| Previous changeset 16:f9353ee6a0d4 (2024-08-20) Next changeset 18:38db3fef7aad (2024-09-19) |
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Commit message:
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 91121b1e72696f17478dae383badaa71e9f96dbb |
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modified:
README.md cluster_reduce_dimension.xml macros.xml test-data/cosg.rank_genes_groups.newton-cg.pbmc68k_highly_reduced_1.h5ad test-data/pbmc68k_reduced.h5ad test-data/pl.clustermap.krumsiek11.png test-data/pl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.png test-data/pl.dotplot.krumsiek11.png test-data/pl.dpt_timeseries.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.png test-data/pl.draw_graph.png test-data/pl.embedding_density.pbmc68k_reduced.png test-data/pl.heatmap.krumsiek11.png test-data/pl.highest_expr_genes.filter_genes_dispersion.krumsiek11-seurat.png test-data/pl.highly_variable_genes.seurat.blobs.png test-data/pl.matrixplot.krumsiek11.png test-data/pl.paga.paul15_gauss_braycurtis.png test-data/pl.paga_compare.paul15_gauss_braycurtis.png test-data/pl.pca.pbmc68k_reduced.CD3D_CD79A_components_2d.pdf test-data/pl.pca_loadings.pp.pca.krumsiek11.png test-data/pl.pca_overview.pp.pca.krumsiek11.png test-data/pl.pca_variance_ratio.pp.pca.krumsiek11.png test-data/pl.rank_genes_groups.newton-cg.pbmc68k_highly_reduced_marker_1.png test-data/pl.rank_genes_groups.newton-cg.pbmc68k_highly_reduced_marker_filtered_1.png test-data/pl.rank_genes_groups.rank_genes_groups.krumsiek11.png test-data/pl.rank_genes_groups_dotplot.rank_genes_groups.krumsiek11.png test-data/pl.rank_genes_groups_heatmap.rank_genes_groups.krumsiek11.png test-data/pl.rank_genes_groups_matrixplot.rank_genes_groups.krumsiek11.png test-data/pl.rank_genes_groups_stacked_violin.rank_genes_groups.krumsiek11.png test-data/pl.rank_genes_groups_violin.Ery.png test-data/pl.rank_genes_groups_violin.Mk.png test-data/pl.rank_genes_groups_violin.Mo.png test-data/pl.rank_genes_groups_violin.Neu.png test-data/pl.rank_genes_groups_violin.progenitor.png test-data/pl.scatter.krumsiek11.png test-data/pl.scatter.pbmc68k_reduced.png test-data/pl.scatter.umap.pbmc68k_reduced.png test-data/pl.stacked_violin.krumsiek11.png test-data/pl.tsne.krumsiek11.png test-data/pl.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.png test-data/pl.violin.pbmc68k_reduced_custom.png test-data/pp.recipe_zheng17.random-randint.h5ad test-data/random-randint.h5ad test-data/sparce_csr_matrix.h5ad test-data/tl.embedding_density.umap.pbmc68k_reduced.h5ad test-data/tl.rank_genes_groups.newton-cg.pbmc68k_reduced.h5ad |
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test-data/external.pp.bbknn.krumsiek11.h5ad test-data/pl.rank_genes_groups_tracksplot.newton-cg.pbmc68k_highly_reduced_marker_filtered_1.png test-data/pl.scrublet_score_distribution.png test-data/pl.stacked_violin_pp.filter_genes_dispersion.krumsiek11-seurat.png test-data/pp.pca.krumsiek11.batch.h5ad test-data/pp.scrublet.krumsiek11.h5ad |
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test-data/external.pp.magic.all_genes.krumsiek11.h5ad test-data/external.pp.magic.pca_only.krumsiek11.h5ad test-data/pp.calculate_qc_metrics.sparce_csr_matrix.h5ad test-data/pp.downsample_counts.random-randint.h5ad test-data/pp.filter_cells.krumsiek11-max_genes.h5ad test-data/pp.filter_cells.krumsiek11-min_counts.h5ad test-data/pp.filter_genes.krumsiek11-min_counts.h5ad test-data/pp.filter_rank_genes_groups.h5ad test-data/pp.highly_variable_genes.krumsiek11-cell_ranger.h5ad test-data/pp.log1p.krumsiek11.h5ad test-data/pp.normalize_total.krumsiek11.h5ad test-data/pp.pca.krumsiek11_chunk.h5ad test-data/pp.recipe_seurat.recipe_zheng17.h5ad test-data/pp.recipe_weinreb17.paul15_subsample.updated.h5ad test-data/pp.scale.krumsiek11.h5ad test-data/pp.scale_max_value.krumsiek11.h5ad test-data/pp.sqrt.krumsiek11.h5ad test-data/pp.subsample.krumsiek11_fraction.h5ad test-data/pp.subsample.krumsiek11_n_obs.h5ad test-data/tl.embedding_density.umap.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.leiden.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad test-data/tl.rank_genes_groups.liblinear.krumsiek11.h5ad test-data/tl.score_genes.krumsiek11.h5ad test-data/tl.score_genes_cell_cycle.krumsiek11.h5ad |
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| diff -r f9353ee6a0d4 -r 178242b82297 README.md --- a/README.md Tue Aug 20 09:50:17 2024 +0000 +++ b/README.md Sat Sep 14 12:45:46 2024 +0000 |
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| @@ -25,6 +25,7 @@ `pp.highly_variable_genes` | Extract highly variable genes `pp.subsample` | Subsample to a fraction of the number of observations `pp.downsample_counts` | Downsample counts so that each cell has no more than target_counts + `pp.scrublet` | Predict doublets 3. Normalize (`normalize.xml`) @@ -34,14 +35,18 @@ `pp.recipe_zheng17` | Normalization and filtering as of [Zheng17] `pp.recipe_weinreb17` | Normalization and filtering as of [Weinreb17] `pp.recipe_seurat` | Normalization and filtering as of Seurat [Satija15] + `external.pp.magic` | Denoising using Markov Affinity-based Graph Imputation of Cells (MAGIC) API 4. Remove confounders (`remove_confounder.xml`) Methods | Description --- | --- `pp.regress_out` | Regress out unwanted sources of variation - `pp.mnn_correct` | Correct batch effects by matching mutual nearest neighbors + <!-- `pp.mnn_correct` | Correct batch effects by matching mutual nearest neighbors --> `pp.combat` | ComBat function for batch effect correction + `external.pp.bbknn` | Batch effect removal with Batch balanced KNN (BBKNN) + `external.pp.harmony_integrate` | Integrate multiple single-cell experiments with Harmony + `external.pp.scanorama_integrate` | Integrate multiple single-cell experiments with Scanorama 5. Clustering, embedding and trajectory inference (`cluster_reduce_dimension.xml`) @@ -49,14 +54,14 @@ --- | --- `tl.louvain` | Cluster cells into subgroups `tl.leiden` | Cluster cells into subgroups - `tl.pca` | Principal component analysis - `pp.pca` | Principal component analysis (appears to be the same func...) + `pp.pca` | Principal component analysis `tl.diffmap` | Diffusion Maps `tl.tsne` | t-SNE `tl.umap` | Embed the neighborhood graph using UMAP `tl.draw_graph` | Force-directed graph drawing `tl.dpt` | Infer progression of cells through geodesic distance along the graph `tl.paga` | Mapping out the coarse-grained connectivity structures of complex manifolds + `tl.embedding_density` | Calculate the density of cells in an embedding (per condition) 6. Plot (`plot.xml`) @@ -66,18 +71,20 @@ --- | --- `pl.scatter` | Scatter plot along observations or variables axes `pl.heatmap` | Heatmap of the expression values of set of genes + `pl.tracksplot` | Tracks plot of the expression values per cell `pl.dotplot` | Makes a dot plot of the expression values `pl.violin` | Violin plot `pl.stacked_violin` | Stacked violin plots `pl.matrixplot` | Heatmap of the mean expression values per cluster `pl.clustermap` | Hierarchically-clustered heatmap - + 2. Preprocessing Methods | Description --- | --- `pl.highest_expr_genes` | Plot the fraction of counts assigned to each gene over all cells `pl.highly_variable_genes` | Plot dispersions versus means for genes + `pl.scrublet_score_distribution` | Histogram of doublet scores 3. PCA @@ -96,12 +103,13 @@ `pl.umap` | Scatter plot in UMAP basis `pl.diffmap` | Scatter plot in Diffusion Map basis `pl.draw_graph` | Scatter plot in graph-drawing basis + `pl.embedding_density` | Density of cells in an embedding (per condition) 5. Branching trajectories and pseudotime, clustering Methods | Description --- | --- - `pl.dpt_groups_pseudotime` | Plot groups and pseudotime + <!-- `pl.dpt_groups_pseudotime` | Plot groups and pseudotime --> `pl.dpt_timeseries` | Heatmap of pseudotime series `pl.paga` | Plot the abstracted graph through thresholding low-connectivity edges `pl.paga_compare` | Scatter and PAGA graph side-by-side @@ -113,3 +121,8 @@ --- | --- `pl.rank_genes_groups` | Plot ranking of genes using dotplot plot `pl.rank_genes_groups_violin` | Plot ranking of genes for all tested comparisons + `pl.rank_genes_groups_stacked_violin` | Plot ranking of genes as stacked violin plot + `pl.rank_genes_groups_heatmap` | Plot ranking of genes as heatmap plot + `pl.rank_genes_groups_dotplot` | Plot ranking of genes as dotplot plot + `pl.rank_genes_groups_matrixplot` | Plot ranking of genes as matrixplot plot + `pl.rank_genes_groups_tracksplot` | Plot ranking of genes as tracksplot plot |
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| diff -r f9353ee6a0d4 -r 178242b82297 cluster_reduce_dimension.xml --- a/cluster_reduce_dimension.xml Tue Aug 20 09:50:17 2024 +0000 +++ b/cluster_reduce_dimension.xml Sat Sep 14 12:45:46 2024 +0000 |
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| b'@@ -1,37 +1,44 @@\n-<tool id="scanpy_cluster_reduce_dimension" name="Cluster, infer trajectories and embed" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@profile@">\n- <description>with scanpy</description>\n+<tool id="scanpy_cluster_reduce_dimension" name="Scanpy cluster, embed" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="@PROFILE@">\n+ <description>and infer trajectories</description>\n <macros>\n <import>macros.xml</import>\n <xml name="pca_inputs">\n <param argument="n_comps" type="integer" min="0" value="50" label="Number of principal components to compute" help="If the value is larger than the number of observations the number of observations is used instead"/>\n- <param argument="dtype" type="text" value="float32" label="Numpy data type string to which to convert the result" help="">\n- <expand macro="sanitize_query" />\n+ <param argument="layer" type="text" value="" optional="true" label="If provided, which element of layers to use for PCA">\n+ <expand macro="sanitize_query"/>\n+ </param>\n+ <param argument="dtype" type="select" label="Numpy data type string to which to convert the result">\n+ <option value="float32" selected="true">float32</option>\n+ <option value="int32">int32</option>\n+ <option value="int64">int64</option>\n+ <option value="uint32">uint32</option>\n+ <option value="uint64">uint64</option>\n+ <option value="float16">float16</option>\n+ <option value="float64">float64</option>\n </param>\n <conditional name="pca">\n <param argument="chunked" type="select" label="Type of PCA?">\n+ <option value="False" selected="true">Full PCA</option>\n <option value="True">Incremental PCA on segments (incremental PCA automatically zero centers and ignores settings of \'random_seed\' and \'svd_solver\')</option>\n- <option value="False" selected="true">Full PCA</option>\n </param>\n <when value="True">\n <param argument="chunk_size" type="integer" min="0" value="" label="chunk_size" help="Number of observations to include in each chunk"/>\n </when>\n <when value="False">\n- <param argument="zero_center" type="boolean" truevalue="True" falsevalue="False" checked="true"\n- label="Compute standard PCA from covariance matrix?"\n- help="If not, it omits zero-centering variables (uses *TruncatedSVD* from scikit-learn), which allows to handle sparse input efficiently."/>\n+ <param argument="zero_center" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Compute standard PCA from covariance matrix?" help="If not, it omits zero-centering variables (uses *TruncatedSVD* from scikit-learn), which allows to handle sparse input efficiently."/>\n <expand macro="svd_solver"/>\n- <param argument="random_state" type="integer" value="0" label="Initial states for the optimization" help=""/>\n+ <param argument="random_state" type="integer" value="0" label="Change to use different initial states for the optimization"/>\n </when>\n </conditional>\n- <param argument="use_highly_variable" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Use highly variable genes only?" help="They should be use if they have been determined beforehand."/>\n+ <param argument="mask_var" type="text" value="" optional="true" label="To run only on a certain set of genes given by a string referring to an array in" help="By default, uses .var[\'highly_variable\'] if available, else everything"/>\n </xml>\n <xml name="param_random_state">\n '..b'help><![CDATA[\n+ \n Cluster cells into subgroups (`tl.louvain`)\n ===========================================\n \n@@ -650,6 +678,7 @@\n More details on the `tl.louvain scanpy documentation\n <https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.louvain.html>`_\n \n+\n Cluster cells into subgroups (`tl.leiden`)\n ==========================================\n \n@@ -660,21 +689,13 @@\n More details on the `tl.leiden scanpy documentation\n <https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.leiden.html>`_\n \n+\n Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca`\n ============================================================================================================\n \n-@CMD_pca_outputs@\n-\n More details on the `pp.pca scanpy documentation\n <https://scanpy.readthedocs.io/en/stable/api/scanpy.pp.pca.html>`__\n \n-Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca`\n-============================================================================================================\n-\n-@CMD_pca_outputs@\n-\n-More details on the `tl.pca scanpy documentation\n-<https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.pca.html>`__\n \n Diffusion Maps, using `tl.diffmap`\n ==================================\n@@ -698,6 +719,7 @@\n More details on the `tl.diffmap scanpy documentation\n <https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.diffmap.html>`__\n \n+\n t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne`\n =======================================================================\n \n@@ -710,6 +732,7 @@\n More details on the `tl.tsne scanpy documentation\n <https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.tsne.html>`__\n \n+\n Embed the neighborhood graph using UMAP, using `tl.umap`\n ========================================================\n \n@@ -721,8 +744,8 @@\n nearest-neighbor distances in the embedding such that these best match the\n distribution of distances in the high-dimensional space. We use the\n implementation of `umap-learn <https://github.com/lmcinnes/umap>`__\n-(McInnes et al, 2018). For a few comparisons of UMAP with tSNE, see this `preprint\n-<https://doi.org/10.1101/298430>`__.\n+(McInnes et al, 2018). For a few comparisons of UMAP with tSNE, see this `paper\n+<https://www.nature.com/articles/nbt.4314>`__.\n \n The UMAP coordinates of data are added to the return AnnData in the multi-dimensional\n observations annotation (obsm). This data is accessible using the inspect tool for AnnData\n@@ -730,6 +753,7 @@\n More details on the `tl.umap scanpy documentation\n <https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.umap.html>`__\n \n+\n Force-directed graph drawing, using `tl.draw_graph`\n ===================================================\n \n@@ -749,6 +773,7 @@\n More details on the `tl.draw_graph scanpy documentation\n <https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.draw_graph.html>`__\n \n+\n Infer progression of cells through geodesic distance along the graph (`tl.dpt`)\n ===============================================================================\n \n@@ -808,6 +833,18 @@\n \n More details on the `tl.paga scanpy documentation\n <https://scanpy.readthedocs.io/en/stable/api/scanpy.tl.paga.html>`_\n- ]]></help>\n+\n+\n+Calculates the density of cells in an embedding (per condition). (`tl.embedding_density`)\n+=========================================================================================\n+\n+Gaussian kernel density estimation is used to calculate the density of cells in an embedded space. This can be performed per category over a categorical cell annotation.\n+\n+Note that density values are scaled to be between 0 and 1. Thus, the density value at each cell is only comparable to densities in the same category.\n+\n+More details on the `tl.embedding_density scanpy documentation\n+<https://scanpy.readthedocs.io/en/stable/generated/scanpy.tl.embedding_density.html>`_\n+ ]]>\n+ </help>\n <expand macro="citations"/>\n </tool>\n' |
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| diff -r f9353ee6a0d4 -r 178242b82297 macros.xml --- a/macros.xml Tue Aug 20 09:50:17 2024 +0000 +++ b/macros.xml Sat Sep 14 12:45:46 2024 +0000 |
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| b'@@ -1,17 +1,15 @@\n <macros>\n- <token name="@TOOL_VERSION@">1.9.6</token>\n- <token name="@VERSION_SUFFIX@">4</token>\n- <token name="@profile@">21.09</token>\n+ <token name="@TOOL_VERSION@">1.10.2</token>\n+ <token name="@VERSION_SUFFIX@">0</token>\n+ <token name="@PROFILE@">21.09</token>\n <xml name="requirements">\n <requirements>\n <requirement type="package" version="@TOOL_VERSION@">scanpy</requirement>\n- <requirement type="package" version="3.0.6">loompy</requirement>\n- <requirement type="package" version="0.10.1">leidenalg</requirement>\n- <requirement type="package" version="0.8.1">louvain</requirement>\n- <requirement type="package" version="1.5.3">pandas</requirement>\n- <requirement type="package" version="3.7">matplotlib</requirement>\n- <requirement type="package" version="0.12.2">seaborn</requirement>\n- <requirement type="package" version="3.0.0">magic-impute</requirement>\n+ <requirement type="package" version="0.10.3">anndata</requirement>\n+ <requirement type="package" version="1.26.4">numpy</requirement>\n+ <requirement type="package" version="2.2.2">pandas</requirement>\n+ <requirement type="package" version="1.14.1">scipy</requirement>\n+ <requirement type="package" version="0.14.2">statsmodels</requirement> \n <yield />\n </requirements>\n </xml>\n@@ -22,7 +20,7 @@\n </xml>\n <xml name="creators">\n <creator>\n- <organization name="European Galaxy Team" url="https://galaxyproject.org/eu/" />\n+ <organization name="European Galaxy Team" url="https://galaxyproject.org/eu/"/>\n </creator>\n </xml>\n <xml name="citations">\n@@ -31,28 +29,13 @@\n <citation type="doi">10.1093/gigascience/giaa102</citation>\n </citations>\n </xml>\n- <xml name="version_command">\n- <version_command><![CDATA[python -c "import scanpy as sc;print(\'scanpy version: %s\' % sc.__version__)"]]></version_command>\n- </xml>\n- <token name="@CMD@"><![CDATA[\n-cp \'$adata\' \'anndata.h5ad\' &&\n-cat \'$script_file\' > \'$hidden_output\' &&\n-python \'$script_file\' >> \'$hidden_output\' &&\n-ls . >> \'$hidden_output\' &&\n-touch \'anndata_info.txt\' &&\n-cat \'anndata_info.txt\' @CMD_prettify_stdout@\n- ]]>\n- </token>\n- <token name="@CMD_imports@"><![CDATA[\n-import scanpy as sc\n-import pandas as pd\n-import numpy as np\n- ]]>\n- </token>\n+ \n+ \n+ <!-- param macros -->\n <xml name="sanitize_query" token_validinitial="string.printable">\n <sanitizer>\n <valid initial="@VALIDINITIAL@">\n- <remove value="'" />\n+ <remove value="'"/>\n </valid>\n </sanitizer>\n </xml>\n@@ -62,58 +45,55 @@\n <add value=","/>\n </valid>\n </sanitizer>\n- </xml>\n+ </xml> \n <xml name="inputs_anndata">\n <param name="adata" type="data" format="h5ad" label="Annotated data matrix"/>\n </xml>\n- <token name="@CMD_read_inputs@"><![CDATA[\n-adata = sc.read_h5ad(\'anndata.h5ad\')\n-]]>\n- </token>\n <xml name="inputs_common_advanced">\n <section name="advanced_common" title="Advanced Options" expanded="false">\n- <param name="show_log" type="boolean" checked="false" label="Output Log?" />\n+ <param name="show_log" type="boolean" checked="false" label="Output Log?"/>\n </section>\n </xml>\n <xml name="anndata_outputs">\n- <data name="anndata_out" format="h5ad" from_work_dir="anndata.h5ad" label="${tool.name} (${method.method}) on ${on_string}: Annotated data matrix"/>\n- <data name="hidden_output" format="txt" label="Log file" >\n+ <data name="anndata_out" format="h5ad" from_work_dir="anndata.h5ad" label="${tool.name} (${method.method}) on ${on_string}: Annotated data matrix">\n+ <yield />\n+ </data>\n+ '..b'row_palette" type="select" optional="true" label="Colors to use in each of the stacked violin plots">\n <option value="muted">muted</option>\n <expand macro="seaborn_color_palette_options"/>\n </param>\n- <param argument="standard_scale" type="select" label="Standardize a dimension between 0 and 1" help="Each variable or observation is subtracted by the minimum and divided each by its maximum.">\n- <option value="None">No standardization</option>\n- <option value="var">Standardization on variable</option>\n- <option value="obs">Standardization on observation</option>\n- </param>\n- <expand macro="seaborn_violinplot"/>\n+ <expand macro="param_standard_scale"/>\n+ <expand macro="params_seaborn_violinplot"/>\n+ <param argument="yticklabels" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Set to view the y tick labels"/>\n+ <expand macro="param_vmin" label="The value representing the lower limit of the color scale" help="Values smaller than vmin are plotted with the same color as vmin."/>\n+ <expand macro="param_vmax" label="The value representing the upper limit of the color scale" help="Values larger than vmax are plotted with the same color as vmax."/>\n+ <expand macro="param_vcenter"/>\n </xml>\n- <token name="@CMD_pl_stacked_violin@"><![CDATA[\n+ <token name="@CMD_PARAMS_PL_STACKED_VIOLIN@"><![CDATA[\n swap_axes=$method.swap_axes,\n- @CMD_conditional_stripplot@\n- scale=\'$method.violin_plot.scale\',\n- #if $method.row_palette\n+ @CMD_CONDITIONAL_STRIPPLOT@\n+ density_norm=\'$method.violin_plot.density_norm\',\n+ #if $method.row_palette:\n row_palette=\'$method.row_palette\',\n #end if\n- #if str($method.standard_scale) != \'None\'\n- standard_scale=\'$method.standard_scale\',\n+ @CMD_STANDARD_SCALE@\n+ @CMD_PARAMS_SEABORN_VIOLINPLOT@\n+ yticklabels=$method.yticklabels,\n+ #if str($method.vmin) != \'\':\n+ vmin=$method.vmin,\n #end if\n- @CMD_params_seaborn_violinplot@\n+ #if str($method.vmax) != \'\':\n+ vmax=$method.vmax,\n+ #end if\n+ #if str($method.vcenter) != \'\':\n+ vcenter=$method.vcenter,\n+ #end if\n ]]>\n </token>\n+\n+ <xml name="params_scatter_outine">\n+ <param argument="add_outline" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Add a thin border around groups of dots" help="In some situations this can enhance the aesthetics of the resulting image"/>\n+ <param name="outline_color_border" type="select" optional="true" label="Border color around the scatter dot" help="Default: black">\n+ <expand macro="matplotlib_color"/>\n+ </param>\n+ <param name="outline_color_gap" type="select" optional="true" label="Gap color between the border color and the scatter dot" help="Default: white">\n+ <expand macro="matplotlib_color"/>\n+ </param>\n+ <param argument="outline_width_border" type="float" value="0.3" optional="true" label=" width of the border color as a fraction of the scatter dot size "/>\n+ <param argument="outline_width_gap" type="float" value="0.05" optional="true" label="The width of the gap color"/>\n+ </xml>\n+ <token name="@CMD_SCATTER_OUTINE@"><![CDATA[\n+ add_outline=$method.add_outline,\n+ #if $method.outline_color_border and $method.outline_color_gap:\n+ outline_color=($method.outline_color_border, $method.outline_color_gap),\n+ #end if\n+ outline_width=($method.outline_width_border, $method.outline_width_gap),\n+ ]]>\n+ </token>\n+\n+\n+ <!-- unused macros -->\n+ <!-- <xml name="param_right_margin">\n+ <param argument="right_margin" type="float" value="1" label="Width of the space right of each plotting panel"/>\n+ </xml>\n+ <xml name="param_left_margin">\n+ <param argument="left_margin" type="float" value="1" label="Width of the space left of each plotting panel"/>\n+ </xml> -->\n </macros>\n' |
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