command) + .append($stdoutLabel) + .append($stdout) + .append($stderrLabel) + .append($stderr); + if(!testResult.passed) { + var $logLabel = $('<div>log:</div>'); + var $log = $('<pre class="pre-scrollable" style="margin-left: 10px;">').text(testResult.problemLog); + $panelBody.append($logLabel).append($log); + } + + $panel.append($panelHeading).append($panelBody); + $(".main").append($panel); + } +} + +var TestResult = function(data) { + this.rawId = data["id"]; + + var testMethod = this.rawId.split("TestForTool_")[1]; + var toolName = testMethod.split(".test_tool_")[0]; + var testIndex = testMethod.split(".test_tool_")[1]; + this.toolName = toolName; + this.testIndex = parseInt(testIndex); + console.log(data); + this.status = data["data"]["status"]; + var job = data["data"]["job"]; + if(job) { + this.stdout = data["data"]["job"]["stdout"]; + this.stderr = data["data"]["job"]["stderr"]; + this.command = data["data"]["job"]["command_line"]; + } else { + this.stdout = null; + this.stderr = null; + this.command = null; + } + this.problems = []; + var outputProblems = data["data"]["output_problems"] || []; + var executionProblem = data["data"]["execution_problem"]; + this.problems.push.apply(this.problems, outputProblems); + if(executionProblem) { + this.problems.push(executionProblem); + } + this.problemLog = data["data"]["problem_log"]; + this.passed = (this.status == "success"); +} + + +// http://stackoverflow.com/questions/19491336/get-url-parameter-jquery +function getUrlParameter(sParam) +{ + var sPageURL = window.location.search.substring(1); + var sURLVariables = sPageURL.split('&'); + for (var i = 0; i < sURLVariables.length; i++) + { + var sParameterName = sURLVariables[i].split('='); + if (sParameterName[0] == sParam) + { + return sParameterName[1]; + } + } +} +</script> + <script> + var testDataUrl = getUrlParameter("test_data_url"); + if(testDataUrl) { + $.ajax( + {'url': testDataUrl, + 'type': 'GET', + } + ) + .success(function(content) { renderTestResults( $.parseJSON(content) ); }) + .failure(function() { alert("Failed to load test data.")} ); + } else { + var test_data = {"tests": [{"data": {"status": "failure", "inputs": {"input": {"src": "hda", "id": "2891970512fa2d5a"}, "cond": "c2"}, "output_problems": ["Job in error state."], "job": {"inputs": {"input": {"src": "hda", "id": "2891970512fa2d5a"}}, "update_time": "2015-01-08T22:31:55.343681", "tool_id": "Summary_Statistics1", "outputs": {"out_file1": {"src": "hda", "id": "5729865256bc2525"}}, "stdout": "Error in dyn.load(file, DLLpath = DLLpath, ...) : \n kann shared object '/home/bag/projects/code/galaxy-central/tool_deps/R_3_0_1/3.0.1/iuc/package_r_3_0_1/1c0e86a44f73/lib/R/library/stats/libs/stats.so' nicht laden:\n /home/bag/projects/code/galaxy-central/tool_deps/R/3.1.2/iuc/package_r_3_1_2/f0626dac6765/lib/R/lib/libRlapack.so: undefined symbol: _gfortran_pow_r8_i4\nBeim Start - Warnmeldung:\n \nFehler: 'rho' must be an environment not NULL: detected in C-level eval\n", "command_line": "python /home/bag/projects/code/galaxytools/rpy_statistics_collection/gsummary.py /tmp/tmprD44IMfiles/000/dataset_1.dat /tmp/tmprD44IMfiles/000/dataset_2.dat \"c2\"", "exit_code": 139, "state": "error", "create_time": "2015-01-08T22:31:51.004258", "params": {"chromInfo": "\"/home/bag/projects/code/galaxy-central/tool-data/shared/ucsc/chrom/hg17.len\"", "cond": "\"c2\"", "dbkey": "\"hg17\""}, "stderr": "Segmentation fault (core dumped)\n", "job_metrics": [], "model_class": "Job", "id": "5729865256bc2525"}, "problem_log": "Traceback (most recent call last):\n File \"/usr/lib/python2.7/unittest/case.py\", line 329, in run\n testMethod()\n File \"/home/bag/projects/code/galaxy-central/test/functional/test_toolbox.py\", line 182, in test_tool\n self.do_it( td )\n File \"/home/bag/projects/code/galaxy-central/test/functional/test_toolbox.py\", line 65, in do_it\n raise e\nJobOutputsError: Job in error state.\n-------------------- >> begin captured stdout << ---------------------\nHistory with id 2891970512fa2d5a in error - summary of datasets in error below.\n--------------------------------------\n| 2 - Summary Statistics on data 1 (HID - NAME) \n| Dataset Blurb:\n| error\n| Dataset Info:\n| Error in dyn.load(file, DLLpath = DLLpath, ...) :\n| kann shared object '/home/bag/projects/code/galaxy-central/tool_deps/R_3_0_1/3.0.1/iuc/package_r_3_0_1/1c0e86a44f73/lib/R/library/stats/libs/stats.so' nicht laden:\n| /home/bag/projects/code/galaxy-cent\n| Dataset Job Standard Output:\n| Error in dyn.load(file, DLLpath = DLLpath, ...) :\n| kann shared object '/home/bag/projects/code/galaxy-central/tool_deps/R_3_0_1/3.0.1/iuc/package_r_3_0_1/1c0e86a44f73/lib/R/library/stats/libs/stats.so' nicht laden:\n| /home/bag/projects/code/galaxy-central/tool_deps/R/3.1.2/iuc/package_r_3_1_2/f0626dac6765/lib/R/lib/libRlapack.so: undefined symbol: _gfortran_pow_r8_i4\n| Beim Start - Warnmeldung:\n| \n| Fehler: 'rho' must be an environment not NULL: detected in C-level eval\n| Dataset Job Standard Error:\n| Segmentation fault (core dumped)\n|\n--------------------------------------\n\n--------------------- >> end captured stdout << ----------------------\n-------------------- >> begin captured logging << --------------------\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.web.framework.webapp: INFO: Session authenticated using Galaxy master api key\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/users?key=test_key HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.web.framework.webapp: INFO: Session authenticated using Galaxy master api key\nrequests.packages.urllib3.connectionpool: DEBUG: \"POST /api/users HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.web.framework.webapp: INFO: Session authenticated using Galaxy master api key\nrequests.packages.urllib3.connectionpool: DEBUG: \"POST /api/users/2891970512fa2d5a/api_key HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"POST /api/histories HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.tools.actions.upload_common: INFO: tool upload1 created job id 1\nrequests.packages.urllib3.connectionpool: DEBUG: \"POST /api/tools HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.jobs: DEBUG: (1) Working directory for job is: /tmp/tmprD44IM/job_working_directory/000/1\ngalaxy.jobs.handler: DEBUG: (1) Dispatching to local runner\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.jobs: DEBUG: (1) Persisting job destination (destination id: local:///)\ngalaxy.jobs.handler: INFO: (1) Job dispatched\ngalaxy.jobs.runners: DEBUG: (1) command is: python /home/bag/projects/code/galaxy-central/tools/data_source/upload.py /home/bag/projects/code/galaxy-central /tmp/tmprD44IM/tmp/tmpBJjmke /tmp/tmprD44IM/tmp/tmpzDrmot 1:/tmp/tmprD44IM/job_working_directory/000/1/dataset_1_files:/tmp/tmprD44IMfiles/000/dataset_1.dat; return_code=$?; cd /home/bag/projects/code/galaxy-central; /home/bag/projects/code/galaxy-central/set_metadata.sh /tmp/tmprD44IMfiles /tmp/tmprD44IM/job_working_directory/000/1 . /tmp/tmp_QrpB0/functional_tests_wsgi.ini /tmp/tmprD44IM/tmp/tmpBJjmke /tmp/tmprD44IM/job_working_directory/000/1/galaxy.json /tmp/tmprD44IM/job_working_directory/000/1/metadata_in_HistoryDatasetAssociation_1_od2880,/tmp/tmprD44IM/job_working_directory/000/1/metadata_kwds_HistoryDatasetAssociation_1_LAC5zI,/tmp/tmprD44IM/job_working_directory/000/1/metadata_out_HistoryDatasetAssociation_1_oWzbfn,/tmp/tmprD44IM/job_working_directory/000/1/metadata_results_HistoryDatasetAssociation_1_dxBMBo,,/tmp/tmprD44IM/job_working_directory/000/1/metadata_override_HistoryDatasetAssociation_1_kZQ7D7; sh -c \"exit $return_code\"\ngalaxy.jobs.runners.local: DEBUG: (1) executing job script: /tmp/tmprD44IM/job_working_directory/000/1/galaxy_1.sh\ngalaxy.jobs: DEBUG: (1) Persisting job destination (destination id: local:///)\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\ngalaxy.jobs.runners.local: DEBUG: execution finished: /tmp/tmprD44IM/job_working_directory/000/1/galaxy_1.sh\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.datatypes.metadata: DEBUG: loading metadata from file for: HistoryDatasetAssociation 1\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.jobs: DEBUG: job 1 ended\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"POST /api/tools HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.jobs: DEBUG: (2) Working directory for job is: /tmp/tmprD44IM/job_working_directory/000/2\ngalaxy.jobs.handler: DEBUG: (2) Dispatching to local runner\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/jobs/5729865256bc2525?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\ngalaxy.jobs: DEBUG: (2) Persisting job destination (destination id: local:///)\ngalaxy.jobs.handler: INFO: (2) Job dispatched\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/jobs/5729865256bc2525?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\ngalaxy.jobs.runners: DEBUG: (2) command is: python /home/bag/projects/code/galaxytools/rpy_statistics_collection/gsummary.py /tmp/tmprD44IMfiles/000/dataset_1.dat /tmp/tmprD44IMfiles/000/dataset_2.dat \"c2\"; return_code=$?; cd /home/bag/projects/code/galaxy-central; /home/bag/projects/code/galaxy-central/set_metadata.sh /tmp/tmprD44IMfiles /tmp/tmprD44IM/job_working_directory/000/2 . /tmp/tmp_QrpB0/functional_tests_wsgi.ini /tmp/tmprD44IM/tmp/tmpBJjmke /tmp/tmprD44IM/job_working_directory/000/2/galaxy.json /tmp/tmprD44IM/job_working_directory/000/2/metadata_in_HistoryDatasetAssociation_2_MCE7SE,/tmp/tmprD44IM/job_working_directory/000/2/metadata_kwds_HistoryDatasetAssociation_2_AtXwgq,/tmp/tmprD44IM/job_working_directory/000/2/metadata_out_HistoryDatasetAssociation_2_NX8vCK,/tmp/tmprD44IM/job_working_directory/000/2/metadata_results_HistoryDatasetAssociation_2_Z2348A,,/tmp/tmprD44IM/job_working_directory/000/2/metadata_override_HistoryDatasetAssociation_2_CBptA2; sh -c \"exit $return_code\"\ngalaxy.jobs.runners.local: DEBUG: (2) executing job script: /tmp/tmprD44IM/job_working_directory/000/2/galaxy_2.sh\ngalaxy.jobs: DEBUG: (2) Persisting job destination (destination id: local:///)\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/jobs/5729865256bc2525?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/jobs/5729865256bc2525?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\ngalaxy.jobs.runners.local: DEBUG: execution finished: /tmp/tmprD44IM/job_working_directory/000/2/galaxy_2.sh\ngalaxy.jobs: DEBUG: setting dataset state to ERROR\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/jobs/5729865256bc2525?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\ngalaxy.jobs: DEBUG: job 2 ended\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/jobs/5729865256bc2525?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a/contents?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a/contents/5729865256bc2525?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a/contents/5729865256bc2525/provenance?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/jobs/5729865256bc2525?full=true&key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\n--------------------- >> end captured logging << ---------------------\n", "problem_type": "functional.test_toolbox.JobOutputsError"}, "id": "functional.test_toolbox.TestForTool_Summary_Statistics1.test_tool_000000", "has_data": true}], "version": "0.1", "summary": {"num_skips": 0, "num_errors": 0, "num_failures": 1, "num_tests": 1}}; + renderTestResults(test_data); + } + </script> + </body> +</html>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_test_output.json Fri Jan 09 12:56:07 2015 -0500 @@ -0,0 +1,1 @@ +{"tests": [{"data": {"status": "failure", "inputs": {"input": {"src": "hda", "id": "2891970512fa2d5a"}, "cond": "c2"}, "output_problems": ["Job in error state."], "job": {"inputs": {"input": {"src": "hda", "id": "2891970512fa2d5a"}}, "update_time": "2015-01-08T22:31:55.343681", "tool_id": "Summary_Statistics1", "outputs": {"out_file1": {"src": "hda", "id": "5729865256bc2525"}}, "stdout": "Error in dyn.load(file, DLLpath = DLLpath, ...) : \n kann shared object '/home/bag/projects/code/galaxy-central/tool_deps/R_3_0_1/3.0.1/iuc/package_r_3_0_1/1c0e86a44f73/lib/R/library/stats/libs/stats.so' nicht laden:\n /home/bag/projects/code/galaxy-central/tool_deps/R/3.1.2/iuc/package_r_3_1_2/f0626dac6765/lib/R/lib/libRlapack.so: undefined symbol: _gfortran_pow_r8_i4\nBeim Start - Warnmeldung:\n \nFehler: 'rho' must be an environment not NULL: detected in C-level eval\n", "command_line": "python /home/bag/projects/code/galaxytools/rpy_statistics_collection/gsummary.py /tmp/tmprD44IMfiles/000/dataset_1.dat /tmp/tmprD44IMfiles/000/dataset_2.dat \"c2\"", "exit_code": 139, "state": "error", "create_time": "2015-01-08T22:31:51.004258", "params": {"chromInfo": "\"/home/bag/projects/code/galaxy-central/tool-data/shared/ucsc/chrom/hg17.len\"", "cond": "\"c2\"", "dbkey": "\"hg17\""}, "stderr": "Segmentation fault (core dumped)\n", "job_metrics": [], "model_class": "Job", "id": "5729865256bc2525"}, "problem_log": "Traceback (most recent call last):\n File \"/usr/lib/python2.7/unittest/case.py\", line 329, in run\n testMethod()\n File \"/home/bag/projects/code/galaxy-central/test/functional/test_toolbox.py\", line 182, in test_tool\n self.do_it( td )\n File \"/home/bag/projects/code/galaxy-central/test/functional/test_toolbox.py\", line 65, in do_it\n raise e\nJobOutputsError: Job in error state.\n-------------------- >> begin captured stdout << ---------------------\nHistory with id 2891970512fa2d5a in error - summary of datasets in error below.\n--------------------------------------\n| 2 - Summary Statistics on data 1 (HID - NAME) \n| Dataset Blurb:\n| error\n| Dataset Info:\n| Error in dyn.load(file, DLLpath = DLLpath, ...) :\n| kann shared object '/home/bag/projects/code/galaxy-central/tool_deps/R_3_0_1/3.0.1/iuc/package_r_3_0_1/1c0e86a44f73/lib/R/library/stats/libs/stats.so' nicht laden:\n| /home/bag/projects/code/galaxy-cent\n| Dataset Job Standard Output:\n| Error in dyn.load(file, DLLpath = DLLpath, ...) :\n| kann shared object '/home/bag/projects/code/galaxy-central/tool_deps/R_3_0_1/3.0.1/iuc/package_r_3_0_1/1c0e86a44f73/lib/R/library/stats/libs/stats.so' nicht laden:\n| /home/bag/projects/code/galaxy-central/tool_deps/R/3.1.2/iuc/package_r_3_1_2/f0626dac6765/lib/R/lib/libRlapack.so: undefined symbol: _gfortran_pow_r8_i4\n| Beim Start - Warnmeldung:\n| \n| Fehler: 'rho' must be an environment not NULL: detected in C-level eval\n| Dataset Job Standard Error:\n| Segmentation fault (core dumped)\n|\n--------------------------------------\n\n--------------------- >> end captured stdout << ----------------------\n-------------------- >> begin captured logging << --------------------\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.web.framework.webapp: INFO: Session authenticated using Galaxy master api key\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/users?key=test_key HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.web.framework.webapp: INFO: Session authenticated using Galaxy master api key\nrequests.packages.urllib3.connectionpool: DEBUG: \"POST /api/users HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.web.framework.webapp: INFO: Session authenticated using Galaxy master api key\nrequests.packages.urllib3.connectionpool: DEBUG: \"POST /api/users/2891970512fa2d5a/api_key HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"POST /api/histories HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.tools.actions.upload_common: INFO: tool upload1 created job id 1\nrequests.packages.urllib3.connectionpool: DEBUG: \"POST /api/tools HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.jobs: DEBUG: (1) Working directory for job is: /tmp/tmprD44IM/job_working_directory/000/1\ngalaxy.jobs.handler: DEBUG: (1) Dispatching to local runner\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.jobs: DEBUG: (1) Persisting job destination (destination id: local:///)\ngalaxy.jobs.handler: INFO: (1) Job dispatched\ngalaxy.jobs.runners: DEBUG: (1) command is: python /home/bag/projects/code/galaxy-central/tools/data_source/upload.py /home/bag/projects/code/galaxy-central /tmp/tmprD44IM/tmp/tmpBJjmke /tmp/tmprD44IM/tmp/tmpzDrmot 1:/tmp/tmprD44IM/job_working_directory/000/1/dataset_1_files:/tmp/tmprD44IMfiles/000/dataset_1.dat; return_code=$?; cd /home/bag/projects/code/galaxy-central; /home/bag/projects/code/galaxy-central/set_metadata.sh /tmp/tmprD44IMfiles /tmp/tmprD44IM/job_working_directory/000/1 . /tmp/tmp_QrpB0/functional_tests_wsgi.ini /tmp/tmprD44IM/tmp/tmpBJjmke /tmp/tmprD44IM/job_working_directory/000/1/galaxy.json /tmp/tmprD44IM/job_working_directory/000/1/metadata_in_HistoryDatasetAssociation_1_od2880,/tmp/tmprD44IM/job_working_directory/000/1/metadata_kwds_HistoryDatasetAssociation_1_LAC5zI,/tmp/tmprD44IM/job_working_directory/000/1/metadata_out_HistoryDatasetAssociation_1_oWzbfn,/tmp/tmprD44IM/job_working_directory/000/1/metadata_results_HistoryDatasetAssociation_1_dxBMBo,,/tmp/tmprD44IM/job_working_directory/000/1/metadata_override_HistoryDatasetAssociation_1_kZQ7D7; sh -c \"exit $return_code\"\ngalaxy.jobs.runners.local: DEBUG: (1) executing job script: /tmp/tmprD44IM/job_working_directory/000/1/galaxy_1.sh\ngalaxy.jobs: DEBUG: (1) Persisting job destination (destination id: local:///)\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\ngalaxy.jobs.runners.local: DEBUG: execution finished: /tmp/tmprD44IM/job_working_directory/000/1/galaxy_1.sh\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.datatypes.metadata: DEBUG: loading metadata from file for: HistoryDatasetAssociation 1\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.jobs: DEBUG: job 1 ended\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"POST /api/tools HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\ngalaxy.jobs: DEBUG: (2) Working directory for job is: /tmp/tmprD44IM/job_working_directory/000/2\ngalaxy.jobs.handler: DEBUG: (2) Dispatching to local runner\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/jobs/5729865256bc2525?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\ngalaxy.jobs: DEBUG: (2) Persisting job destination (destination id: local:///)\ngalaxy.jobs.handler: INFO: (2) Job dispatched\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/jobs/5729865256bc2525?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\ngalaxy.jobs.runners: DEBUG: (2) command is: python /home/bag/projects/code/galaxytools/rpy_statistics_collection/gsummary.py /tmp/tmprD44IMfiles/000/dataset_1.dat /tmp/tmprD44IMfiles/000/dataset_2.dat \"c2\"; return_code=$?; cd /home/bag/projects/code/galaxy-central; /home/bag/projects/code/galaxy-central/set_metadata.sh /tmp/tmprD44IMfiles /tmp/tmprD44IM/job_working_directory/000/2 . /tmp/tmp_QrpB0/functional_tests_wsgi.ini /tmp/tmprD44IM/tmp/tmpBJjmke /tmp/tmprD44IM/job_working_directory/000/2/galaxy.json /tmp/tmprD44IM/job_working_directory/000/2/metadata_in_HistoryDatasetAssociation_2_MCE7SE,/tmp/tmprD44IM/job_working_directory/000/2/metadata_kwds_HistoryDatasetAssociation_2_AtXwgq,/tmp/tmprD44IM/job_working_directory/000/2/metadata_out_HistoryDatasetAssociation_2_NX8vCK,/tmp/tmprD44IM/job_working_directory/000/2/metadata_results_HistoryDatasetAssociation_2_Z2348A,,/tmp/tmprD44IM/job_working_directory/000/2/metadata_override_HistoryDatasetAssociation_2_CBptA2; sh -c \"exit $return_code\"\ngalaxy.jobs.runners.local: DEBUG: (2) executing job script: /tmp/tmprD44IM/job_working_directory/000/2/galaxy_2.sh\ngalaxy.jobs: DEBUG: (2) Persisting job destination (destination id: local:///)\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/jobs/5729865256bc2525?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/jobs/5729865256bc2525?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\ngalaxy.jobs.runners.local: DEBUG: execution finished: /tmp/tmprD44IM/job_working_directory/000/2/galaxy_2.sh\ngalaxy.jobs: DEBUG: setting dataset state to ERROR\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/jobs/5729865256bc2525?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\ngalaxy.jobs: DEBUG: job 2 ended\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/jobs/5729865256bc2525?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a/contents?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a/contents/5729865256bc2525?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/histories/2891970512fa2d5a/contents/5729865256bc2525/provenance?key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\nrequests.packages.urllib3.connectionpool: INFO: Starting new HTTP connection (1): localhost\nrequests.packages.urllib3.connectionpool: DEBUG: \"GET /api/jobs/5729865256bc2525?full=true&key=1ad2653278529aa1e63296a7e0b9dc50 HTTP/1.1\" 200 None\n--------------------- >> end captured logging << ---------------------\n", "problem_type": "functional.test_toolbox.JobOutputsError"}, "id": "functional.test_toolbox.TestForTool_Summary_Statistics1.test_tool_000000", "has_data": true}], "version": "0.1", "summary": {"num_skips": 0, "num_errors": 0, "num_failures": 1, "num_tests": 1}} \ No newline at end of file