diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..b14947a --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +statsplot/_version.py export-subst diff --git a/.github/README.md b/.github/README.md index afd15bd..18abbca 100644 --- a/.github/README.md +++ b/.github/README.md @@ -1,18 +1,19 @@ +[![Latest Version on PyPI](https://img.shields.io/pypi/v/statsplot.svg)](https://pypi.python.org/pypi/statsplot/) + # Statsplot A package that allows you easily to calculate and plot statistics. Seaborn | Statsplot :-------------------------:|:-------------------------: -![boxplot seaborn](../docs/images/nested_seaborn.png) | ![boxplot statsplot](../docs/images/nested_statsplot.png) - +![boxplot seaborn](https://github.com/SilasK/statsplot/blob/master/docs/images/nested_seaborn.png) | ![boxplot statsplot](https://github.com/SilasK/statsplot/blob/master/docs/images/nested_statsplot.png) > + Setup : + + ``` + pip install statsplot + ``` Setup dev version: @@ -37,5 +43,6 @@ pip install --editable . ``` -See the [Example](../example.ipynb) + +See the [Example](https://github.com/SilasK/statsplot/blob/master/example.ipynb) or run it on [Colab](https://colab.research.google.com/github/SilasK/statsplot/blob/master/example.ipynb) diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md new file mode 100644 index 0000000..b707194 --- /dev/null +++ b/.github/copilot-instructions.md @@ -0,0 +1,7 @@ +# Copilot Instructions + +- Use Pixi for local development workflows in this repository. +- Prefer `pixi run test` for running tests. +- Prefer `pixi run test-mpl` for matplotlib image-comparison tests. +- When adding dependencies for local workflows, update `pixi.toml` first. +- Keep CI workflows independent from Pixi unless explicitly requested. diff --git a/.github/workflows/publish-to-test-pypi.yml b/.github/workflows/publish-to-test-pypi.yml new file mode 100644 index 0000000..26e7208 --- /dev/null +++ b/.github/workflows/publish-to-test-pypi.yml @@ -0,0 +1,46 @@ +name: Publish package +on: [push] +jobs: + Publish-to-PyPI: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@master + - name: Set up Python 3.9 + uses: actions/setup-python@v1 + with: + python-version: 3.9 + + - name: Install pypa/build + run: >- + python -m + pip install + build + --user + + - name: Build a binary wheel and a source tarball + run: >- + python -m + build + --sdist + --wheel + --outdir dist/ + . + + - name: Publish package to TestPyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + user: __token__ + password: ${{ secrets.TEST_PYPI_TOKEN }} + repository_url: https://test.pypi.org/legacy/ + verbose: true + + - name: Publish package + if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags') + uses: pypa/gh-action-pypi-publish@release/v1 + with: + user: __token__ + password: ${{ secrets.PYPI_TOKEN }} + + + + diff --git a/.gitignore b/.gitignore index 543d38a..0fae21d 100644 --- a/.gitignore +++ b/.gitignore @@ -11,6 +11,8 @@ docs/_build old *.ipynb_checkpoints +test/_actual + *__pycache__ .vscode diff --git a/example.ipynb b/example.ipynb index 3981d24..2373f3b 100644 --- a/example.ipynb +++ b/example.ipynb @@ -5,17 +5,44 @@ "execution_count": 1, "id": "b24bab44", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You have statsplot v 0.3.0\n" + ] + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pylab as plt\n", "import seaborn as sns\n", "\n", + "try:\n", + "\n", + " import statsplot as stp\n", + "except ImportError:\n", + " # Install statsplot\n", + " ! pip install statsplot\n", + " import statsplot as stp\n", "\n", - "import statsplot as stp\n", + "from statsplot import MetaTable\n", + "print(f\"You have statsplot v {stp.__version__}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "00cfe8e5", + "metadata": {}, + "outputs": [], + "source": [ "\n", - "import anndata" + "sns.set_context(font_scale=1.5)\n", + "# Nice plots when you have a mac\n", + "%config InlineBackend.figure_format = 'retina'" ] }, { @@ -30,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "98b56061", "metadata": {}, "outputs": [], @@ -52,7 +79,7 @@ " name=\"Weight\",\n", ")\n", "# add difference between groups\n", - "weights += groups.map(dict(zip(group_names, [0, 0.1, 10]))).values\n" + "weights += groups.map(dict(zip(group_names, [0, 0.1, 10]))).values" ] }, { @@ -65,13 +92,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "668f3083", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -87,7 +114,7 @@ } ], "source": [ - "ax = sns.boxplot(y=weights, x=groups)\n" + "ax = sns.boxplot(y=weights, x=groups)" ] }, { @@ -100,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "b17c3b88", "metadata": {}, "outputs": [ @@ -113,13 +140,13 @@ "Name: Weight, dtype: float64" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -150,13 +177,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "b80d3874", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -175,8 +202,8 @@ "ax, stats = stp.statsplot(\n", " variable=weights,\n", " test_variable=groups,\n", - " labelkws={\"show_ns\": True, \"use_stars\": False},\n", - ")\n" + " show_not_significant = True\n", + ")" ] }, { @@ -189,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "da475e1c", "metadata": {}, "outputs": [ @@ -269,7 +296,7 @@ "Sample_4 Participant_5 Control before 19.663896" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -292,18 +319,18 @@ "\n", "df.index = \"Sample_\" + df.index.astype(str)\n", "\n", - "df.head()\n" + "df.head()" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "d1cdeca6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -321,12 +348,12 @@ "source": [ "ax = sns.boxplot(\n", " data=df, y=\"Measurement\", hue=\"Timepoint\", x=\"Group\", hue_order=[\"before\", \"after\"]\n", - ")\n" + ")" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "1a87a451", "metadata": {}, "outputs": [ @@ -339,13 +366,13 @@ "Name: Measurement, dtype: object" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -381,162 +408,419 @@ "source": [ "# Example with many variables\n", "\n", - "Based on microbiome profiling" + "If you have many similar values you can put them in a `StatsTable` and then apply statistics once.\n", + "This example is based on on microbiome profiling" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "556680ad", + "execution_count": 10, + "id": "cbf3fabd", "metadata": {}, "outputs": [], "source": [ - "# Functions to parse taxonomy\n", - "import pandas as pd\n", - "import numpy as np\n", - "import warnings\n", - "\n", - "TAXONMIC_LEVELS = [\"Domain\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\"]\n", + "relab = pd.read_table(\"test/data/micobiota_relab.tsv.gz\", index_col=0)\n", "\n", + "Tax = pd.read_table(\"test/data/micobiota_taxonomy.tsv.gz\", index_col=0)\n", "\n", - "def tax2table(Taxonomy_Series, split_character=\";\", remove_prefix=False):\n", - " \"\"\"\n", - " Transforms (green_genes) taxonomy to a table\n", - " Expect the following input format:\n", - " d__Bacteria;p__Bacteroidota;c__Bacteroidia;f__\n", - " Replaces empty values and can remove prefix 'c__'\n", - " \"\"\"\n", - "\n", - " # drop missing values\n", - " if Taxonomy_Series.isnull().any():\n", - " warnings.warn(\n", - " \"Some samples have no taxonomy asigned. Samples:\\n\"\n", - " + \", \".join(Taxonomy_Series.index[Taxonomy_Series.isnull()].astype(str))\n", - " )\n", - "\n", - " Tax = Taxonomy_Series.dropna().astype(str).str.split(split_character, expand=True)\n", - " # Add headers as long as we have columns\n", - " Tax.columns = TAXONMIC_LEVELS[: len(Tax.columns)]\n", - "\n", - " if remove_prefix:\n", - " Tax = Tax.applymap(lambda s: s[3:], na_action=\"ignore\").replace(\"\", np.nan)\n", - " else:\n", - " Tax[Tax.applymap(len, na_action=\"ignore\") == 3] = np.nan\n", - "\n", - " # add missing values again\n", - "\n", - " Tax = Tax.reindex(Taxonomy_Series.index)\n", + "metadata = pd.read_table(\"test/data/micobiota_metadata.tsv.gz\", index_col=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0193b0c2", + "metadata": {}, + "outputs": [], + "source": [ + "# transform data with centered log transform\n", + "from statsplot import transformations\n", "\n", - " return Tax\n", + "clr_data = transformations.clr(relab, log=np.log2)\n", "\n", + "# put everithing together in a MetaTable\n", "\n", - "def load_gtdb_tax(taxonomy_file, remove_prefix=False):\n", + "D = MetaTable(clr_data, obs=metadata, var=Tax)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "347b317f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Your labels are not unique. but I should be able to handle this.\n" + ] + } + ], + "source": [ + "# create stats table\n", "\n", - " D = pd.read_table(taxonomy_file, index_col=0)\n", + "ST = stp.StatsTable(\n", + " D,\n", + " test_variable=\"Group\",\n", + " grouping_variable=\"Source\",\n", + " label_variable=\"Label\",\n", + " data_unit=\"centered log$_2$ ratio\",\n", + " test=\"welch\",\n", + " ref_group=\"RT\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "incoming-meeting", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 386, + "width": 511 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# make a vulcanot\n", "\n", - " Tax = tax2table(D[\"classification\"], remove_prefix=remove_prefix)\n", + "axes = ST.vulcanoplot(hue=\"phylum\")" + ] + }, + { + "cell_type": "markdown", + "id": "b35b6907", + "metadata": {}, + "source": [ + "# PCA\n", "\n", - " return Tax" + "The following functions represent commonly used plots for dimensional reduction\n" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "vocal-publicity", + "execution_count": 15, + "id": "b5986bb2", "metadata": {}, "outputs": [], "source": [ - "# load microbiome data\n", - "# The paths here are existing only on my computer\n", - "\n", - "# 1. counts/ relab\n", - "# 2. taxonomy\n", - "# 3. metadata\n", - "\n", - "Tax = load_gtdb_tax(\n", - " \"../WarmMicrobiota/Metagenome/WD/genomes/taxonomy/gtdbtk.bac120.summary.tsv\",\n", - " remove_prefix=True,\n", - ").sort_index()\n", - "\n", - "# create a label for each genome even if species is not defined\n", - "\n", - "Tax[\"Label\"] = Tax.ffill(axis=1)[\"species\"]\n", - "Tax.loc[Tax.species.isnull(), \"Label\"] += \" \" + Tax.index[Tax.species.isnull()]\n", + "from statsplot import DimRed\n", "\n", - "\n", - "metadata = pd.read_table(\"../WarmMicrobiota/Metagenome/metadata.tsv\", index_col=0)\n", - "D = pd.read_table(\n", - " \"../WarmMicrobiota/Metagenome/WD/genomes/counts/median_coverage_genomes.tsv\",\n", - " index_col=0,\n", - ")\n", - "# relative abundance\n", - "relab = (D.T / D.sum(1)).T * 100\n" + "pca = DimRed(clr_data)" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "a09a118c", + "execution_count": 16, + "id": "20c4a682", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "image/png": { + "height": 261, + "width": 398 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pca.plot_explained_variance_ratio()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "40ebcd48", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "AnnData object with n_obs × n_vars = 32 × 147\n", - " obs: 'Source', 'Group'\n", - " var: 'Domain', 'phylum', 'class', 'order', 'family', 'genus', 'species', 'Label'" + "" ] }, - "execution_count": 11, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 261, + "width": 390 + }, + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "# But everithing in an anndata object that keeps the data togehter.\n", - "\n", - "D = anndata.AnnData(relab, obs=metadata, var=Tax)\n", - "D" + "pca.plot_components(\n", + " plot_ellipse=True,\n", + " groups=metadata.Group,\n", + " order_groups=[\"RT\", \"Hot\"],\n", + " colors=[\"grey\", \"darkred\"],\n", + ")" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "0193b0c2", + "execution_count": 18, + "id": "2299fd46", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 261, + "width": 390 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# transform data with centered log transform\n", - "\n", - "from statsplot import transformations\n", + "pca.plot_components(label_points=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9b54a6a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "automatic selection selected 10 to visualize, which is probably to much. I select only 8\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 261, + "width": 390 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pca.plot_biplot(labels=Tax.Label)" + ] + }, + { + "cell_type": "markdown", + "id": "c49d9279", + "metadata": {}, + "source": [ + "# Stats table with one grouping variable \n", "\n", - "clr_data = transformations.clr(D, log=np.log2)\n" + "This is to show how to construct a statstable without the MetaTable and for testing" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "347b317f", + "execution_count": 20, + "id": "5ae251a8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Your labels are not unique. but I should be able to handle this.\n" + ] + } + ], "source": [ "# create stats table\n", "\n", "ST = stp.StatsTable(\n", - " clr_data,\n", - " test_variable=\"Group\",\n", - " grouping_variable=\"Source\",\n", - " label_variable=\"Label\",\n", - " data_unit=\"centered log$_2$ ratio\",\n", - " test=\"welch\",\n", + " relab,\n", + " test_variable=metadata.Group,\n", + " label_variable=Tax.Label,\n", + " data_unit=\"Relative abundance\",\n", + " test=\"mannwhitneyu\",\n", " ref_group=\"RT\",\n", - ")\n" + ")" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "aggressive-trial", + "execution_count": 21, + "id": "d2f803f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 372, + "width": 490 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ST.vulcanoplot()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a66f7a25", "metadata": {}, "outputs": [ { @@ -555,30 +839,17 @@ " .dataframe thead tr th {\n", " text-align: left;\n", " }\n", - "\n", - " .dataframe thead tr:last-of-type th {\n", - " text-align: right;\n", - " }\n", "\n", "\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -587,77 +858,54 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -667,123 +915,108 @@ " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
PvalueStatisticmedian_diffpBH
CecumFecesCecumFecesCecumFecesCecumFecesPvalueStatisticlog2FCmedian_diffpBHDescription
Hot_vs_RTHot_vs_RTHot_vs_RTHot_vs_RTHot_vs_RTHot_vs_RT
user_genomeLabel
MAG0010.0000869.776737e-08-7.182472-10.0311653.8711364.2340780.0015760.0000140.0000020.03.9790374.3554200.000038CAG-510 MAG001
MAG0020.8365155.090098e-01-0.210301-0.680391-0.065014-0.1131450.9245690.6992940.462380108.00.4135650.1550430.596227Lachnospiraceae MAG002
MAG0030.5829337.263051e-01-0.563255-0.3580600.0184950.1920790.7451400.8473560.338839106.01.7458710.0000000.488326Bacteroides sp002491635
MAG0040.0264534.278594e-022.5754952.252542-1.051217-1.6388590.0925160.1310320.010959196.0-1.138110-0.0819530.032220Lachnospiraceae MAG004
MAG0050.0061275.384853e-023.2677592.104872-1.349585-0.6500920.0321680.1539590.000975216.0-1.211975-0.6682750.003872UBA7050 MAG005
..................
MAG1430.0119261.485765e-022.9720292.840871-2.133191-2.0725220.0536980.0657560.003091207.0-1.311170-0.8512870.010096Zag111 MAG143
MAG1440.1540941.801575e-011.5955901.483567-0.274401-0.1784210.3006870.3430930.038849160.0-9.2478870.0000000.081583Oscillospiraceae MAG144
MAG1450.1140075.550917e-02-1.685910-2.1019330.3528191.0376890.2464550.1539590.00560354.00.8248690.2338110.017160CAG-180 MAG145
MAG1460.1217078.657417e-021.6716721.863180-2.149775-2.0223520.2591690.2157020.024467188.0-0.985527-0.3137070.057089UBA3700 MAG146
MAG1470.6848183.889707e-010.4147110.898178-0.281860-0.2383570.8294020.5898870.362262150.0-0.800029-0.0039890.512043UBA3263 sp001689615
\n", - "

147 rows × 8 columns

\n", + "

147 rows × 6 columns

\n", "" ], "text/plain": [ - " Pvalue Statistic median_diff \\\n", - " Cecum Feces Cecum Feces Cecum \n", - " Hot_vs_RT Hot_vs_RT Hot_vs_RT Hot_vs_RT Hot_vs_RT \n", - "user_genome \n", - "MAG001 0.000086 9.776737e-08 -7.182472 -10.031165 3.871136 \n", - "MAG002 0.836515 5.090098e-01 -0.210301 -0.680391 -0.065014 \n", - "MAG003 0.582933 7.263051e-01 -0.563255 -0.358060 0.018495 \n", - "MAG004 0.026453 4.278594e-02 2.575495 2.252542 -1.051217 \n", - "MAG005 0.006127 5.384853e-02 3.267759 2.104872 -1.349585 \n", - "... ... ... ... ... ... \n", - "MAG143 0.011926 1.485765e-02 2.972029 2.840871 -2.133191 \n", - "MAG144 0.154094 1.801575e-01 1.595590 1.483567 -0.274401 \n", - "MAG145 0.114007 5.550917e-02 -1.685910 -2.101933 0.352819 \n", - "MAG146 0.121707 8.657417e-02 1.671672 1.863180 -2.149775 \n", - "MAG147 0.684818 3.889707e-01 0.414711 0.898178 -0.281860 \n", + " Pvalue Statistic log2FC median_diff pBH \\\n", + " Hot_vs_RT Hot_vs_RT Hot_vs_RT Hot_vs_RT Hot_vs_RT \n", + "MAG001 0.000002 0.0 3.979037 4.355420 0.000038 \n", + "MAG002 0.462380 108.0 0.413565 0.155043 0.596227 \n", + "MAG003 0.338839 106.0 1.745871 0.000000 0.488326 \n", + "MAG004 0.010959 196.0 -1.138110 -0.081953 0.032220 \n", + "MAG005 0.000975 216.0 -1.211975 -0.668275 0.003872 \n", + "... ... ... ... ... ... \n", + "MAG143 0.003091 207.0 -1.311170 -0.851287 0.010096 \n", + "MAG144 0.038849 160.0 -9.247887 0.000000 0.081583 \n", + "MAG145 0.005603 54.0 0.824869 0.233811 0.017160 \n", + "MAG146 0.024467 188.0 -0.985527 -0.313707 0.057089 \n", + "MAG147 0.362262 150.0 -0.800029 -0.003989 0.512043 \n", "\n", - " pBH \n", - " Feces Cecum Feces \n", - " Hot_vs_RT Hot_vs_RT Hot_vs_RT \n", - "user_genome \n", - "MAG001 4.234078 0.001576 0.000014 \n", - "MAG002 -0.113145 0.924569 0.699294 \n", - "MAG003 0.192079 0.745140 0.847356 \n", - "MAG004 -1.638859 0.092516 0.131032 \n", - "MAG005 -0.650092 0.032168 0.153959 \n", - "... ... ... ... \n", - "MAG143 -2.072522 0.053698 0.065756 \n", - "MAG144 -0.178421 0.300687 0.343093 \n", - "MAG145 1.037689 0.246455 0.153959 \n", - "MAG146 -2.022352 0.259169 0.215702 \n", - "MAG147 -0.238357 0.829402 0.589887 \n", + " Description \n", + " Label \n", + "MAG001 CAG-510 MAG001 \n", + "MAG002 Lachnospiraceae MAG002 \n", + "MAG003 Bacteroides sp002491635 \n", + "MAG004 Lachnospiraceae MAG004 \n", + "MAG005 UBA7050 MAG005 \n", + "... ... \n", + "MAG143 Zag111 MAG143 \n", + "MAG144 Oscillospiraceae MAG144 \n", + "MAG145 CAG-180 MAG145 \n", + "MAG146 UBA3700 MAG146 \n", + "MAG147 UBA3263 sp001689615 \n", "\n", - "[147 rows x 8 columns]" + "[147 rows x 6 columns]" ] }, - "execution_count": 14, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ST.stats" + "ST.stats\n", + "# keep in mind that the stats table here has one header row less than if used with a grouping variable" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "incoming-meeting", + "execution_count": 23, + "id": "fde73e94", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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r'__version__ = [\'"]([^\'"]*)[\'"]', read(NAME.replace("-", "_") + "/__init__.py") -).group(1) LONG_DESCRIPTION = read(README) if __name__ == "__main__": setuptools.setup( name=NAME, - version=VERSION, + version=versioneer.get_version(), + cmdclass=versioneer.get_cmdclass(), packages=setuptools.find_packages(), author=AUTHOR, description=DESCRIPTION, diff --git a/statsplot/__init__.py b/statsplot/__init__.py index a7e0b73..19c52af 100644 --- a/statsplot/__init__.py +++ b/statsplot/__init__.py @@ -1,6 +1,8 @@ -__version__ = "0.0.2" -from .statstable import StatsTable +from .statstable import StatsTable, MetaTable from .plot import statsplot, vulcanoplot from .stats import calculate_stats from .dimred import DimRed + +from . import _version +__version__ = _version.get_versions()['version'] diff --git a/statsplot/_version.py b/statsplot/_version.py new file mode 100644 index 0000000..6a92464 --- /dev/null +++ b/statsplot/_version.py @@ -0,0 +1,658 @@ + +# This file helps to compute a version number in source trees obtained from +# git-archive tarball (such as those provided by githubs download-from-tag +# feature). Distribution tarballs (built by setup.py sdist) and build +# directories (produced by setup.py build) will contain a much shorter file +# that just contains the computed version number. + +# This file is released into the public domain. +# Generated by versioneer-0.28 +# https://github.com/python-versioneer/python-versioneer + +"""Git implementation of _version.py.""" + +import errno +import os +import re +import subprocess +import sys +from typing import Callable, Dict +import functools + + +def get_keywords(): + """Get the keywords needed to look up the version information.""" + # these strings will be replaced by git during git-archive. + # setup.py/versioneer.py will grep for the variable names, so they must + # each be defined on a line of their own. _version.py will just call + # get_keywords(). + git_refnames = "$Format:%d$" + git_full = "$Format:%H$" + git_date = "$Format:%ci$" + keywords = {"refnames": git_refnames, "full": git_full, "date": git_date} + return keywords + + +class VersioneerConfig: + """Container for Versioneer configuration parameters.""" + + +def get_config(): + """Create, populate and return the VersioneerConfig() object.""" + # these strings are filled in when 'setup.py versioneer' creates + # _version.py + cfg = VersioneerConfig() + cfg.VCS = "git" + cfg.style = "pep440" + cfg.tag_prefix = "" + cfg.parentdir_prefix = "statsplot-" + cfg.versionfile_source = "statsplot/_version.py" + cfg.verbose = False + return cfg + + +class NotThisMethod(Exception): + """Exception raised if a method is not valid for the current scenario.""" + + +LONG_VERSION_PY: Dict[str, str] = {} +HANDLERS: Dict[str, Dict[str, Callable]] = {} + + +def register_vcs_handler(vcs, method): # decorator + """Create decorator to mark a method as the handler of a VCS.""" + def decorate(f): + """Store f in HANDLERS[vcs][method].""" + if vcs not in HANDLERS: + HANDLERS[vcs] = {} + HANDLERS[vcs][method] = f + return f + return decorate + + +def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, + env=None): + """Call the given command(s).""" + assert isinstance(commands, list) + process = None + + popen_kwargs = {} + if sys.platform == "win32": + # This hides the console window if pythonw.exe is used + startupinfo = subprocess.STARTUPINFO() + startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW + popen_kwargs["startupinfo"] = startupinfo + + for command in commands: + try: + dispcmd = str([command] + args) + # remember shell=False, so use git.cmd on windows, not just git + process = subprocess.Popen([command] + args, cwd=cwd, env=env, + stdout=subprocess.PIPE, + stderr=(subprocess.PIPE if hide_stderr + else None), **popen_kwargs) + break + except OSError: + e = sys.exc_info()[1] + if e.errno == errno.ENOENT: + continue + if verbose: + print("unable to run %s" % dispcmd) + print(e) + return None, None + else: + if verbose: + print("unable to find command, tried %s" % (commands,)) + return None, None + stdout = process.communicate()[0].strip().decode() + if process.returncode != 0: + if verbose: + print("unable to run %s (error)" % dispcmd) + print("stdout was %s" % stdout) + return None, process.returncode + return stdout, process.returncode + + +def versions_from_parentdir(parentdir_prefix, root, verbose): + """Try to determine the version from the parent directory name. + + Source tarballs conventionally unpack into a directory that includes both + the project name and a version string. We will also support searching up + two directory levels for an appropriately named parent directory + """ + rootdirs = [] + + for _ in range(3): + dirname = os.path.basename(root) + if dirname.startswith(parentdir_prefix): + return {"version": dirname[len(parentdir_prefix):], + "full-revisionid": None, + "dirty": False, "error": None, "date": None} + rootdirs.append(root) + root = os.path.dirname(root) # up a level + + if verbose: + print("Tried directories %s but none started with prefix %s" % + (str(rootdirs), parentdir_prefix)) + raise NotThisMethod("rootdir doesn't start with parentdir_prefix") + + +@register_vcs_handler("git", "get_keywords") +def git_get_keywords(versionfile_abs): + """Extract version information from the given file.""" + # the code embedded in _version.py can just fetch the value of these + # keywords. When used from setup.py, we don't want to import _version.py, + # so we do it with a regexp instead. This function is not used from + # _version.py. + keywords = {} + try: + with open(versionfile_abs, "r") as fobj: + for line in fobj: + if line.strip().startswith("git_refnames ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["refnames"] = mo.group(1) + if line.strip().startswith("git_full ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["full"] = mo.group(1) + if line.strip().startswith("git_date ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["date"] = mo.group(1) + except OSError: + pass + return keywords + + +@register_vcs_handler("git", "keywords") +def git_versions_from_keywords(keywords, tag_prefix, verbose): + """Get version information from git keywords.""" + if "refnames" not in keywords: + raise NotThisMethod("Short version file found") + date = keywords.get("date") + if date is not None: + # Use only the last line. Previous lines may contain GPG signature + # information. + date = date.splitlines()[-1] + + # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant + # datestamp. However we prefer "%ci" (which expands to an "ISO-8601 + # -like" string, which we must then edit to make compliant), because + # it's been around since git-1.5.3, and it's too difficult to + # discover which version we're using, or to work around using an + # older one. + date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) + refnames = keywords["refnames"].strip() + if refnames.startswith("$Format"): + if verbose: + print("keywords are unexpanded, not using") + raise NotThisMethod("unexpanded keywords, not a git-archive tarball") + refs = {r.strip() for r in refnames.strip("()").split(",")} + # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of + # just "foo-1.0". If we see a "tag: " prefix, prefer those. + TAG = "tag: " + tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} + if not tags: + # Either we're using git < 1.8.3, or there really are no tags. We use + # a heuristic: assume all version tags have a digit. The old git %d + # expansion behaves like git log --decorate=short and strips out the + # refs/heads/ and refs/tags/ prefixes that would let us distinguish + # between branches and tags. By ignoring refnames without digits, we + # filter out many common branch names like "release" and + # "stabilization", as well as "HEAD" and "master". + tags = {r for r in refs if re.search(r'\d', r)} + if verbose: + print("discarding '%s', no digits" % ",".join(refs - tags)) + if verbose: + print("likely tags: %s" % ",".join(sorted(tags))) + for ref in sorted(tags): + # sorting will prefer e.g. "2.0" over "2.0rc1" + if ref.startswith(tag_prefix): + r = ref[len(tag_prefix):] + # Filter out refs that exactly match prefix or that don't start + # with a number once the prefix is stripped (mostly a concern + # when prefix is '') + if not re.match(r'\d', r): + continue + if verbose: + print("picking %s" % r) + return {"version": r, + "full-revisionid": keywords["full"].strip(), + "dirty": False, "error": None, + "date": date} + # no suitable tags, so version is "0+unknown", but full hex is still there + if verbose: + print("no suitable tags, using unknown + full revision id") + return {"version": "0+unknown", + "full-revisionid": keywords["full"].strip(), + "dirty": False, "error": "no suitable tags", "date": None} + + +@register_vcs_handler("git", "pieces_from_vcs") +def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): + """Get version from 'git describe' in the root of the source tree. + + This only gets called if the git-archive 'subst' keywords were *not* + expanded, and _version.py hasn't already been rewritten with a short + version string, meaning we're inside a checked out source tree. + """ + GITS = ["git"] + if sys.platform == "win32": + GITS = ["git.cmd", "git.exe"] + + # GIT_DIR can interfere with correct operation of Versioneer. + # It may be intended to be passed to the Versioneer-versioned project, + # but that should not change where we get our version from. + env = os.environ.copy() + env.pop("GIT_DIR", None) + runner = functools.partial(runner, env=env) + + _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, + hide_stderr=not verbose) + if rc != 0: + if verbose: + print("Directory %s not under git control" % root) + raise NotThisMethod("'git rev-parse --git-dir' returned error") + + # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] + # if there isn't one, this yields HEX[-dirty] (no NUM) + describe_out, rc = runner(GITS, [ + "describe", "--tags", "--dirty", "--always", "--long", + "--match", f"{tag_prefix}[[:digit:]]*" + ], cwd=root) + # --long was added in git-1.5.5 + if describe_out is None: + raise NotThisMethod("'git describe' failed") + describe_out = describe_out.strip() + full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) + if full_out is None: + raise NotThisMethod("'git rev-parse' failed") + full_out = full_out.strip() + + pieces = {} + pieces["long"] = full_out + pieces["short"] = full_out[:7] # maybe improved later + pieces["error"] = None + + branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], + cwd=root) + # --abbrev-ref was added in git-1.6.3 + if rc != 0 or branch_name is None: + raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") + branch_name = branch_name.strip() + + if branch_name == "HEAD": + # If we aren't exactly on a branch, pick a branch which represents + # the current commit. If all else fails, we are on a branchless + # commit. + branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) + # --contains was added in git-1.5.4 + if rc != 0 or branches is None: + raise NotThisMethod("'git branch --contains' returned error") + branches = branches.split("\n") + + # Remove the first line if we're running detached + if "(" in branches[0]: + branches.pop(0) + + # Strip off the leading "* " from the list of branches. + branches = [branch[2:] for branch in branches] + if "master" in branches: + branch_name = "master" + elif not branches: + branch_name = None + else: + # Pick the first branch that is returned. Good or bad. + branch_name = branches[0] + + pieces["branch"] = branch_name + + # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] + # TAG might have hyphens. + git_describe = describe_out + + # look for -dirty suffix + dirty = git_describe.endswith("-dirty") + pieces["dirty"] = dirty + if dirty: + git_describe = git_describe[:git_describe.rindex("-dirty")] + + # now we have TAG-NUM-gHEX or HEX + + if "-" in git_describe: + # TAG-NUM-gHEX + mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) + if not mo: + # unparsable. Maybe git-describe is misbehaving? + pieces["error"] = ("unable to parse git-describe output: '%s'" + % describe_out) + return pieces + + # tag + full_tag = mo.group(1) + if not full_tag.startswith(tag_prefix): + if verbose: + fmt = "tag '%s' doesn't start with prefix '%s'" + print(fmt % (full_tag, tag_prefix)) + pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" + % (full_tag, tag_prefix)) + return pieces + pieces["closest-tag"] = full_tag[len(tag_prefix):] + + # distance: number of commits since tag + pieces["distance"] = int(mo.group(2)) + + # commit: short hex revision ID + pieces["short"] = mo.group(3) + + else: + # HEX: no tags + pieces["closest-tag"] = None + out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root) + pieces["distance"] = len(out.split()) # total number of commits + + # commit date: see ISO-8601 comment in git_versions_from_keywords() + date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip() + # Use only the last line. Previous lines may contain GPG signature + # information. + date = date.splitlines()[-1] + pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) + + return pieces + + +def plus_or_dot(pieces): + """Return a + if we don't already have one, else return a .""" + if "+" in pieces.get("closest-tag", ""): + return "." + return "+" + + +def render_pep440(pieces): + """Build up version string, with post-release "local version identifier". + + Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you + get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty + + Exceptions: + 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += plus_or_dot(pieces) + rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0+untagged.%d.g%s" % (pieces["distance"], + pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def render_pep440_branch(pieces): + """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . + + The ".dev0" means not master branch. Note that .dev0 sorts backwards + (a feature branch will appear "older" than the master branch). + + Exceptions: + 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0" + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += "+untagged.%d.g%s" % (pieces["distance"], + pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def pep440_split_post(ver): + """Split pep440 version string at the post-release segment. + + Returns the release segments before the post-release and the + post-release version number (or -1 if no post-release segment is present). + """ + vc = str.split(ver, ".post") + return vc[0], int(vc[1] or 0) if len(vc) == 2 else None + + +def render_pep440_pre(pieces): + """TAG[.postN.devDISTANCE] -- No -dirty. + + Exceptions: + 1: no tags. 0.post0.devDISTANCE + """ + if pieces["closest-tag"]: + if pieces["distance"]: + # update the post release segment + tag_version, post_version = pep440_split_post(pieces["closest-tag"]) + rendered = tag_version + if post_version is not None: + rendered += ".post%d.dev%d" % (post_version + 1, pieces["distance"]) + else: + rendered += ".post0.dev%d" % (pieces["distance"]) + else: + # no commits, use the tag as the version + rendered = pieces["closest-tag"] + else: + # exception #1 + rendered = "0.post0.dev%d" % pieces["distance"] + return rendered + + +def render_pep440_post(pieces): + """TAG[.postDISTANCE[.dev0]+gHEX] . + + The ".dev0" means dirty. Note that .dev0 sorts backwards + (a dirty tree will appear "older" than the corresponding clean one), + but you shouldn't be releasing software with -dirty anyways. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%d" % pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "g%s" % pieces["short"] + else: + # exception #1 + rendered = "0.post%d" % pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + rendered += "+g%s" % pieces["short"] + return rendered + + +def render_pep440_post_branch(pieces): + """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . + + The ".dev0" means not master branch. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%d" % pieces["distance"] + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "g%s" % pieces["short"] + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0.post%d" % pieces["distance"] + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += "+g%s" % pieces["short"] + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def render_pep440_old(pieces): + """TAG[.postDISTANCE[.dev0]] . + + The ".dev0" means dirty. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%d" % pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + else: + # exception #1 + rendered = "0.post%d" % pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + return rendered + + +def render_git_describe(pieces): + """TAG[-DISTANCE-gHEX][-dirty]. + + Like 'git describe --tags --dirty --always'. + + Exceptions: + 1: no tags. HEX[-dirty] (note: no 'g' prefix) + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"]: + rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) + else: + # exception #1 + rendered = pieces["short"] + if pieces["dirty"]: + rendered += "-dirty" + return rendered + + +def render_git_describe_long(pieces): + """TAG-DISTANCE-gHEX[-dirty]. + + Like 'git describe --tags --dirty --always -long'. + The distance/hash is unconditional. + + Exceptions: + 1: no tags. HEX[-dirty] (note: no 'g' prefix) + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) + else: + # exception #1 + rendered = pieces["short"] + if pieces["dirty"]: + rendered += "-dirty" + return rendered + + +def render(pieces, style): + """Render the given version pieces into the requested style.""" + if pieces["error"]: + return {"version": "unknown", + "full-revisionid": pieces.get("long"), + "dirty": None, + "error": pieces["error"], + "date": None} + + if not style or style == "default": + style = "pep440" # the default + + if style == "pep440": + rendered = render_pep440(pieces) + elif style == "pep440-branch": + rendered = render_pep440_branch(pieces) + elif style == "pep440-pre": + rendered = render_pep440_pre(pieces) + elif style == "pep440-post": + rendered = render_pep440_post(pieces) + elif style == "pep440-post-branch": + rendered = render_pep440_post_branch(pieces) + elif style == "pep440-old": + rendered = render_pep440_old(pieces) + elif style == "git-describe": + rendered = render_git_describe(pieces) + elif style == "git-describe-long": + rendered = render_git_describe_long(pieces) + else: + raise ValueError("unknown style '%s'" % style) + + return {"version": rendered, "full-revisionid": pieces["long"], + "dirty": pieces["dirty"], "error": None, + "date": pieces.get("date")} + + +def get_versions(): + """Get version information or return default if unable to do so.""" + # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have + # __file__, we can work backwards from there to the root. Some + # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which + # case we can only use expanded keywords. + + cfg = get_config() + verbose = cfg.verbose + + try: + return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, + verbose) + except NotThisMethod: + pass + + try: + root = os.path.realpath(__file__) + # versionfile_source is the relative path from the top of the source + # tree (where the .git directory might live) to this file. Invert + # this to find the root from __file__. + for _ in cfg.versionfile_source.split('/'): + root = os.path.dirname(root) + except NameError: + return {"version": "0+unknown", "full-revisionid": None, + "dirty": None, + "error": "unable to find root of source tree", + "date": None} + + try: + pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) + return render(pieces, cfg.style) + except NotThisMethod: + pass + + try: + if cfg.parentdir_prefix: + return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) + except NotThisMethod: + pass + + return {"version": "0+unknown", "full-revisionid": None, + "dirty": None, + "error": "unable to compute version", "date": None} diff --git a/statsplot/dimred.py b/statsplot/dimred.py index 1af3de4..9ea1800 100644 --- a/statsplot/dimred.py +++ b/statsplot/dimred.py @@ -1,60 +1,23 @@ -from sklearn.decomposition import PCA, SparsePCA +from logging import getLogger + +logger = getLogger("__name__") + +from sklearn.decomposition import PCA import pandas as pd import numpy as np import matplotlib.pylab as plt import warnings - +from .plot import annotate_points, _def_label_alignment import seaborn as sns -def label_points_(data, ax, max_labels=10): - - assert data.shape[1] == 2, "Expect data n x 2" - - sample_names = data.index - - N_samples = len(sample_names) - if N_samples < max_labels: - - for i in range(N_samples): - ax.annotate( - s=sample_names[i], - xy=(data.iloc[i, 0], data.iloc[i, 1]), - xytext=(0, 10), - textcoords="offset points", - ) - -def _def_label_alignment(x,y): - - ha="center" - - if abs(x)> abs(y): - if x>0: - ha="left" - else: - ha="right" - - va="center" - - if abs(y)> abs(x): - if y>0: - va="bottom" - else: - va="top" - - return {'ha':ha,'va':va} - - - - - from matplotlib.patches import Ellipse import matplotlib.transforms as transforms from scipy.stats import chi2 -def confidence_ellipse(x, y, ax,ci=0.95, color='red',facecolor='none', **kwargs): +def confidence_ellipse(x, y, ax, ci=0.95, color="red", facecolor="none", **kwargs): """ Create a plot of the covariance confidence ellipse of *x* and *y*. @@ -78,7 +41,7 @@ def confidence_ellipse(x, y, ax,ci=0.95, color='red',facecolor='none', **kwargs) """ if ax is None: - ax=plt.gca() + ax = plt.gca() if len(x) < 4: raise Exception("need more than 3 data points") @@ -87,16 +50,21 @@ def confidence_ellipse(x, y, ax,ci=0.95, color='red',facecolor='none', **kwargs) raise ValueError("x and y must be the same size") cov = np.cov(x, y) - pearson = cov[0, 1]/np.sqrt(cov[0, 0] * cov[1, 1]) + pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1]) # Using a special case to obtain the eigenvalues of this # two-dimensionl dataset. ell_radius_x = np.sqrt(1 + pearson) ell_radius_y = np.sqrt(1 - pearson) - ellipse = Ellipse((0, 0), width=ell_radius_x * 2, height=ell_radius_y * 2, - facecolor=facecolor,edgecolor=color ,**kwargs) - + ellipse = Ellipse( + (0, 0), + width=ell_radius_x * 2, + height=ell_radius_y * 2, + facecolor=facecolor, + edgecolor=color, + **kwargs, + ) - s= chi2.ppf(ci,2) + s = chi2.ppf(ci, 2) # Calculating the stdandard deviation of x from # the squareroot of the variance and multiplying @@ -108,33 +76,72 @@ def confidence_ellipse(x, y, ax,ci=0.95, color='red',facecolor='none', **kwargs) scale_y = np.sqrt(cov[1, 1] * s) mean_y = np.mean(y) - transf = transforms.Affine2D() \ - .rotate_deg(45) \ - .scale(scale_x, scale_y) \ + transf = ( + transforms.Affine2D() + .rotate_deg(45) + .scale(scale_x, scale_y) .translate(mean_x, mean_y) + ) ellipse.set_transform(transf + ax.transData) return ax.add_patch(ellipse) +def plot_confidence_ellipses( + x, + y, + groups, + order=None, + colors=None, + confidence_interval=0.95, + facecolor="none", + ax=None, + **kwargs, +): + x = np.array(x) + y = np.array(y) + + if ax is None: + ax = plt.subplot(111) + if order is None: + order = np.unique(groups) + + if colors is None: + colors = sns.color_palette(n_colors=len(order)) + + if kwargs is None: + kwargs = {} + + for n, g in enumerate(order): + confidence_ellipse( + x[groups == g], + y=y[groups == g], + ax=ax, + color=colors[n], + ci=confidence_interval, + facecolor=facecolor, + **kwargs, + ) + + return ax class DimRed: def __init__( - self, data, decomposition=PCA, transformation=None, n_components=None, **kargs + self, data, method=PCA, transformation=None, n_components=None, **kargs ): - if n_components is None: - n_components = data.shape[0] - if data.shape[0] > data.shape[1]: - print( + warnings.warn( "you don't need to reduce dimensionality or your dataset is transposed." ) - self.decomposition = decomposition(n_components=n_components, **kargs) + if n_components is None: + n_components = min(data.shape) + + self.decomposition = method(n_components=n_components, **kargs) self.rawdata = data @@ -147,7 +154,7 @@ def __init__( else: - self.data_ = data.applymap(transformation) + self.data_ = data.map(transformation) Xt = self.decomposition.fit_transform(self.data_) @@ -200,9 +207,26 @@ def plot_explained_variance_ratio(self, n_components=25, **kwargs): explained_variance_ratio = self.decomposition.explained_variance_ratio_ n = min(n_components, len(explained_variance_ratio)) - return plt.bar(np.arange(n), explained_variance_ratio[:n], **kwargs) + plt.bar(np.arange(n), explained_variance_ratio[:n], **kwargs) - def plot_Components_2D(self, components=(1, 2), ax=None,plot_ellipse=False,hue=None, **scatter_args): + ax = plt.gca() + ax.set_xlabel("Principal Component") + ax.set_ylabel("Explained Variance Ratio") + + return ax + + def plot_components( + self, + components=(1, 2), + ax=None, + groups=None, + plot_ellipse=False, + label_points=False, + confidence_interval=0.95, + order_groups=None, + colors=None, + **scatter_args, + ): components = list(components) assert len(components) == 2, "expect two components" @@ -210,47 +234,58 @@ def plot_Components_2D(self, components=(1, 2), ax=None,plot_ellipse=False,hue=N if ax is None: ax = plt.subplot(111) - x,y = self.transformed_data[components[0]], self.transformed_data[components[1]] + if (groups is not None) and (order_groups is None): + order_groups = np.unique(groups) + + x, y = ( + self.transformed_data[components[0]], + self.transformed_data[components[1]], + ) + + overwritten_seaborn_kargs = { + "hue": "groups", + "hue_order": "order_groups", + "palette": "colros", + } + + for k in overwritten_seaborn_kargs: + if k in scatter_args: + raise ValueError( + f"You provided `{k}` as keyword. However `{k}` is overwritten by the `{overwritten_seaborn_kargs[k]}` argument." + ) sns.scatterplot( - x=x,y=y, + x=x, + y=y, ax=ax, - hue=hue, + hue=groups, + hue_order=order_groups, + palette=colors, **scatter_args, ) ax.axis("equal") self.set_axes_labels_(ax, components) - label_points_(self.transformed_data[components], ax) - - # if plot_ellipse: - - # if hue is None: - - # raise Exception("hue is required for plotting ellipse") - - - - # if "hue_order" not in scatter_args: - # scatter_args["hue_order"] = np.unique(hue) - - # if "palette" not in scatter_args: - # scatter_args["palette"] = sns.color_palette() - - # palette = colors - # #palette= sns.color_palette() - - # for n,group in enumerate(hue_order): - # confidence_ellipse(x.loc[hue==group],y=y.loc[hue==group],ax=ax,color= palette[n]) - - - - - + if label_points: + annotate_points(data=self.transformed_data[components], ax=ax) + + if plot_ellipse: + if groups is None: + raise Exception("`groups`` is required for plotting confidence ellipse") + + plot_confidence_ellipses( + x, + y, + groups, + order=order_groups, + colors=colors, + confidence_interval=confidence_interval, + ax=ax, + ) return ax - def plot_Loadings_2D(self, components=(1, 2), ax=None, **scatter_args): + def plot_loadings(self, components=(1, 2), ax=None, **scatter_args): if ax is None: ax = plt.subplot(111) @@ -269,11 +304,11 @@ def plot_Loadings_2D(self, components=(1, 2), ax=None, **scatter_args): return ax - def _detect_which_arrows_to_vizualize(self,loadings, n_arrows=None): + def _detect_which_arrows_to_vizualize(self, loadings, n_arrows=None): assert loadings.shape[0] == 2 - radius = np.sqrt(sum(loadings.values ** 2)) + radius = np.sqrt(sum(loadings.values**2)) radius = pd.Series(radius, self.components.columns).sort_values(ascending=False) @@ -309,16 +344,11 @@ def _detect_which_arrows_to_vizualize(self,loadings, n_arrows=None): return list(radius.index[:n_arrows]) - - - - - - def biplot( + def plot_biplot( self, components=[1, 2], n_arrows=None, scale_factor=None, labels=None, **kws ): - ax = self.plot_Components_2D(**kws) + ax = self.plot_components(**kws) if scale_factor is None: scale_factor = max( @@ -332,6 +362,7 @@ def biplot( loadings, n_arrows=n_arrows ) + Texts = [] for c in interesting_components: x, y = loadings[c] * scale_factor @@ -343,7 +374,23 @@ def biplot( else: label = labels[c] - ax.text(x * 1.3, y * 1.3, label, color="k", **_def_label_alignment(x,y)) + Texts.append( + ax.text( + x * 1.3, y * 1.3, label, color="k", **_def_label_alignment(x, y) + ) + ) + + try: + from adjustText import adjust_text + + adjust_text(Texts, x=[0], y=[0], ax=ax) + + except ImportError: + logger.warning( + "Want to optimize label placement but adjustText is not installed." + "This will inevitabely lead to overlapping labels." + "You need to install it: `conda install -c conda-forge adjusttext` " + ) return ax @@ -385,6 +432,5 @@ def altair_plot2D(data, variables=None, **kws): return chart - except ImportError: warnings.warn("Altair is not installed. Interactive plots are not available") diff --git a/statsplot/example_iris.py b/statsplot/example_iris.py new file mode 100644 index 0000000..6994718 --- /dev/null +++ b/statsplot/example_iris.py @@ -0,0 +1,12 @@ +import seaborn as sns; sns.set_theme(color_codes=True) +iris = sns.load_dataset("iris") +species = iris.pop("species") +g = sns.clustermap(iris) + + +g = sns.clustermap(iris, + figsize=(7, 5), + row_cluster=False, + dendrogram_ratio=(.1, .2), + cbar_pos=(0, .2, .03, .4) + ) \ No newline at end of file diff --git a/statsplot/plot.py b/statsplot/plot.py index 2dc76b2..893536c 100644 --- a/statsplot/plot.py +++ b/statsplot/plot.py @@ -1,59 +1,206 @@ +from logging import getLogger +from textwrap import dedent + +logger = getLogger("__name__") + import matplotlib.pyplot as plt import seaborn as sns -from numpy import unique, log10,abs +from numpy import unique, log10, abs import pandas as pd -from .stats import calculate_stats +from .stats import calculate_stats from .siglabels import plot_all_sig_labels -def vulcanoplot(p_values,effect,hue = None,threshold_p = 0.05, figsize=(4,4),**kws): - f= plt.figure(figsize=figsize) +def _def_label_alignment(x, y): + + ha = "center" + + if abs(x) > abs(y): + if x > 0: + ha = "left" + else: + ha = "right" + + va = "center" + + if abs(y) > abs(x): + if y > 0: + va = "bottom" + else: + va = "top" + + return {"ha": ha, "va": va} + + +def annotate_points( + *, + data=None, + x=None, + y=None, + labels=None, + ax=None, + max_labels=50, + arrowprops=dict(arrowstyle="-", color="k", lw=0.5), +): + + if ax is None: + ax = plt.gca() + + # parse data + if data is None: + assert ( + x is not None and y is not None + ), "Either data or x and y should be provided" + else: + if x is None: + assert data.shape[1] == 2, "Expect data n x 2" + x = data.iloc[:, 0] + elif type(x) == str: + x = data.loc[:, x] + + if y is None: + assert data.shape[1] == 2, "Expect data n x 2" + y = data.iloc[:, 1] + elif type(y) == str: + y = data.loc[:, y] + + if labels is None: + labels = data.index + + N_labels = len(labels) + assert len(x) == N_labels, "x and labels should have the same length" + assert len(y) == N_labels, "y and labels should have the same length" + + if N_labels > max_labels: + + logger.error( + f"You want to label more than {max_labels} points." + " This is would overcroud the plot. " + ) + + else: + + Texts = [] + for i in range(N_labels): + + Texts.append( + plt.text(x[i], y[i], labels[i], **_def_label_alignment(x[i], y[i])) + ) + + try: + from adjustText import adjust_text + + adjust_text(Texts, ax=ax, arrowprops=arrowprops) + + except ImportError: + logger.warning( + "Want to optimize label placement but adjustText is not installed." + "This will inevitabely lead to overlapping labels." + "You need to install it: `conda install -c conda-forge adjusttext` " + ) + + +def vulcanoplot( + p_values, + effect, + hue=None, + labels=None, + threshold_p=0.05, + figsize=(6, 6), + label_points="auto", + max_labels=5, + **kws, +): + f = plt.figure(figsize=figsize) - assert effect.ndim==1, "effect should be one dimensional" - assert p_values.ndim==1, "p_values should be one dimensional" + assert effect.ndim == 1, "effect should be one dimensional" + assert p_values.ndim == 1, "p_values should be one dimensional" - - logPvalues = - log10( p_values.astype(float) ) - logPvalues.name= "$-\log(P)$" + logPvalues = -log10(p_values.astype(float)) + logPvalues.name = "$-\log(P)$" - threshold = - log10(threshold_p) + threshold = -log10(threshold_p) if hue is None: - hue= pd.Series("Significant", index= logPvalues.index) + hue = pd.Series("Significant", index=logPvalues.index) + ax = sns.scatterplot( + y=logPvalues.loc[logPvalues > threshold], + x=effect, + hue=hue.loc[logPvalues > threshold], + ) - ax=sns.scatterplot(y= logPvalues.loc[logPvalues> threshold] ,x= effect, - hue= hue.loc[logPvalues> threshold]) + ax = sns.scatterplot( + y=logPvalues.loc[logPvalues <= threshold], + x=effect, + color="grey", + marker=".", + label="not significant", + ) - ax=sns.scatterplot(y= logPvalues.loc[logPvalues<= threshold] ,x= effect, - color='grey',marker='.', - label='not significant' - ) + if (label_points == "auto") or (label_points == True): + label_data = pd.concat([effect, logPvalues], axis=1) + label_data.columns = ["effect", "logP"] + if labels is not None: + label_data["Labels"] = labels + if label_points == "auto": + # select only significant points + label_data = label_data.query("logP > @threshold") - ax.legend(bbox_to_anchor=(1,1)) - ax_lim= abs(ax.get_xlim()).max() - ax.set_xlim([-ax_lim,ax_lim]) + max_labels = min(max_labels, label_data.shape[0]) - if '_vs_' in effect.name: - g1,g2 = effect.name.split('_vs_') + if max_labels > 0: + + # calculate radius to detect outermost labels based on effect and logP + effect_range = abs(label_data.effect.max() - label_data.effect.min()) + logP_range = label_data.logP.max() - label_data.logP.min() + # subtract minimum log P value this changes the radius if only significat points are selected + min_log_p = label_data.logP.min() + + label_data.eval( + "radius = (abs(effect)/ @effect_range)**2 + ((logP - @min_log_p )/@logP_range )**2 ", + inplace=True, + ) + + label_data = label_data.sort_values("radius", ascending=False) + + label_data = label_data.iloc[:max_labels] + + annotate_points( + data=label_data.iloc[:, :2], + labels=label_data.Labels, + max_labels=max_labels, + ) + + # legend + ax.legend(bbox_to_anchor=(1, 1), loc="upper left", fontsize=10) + # equalize axis + ax_lim = abs(ax.get_xlim()).max() + ax.set_xlim([-ax_lim, ax_lim]) + + if "_vs_" in effect.name: + g1, g2 = effect.name.split("_vs_") + + ax.annotate(g1, (ax_lim * 0.9, 0), ha="right") + ax.annotate(g2, (-ax_lim * 0.9, 0), ha="left") + + +# TODO: handle unaligned input. - ax.annotate(g1, (ax_lim*0.9,0), ha='right') - ax.annotate(g2, (-ax_lim*0.9,0), ha='left') - - def statsplot( variable, test_variable, - data = None, + data=None, order_test=None, grouping_variable=None, order_grouping=None, + show_dots=True, box_params=None, swarm_params=None, labelkws=None, @@ -61,59 +208,93 @@ def statsplot( palette=None, p_values=None, test="ttest_ind", + show_not_significant=False, ax=None, ): + """Main function for plotting statistical tests.""" + + default_box_params = {} + if show_dots: + # Dot overlays already show sample-level values; hide boxplot outliers. + default_box_params.update(dict(showfliers=False, fliersize=0)) + default_swarm_params = {} if ax is None: ax = plt.subplot(111) - - if type(variable) == str: assert data is not None, "If variable is a string, data must be provided" variable = data[variable] - + if type(test_variable) == str: assert data is not None, "If test_variable is a string, data must be provided" test_variable = data[test_variable] - + params = dict(y=variable, ax=ax) - if order_test is None: order_test = unique(test_variable) + else: + assert set(order_test) == set( + test_variable + ), "test_variable has more values than order test. order_test cannot be used to subset the data. Do this prior." # use subgrouping if grouping_variable is None: params.update(dict(x=test_variable, order=order_test)) + default_swarm_params.update(dict(color="k")) else: + if type(grouping_variable) == str: + assert ( + data is not None + ), "If grouping_variable is a string, data must be provided" + grouping_variable = data[grouping_variable] + if order_grouping is None: order_grouping = unique(grouping_variable) + else: - params.update( - dict( - x=grouping_variable, - order=order_grouping, - hue=test_variable, - hue_order=order_test, - ) + assert set(order_grouping) >= set( + grouping_variable + ), "grouping_variable has more values than order_grouping. order_grouping cannot be used to subset the data. Do this prior." + + params.update(dict(x=grouping_variable, order=order_grouping)) + + default_swarm_params.update( + dict(dodge=True, palette="dark:k", hue=test_variable, hue_order=order_test) ) + # apply new keyword params if box_params is None: box_params = {} + box_params = {**default_box_params, **box_params} + if swarm_params is None: swarm_params = {} - - sns.boxplot(palette=palette, **params, **box_params) - - legend = ax.get_legend_handles_labels() - - sns.swarmplot(**params, color="k", dodge=True, **swarm_params) - - if grouping_variable is not None: - ax.legend(*legend, bbox_to_anchor=(1, 1)) + swarm_params = {**default_swarm_params, **swarm_params} + + # Ensure dodge-capable swarm defaults stay intact when users pass partial kwargs. + if show_dots and (grouping_variable is not None): + swarm_params.setdefault("dodge", True) + if swarm_params.get("dodge"): + swarm_params.setdefault("hue", test_variable) + swarm_params.setdefault("hue_order", order_test) + swarm_params.setdefault("palette", "dark:k") + + sns.boxplot( + palette=palette, + legend=grouping_variable is not None, + hue_order=order_test, + hue=test_variable, + **params, + **box_params, + ) + + if show_dots: + logger.debug(f"Showing dots with params: {dict(**params, **swarm_params)}") + sns.swarmplot(legend=False, **params, **swarm_params) # Statistics if p_values is None: @@ -131,8 +312,88 @@ def statsplot( if labelkws is None: labelkws = dict(deltay="auto") - - plot_all_sig_labels(p_values, order_test, order_grouping, ax=ax, **labelkws) + if show_not_significant: + labelkws.update(use_stars=False) + + plot_all_sig_labels( + p_values, + order_test, + order_grouping, + show_ns=show_not_significant, + ax=ax, + **labelkws, + ) return ax, p_values + +statsplot.__doc__ = dedent( + """\ + Plot Boxplot with statistical significance. + + Parameters + ---------- + variable : str or pandas.Series + The variable to be tested. + test_variable : str or pandas.Series + The variable to be tested against. + data : pandas.DataFrame + The dataframe containing the variable, test_variable and grouping_variable. + order_test : list + The order of the test_variable. + grouping_variable : str or pandas.Series + The variable to be used for grouping. + order_grouping : list + The order of the grouping_variable. + show_dots : bool + If True, show dots on top of boxplot. + box_params : dict + Parameters for the boxplot. + swarm_params : dict + Parameters for the swarmplot. + labelkws : dict + Parameters for the labels. + stats_kws : dict + Parameters for the statistical test. + palette : list + The color palette. + p_values : pandas.Series + The p-values of the statistical test. + test : str + The statistical test. + show_not_significant : bool + If True, show not significant labels. + ax : matplotlib.axes.Axes + The axis to plot on. + + Returns + ------- + ax : matplotlib.axes.Axes + The axis with the plot. + p_values : pandas.Series + The p-values of the statistical test. + + Examples + -------- + + + .. plot:: + :context: close-figs + :format: doctest + :include-source: True + + >>> import seaborn as sns + >>> import statsplot as stp + >>> iris = sns.load_dataset("iris") + >>> ax,stats = stp.statsplot(data=iris, variable="sepal_length", test_variable="species") + >>> print(stats) + versicolor_vs_setosa 8.985235e-18 + virginica_vs_setosa 6.892546e-28 + virginica_vs_versicolor 1.724856e-07 + Name: sepal_length, dtype: float64 + >>> plt.show() + + + + """ +) diff --git a/statsplot/siglabels.py b/statsplot/siglabels.py index 6b2f959..1ecbe4d 100644 --- a/statsplot/siglabels.py +++ b/statsplot/siglabels.py @@ -62,7 +62,7 @@ def __plot_sig_labels_hue( show_ns=True, width=0.8, use_stars=True, - labelkws=None, + **labelkws, ): assert type(P_values) == pd.Series, "P values should be a series" @@ -72,13 +72,10 @@ def __plot_sig_labels_hue( P_values = P_values.apply(format_p_value, use_stars=use_stars) - if labelkws is None: - labelkws = {} - # start with y0 y = y0 - for idx, text in P_values.iteritems(): + for idx, text in P_values.items(): def calculate_hue_offset(group, order): return (order.index(group) - len(order) * 0.5 + 0.5) / len(order) * width @@ -100,7 +97,7 @@ def ___plot_sig_labels_xaxis( show_ns=True, width=0.8, use_stars=True, - labelkws=None, + **labelkws, ): assert type(P_values) == pd.Series, "P values should be a series" @@ -114,11 +111,8 @@ def ___plot_sig_labels_xaxis( P_values = P_values.apply(format_p_value, use_stars=use_stars) - if labelkws is None: - labelkws = {} - y = y0 - for idx, text in P_values.iteritems(): + for idx, text in P_values.items(): def calculate_x_offset(group, order): return order.index(group) @@ -139,9 +133,8 @@ def plot_all_sig_labels( y0="auto", deltay="auto", ax=None, - **kws + **kws, ): - """""" # define y0 and deltay @@ -177,5 +170,5 @@ def plot_all_sig_labels( deltay=deltay, y0=y0, show_ns=show_ns, - **kws + **kws, ) diff --git a/statsplot/stats.py b/statsplot/stats.py index 5d66a14..2ce1e31 100644 --- a/statsplot/stats.py +++ b/statsplot/stats.py @@ -1,25 +1,28 @@ import pandas as pd import numpy as np from scipy import stats -from xarray import corr + import logging + logger = logging.getLogger("statsplot") -def correct_pvalues_for_multiple_testing(p_values, correction_type="Benjamini-Hochberg"): +def correct_pvalues_for_multiple_testing( + p_values, correction_type="Benjamini-Hochberg" +): """ correction_type: one of "Bonferroni", "Bonferroni-Holm", "Benjamini-Hochberg" consistent with R - print correct_pvalues_for_multiple_testing([0.0, 0.01, 0.029, 0.03, 0.031, 0.05, 0.069, 0.07, 0.071, 0.09, 0.1]) """ - from numpy import array, empty, isnan,where + from numpy import array, empty, isnan, where # remove na values convert to array, store indexes of non NA - p_values_with_nan = array(p_values,dtype=float) + p_values_with_nan = array(p_values, dtype=float) - not_na_positions= where(~ isnan(p_values_with_nan))[0] + not_na_positions = where(~isnan(p_values_with_nan))[0] - pvalues = p_values_with_nan[ not_na_positions] + pvalues = p_values_with_nan[not_na_positions] # sort p vlaues and prepare unsort index sort_index = np.argsort(pvalues) @@ -58,12 +61,13 @@ def correct_pvalues_for_multiple_testing(p_values, correction_type="Benjamini-Ho # add NAn if present - if len(not_na_positions)< len(p_values_with_nan): - logger.warn(f"{len(p_values_with_nan) - len(not_na_positions)} p values are NA, I don't take them into account") - + if len(not_na_positions) < len(p_values_with_nan): + logger.warn( + f"{len(p_values_with_nan) - len(not_na_positions)} p values are NA, I don't take them into account" + ) corrected_p_values_wiht_na = np.empty_like(p_values_with_nan) * np.nan - corrected_p_values_wiht_na[ not_na_positions] = new_pvalues + corrected_p_values_wiht_na[not_na_positions] = new_pvalues return corrected_p_values_wiht_na @@ -73,6 +77,7 @@ def __stats_test_all_on_once(values1, values2, test, **test_kws): res = test(values1, values2, **test_kws) ResultsDB["Statistic"] = res.statistic ResultsDB["Pvalue"] = res.pvalue + return ResultsDB @@ -105,9 +110,9 @@ def two_group_test( test_kws=None, correct_for_multiple_testing=True, ): - """test: a parwise statistical test found in scipy e.g ['mannwhitneyu','ttest_ind'] - or a function wich takes two argumens. Additional keyword arguments can be specified by test_kws""" + or a function wich takes two argumens. Additional keyword arguments can be specified by test_kws + """ # Define test if test_kws is None: @@ -142,24 +147,20 @@ def two_group_test( data = pd.DataFrame(data) - - Groups = np.unique(test_variable) # min value for logFC caluclation min_value = data.min().min() - if min_value ==0: + if min_value == 0: - log_delta = data.values[data>0].min() * 0.65 - elif min_value >0: - log_delta =0 + log_delta = data.values[data > 0].min() * 0.65 + elif min_value > 0: + log_delta = 0 else: logger.info("lowest value is negative I don't calculate log2 Fold change") - - if ref_group is not None: assert ref_group in Groups, "ref_group: {} is not in the groups: {}".format( ref_group, Groups @@ -189,25 +190,22 @@ def two_group_test( Pairwise_comp = Test(values1, values2) Pairwise_comp["median_diff"] = values2.median() - values1.median() + Pairwise_comp["mean_diff"] = values2.mean() - values1.mean() + Pairwise_comp["Median1"] = values1.median() + Pairwise_comp["Median2"] = values2.median() - - if min_value >=0: - Pairwise_comp["log2FC"] = np.log2(values2.mean() + log_delta) - np.log2(values1.mean() + log_delta ) - - + if min_value >= 0: + Pairwise_comp["log2FC"] = np.log2(values2.mean() + log_delta) - np.log2( + values1.mean() + log_delta + ) if correct_for_multiple_testing: - # what to do when some P values are Nan - - Pairwise_comp["pBH"] = Pairwise_comp[["Pvalue"]]\ - .apply( - correct_pvalues_for_multiple_testing, - axis=0, - correction_type="Benjamini-Hochberg", - ) - - + Pairwise_comp["pBH"] = Pairwise_comp[["Pvalue"]].apply( + correct_pvalues_for_multiple_testing, + axis=0, + correction_type="Benjamini-Hochberg", + ) Results[group2 + "_vs_" + group1] = Pairwise_comp @@ -236,7 +234,6 @@ def calculate_stats( test="ttest_ind", **test_kws, ): - """Calculate pairewise statistical tests optioonally grouped by a grouping variable""" kws = dict( diff --git a/statsplot/statstable.py b/statsplot/statstable.py index 1542820..9b511f8 100644 --- a/statsplot/statstable.py +++ b/statsplot/statstable.py @@ -1,12 +1,8 @@ -import imp import logging -from matplotlib.pyplot import violinplot - logger = logging.getLogger("statstable") -from anndata import AnnData -from numpy import unique +from numpy import dtype, unique import pandas as pd import matplotlib.pylab as plt @@ -17,8 +13,142 @@ import seaborn as sns +def is_anndata(instance): + """Function to check if an object is a anndata without importing the package""" + + anndata_attr = ["obs", "var", "to_df", "X"] + return all([hasattr(instance, a) for a in anndata_attr]) + + +def set_string_indexes(df): + + df.index = df.index.astype(str) + df.columns = df.columns.astype(str) + + +# TODO: groupby with function such as sum + + +class MetaTable: + def __check_consistency(self): + + if self.data.shape[0] != self.obs.shape[0]: + raise Exception("data and obs are not alligned") + if self.data.shape[1] != self.var.shape[0]: + raise Exception("data and var are not alligned") + + def __set_names_and_size(self): + """Set shape and indexes""" + self.var_names = self.data.columns + self.obs_names = self.data.index + self.shape = self.data.shape + + def __init__(self, data, obs=None, var=None) -> None: + + if type(data) == MetaTable: + self.data = data.data + self.obs = data.obs + self.var = data.var + + elif is_anndata(data): + self.data = data.to_df() + self.obs = data.obs + self.var = data.var + + elif type(data) == pd.DataFrame: + # parse data + assert data.shape[0] > 0, "data is empty" + assert data.index.is_unique, "data has duplicate indices" + assert data.columns.is_unique, "data has duplicate columns" + + self.data = data + set_string_indexes(self.data) + + # parse obs + + if obs is None: + self.obs = pd.DataFrame(index=self.data.index) + elif type(obs) == pd.DataFrame: + + assert obs.index.is_unique, "obs has duplicate indices" + self.obs = obs + set_string_indexes(obs) + + if self.obs.shape[0] != self.data.shape[0]: + self.obs = self.obs.loc[self.data.index].copy() -class StatsTable(AnnData): + else: + raise AttributeError("`obs` should be of type DataFrame or None") + + # parse var + + if var is None: + self.var = pd.DataFrame(index=self.data.columns) + elif type(var) == pd.DataFrame: + + assert var.index.is_unique, "var has duplicate indices" + self.var = var + set_string_indexes(var) + + if self.var.shape[0] != self.data.shape[0]: + self.var = self.var.loc[self.data.columns].copy() + + else: + raise AttributeError("`var` should be of type DataFrame or None") + + self.__check_consistency() + + else: + raise AttributeError("`data` needst to be one of [pandas.DataFrame, ") + + # other attributes commmon to all + self.__set_names_and_size() + + # link functions from self data to self + functions_to_link = ["mean", "median", "sum", "std"] + for f in functions_to_link: + setattr(self, f, getattr(self.data, f)) + + def subset(self, index=None, columns=None): + assert not ( + (index is None) and (columns is None) + ), "either indexes or columns needs to be given" + + # fill indexes if None + if columns is None: + columns = self.var_names + elif index is None: + index = self.obs_names + + return MetaTable( + data=self.data.loc[index, columns], + obs=self.obs.loc[index], + var=self.var.loc[columns], + ) + + def groupby(self, groupby, axis=0): + if axis == 0: + G = self.obs.groupby(groupby) + + for group in G.indices: + yield (group, self.subset(index=self.obs_names[G.indices[group]])) + + elif axis == 1: + # Group by on axis 0. var indexes contain data.columns + G = self.var.groupby(groupby) + for group in G.indices: + yield group, self.subset(columns=self.var_names[G.indices[group]]) + else: + raise Exception("axis should be 1 or 2") + + def __repr__(self): + value = f"MetaTable with {self.shape[0]} samples x {self.shape[1]} features\n" + f"Sample annotations: {list(self.obs.columns)}\n" + f"Feature annotations: {list(self.var.columns)} " + return value + + +class StatsTable(MetaTable): def __init__( self, data, @@ -35,8 +165,8 @@ def __init__( label_variable=None, ): - # create AnnData object - super().__init__(data, obs=None, var=None) + # create MetaTable object + super().__init__(data) if type(test_variable) == str: self.test_variable = self.obs[test_variable] @@ -77,24 +207,37 @@ def __init__( else: self.order_grouping, self.grouping_variable = None, None - self.__calculate_stats__( - test=test, test_kws=test_kws, comparisons=comparisons, ref_group=ref_group - ) - self.data_unit = data_unit if label_variable is None: self.labels = pd.Series(self.var_names, self.var_names) elif type(label_variable) == str: + self.labels = self.var[label_variable] + elif type(label_variable) == pd.Series: + if label_variable.name is None: + label_variable.name = "Label" + else: + assert ( + label_variable.name not in self.var.columns + ), "Label is already found in var" - self.var = self.var.join(label_variable) + self.var[label_variable.name] = label_variable self.labels = self.var[label_variable.name] else: raise IOError("label_variable must be None, a string, or a pandas series") + if not self.labels.is_unique: + logger.warn( + "Your labels are not unique. but I should be able to handle this." + ) + + self.__calculate_stats__( + test=test, test_kws=test_kws, comparisons=comparisons, ref_group=ref_group + ) + def __repr__(self) -> str: annadata_str = super().__repr__() annadata_str += f"\n test_variable: {self.test_variable.name} with groups {self.order_test} " @@ -112,12 +255,12 @@ def __apply_to_subsets(self, function, **kws): results = {} - for subset, subset_data in self.to_df().groupby(self.grouping_variable): - results[subset] = function(subset_data, **kws) + for subset, subset_metatable in self.groupby(self.grouping_variable): + results[subset] = function(subset_metatable.data, **kws) return results def __calculate_stats__( - self, comparisons=None, ref_group=None, test="welch", test_kws=None + self, comparisons=None, ref_group=None, test="welch", **test_kws ): if ref_group is not None: @@ -134,9 +277,6 @@ def __calculate_stats__( function = two_group_test - if test_kws is None: - test_kws = {} - kws = dict( test_variable=self.test_variable, ref_group=ref_group, @@ -146,7 +286,7 @@ def __calculate_stats__( ) if self.grouping_variable is None: - results = function(self.to_df(), **kws) + results = function(self.data, **kws) else: results = self.__apply_to_subsets(function, **kws) @@ -155,16 +295,35 @@ def __calculate_stats__( results.columns = results.columns.swaplevel(0, 1) results.sort_index(axis=1, inplace=True) - self.stats = results.astype(float) + ## Add description to stats + + description = self.var.copy() + + if len(results.columns.levshape) == 3: + description.columns = pd.MultiIndex.from_arrays( + [ + ["Description"] * description.shape[1], + ["All"] * description.shape[1], + description.columns, + ] + ) + elif len(results.columns.levshape) == 2: + description.columns = pd.MultiIndex.from_arrays( + [["Description"] * description.shape[1], description.columns] + ) + self.stats = results.astype(float).join(description) def plot( self, variable, + show_dots=True, distance_between_sig_labels="auto", box_params=None, swarm_params=None, + corrected_pvalues=False, + show_not_significant=False, ax=None, **labelkws, ): @@ -174,111 +333,161 @@ def plot( if ax is None: ax = plt.subplot(111) + if corrected_pvalues: + p_value_name = "pBH" + else: + p_value_name = "Pvalue" + statsplot( - self[:, variable].to_df()[variable], + self.data[variable], self.test_variable, order_test=self.order_test, grouping_variable=self.grouping_variable, order_grouping=self.order_grouping, + show_dots=show_dots, box_params=box_params, swarm_params=swarm_params, + show_not_significant=show_not_significant, labelkws=labelkws, palette=self.colors, - p_values=self.stats.Pvalue.loc[variable].T, + p_values=self.stats[p_value_name].loc[variable].T, ax=ax, ) ax.set_title(self.labels[variable]) ax.set_ylabel(self.data_unit) - def __get_groups(self, subset = None): + def __get_groups(self, subset=None): "Check if given subset are in header of statstable" "Otherwise return all in a row, if not defined return None" - # check if grouped + # check if grouped if self.grouping_variable is None: return None else: - all_groups = list(self.stats.columns.levels[-2]) + all_groups = list(self.stats.Pvalue.columns.get_level_values(-2).unique()) if subset is None: return all_groups elif type(subset) == str: + assert subset in all_groups, f"{g} is not in the Groups" + return [subset] else: for g in subset: assert g in all_groups, f"{g} is not in the Groups" - + return list(subset) - def __get_comparisons(self, subset = None): + def __get_comparisons(self, subset=None): "Check if given subset are in comparisons of statstable" "Otherwise return all in a row, if not defined return None" - - all_comparisons = list(self.stats.columns.levels[-1]) + all_comparisons = list(self.stats.Pvalue.columns.get_level_values(-1).unique()) if subset is None: return all_comparisons elif type(subset) == str: + assert subset in all_comparisons, f"{g} is not in the Comparisons" return [subset] else: for g in subset: assert g in all_comparisons, f"{g} is not in the Comparisons" - - return list(subset) - - - + return list(subset) - def vulcanoplot(self, comparisons = None, groups = None, corrected_pvalues= True, threshold_p= None , **kws): + # TODO: Hide output axes labesls + def vulcanoplot( + self, + comparisons=None, + groups=None, + corrected_pvalues=False, + threshold_p=None, + hue=None, + figsize=(6, 6), + label_points="auto", + max_labels=5, + effect_label=None, + pvalue_label=None, + **kws, + ): if "log2FC" in self.stats.columns: - effect_name= "log2FC" + effect_name = "log2FC" + x_label = "$\log_2FC$" else: - effect_name= "median_diff" + effect_name = "median_diff" + x_label = "median difference" logger.info("Don't have log2FC in stats, using median_diff for vulcanoplot") + if effect_label is not None: + x_label = effect_label + + def rename_vulcano_axis_labels(): + ax = plt.gca() + ax.set_xlabel(x_label) + + if pvalue_label is not None: + ax.set_ylabel(pvalue_label) + elif corrected_pvalues: + ax.set_ylabel("$-\log(P_{BH})$") + # ellse default label from vulcano plot + groups = self.__get_groups(groups) - comparisons = self.__get_comparisons(comparisons) + axes = [] if corrected_pvalues: - p_value_name= 'pBH' + p_value_name = "pBH" if threshold_p is None: - threshold_p= 0.1 + threshold_p = 0.1 else: p_value_name = "Pvalue" if threshold_p is None: - threshold_p= 0.05 - + threshold_p = 0.05 + + if hue is not None and (type(hue) == str): + hue = self.var[hue] + + # collect general arguments + kws["threshold_p"] = threshold_p + kws["hue"] = hue + kws["labels"] = self.labels + + # map kws to vulcanoplot + kws["label_points"] = label_points + kws["max_labels"] = max_labels + kws["figsize"] = figsize + if groups is not None: for g in groups: for c in comparisons: - - vulcanoplot( p_values=self.stats[p_value_name][g][c], - effect= self.stats[effect_name][g][c], - threshold_p= threshold_p, - **kws - ) - ax= plt.gca() + vulcanoplot( + p_values=self.stats[p_value_name][g][c], + effect=self.stats[effect_name][g][c], + **kws, + ) + ax = plt.gca() ax.set_title(g) + rename_vulcano_axis_labels() + axes.append(ax) + else: - for c in comparisons: - vulcanoplot( p_values=self.stats[p_value_name][c], - effect= self.stats[effect_name][c], - threshold_p= threshold_p, - **kws - ) - + vulcanoplot( + p_values=self.stats[p_value_name][c], + effect=self.stats[effect_name][c], + **kws, + ) + rename_vulcano_axis_labels() + axes.append(plt.gca()) + return axes diff --git a/statsplot/transformations.py b/statsplot/transformations.py index 07c37b6..752c0d9 100644 --- a/statsplot/transformations.py +++ b/statsplot/transformations.py @@ -1,19 +1,114 @@ -try: - from skbio.stats import composition -except ImportError as e: - raise Exception( - "'scikit-bio' is required for this sub-package. Install id with pip or conda" - ) from e - - from numpy import log import pandas as pd -import anndata +import numpy as np + + +# copied from scikit-bio +# because I cannot install it +def closure(mat): + """ + Performs closure to ensure that all elements add up to 1. + Parameters + ---------- + mat : array_like + a matrix of proportions where + rows = compositions + columns = components + Returns + ------- + array_like, np.float64 + A matrix of proportions where all of the values + are nonzero and each composition (row) adds up to 1 + Raises + ------ + ValueError + Raises an error if any values are negative. + ValueError + Raises an error if the matrix has more than 2 dimension. + ValueError + Raises an error if there is a row that has all zeros. + Examples + -------- + >>> import numpy as np + >>> from skbio.stats.composition import closure + >>> X = np.array([[2, 2, 6], [4, 4, 2]]) + >>> closure(X) + array([[ 0.2, 0.2, 0.6], + [ 0.4, 0.4, 0.2]]) + """ + mat = np.atleast_2d(mat) + if np.any(mat < 0): + raise ValueError("Cannot have negative proportions") + if mat.ndim > 2: + raise ValueError("Input matrix can only have two dimensions or less") + if np.all(mat == 0, axis=1).sum() > 0: + raise ValueError("Input matrix cannot have rows with all zeros") + mat = mat / mat.sum(axis=1, keepdims=True) + return mat.squeeze() + + +def multiplicative_replacement(mat, delta=None): + r"""Replace all zeros with small non-zero values + It uses the multiplicative replacement strategy [1]_ , + replacing zeros with a small positive :math:`\delta` + and ensuring that the compositions still add up to 1. + Parameters + ---------- + mat: array_like + a matrix of proportions where + rows = compositions and + columns = components + delta: float, optional + a small number to be used to replace zeros + If delta is not specified, then the default delta is + :math:`\delta = \frac{1}{N^2}` where :math:`N` + is the number of components + Returns + ------- + numpy.ndarray, np.float64 + A matrix of proportions where all of the values + are nonzero and each composition (row) adds up to 1 + Raises + ------ + ValueError + Raises an error if negative proportions are created due to a large + `delta`. + Notes + ----- + This method will result in negative proportions if a large delta is chosen. + References + ---------- + .. [1] J. A. Martin-Fernandez. "Dealing With Zeros and Missing Values in + Compositional Data Sets Using Nonparametric Imputation" + Examples + -------- + >>> import numpy as np + >>> from skbio.stats.composition import multiplicative_replacement + >>> X = np.array([[.2,.4,.4, 0],[0,.5,.5,0]]) + >>> multiplicative_replacement(X) + array([[ 0.1875, 0.375 , 0.375 , 0.0625], + [ 0.0625, 0.4375, 0.4375, 0.0625]]) + """ + mat = closure(mat) + z_mat = mat == 0 -from typing import Union + num_feats = mat.shape[-1] + tot = z_mat.sum(axis=-1, keepdims=True) + if delta is None: + delta = (1.0 / num_feats) ** 2 -def clr(data: Union[pd.DataFrame, anndata.AnnData], log=log): + zcnts = 1 - tot * delta + if np.any(zcnts) < 0: + raise ValueError( + "The multiplicative replacement created negative " + "proportions. Consider using a smaller `delta`." + ) + mat = np.where(z_mat, delta, zcnts * mat) + return mat.squeeze() + + +def clr(data: pd.DataFrame, log=log, features="all"): """ Centered log ratio (CLR) with multiplicative replacement implemented in scikit-bio """ @@ -25,26 +120,33 @@ def clr(data: Union[pd.DataFrame, anndata.AnnData], log=log): d = data.loc[:, ~(data == 0).all()] # get data as matrix matrix = d.values - elif type(data) == anndata.AnnData: - # remove columns with all zeros - d = data[:, ~(data.X == 0).all(axis=0)].copy() - # get data as matrix - matrix = d.X else: - raise Exception("data must be a pandas.DataFrame or anndata.AnnData") + raise Exception("data must be a pandas.DataFrame") # Fill in zeros with multiplicative replacement - matrix = composition.multiplicative_replacement(matrix) + matrix = multiplicative_replacement(matrix) + + matrix = pd.DataFrame(matrix, index=d.index, columns=d.columns) # CLR matrix = log(matrix) - matrix = (matrix.T - matrix.mean(1)).T + # Center + if features.lower() == "all": - if type(data) == pd.DataFrame: + mean = matrix.mean(1) + + elif features.lower() == "nz": + + mean = matrix[matrix != 0].mean(1) + elif features.lower() == "iql": + # use mean of features in interquartile range + q1 = matrix.quantile(0.25, axis=1) + q3 = matrix.quantile(0.75, axis=1) + mean = matrix[(matrix > q1) & (matrix < q3)].mean(1) + else: + raise Exception("features must be 'all', 'nz', or 'iql'") - return pd.DataFrame(matrix, index=d.index, columns=d.columns) + matrix = (matrix.T - mean).T - elif type(data) == anndata.AnnData: - d.X = matrix - return d + return matrix diff --git a/test/baseline_images/grouped_dots_default.png b/test/baseline_images/grouped_dots_default.png new file mode 100644 index 0000000..6483300 Binary files /dev/null and b/test/baseline_images/grouped_dots_default.png differ diff --git a/test/baseline_images/grouped_dots_partial_swarm.png b/test/baseline_images/grouped_dots_partial_swarm.png new file mode 100644 index 0000000..49752d6 Binary files /dev/null and b/test/baseline_images/grouped_dots_partial_swarm.png differ diff --git a/test/baseline_images/ungrouped_dots.png b/test/baseline_images/ungrouped_dots.png new file mode 100644 index 0000000..410edd0 Binary files /dev/null and b/test/baseline_images/ungrouped_dots.png differ diff --git a/test/conftest.py b/test/conftest.py new file mode 100644 index 0000000..110f0b8 --- /dev/null +++ b/test/conftest.py @@ -0,0 +1,56 @@ +from __future__ import annotations + +import matplotlib +import numpy as np +import pandas as pd +import pytest + + +# CI-safe, headless backend for deterministic rendering. +matplotlib.use("Agg") + + +@pytest.fixture +def synthetic_data() -> pd.DataFrame: + rng = np.random.default_rng(7) + + n = 20 + rows = [] + for group_bin, offset in (("G1", 0.0), ("G2", 0.35)): + for test_group, mean in (("A", 0.0 + offset), ("B", 0.9 + offset)): + values = rng.normal(loc=mean, scale=0.25, size=n) + rows.extend( + { + "value": float(v), + "test_group": test_group, + "group_bin": group_bin, + } + for v in values + ) + + data = pd.DataFrame(rows) + + # Inject explicit outliers so showfliers behavior is testable. + outliers = pd.DataFrame( + [ + {"value": 4.2, "test_group": "A", "group_bin": "G1"}, + {"value": -3.4, "test_group": "B", "group_bin": "G1"}, + {"value": 4.8, "test_group": "A", "group_bin": "G2"}, + {"value": -3.9, "test_group": "B", "group_bin": "G2"}, + ] + ) + return pd.concat([data, outliers], ignore_index=True) + + +@pytest.fixture +def p_values_ungrouped() -> pd.Series: + return pd.Series({"B_vs_A": 0.01}, name="value") + + +@pytest.fixture +def p_values_grouped() -> pd.Series: + idx = pd.MultiIndex.from_tuples( + [("G1", "B_vs_A"), ("G2", "B_vs_A")], + names=["group_bin", "comparison"], + ) + return pd.Series([0.01, 0.02], index=idx, name="value") diff --git a/test/data/micobiota_metadata.tsv.gz b/test/data/micobiota_metadata.tsv.gz new file mode 100644 index 0000000..a71ed95 Binary files /dev/null and b/test/data/micobiota_metadata.tsv.gz differ diff --git a/test/data/micobiota_relab.tsv.gz b/test/data/micobiota_relab.tsv.gz new file mode 100644 index 0000000..9b5f93a Binary files /dev/null and b/test/data/micobiota_relab.tsv.gz differ diff --git a/test/data/micobiota_taxonomy.tsv.gz b/test/data/micobiota_taxonomy.tsv.gz new file mode 100644 index 0000000..65793e6 Binary files /dev/null and b/test/data/micobiota_taxonomy.tsv.gz differ diff --git a/test/test_statsplot_matrix.py b/test/test_statsplot_matrix.py new file mode 100644 index 0000000..4509729 --- /dev/null +++ b/test/test_statsplot_matrix.py @@ -0,0 +1,140 @@ +from __future__ import annotations + +import pandas as pd +import pytest +import seaborn as sns +from matplotlib.axes import Axes + +from statsplot import statsplot + + +@pytest.mark.parametrize( + "case", + [ + { + "name": "ungrouped_no_dots", + "grouped": False, + "show_dots": False, + "swarm_params": None, + }, + { + "name": "ungrouped_with_dots", + "grouped": False, + "show_dots": True, + "swarm_params": None, + }, + { + "name": "grouped_no_dots", + "grouped": True, + "show_dots": False, + "swarm_params": None, + }, + { + "name": "grouped_with_dots_default", + "grouped": True, + "show_dots": True, + "swarm_params": None, + }, + { + "name": "grouped_with_dots_dodge_only", + "grouped": True, + "show_dots": True, + "swarm_params": {"dodge": True}, + }, + { + "name": "grouped_with_dots_partial_swarm", + "grouped": True, + "show_dots": True, + "swarm_params": {"size": 2}, + }, + { + "name": "grouped_with_dots_override_dodge_false", + "grouped": True, + "show_dots": True, + "swarm_params": {"dodge": False}, + }, + ], + ids=lambda c: c["name"], +) +def test_statsplot_behavior_matrix( + case, + synthetic_data: pd.DataFrame, + p_values_ungrouped: pd.Series, + p_values_grouped: pd.Series, + monkeypatch: pytest.MonkeyPatch, +): + captured: dict[str, dict] = {} + + original_boxplot = sns.boxplot + original_swarmplot = sns.swarmplot + + def boxplot_spy(*args, **kwargs): + captured["box"] = dict(kwargs) + return original_boxplot(*args, **kwargs) + + def swarmplot_spy(*args, **kwargs): + captured["swarm"] = dict(kwargs) + return original_swarmplot(*args, **kwargs) + + monkeypatch.setattr(sns, "boxplot", boxplot_spy) + monkeypatch.setattr(sns, "swarmplot", swarmplot_spy) + + grouped = case["grouped"] + show_dots = case["show_dots"] + + p_values = p_values_grouped if grouped else p_values_ungrouped + grouping_variable = "group_bin" if grouped else None + order_grouping = ["G1", "G2"] if grouped else None + + ax, returned_p_values = statsplot( + variable="value", + test_variable="test_group", + data=synthetic_data, + order_test=["A", "B"], + grouping_variable=grouping_variable, + order_grouping=order_grouping, + show_dots=show_dots, + swarm_params=case["swarm_params"], + p_values=p_values, + ) + + # Smoke checks: all matrix rows should execute and return axis + p-values. + assert isinstance(ax, Axes) + assert isinstance(returned_p_values, pd.Series) + + box_kwargs = captured["box"] + if show_dots: + assert box_kwargs["showfliers"] is False + assert box_kwargs["fliersize"] == 0 + else: + assert "showfliers" not in box_kwargs + assert "fliersize" not in box_kwargs + + if not show_dots: + assert "swarm" not in captured + return + + swarm_kwargs = captured["swarm"] + + if not grouped: + # No-grouping path should not require grouped hue/dodge defaults. + assert "dodge" not in swarm_kwargs + assert "hue" not in swarm_kwargs + assert "hue_order" not in swarm_kwargs + assert swarm_kwargs.get("color") == "k" + return + + # Grouped + dots path should use dodge defaults unless explicitly overridden. + if case["swarm_params"] == {"dodge": False}: + assert swarm_kwargs["dodge"] is False + else: + assert swarm_kwargs["dodge"] is True + + # Grouped path should preserve hue defaults even with partial swarm kwargs. + assert "hue" in swarm_kwargs + assert set(pd.unique(swarm_kwargs["hue"])) == {"A", "B"} + assert swarm_kwargs["hue_order"] == ["A", "B"] + assert swarm_kwargs["palette"] == "dark:k" + + if case["swarm_params"] == {"size": 2}: + assert swarm_kwargs["size"] == 2 diff --git a/test/test_statsplot_mpl.py b/test/test_statsplot_mpl.py new file mode 100644 index 0000000..47b33fc --- /dev/null +++ b/test/test_statsplot_mpl.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +from pathlib import Path + +import matplotlib.pyplot as plt +from matplotlib.testing.compare import compare_images + +from statsplot import statsplot + + +BASELINE_DIR = Path(__file__).parent / "baseline_images" + + +def _assert_matches_baseline(fig, baseline_name: str, tolerance: float = 2.0): + actual_dir = Path(__file__).parent / "_actual" + actual_dir.mkdir(exist_ok=True) + + actual_path = actual_dir / baseline_name + fig.savefig(actual_path, dpi=100, bbox_inches="tight") + + expected_path = BASELINE_DIR / baseline_name + assert expected_path.exists(), f"Missing baseline image: {expected_path}" + + result = compare_images(str(expected_path), str(actual_path), tol=tolerance) + assert result is None, result + + +def test_mpl_grouped_dots_default(synthetic_data, p_values_grouped): + fig, ax = plt.subplots(figsize=(7, 4), dpi=100) + statsplot( + variable="value", + test_variable="test_group", + data=synthetic_data, + order_test=["A", "B"], + grouping_variable="group_bin", + order_grouping=["G1", "G2"], + show_dots=True, + p_values=p_values_grouped, + ax=ax, + ) + _assert_matches_baseline(fig, "grouped_dots_default.png") + plt.close(fig) + + +def test_mpl_grouped_dots_partial_swarm_params(synthetic_data, p_values_grouped): + fig, ax = plt.subplots(figsize=(7, 4), dpi=100) + statsplot( + variable="value", + test_variable="test_group", + data=synthetic_data, + order_test=["A", "B"], + grouping_variable="group_bin", + order_grouping=["G1", "G2"], + show_dots=True, + swarm_params={"size": 2}, + p_values=p_values_grouped, + ax=ax, + ) + _assert_matches_baseline(fig, "grouped_dots_partial_swarm.png") + plt.close(fig) + + +def test_mpl_ungrouped_dots(synthetic_data, p_values_ungrouped): + fig, ax = plt.subplots(figsize=(7, 4), dpi=100) + statsplot( + variable="value", + test_variable="test_group", + data=synthetic_data, + order_test=["A", "B"], + show_dots=True, + p_values=p_values_ungrouped, + ax=ax, + ) + _assert_matches_baseline(fig, "ungrouped_dots.png") + plt.close(fig) diff --git a/versioneer.py b/versioneer.py new file mode 100644 index 0000000..18e34c2 --- /dev/null +++ b/versioneer.py @@ -0,0 +1,2205 @@ + +# Version: 0.28 + +"""The Versioneer - like a rocketeer, but for versions. + +The Versioneer +============== + +* like a rocketeer, but for versions! +* https://github.com/python-versioneer/python-versioneer +* Brian Warner +* License: Public Domain (Unlicense) +* Compatible with: Python 3.7, 3.8, 3.9, 3.10 and pypy3 +* [![Latest Version][pypi-image]][pypi-url] +* [![Build Status][travis-image]][travis-url] + +This is a tool for managing a recorded version number in setuptools-based +python projects. The goal is to remove the tedious and error-prone "update +the embedded version string" step from your release process. Making a new +release should be as easy as recording a new tag in your version-control +system, and maybe making new tarballs. + + +## Quick Install + +Versioneer provides two installation modes. The "classic" vendored mode installs +a copy of versioneer into your repository. The experimental build-time dependency mode +is intended to allow you to skip this step and simplify the process of upgrading. + +### Vendored mode + +* `pip install versioneer` to somewhere in your $PATH + * A [conda-forge recipe](https://github.com/conda-forge/versioneer-feedstock) is + available, so you can also use `conda install -c conda-forge versioneer` +* add a `[tool.versioneer]` section to your `pyproject.toml` or a + `[versioneer]` section to your `setup.cfg` (see [Install](INSTALL.md)) + * Note that you will need to add `tomli; python_version < "3.11"` to your + build-time dependencies if you use `pyproject.toml` +* run `versioneer install --vendor` in your source tree, commit the results +* verify version information with `python setup.py version` + +### Build-time dependency mode + +* `pip install versioneer` to somewhere in your $PATH + * A [conda-forge recipe](https://github.com/conda-forge/versioneer-feedstock) is + available, so you can also use `conda install -c conda-forge versioneer` +* add a `[tool.versioneer]` section to your `pyproject.toml` or a + `[versioneer]` section to your `setup.cfg` (see [Install](INSTALL.md)) +* add `versioneer` (with `[toml]` extra, if configuring in `pyproject.toml`) + to the `requires` key of the `build-system` table in `pyproject.toml`: + ```toml + [build-system] + requires = ["setuptools", "versioneer[toml]"] + build-backend = "setuptools.build_meta" + ``` +* run `versioneer install --no-vendor` in your source tree, commit the results +* verify version information with `python setup.py version` + +## Version Identifiers + +Source trees come from a variety of places: + +* a version-control system checkout (mostly used by developers) +* a nightly tarball, produced by build automation +* a snapshot tarball, produced by a web-based VCS browser, like github's + "tarball from tag" feature +* a release tarball, produced by "setup.py sdist", distributed through PyPI + +Within each source tree, the version identifier (either a string or a number, +this tool is format-agnostic) can come from a variety of places: + +* ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows + about recent "tags" and an absolute revision-id +* the name of the directory into which the tarball was unpacked +* an expanded VCS keyword ($Id$, etc) +* a `_version.py` created by some earlier build step + +For released software, the version identifier is closely related to a VCS +tag. Some projects use tag names that include more than just the version +string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool +needs to strip the tag prefix to extract the version identifier. For +unreleased software (between tags), the version identifier should provide +enough information to help developers recreate the same tree, while also +giving them an idea of roughly how old the tree is (after version 1.2, before +version 1.3). Many VCS systems can report a description that captures this, +for example `git describe --tags --dirty --always` reports things like +"0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the +0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has +uncommitted changes). + +The version identifier is used for multiple purposes: + +* to allow the module to self-identify its version: `myproject.__version__` +* to choose a name and prefix for a 'setup.py sdist' tarball + +## Theory of Operation + +Versioneer works by adding a special `_version.py` file into your source +tree, where your `__init__.py` can import it. This `_version.py` knows how to +dynamically ask the VCS tool for version information at import time. + +`_version.py` also contains `$Revision$` markers, and the installation +process marks `_version.py` to have this marker rewritten with a tag name +during the `git archive` command. As a result, generated tarballs will +contain enough information to get the proper version. + +To allow `setup.py` to compute a version too, a `versioneer.py` is added to +the top level of your source tree, next to `setup.py` and the `setup.cfg` +that configures it. This overrides several distutils/setuptools commands to +compute the version when invoked, and changes `setup.py build` and `setup.py +sdist` to replace `_version.py` with a small static file that contains just +the generated version data. + +## Installation + +See [INSTALL.md](./INSTALL.md) for detailed installation instructions. + +## Version-String Flavors + +Code which uses Versioneer can learn about its version string at runtime by +importing `_version` from your main `__init__.py` file and running the +`get_versions()` function. From the "outside" (e.g. in `setup.py`), you can +import the top-level `versioneer.py` and run `get_versions()`. + +Both functions return a dictionary with different flavors of version +information: + +* `['version']`: A condensed version string, rendered using the selected + style. This is the most commonly used value for the project's version + string. The default "pep440" style yields strings like `0.11`, + `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section + below for alternative styles. + +* `['full-revisionid']`: detailed revision identifier. For Git, this is the + full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac". + +* `['date']`: Date and time of the latest `HEAD` commit. For Git, it is the + commit date in ISO 8601 format. This will be None if the date is not + available. + +* `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that + this is only accurate if run in a VCS checkout, otherwise it is likely to + be False or None + +* `['error']`: if the version string could not be computed, this will be set + to a string describing the problem, otherwise it will be None. It may be + useful to throw an exception in setup.py if this is set, to avoid e.g. + creating tarballs with a version string of "unknown". + +Some variants are more useful than others. Including `full-revisionid` in a +bug report should allow developers to reconstruct the exact code being tested +(or indicate the presence of local changes that should be shared with the +developers). `version` is suitable for display in an "about" box or a CLI +`--version` output: it can be easily compared against release notes and lists +of bugs fixed in various releases. + +The installer adds the following text to your `__init__.py` to place a basic +version in `YOURPROJECT.__version__`: + + from ._version import get_versions + __version__ = get_versions()['version'] + del get_versions + +## Styles + +The setup.cfg `style=` configuration controls how the VCS information is +rendered into a version string. + +The default style, "pep440", produces a PEP440-compliant string, equal to the +un-prefixed tag name for actual releases, and containing an additional "local +version" section with more detail for in-between builds. For Git, this is +TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags +--dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the +tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and +that this commit is two revisions ("+2") beyond the "0.11" tag. For released +software (exactly equal to a known tag), the identifier will only contain the +stripped tag, e.g. "0.11". + +Other styles are available. See [details.md](details.md) in the Versioneer +source tree for descriptions. + +## Debugging + +Versioneer tries to avoid fatal errors: if something goes wrong, it will tend +to return a version of "0+unknown". To investigate the problem, run `setup.py +version`, which will run the version-lookup code in a verbose mode, and will +display the full contents of `get_versions()` (including the `error` string, +which may help identify what went wrong). + +## Known Limitations + +Some situations are known to cause problems for Versioneer. This details the +most significant ones. More can be found on Github +[issues page](https://github.com/python-versioneer/python-versioneer/issues). + +### Subprojects + +Versioneer has limited support for source trees in which `setup.py` is not in +the root directory (e.g. `setup.py` and `.git/` are *not* siblings). The are +two common reasons why `setup.py` might not be in the root: + +* Source trees which contain multiple subprojects, such as + [Buildbot](https://github.com/buildbot/buildbot), which contains both + "master" and "slave" subprojects, each with their own `setup.py`, + `setup.cfg`, and `tox.ini`. Projects like these produce multiple PyPI + distributions (and upload multiple independently-installable tarballs). +* Source trees whose main purpose is to contain a C library, but which also + provide bindings to Python (and perhaps other languages) in subdirectories. + +Versioneer will look for `.git` in parent directories, and most operations +should get the right version string. However `pip` and `setuptools` have bugs +and implementation details which frequently cause `pip install .` from a +subproject directory to fail to find a correct version string (so it usually +defaults to `0+unknown`). + +`pip install --editable .` should work correctly. `setup.py install` might +work too. + +Pip-8.1.1 is known to have this problem, but hopefully it will get fixed in +some later version. + +[Bug #38](https://github.com/python-versioneer/python-versioneer/issues/38) is tracking +this issue. The discussion in +[PR #61](https://github.com/python-versioneer/python-versioneer/pull/61) describes the +issue from the Versioneer side in more detail. +[pip PR#3176](https://github.com/pypa/pip/pull/3176) and +[pip PR#3615](https://github.com/pypa/pip/pull/3615) contain work to improve +pip to let Versioneer work correctly. + +Versioneer-0.16 and earlier only looked for a `.git` directory next to the +`setup.cfg`, so subprojects were completely unsupported with those releases. + +### Editable installs with setuptools <= 18.5 + +`setup.py develop` and `pip install --editable .` allow you to install a +project into a virtualenv once, then continue editing the source code (and +test) without re-installing after every change. + +"Entry-point scripts" (`setup(entry_points={"console_scripts": ..})`) are a +convenient way to specify executable scripts that should be installed along +with the python package. + +These both work as expected when using modern setuptools. When using +setuptools-18.5 or earlier, however, certain operations will cause +`pkg_resources.DistributionNotFound` errors when running the entrypoint +script, which must be resolved by re-installing the package. This happens +when the install happens with one version, then the egg_info data is +regenerated while a different version is checked out. Many setup.py commands +cause egg_info to be rebuilt (including `sdist`, `wheel`, and installing into +a different virtualenv), so this can be surprising. + +[Bug #83](https://github.com/python-versioneer/python-versioneer/issues/83) describes +this one, but upgrading to a newer version of setuptools should probably +resolve it. + + +## Updating Versioneer + +To upgrade your project to a new release of Versioneer, do the following: + +* install the new Versioneer (`pip install -U versioneer` or equivalent) +* edit `setup.cfg` and `pyproject.toml`, if necessary, + to include any new configuration settings indicated by the release notes. + See [UPGRADING](./UPGRADING.md) for details. +* re-run `versioneer install --[no-]vendor` in your source tree, to replace + `SRC/_version.py` +* commit any changed files + +## Future Directions + +This tool is designed to make it easily extended to other version-control +systems: all VCS-specific components are in separate directories like +src/git/ . The top-level `versioneer.py` script is assembled from these +components by running make-versioneer.py . In the future, make-versioneer.py +will take a VCS name as an argument, and will construct a version of +`versioneer.py` that is specific to the given VCS. It might also take the +configuration arguments that are currently provided manually during +installation by editing setup.py . Alternatively, it might go the other +direction and include code from all supported VCS systems, reducing the +number of intermediate scripts. + +## Similar projects + +* [setuptools_scm](https://github.com/pypa/setuptools_scm/) - a non-vendored build-time + dependency +* [minver](https://github.com/jbweston/miniver) - a lightweight reimplementation of + versioneer +* [versioningit](https://github.com/jwodder/versioningit) - a PEP 518-based setuptools + plugin + +## License + +To make Versioneer easier to embed, all its code is dedicated to the public +domain. The `_version.py` that it creates is also in the public domain. +Specifically, both are released under the "Unlicense", as described in +https://unlicense.org/. + +[pypi-image]: https://img.shields.io/pypi/v/versioneer.svg +[pypi-url]: https://pypi.python.org/pypi/versioneer/ +[travis-image]: +https://img.shields.io/travis/com/python-versioneer/python-versioneer.svg +[travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer + +""" +# pylint:disable=invalid-name,import-outside-toplevel,missing-function-docstring +# pylint:disable=missing-class-docstring,too-many-branches,too-many-statements +# pylint:disable=raise-missing-from,too-many-lines,too-many-locals,import-error +# pylint:disable=too-few-public-methods,redefined-outer-name,consider-using-with +# pylint:disable=attribute-defined-outside-init,too-many-arguments + +import configparser +import errno +import json +import os +import re +import subprocess +import sys +from pathlib import Path +from typing import Callable, Dict +import functools + +have_tomllib = True +if sys.version_info >= (3, 11): + import tomllib +else: + try: + import tomli as tomllib + except ImportError: + have_tomllib = False + + +class VersioneerConfig: + """Container for Versioneer configuration parameters.""" + + +def get_root(): + """Get the project root directory. + + We require that all commands are run from the project root, i.e. the + directory that contains setup.py, setup.cfg, and versioneer.py . + """ + root = os.path.realpath(os.path.abspath(os.getcwd())) + setup_py = os.path.join(root, "setup.py") + versioneer_py = os.path.join(root, "versioneer.py") + if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): + # allow 'python path/to/setup.py COMMAND' + root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0]))) + setup_py = os.path.join(root, "setup.py") + versioneer_py = os.path.join(root, "versioneer.py") + if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): + err = ("Versioneer was unable to run the project root directory. " + "Versioneer requires setup.py to be executed from " + "its immediate directory (like 'python setup.py COMMAND'), " + "or in a way that lets it use sys.argv[0] to find the root " + "(like 'python path/to/setup.py COMMAND').") + raise VersioneerBadRootError(err) + try: + # Certain runtime workflows (setup.py install/develop in a setuptools + # tree) execute all dependencies in a single python process, so + # "versioneer" may be imported multiple times, and python's shared + # module-import table will cache the first one. So we can't use + # os.path.dirname(__file__), as that will find whichever + # versioneer.py was first imported, even in later projects. + my_path = os.path.realpath(os.path.abspath(__file__)) + me_dir = os.path.normcase(os.path.splitext(my_path)[0]) + vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0]) + if me_dir != vsr_dir and "VERSIONEER_PEP518" not in globals(): + print("Warning: build in %s is using versioneer.py from %s" + % (os.path.dirname(my_path), versioneer_py)) + except NameError: + pass + return root + + +def get_config_from_root(root): + """Read the project setup.cfg file to determine Versioneer config.""" + # This might raise OSError (if setup.cfg is missing), or + # configparser.NoSectionError (if it lacks a [versioneer] section), or + # configparser.NoOptionError (if it lacks "VCS="). See the docstring at + # the top of versioneer.py for instructions on writing your setup.cfg . + root = Path(root) + pyproject_toml = root / "pyproject.toml" + setup_cfg = root / "setup.cfg" + section = None + if pyproject_toml.exists() and have_tomllib: + try: + with open(pyproject_toml, 'rb') as fobj: + pp = tomllib.load(fobj) + section = pp['tool']['versioneer'] + except (tomllib.TOMLDecodeError, KeyError): + pass + if not section: + parser = configparser.ConfigParser() + with open(setup_cfg) as cfg_file: + parser.read_file(cfg_file) + parser.get("versioneer", "VCS") # raise error if missing + + section = parser["versioneer"] + + cfg = VersioneerConfig() + cfg.VCS = section['VCS'] + cfg.style = section.get("style", "") + cfg.versionfile_source = section.get("versionfile_source") + cfg.versionfile_build = section.get("versionfile_build") + cfg.tag_prefix = section.get("tag_prefix") + if cfg.tag_prefix in ("''", '""', None): + cfg.tag_prefix = "" + cfg.parentdir_prefix = section.get("parentdir_prefix") + cfg.verbose = section.get("verbose") + return cfg + + +class NotThisMethod(Exception): + """Exception raised if a method is not valid for the current scenario.""" + + +# these dictionaries contain VCS-specific tools +LONG_VERSION_PY: Dict[str, str] = {} +HANDLERS: Dict[str, Dict[str, Callable]] = {} + + +def register_vcs_handler(vcs, method): # decorator + """Create decorator to mark a method as the handler of a VCS.""" + def decorate(f): + """Store f in HANDLERS[vcs][method].""" + HANDLERS.setdefault(vcs, {})[method] = f + return f + return decorate + + +def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, + env=None): + """Call the given command(s).""" + assert isinstance(commands, list) + process = None + + popen_kwargs = {} + if sys.platform == "win32": + # This hides the console window if pythonw.exe is used + startupinfo = subprocess.STARTUPINFO() + startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW + popen_kwargs["startupinfo"] = startupinfo + + for command in commands: + try: + dispcmd = str([command] + args) + # remember shell=False, so use git.cmd on windows, not just git + process = subprocess.Popen([command] + args, cwd=cwd, env=env, + stdout=subprocess.PIPE, + stderr=(subprocess.PIPE if hide_stderr + else None), **popen_kwargs) + break + except OSError: + e = sys.exc_info()[1] + if e.errno == errno.ENOENT: + continue + if verbose: + print("unable to run %s" % dispcmd) + print(e) + return None, None + else: + if verbose: + print("unable to find command, tried %s" % (commands,)) + return None, None + stdout = process.communicate()[0].strip().decode() + if process.returncode != 0: + if verbose: + print("unable to run %s (error)" % dispcmd) + print("stdout was %s" % stdout) + return None, process.returncode + return stdout, process.returncode + + +LONG_VERSION_PY['git'] = r''' +# This file helps to compute a version number in source trees obtained from +# git-archive tarball (such as those provided by githubs download-from-tag +# feature). Distribution tarballs (built by setup.py sdist) and build +# directories (produced by setup.py build) will contain a much shorter file +# that just contains the computed version number. + +# This file is released into the public domain. +# Generated by versioneer-0.28 +# https://github.com/python-versioneer/python-versioneer + +"""Git implementation of _version.py.""" + +import errno +import os +import re +import subprocess +import sys +from typing import Callable, Dict +import functools + + +def get_keywords(): + """Get the keywords needed to look up the version information.""" + # these strings will be replaced by git during git-archive. + # setup.py/versioneer.py will grep for the variable names, so they must + # each be defined on a line of their own. _version.py will just call + # get_keywords(). + git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s" + git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s" + git_date = "%(DOLLAR)sFormat:%%ci%(DOLLAR)s" + keywords = {"refnames": git_refnames, "full": git_full, "date": git_date} + return keywords + + +class VersioneerConfig: + """Container for Versioneer configuration parameters.""" + + +def get_config(): + """Create, populate and return the VersioneerConfig() object.""" + # these strings are filled in when 'setup.py versioneer' creates + # _version.py + cfg = VersioneerConfig() + cfg.VCS = "git" + cfg.style = "%(STYLE)s" + cfg.tag_prefix = "%(TAG_PREFIX)s" + cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s" + cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s" + cfg.verbose = False + return cfg + + +class NotThisMethod(Exception): + """Exception raised if a method is not valid for the current scenario.""" + + +LONG_VERSION_PY: Dict[str, str] = {} +HANDLERS: Dict[str, Dict[str, Callable]] = {} + + +def register_vcs_handler(vcs, method): # decorator + """Create decorator to mark a method as the handler of a VCS.""" + def decorate(f): + """Store f in HANDLERS[vcs][method].""" + if vcs not in HANDLERS: + HANDLERS[vcs] = {} + HANDLERS[vcs][method] = f + return f + return decorate + + +def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, + env=None): + """Call the given command(s).""" + assert isinstance(commands, list) + process = None + + popen_kwargs = {} + if sys.platform == "win32": + # This hides the console window if pythonw.exe is used + startupinfo = subprocess.STARTUPINFO() + startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW + popen_kwargs["startupinfo"] = startupinfo + + for command in commands: + try: + dispcmd = str([command] + args) + # remember shell=False, so use git.cmd on windows, not just git + process = subprocess.Popen([command] + args, cwd=cwd, env=env, + stdout=subprocess.PIPE, + stderr=(subprocess.PIPE if hide_stderr + else None), **popen_kwargs) + break + except OSError: + e = sys.exc_info()[1] + if e.errno == errno.ENOENT: + continue + if verbose: + print("unable to run %%s" %% dispcmd) + print(e) + return None, None + else: + if verbose: + print("unable to find command, tried %%s" %% (commands,)) + return None, None + stdout = process.communicate()[0].strip().decode() + if process.returncode != 0: + if verbose: + print("unable to run %%s (error)" %% dispcmd) + print("stdout was %%s" %% stdout) + return None, process.returncode + return stdout, process.returncode + + +def versions_from_parentdir(parentdir_prefix, root, verbose): + """Try to determine the version from the parent directory name. + + Source tarballs conventionally unpack into a directory that includes both + the project name and a version string. We will also support searching up + two directory levels for an appropriately named parent directory + """ + rootdirs = [] + + for _ in range(3): + dirname = os.path.basename(root) + if dirname.startswith(parentdir_prefix): + return {"version": dirname[len(parentdir_prefix):], + "full-revisionid": None, + "dirty": False, "error": None, "date": None} + rootdirs.append(root) + root = os.path.dirname(root) # up a level + + if verbose: + print("Tried directories %%s but none started with prefix %%s" %% + (str(rootdirs), parentdir_prefix)) + raise NotThisMethod("rootdir doesn't start with parentdir_prefix") + + +@register_vcs_handler("git", "get_keywords") +def git_get_keywords(versionfile_abs): + """Extract version information from the given file.""" + # the code embedded in _version.py can just fetch the value of these + # keywords. When used from setup.py, we don't want to import _version.py, + # so we do it with a regexp instead. This function is not used from + # _version.py. + keywords = {} + try: + with open(versionfile_abs, "r") as fobj: + for line in fobj: + if line.strip().startswith("git_refnames ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["refnames"] = mo.group(1) + if line.strip().startswith("git_full ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["full"] = mo.group(1) + if line.strip().startswith("git_date ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["date"] = mo.group(1) + except OSError: + pass + return keywords + + +@register_vcs_handler("git", "keywords") +def git_versions_from_keywords(keywords, tag_prefix, verbose): + """Get version information from git keywords.""" + if "refnames" not in keywords: + raise NotThisMethod("Short version file found") + date = keywords.get("date") + if date is not None: + # Use only the last line. Previous lines may contain GPG signature + # information. + date = date.splitlines()[-1] + + # git-2.2.0 added "%%cI", which expands to an ISO-8601 -compliant + # datestamp. However we prefer "%%ci" (which expands to an "ISO-8601 + # -like" string, which we must then edit to make compliant), because + # it's been around since git-1.5.3, and it's too difficult to + # discover which version we're using, or to work around using an + # older one. + date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) + refnames = keywords["refnames"].strip() + if refnames.startswith("$Format"): + if verbose: + print("keywords are unexpanded, not using") + raise NotThisMethod("unexpanded keywords, not a git-archive tarball") + refs = {r.strip() for r in refnames.strip("()").split(",")} + # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of + # just "foo-1.0". If we see a "tag: " prefix, prefer those. + TAG = "tag: " + tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} + if not tags: + # Either we're using git < 1.8.3, or there really are no tags. We use + # a heuristic: assume all version tags have a digit. The old git %%d + # expansion behaves like git log --decorate=short and strips out the + # refs/heads/ and refs/tags/ prefixes that would let us distinguish + # between branches and tags. By ignoring refnames without digits, we + # filter out many common branch names like "release" and + # "stabilization", as well as "HEAD" and "master". + tags = {r for r in refs if re.search(r'\d', r)} + if verbose: + print("discarding '%%s', no digits" %% ",".join(refs - tags)) + if verbose: + print("likely tags: %%s" %% ",".join(sorted(tags))) + for ref in sorted(tags): + # sorting will prefer e.g. "2.0" over "2.0rc1" + if ref.startswith(tag_prefix): + r = ref[len(tag_prefix):] + # Filter out refs that exactly match prefix or that don't start + # with a number once the prefix is stripped (mostly a concern + # when prefix is '') + if not re.match(r'\d', r): + continue + if verbose: + print("picking %%s" %% r) + return {"version": r, + "full-revisionid": keywords["full"].strip(), + "dirty": False, "error": None, + "date": date} + # no suitable tags, so version is "0+unknown", but full hex is still there + if verbose: + print("no suitable tags, using unknown + full revision id") + return {"version": "0+unknown", + "full-revisionid": keywords["full"].strip(), + "dirty": False, "error": "no suitable tags", "date": None} + + +@register_vcs_handler("git", "pieces_from_vcs") +def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): + """Get version from 'git describe' in the root of the source tree. + + This only gets called if the git-archive 'subst' keywords were *not* + expanded, and _version.py hasn't already been rewritten with a short + version string, meaning we're inside a checked out source tree. + """ + GITS = ["git"] + if sys.platform == "win32": + GITS = ["git.cmd", "git.exe"] + + # GIT_DIR can interfere with correct operation of Versioneer. + # It may be intended to be passed to the Versioneer-versioned project, + # but that should not change where we get our version from. + env = os.environ.copy() + env.pop("GIT_DIR", None) + runner = functools.partial(runner, env=env) + + _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, + hide_stderr=not verbose) + if rc != 0: + if verbose: + print("Directory %%s not under git control" %% root) + raise NotThisMethod("'git rev-parse --git-dir' returned error") + + # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] + # if there isn't one, this yields HEX[-dirty] (no NUM) + describe_out, rc = runner(GITS, [ + "describe", "--tags", "--dirty", "--always", "--long", + "--match", f"{tag_prefix}[[:digit:]]*" + ], cwd=root) + # --long was added in git-1.5.5 + if describe_out is None: + raise NotThisMethod("'git describe' failed") + describe_out = describe_out.strip() + full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) + if full_out is None: + raise NotThisMethod("'git rev-parse' failed") + full_out = full_out.strip() + + pieces = {} + pieces["long"] = full_out + pieces["short"] = full_out[:7] # maybe improved later + pieces["error"] = None + + branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], + cwd=root) + # --abbrev-ref was added in git-1.6.3 + if rc != 0 or branch_name is None: + raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") + branch_name = branch_name.strip() + + if branch_name == "HEAD": + # If we aren't exactly on a branch, pick a branch which represents + # the current commit. If all else fails, we are on a branchless + # commit. + branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) + # --contains was added in git-1.5.4 + if rc != 0 or branches is None: + raise NotThisMethod("'git branch --contains' returned error") + branches = branches.split("\n") + + # Remove the first line if we're running detached + if "(" in branches[0]: + branches.pop(0) + + # Strip off the leading "* " from the list of branches. + branches = [branch[2:] for branch in branches] + if "master" in branches: + branch_name = "master" + elif not branches: + branch_name = None + else: + # Pick the first branch that is returned. Good or bad. + branch_name = branches[0] + + pieces["branch"] = branch_name + + # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] + # TAG might have hyphens. + git_describe = describe_out + + # look for -dirty suffix + dirty = git_describe.endswith("-dirty") + pieces["dirty"] = dirty + if dirty: + git_describe = git_describe[:git_describe.rindex("-dirty")] + + # now we have TAG-NUM-gHEX or HEX + + if "-" in git_describe: + # TAG-NUM-gHEX + mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) + if not mo: + # unparsable. Maybe git-describe is misbehaving? + pieces["error"] = ("unable to parse git-describe output: '%%s'" + %% describe_out) + return pieces + + # tag + full_tag = mo.group(1) + if not full_tag.startswith(tag_prefix): + if verbose: + fmt = "tag '%%s' doesn't start with prefix '%%s'" + print(fmt %% (full_tag, tag_prefix)) + pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'" + %% (full_tag, tag_prefix)) + return pieces + pieces["closest-tag"] = full_tag[len(tag_prefix):] + + # distance: number of commits since tag + pieces["distance"] = int(mo.group(2)) + + # commit: short hex revision ID + pieces["short"] = mo.group(3) + + else: + # HEX: no tags + pieces["closest-tag"] = None + out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root) + pieces["distance"] = len(out.split()) # total number of commits + + # commit date: see ISO-8601 comment in git_versions_from_keywords() + date = runner(GITS, ["show", "-s", "--format=%%ci", "HEAD"], cwd=root)[0].strip() + # Use only the last line. Previous lines may contain GPG signature + # information. + date = date.splitlines()[-1] + pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) + + return pieces + + +def plus_or_dot(pieces): + """Return a + if we don't already have one, else return a .""" + if "+" in pieces.get("closest-tag", ""): + return "." + return "+" + + +def render_pep440(pieces): + """Build up version string, with post-release "local version identifier". + + Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you + get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty + + Exceptions: + 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += plus_or_dot(pieces) + rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"], + pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def render_pep440_branch(pieces): + """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . + + The ".dev0" means not master branch. Note that .dev0 sorts backwards + (a feature branch will appear "older" than the master branch). + + Exceptions: + 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0" + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += "+untagged.%%d.g%%s" %% (pieces["distance"], + pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def pep440_split_post(ver): + """Split pep440 version string at the post-release segment. + + Returns the release segments before the post-release and the + post-release version number (or -1 if no post-release segment is present). + """ + vc = str.split(ver, ".post") + return vc[0], int(vc[1] or 0) if len(vc) == 2 else None + + +def render_pep440_pre(pieces): + """TAG[.postN.devDISTANCE] -- No -dirty. + + Exceptions: + 1: no tags. 0.post0.devDISTANCE + """ + if pieces["closest-tag"]: + if pieces["distance"]: + # update the post release segment + tag_version, post_version = pep440_split_post(pieces["closest-tag"]) + rendered = tag_version + if post_version is not None: + rendered += ".post%%d.dev%%d" %% (post_version + 1, pieces["distance"]) + else: + rendered += ".post0.dev%%d" %% (pieces["distance"]) + else: + # no commits, use the tag as the version + rendered = pieces["closest-tag"] + else: + # exception #1 + rendered = "0.post0.dev%%d" %% pieces["distance"] + return rendered + + +def render_pep440_post(pieces): + """TAG[.postDISTANCE[.dev0]+gHEX] . + + The ".dev0" means dirty. Note that .dev0 sorts backwards + (a dirty tree will appear "older" than the corresponding clean one), + but you shouldn't be releasing software with -dirty anyways. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%%d" %% pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "g%%s" %% pieces["short"] + else: + # exception #1 + rendered = "0.post%%d" %% pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + rendered += "+g%%s" %% pieces["short"] + return rendered + + +def render_pep440_post_branch(pieces): + """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . + + The ".dev0" means not master branch. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%%d" %% pieces["distance"] + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "g%%s" %% pieces["short"] + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0.post%%d" %% pieces["distance"] + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += "+g%%s" %% pieces["short"] + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def render_pep440_old(pieces): + """TAG[.postDISTANCE[.dev0]] . + + The ".dev0" means dirty. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%%d" %% pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + else: + # exception #1 + rendered = "0.post%%d" %% pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + return rendered + + +def render_git_describe(pieces): + """TAG[-DISTANCE-gHEX][-dirty]. + + Like 'git describe --tags --dirty --always'. + + Exceptions: + 1: no tags. HEX[-dirty] (note: no 'g' prefix) + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"]: + rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) + else: + # exception #1 + rendered = pieces["short"] + if pieces["dirty"]: + rendered += "-dirty" + return rendered + + +def render_git_describe_long(pieces): + """TAG-DISTANCE-gHEX[-dirty]. + + Like 'git describe --tags --dirty --always -long'. + The distance/hash is unconditional. + + Exceptions: + 1: no tags. HEX[-dirty] (note: no 'g' prefix) + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) + else: + # exception #1 + rendered = pieces["short"] + if pieces["dirty"]: + rendered += "-dirty" + return rendered + + +def render(pieces, style): + """Render the given version pieces into the requested style.""" + if pieces["error"]: + return {"version": "unknown", + "full-revisionid": pieces.get("long"), + "dirty": None, + "error": pieces["error"], + "date": None} + + if not style or style == "default": + style = "pep440" # the default + + if style == "pep440": + rendered = render_pep440(pieces) + elif style == "pep440-branch": + rendered = render_pep440_branch(pieces) + elif style == "pep440-pre": + rendered = render_pep440_pre(pieces) + elif style == "pep440-post": + rendered = render_pep440_post(pieces) + elif style == "pep440-post-branch": + rendered = render_pep440_post_branch(pieces) + elif style == "pep440-old": + rendered = render_pep440_old(pieces) + elif style == "git-describe": + rendered = render_git_describe(pieces) + elif style == "git-describe-long": + rendered = render_git_describe_long(pieces) + else: + raise ValueError("unknown style '%%s'" %% style) + + return {"version": rendered, "full-revisionid": pieces["long"], + "dirty": pieces["dirty"], "error": None, + "date": pieces.get("date")} + + +def get_versions(): + """Get version information or return default if unable to do so.""" + # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have + # __file__, we can work backwards from there to the root. Some + # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which + # case we can only use expanded keywords. + + cfg = get_config() + verbose = cfg.verbose + + try: + return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, + verbose) + except NotThisMethod: + pass + + try: + root = os.path.realpath(__file__) + # versionfile_source is the relative path from the top of the source + # tree (where the .git directory might live) to this file. Invert + # this to find the root from __file__. + for _ in cfg.versionfile_source.split('/'): + root = os.path.dirname(root) + except NameError: + return {"version": "0+unknown", "full-revisionid": None, + "dirty": None, + "error": "unable to find root of source tree", + "date": None} + + try: + pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) + return render(pieces, cfg.style) + except NotThisMethod: + pass + + try: + if cfg.parentdir_prefix: + return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) + except NotThisMethod: + pass + + return {"version": "0+unknown", "full-revisionid": None, + "dirty": None, + "error": "unable to compute version", "date": None} +''' + + +@register_vcs_handler("git", "get_keywords") +def git_get_keywords(versionfile_abs): + """Extract version information from the given file.""" + # the code embedded in _version.py can just fetch the value of these + # keywords. When used from setup.py, we don't want to import _version.py, + # so we do it with a regexp instead. This function is not used from + # _version.py. + keywords = {} + try: + with open(versionfile_abs, "r") as fobj: + for line in fobj: + if line.strip().startswith("git_refnames ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["refnames"] = mo.group(1) + if line.strip().startswith("git_full ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["full"] = mo.group(1) + if line.strip().startswith("git_date ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["date"] = mo.group(1) + except OSError: + pass + return keywords + + +@register_vcs_handler("git", "keywords") +def git_versions_from_keywords(keywords, tag_prefix, verbose): + """Get version information from git keywords.""" + if "refnames" not in keywords: + raise NotThisMethod("Short version file found") + date = keywords.get("date") + if date is not None: + # Use only the last line. Previous lines may contain GPG signature + # information. + date = date.splitlines()[-1] + + # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant + # datestamp. However we prefer "%ci" (which expands to an "ISO-8601 + # -like" string, which we must then edit to make compliant), because + # it's been around since git-1.5.3, and it's too difficult to + # discover which version we're using, or to work around using an + # older one. + date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) + refnames = keywords["refnames"].strip() + if refnames.startswith("$Format"): + if verbose: + print("keywords are unexpanded, not using") + raise NotThisMethod("unexpanded keywords, not a git-archive tarball") + refs = {r.strip() for r in refnames.strip("()").split(",")} + # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of + # just "foo-1.0". If we see a "tag: " prefix, prefer those. + TAG = "tag: " + tags = {r[len(TAG):] for r in refs if r.startswith(TAG)} + if not tags: + # Either we're using git < 1.8.3, or there really are no tags. We use + # a heuristic: assume all version tags have a digit. The old git %d + # expansion behaves like git log --decorate=short and strips out the + # refs/heads/ and refs/tags/ prefixes that would let us distinguish + # between branches and tags. By ignoring refnames without digits, we + # filter out many common branch names like "release" and + # "stabilization", as well as "HEAD" and "master". + tags = {r for r in refs if re.search(r'\d', r)} + if verbose: + print("discarding '%s', no digits" % ",".join(refs - tags)) + if verbose: + print("likely tags: %s" % ",".join(sorted(tags))) + for ref in sorted(tags): + # sorting will prefer e.g. "2.0" over "2.0rc1" + if ref.startswith(tag_prefix): + r = ref[len(tag_prefix):] + # Filter out refs that exactly match prefix or that don't start + # with a number once the prefix is stripped (mostly a concern + # when prefix is '') + if not re.match(r'\d', r): + continue + if verbose: + print("picking %s" % r) + return {"version": r, + "full-revisionid": keywords["full"].strip(), + "dirty": False, "error": None, + "date": date} + # no suitable tags, so version is "0+unknown", but full hex is still there + if verbose: + print("no suitable tags, using unknown + full revision id") + return {"version": "0+unknown", + "full-revisionid": keywords["full"].strip(), + "dirty": False, "error": "no suitable tags", "date": None} + + +@register_vcs_handler("git", "pieces_from_vcs") +def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): + """Get version from 'git describe' in the root of the source tree. + + This only gets called if the git-archive 'subst' keywords were *not* + expanded, and _version.py hasn't already been rewritten with a short + version string, meaning we're inside a checked out source tree. + """ + GITS = ["git"] + if sys.platform == "win32": + GITS = ["git.cmd", "git.exe"] + + # GIT_DIR can interfere with correct operation of Versioneer. + # It may be intended to be passed to the Versioneer-versioned project, + # but that should not change where we get our version from. + env = os.environ.copy() + env.pop("GIT_DIR", None) + runner = functools.partial(runner, env=env) + + _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, + hide_stderr=not verbose) + if rc != 0: + if verbose: + print("Directory %s not under git control" % root) + raise NotThisMethod("'git rev-parse --git-dir' returned error") + + # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] + # if there isn't one, this yields HEX[-dirty] (no NUM) + describe_out, rc = runner(GITS, [ + "describe", "--tags", "--dirty", "--always", "--long", + "--match", f"{tag_prefix}[[:digit:]]*" + ], cwd=root) + # --long was added in git-1.5.5 + if describe_out is None: + raise NotThisMethod("'git describe' failed") + describe_out = describe_out.strip() + full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) + if full_out is None: + raise NotThisMethod("'git rev-parse' failed") + full_out = full_out.strip() + + pieces = {} + pieces["long"] = full_out + pieces["short"] = full_out[:7] # maybe improved later + pieces["error"] = None + + branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], + cwd=root) + # --abbrev-ref was added in git-1.6.3 + if rc != 0 or branch_name is None: + raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") + branch_name = branch_name.strip() + + if branch_name == "HEAD": + # If we aren't exactly on a branch, pick a branch which represents + # the current commit. If all else fails, we are on a branchless + # commit. + branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) + # --contains was added in git-1.5.4 + if rc != 0 or branches is None: + raise NotThisMethod("'git branch --contains' returned error") + branches = branches.split("\n") + + # Remove the first line if we're running detached + if "(" in branches[0]: + branches.pop(0) + + # Strip off the leading "* " from the list of branches. + branches = [branch[2:] for branch in branches] + if "master" in branches: + branch_name = "master" + elif not branches: + branch_name = None + else: + # Pick the first branch that is returned. Good or bad. + branch_name = branches[0] + + pieces["branch"] = branch_name + + # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] + # TAG might have hyphens. + git_describe = describe_out + + # look for -dirty suffix + dirty = git_describe.endswith("-dirty") + pieces["dirty"] = dirty + if dirty: + git_describe = git_describe[:git_describe.rindex("-dirty")] + + # now we have TAG-NUM-gHEX or HEX + + if "-" in git_describe: + # TAG-NUM-gHEX + mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) + if not mo: + # unparsable. Maybe git-describe is misbehaving? + pieces["error"] = ("unable to parse git-describe output: '%s'" + % describe_out) + return pieces + + # tag + full_tag = mo.group(1) + if not full_tag.startswith(tag_prefix): + if verbose: + fmt = "tag '%s' doesn't start with prefix '%s'" + print(fmt % (full_tag, tag_prefix)) + pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" + % (full_tag, tag_prefix)) + return pieces + pieces["closest-tag"] = full_tag[len(tag_prefix):] + + # distance: number of commits since tag + pieces["distance"] = int(mo.group(2)) + + # commit: short hex revision ID + pieces["short"] = mo.group(3) + + else: + # HEX: no tags + pieces["closest-tag"] = None + out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root) + pieces["distance"] = len(out.split()) # total number of commits + + # commit date: see ISO-8601 comment in git_versions_from_keywords() + date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip() + # Use only the last line. Previous lines may contain GPG signature + # information. + date = date.splitlines()[-1] + pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) + + return pieces + + +def do_vcs_install(versionfile_source, ipy): + """Git-specific installation logic for Versioneer. + + For Git, this means creating/changing .gitattributes to mark _version.py + for export-subst keyword substitution. + """ + GITS = ["git"] + if sys.platform == "win32": + GITS = ["git.cmd", "git.exe"] + files = [versionfile_source] + if ipy: + files.append(ipy) + if "VERSIONEER_PEP518" not in globals(): + try: + my_path = __file__ + if my_path.endswith((".pyc", ".pyo")): + my_path = os.path.splitext(my_path)[0] + ".py" + versioneer_file = os.path.relpath(my_path) + except NameError: + versioneer_file = "versioneer.py" + files.append(versioneer_file) + present = False + try: + with open(".gitattributes", "r") as fobj: + for line in fobj: + if line.strip().startswith(versionfile_source): + if "export-subst" in line.strip().split()[1:]: + present = True + break + except OSError: + pass + if not present: + with open(".gitattributes", "a+") as fobj: + fobj.write(f"{versionfile_source} export-subst\n") + files.append(".gitattributes") + run_command(GITS, ["add", "--"] + files) + + +def versions_from_parentdir(parentdir_prefix, root, verbose): + """Try to determine the version from the parent directory name. + + Source tarballs conventionally unpack into a directory that includes both + the project name and a version string. We will also support searching up + two directory levels for an appropriately named parent directory + """ + rootdirs = [] + + for _ in range(3): + dirname = os.path.basename(root) + if dirname.startswith(parentdir_prefix): + return {"version": dirname[len(parentdir_prefix):], + "full-revisionid": None, + "dirty": False, "error": None, "date": None} + rootdirs.append(root) + root = os.path.dirname(root) # up a level + + if verbose: + print("Tried directories %s but none started with prefix %s" % + (str(rootdirs), parentdir_prefix)) + raise NotThisMethod("rootdir doesn't start with parentdir_prefix") + + +SHORT_VERSION_PY = """ +# This file was generated by 'versioneer.py' (0.28) from +# revision-control system data, or from the parent directory name of an +# unpacked source archive. Distribution tarballs contain a pre-generated copy +# of this file. + +import json + +version_json = ''' +%s +''' # END VERSION_JSON + + +def get_versions(): + return json.loads(version_json) +""" + + +def versions_from_file(filename): + """Try to determine the version from _version.py if present.""" + try: + with open(filename) as f: + contents = f.read() + except OSError: + raise NotThisMethod("unable to read _version.py") + mo = re.search(r"version_json = '''\n(.*)''' # END VERSION_JSON", + contents, re.M | re.S) + if not mo: + mo = re.search(r"version_json = '''\r\n(.*)''' # END VERSION_JSON", + contents, re.M | re.S) + if not mo: + raise NotThisMethod("no version_json in _version.py") + return json.loads(mo.group(1)) + + +def write_to_version_file(filename, versions): + """Write the given version number to the given _version.py file.""" + os.unlink(filename) + contents = json.dumps(versions, sort_keys=True, + indent=1, separators=(",", ": ")) + with open(filename, "w") as f: + f.write(SHORT_VERSION_PY % contents) + + print("set %s to '%s'" % (filename, versions["version"])) + + +def plus_or_dot(pieces): + """Return a + if we don't already have one, else return a .""" + if "+" in pieces.get("closest-tag", ""): + return "." + return "+" + + +def render_pep440(pieces): + """Build up version string, with post-release "local version identifier". + + Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you + get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty + + Exceptions: + 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += plus_or_dot(pieces) + rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0+untagged.%d.g%s" % (pieces["distance"], + pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def render_pep440_branch(pieces): + """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . + + The ".dev0" means not master branch. Note that .dev0 sorts backwards + (a feature branch will appear "older" than the master branch). + + Exceptions: + 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0" + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += "+untagged.%d.g%s" % (pieces["distance"], + pieces["short"]) + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def pep440_split_post(ver): + """Split pep440 version string at the post-release segment. + + Returns the release segments before the post-release and the + post-release version number (or -1 if no post-release segment is present). + """ + vc = str.split(ver, ".post") + return vc[0], int(vc[1] or 0) if len(vc) == 2 else None + + +def render_pep440_pre(pieces): + """TAG[.postN.devDISTANCE] -- No -dirty. + + Exceptions: + 1: no tags. 0.post0.devDISTANCE + """ + if pieces["closest-tag"]: + if pieces["distance"]: + # update the post release segment + tag_version, post_version = pep440_split_post(pieces["closest-tag"]) + rendered = tag_version + if post_version is not None: + rendered += ".post%d.dev%d" % (post_version + 1, pieces["distance"]) + else: + rendered += ".post0.dev%d" % (pieces["distance"]) + else: + # no commits, use the tag as the version + rendered = pieces["closest-tag"] + else: + # exception #1 + rendered = "0.post0.dev%d" % pieces["distance"] + return rendered + + +def render_pep440_post(pieces): + """TAG[.postDISTANCE[.dev0]+gHEX] . + + The ".dev0" means dirty. Note that .dev0 sorts backwards + (a dirty tree will appear "older" than the corresponding clean one), + but you shouldn't be releasing software with -dirty anyways. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%d" % pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "g%s" % pieces["short"] + else: + # exception #1 + rendered = "0.post%d" % pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + rendered += "+g%s" % pieces["short"] + return rendered + + +def render_pep440_post_branch(pieces): + """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . + + The ".dev0" means not master branch. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%d" % pieces["distance"] + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += "g%s" % pieces["short"] + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0.post%d" % pieces["distance"] + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += "+g%s" % pieces["short"] + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def render_pep440_old(pieces): + """TAG[.postDISTANCE[.dev0]] . + + The ".dev0" means dirty. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += ".post%d" % pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + else: + # exception #1 + rendered = "0.post%d" % pieces["distance"] + if pieces["dirty"]: + rendered += ".dev0" + return rendered + + +def render_git_describe(pieces): + """TAG[-DISTANCE-gHEX][-dirty]. + + Like 'git describe --tags --dirty --always'. + + Exceptions: + 1: no tags. HEX[-dirty] (note: no 'g' prefix) + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"]: + rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) + else: + # exception #1 + rendered = pieces["short"] + if pieces["dirty"]: + rendered += "-dirty" + return rendered + + +def render_git_describe_long(pieces): + """TAG-DISTANCE-gHEX[-dirty]. + + Like 'git describe --tags --dirty --always -long'. + The distance/hash is unconditional. + + Exceptions: + 1: no tags. HEX[-dirty] (note: no 'g' prefix) + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) + else: + # exception #1 + rendered = pieces["short"] + if pieces["dirty"]: + rendered += "-dirty" + return rendered + + +def render(pieces, style): + """Render the given version pieces into the requested style.""" + if pieces["error"]: + return {"version": "unknown", + "full-revisionid": pieces.get("long"), + "dirty": None, + "error": pieces["error"], + "date": None} + + if not style or style == "default": + style = "pep440" # the default + + if style == "pep440": + rendered = render_pep440(pieces) + elif style == "pep440-branch": + rendered = render_pep440_branch(pieces) + elif style == "pep440-pre": + rendered = render_pep440_pre(pieces) + elif style == "pep440-post": + rendered = render_pep440_post(pieces) + elif style == "pep440-post-branch": + rendered = render_pep440_post_branch(pieces) + elif style == "pep440-old": + rendered = render_pep440_old(pieces) + elif style == "git-describe": + rendered = render_git_describe(pieces) + elif style == "git-describe-long": + rendered = render_git_describe_long(pieces) + else: + raise ValueError("unknown style '%s'" % style) + + return {"version": rendered, "full-revisionid": pieces["long"], + "dirty": pieces["dirty"], "error": None, + "date": pieces.get("date")} + + +class VersioneerBadRootError(Exception): + """The project root directory is unknown or missing key files.""" + + +def get_versions(verbose=False): + """Get the project version from whatever source is available. + + Returns dict with two keys: 'version' and 'full'. + """ + if "versioneer" in sys.modules: + # see the discussion in cmdclass.py:get_cmdclass() + del sys.modules["versioneer"] + + root = get_root() + cfg = get_config_from_root(root) + + assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg" + handlers = HANDLERS.get(cfg.VCS) + assert handlers, "unrecognized VCS '%s'" % cfg.VCS + verbose = verbose or cfg.verbose + assert cfg.versionfile_source is not None, \ + "please set versioneer.versionfile_source" + assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix" + + versionfile_abs = os.path.join(root, cfg.versionfile_source) + + # extract version from first of: _version.py, VCS command (e.g. 'git + # describe'), parentdir. This is meant to work for developers using a + # source checkout, for users of a tarball created by 'setup.py sdist', + # and for users of a tarball/zipball created by 'git archive' or github's + # download-from-tag feature or the equivalent in other VCSes. + + get_keywords_f = handlers.get("get_keywords") + from_keywords_f = handlers.get("keywords") + if get_keywords_f and from_keywords_f: + try: + keywords = get_keywords_f(versionfile_abs) + ver = from_keywords_f(keywords, cfg.tag_prefix, verbose) + if verbose: + print("got version from expanded keyword %s" % ver) + return ver + except NotThisMethod: + pass + + try: + ver = versions_from_file(versionfile_abs) + if verbose: + print("got version from file %s %s" % (versionfile_abs, ver)) + return ver + except NotThisMethod: + pass + + from_vcs_f = handlers.get("pieces_from_vcs") + if from_vcs_f: + try: + pieces = from_vcs_f(cfg.tag_prefix, root, verbose) + ver = render(pieces, cfg.style) + if verbose: + print("got version from VCS %s" % ver) + return ver + except NotThisMethod: + pass + + try: + if cfg.parentdir_prefix: + ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose) + if verbose: + print("got version from parentdir %s" % ver) + return ver + except NotThisMethod: + pass + + if verbose: + print("unable to compute version") + + return {"version": "0+unknown", "full-revisionid": None, + "dirty": None, "error": "unable to compute version", + "date": None} + + +def get_version(): + """Get the short version string for this project.""" + return get_versions()["version"] + + +def get_cmdclass(cmdclass=None): + """Get the custom setuptools subclasses used by Versioneer. + + If the package uses a different cmdclass (e.g. one from numpy), it + should be provide as an argument. + """ + if "versioneer" in sys.modules: + del sys.modules["versioneer"] + # this fixes the "python setup.py develop" case (also 'install' and + # 'easy_install .'), in which subdependencies of the main project are + # built (using setup.py bdist_egg) in the same python process. Assume + # a main project A and a dependency B, which use different versions + # of Versioneer. A's setup.py imports A's Versioneer, leaving it in + # sys.modules by the time B's setup.py is executed, causing B to run + # with the wrong versioneer. Setuptools wraps the sub-dep builds in a + # sandbox that restores sys.modules to it's pre-build state, so the + # parent is protected against the child's "import versioneer". By + # removing ourselves from sys.modules here, before the child build + # happens, we protect the child from the parent's versioneer too. + # Also see https://github.com/python-versioneer/python-versioneer/issues/52 + + cmds = {} if cmdclass is None else cmdclass.copy() + + # we add "version" to setuptools + from setuptools import Command + + class cmd_version(Command): + description = "report generated version string" + user_options = [] + boolean_options = [] + + def initialize_options(self): + pass + + def finalize_options(self): + pass + + def run(self): + vers = get_versions(verbose=True) + print("Version: %s" % vers["version"]) + print(" full-revisionid: %s" % vers.get("full-revisionid")) + print(" dirty: %s" % vers.get("dirty")) + print(" date: %s" % vers.get("date")) + if vers["error"]: + print(" error: %s" % vers["error"]) + cmds["version"] = cmd_version + + # we override "build_py" in setuptools + # + # most invocation pathways end up running build_py: + # distutils/build -> build_py + # distutils/install -> distutils/build ->.. + # setuptools/bdist_wheel -> distutils/install ->.. + # setuptools/bdist_egg -> distutils/install_lib -> build_py + # setuptools/install -> bdist_egg ->.. + # setuptools/develop -> ? + # pip install: + # copies source tree to a tempdir before running egg_info/etc + # if .git isn't copied too, 'git describe' will fail + # then does setup.py bdist_wheel, or sometimes setup.py install + # setup.py egg_info -> ? + + # pip install -e . and setuptool/editable_wheel will invoke build_py + # but the build_py command is not expected to copy any files. + + # we override different "build_py" commands for both environments + if 'build_py' in cmds: + _build_py = cmds['build_py'] + else: + from setuptools.command.build_py import build_py as _build_py + + class cmd_build_py(_build_py): + def run(self): + root = get_root() + cfg = get_config_from_root(root) + versions = get_versions() + _build_py.run(self) + if getattr(self, "editable_mode", False): + # During editable installs `.py` and data files are + # not copied to build_lib + return + # now locate _version.py in the new build/ directory and replace + # it with an updated value + if cfg.versionfile_build: + target_versionfile = os.path.join(self.build_lib, + cfg.versionfile_build) + print("UPDATING %s" % target_versionfile) + write_to_version_file(target_versionfile, versions) + cmds["build_py"] = cmd_build_py + + if 'build_ext' in cmds: + _build_ext = cmds['build_ext'] + else: + from setuptools.command.build_ext import build_ext as _build_ext + + class cmd_build_ext(_build_ext): + def run(self): + root = get_root() + cfg = get_config_from_root(root) + versions = get_versions() + _build_ext.run(self) + if self.inplace: + # build_ext --inplace will only build extensions in + # build/lib<..> dir with no _version.py to write to. + # As in place builds will already have a _version.py + # in the module dir, we do not need to write one. + return + # now locate _version.py in the new build/ directory and replace + # it with an updated value + if not cfg.versionfile_build: + return + target_versionfile = os.path.join(self.build_lib, + cfg.versionfile_build) + if not os.path.exists(target_versionfile): + print(f"Warning: {target_versionfile} does not exist, skipping " + "version update. This can happen if you are running build_ext " + "without first running build_py.") + return + print("UPDATING %s" % target_versionfile) + write_to_version_file(target_versionfile, versions) + cmds["build_ext"] = cmd_build_ext + + if "cx_Freeze" in sys.modules: # cx_freeze enabled? + from cx_Freeze.dist import build_exe as _build_exe + # nczeczulin reports that py2exe won't like the pep440-style string + # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g. + # setup(console=[{ + # "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION + # "product_version": versioneer.get_version(), + # ... + + class cmd_build_exe(_build_exe): + def run(self): + root = get_root() + cfg = get_config_from_root(root) + versions = get_versions() + target_versionfile = cfg.versionfile_source + print("UPDATING %s" % target_versionfile) + write_to_version_file(target_versionfile, versions) + + _build_exe.run(self) + os.unlink(target_versionfile) + with open(cfg.versionfile_source, "w") as f: + LONG = LONG_VERSION_PY[cfg.VCS] + f.write(LONG % + {"DOLLAR": "$", + "STYLE": cfg.style, + "TAG_PREFIX": cfg.tag_prefix, + "PARENTDIR_PREFIX": cfg.parentdir_prefix, + "VERSIONFILE_SOURCE": cfg.versionfile_source, + }) + cmds["build_exe"] = cmd_build_exe + del cmds["build_py"] + + if 'py2exe' in sys.modules: # py2exe enabled? + try: + from py2exe.setuptools_buildexe import py2exe as _py2exe + except ImportError: + from py2exe.distutils_buildexe import py2exe as _py2exe + + class cmd_py2exe(_py2exe): + def run(self): + root = get_root() + cfg = get_config_from_root(root) + versions = get_versions() + target_versionfile = cfg.versionfile_source + print("UPDATING %s" % target_versionfile) + write_to_version_file(target_versionfile, versions) + + _py2exe.run(self) + os.unlink(target_versionfile) + with open(cfg.versionfile_source, "w") as f: + LONG = LONG_VERSION_PY[cfg.VCS] + f.write(LONG % + {"DOLLAR": "$", + "STYLE": cfg.style, + "TAG_PREFIX": cfg.tag_prefix, + "PARENTDIR_PREFIX": cfg.parentdir_prefix, + "VERSIONFILE_SOURCE": cfg.versionfile_source, + }) + cmds["py2exe"] = cmd_py2exe + + # sdist farms its file list building out to egg_info + if 'egg_info' in cmds: + _egg_info = cmds['egg_info'] + else: + from setuptools.command.egg_info import egg_info as _egg_info + + class cmd_egg_info(_egg_info): + def find_sources(self): + # egg_info.find_sources builds the manifest list and writes it + # in one shot + super().find_sources() + + # Modify the filelist and normalize it + root = get_root() + cfg = get_config_from_root(root) + self.filelist.append('versioneer.py') + if cfg.versionfile_source: + # There are rare cases where versionfile_source might not be + # included by default, so we must be explicit + self.filelist.append(cfg.versionfile_source) + self.filelist.sort() + self.filelist.remove_duplicates() + + # The write method is hidden in the manifest_maker instance that + # generated the filelist and was thrown away + # We will instead replicate their final normalization (to unicode, + # and POSIX-style paths) + from setuptools import unicode_utils + normalized = [unicode_utils.filesys_decode(f).replace(os.sep, '/') + for f in self.filelist.files] + + manifest_filename = os.path.join(self.egg_info, 'SOURCES.txt') + with open(manifest_filename, 'w') as fobj: + fobj.write('\n'.join(normalized)) + + cmds['egg_info'] = cmd_egg_info + + # we override different "sdist" commands for both environments + if 'sdist' in cmds: + _sdist = cmds['sdist'] + else: + from setuptools.command.sdist import sdist as _sdist + + class cmd_sdist(_sdist): + def run(self): + versions = get_versions() + self._versioneer_generated_versions = versions + # unless we update this, the command will keep using the old + # version + self.distribution.metadata.version = versions["version"] + return _sdist.run(self) + + def make_release_tree(self, base_dir, files): + root = get_root() + cfg = get_config_from_root(root) + _sdist.make_release_tree(self, base_dir, files) + # now locate _version.py in the new base_dir directory + # (remembering that it may be a hardlink) and replace it with an + # updated value + target_versionfile = os.path.join(base_dir, cfg.versionfile_source) + print("UPDATING %s" % target_versionfile) + write_to_version_file(target_versionfile, + self._versioneer_generated_versions) + cmds["sdist"] = cmd_sdist + + return cmds + + +CONFIG_ERROR = """ +setup.cfg is missing the necessary Versioneer configuration. You need +a section like: + + [versioneer] + VCS = git + style = pep440 + versionfile_source = src/myproject/_version.py + versionfile_build = myproject/_version.py + tag_prefix = + parentdir_prefix = myproject- + +You will also need to edit your setup.py to use the results: + + import versioneer + setup(version=versioneer.get_version(), + cmdclass=versioneer.get_cmdclass(), ...) + +Please read the docstring in ./versioneer.py for configuration instructions, +edit setup.cfg, and re-run the installer or 'python versioneer.py setup'. +""" + +SAMPLE_CONFIG = """ +# See the docstring in versioneer.py for instructions. Note that you must +# re-run 'versioneer.py setup' after changing this section, and commit the +# resulting files. + +[versioneer] +#VCS = git +#style = pep440 +#versionfile_source = +#versionfile_build = +#tag_prefix = +#parentdir_prefix = + +""" + +OLD_SNIPPET = """ +from ._version import get_versions +__version__ = get_versions()['version'] +del get_versions +""" + +INIT_PY_SNIPPET = """ +from . import {0} +__version__ = {0}.get_versions()['version'] +""" + + +def do_setup(): + """Do main VCS-independent setup function for installing Versioneer.""" + root = get_root() + try: + cfg = get_config_from_root(root) + except (OSError, configparser.NoSectionError, + configparser.NoOptionError) as e: + if isinstance(e, (OSError, configparser.NoSectionError)): + print("Adding sample versioneer config to setup.cfg", + file=sys.stderr) + with open(os.path.join(root, "setup.cfg"), "a") as f: + f.write(SAMPLE_CONFIG) + print(CONFIG_ERROR, file=sys.stderr) + return 1 + + print(" creating %s" % cfg.versionfile_source) + with open(cfg.versionfile_source, "w") as f: + LONG = LONG_VERSION_PY[cfg.VCS] + f.write(LONG % {"DOLLAR": "$", + "STYLE": cfg.style, + "TAG_PREFIX": cfg.tag_prefix, + "PARENTDIR_PREFIX": cfg.parentdir_prefix, + "VERSIONFILE_SOURCE": cfg.versionfile_source, + }) + + ipy = os.path.join(os.path.dirname(cfg.versionfile_source), + "__init__.py") + if os.path.exists(ipy): + try: + with open(ipy, "r") as f: + old = f.read() + except OSError: + old = "" + module = os.path.splitext(os.path.basename(cfg.versionfile_source))[0] + snippet = INIT_PY_SNIPPET.format(module) + if OLD_SNIPPET in old: + print(" replacing boilerplate in %s" % ipy) + with open(ipy, "w") as f: + f.write(old.replace(OLD_SNIPPET, snippet)) + elif snippet not in old: + print(" appending to %s" % ipy) + with open(ipy, "a") as f: + f.write(snippet) + else: + print(" %s unmodified" % ipy) + else: + print(" %s doesn't exist, ok" % ipy) + ipy = None + + # Make VCS-specific changes. For git, this means creating/changing + # .gitattributes to mark _version.py for export-subst keyword + # substitution. + do_vcs_install(cfg.versionfile_source, ipy) + return 0 + + +def scan_setup_py(): + """Validate the contents of setup.py against Versioneer's expectations.""" + found = set() + setters = False + errors = 0 + with open("setup.py", "r") as f: + for line in f.readlines(): + if "import versioneer" in line: + found.add("import") + if "versioneer.get_cmdclass()" in line: + found.add("cmdclass") + if "versioneer.get_version()" in line: + found.add("get_version") + if "versioneer.VCS" in line: + setters = True + if "versioneer.versionfile_source" in line: + setters = True + if len(found) != 3: + print("") + print("Your setup.py appears to be missing some important items") + print("(but I might be wrong). Please make sure it has something") + print("roughly like the following:") + print("") + print(" import versioneer") + print(" setup( version=versioneer.get_version(),") + print(" cmdclass=versioneer.get_cmdclass(), ...)") + print("") + errors += 1 + if setters: + print("You should remove lines like 'versioneer.VCS = ' and") + print("'versioneer.versionfile_source = ' . This configuration") + print("now lives in setup.cfg, and should be removed from setup.py") + print("") + errors += 1 + return errors + + +def setup_command(): + """Set up Versioneer and exit with appropriate error code.""" + errors = do_setup() + errors += scan_setup_py() + sys.exit(1 if errors else 0) + + +if __name__ == "__main__": + cmd = sys.argv[1] + if cmd == "setup": + setup_command()