Mercurial > repos > iuc > scanpy_normalize
changeset 1:a9f14e2d1655 draft
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scanpy/ commit 8ef5f7c6f8728608a3f05bb51e11b642b84a05f5"
line wrap: on
line diff
--- a/README.md Mon Mar 04 10:16:12 2019 -0500 +++ b/README.md Wed Oct 16 06:31:10 2019 -0400 @@ -1,138 +1,115 @@ Scanpy ====== -## Classification of methods into steps +1. Inspect & Manipulate (`inspect.xml`) -Steps: + Methods | Description + --- | --- + `pp.calculate_qc_metrics` | Calculate quality control metrics + `pp.neighbors` | Compute a neighborhood graph of observations + `tl.score_genes` | Score a set of genes + `tl.score_genes_cell_cycle` | Score cell cycle gene + `tl.rank_genes_groups` | Rank genes for characterizing groups + `tl.marker_gene_overlap` | Calculate an overlap score between data-deriven marker genes and provided markers (**not working for now**) + `pp.log1p` | Logarithmize the data matrix. + `pp.scale` | Scale data to unit variance and zero mean + `pp.sqrt` | Square root the data matrix -1. Filtering +2. Filter (`filter.xml`) Methods | Description --- | --- `pp.filter_cells` | Filter cell outliers based on counts and numbers of genes expressed. `pp.filter_genes` | Filter genes based on number of cells or counts. - `pp.filter_genes_dispersion` | Extract highly variable genes + `tl.filter_rank_genes_groups` | Filters out genes based on fold change and fraction of genes expressing the gene within and outside the groupby categories (**to fix**) `pp.highly_variable_genes` | Extract highly variable genes `pp.subsample` | Subsample to a fraction of the number of observations - `queries.gene_coordinates` | (Could not find...) - `queries.mitochondrial_genes` | Retrieves Mitochondrial gene symbols for specific organism through BioMart for filtering - -2. Quality Plots - - These are in-between stages used to measure the effectiveness of a Filtering/Normalisation/Conf.Removal stage either after processing or prior to. + `pp.downsample_counts` | Downsample counts so that each cell has no more than target_counts - Methods | Description | Notes - --- | --- | --- - `pp.calculate_qc_metrics` | Calculate quality control metrics - `pl.violin` | violin plot of features, lib. size, or subsets of. - `pl.stacked_violin` | Same as above but for multiple series of features or cells - -3. Normalization +3. Normalize (`normalize.xml`) Methods | Description --- | --- - `pp.normalize_per_cell` | Normalize total counts per cell + `pp.normalize_total` | Normalize counts per cell `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] - `pp.log1p` | Logarithmize the data matrix. - `pp.scale` | Scale data to unit variance and zero mean - `pp.sqrt` | - `pp.downsample_counts` | Downsample counts so that each cell has no more than target_counts -4. Conf. removal +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.dca` | Deep count autoencoder to denoise the data - `pp.magic` | Markov Affinity-based Graph Imputation of Cells (MAGIC) API to denoise - `tl.sim` | Simulate dynamic gene expression data [Wittman09] - `pp.calculate_qc_metrics` | Calculate quality control metrics - `tl.score_genes` | Score a set of genes - `tl.score_genes_cell_cycle` | Score cell cycle genes - `tl.cyclone` | Assigns scores and predicted class to observations based on cell-cycle genes [Scialdone15] - `tl.sandbag` | Calculates pairs of genes serving as markers for each cell-cycle phase [Scialdone15] + `pp.combat` | ComBat function for batch effect correction -5. Clustering and Heatmaps +5. Clustering, embedding and trajectory inference (`cluster_reduce_dimension.xml`) Methods | Description --- | --- - `tl.leiden` | Cluster cells into subgroups [Traag18] [Levine15] - `tl.louvain` | Cluster cells into subgroups [Blondel08] [Levine15] [Traag17] + `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...) `tl.diffmap` | Diffusion Maps `tl.tsne` | t-SNE `tl.umap` | Embed the neighborhood graph using UMAP - `tl.phate` | PHATE - `pp.neighbors` | Compute a neighborhood graph of observations - `tl.rank_genes_groups` | Rank genes for characterizing groups - `pl.rank_genes_groups` | - `pl.rank_genes_groups_dotplot` | - `pl.rank_genes_groups_heatmap` | - `pl.rank_genes_groups_matrixplot` | - `pl.rank_genes_groups_stacked_violin` | - `pl.rank_genes_groups_violin` | - `pl.matrix_plot` | - `pl.heatmap` | - `pl.highest_expr_genes` | - `pl.diffmap` | + `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 + +6. Plot (`plot.xml`) + + 1. Generic + + Methods | Description + --- | --- + `pl.scatter` | Scatter plot along observations or variables axes + `pl.heatmap` | Heatmap of the expression values of set of genes + `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 -6. Cluster Inspection and plotting + 2. Preprocessing - Methods that draw out the clusters computed in the previous stage, not heatmap or pseudotime related. + 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 + + 3. PCA - Methods | Description - --- | --- - `pl.clustermap` | - `pl.phate` | - `pl.dotplot` | - `pl.draw_graph` | (really general purpose, would not implement directly) - `pl.filter_genes_dispersion` | (depreciated for 'highly_variable_genes') - `pl.matrix` | (could not find in API) - `pl.pca` | - `pl.pca_loadings` | - `pl.pca_overview` | - `pl.pca_variance_ratio` | - `pl.ranking` | (not sure what this does...) - `pl.scatter` | ([very general purpose](https://icb-scanpy.readthedocs-hosted.com/en/latest/api/scanpy.api.pl.scatter.html), would not implement directly) - `pl.set_rcParams_defaults` | - `pl.set_rcParams_scanpy` | - `pl.sim` | - `pl.tsne` | - `pl.umap` | + Methods | Description + --- | --- + `pl.pca` | Scatter plot in PCA coordinates + `pl.pca_loadings` | Rank genes according to contributions to PCs + `pl.pca_variance_ratio` | Scatter plot in PCA coordinates + `pl.pca_overview` | Plot PCA results -7. Branch/Between-Cluster Inspection + 4. Embeddings - Pseudotime analysis, relies on initial clustering. + Methods | Description + --- | --- + `pl.tsne` | Scatter plot in tSNE basis + `pl.umap` | Scatter plot in UMAP basis + `pl.diffmap` | Scatter plot in Diffusion Map basis + `pl.draw_graph` | Scatter plot in graph-drawing basis - Methods | Description - --- | --- - `tl.dpt` | Infer progression of cells through geodesic distance along the graph [Haghverdi16] [Wolf17i] - `pl.dpt_groups_pseudotime` | - `pl.dpt_timeseries` | - `tl.paga_compare_paths` | - `tl.paga_degrees` | - `tl.paga_expression_entropies` | - `tl.paga` | Generate cellular maps of differentiation manifolds with complex topologies [Wolf17i] - `pl.paga` | - `pl.paga_adjacency` | - `pl.paga_compare` | - `pl.paga_path` | - `pl.timeseries` | - `pl.timeseries_as_heatmap` | - `pl.timeseries_subplot` | + 5. Branching trajectories and pseudotime, clustering + Methods | Description + --- | --- + `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 + `pl.paga_path` | Gene expression and annotation changes along paths -Methods to sort | Description ---- | --- -`tl.ROC_AUC_analysis` | (could not find in API) -`tl.correlation_matrix` | (could not find in API) -`rtools.mnn_concatenate` | (could not find in API) -`utils.compute_association_matrix_of_groups` | (could not find in API) -`utils.cross_entropy_neighbors_in_rep` | (could not find in API) -`utils.merge_groups` | (could not find in API) -`utils.plot_category_association` | (could not find in API) -`utils.select_groups` | (could not find in API) \ No newline at end of file + 6. Marker genes + + Methods | Description + --- | --- + `pl.rank_genes_groups` | Plot ranking of genes using dotplot plot + `pl.rank_genes_groups_violin` | Plot ranking of genes for all tested comparisons
--- a/README.rst Mon Mar 04 10:16:12 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,105 +0,0 @@ -The different methods from Scanpy have been grouped by themes: - -1. Filter in `filter.xml` - - Filter cell outliers based on counts and numbers of genes expressed, using `pp.filter_cells` - - Filter genes based on number of cells or counts, using `pp.filter_genes` - - Extract highly variable genes, using `pp.filter_genes_dispersion` - - `tl.highly_variable_genes` (need to be added) - - Subsample to a fraction of the number of observations, using `pp.subsample` - - `queries.gene_coordinates` (need to be added) - - `queries.mitochondrial_genes` (need to be added) - -2. Normalize in `normalize.xml` - - Normalize total counts per cell, using `pp.normalize_per_cell` - - Normalization and filtering as of Zheng et al. (2017), using `pp.recipe_zheng17` - - Normalization and filtering as of Weinreb et al (2017), using `pp.recipe_weinreb17` - - Normalization and filtering as of Seurat et al (2015), using `pp.recipe_seurat` - - Logarithmize the data matrix, using `pp.log1p` - - Scale data to unit variance and zero mean, using `pp.scale` - - Square root the data matrix, using `pp.sqrt` - - Downsample counts, using `pp.downsample_counts` - -3. Remove confounder in `remove_confounders.xml` - - Regress out unwanted sources of variation, using `pp.regress_out` - - `pp.mnn_correct` (need to be added) - - `pp.mnn_correct` (need to be added) - - `pp.magic` (need to be added) - - `tl.sim` (need to be added) - - `pp.calculate_qc_metrics` (need to be added) - - Score a set of genes, using `tl.score_genes` - - Score cell cycle genes, using `tl.score_genes_cell_cycle` - - `tl.cyclone` (need to be added) - - `tl.andbag` (need to be added) - -4. Cluster and reduce dimension in `cluster_reduce_dimension.xml` - - `tl.leiden` (need to be added) - - Cluster cells into subgroups, using `tl.louvain` - - Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `pp.pca` - - Computes PCA (principal component analysis) coordinates, loadings and variance decomposition, using `tl.pca` - - Diffusion Maps, using `tl.diffmap` - - t-distributed stochastic neighborhood embedding (tSNE), using `tl.tsne` - - Embed the neighborhood graph using UMAP, using `tl.umap` - - `tl.phate` (need to be added) - - Compute a neighborhood graph of observations, using `pp.neighbors` - - Rank genes for characterizing groups, using `tl.rank_genes_groups` - -4. Inspect - - `tl.paga_compare_paths` (need to be added) - - `tl.paga_degrees` (need to be added) - - `tl.paga_expression_entropies` (need to be added) - - Generate cellular maps of differentiation manifolds with complex topologies, using `tl.paga` - - Infer progression of cells through geodesic distance along the graph, using `tl.dpt` - -5. Plot - 1. Generic - - Scatter plot along observations or variables axes, using `pl.scatter` - - Heatmap of the expression values of set of genes, using `pl.heatmap` - - Makes a dot plot of the expression values, using `pl.dotplot` - - Violin plot, using `pl.violin` - - `pl.stacked_violin` (need to be added) - - Heatmap of the mean expression values per cluster, using `pl.matrixplot` - - Hierarchically-clustered heatmap, using `pl.clustermap` - - `pl.ranking` - - 2. Preprocessing - - Plot the fraction of counts assigned to each gene over all cells, using `pl.highest_expr_genes` - - Plot dispersions versus means for genes, using `pl.filter_genes_dispersion` - - `pl.highly_variable_genes` (need to be added) - - `pl.calculate_qc_metrics` (need to be added) - - 3. PCA - - Scatter plot in PCA coordinates, using `pl.pca` - - Rank genes according to contributions to PCs, using `pl.pca_loadings` - - Scatter plot in PCA coordinates, using `pl.pca_variance_ratio` - - Plot PCA results, using `pl.pca_overview` - - 4. Embeddings - - Scatter plot in tSNE basis, using `pl.tsne` - - Scatter plot in UMAP basis, using `pl.umap` - - Scatter plot in Diffusion Map basis, using `pl.diffmap` - - `pl.draw_graph` (need to be added) - - 5. Branching trajectories and pseudotime, clustering - - Plot groups and pseudotime, using `pl.dpt_groups_pseudotime` - - Heatmap of pseudotime series, using `pl.dpt_timeseries` - - Plot the abstracted graph through thresholding low-connectivity edges, using `pl.paga` - - `pl.paga_compare` (need to be added) - - `pl.paga_path` (need to be added) - - 6. Marker genes: - - Plot ranking of genes using dotplot plot, using `pl.rank_gene_groups` - - `pl.rank_genes_groups_dotplot` (need to be added) - - `pl.rank_genes_groups_heatmap` (need to be added) - - `pl.rank_genes_groups_matrixplot` (need to be added) - - `pl.rank_genes_groups_stacked_violin` (need to be added) - - `pl.rank_genes_groups_violin` (need to be added) - - 7. Misc - - `pl.phate` (need to be added) - - `pl.matrix` (need to be added) - - `pl.paga_adjacency` (need to be added) - - `pl.timeseries` (need to be added) - - `pl.timeseries_as_heatmap` (need to be added) - - `pl.timeseries_subplot` (need to be added) - - \ No newline at end of file
--- a/macros.xml Mon Mar 04 10:16:12 2019 -0500 +++ b/macros.xml Wed Oct 16 06:31:10 2019 -0400 @@ -1,10 +1,12 @@ <macros> - <token name="@version@">1.4</token> + <token name="@version@">1.4.4</token> <token name="@galaxy_version@"><![CDATA[@version@+galaxy0]]></token> <xml name="requirements"> <requirements> <requirement type="package" version="@version@">scanpy</requirement> <requirement type="package" version="2.0.17">loompy</requirement> + <requirement type="package" version="2.9.0">h5py</requirement> + <requirement type="package" version="0.7.0">leidenalg</requirement> <yield /> </requirements> </xml> @@ -14,102 +16,33 @@ </citations> </xml> <xml name="version_command"> - <version_command><![CDATA[python -c "import scanpy.api as sc;print('scanpy version: %s' % sc.__version__)"]]></version_command> + <version_command><![CDATA[python -c "import scanpy as sc;print('scanpy version: %s' % sc.__version__)"]]></version_command> </xml> <token name="@CMD@"><![CDATA[ +cp '$adata' 'anndata.h5ad' && cat '$script_file' && -python '$script_file' +python '$script_file' && +ls . ]]> </token> <token name="@CMD_imports@"><![CDATA[ -import scanpy.api as sc +import scanpy as sc import pandas as pd import numpy as np ]]> </token> <xml name="inputs_anndata"> - <conditional name="input"> - <param name="format" type="select" label="Format for the annotated data matrix"> - <option value="loom">loom</option> - <option value="h5ad">h5ad-formatted hdf5 (anndata)</option> - </param> - <when value="loom"> - <param name="adata" type="data" format="loom" label="Annotated data matrix"/> - <param name="sparse" type="boolean" truevalue="True" falsevalue="False" checked="true" label="Is the data matrix to read sparse?"/> - <param name="cleanup" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Cleanup?"/> - <param name="x_name" type="text" value="spliced" label="X_name"/> - <param name="obs_names" type="text" value="CellID" label="obs_names"/> - <param name="var_names" type="text" value="Gene" label="var_names"/> - </when> - <when value="h5ad"> - <param name="adata" type="data" format="h5" label="Annotated data matrix"/> - </when> - </conditional> + <param name="adata" type="data" format="h5ad" label="Annotated data matrix"/> </xml> <token name="@CMD_read_inputs@"><![CDATA[ -#if $input.format == 'loom' -adata = sc.read_loom( - '$input.adata', - sparse=$input.sparse, - cleanup=$input.cleanup, - X_name='$input.x_name', - obs_names='$input.obs_names', - var_names='$input.var_names') -#else if $input.format == 'h5ad' -adata = sc.read_h5ad('$input.adata') -#end if +adata = sc.read('anndata.h5ad') ]]> </token> - <xml name="anndata_output_format"> - <param name="anndata_output_format" type="select" label="Format to write the annotated data matrix"> - <option value="loom">loom</option> - <option value="h5ad">h5ad-formatted hdf5 (anndata)</option> - </param> - </xml> - <xml name="anndata_modify_output_input"> - <conditional name="modify_anndata"> - <param name="modify_anndata" type="select" label="Return modify annotate data matrix?"> - <option value="true">Yes</option> - <option value="false">No</option> - </param> - <when value="true"> - <expand macro="anndata_output_format"/> - </when> - <when value="false"/> - </conditional> - </xml> <xml name="anndata_outputs"> - <data name="anndata_out_h5ad" format="h5" from_work_dir="anndata.h5ad" label="${tool.name} on ${on_string}: Annotated data matrix"> - <filter>anndata_output_format == 'h5ad'</filter> - </data> - <data name="anndata_out_loom" format="loom" from_work_dir="anndata.loom" label="${tool.name} on ${on_string}: Annotated data matrix"> - <filter>anndata_output_format == 'loom'</filter> - </data> - </xml> - <xml name="anndata_modify_outputs"> - <data name="anndata_out_h5ad" format="h5" from_work_dir="anndata.h5ad" label="${tool.name} on ${on_string}: Annotated data matrix"> - <filter>modify_anndata['modify_anndata'] == 'true' and modify_anndata['anndata_output_format'] == 'h5ad'</filter> - </data> - <data name="anndata_out_loom" format="loom" from_work_dir="anndata.loom" label="${tool.name} on ${on_string}: Annotated data matrix"> - <filter>modify_anndata['modify_anndata'] == 'true' and modify_anndata['anndata_output_format'] == 'loom'</filter> - </data> + <data name="anndata_out" format="h5ad" from_work_dir="anndata.h5ad" label="${tool.name} (${method.method}) on ${on_string}: Annotated data matrix"/> </xml> <token name="@CMD_anndata_write_outputs@"><![CDATA[ -#if $anndata_output_format == 'loom' -adata.write_loom('anndata.loom') -#else if $anndata_output_format == 'h5ad' adata.write('anndata.h5ad') -#end if -]]> - </token> - <token name="@CMD_anndata_write_modify_outputs@"><![CDATA[ -#if $modify_anndata.modify_anndata == 'true' - #if $modify_anndata.anndata_output_format == 'loom' -adata.write_loom('anndata.loom') - #elif $modify_anndata.anndata_output_format == 'h5ad' -adata.write('anndata.h5ad') - #end if -#end if ]]> </token> <xml name="svd_solver"> @@ -423,7 +356,7 @@ <param argument="use_raw" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Use `raw` attribute of input if present" help=""/> </xml> <xml name="param_log"> - <param argument="log" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Use the log of the values?" help=""/> + <param argument="log" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Use the log of the values?"/> </xml> <xml name="pl_figsize"> <conditional name="figsize"> @@ -473,7 +406,7 @@ <param argument="layer" type="text" value="" label="Name of the AnnData object layer that wants to be plotted" help="By default `adata.raw.X` is plotted. If `use_raw=False` is set, then `adata.X` is plotted. If layer is set to a valid layer name, then the layer is plotted. layer takes precedence over `use_raw`."/> </xml> <token name="@CMD_param_plot_inputs@"><![CDATA[ - adata=adata, + adata, save='.$format', show=False, ]]></token> @@ -512,9 +445,6 @@ #end for var_group_positions=$var_group_positions, var_group_labels=$var_group_labels, - #else - var_group_positions=None, - var_group_labels=None, #end if #if $method.var_group_rotation var_group_rotation=$method.var_group_rotation, @@ -729,44 +659,42 @@ linewidths=$method.matplotlib_pyplot_scatter.linewidths, edgecolors='$method.matplotlib_pyplot_scatter.edgecolors' ]]></token> - <xml name="section_violin_plots"> - <section name="violin_plot" title="Violin plot attributes"> - <conditional name="stripplot"> - <param argument="stripplot" type="select" label="Add a stripplot on top of the violin plot" help=""> - <option value="True">Yes</option> - <option value="False">No</option> - </param> - <when value="True"> - <conditional name="jitter"> - <param argument="jitter" type="select" label="Add a jitter to the stripplot" help=""> - <option value="True">Yes</option> - <option value="False">No</option> - </param> - <when value="True"> - <param argument="size" type="integer" min="0" value="1" label="Size of the jitter points" help=""/> - </when> - <when value="False"/> - </conditional> - </when> - <when value="False"/> - </conditional> - <conditional name="multi_panel"> - <param argument="multi_panel" type="select" label="Display keys in multiple panels" help="Also when `groupby is not provided"> - <option value="True">Yes</option> - <option value="False" selected="true">No</option> - </param> - <when value="True"> - <param argument="width" type="integer" min="0" value="" optional="true" label="Width of the figure" help=""/> - <param argument="height" type="integer" min="0" value="" optional="true" label="Height of the figure" help=""/> - </when> - <when value="False"/> - </conditional> - <param argument="scale" type="select" label="Method used to scale the width of each violin"> - <option value="area">area: each violin will have the same area</option> - <option value="count">count: the width of the violins will be scaled by the number of observations in that bin</option> - <option value="width" selected="true">width: each violin will have the same width</option> + <xml name="conditional_stripplot"> + <conditional name="stripplot"> + <param argument="stripplot" type="select" label="Add a stripplot on top of the violin plot" help=""> + <option value="True">Yes</option> + <option value="False">No</option> </param> - </section> + <when value="True"> + <conditional name="jitter"> + <param argument="jitter" type="select" label="Add a jitter to the stripplot" help=""> + <option value="True">Yes</option> + <option value="False">No</option> + </param> + <when value="True"> + <param argument="size" type="integer" min="0" value="1" label="Size of the jitter points" help=""/> + </when> + <when value="False"/> + </conditional> + </when> + <when value="False"/> + </conditional> + </xml> + <token name="@CMD_conditional_stripplot@"><![CDATA[ + stripplot=$method.violin_plot.stripplot.stripplot, +#if $method.violin_plot.stripplot.stripplot == "True" + jitter=$method.violin_plot.stripplot.jitter.jitter, + #if $method.violin_plot.stripplot.jitter.jitter == "True" + size=$method.violin_plot.stripplot.jitter.size, + #end if +#end if + ]]></token> + <xml name="param_scale"> + <param argument="scale" type="select" label="Method used to scale the width of each violin"> + <option value="area">area: each violin will have the same area</option> + <option value="count">count: the width of the violins will be scaled by the number of observations in that bin</option> + <option value="width" selected="true">width: each violin will have the same width</option> + </param> </xml> <token name="@CMD_params_violin_plots@"><![CDATA[ stripplot=$method.violin_plot.stripplot.stripplot, @@ -777,7 +705,7 @@ #end if #end if multi_panel=$method.violin_plot.multi_panel.multi_panel, -#if $method.multi_panel.violin_plot.multi_panel == "True" and $method.violin_plot.multi_panel.width and $method.violin_plot.multi_panel.height +#if $method.multi_panel.violin_plot.multi_panel == "True" and str($method.violin_plot.multi_panel.width) != '' and str($method.violin_plot.multi_panel.height) != '' figsize=($method.violin_plot.multi_panel.width, $method.violin_plot.multi_panel.height) #end if scale='$method.violin_plot.scale', @@ -813,14 +741,12 @@ saturation=$method.seaborn_violinplot.saturation, ]]></token> <xml name="param_color"> - <param argument="color" type="text" value="" optional="true" label="Keys for annotations of observations/cells or variables/genes`" help="One or a list of comma-separated index or key from either `.obs` or `.var`"/> + <param argument="color" type="text" value="" optional="true" label="Keys for annotations of observations/cells or variables/genes" help="One or a list of comma-separated index or key from either `.obs` or `.var`"/> </xml> <token name="@CMD_param_color@"><![CDATA[ #if str($method.color) != '' #set $color = ([x.strip() for x in str($method.color).split(',')]) color=$color, -#else - color=None, #end if ]]></token> <xml name="pl_groups"> @@ -830,8 +756,6 @@ #if str($method.groups) != '' #set $groups=([x.strip() for x in str($method.groups).split(',')]) groups=$groups, -#else - groups=None, #end if ]]></token> <xml name="pl_components"> @@ -847,8 +771,6 @@ #silent $components.append(str($s.axis1) + ',' + str($s.axis2)) #end for components=$components, -#else - components=None, #end if ]]> </token> @@ -877,7 +799,7 @@ </param> </xml> <xml name="param_legend_fontsize"> - <param argument="legend_fontsize" type="integer" min="0" value="1" label="Legend font size" help=""/> + <param argument="legend_fontsize" type="integer" optional="true" value="" label="Legend font size" help=""/> </xml> <xml name="param_legend_fontweight"> <param argument="legend_fontweight" type="select" label="Legend font weight" help=""> @@ -910,7 +832,7 @@ <param argument="left_margin" type="float" value="1" label="Width of the space left of each plotting panel" help=""/> </xml> <xml name="param_size"> - <param argument="size" type="float" value="1" label="Point size" help=""/> + <param argument="size" type="float" optional="true" value="" label="Point size" help=""/> </xml> <xml name="param_title"> <param argument="title" type="text" value="" optional="true" label="Title for panels" help="Titles must be separated by a comma"/> @@ -937,8 +859,8 @@ <option value="False" selected="true">No</option> </param> <when value="True"> - <param name="edges_width" type="float" min="0" value="0.1" label="Width of edges"/> - <param name="edges_color" type="select" label="Color of edges"> + <param argument="edges_width" type="float" min="0" value="0.1" label="Width of edges"/> + <param argument="edges_color" type="select" label="Color of edges"> <expand macro="matplotlib_color"/> </param> </when> @@ -956,7 +878,7 @@ ]]> </token> <xml name="param_arrows"> - <param name="arrows" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Show arrows?" help="It requires to run `tl.rna_velocity` before."/> + <param argument="arrows" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Show arrows?" help="It requires to run `tl.rna_velocity` before."/> </xml> <xml name="param_cmap"> <param argument="cmap" type="select" label="Colors to use for plotting categorical annotation groups" help=""> @@ -982,9 +904,13 @@ <token name="@CMD_pl_attribute_section@"><![CDATA[ projection='$method.plot.projection', legend_loc='$method.plot.legend_loc', + #if str($method.plot.legend_fontsize) != '' legend_fontsize=$method.plot.legend_fontsize, + #end if legend_fontweight='$method.plot.legend_fontweight', + #if str($method.plot.size) != '' size=$method.plot.size, + #end if palette='$method.plot.palette', frameon=$method.plot.frameon, ncols=$method.plot.ncols, @@ -995,24 +921,39 @@ #end if ]]> </token> + <xml name="options_layout"> + <option value="fa">fa: ForceAtlas2</option> + <option value="fr">fr: Fruchterman-Reingold</option> + <option value="grid_fr">grid_fr: Grid Fruchterman Reingold, faster than "fr"</option> + <option value="kk">kk: Kamadi Kawai’, slower than "fr"</option> + <option value="drl">drl: Distributed Recursive Layout, pretty fast</option> + <option value="rt">rt: Reingold Tilford tree layout</option> + <option value="eq_tree">eq_tree: Equally spaced tree</option> + </xml> + <xml name="param_layout"> + <param argument="layout" type="select" label="Plotting layout" help=""> + <expand macro="options_layout"/> + </param> + </xml> + <xml name="param_root"> + <param argument="root" type="text" value="" label="Comma-separated roots" help="If choosing a tree layout, this is the index of the root node or a list of root node indices. If this is a non-empty vector then the supplied node IDs are used as the roots of the trees (or a single tree if the graph is connected). If this is `None` or an empty list, the root vertices are automatically calculated based on topological sorting."/> + </xml> + <xml name="param_random_state"> + <param argument="random_state" type="integer" value="0" label="Random state" help="For layouts with random initialization like 'fr', change this to use different intial states for the optimization. If `None`, the initial state is not reproducible."/> + </xml> <xml name="inputs_paga"> <param argument="threshold" type="float" min="0" value="0.01" label="Threshold to draw edges" help="Do not draw edges for weights below this threshold. Set to 0 if you want all edges. Discarding low-connectivity edges helps in getting a much clearer picture of the graph."/> <expand macro="pl_groups"/> <param argument="color" type="text" value="" label="The node colors" help="Gene name or obs. annotation, and also plots the degree of the abstracted graph when passing 'degree_dashed', 'degree_solid'."/> <param argument="pos" type="data" format="tabular,csv,tsv" optional="true" label="Two-column tabular file storing the x and y coordinates for drawing" help=""/> <param argument="labels" type="text" value="" label="Comma-separated node labels" help="If none is provided, this defaults to the group labels stored in the categorical for which `tl.paga` has been computed."/> - <param argument="layout" type="select" value="" label="Plotting layout" help=""> - <option value="fa">fa: ForceAtlas2</option> - <option value="fr">fr: Fruchterman-Reingold</option> - <option value="fr">rt: stands for Reingold Tilford</option> - <option value="fr">eq_tree: equally spaced tree</option> - </param> + <expand macro="param_layout"/> <param argument="init_pos" type="data" format="tabular,csv,tsv" optional="true" label="Two-column tabular file storing the x and y coordinates for initializing the layout" help=""/> - <param argument="random_state" type="integer" value="0" label="Random state" help="For layouts with random initialization like 'fr', change this to use different intial states for the optimization. If `None`, the initial state is not reproducible."/> - <param argument="root" type="text" value="" label="Comma-separated roots" help="If choosing a tree layout, this is the index of the root node or a list of root node indices. If this is a non-empty vector then the supplied node IDs are used as the roots of the trees (or a single tree if the graph is connected). If this is `None` or an empty list, the root vertices are automatically calculated based on topological sorting."/> + <expand macro="param_random_state"/> + <expand macro="param_root"/> <param argument="transitions" type="text" value="" label="Key corresponding to the matrix storing the arrows" help="Key for `.uns['paga']`, e.g. 'transistions_confidence'"/> - <param argument="solid_edges" type="text" value="paga_connectivities" label="Key corresponding to the matrix storing the edges to be drawn solid black" help="Key for `.uns['paga']`"/> - <param argument="dashed_edges" type="text" value="" optional="true" label="Key corresponding to the matrix storing the edges to be drawn dashed grey" help="Key for `.uns['paga']`. If not set, no dashed edges are drawn."/> + <param argument="solid_edges" type="text" value="connectivities" label="Key corresponding to the matrix storing the edges to be drawn solid black" help="Key for uns/paga"/> + <param argument="dashed_edges" type="text" value="" optional="true" label="Key corresponding to the matrix storing the edges to be drawn dashed grey" help="Key for uns/paga. If not set, no dashed edges are drawn."/> <param argument="single_component" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Restrict to largest connected component?" help=""/> <param argument="fontsize" type="integer" min="0" value="1" label="Font size for node labels" help=""/> <param argument="node_size_scale" type="float" min="0" value="1.0" label="Size of the nodes" help=""/> @@ -1031,10 +972,11 @@ #if str($method.groups) != '' #set $groups=([x.strip() for x in str($method.groups).split(',')]) groups=$groups, -#else - groups=None, #end if - color='$method.color', +#if str($method.color) != '' + #set $color=([x.strip() for x in str($method.color).split(',')]) + color=$color, +#end if #if $method.pos pos=np.fromfile($method.pos, dtype=dt), #end if @@ -1081,4 +1023,10 @@ <xml name="param_swap_axes"> <param argument="swap_axes" type="boolean" truevalue="True" falsevalue="False" checked="false" label="Swap axes?" help="By default, the x axis contains `var_names` (e.g. genes) and the y axis the `groupby` categories (if any). By setting `swap_axes` then x are the `groupby` categories and y the `var_names`."/> </xml> + <xml name="gene_symbols"> + <param argument="gene_symbols" type="text" value="" optional="true" label="Key for field in `.var` that stores gene symbols"/> + </xml> + <xml name="n_genes"> + <param argument="n_genes" type="integer" min="0" value="20" label="Number of genes to show" help=""/> + </xml> </macros>
--- a/normalize.xml Mon Mar 04 10:16:12 2019 -0500 +++ b/normalize.xml Wed Oct 16 06:31:10 2019 -0400 @@ -1,5 +1,5 @@ -<tool id="scanpy_normalize" name="Normalize with scanpy" version="@galaxy_version@"> - <description></description> +<tool id="scanpy_normalize" name="Normalize" version="@galaxy_version@"> + <description>with scanpy</description> <macros> <import>macros.xml</import> </macros> @@ -13,26 +13,36 @@ @CMD_imports@ @CMD_read_inputs@ -#if $method.method == "pp.normalize_per_cell" -sc.pp.normalize_per_cell( - data=adata, - #if $method.counts_per_cell_after - counts_per_cell_after=$method.counts_per_cell_after, +#if $method.method == "pp.normalize_total" +sc.pp.normalize_total( + adata, + #if str($method.target_sum)!= '' + target_sum=$method.target_sum, + #end if + exclude_highly_expressed=$method.exclude_highly_expressed.exclude_highly_expressed, + #if $method.exclude_highly_expressed.exclude_highly_expressed == "True" + max_fraction=$method.exclude_highly_expressed.max_fraction, #end if - #if $method.counts_per_cell - counts_per_cell=np.loadtxt('$method.counts_per_cell'), + key_added='$method.key_added', + #if str($method.layers) != 'all' + layers[str(x.strip()) for x in str($method.layers).split(',')], + #else + layers='$method.layers', #end if - key_n_counts='$method.key_n_counts', - copy=False) -adata.obs.to_csv('$anndata_obs', sep='\t') -#elif $method.method == "pp.recipe_zheng17" + #if str($method.layer_norm) != "None" + layer_norm='$method.layer_norm', + #end if + inplace=True) + +#else if $method.method == "pp.recipe_zheng17" sc.pp.recipe_zheng17( adata=adata, n_top_genes=$method.n_top_genes, log=$method.log, plot=False, copy=False) -#elif $method.method == "pp.recipe_weinreb17" + +#else if $method.method == "pp.recipe_weinreb17" sc.pp.recipe_weinreb17( adata=adata, log=$method.log, @@ -42,34 +52,14 @@ svd_solver='$method.svd_solver', random_state=$method.random_state, copy=False) -#elif $method.method == "pp.recipe_seurat" + +#else if $method.method == "pp.recipe_seurat" sc.pp.recipe_seurat( adata=adata, log=$method.log, plot=False, copy=False) -#elif $method.method == "pp.log1p" -sc.pp.log1p( - data=adata, - copy=False) -#elif $method.method == "pp.scale" -sc.pp.scale( - data=adata, - zero_center=$method.zero_center, - #if $method.max_value - max_value=$method.max_value, - #end if - copy=False) -#elif $method.method == "pp.sqrt" -sc.pp.sqrt( - data=adata, - copy=False) -#elif $method.method == "pp.downsample_counts" -sc.pp.downsample_counts( - adata=adata, - target_counts=$method.target_counts, - random_state=$method.random_state, - copy=False) + #end if @CMD_anndata_write_outputs@ @@ -79,20 +69,31 @@ <inputs> <expand macro="inputs_anndata"/> <conditional name="method"> - <param argument="method" type="select" label="Method used for plotting"> - <option value="pp.normalize_per_cell">Normalize total counts per cell, using `pp.normalize_per_cell`</option> + <param argument="method" type="select" label="Method used for normalization"> + <option value="pp.normalize_total">Normalize counts per cell, using `pp.normalize_total`</option> <option value="pp.recipe_zheng17">Normalization and filtering as of Zheng et al. (2017), using `pp.recipe_zheng17`</option> <option value="pp.recipe_weinreb17">Normalization and filtering as of Weinreb et al (2017), using `pp.recipe_weinreb17`</option> <option value="pp.recipe_seurat">Normalization and filtering as of Seurat et al (2015), using `pp.recipe_seurat`</option> - <option value="pp.log1p">Logarithmize the data matrix, using `pp.log1p`</option> - <option value="pp.scale">Scale data to unit variance and zero mean, using `pp.scale`</option> - <option value="pp.sqrt">Square root the data matrix, using `pp.sqrt`</option> - <option value="pp.downsample_counts">Downsample counts, using `pp.downsample_counts`</option> </param> - <when value="pp.normalize_per_cell"> - <param argument="counts_per_cell_after" type="float" value="" optional="true" label="Counts per cell after" help="If not provided, after normalization, each cell has a total count equal to the median of the *counts_per_cell* before normalization."/> - <param argument="counts_per_cell" type="data" format="tabular,txt" optional="true" label="Precomputed counts per cell" help=""/> - <param argument="key_n_counts" type="text" value="n_counts" label="Name of the field in `adata.obs` where the total counts per cell will be stored" help=""/> + <when value="pp.normalize_total"> + <param argument="target_sum" type="float" value="" optional="true" label="Target sum" help="If not provided, after normalization, each observation (cell) has a total count equal to the median of the total counts (cells) before normalization."/> + <conditional name="exclude_highly_expressed"> + <param argument="exclude_highly_expressed" type="select" label="Exclude (very) highly expressed genes for the computation of the normalization factor (size factor) for each cell" help=" A gene is considered highly expressed, if it has more than max_fraction of the total counts in at least one cell. The not-excluded genes will sum up to target_sum"> + <option value="True">Yes</option> + <option value="False" selected="true">No</option> + </param> + <when value="True"> + <param argument="max_fraction" type="float" value="0.05" label="Target sum" help="If not provided, after normalization, each observation (cell) has a total count equal to the median of the total counts (cells) before normalization."/> + </when> + <when value="False"/> + </conditional> + <param argument="key_added" type="text" value="n_counts" label="Name of the field in `adata.obs` where the normalization factor is stored" help=""/> + <param argument="layers" type="text" value="all" label="List of layers to normalize" help="'All' will normalize all layers. The list should be comma-separated."/> + <param argument="layer_norm" type="select" label="How to normalize layers?"> + <option value="None">None: after normalization, for each layer in layers each cell has a total count equal to the median of the median of the total counts (cells) before normalization of the layer.</option> + <option value="after">After: for each layer in layers each cell has a total count equal to target_sum.</option> + <option value="X">X: for each layer in layers each cell has a total count equal to the median of total counts for observations (cells) of adata.X before normalization.</option> + </param> </when> <when value="pp.recipe_zheng17"> <param argument="n_top_genes" type="integer" min="0" value="1000" label="Number of genes to keep" help=""/> @@ -109,73 +110,50 @@ <when value="pp.recipe_seurat"> <expand macro="param_log"/> </when> - <when value="pp.log1p"/> - <when value="pp.scale"> - <param argument="zero_center" type="boolean" truevalue="True" falsevalue="False" checked="true" - label="Zero center?" help="If not, it omits zero-centering variables, which allows to handle sparse input efficiently."/> - <param argument="max_value" type="float" value="" optional="true" label="Maximum value" - help="Clip (truncate) to this value after scaling. If not set, it does not clip."/> - </when> - <when value="pp.sqrt"/> - <when value="pp.downsample_counts"> - <param argument="target_counts" type="integer" min="0" value="20000" - label="Target number of counts for downsampling" help="Cells with more counts than 'target_counts' will be downsampled to have 'target_counts' counts."/> - <param argument="random_state" type="integer" value="0" label="Random seed to change subsampling" help=""/> - </when> </conditional> - <expand macro="anndata_output_format"/> </inputs> <outputs> <expand macro="anndata_outputs"/> - <data name="anndata_obs" format="tabular" label="${tool.name} on ${on_string}: Annotation of observations"> - <filter>method['method'] == 'pp.normalize_per_cell'</filter> - </data> </outputs> <tests> <test> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="krumsiek11.h5ad" /> - </conditional> + <!-- test 1 --> + <param name="adata" value="krumsiek11.h5ad" /> <conditional name="method"> - <param name="method" value="pp.normalize_per_cell"/> - <param name="counts_per_cell_after" value="2"/> - <param name="counts_per_cell" value="krumsiek11_counts_per_cell"/> - <param name="key_n_counts" value="n_counts"/> + <param name="method" value="pp.normalize_total"/> + <conditional name="exclude_highly_expressed"> + <param name="exclude_highly_expressed" value="False"/> + </conditional> + <param name="key_added" value="n_counts"/> + <param name="layers" value="all"/> + <param name="layer_norm" value="None"/> </conditional> - <param name="anndata_output_format" value="h5ad"/> <assert_stdout> - <has_text_matching expression="sc.pp.normalize_per_cell"/> - <has_text_matching expression="counts_per_cell_after=2.0"/> - <has_text_matching expression="counts_per_cell=np.loadtxt"/> - <has_text_matching expression="key_n_counts='n_counts'"/> + <has_text_matching expression="sc.pp.normalize_total"/> + <has_text_matching expression="exclude_highly_expressed=False"/> + <has_text_matching expression="key_added='n_counts'"/> + <has_text_matching expression="layers='all'"/> </assert_stdout> - <output name="anndata_out_h5ad" file="pp.normalize_per_cell.krumsiek11.h5ad" ftype="h5" compare="sim_size"/> - <output name="anndata_obs" file="pp.normalize_per_cell.obs.krumsiek11.tabular"/> + <output name="anndata_out" file="pp.normalize_total.krumsiek11.h5ad" ftype="h5ad" compare="sim_size"/> </test> <test> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="random-randint.h5ad"/> - </conditional> + <!-- test 2 --> + <param name="adata" value="random-randint.h5ad"/> <conditional name="method"> <param name="method" value="pp.recipe_zheng17"/> <param name="n_top_genes" value="1000"/> <param name="log" value="True"/> </conditional> - <param name="anndata_output_format" value="h5ad"/> <assert_stdout> <has_text_matching expression="sc.pp.recipe_zheng17"/> <has_text_matching expression="n_top_genes=1000"/> <has_text_matching expression="log=True"/> </assert_stdout> - <output name="anndata_out_h5ad" file="pp.recipe_zheng17.random-randint.h5ad" ftype="h5" compare="sim_size"/> + <output name="anndata_out" file="pp.recipe_zheng17.random-randint.h5ad" ftype="h5ad" compare="sim_size"/> </test> <test> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="paul15_subsample.h5ad" /> - </conditional> + <!-- test 3 --> + <param name="adata" value="paul15_subsample.h5ad" /> <conditional name="method"> <param name="method" value="pp.recipe_weinreb17"/> <param name="log" value="True"/> @@ -185,7 +163,6 @@ <param name="svd_solver" value="randomized"/> <param name="random_state" value="0"/> </conditional> - <param name="anndata_output_format" value="h5ad" /> <assert_stdout> <has_text_matching expression="sc.pp.recipe_weinreb17"/> <has_text_matching expression="log=True"/> @@ -195,108 +172,22 @@ <has_text_matching expression="svd_solver='randomized'"/> <has_text_matching expression="random_state=0"/> </assert_stdout> - <output name="anndata_out_h5ad" file="pp.recipe_weinreb17.paul15_subsample.h5ad" ftype="h5" compare="sim_size"/> + <output name="anndata_out" file="pp.recipe_weinreb17.paul15_subsample.updated.h5ad" ftype="h5ad" compare="sim_size"/> </test> <test> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="pp.recipe_zheng17.random-randint.h5ad" /> - </conditional> + <!-- test 4 --> + <param name="adata" value="pp.recipe_zheng17.random-randint.h5ad" /> <conditional name="method"> <param name="method" value="pp.recipe_seurat"/> <param name="log" value="True"/> </conditional> - <param name="anndata_output_format" value="h5ad"/> <assert_stdout> <has_text_matching expression="sc.pp.recipe_seurat"/> <has_text_matching expression="log=True"/> </assert_stdout> - <output name="anndata_out_h5ad" file="pp.recipe_seurat.recipe_zheng17.h5ad" ftype="h5" compare="sim_size"/> - </test> - <test> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="krumsiek11.h5ad" /> - </conditional> - <conditional name="method"> - <param name="method" value="pp.log1p"/> - </conditional> - <param name="anndata_output_format" value="h5ad" /> - <assert_stdout> - <has_text_matching expression="sc.pp.log1p"/> - </assert_stdout> - <output name="anndata_out_h5ad" file="pp.log1p.krumsiek11.h5ad" ftype="h5" compare="sim_size"/> - </test> - <test> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="krumsiek11.h5ad" /> - </conditional> - <conditional name="method"> - <param name="method" value="pp.scale"/> - <param name="zero_center" value="true"/> - </conditional> - <param name="anndata_output_format" value="h5ad" /> - <assert_stdout> - <has_text_matching expression="sc.pp.scale"/> - <has_text_matching expression="zero_center=True"/> - </assert_stdout> - <output name="anndata_out_h5ad" file="pp.scale.krumsiek11.h5ad" ftype="h5" compare="sim_size"/> + <output name="anndata_out" file="pp.recipe_seurat.recipe_zheng17.h5ad" ftype="h5ad" compare="sim_size"/> </test> - <test> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="krumsiek11.h5ad" /> - </conditional> - <conditional name="method"> - <param name="method" value="pp.scale"/> - <param name="zero_center" value="true"/> - <param name="max_value" value="10"/> - </conditional> - <param name="anndata_output_format" value="h5ad" /> - <assert_stdout> - <has_text_matching expression="sc.pp.scale"/> - <has_text_matching expression="zero_center=True"/> - <has_text_matching expression="max_value=10.0"/> - </assert_stdout> - <output name="anndata_out_h5ad" file="pp.scale_max_value.krumsiek11.h5ad" ftype="h5" compare="sim_size"/> - </test> - <test> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="krumsiek11.h5ad" /> - </conditional> - <conditional name="method"> - <param name="method" value="pp.sqrt"/> - </conditional> - <param name="anndata_output_format" value="h5ad" /> - <assert_stdout> - <has_text_matching expression="sc.pp.sqrt"/> - </assert_stdout> - <output name="anndata_out_h5ad" file="pp.sqrt.krumsiek11.h5ad" ftype="h5" compare="sim_size"/> - </test> - <test> - <conditional name="input"> - <param name="format" value="h5ad" /> - <param name="adata" value="random-randint.h5ad" /> - </conditional> - <conditional name="method"> - <param name="method" value="pp.downsample_counts"/> - <param name="target_counts" value="20000"/> - <param name="random_state" value="0"/> - </conditional> - <param name="anndata_output_format" value="h5ad" /> - <assert_stdout> - <has_text_matching expression="sc.pp.downsample_counts"/> - <has_text_matching expression="target_counts=20000"/> - <has_text_matching expression="random_state=0"/> - </assert_stdout> - <output name="anndata_out_h5ad" ftype="h5"> - <assert_contents> - <has_h5_keys keys="X, obs, var" /> - </assert_contents> - </output> - </test> + </tests> <help><![CDATA[ Normalize total counts per cell (`pp.normalize_per_cell`) @@ -308,7 +199,7 @@ Similar functions are used, for example, by Seurat, Cell Ranger or SPRING. More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.pp.normalize_per_cell.html>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.normalize_per_cell.html>`__ Normalization and filtering as of Zheng et al. (2017), the Cell Ranger R Kit of 10x Genomics (`pp.recipe_zheng17`) @@ -327,7 +218,7 @@ - scale to unit variance and shift to zero mean More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.pp.recipe_zheng17.html>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.recipe_zheng17.html>`__ Normalization and filtering as of Weinreb et al (2017) (`pp.recipe_weinreb17`) @@ -336,7 +227,7 @@ Expects non-logarithmized data. If using logarithmized data, pass `log=False`. More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.pp.recipe_weinreb17.html>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.recipe_weinreb17.html>`__ Normalization and filtering as of Seurat et al (2015) (`pp.recipe_seurat`) @@ -347,33 +238,7 @@ Expects non-logarithmized data. If using logarithmized data, pass `log=False`. More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.pp.recipe_seurat.html>`__ - -Logarithmize the data matrix (`pp.log1p`) -========================================= - -More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.pp.log1p.html>`__ - -Scale data to unit variance and zero mean (`pp.scale`) -====================================================== - -More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.pp.scale.html>`__ - -Computes the square root the data matrix (`pp.sqrt`) -==================================================== - -`X = sqrt(X)` - -Downsample counts (`pp.downsample_counts`) -========================================== - -Downsample counts so that each cell has no more than `target_counts`. Cells with fewer counts than `target_counts` are unaffected by this. This -has been implemented by M. D. Luecken. - -More details on the `scanpy documentation -<https://scanpy.readthedocs.io/en/latest/api/scanpy.pp.downsample_counts.html>`__ +<https://icb-scanpy.readthedocs-hosted.com/en/stable/api/scanpy.pp.recipe_seurat.html>`__ ]]></help> <expand macro="citations"/>
--- a/test-data/pp.filter_cells.number_per_cell.krumsiek11-max_genes.tabular Mon Mar 04 10:16:12 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,641 +0,0 @@ - cell_subset number_per_cell -0 True 9 -1 True 9 -2 True 9 -3 True 8 -4 True 8 -5 True 8 -6 True 8 -7 True 7 -8 True 8 -9 True 8 -10 True 7 -11 True 7 -12 True 7 -13 True 7 -14 True 8 -15 True 10 -16 True 10 -17 True 10 -18 True 11 -19 True 11 -20 True 11 -21 True 11 -22 True 11 -23 True 11 -24 True 11 -25 True 11 -26 True 11 -27 True 11 -28 True 11 -29 True 11 -30 True 11 -31 True 11 -32 True 11 -33 True 11 -34 True 11 -35 True 11 -36 True 11 -37 True 11 -38 True 11 -39 True 11 -40 True 11 -41 True 11 -42 True 11 -43 True 11 -44 True 11 -45 True 11 -46 True 11 -47 True 11 -48 True 10 -49 True 10 -50 True 10 -51 True 10 -52 True 10 -53 True 10 -54 True 10 -55 True 10 -56 True 11 -57 True 11 -58 True 11 -59 True 10 -60 True 10 -61 True 11 -62 True 10 -63 True 11 -64 True 10 -65 True 10 -66 True 11 -67 True 11 -68 True 11 -69 True 10 -70 True 10 -71 True 10 -72 True 10 -73 True 10 -74 True 11 -75 True 10 -76 True 10 -77 True 10 -78 True 9 -79 True 10 -80 True 9 -81 True 9 -82 True 10 -83 True 9 -84 True 9 -85 True 9 -86 True 9 -87 True 9 -88 True 9 -89 True 9 -90 True 9 -91 True 10 -92 True 10 -93 True 9 -94 True 9 -95 True 9 -96 True 9 -97 True 7 -98 True 7 -99 True 6 -100 True 6 -101 True 7 -102 True 8 -103 True 8 -104 True 8 -105 True 8 -106 True 9 -107 True 9 -108 True 9 -109 True 8 -110 True 8 -111 True 10 -112 True 9 -113 True 8 -114 True 9 -115 True 10 -116 True 9 -117 True 8 -118 True 7 -119 True 7 -120 True 7 -121 True 7 -122 True 9 -123 True 9 -124 True 9 -125 True 8 -126 True 7 -127 True 6 -128 True 6 -129 True 8 -130 True 8 -131 True 8 -132 True 8 -133 True 10 -134 True 10 -135 True 8 -136 True 6 -137 True 6 -138 True 8 -139 True 9 -140 True 8 -141 True 7 -142 True 7 -143 True 8 -144 True 7 -145 True 7 -146 True 7 -147 True 5 -148 True 6 -149 True 8 -150 True 9 -151 True 6 -152 True 6 -153 True 6 -154 True 7 -155 True 8 -156 True 7 -157 True 7 -158 True 7 -159 True 8 -160 True 9 -161 True 8 -162 True 8 -163 True 9 -164 True 9 -165 True 9 -166 True 8 -167 True 8 -168 True 9 -169 True 9 -170 True 8 -171 True 9 -172 True 9 -173 True 10 -174 True 10 -175 True 10 -176 True 10 -177 True 10 -178 True 10 -179 True 10 -180 True 10 -181 True 10 -182 True 10 -183 True 10 -184 True 10 -185 True 10 -186 True 10 -187 True 10 -188 True 11 -189 True 11 -190 True 11 -191 True 11 -192 True 11 -193 True 11 -194 True 11 -195 True 11 -196 True 11 -197 True 11 -198 True 11 -199 True 11 -200 True 11 -201 True 11 -202 True 11 -203 True 11 -204 True 11 -205 True 11 -206 True 11 -207 True 11 -208 True 11 -209 True 11 -210 True 11 -211 True 11 -212 True 11 -213 True 11 -214 True 11 -215 True 11 -216 True 11 -217 True 11 -218 True 11 -219 True 11 -220 True 11 -221 True 11 -222 True 11 -223 True 11 -224 True 11 -225 True 11 -226 True 11 -227 True 11 -228 True 11 -229 True 11 -230 True 11 -231 True 11 -232 True 11 -233 True 11 -234 True 11 -235 True 11 -236 True 11 -237 True 11 -238 True 11 -239 True 11 -240 True 11 -241 True 11 -242 True 11 -243 True 11 -244 True 11 -245 True 11 -246 True 11 -247 True 11 -248 True 11 -249 True 11 -250 True 11 -251 True 11 -252 True 11 -253 True 11 -254 True 11 -255 True 11 -256 True 11 -257 True 11 -258 True 11 -259 True 11 -260 True 11 -261 True 11 -262 True 11 -263 True 11 -264 True 11 -265 True 11 -266 True 11 -267 True 11 -268 True 11 -269 True 11 -270 True 11 -271 True 11 -272 True 10 -273 True 11 -274 True 11 -275 True 11 -276 True 11 -277 True 11 -278 True 11 -279 True 10 -280 True 11 -281 True 9 -282 True 9 -283 True 8 -284 True 9 -285 True 9 -286 True 10 -287 True 9 -288 True 9 -289 True 9 -290 True 9 -291 True 9 -292 True 9 -293 True 10 -294 True 10 -295 True 10 -296 True 10 -297 True 9 -298 True 10 -299 True 9 -300 True 8 -301 True 8 -302 True 8 -303 True 8 -304 True 8 -305 True 9 -306 True 8 -307 True 8 -308 True 8 -309 True 8 -310 True 9 -311 True 8 -312 True 9 -313 True 9 -314 True 10 -315 True 10 -316 True 10 -317 True 10 -318 True 10 -319 True 10 -320 True 4 -321 True 8 -322 True 8 -323 True 8 -324 True 8 -325 True 7 -326 True 8 -327 True 8 -328 True 7 -329 True 9 -330 True 8 -331 True 9 -332 True 8 -333 True 8 -334 True 8 -335 True 10 -336 True 10 -337 True 9 -338 True 10 -339 True 10 -340 True 10 -341 True 10 -342 True 10 -343 True 10 -344 True 10 -345 True 11 -346 True 11 -347 True 11 -348 True 11 -349 True 11 -350 True 11 -351 True 11 -352 True 11 -353 True 11 -354 True 11 -355 True 11 -356 True 11 -357 True 11 -358 True 11 -359 True 11 -360 True 11 -361 True 11 -362 True 11 -363 True 11 -364 True 11 -365 True 11 -366 True 11 -367 True 11 -368 True 11 -369 True 11 -370 True 11 -371 True 11 -372 True 11 -373 True 11 -374 True 11 -375 True 11 -376 True 11 -377 True 11 -378 True 11 -379 True 11 -380 True 11 -381 True 11 -382 True 11 -383 True 11 -384 True 11 -385 True 11 -386 True 11 -387 True 11 -388 True 11 -389 True 11 -390 True 11 -391 True 11 -392 True 11 -393 True 11 -394 True 11 -395 True 11 -396 True 11 -397 True 11 -398 True 11 -399 True 11 -400 True 11 -401 True 11 -402 True 11 -403 True 11 -404 True 11 -405 True 11 -406 True 11 -407 True 11 -408 True 11 -409 True 11 -410 True 11 -411 True 11 -412 True 11 -413 True 11 -414 True 11 -415 True 11 -416 True 11 -417 True 11 -418 True 11 -419 True 11 -420 True 11 -421 True 11 -422 True 11 -423 True 11 -424 True 11 -425 True 11 -426 True 11 -427 True 11 -428 True 11 -429 True 11 -430 True 11 -431 True 11 -432 True 11 -433 True 11 -434 True 11 -435 True 11 -436 True 11 -437 True 11 -438 True 11 -439 True 11 -440 True 11 -441 True 11 -442 True 11 -443 True 11 -444 True 11 -445 True 11 -446 True 10 -447 True 10 -448 True 10 -449 True 10 -450 True 10 -451 True 11 -452 True 11 -453 True 11 -454 True 10 -455 True 10 -456 True 11 -457 True 10 -458 True 10 -459 True 11 -460 True 11 -461 True 10 -462 True 9 -463 True 10 -464 True 10 -465 True 10 -466 True 10 -467 True 9 -468 True 10 -469 True 10 -470 True 11 -471 True 11 -472 True 9 -473 True 9 -474 True 9 -475 True 9 -476 True 9 -477 True 10 -478 True 10 -479 True 9 -480 True 8 -481 True 10 -482 True 10 -483 True 10 -484 True 8 -485 True 8 -486 True 9 -487 True 8 -488 True 9 -489 True 10 -490 True 11 -491 True 11 -492 True 11 -493 True 9 -494 True 10 -495 True 10 -496 True 10 -497 True 10 -498 True 11 -499 True 11 -500 True 11 -501 True 11 -502 True 11 -503 True 11 -504 True 11 -505 True 11 -506 True 11 -507 True 11 -508 True 11 -509 True 11 -510 True 11 -511 True 11 -512 True 11 -513 True 11 -514 True 11 -515 True 11 -516 True 11 -517 True 11 -518 True 11 -519 True 11 -520 True 11 -521 True 11 -522 True 11 -523 True 11 -524 True 11 -525 True 11 -526 True 11 -527 True 11 -528 True 11 -529 True 11 -530 True 11 -531 True 11 -532 True 11 -533 True 11 -534 True 11 -535 True 11 -536 True 10 -537 True 10 -538 True 10 -539 True 10 -540 True 10 -541 True 10 -542 True 11 -543 True 11 -544 True 11 -545 True 11 -546 True 11 -547 True 10 -548 True 9 -549 True 9 -550 True 10 -551 True 11 -552 True 10 -553 True 9 -554 True 9 -555 True 9 -556 True 8 -557 True 9 -558 True 7 -559 True 8 -560 True 8 -561 True 10 -562 True 9 -563 True 8 -564 True 8 -565 True 8 -566 True 8 -567 True 8 -568 True 6 -569 True 6 -570 True 6 -571 True 6 -572 True 8 -573 True 8 -574 True 7 -575 True 9 -576 True 7 -577 True 7 -578 True 8 -579 True 8 -580 True 6 -581 True 7 -582 True 7 -583 True 8 -584 True 6 -585 True 5 -586 True 5 -587 True 5 -588 True 6 -589 True 7 -590 True 6 -591 True 8 -592 True 7 -593 True 7 -594 True 8 -595 True 7 -596 True 7 -597 True 8 -598 True 5 -599 True 4 -600 True 5 -601 True 6 -602 True 5 -603 True 6 -604 True 7 -605 True 7 -606 True 9 -607 True 10 -608 True 8 -609 True 8 -610 True 10 -611 True 10 -612 True 9 -613 True 8 -614 True 8 -615 True 8 -616 True 7 -617 True 8 -618 True 7 -619 True 6 -620 True 6 -621 True 7 -622 True 7 -623 True 7 -624 True 8 -625 True 6 -626 True 7 -627 True 7 -628 True 7 -629 True 6 -630 True 5 -631 True 7 -632 True 6 -633 True 6 -634 True 7 -635 True 6 -636 True 8 -637 True 8 -638 True 6 -639 True 8
--- a/test-data/pp.filter_genes.number_per_gene.krumsiek11-min_counts.tabular Mon Mar 04 10:16:12 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,12 +0,0 @@ -index n_counts -Gata2 163.95355 -Gata1 203.95117 -Fog1 83.94181 -EKLF 70.69286 -Fli1 57.56072 -SCL 202.67444 -Cebpa 469.87094 -Pu.1 250.78569 -cJun 188.10158 -EgrNab 164.99693 -Gfi1 159.99155
--- a/test-data/pp.filter_genes.number_per_gene.pbmc68k_reduced-max_cells.tabular Mon Mar 04 10:16:12 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,222 +0,0 @@ - gene_subset number_per_gene -0 True 34 -1 True 123 -2 True 281 -3 True 54 -4 True 253 -5 True 63 -6 True 9 -7 True 266 -8 True 101 -9 True 233 -10 True 267 -11 True 285 -12 True 332 -13 True 197 -14 True 158 -15 True 64 -16 True 285 -17 True 229 -18 True 43 -19 True 199 -20 True 271 -21 True 318 -22 True 132 -23 True 83 -24 True 88 -25 True 87 -26 True 71 -27 True 258 -28 True 58 -29 True 348 -30 True 280 -31 True 150 -32 True 121 -33 True 237 -34 True 29 -35 True 220 -36 True 103 -37 True 87 -38 True 115 -39 True 100 -40 True 139 -41 True 23 -42 True 162 -43 True 76 -44 True 180 -45 True 51 -46 True 244 -47 True 132 -48 True 244 -49 True 82 -50 True 172 -51 True 27 -52 True 100 -53 True 327 -54 True 277 -55 True 282 -56 True 245 -57 True 21 -58 True 52 -59 True 19 -60 True 227 -61 True 288 -62 True 274 -63 True 301 -64 True 316 -65 True 314 -66 True 271 -67 True 270 -68 True 283 -69 True 245 -70 True 263 -71 True 312 -72 True 285 -73 True 228 -74 True 170 -75 True 11 -76 True 228 -77 True 192 -78 True 140 -79 True 15 -80 True 22 -81 True 10 -82 True 233 -83 True 129 -84 True 12 -85 True 297 -86 True 295 -87 True 127 -88 True 208 -89 True 281 -90 True 265 -91 True 254 -92 True 122 -93 True 76 -94 True 237 -95 True 74 -96 True 65 -97 True 45 -98 True 90 -99 True 147 -100 True 189 -101 True 170 -102 True 207 -103 True 14 -104 True 307 -105 True 267 -106 True 111 -107 True 94 -108 True 306 -109 True 126 -110 True 269 -111 True 116 -112 True 140 -113 True 260 -114 True 201 -115 True 198 -116 True 155 -117 True 256 -118 True 214 -119 True 70 -120 True 304 -121 True 336 -122 True 201 -123 True 305 -124 True 301 -125 True 301 -126 True 338 -127 True 81 -128 True 256 -129 True 277 -130 True 237 -131 True 173 -132 True 228 -133 True 64 -134 True 52 -135 True 34 -136 True 333 -137 True 285 -138 True 132 -139 True 32 -140 True 275 -141 True 31 -142 True 244 -143 True 15 -144 True 54 -145 True 289 -146 True 186 -147 True 283 -148 True 333 -149 True 53 -150 True 26 -151 True 173 -152 True 19 -153 True 109 -154 True 138 -155 True 264 -156 True 293 -157 True 225 -158 True 150 -159 True 62 -160 True 350 -161 True 13 -162 True 341 -163 True 223 -164 True 177 -165 True 15 -166 True 202 -167 True 101 -168 True 203 -169 True 271 -170 True 305 -171 True 45 -172 True 322 -173 True 164 -174 True 213 -175 True 55 -176 True 143 -177 True 112 -178 True 266 -179 True 168 -180 True 9 -181 True 300 -182 True 249 -183 True 101 -184 True 55 -185 True 312 -186 True 181 -187 True 256 -188 True 27 -189 True 242 -190 True 210 -191 True 12 -192 True 203 -193 True 41 -194 True 205 -195 True 315 -196 True 94 -197 True 262 -198 True 316 -199 True 13 -200 True 94 -201 True 204 -202 True 245 -203 True 11 -204 True 238 -205 True 301 -206 True 219 -207 True 106 -208 True 253 -209 True 134 -210 True 262 -211 True 222 -212 True 82 -213 True 153 -214 True 122 -215 True 211 -216 True 49 -217 True 211 -218 True 176 -219 True 329 -220 True 8
--- a/test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-cell_ranger.tabular Mon Mar 04 10:16:12 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,12 +0,0 @@ - gene_subset means dispersions dispersions_norm -0 False 0.22807331 -1.513815 -1 False 0.27662647 -0.6374868 -2 False 0.12324284 -1.1931922 -3 True 0.10477218 -0.8270577 0.67448974 -4 True 0.08612139 -0.880823 0.67448974 -5 False 0.2751125 -0.6042374 -6 False 0.55053085 -1.5924454 -7 False 0.3306357 -0.91260546 -8 False 0.25766766 -0.86990273 -9 False 0.22937028 -0.7354343 -10 False 0.223133 -0.96748924
--- a/test-data/pp.filter_genes_dispersion.per_gene.krumsiek11-seurat.tabular Mon Mar 04 10:16:12 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,9 +0,0 @@ -index means dispersions dispersions_norm -Fog1 0.12324284 -1.1931922 1.0 -EKLF 0.10477218 -0.8270577 0.70710677 -SCL 0.2751125 -0.6042374 0.707108 -Cebpa 0.55053085 -1.5924454 1.0 -Pu.1 0.3306357 -0.91260546 1.0 -cJun 0.25766766 -0.86990273 1.0 -EgrNab 0.22937028 -0.7354343 0.7071069 -Gfi1 0.223133 -0.96748924 1.0
Binary file test-data/pp.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
Binary file test-data/pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad has changed
--- a/test-data/pp.normalize_per_cell.obs.krumsiek11.tabular Mon Mar 04 10:16:12 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,641 +0,0 @@ -index cell_type -0 progenitor -1 progenitor -2 progenitor -3 progenitor -4 progenitor -5 progenitor -6 progenitor -7 progenitor -8 progenitor -9 progenitor -10 progenitor -11 progenitor -12 progenitor -13 progenitor -14 progenitor -15 progenitor -16 progenitor -17 progenitor -18 progenitor -19 progenitor -20 progenitor -21 progenitor -22 progenitor -23 progenitor -24 progenitor -25 progenitor -26 progenitor -27 progenitor -28 progenitor -29 progenitor -30 progenitor -31 progenitor -32 progenitor -33 progenitor -34 progenitor -35 progenitor -36 progenitor -37 progenitor -38 progenitor -39 progenitor -40 progenitor -41 progenitor -42 progenitor -43 progenitor -44 progenitor -45 progenitor -46 progenitor -47 progenitor -48 progenitor -49 progenitor -50 progenitor -51 progenitor -52 progenitor -53 progenitor -54 progenitor -55 progenitor -56 progenitor -57 progenitor -58 progenitor -59 progenitor -60 progenitor -61 progenitor -62 progenitor -63 progenitor -64 progenitor -65 progenitor -66 progenitor -67 progenitor -68 progenitor -69 progenitor -70 progenitor -71 progenitor -72 progenitor -73 progenitor -74 progenitor -75 progenitor -76 progenitor -77 progenitor -78 progenitor -79 progenitor -80 Mo -81 Mo -82 Mo -83 Mo -84 Mo -85 Mo -86 Mo -87 Mo -88 Mo -89 Mo -90 Mo -91 Mo -92 Mo -93 Mo -94 Mo -95 Mo -96 Mo -97 Mo -98 Mo -99 Mo -100 Mo -101 Mo -102 Mo -103 Mo -104 Mo -105 Mo -106 Mo -107 Mo -108 Mo -109 Mo -110 Mo -111 Mo -112 Mo -113 Mo -114 Mo -115 Mo -116 Mo -117 Mo -118 Mo -119 Mo -120 Mo -121 Mo -122 Mo -123 Mo -124 Mo -125 Mo -126 Mo -127 Mo -128 Mo -129 Mo -130 Mo -131 Mo -132 Mo -133 Mo -134 Mo -135 Mo -136 Mo -137 Mo -138 Mo -139 Mo -140 Mo -141 Mo -142 Mo -143 Mo -144 Mo -145 Mo -146 Mo -147 Mo -148 Mo -149 Mo -150 Mo -151 Mo -152 Mo -153 Mo -154 Mo -155 Mo -156 Mo -157 Mo -158 Mo -159 Mo -0 progenitor -1 progenitor -2 progenitor -3 progenitor -4 progenitor -5 progenitor -6 progenitor -7 progenitor -8 progenitor -9 progenitor -10 progenitor -11 progenitor -12 progenitor -13 progenitor -14 progenitor -15 progenitor -16 progenitor -17 progenitor -18 progenitor -19 progenitor -20 progenitor -21 progenitor -22 progenitor -23 progenitor -24 progenitor -25 progenitor -26 progenitor -27 progenitor -28 progenitor -29 progenitor -30 progenitor -31 progenitor -32 progenitor -33 progenitor -34 progenitor -35 progenitor -36 progenitor -37 progenitor -38 progenitor -39 progenitor -40 progenitor -41 progenitor -42 progenitor -43 progenitor -44 progenitor -45 progenitor -46 progenitor -47 progenitor -48 progenitor -49 progenitor -50 progenitor -51 progenitor -52 progenitor -53 progenitor -54 progenitor -55 progenitor -56 progenitor -57 progenitor -58 progenitor -59 progenitor -60 progenitor -61 progenitor -62 progenitor -63 progenitor -64 progenitor -65 progenitor -66 progenitor -67 progenitor -68 progenitor -69 progenitor -70 progenitor -71 progenitor -72 progenitor -73 progenitor -74 progenitor -75 progenitor -76 progenitor -77 progenitor -78 progenitor -79 progenitor -80 Ery -81 Ery -82 Ery -83 Ery -84 Ery -85 Ery -86 Ery -87 Ery -88 Ery -89 Ery -90 Ery -91 Ery -92 Ery -93 Ery -94 Ery -95 Ery -96 Ery -97 Ery -98 Ery -99 Ery -100 Ery -101 Ery -102 Ery -103 Ery -104 Ery -105 Ery -106 Ery -107 Ery -108 Ery -109 Ery -110 Ery -111 Ery -112 Ery -113 Ery -114 Ery -115 Ery -116 Ery -117 Ery -118 Ery -119 Ery -120 Ery -121 Ery -122 Ery -123 Ery -124 Ery -125 Ery -126 Ery -127 Ery -128 Ery -129 Ery -130 Ery -131 Ery -132 Ery -133 Ery -134 Ery -135 Ery -136 Ery -137 Ery -138 Ery -139 Ery -140 Ery -141 Ery -142 Ery -143 Ery -144 Ery -145 Ery -146 Ery -147 Ery -148 Ery -149 Ery -150 Ery -151 Ery -152 Ery -153 Ery -154 Ery -155 Ery -156 Ery -157 Ery -158 Ery -159 Ery -0 progenitor -1 progenitor -2 progenitor -3 progenitor -4 progenitor -5 progenitor -6 progenitor -7 progenitor -8 progenitor -9 progenitor -10 progenitor -11 progenitor -12 progenitor -13 progenitor -14 progenitor -15 progenitor -16 progenitor -17 progenitor -18 progenitor -19 progenitor -20 progenitor -21 progenitor -22 progenitor -23 progenitor -24 progenitor -25 progenitor -26 progenitor -27 progenitor -28 progenitor -29 progenitor -30 progenitor -31 progenitor -32 progenitor -33 progenitor -34 progenitor -35 progenitor -36 progenitor -37 progenitor -38 progenitor -39 progenitor -40 progenitor -41 progenitor -42 progenitor -43 progenitor -44 progenitor -45 progenitor -46 progenitor -47 progenitor -48 progenitor -49 progenitor -50 progenitor -51 progenitor -52 progenitor -53 progenitor -54 progenitor -55 progenitor -56 progenitor -57 progenitor -58 progenitor -59 progenitor -60 progenitor -61 progenitor -62 progenitor -63 progenitor -64 progenitor -65 progenitor -66 progenitor -67 progenitor -68 progenitor -69 progenitor -70 progenitor -71 progenitor -72 progenitor -73 progenitor -74 progenitor -75 progenitor -76 progenitor -77 progenitor -78 progenitor -79 progenitor -80 Mk -81 Mk -82 Mk -83 Mk -84 Mk -85 Mk -86 Mk -87 Mk -88 Mk -89 Mk -90 Mk -91 Mk -92 Mk -93 Mk -94 Mk -95 Mk -96 Mk -97 Mk -98 Mk -99 Mk -100 Mk -101 Mk -102 Mk -103 Mk -104 Mk -105 Mk -106 Mk -107 Mk -108 Mk -109 Mk -110 Mk -111 Mk -112 Mk -113 Mk -114 Mk -115 Mk -116 Mk -117 Mk -118 Mk -119 Mk -120 Mk -121 Mk -122 Mk -123 Mk -124 Mk -125 Mk -126 Mk -127 Mk -128 Mk -129 Mk -130 Mk -131 Mk -132 Mk -133 Mk -134 Mk -135 Mk -136 Mk -137 Mk -138 Mk -139 Mk -140 Mk -141 Mk -142 Mk -143 Mk -144 Mk -145 Mk -146 Mk -147 Mk -148 Mk -149 Mk -150 Mk -151 Mk -152 Mk -153 Mk -154 Mk -155 Mk -156 Mk -157 Mk -158 Mk -159 Mk -0 progenitor -1 progenitor -2 progenitor -3 progenitor -4 progenitor -5 progenitor -6 progenitor -7 progenitor -8 progenitor -9 progenitor -10 progenitor -11 progenitor -12 progenitor -13 progenitor -14 progenitor -15 progenitor -16 progenitor -17 progenitor -18 progenitor -19 progenitor -20 progenitor -21 progenitor -22 progenitor -23 progenitor -24 progenitor -25 progenitor -26 progenitor -27 progenitor -28 progenitor -29 progenitor -30 progenitor -31 progenitor -32 progenitor -33 progenitor -34 progenitor -35 progenitor -36 progenitor -37 progenitor -38 progenitor -39 progenitor -40 progenitor -41 progenitor -42 progenitor -43 progenitor -44 progenitor -45 progenitor -46 progenitor -47 progenitor -48 progenitor -49 progenitor -50 progenitor -51 progenitor -52 progenitor -53 progenitor -54 progenitor -55 progenitor -56 progenitor -57 progenitor -58 progenitor -59 progenitor -60 progenitor -61 progenitor -62 progenitor -63 progenitor -64 progenitor -65 progenitor -66 progenitor -67 progenitor -68 progenitor -69 progenitor -70 progenitor -71 progenitor -72 progenitor -73 progenitor -74 progenitor -75 progenitor -76 progenitor -77 progenitor -78 progenitor -79 progenitor -80 Neu -81 Neu -82 Neu -83 Neu -84 Neu -85 Neu -86 Neu -87 Neu -88 Neu -89 Neu -90 Neu -91 Neu -92 Neu -93 Neu -94 Neu -95 Neu -96 Neu -97 Neu -98 Neu -99 Neu -100 Neu -101 Neu -102 Neu -103 Neu -104 Neu -105 Neu -106 Neu -107 Neu -108 Neu -109 Neu -110 Neu -111 Neu -112 Neu -113 Neu -114 Neu -115 Neu -116 Neu -117 Neu -118 Neu -119 Neu -120 Neu -121 Neu -122 Neu -123 Neu -124 Neu -125 Neu -126 Neu -127 Neu -128 Neu -129 Neu -130 Neu -131 Neu -132 Neu -133 Neu -134 Neu -135 Neu -136 Neu -137 Neu -138 Neu -139 Neu -140 Neu -141 Neu -142 Neu -143 Neu -144 Neu -145 Neu -146 Neu -147 Neu -148 Neu -149 Neu -150 Neu -151 Neu -152 Neu -153 Neu -154 Neu -155 Neu -156 Neu -157 Neu -158 Neu -159 Neu
--- a/test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.X_diffmap.tabular Mon Mar 04 10:16:12 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,100 +0,0 @@ -1.006254479289054871e-01 7.390013337135314941e-02 5.162549763917922974e-02 -6.088243797421455383e-02 1.660361140966415405e-02 2.669865079224109650e-02 -1.013666391372680664e-01 5.378784239292144775e-02 2.008118629455566406e-01 1.484276503324508667e-01 1.083310469985008240e-01 1.318635195493698120e-01 1.997928470373153687e-01 -7.899370044469833374e-02 -2.425468564033508301e-01 -9.916571527719497681e-02 -6.192789599299430847e-02 3.743748366832733154e-02 5.766532197594642639e-02 2.186784986406564713e-03 -1.058542281389236450e-01 5.377947166562080383e-02 1.402157917618751526e-02 1.486204266548156738e-01 8.553525805473327637e-02 2.134956121444702148e-01 -9.449188411235809326e-02 -9.736447781324386597e-02 -6.411797553300857544e-02 -3.109178543090820312e-01 -1.063194051384925842e-01 8.235514909029006958e-02 9.153002500534057617e-02 -1.288201361894607544e-01 2.123118191957473755e-02 -5.571385100483894348e-02 4.388944245874881744e-03 -1.907968819141387939e-01 5.077145248651504517e-02 -5.320586264133453369e-02 -2.017502486705780029e-02 2.888677455484867096e-02 -3.507991880178451538e-02 5.667190253734588623e-02 7.913256995379924774e-03 -9.840648621320724487e-02 -4.607489332556724548e-02 3.584916703402996063e-03 1.064319070428609848e-02 -1.083880066871643066e-01 1.248296573758125305e-01 6.351136416196823120e-02 1.885401159524917603e-01 -2.020059973001480103e-01 3.631909489631652832e-01 1.699842363595962524e-01 1.017943695187568665e-01 -1.385858207941055298e-01 3.727469593286514282e-02 8.702995628118515015e-02 -1.002686843276023865e-01 8.042127639055252075e-02 -1.485065966844558716e-01 -1.834145635366439819e-01 1.185779422521591187e-01 -4.452232271432876587e-03 2.401492185890674591e-02 1.709450007183477283e-04 -1.044583246111869812e-01 1.082587465643882751e-01 8.498968929052352905e-02 1.078867260366678238e-02 1.678988523781299591e-02 -4.863050952553749084e-02 -8.437795564532279968e-03 -1.039626300334930420e-01 6.898555904626846313e-02 -5.245065316557884216e-02 -1.401370912790298462e-01 1.724163144826889038e-01 -9.417925775051116943e-02 5.670287460088729858e-02 -2.294020541012287140e-02 -1.041278913617134094e-01 -4.751206189393997192e-02 -2.486055344343185425e-02 1.906321793794631958e-01 -1.133706346154212952e-01 1.018952578306198120e-01 -1.696813404560089111e-01 -1.031595841050148010e-01 6.644981354475021362e-02 -3.434765338897705078e-02 -8.121536672115325928e-02 -2.303110063076019287e-01 6.845026742666959763e-03 -3.113305754959583282e-02 3.572509065270423889e-02 -2.340330332517623901e-01 1.681309342384338379e-01 2.042572945356369019e-01 -2.152139544486999512e-01 3.323959186673164368e-02 -1.992796063423156738e-01 1.612142026424407959e-01 -1.063378602266311646e-01 9.444754570722579956e-02 1.147621273994445801e-01 -1.788049340248107910e-01 1.663699001073837280e-01 -1.299377232789993286e-01 9.099455177783966064e-02 8.901253342628479004e-02 -3.782828152179718018e-02 1.340985000133514404e-01 -1.110952645540237427e-01 -1.353431493043899536e-01 9.196916222572326660e-02 -5.004190653562545776e-02 1.283620446920394897e-01 -1.019066423177719116e-01 3.868932276964187622e-02 -5.681320559233427048e-03 2.885684370994567871e-02 -1.618600785732269287e-01 -7.793435454368591309e-02 -6.321301311254501343e-02 -7.030926644802093506e-02 -8.451142162084579468e-02 1.784774512052536011e-01 -5.302700772881507874e-02 2.233742922544479370e-02 -8.568169549107551575e-03 -3.328817337751388550e-02 -2.190946601331233978e-02 -1.055724918842315674e-01 6.597422808408737183e-02 1.207857206463813782e-01 -1.769655644893646240e-01 -1.941676251590251923e-02 -2.537060342729091644e-02 2.017348445951938629e-02 2.019873559474945068e-01 2.603957951068878174e-01 -8.517616987228393555e-02 1.920531690120697021e-01 3.013696968555450439e-01 1.285752207040786743e-01 -6.937998533248901367e-02 2.583270706236362457e-02 -1.013405695557594299e-01 7.315808534622192383e-02 -4.366664588451385498e-02 2.623320743441581726e-02 -1.268581300973892212e-01 -9.996589273214340210e-02 3.801496233791112900e-03 -1.574327796697616577e-01 8.082801103591918945e-02 -1.715112775564193726e-01 1.981573849916458130e-01 -9.785132110118865967e-02 -4.965898115187883377e-03 9.352693706750869751e-02 3.635504096746444702e-02 -1.001976355910301208e-01 7.669639587402343750e-02 5.201552435755729675e-02 2.700087428092956543e-01 2.118834294378757477e-02 -5.663126334547996521e-02 -8.477722853422164917e-02 9.727138280868530273e-02 -8.382367901504039764e-03 -1.135666593909263611e-01 1.608509272336959839e-01 1.184404194355010986e-01 1.566798686981201172e-01 -7.676979992538690567e-03 4.270016402006149292e-02 -9.744150936603546143e-02 -1.296482831239700317e-01 -1.239879243075847626e-02 1.855848357081413269e-02 -2.284491062164306641e-02 -1.009282022714614868e-01 7.055556029081344604e-02 4.106458649039268494e-02 -6.035594642162322998e-02 -1.043885275721549988e-01 -1.532001048326492310e-01 4.817606508731842041e-02 7.467575371265411377e-02 -1.860609161667525768e-03 3.278955444693565369e-02 -9.364669770002365112e-02 -1.809429824352264404e-01 -1.568255946040153503e-02 -1.347039826214313507e-02 5.973972380161285400e-03 -5.848481878638267517e-02 -5.189557373523712158e-02 -3.990079089999198914e-02 2.690394036471843719e-02 3.291362524032592773e-02 5.178750306367874146e-02 -1.781906234100461006e-03 1.922948169521987438e-03 3.529559448361396790e-02 -4.709535860456526279e-04 -9.947066009044647217e-02 8.559655398130416870e-02 -1.963858157396316528e-01 -5.146042443811893463e-03 -2.873218990862369537e-02 3.527766466140747070e-02 3.702211426571011543e-03 -5.826206132769584656e-02 -5.298026651144027710e-02 1.265548728406429291e-02 -9.924994409084320068e-02 7.643856108188629150e-02 -3.946479875594377518e-03 -6.800348311662673950e-02 1.005024369806051254e-02 -9.972835332155227661e-02 -5.728941038250923157e-02 1.164532150141894817e-03 -2.283337898552417755e-02 -8.941999077796936035e-02 1.752964705228805542e-01 7.918696850538253784e-02 -1.626298427581787109e-01 5.423834919929504395e-02 1.800755560398101807e-01 -3.383423760533332825e-02 3.943570330739021301e-02 2.784142494201660156e-01 1.194428130984306335e-01 -1.088062301278114319e-01 -1.063553839921951294e-01 6.921559572219848633e-02 2.080408483743667603e-01 -7.342543452978134155e-02 1.763515174388885498e-01 -5.611616000533103943e-02 6.127767637372016907e-02 -1.789846867322921753e-01 -1.116973087191581726e-01 5.425352230668067932e-02 4.038978368043899536e-02 -1.916505545377731323e-01 1.254186034202575684e-01 6.859276443719863892e-02 6.871620565652847290e-02 -9.730846434831619263e-02 -1.349723786115646362e-01 -3.723482042551040649e-02 3.166849538683891296e-02 4.175714775919914246e-02 -8.143801242113113403e-02 1.949654072523117065e-01 5.183657631278038025e-02 6.802517175674438477e-02 5.463999882340431213e-02 -4.301572218537330627e-03 -7.239108532667160034e-02 1.697570271790027618e-02 -1.546490471810102463e-02 8.058656007051467896e-02 -9.557868540287017822e-02 -1.378565728664398193e-01 -1.682972908020019531e-02 -1.662165974266827106e-03 -2.648654952645301819e-02 -9.425739943981170654e-02 6.581720709800720215e-02 -1.690089106559753418e-01 3.114995732903480530e-02 -2.066969871520996094e-02 1.702432520687580109e-02 -4.617675766348838806e-02 -3.817678987979888916e-02 -4.838168621063232422e-02 -4.461381956934928894e-02 -9.572254866361618042e-02 -1.157653778791427612e-01 -2.745305374264717102e-02 2.824329398572444916e-02 1.683281175792217255e-02 -7.204449176788330078e-02 1.442870348691940308e-01 -1.112484484910964966e-01 5.254449322819709778e-02 -4.310525953769683838e-02 -6.537265330553054810e-02 9.152984619140625000e-02 1.099050277844071388e-03 4.806879535317420959e-02 8.303747326135635376e-02 -1.017507240176200867e-01 6.871048361063003540e-02 -2.869148179888725281e-02 -1.148616150021553040e-01 -2.352946251630783081e-01 -2.909171022474765778e-02 5.929908156394958496e-02 1.573819369077682495e-01 -2.147024124860763550e-01 -3.502381592988967896e-02 -5.037564784288406372e-02 7.337512075901031494e-02 -1.265074359253048897e-03 1.872758269309997559e-01 4.964049533009529114e-02 -9.428320080041885376e-02 -1.354417204856872559e-01 8.272815495729446411e-03 2.385292761027812958e-02 -8.242442272603511810e-03 -7.430975139141082764e-02 -1.742218136787414551e-01 -5.538051947951316833e-03 -1.018167193979024887e-02 1.547044795006513596e-02 8.455148199573159218e-04 5.404802784323692322e-02 5.126107111573219299e-02 -6.693445146083831787e-03 -1.268707681447267532e-02 -9.686828404664993286e-02 7.426099479198455811e-02 -1.982558220624923706e-01 -3.170682583004236221e-03 1.799695640802383423e-01 -3.648606827482581139e-03 2.222890593111515045e-02 2.509209886193275452e-02 3.848403692245483398e-02 -9.611283242702484131e-02 -1.264516962692141533e-03 7.569468766450881958e-02 -4.498184472322463989e-02 4.383683577179908752e-02 -5.853280797600746155e-02 -1.042621433734893799e-01 6.099966540932655334e-02 1.840294301509857178e-01 1.754992753267288208e-01 6.823045015335083008e-02 -6.831583380699157715e-02 -6.362004578113555908e-02 -1.517322100698947906e-02 -1.908881068229675293e-01 2.010526834055781364e-03 -2.508206292986869812e-02 -1.952427774667739868e-01 1.529918909072875977e-01 8.812237530946731567e-03 -3.321791067719459534e-02 -9.637255966663360596e-02 7.963524758815765381e-02 -1.717496514320373535e-01 5.302957445383071899e-02 8.758340775966644287e-02 4.953101277351379395e-02 -1.924332347698509693e-03 1.019738893955945969e-02 5.325353518128395081e-02 2.628941275179386139e-02 -8.831731975078582764e-02 1.133090700022876263e-03 3.478634357452392578e-02 -4.082480445504188538e-02 2.803635410964488983e-02 -1.061019226908683777e-01 -1.301048509776592255e-02 8.831021934747695923e-02 5.576367303729057312e-02 -2.309499494731426239e-02 3.766698539257049561e-01 6.932982802391052246e-02 -1.278860960155725479e-02 -9.938260912895202637e-02 -1.299702078104019165e-01 -1.411890983581542969e-01 2.314591221511363983e-02 2.776186168193817139e-01 1.167302802205085754e-01 -1.185707077383995056e-01 -1.057639271020889282e-01 8.270046114921569824e-02 1.476289331912994385e-01 -6.666278839111328125e-02 -5.550191178917884827e-02 -3.524509444832801819e-02 -7.368044555187225342e-02 1.785134151577949524e-02 1.306160390377044678e-01 -2.507601305842399597e-02 -3.894129768013954163e-02 -1.468869149684906006e-01 -4.083442315459251404e-02 -1.527885906398296356e-02 1.039780024439096451e-02 -1.033702343702316284e-01 4.658226296305656433e-02 9.960353374481201172e-02 2.222209423780441284e-01 9.922166168689727783e-02 1.302605494856834412e-02 -5.205357074737548828e-02 4.135281778872013092e-04 -9.018164128065109253e-02 1.557689346373081207e-02 -1.260346621274948120e-01 4.821802303194999695e-02 -7.068537920713424683e-02 -2.585342526435852051e-01 1.349274534732103348e-02 -9.477795660495758057e-02 -1.554774045944213867e-01 -6.355965044349431992e-03 -3.065563924610614777e-02 -2.320274477824568748e-03 2.243204414844512939e-02 8.106533437967300415e-03 -5.485361907631158829e-03 -7.122739404439926147e-02 -4.912284016609191895e-02 -1.642951183021068573e-02 -3.334269672632217407e-02 9.506576508283615112e-02 3.177214413881301880e-02 -7.037246599793434143e-03 -1.041858941316604614e-01 4.901642724871635437e-02 1.881349086761474609e-01 2.629276830703020096e-03 2.207450568675994873e-02 1.956734210252761841e-01 -5.738193169236183167e-02 -1.309214830398559570e-01 5.612225830554962158e-02 -4.862225800752639771e-02 1.449161618947982788e-01 2.058826237916946411e-01 -6.669452041387557983e-02 -1.336530447006225586e-01 -4.366984590888023376e-02 -1.014385446906089783e-01 -4.612392559647560120e-02 -6.257595028728246689e-03 6.506688147783279419e-02 5.844650417566299438e-02 1.481245607137680054e-01 7.974769175052642822e-02 -5.714906007051467896e-02 2.003025263547897339e-02 -8.641435950994491577e-02 -2.218966186046600342e-02 2.825243771076202393e-02 -5.980410613119602203e-03 -4.802919924259185791e-02 -2.706299908459186554e-02 -1.019275709986686707e-01 -9.805458039045333862e-02 -2.528023440390825272e-03 -2.206768654286861420e-02 -6.094220653176307678e-02 1.697291433811187744e-01 2.995367646217346191e-01 1.458646506071090698e-01 -1.148837730288505554e-01 -1.631249487400054932e-01 6.046377122402191162e-02 -6.091985478997230530e-02 -9.345711767673492432e-02 -1.146302819252014160e-01 -6.680992990732192993e-02 -9.670515358448028564e-02 -1.625915020704269409e-01 -2.481849864125251770e-03 -4.520184919238090515e-02 -1.349300728179514408e-03 -2.573280408978462219e-02 -3.020944632589817047e-02 7.099823653697967529e-02 4.214366152882575989e-02 -2.898849919438362122e-02 2.248373813927173615e-02 -5.621459335088729858e-02 -8.263271301984786987e-02 -7.671217899769544601e-03 -7.041502743959426880e-03 -9.790167957544326782e-02 7.039222121238708496e-02 -1.774167567491531372e-01 -3.065176308155059814e-02 1.744043976068496704e-01 9.052980691194534302e-02 -3.136295452713966370e-02 -2.621344476938247681e-02 -1.041591726243495941e-02 -1.250211596488952637e-01 -2.442165464162826538e-02 -5.774322524666786194e-02 -1.320297718048095703e-01 -9.654930979013442993e-02 1.349056810140609741e-01 -9.792804718017578125e-02 5.769139528274536133e-02 -9.987217187881469727e-02 9.300402551889419556e-02 -6.949677597731351852e-03 -3.404668346047401428e-02 9.231559932231903076e-02 5.160513147711753845e-02 1.609171479940414429e-01 1.343503147363662720e-01 -1.734616011381149292e-01 -1.384260654449462891e-01 -9.630931913852691650e-02 -7.621422410011291504e-02 -1.551136225461959839e-01 -9.476174414157867432e-02 -1.110036522150039673e-01 -1.526500005275011063e-02 -3.676902130246162415e-02 -9.109597653150558472e-02 -9.239159524440765381e-02 1.616749167442321777e-02 -1.986319273710250854e-01 -8.253418840467929840e-03 2.224889211356639862e-02 -1.059062080457806587e-03 -4.505279660224914551e-02 3.057709522545337677e-02 -2.214481681585311890e-02 4.315280541777610779e-02 -9.417848289012908936e-02 6.193256750702857971e-02 -1.422626525163650513e-01 1.917927898466587067e-02 -1.250294223427772522e-02 -4.498282819986343384e-02 6.364709883928298950e-02 -2.036240696907043457e-02 2.362496964633464813e-02 -7.819373160600662231e-02 1.323003172874450684e-01 2.130920812487602234e-02 -5.405363161116838455e-03 6.362792104482650757e-02 1.635468006134033203e-02 -9.548019617795944214e-02 -1.779708117246627808e-01 -8.975042030215263367e-03 -2.999478019773960114e-02 4.545003920793533325e-02 5.566985532641410828e-02 -5.788084492087364197e-02 1.256971210241317749e-01 -4.598109424114227295e-02 -4.915640503168106079e-02 -4.440484568476676941e-02 2.092318236827850342e-02 2.757432311773300171e-02 1.070784498006105423e-02 2.980512753129005432e-02 -9.977434575557708740e-02 5.892081186175346375e-02 -9.230908751487731934e-02 9.639902040362358093e-03 -1.274359226226806641e-01 -1.662229187786579132e-02 4.402523860335350037e-02 -8.202790468931198120e-02 1.435073465108871460e-02 5.877543985843658447e-02 9.935144335031509399e-02 2.146081533282995224e-03 -2.864263765513896942e-02 -4.906255006790161133e-02 -3.847812861204147339e-02 -1.027446687221527100e-01 7.624021172523498535e-02 1.120918523520231247e-02 8.831836283206939697e-02 -1.509995460510253906e-01 6.561069935560226440e-02 -8.483945578336715698e-02 -1.304500848054885864e-01 3.499194234609603882e-02 -6.071432679891586304e-02 1.464422196149826050e-01 -4.895902425050735474e-02 2.498662322759628296e-01 -1.350328773260116577e-01 5.197114497423171997e-02 -1.007124558091163635e-01 -9.498898684978485107e-02 1.600779406726360321e-02 -6.713904440402984619e-02 9.510654956102371216e-02 2.613145112991333008e-01 5.298715829849243164e-02 2.186571434140205383e-02 -1.953593045473098755e-01 -6.295756995677947998e-02 -4.816478863358497620e-02 -7.176841050386428833e-02 1.176497116684913635e-01 7.941282540559768677e-02 -1.130634639412164688e-02 -1.058567389845848083e-01 5.670538917183876038e-02 9.846317023038864136e-02 2.081715762615203857e-01 -6.757875531911849976e-02 -1.148305535316467285e-01 3.380669280886650085e-02 2.123793512582778931e-01 -1.718706488609313965e-01 -2.019357532262802124e-01 2.760044531896710396e-03 -1.770434081554412842e-01 2.421746402978897095e-02 -2.580799460411071777e-01 -1.511664241552352905e-01 -9.994039684534072876e-02 -5.237572267651557922e-02 -3.030188940465450287e-02 1.411070674657821655e-01 -2.770690061151981354e-02 1.953638531267642975e-02 1.266712099313735962e-01 9.288331866264343262e-02 7.042742520570755005e-02 -1.118599325418472290e-01 -1.744727045297622681e-01 -6.401036866009235382e-03 1.090899556875228882e-01 -1.459762454032897949e-01 -1.650056093931198120e-01 -9.939632564783096313e-02 8.044686913490295410e-02 -1.665522307157516479e-01 -3.347875922918319702e-02 -1.193663105368614197e-01 2.643955498933792114e-02 -2.088110893964767456e-02 -2.552646957337856293e-02 -8.814702183008193970e-02 2.117055468261241913e-02 -3.291779011487960815e-02 1.364665105938911438e-02 2.119231596589088440e-02 -9.942702949047088623e-02 4.783578217029571533e-02 -9.181451052427291870e-02 -1.824584603309631348e-01 -1.307924278080463409e-02 -4.180047288537025452e-02 7.577048614621162415e-03 -7.718593627214431763e-02 -1.775113046169281006e-01 -3.451743349432945251e-02 1.008456572890281677e-02 -1.162463147193193436e-02 6.200802046805620193e-03 5.095251370221376419e-03 3.772788448259234428e-03 1.403792575001716614e-02 -7.958821021020412445e-03 -1.020366400480270386e-01 5.088304728269577026e-02 3.272717446088790894e-02 -1.456688642501831055e-01 -2.295345254242420197e-02 -1.016464307904243469e-01 1.475215405225753784e-01 1.740464121103286743e-01 -4.927166271954774857e-03 2.739141285419464111e-01 -1.155362352728843689e-01 -4.547961056232452393e-02 6.654956191778182983e-02 -7.726808544248342514e-03 4.666358605027198792e-02 -1.017608344554901123e-01 1.159704197198152542e-02 1.786869317293167114e-01 -8.475835621356964111e-02 1.061371788382530212e-01 6.321826577186584473e-02 3.605883941054344177e-02 -2.101089507341384888e-01 -1.602706909179687500e-01 6.894896179437637329e-02 5.363681167364120483e-02 1.400205790996551514e-01 -1.765108257532119751e-01 -2.672278694808483124e-02 3.528797999024391174e-02 -1.053973138332366943e-01 5.980503559112548828e-02 1.294687092304229736e-01 8.599842339754104614e-02 -5.153755843639373779e-02 -1.123336516320705414e-02 -8.372586220502853394e-02 6.772116571664810181e-02 -2.525310218334197998e-02 -1.527518033981323242e-01 -6.109386309981346130e-02 9.316059201955795288e-02 3.940534591674804688e-02 6.032083183526992798e-02 1.056960150599479675e-01 -9.307835251092910767e-02 -1.894880682229995728e-01 -1.744995266199111938e-02 -2.943326905369758606e-02 2.312117069959640503e-02 -7.852952927350997925e-02 -9.915205091238021851e-02 -7.527264300733804703e-03 2.069273591041564941e-02 1.242756377905607224e-02 1.593445427715778351e-02 2.069955226033926010e-03 -1.014956273138523102e-02 -1.058064633980393410e-03 -2.485149540007114410e-02 -1.096552833914756775e-01 8.830883353948593140e-02 1.838217973709106445e-01 -1.679951250553131104e-01 1.281235069036483765e-01 -2.967997267842292786e-02 6.718355417251586914e-02 -6.985174864530563354e-02 -1.050625219941139221e-01 -1.021873503923416138e-01 1.065925285220146179e-01 1.964906789362430573e-02 -9.339028620161116123e-04 -1.147233247756958008e-01 -9.296967089176177979e-02 -1.084800511598587036e-01 6.826906651258468628e-02 2.033265233039855957e-01 -1.296351104974746704e-01 3.208687156438827515e-02 9.674745611846446991e-03 1.818936690688133240e-02 -9.582243114709854126e-02 7.843807339668273926e-02 4.039196297526359558e-02 -2.155345827341079712e-01 -1.577789783477783203e-01 1.516564339399337769e-01 6.666070222854614258e-02 7.346466928720474243e-02 -1.039668172597885132e-01 8.601240813732147217e-02 4.633598774671554565e-02 -1.770939119160175323e-02 5.830590799450874329e-02 9.115639328956604004e-02 -2.177701704204082489e-02 5.951844155788421631e-02 5.083320289850234985e-02 -2.459524758160114288e-02 -1.903394423425197601e-02 1.292291432619094849e-01 -1.702900677919387817e-01 -1.560598313808441162e-01 -8.106053620576858521e-02 -1.049690097570419312e-01 7.046835869550704956e-02 1.809345781803131104e-01 1.191162243485450745e-01 4.144360497593879700e-02 9.311024099588394165e-02 -6.507906317710876465e-02 -3.033739328384399414e-02 -6.379290670156478882e-02 1.814640760421752930e-01 -9.738548099994659424e-02 -7.513097673654556274e-02 -1.740936338901519775e-01 2.220288068056106567e-01 1.151594594120979309e-01 -1.052022203803062439e-01 6.111931055784225464e-02 6.439048796892166138e-02 2.338735014200210571e-01 2.994438074529170990e-02 -6.889136880636215210e-02 2.095837378874421120e-03 -4.114958271384239197e-02 -1.069214381277561188e-02 7.941628992557525635e-02 -1.962262839078903198e-01 1.201168522238731384e-01 -8.384832739830017090e-02 -9.490247815847396851e-03 1.106658428907394409e-01 -9.998116642236709595e-02 2.039877325296401978e-02 4.376422241330146790e-02 2.977356314659118652e-01 1.436971500515937805e-02 -1.688353270292282104e-01 -5.432502180337905884e-02 7.137454301118850708e-02 -1.399741619825363159e-01 1.189125776290893555e-01 1.475899368524551392e-01 -3.964446485042572021e-02 -7.533613592386245728e-02 2.176573425531387329e-01 5.747236777096986771e-03 -1.002084985375404358e-01 7.954944670200347900e-02 -8.533553779125213623e-02 -4.217643290758132935e-02 1.198484152555465698e-01 -3.491419553756713867e-02 6.017883121967315674e-02 1.294819116592407227e-01 3.572440519928932190e-02 1.924678124487400055e-02 1.396540645509958267e-02 3.103173151612281799e-02 1.801325939595699310e-02 -2.653031144291162491e-03 2.304823510348796844e-02 -9.777025133371353149e-02 -1.042842194437980652e-01 2.082869783043861389e-02 -3.860146924853324890e-02 5.125629529356956482e-02 2.080539464950561523e-01 -1.654386669397354126e-01 4.806843400001525879e-02 3.355716541409492493e-02 8.775050938129425049e-02 -1.434527039527893066e-01 -9.607073850929737091e-03 -5.951974168419837952e-02 -8.223409205675125122e-02 -1.461203955113887787e-02 -1.019103005528450012e-01 8.671076595783233643e-02 -7.325944304466247559e-02 -1.174783334136009216e-01 1.129657402634620667e-01 -3.813041374087333679e-02 -1.566699706017971039e-02 -5.187815427780151367e-02 8.993184566497802734e-02 -4.521354660391807556e-02 -4.131594672799110413e-02 -4.266967996954917908e-02 3.811590373516082764e-02 -4.174317046999931335e-02 2.299142330884933472e-01 -9.619058668613433838e-02 6.313768029212951660e-02 -1.249785348773002625e-01 3.527817502617835999e-02 7.962471991777420044e-02 2.284124493598937988e-02 -7.864867150783538818e-02 -3.863862156867980957e-02 8.502854034304618835e-04 1.686216890811920166e-02 1.423155814409255981e-01 -1.534306257963180542e-01 1.124335378408432007e-01 1.857865899801254272e-01 -5.905169993638992310e-02 -1.007009595632553101e-01 7.516346126794815063e-02 -1.312321573495864868e-01 -7.602021098136901855e-02 -1.610904335975646973e-01 -7.157172262668609619e-02 -3.855035081505775452e-02 -1.936252266168594360e-01 -9.300149232149124146e-02 4.600097984075546265e-02 -1.138332858681678772e-01 4.203705117106437683e-02 -4.520619660615921021e-02 1.347151994705200195e-01 -1.294217556715011597e-01 -1.001875400543212891e-01 -1.132487505674362183e-01 -4.163928329944610596e-03 6.681708991527557373e-02 -4.600565880537033081e-02 1.763418167829513550e-01 3.313385546207427979e-01 -1.129601299762725830e-01 2.431528642773628235e-02 6.325863301753997803e-02 -4.059148952364921570e-02 -1.118210423737764359e-02 -2.038956433534622192e-01 -1.425022631883621216e-01 -5.723150447010993958e-03 -9.407982230186462402e-02 8.001462370157241821e-02 -2.035376876592636108e-01 8.062360808253288269e-03 1.538903564214706421e-01 2.830612286925315857e-02 -1.518529467284679413e-02 -3.077569231390953064e-02 4.123907536268234253e-03 -2.103170193731784821e-02 9.613325446844100952e-02 -7.903409004211425781e-02 3.125510737299919128e-02 8.903456619009375572e-04 -6.428617238998413086e-02 -9.406865388154983521e-02 -1.791881918907165527e-01 -8.276556618511676788e-03 -2.965002134442329407e-02 5.700163543224334717e-02 -1.148957666009664536e-02 -1.332757174968719482e-01 5.545758455991744995e-02 -6.686314940452575684e-03 2.468170970678329468e-02 -2.053959295153617859e-02 1.643142849206924438e-02 1.258462551049888134e-03 -5.143086309544742107e-04 5.937817506492137909e-03 -9.649397432804107666e-02 8.027059584856033325e-02 -1.631885468959808350e-01 2.333489991724491119e-02 2.121080607175827026e-01 1.130969263613224030e-02 -2.103595063090324402e-02 4.660079628229141235e-02 2.096206415444612503e-03 1.953577436506748199e-02 2.901989594101905823e-02 -3.907585889101028442e-02 4.627696797251701355e-02 1.089777275919914246e-01 1.461862493306398392e-02 -1.033540964126586914e-01 7.794149965047836304e-02 1.151292398571968079e-01 2.309635728597640991e-01 3.235552087426185608e-02 -1.148650571703910828e-01 -4.997650533914566040e-02 5.795754119753837585e-02 -1.963078230619430542e-01 -4.085578769445419312e-02 -6.560879200696945190e-02 1.799988597631454468e-01 -1.434233337640762329e-01 1.321845650672912598e-01 -1.391446888446807861e-01 -9.632616490125656128e-02 7.860046625137329102e-02 -1.156974583864212036e-01 6.384550780057907104e-02 9.553812444210052490e-02 1.855529658496379852e-02 -7.110892981290817261e-02 2.114013768732547760e-03 3.175996243953704834e-02 7.492856681346893311e-02 5.438266322016716003e-02 1.752818352542817593e-03 5.380610749125480652e-02 1.135067045688629150e-01 -7.447752356529235840e-02 -1.018153354525566101e-01 4.603661969304084778e-02 -1.248271390795707703e-01 -8.571246266365051270e-02 -1.614183336496353149e-01 -3.166115283966064453e-02 3.251006454229354858e-02 -8.094025403261184692e-02 -8.872838318347930908e-02 -6.665214151144027710e-02 -7.572681456804275513e-02 8.980319648981094360e-02 -1.394795030355453491e-01 -1.250675879418849945e-02 1.468008477240800858e-02 -1.032287180423736572e-01 9.694153815507888794e-02 -2.908578841015696526e-03 -2.182907015085220337e-01 1.721379160881042480e-01 -7.366006076335906982e-02 5.815453082323074341e-02 1.759700179100036621e-01 6.568897515535354614e-02 -1.107818484306335449e-01 -7.642934471368789673e-02 -1.505851596593856812e-01 -7.711775600910186768e-02 7.467343658208847046e-02 7.080453447997570038e-03 -1.065864339470863342e-01 5.720582604408264160e-02 1.948722153902053833e-01 -7.787132263183593750e-02 1.477451622486114502e-01 -5.413819104433059692e-02 6.531537324190139771e-02 -1.822435259819030762e-01 -1.397956609725952148e-01 -1.097136139869689941e-01 8.924543112516403198e-02 1.279107034206390381e-01 1.993209309875965118e-02 -5.507615953683853149e-02 -3.446640679612755775e-03 -9.265841543674468994e-02 -1.751434653997421265e-01 -2.025607787072658539e-02 -2.595988102257251740e-03 5.127311125397682190e-02 -8.045714930631220341e-04 -6.226925179362297058e-02 9.989995509386062622e-02 5.121784750372171402e-03 1.036263331770896912e-01 7.005699723958969116e-02 3.461425006389617920e-02 -7.322895526885986328e-02 1.146115548908710480e-02 -1.859322073869407177e-03 -9.542080014944076538e-02 -1.547558456659317017e-01 -2.360189519822597504e-02 5.806864798069000244e-02 -6.534500978887081146e-03 -7.163854315876960754e-03 2.721503973007202148e-01 6.798323243856430054e-02 5.726485326886177063e-02 6.443656980991363525e-02 6.527258455753326416e-02 -4.411538690328598022e-02 1.094646845012903214e-02 2.960695512592792511e-02 -2.624839730560779572e-02 -9.617692232131958008e-02 8.400613814592361450e-02 -1.936585009098052979e-01 -6.250149570405483246e-03 6.989545375108718872e-02 5.721643567085266113e-03 -6.330724805593490601e-03 3.107380308210849762e-02 -4.174800962209701538e-02 5.090944096446037292e-02 5.953476950526237488e-02 -6.513056810945272446e-03 5.078724771738052368e-02 -3.625847026705741882e-02 1.019463595002889633e-02 -1.049788594245910645e-01 8.304906636476516724e-02 5.656645819544792175e-02 -1.640451848506927490e-01 -2.389858067035675049e-01 4.443007335066795349e-02 -7.840061932802200317e-02 1.193439811468124390e-01 -1.223825477063655853e-02 1.019432842731475830e-01 3.284224867820739746e-02 1.366260647773742676e-01 1.669918745756149292e-01 -1.151861101388931274e-01 -3.699916601181030273e-02 -9.868630021810531616e-02 8.215790241956710815e-02 -9.707875549793243408e-02 1.376012861728668213e-01 1.388198435306549072e-01 1.623931713402271271e-02 -4.843015596270561218e-02 5.262994766235351562e-02 1.130281016230583191e-02 3.778235986828804016e-02 4.424711223691701889e-03 -2.095900475978851318e-02 1.682416908442974091e-02 -5.163656547665596008e-02 8.480735123157501221e-02 -9.740426391363143921e-02 9.016389399766921997e-02 -1.474756747484207153e-01 7.034499198198318481e-02 8.572756499052047729e-02 2.408424019813537598e-02 -9.561213850975036621e-02 8.399785496294498444e-03 4.017020016908645630e-02 -1.322972401976585388e-02 -2.936635166406631470e-02 -3.563411533832550049e-02 -7.874042540788650513e-02 6.455113738775253296e-02 1.021147053688764572e-02 -9.785865992307662964e-02 2.002363093197345734e-02 -2.186168543994426727e-02 1.725570708513259888e-01 1.872270740568637848e-02 2.384064793586730957e-01 8.567645400762557983e-02 -3.716270625591278076e-02 8.989029377698898315e-02 -4.444942995905876160e-02 3.172029852867126465e-01 -7.634723186492919922e-02 7.728370279073715210e-02 1.805804371833801270e-01 9.173929691314697266e-02 -9.979384392499923706e-02 5.984103679656982422e-02 -5.989267304539680481e-02 -3.894945234060287476e-02 -1.764608323574066162e-01 -2.952952124178409576e-02 -8.314014226198196411e-02 4.754667729139328003e-02 -5.578798055648803711e-02 -9.455468505620956421e-02 9.188339859247207642e-02 -3.511780407279729843e-03 -1.166689470410346985e-01 9.548252820968627930e-02 -7.699102908372879028e-02 -1.049434468150138855e-01 5.845416337251663208e-02 1.641098409891128540e-01 -8.518899790942668915e-03 3.210408240556716919e-02 -1.310225725173950195e-01 8.615318685770034790e-02 -9.180887788534164429e-02 1.344605237245559692e-01 5.034093931317329407e-02 5.061811208724975586e-02 -6.799745559692382812e-02 -9.547847509384155273e-02 -1.987753435969352722e-02 -2.295717298984527588e-01 -1.013745963573455811e-01 3.942378982901573181e-02 -9.149497747421264648e-02 -1.120824217796325684e-01 -2.214438915252685547e-01 -2.176503837108612061e-02 -4.737643059343099594e-03 -1.330705732107162476e-01 -1.559747606515884399e-01 -2.740937769412994385e-01 -4.498530551791191101e-02 -5.058288201689720154e-02 -7.116720080375671387e-02 1.151673570275306702e-01 -4.305647686123847961e-02 -9.708176553249359131e-02 -1.062218919396400452e-01 -2.149707637727260590e-02 2.858676761388778687e-02 -1.854398660361766815e-02 -8.187077194452285767e-02 -8.343760669231414795e-02 -5.610152706503868103e-02 5.596423521637916565e-02 -1.519521772861480713e-01 -5.628714337944984436e-02 -9.399947710335254669e-03 -3.974910452961921692e-02 -3.937162831425666809e-02 1.491279304027557373e-01 -9.829887002706527710e-02 7.933630794286727905e-02 -9.475814551115036011e-02 9.995894879102706909e-02 -3.750766441226005554e-02 9.170225262641906738e-02 -6.639271229505538940e-02 -3.619616851210594177e-02 7.452869415283203125e-02 2.267530746757984161e-02 -5.552818253636360168e-02 -1.913725398480892181e-02 -1.615663059055805206e-02 -6.091057881712913513e-02 -5.618408694863319397e-02 -1.007776707410812378e-01 8.432733267545700073e-02 -8.283563703298568726e-02 2.562934346497058868e-02 -4.028012230992317200e-02 1.287396103143692017e-01 -4.740872979164123535e-02 -9.946762770414352417e-02 7.258672267198562622e-02 9.603270143270492554e-02 -2.146127820014953613e-01 2.005861401557922363e-01 4.016991704702377319e-03 9.881404042243957520e-02 -1.784754395484924316e-01 -9.105507284402847290e-02 -1.669324934482574463e-01 -2.300198376178741455e-02 -1.525159459561109543e-02 -2.397251874208450317e-02 -1.262844651937484741e-01 9.313737973570823669e-03 -1.240491420030593872e-01 3.332263603806495667e-02 -4.020209889858961105e-03 1.576019078493118286e-02 3.266835701651871204e-04 2.482586540281772614e-02 2.369339764118194580e-02 3.293184854555875063e-04 -1.065719574689865112e-01 3.809507191181182861e-02 9.304562211036682129e-02 -4.252380505204200745e-02 -1.260530948638916016e-01 2.520525753498077393e-01 -9.897017478942871094e-02 -2.133223414421081543e-02 2.489262372255325317e-01 -9.046503901481628418e-02 8.635894209146499634e-02 -1.634506434202194214e-01 -3.197543323040008545e-01 4.913535341620445251e-02 1.959136575460433960e-01 -9.264313429594039917e-02 -1.881375163793563843e-01 -1.751124672591686249e-02 -2.905825152993202209e-02 1.871072687208652496e-02 -7.512912154197692871e-02 -1.051722019910812378e-01 -5.402390379458665848e-03 3.364746272563934326e-02 5.512980278581380844e-03 7.305745035409927368e-03 4.229303449392318726e-03 5.940871778875589371e-03 3.234712639823555946e-03 -2.847168780863285065e-02 -1.048633009195327759e-01 7.608785480260848999e-02 1.051750108599662781e-01 7.690056692808866501e-03 -1.414654403924942017e-01 7.320459187030792236e-02 -1.152660772204399109e-01 4.983460530638694763e-02 2.327303886413574219e-01 9.326806664466857910e-02 -1.704614013433456421e-01 -1.848956942558288574e-01 -5.976792424917221069e-02 1.592061072587966919e-01 -1.650659143924713135e-01 -9.381422400474548340e-02 -1.856523603200912476e-01 -3.959448076784610748e-03 -4.706517234444618225e-02 4.505594074726104736e-02 2.624286152422428131e-02 -2.089477628469467163e-01 1.239063516259193420e-01 -2.390718087553977966e-02 -1.709154099225997925e-01 -1.105205416679382324e-01 -1.508438773453235626e-02 1.035469919443130493e-01 1.673432067036628723e-02 5.120269581675529480e-02 -1.030855774879455566e-01 8.343014866113662720e-02 -1.017857119441032410e-01 1.229696534574031830e-02 -3.135662153363227844e-02 -9.643211215734481812e-02 -8.517875522375106812e-02 -9.448844194412231445e-02 -2.268776111304759979e-02 8.870540559291839600e-02 -6.994637101888656616e-02 8.937536925077438354e-02 8.529035001993179321e-02 -3.078667521476745605e-01 1.380416750907897949e-01 -1.029732674360275269e-01 6.471338868141174316e-02 3.752776980400085449e-02 6.338411569595336914e-02 -9.442648291587829590e-02 -7.978357374668121338e-02 -6.381234526634216309e-02 5.312698706984519958e-02 1.511745750904083252e-01 -1.171831693500280380e-02 -4.243907332420349121e-02 2.028077393770217896e-01 3.521358594298362732e-02 2.125239185988903046e-02 2.735166251659393311e-01 -9.390673786401748657e-02 -1.700727343559265137e-01 5.626420490443706512e-03 -5.743062123656272888e-02 6.509607285261154175e-02 4.419220983982086182e-02 -1.206412538886070251e-01 -2.198786288499832153e-02 -6.157751753926277161e-02 6.935269385576248169e-02 5.418083444237709045e-02 -2.927818335592746735e-02 2.737504616379737854e-02 6.675768643617630005e-03 -4.358346015214920044e-02 -1.045850887894630432e-01 3.966004028916358948e-02 7.888220995664596558e-02 1.084793508052825928e-01 -3.825098276138305664e-02 5.556414648890495300e-02 -2.386018633842468262e-03 -1.735868491232395172e-02 1.626232713460922241e-01 -3.189559327438473701e-03 -3.677500039339065552e-02 -1.077032759785652161e-01 -6.530553102493286133e-02 -1.181415840983390808e-01 2.054665982723236084e-01 -1.013580858707427979e-01 6.717060506343841553e-02 -7.871925085783004761e-02 -4.536754917353391647e-03 -8.628232032060623169e-02 -4.364237934350967407e-02 5.463447421789169312e-02 7.559294998645782471e-02 1.829659007489681244e-02 -3.810231760144233704e-02 1.445978041738271713e-02 -1.330154389142990112e-01 7.491165399551391602e-02 -5.850773304700851440e-02 5.279816687107086182e-03 -1.007874831557273865e-01 -6.051312014460563660e-02 -2.818964421749114990e-03 1.158334836363792419e-01 -6.604961305856704712e-02 -6.636043637990951538e-02 3.605647683143615723e-01 6.230036914348602295e-02 1.247871741652488708e-01 4.024268593639135361e-03 -4.460835456848144531e-02 1.964040845632553101e-01 1.187151372432708740e-01 2.093635946512222290e-01 2.515807449817657471e-01 -1.040665134787559509e-01 7.813188433647155762e-02 1.352884173393249512e-01 -9.691929072141647339e-02 2.978404052555561066e-02 -6.218590214848518372e-02 3.118260763585567474e-02 2.100257873535156250e-01 5.309388414025306702e-02 -3.683280944824218750e-02 1.081678271293640137e-01 1.159011572599411011e-01 -9.071216732263565063e-02 1.454317569732666016e-01 1.560889184474945068e-02 -9.596666693687438965e-02 -1.235919669270515442e-01 -6.143675185739994049e-03 4.618058353662490845e-02 -4.854806885123252869e-02 -1.566148698329925537e-01 1.086631417274475098e-01 -5.864029750227928162e-02 7.967603951692581177e-02 -4.821031168103218079e-02 -7.968238554894924164e-03 -5.616628099232912064e-03 -7.662835996598005295e-03 -5.304025113582611084e-02 -9.814745932817459106e-02 -9.450878947973251343e-02 -1.617153137922286987e-01 -1.475114375352859497e-02 -3.400534763932228088e-02 3.202979266643524170e-02 -7.523473352193832397e-03 -7.037699967622756958e-02 5.858044326305389404e-02 -2.339935861527919769e-02 6.765121361240744591e-05 -7.621700875461101532e-03 -2.569160657003521919e-03 1.386285480111837387e-02 -9.449950885027647018e-04 -9.074879810214042664e-03 -9.348812699317932129e-02 -1.875597685575485229e-01 -1.796119287610054016e-02 -2.141974493861198425e-02 2.915703505277633667e-02 -5.883788317441940308e-02 -1.005370840430259705e-01 3.347435966134071350e-02 2.262158133089542389e-02 1.751536689698696136e-02 3.964411094784736633e-02 -3.229109104722738266e-03 -1.950366050004959106e-02 1.035222597420215607e-02 -1.544480863958597183e-02 -9.449574351310729980e-02 -1.709264367818832397e-01 4.125457257032394409e-03 -6.873404979705810547e-02 4.397033154964447021e-02 1.867942214012145996e-01 -1.137310042977333069e-01 6.536182016134262085e-02 -8.733030408620834351e-02 4.486828297376632690e-02 6.879048049449920654e-02 3.395766764879226685e-02 -1.334057748317718506e-01 -3.880335018038749695e-02 5.785062164068222046e-02 -9.526924788951873779e-02 -1.083553209900856018e-01 1.990574225783348083e-02 1.783852465450763702e-02 -5.172537639737129211e-02 -1.314793080091476440e-01 -3.332496806979179382e-02 -1.304124593734741211e-01 7.114066183567047119e-02 4.942291602492332458e-02 1.143850944936275482e-02 3.503248840570449829e-02 8.114560507237911224e-03 2.840096876025199890e-02 6.378748267889022827e-02 -1.035511195659637451e-01 7.710250467061996460e-02 5.883126333355903625e-02 -1.181510612368583679e-01 -1.791930794715881348e-01 -2.050231397151947021e-02 -4.374536871910095215e-02 2.246104925870895386e-01 1.195801421999931335e-02 -1.478822380304336548e-01 -9.886784851551055908e-02 -9.984883666038513184e-02 -6.099858880043029785e-02 8.420736342668533325e-02 -9.065923094749450684e-02
Binary file test-data/tl.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
Binary file test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
--- a/test-data/tl.dpt.diffmap.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.obs.tabular Mon Mar 04 10:16:12 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,101 +0,0 @@ -index paul15_clusters dpt_groups dpt_order dpt_order_indices -578 13Baso 2 53 27 -2242 3Ery 1 30 46 -2690 10GMP 2 66 45 -70 5Ery 1 32 65 -758 15Mo 2 67 8 -465 16Neu 2 68 80 -245 16Neu 2 69 87 -2172 10GMP 2 70 90 -2680 10GMP 0 4 36 -1790 7MEP 2 71 59 -855 11DC 2 72 82 -2721 10GMP 2 73 30 -104 2Ery 1 38 62 -1106 2Ery 1 40 32 -2367 15Mo 3 93 35 -124 2Ery 1 41 37 -2477 8Mk 2 74 31 -1968 2Ery 1 42 78 -563 1Ery 1 43 28 -276 2Ery 1 44 56 -192 16Neu 2 75 42 -2409 2Ery 1 45 44 -2054 15Mo 3 95 75 -720 8Mk 2 76 48 -2225 14Mo 3 97 98 -878 6Ery 1 29 54 -156 7MEP 2 77 79 -1244 8Mk 0 0 40 -10 2Ery 1 18 83 -1108 6Ery 2 65 25 -353 5Ery 1 11 1 -182 5Ery 1 16 97 -2053 3Ery 1 13 3 -2291 16Neu 3 92 96 -2056 10GMP 2 79 95 -1047 2Ery 1 14 94 -1947 14Mo 0 8 92 -1390 3Ery 1 15 60 -2317 14Mo 2 90 12 -2348 11DC 2 82 69 -953 5Ery 1 27 13 -628 9GMP 2 83 15 -2691 5Ery 1 20 17 -1499 16Neu 3 96 18 -1083 2Ery 1 21 19 -831 14Mo 0 2 21 -15 7MEP 0 1 86 -2005 7MEP 2 87 66 -1662 3Ery 1 23 84 -2457 7MEP 2 64 89 -757 7MEP 2 81 70 -1642 14Mo 2 91 68 -2520 10GMP 2 89 67 -1393 7MEP 2 88 0 -2170 6Ery 1 25 73 -988 14Mo 2 86 76 -1338 2Ery 1 19 77 -2189 16Neu 2 85 81 -446 13Baso 2 84 85 -2276 14Mo 0 9 88 -317 2Ery 1 37 91 -1540 16Neu 3 99 93 -2164 4Ery 1 12 72 -227 15Mo 2 78 64 -906 12Baso 2 63 49 -716 15Mo 0 3 29 -912 14Mo 1 47 2 -2688 11DC 2 52 4 -1678 7MEP 2 51 5 -1063 6Ery 1 39 6 -1041 5Ery 1 50 7 -2279 15Mo 3 98 9 -558 13Baso 2 62 10 -2196 14Mo 2 54 11 -1270 13Baso 3 94 16 -2259 3Ery 1 22 20 -2410 13Baso 2 55 23 -886 7MEP 2 56 26 -2072 13Baso 1 17 63 -443 5Ery 1 26 34 -910 13Baso 0 5 99 -2608 15Mo 2 57 50 -2645 1Ery 1 10 39 -616 6Ery 1 28 41 -1866 2Ery 1 48 58 -923 7MEP 2 58 57 -1716 4Ery 1 46 55 -2476 11DC 0 6 47 -1872 10GMP 2 59 53 -1009 4Ery 1 49 52 -1680 6Ery 0 7 38 -1490 14Mo 2 60 51 -1454 2Ery 1 36 33 -2580 9GMP 2 61 14 -958 1Ery 1 35 74 -2626 2Ery 1 34 22 -1677 3Ery 1 33 43 -982 4Ery 1 31 24 -202 2Ery 1 24 71 -891 10GMP 2 80 61
Binary file test-data/tl.draw_graph.pp.neighbors_umap_euclidean.recipe_weinreb17.paul15_subsample.h5ad has changed
Binary file test-data/tl.leiden.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
Binary file test-data/tl.louvain.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
Binary file test-data/tl.paga.neighbors_gauss_braycurtis.recipe_weinreb17.paul15_subsample.h5ad has changed
--- a/test-data/tl.score_genes.krumsiek11.obs.tabular Mon Mar 04 10:16:12 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,641 +0,0 @@ -index cell_type score -0 progenitor -1 progenitor -2 progenitor -3 progenitor -4 progenitor -5 progenitor -6 progenitor -7 progenitor -8 progenitor -9 progenitor -10 progenitor -11 progenitor -12 progenitor -13 progenitor -14 progenitor -15 progenitor -16 progenitor -17 progenitor -18 progenitor -19 progenitor -20 progenitor -21 progenitor -22 progenitor -23 progenitor -24 progenitor -25 progenitor -26 progenitor -27 progenitor -28 progenitor -29 progenitor -30 progenitor -31 progenitor -32 progenitor -33 progenitor -34 progenitor -35 progenitor -36 progenitor -37 progenitor -38 progenitor -39 progenitor -40 progenitor -41 progenitor -42 progenitor -43 progenitor -44 progenitor -45 progenitor -46 progenitor -47 progenitor -48 progenitor -49 progenitor -50 progenitor -51 progenitor -52 progenitor -53 progenitor -54 progenitor -55 progenitor -56 progenitor -57 progenitor -58 progenitor -59 progenitor -60 progenitor -61 progenitor -62 progenitor -63 progenitor -64 progenitor -65 progenitor -66 progenitor -67 progenitor -68 progenitor -69 progenitor -70 progenitor -71 progenitor -72 progenitor -73 progenitor -74 progenitor -75 progenitor -76 progenitor -77 progenitor -78 progenitor -79 progenitor -80 Mo -81 Mo -82 Mo -83 Mo -84 Mo -85 Mo -86 Mo -87 Mo -88 Mo -89 Mo -90 Mo -91 Mo -92 Mo -93 Mo -94 Mo -95 Mo -96 Mo -97 Mo -98 Mo -99 Mo -100 Mo -101 Mo -102 Mo -103 Mo -104 Mo -105 Mo -106 Mo -107 Mo -108 Mo -109 Mo -110 Mo -111 Mo -112 Mo -113 Mo -114 Mo -115 Mo -116 Mo -117 Mo -118 Mo -119 Mo -120 Mo -121 Mo -122 Mo -123 Mo -124 Mo -125 Mo -126 Mo -127 Mo -128 Mo -129 Mo -130 Mo -131 Mo -132 Mo -133 Mo -134 Mo -135 Mo -136 Mo -137 Mo -138 Mo -139 Mo -140 Mo -141 Mo -142 Mo -143 Mo -144 Mo -145 Mo -146 Mo -147 Mo -148 Mo -149 Mo -150 Mo -151 Mo -152 Mo -153 Mo -154 Mo -155 Mo -156 Mo -157 Mo -158 Mo -159 Mo -0 progenitor -1 progenitor -2 progenitor -3 progenitor -4 progenitor -5 progenitor -6 progenitor -7 progenitor -8 progenitor -9 progenitor -10 progenitor -11 progenitor -12 progenitor -13 progenitor -14 progenitor -15 progenitor -16 progenitor -17 progenitor -18 progenitor -19 progenitor -20 progenitor -21 progenitor -22 progenitor -23 progenitor -24 progenitor -25 progenitor -26 progenitor -27 progenitor -28 progenitor -29 progenitor -30 progenitor -31 progenitor -32 progenitor -33 progenitor -34 progenitor -35 progenitor -36 progenitor -37 progenitor -38 progenitor -39 progenitor -40 progenitor -41 progenitor -42 progenitor -43 progenitor -44 progenitor -45 progenitor -46 progenitor -47 progenitor -48 progenitor -49 progenitor -50 progenitor -51 progenitor -52 progenitor -53 progenitor -54 progenitor -55 progenitor -56 progenitor -57 progenitor -58 progenitor -59 progenitor -60 progenitor -61 progenitor -62 progenitor -63 progenitor -64 progenitor -65 progenitor -66 progenitor -67 progenitor -68 progenitor -69 progenitor -70 progenitor -71 progenitor -72 progenitor -73 progenitor -74 progenitor -75 progenitor -76 progenitor -77 progenitor -78 progenitor -79 progenitor -80 Ery -81 Ery -82 Ery -83 Ery -84 Ery -85 Ery -86 Ery -87 Ery -88 Ery -89 Ery -90 Ery -91 Ery -92 Ery -93 Ery -94 Ery -95 Ery -96 Ery -97 Ery -98 Ery -99 Ery -100 Ery -101 Ery -102 Ery -103 Ery -104 Ery -105 Ery -106 Ery -107 Ery -108 Ery -109 Ery -110 Ery -111 Ery -112 Ery -113 Ery -114 Ery -115 Ery -116 Ery -117 Ery -118 Ery -119 Ery -120 Ery -121 Ery -122 Ery -123 Ery -124 Ery -125 Ery -126 Ery -127 Ery -128 Ery -129 Ery -130 Ery -131 Ery -132 Ery -133 Ery -134 Ery -135 Ery -136 Ery -137 Ery -138 Ery -139 Ery -140 Ery -141 Ery -142 Ery -143 Ery -144 Ery -145 Ery -146 Ery -147 Ery -148 Ery -149 Ery -150 Ery -151 Ery -152 Ery -153 Ery -154 Ery -155 Ery -156 Ery -157 Ery -158 Ery -159 Ery -0 progenitor -1 progenitor -2 progenitor -3 progenitor -4 progenitor -5 progenitor -6 progenitor -7 progenitor -8 progenitor -9 progenitor -10 progenitor -11 progenitor -12 progenitor -13 progenitor -14 progenitor -15 progenitor -16 progenitor -17 progenitor -18 progenitor -19 progenitor -20 progenitor -21 progenitor -22 progenitor -23 progenitor -24 progenitor -25 progenitor -26 progenitor -27 progenitor -28 progenitor -29 progenitor -30 progenitor -31 progenitor -32 progenitor -33 progenitor -34 progenitor -35 progenitor -36 progenitor -37 progenitor -38 progenitor -39 progenitor -40 progenitor -41 progenitor -42 progenitor -43 progenitor -44 progenitor -45 progenitor -46 progenitor -47 progenitor -48 progenitor -49 progenitor -50 progenitor -51 progenitor -52 progenitor -53 progenitor -54 progenitor -55 progenitor -56 progenitor -57 progenitor -58 progenitor -59 progenitor -60 progenitor -61 progenitor -62 progenitor -63 progenitor -64 progenitor -65 progenitor -66 progenitor -67 progenitor -68 progenitor -69 progenitor -70 progenitor -71 progenitor -72 progenitor -73 progenitor -74 progenitor -75 progenitor -76 progenitor -77 progenitor -78 progenitor -79 progenitor -80 Mk -81 Mk -82 Mk -83 Mk -84 Mk -85 Mk -86 Mk -87 Mk -88 Mk -89 Mk -90 Mk -91 Mk -92 Mk -93 Mk -94 Mk -95 Mk -96 Mk -97 Mk -98 Mk -99 Mk -100 Mk -101 Mk -102 Mk -103 Mk -104 Mk -105 Mk -106 Mk -107 Mk -108 Mk -109 Mk -110 Mk -111 Mk -112 Mk -113 Mk -114 Mk -115 Mk -116 Mk -117 Mk -118 Mk -119 Mk -120 Mk -121 Mk -122 Mk -123 Mk -124 Mk -125 Mk -126 Mk -127 Mk -128 Mk -129 Mk -130 Mk -131 Mk -132 Mk -133 Mk -134 Mk -135 Mk -136 Mk -137 Mk -138 Mk -139 Mk -140 Mk -141 Mk -142 Mk -143 Mk -144 Mk -145 Mk -146 Mk -147 Mk -148 Mk -149 Mk -150 Mk -151 Mk -152 Mk -153 Mk -154 Mk -155 Mk -156 Mk -157 Mk -158 Mk -159 Mk -0 progenitor -1 progenitor -2 progenitor -3 progenitor -4 progenitor -5 progenitor -6 progenitor -7 progenitor -8 progenitor -9 progenitor -10 progenitor -11 progenitor -12 progenitor -13 progenitor -14 progenitor -15 progenitor -16 progenitor -17 progenitor -18 progenitor -19 progenitor -20 progenitor -21 progenitor -22 progenitor -23 progenitor -24 progenitor -25 progenitor -26 progenitor -27 progenitor -28 progenitor -29 progenitor -30 progenitor -31 progenitor -32 progenitor -33 progenitor -34 progenitor -35 progenitor -36 progenitor -37 progenitor -38 progenitor -39 progenitor -40 progenitor -41 progenitor -42 progenitor -43 progenitor -44 progenitor -45 progenitor -46 progenitor -47 progenitor -48 progenitor -49 progenitor -50 progenitor -51 progenitor -52 progenitor -53 progenitor -54 progenitor -55 progenitor -56 progenitor -57 progenitor -58 progenitor -59 progenitor -60 progenitor -61 progenitor -62 progenitor -63 progenitor -64 progenitor -65 progenitor -66 progenitor -67 progenitor -68 progenitor -69 progenitor -70 progenitor -71 progenitor -72 progenitor -73 progenitor -74 progenitor -75 progenitor -76 progenitor -77 progenitor -78 progenitor -79 progenitor -80 Neu -81 Neu -82 Neu -83 Neu -84 Neu -85 Neu -86 Neu -87 Neu -88 Neu -89 Neu -90 Neu -91 Neu -92 Neu -93 Neu -94 Neu -95 Neu -96 Neu -97 Neu -98 Neu -99 Neu -100 Neu -101 Neu -102 Neu -103 Neu -104 Neu -105 Neu -106 Neu -107 Neu -108 Neu -109 Neu -110 Neu -111 Neu -112 Neu -113 Neu -114 Neu -115 Neu -116 Neu -117 Neu -118 Neu -119 Neu -120 Neu -121 Neu -122 Neu -123 Neu -124 Neu -125 Neu -126 Neu -127 Neu -128 Neu -129 Neu -130 Neu -131 Neu -132 Neu -133 Neu -134 Neu -135 Neu -136 Neu -137 Neu -138 Neu -139 Neu -140 Neu -141 Neu -142 Neu -143 Neu -144 Neu -145 Neu -146 Neu -147 Neu -148 Neu -149 Neu -150 Neu -151 Neu -152 Neu -153 Neu -154 Neu -155 Neu -156 Neu -157 Neu -158 Neu -159 Neu
--- a/test-data/tl.score_genes_cell_cycle.krumsiek11.obs.tabular Mon Mar 04 10:16:12 2019 -0500 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,641 +0,0 @@ -index cell_type S_score G2M_score phase -0 progenitor 0.2681 0.20055 S -1 progenitor 0.24346666 0.15855001 S -2 progenitor 0.2276 0.13482499 S -3 progenitor 0.21043333 0.12637499 S -4 progenitor 0.19113334 0.1272 S -5 progenitor 0.17531666 0.13072497 S -6 progenitor 0.16073334 0.13242501 S -7 progenitor 0.15353334 0.13672501 S -8 progenitor 0.14314999 0.1399 S -9 progenitor 0.1337 0.14515 G2M -10 progenitor 0.12695001 0.15165001 G2M -11 progenitor 0.11726667 0.16077498 G2M -12 progenitor 0.11081667 0.16735 G2M -13 progenitor 0.104849994 0.17429999 G2M -14 progenitor 0.09816667 0.18152499 G2M -15 progenitor 0.095350005 0.186625 G2M -16 progenitor 0.09528333 0.19447501 G2M -17 progenitor 0.09463333 0.199675 G2M -18 progenitor 0.0947 0.205275 G2M -19 progenitor 0.0947 0.20802501 G2M -20 progenitor 0.097733326 0.21100001 G2M -21 progenitor 0.09881667 0.21964999 G2M -22 progenitor 0.10131666 0.22662501 G2M -23 progenitor 0.104849994 0.23022501 G2M -24 progenitor 0.112266675 0.23387499 G2M -25 progenitor 0.120283335 0.2393 G2M -26 progenitor 0.12826668 0.24174997 G2M -27 progenitor 0.13323334 0.24710001 G2M -28 progenitor 0.13971666 0.25280002 G2M -29 progenitor 0.14393334 0.256775 G2M -30 progenitor 0.15066667 0.259775 G2M -31 progenitor 0.15316668 0.26244998 G2M -32 progenitor 0.15993333 0.26487502 G2M -33 progenitor 0.16430001 0.266275 G2M -34 progenitor 0.16598332 0.270625 G2M -35 progenitor 0.17068332 0.2715 G2M -36 progenitor 0.17713334 0.276475 G2M -37 progenitor 0.17893334 0.27514997 G2M -38 progenitor 0.18013333 0.278025 G2M -39 progenitor 0.18251666 0.279675 G2M -40 progenitor 0.18876666 0.27925 G2M -41 progenitor 0.19041668 0.281775 G2M -42 progenitor 0.19083333 0.2824 G2M -43 progenitor 0.19411668 0.281725 G2M -44 progenitor 0.19639999 0.2844 G2M -45 progenitor 0.19843334 0.285375 G2M -46 progenitor 0.20406666 0.284075 G2M -47 progenitor 0.20673332 0.28625 G2M -48 progenitor 0.20769998 0.2885 G2M -49 progenitor 0.21186668 0.28935 G2M -50 progenitor 0.21285 0.28867498 G2M -51 progenitor 0.21443334 0.28855002 G2M -52 progenitor 0.21568334 0.28705 G2M -53 progenitor 0.21788335 0.29035 G2M -54 progenitor 0.22551665 0.28815 G2M -55 progenitor 0.22586668 0.28689998 G2M -56 progenitor 0.23069999 0.2816 G2M -57 progenitor 0.23118332 0.282375 G2M -58 progenitor 0.23160002 0.28230003 G2M -59 progenitor 0.23546667 0.28329998 G2M -60 progenitor 0.23661667 0.28195 G2M -61 progenitor 0.24134998 0.27899998 G2M -62 progenitor 0.24546666 0.27855 G2M -63 progenitor 0.24836665 0.27609998 G2M -64 progenitor 0.25375 0.27562502 G2M -65 progenitor 0.25834998 0.273525 G2M -66 progenitor 0.26393333 0.27015 G2M -67 progenitor 0.26746666 0.26622498 S -68 progenitor 0.2706333 0.267025 S -69 progenitor 0.27618334 0.2651 S -70 progenitor 0.28033334 0.263975 S -71 progenitor 0.2868167 0.2622 S -72 progenitor 0.29141667 0.26174998 S -73 progenitor 0.29198334 0.26385 S -74 progenitor 0.29348332 0.26275003 S -75 progenitor 0.29788333 0.263575 S -76 progenitor 0.30125 0.26232502 S -77 progenitor 0.29955 0.261825 S -78 progenitor 0.30065 0.2623 S -79 progenitor 0.30573332 0.2588 S -80 Mo 0.30818334 0.25555003 S -81 Mo 0.31073332 0.25422502 S -82 Mo 0.31378332 0.25410002 S -83 Mo 0.31268334 0.25304997 S -84 Mo 0.31355 0.25059998 S -85 Mo 0.3157 0.251275 S -86 Mo 0.3139333 0.25072497 S -87 Mo 0.3151833 0.25165 S -88 Mo 0.3149333 0.25079998 S -89 Mo 0.31440002 0.25172502 S -90 Mo 0.31251666 0.254725 S -91 Mo 0.31613332 0.25347498 S -92 Mo 0.31813332 0.25372502 S -93 Mo 0.31543335 0.25340003 S -94 Mo 0.31663334 0.257025 S -95 Mo 0.31793332 0.25435 S -96 Mo 0.3184333 0.2527 S -97 Mo 0.31743336 0.25052497 S -98 Mo 0.3164667 0.24747501 S -99 Mo 0.31841668 0.2466 S -100 Mo 0.31648335 0.24679999 S -101 Mo 0.31504998 0.2501 S -102 Mo 0.31489998 0.250375 S -103 Mo 0.31256667 0.25195 S -104 Mo 0.31425 0.250675 S -105 Mo 0.31441668 0.248675 S -106 Mo 0.31828332 0.24724999 S -107 Mo 0.32236665 0.25105 S -108 Mo 0.32341668 0.2527 S -109 Mo 0.32334998 0.25145 S -110 Mo 0.32061666 0.2516 S -111 Mo 0.3239333 0.24855 S -112 Mo 0.3217833 0.248275 S -113 Mo 0.3194833 0.25055 S -114 Mo 0.32711667 0.24814999 S -115 Mo 0.32861665 0.244375 S -116 Mo 0.33048332 0.244225 S -117 Mo 0.33173332 0.24415 S -118 Mo 0.32801664 0.24665 S -119 Mo 0.3321833 0.245675 S -120 Mo 0.32905 0.24717501 S -121 Mo 0.33133334 0.245975 S -122 Mo 0.33201668 0.24515 S -123 Mo 0.33265 0.24475 S -124 Mo 0.32968336 0.24344999 S -125 Mo 0.32461664 0.245175 S -126 Mo 0.32303333 0.24647498 S -127 Mo 0.32363334 0.24472499 S -128 Mo 0.3234 0.24480002 S -129 Mo 0.32494998 0.24702501 S -130 Mo 0.32526666 0.24975002 S -131 Mo 0.32278332 0.24785002 S -132 Mo 0.3201 0.24885 S -133 Mo 0.32130003 0.25125 S -134 Mo 0.32468334 0.2521 S -135 Mo 0.32040003 0.25545 S -136 Mo 0.31931666 0.25560004 S -137 Mo 0.31963333 0.25262502 S -138 Mo 0.31644997 0.253575 S -139 Mo 0.31913334 0.251575 S -140 Mo 0.32393336 0.24987501 S -141 Mo 0.32683334 0.2504 S -142 Mo 0.32791668 0.24740002 S -143 Mo 0.329 0.247075 S -144 Mo 0.32784998 0.24852501 S -145 Mo 0.32766664 0.24740002 S -146 Mo 0.32676667 0.2485 S -147 Mo 0.3254 0.24985 S -148 Mo 0.32301664 0.24805 S -149 Mo 0.32369998 0.25047502 S -150 Mo 0.3267 0.250475 S -151 Mo 0.3280667 0.252875 S -152 Mo 0.32885 0.25315002 S -153 Mo 0.32688335 0.2515 S -154 Mo 0.32666668 0.25325 S -155 Mo 0.3258167 0.25137502 S -156 Mo 0.32818332 0.2465 S -157 Mo 0.32963336 0.24692501 S -158 Mo 0.3318167 0.24837498 S -159 Mo 0.33176666 0.247625 S -0 progenitor 0.26751667 0.2007 S -1 progenitor 0.24345 0.157325 S -2 progenitor 0.22616667 0.139575 S -3 progenitor 0.20503333 0.13499999 S -4 progenitor 0.18988334 0.1349 S -5 progenitor 0.17425 0.134875 S -6 progenitor 0.16213334 0.13455 S -7 progenitor 0.14678332 0.14125 S -8 progenitor 0.1336 0.146375 G2M -9 progenitor 0.1237 0.15342501 G2M -10 progenitor 0.11621666 0.16017501 G2M -11 progenitor 0.10858333 0.1669 G2M -12 progenitor 0.09945001 0.17515 G2M -13 progenitor 0.09445 0.182875 G2M -14 progenitor 0.091649994 0.18862501 G2M -15 progenitor 0.08881667 0.196275 G2M -16 progenitor 0.08878334 0.20034999 G2M -17 progenitor 0.09183334 0.208125 G2M -18 progenitor 0.094516665 0.21419999 G2M -19 progenitor 0.094349995 0.223 G2M -20 progenitor 0.09643334 0.228775 G2M -21 progenitor 0.09933333 0.232925 G2M -22 progenitor 0.10111666 0.2406 G2M -23 progenitor 0.10683333 0.24365 G2M -24 progenitor 0.11198333 0.24744998 G2M -25 progenitor 0.1187 0.24800001 G2M -26 progenitor 0.12223333 0.253475 G2M -27 progenitor 0.12516668 0.25777498 G2M -28 progenitor 0.13296667 0.261875 G2M -29 progenitor 0.13638332 0.2664 G2M -30 progenitor 0.14060001 0.27075002 G2M -31 progenitor 0.14363334 0.27295002 G2M -32 progenitor 0.14310001 0.277825 G2M -33 progenitor 0.14686668 0.2806 G2M -34 progenitor 0.14746666 0.28705 G2M -35 progenitor 0.1488 0.291375 G2M -36 progenitor 0.14686665 0.295125 G2M -37 progenitor 0.14803334 0.29590002 G2M -38 progenitor 0.14774999 0.30242503 G2M -39 progenitor 0.14461668 0.30615002 G2M -40 progenitor 0.14245 0.3091 G2M -41 progenitor 0.14150001 0.313175 G2M -42 progenitor 0.13565 0.316325 G2M -43 progenitor 0.12808332 0.3198 G2M -44 progenitor 0.12049997 0.3262 G2M -45 progenitor 0.11080001 0.33355 G2M -46 progenitor 0.09916668 0.33852503 G2M -47 progenitor 0.08836666 0.34457502 G2M -48 progenitor 0.0756 0.35015002 G2M -49 progenitor 0.061966658 0.354175 G2M -50 progenitor 0.04515001 0.361325 G2M -51 progenitor 0.026250005 0.365675 G2M -52 progenitor 0.008533329 0.371575 G2M -53 progenitor -0.0058666766 0.36895004 G2M -54 progenitor -0.01971668 0.36967498 G2M -55 progenitor -0.035949945 0.36804998 G2M -56 progenitor -0.04824999 0.36305 G2M -57 progenitor -0.06161666 0.35704997 G2M -58 progenitor -0.07620004 0.34805 G2M -59 progenitor -0.09081668 0.341575 G2M -60 progenitor -0.103000015 0.32840002 G2M -61 progenitor -0.11609998 0.317375 G2M -62 progenitor -0.12736666 0.30952498 G2M -63 progenitor -0.13890001 0.30077502 G2M -64 progenitor -0.15153334 0.28985 G2M -65 progenitor -0.16445002 0.28004998 G2M -66 progenitor -0.17231664 0.26997498 G2M -67 progenitor -0.18360004 0.2623 G2M -68 progenitor -0.19526666 0.250675 G2M -69 progenitor -0.20973334 0.24382502 G2M -70 progenitor -0.22153333 0.23462497 G2M -71 progenitor -0.23176664 0.22642499 G2M -72 progenitor -0.23878333 0.21525 G2M -73 progenitor -0.24899998 0.20759997 G2M -74 progenitor -0.25769997 0.196425 G2M -75 progenitor -0.266 0.190525 G2M -76 progenitor -0.27291664 0.185325 G2M -77 progenitor -0.27909997 0.17939998 G2M -78 progenitor -0.28546664 0.16992497 G2M -79 progenitor -0.2924833 0.16142498 G2M -80 Ery -0.30063334 0.158275 G2M -81 Ery -0.3081833 0.14844999 G2M -82 Ery -0.31410003 0.13829997 G2M -83 Ery -0.32265 0.12857503 G2M -84 Ery -0.33280006 0.12172499 G2M -85 Ery -0.34323335 0.11087498 G2M -86 Ery -0.3550667 0.09400001 G2M -87 Ery -0.36045003 0.074499995 G2M -88 Ery -0.36565 0.056499988 G2M -89 Ery -0.37118334 0.03820002 G2M -90 Ery -0.3749 0.026174992 G2M -91 Ery -0.37931666 0.019950002 G2M -92 Ery -0.38785002 0.012800008 G2M -93 Ery -0.3930334 0.0039000213 G2M -94 Ery -0.39623332 -0.0038750172 G1 -95 Ery -0.40098336 -0.008474976 G1 -96 Ery -0.41044998 -0.008125007 G1 -97 Ery -0.41723332 -0.0113250315 G1 -98 Ery -0.42673334 -0.008574992 G1 -99 Ery -0.43113336 -0.009875029 G1 -100 Ery -0.4387667 -0.009699941 G1 -101 Ery -0.44501665 -0.006850004 G1 -102 Ery -0.44808337 -0.0041999817 G1 -103 Ery -0.45334998 -0.0044499934 G1 -104 Ery -0.4579167 -0.0024499893 G1 -105 Ery -0.4625 0.0014250278 G2M -106 Ery -0.46655002 0.003275007 G2M -107 Ery -0.4705 0.0074749887 G2M -108 Ery -0.47354996 0.011525005 G2M -109 Ery -0.47571668 0.012849987 G2M -110 Ery -0.47741672 0.014274985 G2M -111 Ery -0.47899997 0.015850008 G2M -112 Ery -0.48184994 0.017825007 G2M -113 Ery -0.48310003 0.021875024 G2M -114 Ery -0.48716664 0.023850024 G2M -115 Ery -0.48626667 0.024949968 G2M -116 Ery -0.4891 0.03274998 G2M -117 Ery -0.4909666 0.035274982 G2M -118 Ery -0.4974334 0.037799954 G2M -119 Ery -0.5008334 0.040574998 G2M -120 Ery -0.50525 0.044800013 G2M -121 Ery -0.50745004 0.045899987 G2M -122 Ery -0.51255 0.048699975 G2M -123 Ery -0.5148666 0.051825017 G2M -124 Ery -0.51621664 0.054074973 G2M -125 Ery -0.52003336 0.058725 G2M -126 Ery -0.5201167 0.06525004 G2M -127 Ery -0.5232334 0.06755 G2M -128 Ery -0.5255166 0.06912503 G2M -129 Ery -0.52691674 0.071750015 G2M -130 Ery -0.5294833 0.07469997 G2M -131 Ery -0.5308 0.07885 G2M -132 Ery -0.53328335 0.08200002 G2M -133 Ery -0.53339994 0.082275 G2M -134 Ery -0.5356667 0.08287498 G2M -135 Ery -0.53651667 0.083850026 G2M -136 Ery -0.53586674 0.08415002 G2M -137 Ery -0.5371834 0.08655003 G2M -138 Ery -0.53768337 0.08915001 G2M -139 Ery -0.5387167 0.086775005 G2M -140 Ery -0.5398166 0.08837497 G2M -141 Ery -0.5402333 0.09094998 G2M -142 Ery -0.5395833 0.09077501 G2M -143 Ery -0.5413166 0.094074994 G2M -144 Ery -0.5375334 0.094500035 G2M -145 Ery -0.5376667 0.09659997 G2M -146 Ery -0.5442666 0.09917498 G2M -147 Ery -0.5433 0.10099995 G2M -148 Ery -0.54293334 0.09899998 G2M -149 Ery -0.5396333 0.09729999 G2M -150 Ery -0.53550005 0.09580001 G2M -151 Ery -0.5340333 0.094500005 G2M -152 Ery -0.53835 0.094024986 G2M -153 Ery -0.5339166 0.09344998 G2M -154 Ery -0.5354667 0.095400006 G2M -155 Ery -0.5398333 0.09622499 G2M -156 Ery -0.54073334 0.09739998 G2M -157 Ery -0.54269993 0.09609997 G2M -158 Ery -0.54613334 0.09427503 G2M -159 Ery -0.5469167 0.09325001 G2M -0 progenitor 0.26924998 0.20047499 S -1 progenitor 0.24753334 0.15694998 S -2 progenitor 0.2261 0.129575 S -3 progenitor 0.20549999 0.12205 S -4 progenitor 0.18906666 0.118075006 S -5 progenitor 0.17461666 0.1156 S -6 progenitor 0.1549 0.112900004 S -7 progenitor 0.14206666 0.11277501 S -8 progenitor 0.12825 0.11547499 S -9 progenitor 0.11403333 0.116174996 G2M -10 progenitor 0.10465 0.11955001 G2M -11 progenitor 0.09291667 0.123825 G2M -12 progenitor 0.08646667 0.12455 G2M -13 progenitor 0.07824999 0.13115 G2M -14 progenitor 0.07111666 0.13497499 G2M -15 progenitor 0.06305 0.138225 G2M -16 progenitor 0.059816666 0.14175001 G2M -17 progenitor 0.055983335 0.1487 G2M -18 progenitor 0.05093333 0.15525001 G2M -19 progenitor 0.048833337 0.161075 G2M -20 progenitor 0.047583334 0.16835001 G2M -21 progenitor 0.040233333 0.1783 G2M -22 progenitor 0.038650002 0.18339998 G2M -23 progenitor 0.034033336 0.19080001 G2M -24 progenitor 0.0334 0.19689998 G2M -25 progenitor 0.036050003 0.19765002 G2M -26 progenitor 0.037483335 0.20150003 G2M -27 progenitor 0.0379 0.205475 G2M -28 progenitor 0.03891667 0.21019998 G2M -29 progenitor 0.041166663 0.21605001 G2M -30 progenitor 0.041533336 0.22262499 G2M -31 progenitor 0.0463 0.226375 G2M -32 progenitor 0.048683327 0.22929999 G2M -33 progenitor 0.057249997 0.233375 G2M -34 progenitor 0.06268333 0.236 G2M -35 progenitor 0.06565 0.23992498 G2M -36 progenitor 0.06738335 0.24414998 G2M -37 progenitor 0.07278331 0.24974999 G2M -38 progenitor 0.07835 0.25365 G2M -39 progenitor 0.08574999 0.25655 G2M -40 progenitor 0.089816675 0.25997502 G2M -41 progenitor 0.094816685 0.268325 G2M -42 progenitor 0.10088334 0.27127498 G2M -43 progenitor 0.10618336 0.27574998 G2M -44 progenitor 0.11181665 0.27997503 G2M -45 progenitor 0.12016666 0.28125003 G2M -46 progenitor 0.120766655 0.2857 G2M -47 progenitor 0.12061668 0.289625 G2M -48 progenitor 0.12701666 0.292675 G2M -49 progenitor 0.13323334 0.294025 G2M -50 progenitor 0.13686669 0.29399997 G2M -51 progenitor 0.14141665 0.296375 G2M -52 progenitor 0.14054999 0.29835 G2M -53 progenitor 0.13769999 0.30177498 G2M -54 progenitor 0.13920003 0.306425 G2M -55 progenitor 0.13541666 0.30935 G2M -56 progenitor 0.13395001 0.31435 G2M -57 progenitor 0.12931666 0.319175 G2M -58 progenitor 0.12291667 0.32285002 G2M -59 progenitor 0.11760001 0.32947502 G2M -60 progenitor 0.1109 0.33325002 G2M -61 progenitor 0.098733336 0.33807498 G2M -62 progenitor 0.08863334 0.345725 G2M -63 progenitor 0.074066654 0.347775 G2M -64 progenitor 0.062050015 0.3543 G2M -65 progenitor 0.050833344 0.359575 G2M -66 progenitor 0.038566664 0.36534998 G2M -67 progenitor 0.022033334 0.37015 G2M -68 progenitor 0.009916633 0.37007502 G2M -69 progenitor -0.002099961 0.37010002 G2M -70 progenitor -0.013416678 0.36807504 G2M -71 progenitor -0.026216656 0.36464998 G2M -72 progenitor -0.04154995 0.357625 G2M -73 progenitor -0.054400027 0.35250002 G2M -74 progenitor -0.06606665 0.3451 G2M -75 progenitor -0.07311666 0.33777502 G2M -76 progenitor -0.077833325 0.32635 G2M -77 progenitor -0.08776665 0.3159 G2M -78 progenitor -0.09445 0.30395 G2M -79 progenitor -0.102666676 0.2935 G2M -80 Mk -0.10896668 0.282375 G2M -81 Mk -0.12169999 0.27165002 G2M -82 Mk -0.12861666 0.26255003 G2M -83 Mk -0.13356665 0.2516 G2M -84 Mk -0.1381667 0.2421 G2M -85 Mk -0.14588335 0.23299998 G2M -86 Mk -0.14643335 0.220175 G2M -87 Mk -0.15011665 0.216025 G2M -88 Mk -0.15608332 0.20797502 G2M -89 Mk -0.1635333 0.20320001 G2M -90 Mk -0.1667167 0.19779998 G2M -91 Mk -0.16811666 0.18747498 G2M -92 Mk -0.16958332 0.17795 G2M -93 Mk -0.17056668 0.16855001 G2M -94 Mk -0.17408332 0.16107498 G2M -95 Mk -0.17345 0.1532 G2M -96 Mk -0.17251664 0.147325 G2M -97 Mk -0.17686662 0.141125 G2M -98 Mk -0.17819998 0.1339 G2M -99 Mk -0.18205002 0.12702498 G2M -100 Mk -0.18008336 0.12057501 G2M -101 Mk -0.17778334 0.10987502 G2M -102 Mk -0.17706665 0.10052502 G2M -103 Mk -0.17208335 0.09229997 G2M -104 Mk -0.17455 0.09097502 G2M -105 Mk -0.17273334 0.087374985 G2M -106 Mk -0.17373335 0.08560002 G2M -107 Mk -0.17395002 0.07944998 G2M -108 Mk -0.17468333 0.07655001 G2M -109 Mk -0.1739833 0.07757497 G2M -110 Mk -0.17766666 0.08107501 G2M -111 Mk -0.17615 0.07807499 G2M -112 Mk -0.17605004 0.077325016 G2M -113 Mk -0.17686665 0.07712501 G2M -114 Mk -0.17955002 0.07734999 G2M -115 Mk -0.17851666 0.07519999 G2M -116 Mk -0.17718336 0.076775014 G2M -117 Mk -0.17596671 0.07339999 G2M -118 Mk -0.1750167 0.07412499 G2M -119 Mk -0.17744997 0.076675 G2M -120 Mk -0.1789 0.074625015 G2M -121 Mk -0.17714998 0.071624994 G2M -122 Mk -0.1736333 0.068425 G2M -123 Mk -0.17461663 0.06832498 G2M -124 Mk -0.17366666 0.069875 G2M -125 Mk -0.17350003 0.07087502 G2M -126 Mk -0.17423335 0.073125005 G2M -127 Mk -0.17289999 0.07657498 G2M -128 Mk -0.17336664 0.07489997 G2M -129 Mk -0.16989997 0.07117501 G2M -130 Mk -0.16938332 0.06972501 G2M -131 Mk -0.17073336 0.07189995 G2M -132 Mk -0.16995004 0.07332501 G2M -133 Mk -0.16946661 0.07052502 G2M -134 Mk -0.16478333 0.070250005 G2M -135 Mk -0.16570002 0.072375 G2M -136 Mk -0.16755003 0.073075026 G2M -137 Mk -0.16876668 0.076124996 G2M -138 Mk -0.16663334 0.07460004 G2M -139 Mk -0.1660833 0.07682499 G2M -140 Mk -0.16843331 0.0783 G2M -141 Mk -0.17143327 0.07712501 G2M -142 Mk -0.17213336 0.07727498 G2M -143 Mk -0.16951668 0.07885 G2M -144 Mk -0.16820005 0.078149974 G2M -145 Mk -0.16826665 0.07882503 G2M -146 Mk -0.17055002 0.08182496 G2M -147 Mk -0.17345 0.082975 G2M -148 Mk -0.17216668 0.086125016 G2M -149 Mk -0.17273334 0.09057501 G2M -150 Mk -0.17401668 0.092824996 G2M -151 Mk -0.17518333 0.091575 G2M -152 Mk -0.17483333 0.09237501 G2M -153 Mk -0.17593333 0.092875004 G2M -154 Mk -0.1739333 0.094374955 G2M -155 Mk -0.1740667 0.09417495 G2M -156 Mk -0.17770004 0.09324998 G2M -157 Mk -0.17335 0.09350002 G2M -158 Mk -0.1704 0.09047499 G2M -159 Mk -0.17143336 0.089825004 G2M -0 progenitor 0.2660833 0.20005001 S -1 progenitor 0.24146667 0.1564 S -2 progenitor 0.22096668 0.12695 S -3 progenitor 0.19886668 0.112325005 S -4 progenitor 0.18153334 0.102675 S -5 progenitor 0.16055 0.10249999 S -6 progenitor 0.14478332 0.098000005 S -7 progenitor 0.13021666 0.092875004 S -8 progenitor 0.11686668 0.091899976 S -9 progenitor 0.10476667 0.091975 S -10 progenitor 0.09625 0.094950005 S -11 progenitor 0.09105 0.09615001 G2M -12 progenitor 0.0822 0.102025 G2M -13 progenitor 0.074 0.10612498 G2M -14 progenitor 0.062583335 0.10890001 G2M -15 progenitor 0.052600004 0.11175001 G2M -16 progenitor 0.045050006 0.112574995 G2M -17 progenitor 0.038033333 0.11227499 G2M -18 progenitor 0.03231667 0.11082502 G2M -19 progenitor 0.028383333 0.11277501 G2M -20 progenitor 0.021966662 0.11262502 G2M -21 progenitor 0.02043334 0.110575005 G2M -22 progenitor 0.017400004 0.110875025 G2M -23 progenitor 0.017300002 0.111875 G2M -24 progenitor 0.015683334 0.112075 G2M -25 progenitor 0.014233332 0.11295 G2M -26 progenitor 0.012683332 0.11170004 G2M -27 progenitor 0.011016667 0.112225026 G2M -28 progenitor 0.010216668 0.116024986 G2M -29 progenitor 0.0077833347 0.11819999 G2M -30 progenitor 0.004733335 0.119224995 G2M -31 progenitor 0.002683334 0.12180002 G2M -32 progenitor 0.006133329 0.11839999 G2M -33 progenitor 0.0052000023 0.116349995 G2M -34 progenitor 0.006116666 0.115775004 G2M -35 progenitor 0.0019833297 0.115449995 G2M -36 progenitor 0.0007166676 0.114999995 G2M -37 progenitor 0.00016666204 0.113325 G2M -38 progenitor -0.0018666722 0.116224974 G2M -39 progenitor -0.0030833334 0.11517501 G2M -40 progenitor -0.0030166656 0.11082499 G2M -41 progenitor -0.0019833334 0.10034999 G2M -42 progenitor 0.0010500029 0.094025 G2M -43 progenitor -0.0039666668 0.086150005 G2M -44 progenitor -0.0024000034 0.077549994 G2M -45 progenitor -0.0036166683 0.07370001 G2M -46 progenitor -0.00485 0.0651 G2M -47 progenitor -0.002550004 0.05520001 G2M -48 progenitor 0.0003666673 0.041975006 G2M -49 progenitor -0.0010499991 0.030874997 G2M -50 progenitor 0.0007333346 0.014625013 G2M -51 progenitor 9.999983e-05 -0.0017999709 S -52 progenitor 0.0018333336 -0.01987499 S -53 progenitor 0.00090000033 -0.032900006 S -54 progenitor 0.0029999996 -0.05064997 S -55 progenitor 0.003983334 -0.068574995 S -56 progenitor -0.0008500004 -0.08140004 G1 -57 progenitor -0.0029833335 -0.09470001 G1 -58 progenitor -0.0021333336 -0.106824994 G1 -59 progenitor -0.0015000002 -0.11555001 G1 -60 progenitor -0.0022999998 -0.12255001 G1 -61 progenitor -0.0017666668 -0.13125 G1 -62 progenitor 0.0033999998 -0.14297503 S -63 progenitor 0.006316666 -0.15117502 S -64 progenitor 0.005033333 -0.15187496 S -65 progenitor 0.0031333338 -0.15367502 S -66 progenitor 0.0035666667 -0.15197498 S -67 progenitor 0.005016666 -0.14580002 S -68 progenitor 0.0061 -0.142575 S -69 progenitor 0.00515 -0.13665 S -70 progenitor 0.0028499998 -0.12865001 S -71 progenitor 0.0030833331 -0.12112501 S -72 progenitor 0.0032833333 -0.11669999 S -73 progenitor 0.0006166666 -0.11475003 S -74 progenitor -0.0006833335 -0.11412498 G1 -75 progenitor -2.3283064e-10 -0.11702502 G1 -76 progenitor 0.0007333332 -0.121425 S -77 progenitor 0.0005833333 -0.12972501 S -78 progenitor -0.0015333334 -0.13869998 G1 -79 progenitor -0.0023 -0.14877501 G1 -80 Neu -0.0046166666 -0.157125 G1 -81 Neu -0.0035666665 -0.16639996 G1 -82 Neu -0.0011666667 -0.17409998 G1 -83 Neu -0.0026166667 -0.180325 G1 -84 Neu -0.0001833333 -0.18502498 G1 -85 Neu 0.0035666665 -0.18762502 S -86 Neu 0.0024333335 -0.19062501 S -87 Neu 0.0023 -0.19277498 S -88 Neu 0.0014833333 -0.19762498 S -89 Neu 0.0024333335 -0.201725 S -90 Neu 0.003866667 -0.20430002 S -91 Neu 0.0067999996 -0.20865002 S -92 Neu 0.0079333335 -0.21332502 S -93 Neu 0.0068 -0.21907501 S -94 Neu 0.0058 -0.22292498 S -95 Neu 0.0069666663 -0.22795 S -96 Neu 0.0023833334 -0.22915001 S -97 Neu 0.0056166667 -0.230375 S -98 Neu 0.0051666666 -0.23027502 S -99 Neu 0.001966667 -0.23169999 S -100 Neu 0.0033666668 -0.23169999 S -101 Neu 0.0047333334 -0.233675 S -102 Neu 0.0045666667 -0.23272496 S -103 Neu 0.00060000026 -0.23024999 S -104 Neu 0.0036833333 -0.22662503 S -105 Neu 0.0025166667 -0.22107498 S -106 Neu 0.0038833334 -0.22109997 S -107 Neu 0.0033666666 -0.21907501 S -108 Neu 0.0042666667 -0.218675 S -109 Neu 0.0038 -0.2165 S -110 Neu 0.004633333 -0.21385 S -111 Neu 0.003266667 -0.21305001 S -112 Neu 0.0034166668 -0.21077499 S -113 Neu 0.0017166669 -0.21184999 S -114 Neu 0.0012666667 -0.21437502 S -115 Neu -0.0016333334 -0.2153 G1 -116 Neu -0.00043333336 -0.215675 G1 -117 Neu 0.0018833333 -0.21865001 S -118 Neu 0.0014666667 -0.22187501 S -119 Neu -0.0020833332 -0.21787499 G1 -120 Neu -0.0039000001 -0.21329997 G1 -121 Neu -0.0023 -0.20820004 G1 -122 Neu -0.00195 -0.21035 G1 -123 Neu -0.0058833333 -0.20732501 G1 -124 Neu -0.0070166667 -0.20714998 G1 -125 Neu -0.008633333 -0.20389998 G1 -126 Neu -0.006616667 -0.20832503 G1 -127 Neu -0.003116667 -0.21047503 G1 -128 Neu -0.0055833333 -0.21289998 G1 -129 Neu -0.0042333333 -0.216575 G1 -130 Neu -0.005016667 -0.21945003 G1 -131 Neu -0.0020833334 -0.21799998 G1 -132 Neu -0.0032 -0.21150002 G1 -133 Neu -0.0033333339 -0.20989999 G1 -134 Neu -0.0011666667 -0.21257499 G1 -135 Neu -0.0023 -0.21222499 G1 -136 Neu -0.0034666667 -0.2146 G1 -137 Neu -0.0011833335 -0.21350002 G1 -138 Neu -0.00325 -0.21377501 G1 -139 Neu -0.0040166667 -0.213925 G1 -140 Neu -0.0024666665 -0.21455 G1 -141 Neu 0.0028333336 -0.21975 S -142 Neu 0.0032833333 -0.220325 S -143 Neu 0.004766667 -0.22119999 S -144 Neu 0.00705 -0.22362499 S -145 Neu 0.0036 -0.22555003 S -146 Neu 0.006733333 -0.22377498 S -147 Neu 0.0025000002 -0.22509998 S -148 Neu 0.0018 -0.221075 S -149 Neu 6.666663e-05 -0.22125 S -150 Neu -0.0014499999 -0.219875 G1 -151 Neu -0.0012833332 -0.21900001 G1 -152 Neu -0.0001999999 -0.21767499 G1 -153 Neu -0.0032666668 -0.21407498 G1 -154 Neu -0.0017 -0.2116 G1 -155 Neu -0.0028166666 -0.2077 G1 -156 Neu -0.00245 -0.2053 G1 -157 Neu -0.0011166666 -0.203825 G1 -158 Neu -0.001966667 -0.20535001 G1 -159 Neu -0.0027 -0.20725003 G1