From db970ac8d716e392d779bf420e872edac99f031c Mon Sep 17 00:00:00 2001 From: Felix Hekhorn Date: Wed, 25 Sep 2024 18:42:34 +0300 Subject: [PATCH] Iterate Python introduction nb --- pineappl_py/.gitignore | 3 +- pineappl_py/docs/source/introduction.ipynb | 382 +++++++++++++-------- pineappl_py/pyproject.toml | 3 +- 3 files changed, 234 insertions(+), 154 deletions(-) diff --git a/pineappl_py/.gitignore b/pineappl_py/.gitignore index 83eb351c..be08e5e8 100644 --- a/pineappl_py/.gitignore +++ b/pineappl_py/.gitignore @@ -1,4 +1,5 @@ - +.ipynb_checkpoints +docs/source/*.pineappl.lz4 ### Python ### # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/pineappl_py/docs/source/introduction.ipynb b/pineappl_py/docs/source/introduction.ipynb index 3b5f2d68..63808c6c 100644 --- a/pineappl_py/docs/source/introduction.ipynb +++ b/pineappl_py/docs/source/introduction.ipynb @@ -88,36 +88,6 @@ "grid = pineappl.grid.Grid.read(\"./LHCB_DY_8TEV.pineappl.lz4\")" ] }, - { - "cell_type": "markdown", - "id": "88df7978-cff0-4ca3-b767-552dae5cf095", - "metadata": {}, - "source": [ - "If the grid is actually a fast-kernel (FK) table, then it can loaded using the following:" - ] - }, - { - "cell_type": "markdown", - "id": "e7c9873c-ff9a-43d1-9dea-1c6280b1b973", - "metadata": {}, - "source": [ - "```python\n", - "# Loading a FK table\n", - "fk = pineappl.fk_table.FkTable.read(\"path/to/fktable.pineappl.lz4\")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "f0a17fb4-4216-44bd-a111-8707413c815e", - "metadata": {}, - "source": [ - "The difference between a PineAPPL grid and an FK table is that the latter\n", - "contains the Evolution Kernel Operators ([EKO](https://eko.readthedocs.io/))\n", - "which encode the DGLAP evolution equations. Furthermore, FK tables do not\n", - "have perturbative orders while PineAPPL grids do." - ] - }, { "cell_type": "markdown", "id": "d0449d9b-26fc-4847-9689-5a4361cfef8b", @@ -146,7 +116,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "LHAPDF 6.5.4 loading /home/tanjona/LHAPDF_PATH/NNPDF40_nnlo_as_01180/NNPDF40_nnlo_as_01180_0000.dat\n", + "LHAPDF 6.5.4 loading /home/felix/local/share/LHAPDF/NNPDF40_nnlo_as_01180/NNPDF40_nnlo_as_01180_0000.dat\n", "NNPDF40_nnlo_as_01180 PDF set, member #0, version 1; LHAPDF ID = 331100\n" ] } @@ -179,28 +149,41 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape: (18, 2)\n", - "┌──────┬─────────────┐\n", - "│ bins ┆ predictions │\n", - "│ --- ┆ --- │\n", - "│ i64 ┆ f64 │\n", - "╞══════╪═════════════╡\n", - "│ 0 ┆ 8.159231 │\n", - "│ 1 ┆ 23.890855 │\n", - "│ 2 ┆ 38.306994 │\n", - "│ 3 ┆ 51.278627 │\n", - "│ 4 ┆ 62.349204 │\n", - "│ … ┆ … │\n", - "│ 13 ┆ 22.640245 │\n", - "│ 14 ┆ 12.927123 │\n", - "│ 15 ┆ 6.138797 │\n", - "│ 16 ┆ 1.435175 │\n", - "│ 17 ┆ 0.052598 │\n", - "└──────┴─────────────┘\n" - ] + "data": { + "text/html": [ + "
\n", + "shape: (18, 2)
binspredictions
i64f64
08.159231
123.890855
238.306994
351.278627
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……
1322.640245
1412.927123
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161.435175
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" + ], + "text/plain": [ + "shape: (18, 2)\n", + "┌──────┬─────────────┐\n", + "│ bins ┆ predictions │\n", + "│ --- ┆ --- │\n", + "│ i64 ┆ f64 │\n", + "╞══════╪═════════════╡\n", + "│ 0 ┆ 8.159231 │\n", + "│ 1 ┆ 23.890855 │\n", + "│ 2 ┆ 38.306994 │\n", + "│ 3 ┆ 51.278627 │\n", + "│ 4 ┆ 62.349204 │\n", + "│ … ┆ … │\n", + "│ 13 ┆ 22.640245 │\n", + "│ 14 ┆ 12.927123 │\n", + "│ 15 ┆ 6.138797 │\n", + "│ 16 ┆ 1.435175 │\n", + "│ 17 ┆ 0.052598 │\n", + "└──────┴─────────────┘" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -209,7 +192,7 @@ " \"bins\": range(predictions.size),\n", " \"predictions\": predictions,\n", "})\n", - "print(df_preds)" + "df_preds" ] }, { @@ -235,7 +218,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -395,24 +378,37 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape: (7, 5)\n", - "┌───────┬─────┬─────┬─────┬─────┐\n", - "│ index ┆ as ┆ a ┆ lf ┆ lr │\n", - "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ u32 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │\n", - "╞═══════╪═════╪═════╪═════╪═════╡\n", - "│ 0 ┆ 0 ┆ 2 ┆ 0 ┆ 0 │\n", - "│ 1 ┆ 1 ┆ 2 ┆ 0 ┆ 0 │\n", - "│ 2 ┆ 1 ┆ 2 ┆ 1 ┆ 0 │\n", - "│ 3 ┆ 1 ┆ 2 ┆ 0 ┆ 1 │\n", - "│ 4 ┆ 0 ┆ 3 ┆ 0 ┆ 0 │\n", - "│ 5 ┆ 0 ┆ 3 ┆ 1 ┆ 0 │\n", - "│ 6 ┆ 0 ┆ 3 ┆ 0 ┆ 1 │\n", - "└───────┴─────┴─────┴─────┴─────┘\n" - ] + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "shape: (7, 5)\n", + "┌───────┬─────┬─────┬─────┬─────┐\n", + "│ index ┆ as ┆ a ┆ lf ┆ lr │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ u32 ┆ i64 ┆ i64 ┆ i64 ┆ i64 │\n", + "╞═══════╪═════╪═════╪═════╪═════╡\n", + "│ 0 ┆ 0 ┆ 2 ┆ 0 ┆ 0 │\n", + "│ 1 ┆ 1 ┆ 2 ┆ 0 ┆ 0 │\n", + "│ 2 ┆ 1 ┆ 2 ┆ 1 ┆ 0 │\n", + "│ 3 ┆ 1 ┆ 2 ┆ 0 ┆ 1 │\n", + "│ 4 ┆ 0 ┆ 3 ┆ 0 ┆ 0 │\n", + "│ 5 ┆ 0 ┆ 3 ┆ 1 ┆ 0 │\n", + "│ 6 ┆ 0 ┆ 3 ┆ 0 ┆ 1 │\n", + "└───────┴─────┴─────┴─────┴─────┘" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -425,7 +421,7 @@ " np.array(orders),\n", " schema=[\"as\", \"a\", \"lf\", \"lr\"]\n", ")\n", - "print(df_orders.with_row_index())" + "df_orders.with_row_index()" ] }, { @@ -435,7 +431,7 @@ "source": [ "The table above lists the perturbative orders contained in the\n", "grid where the powers of the strong coupling $a_s$, the electroweak\n", - "coupling $a$, the factorization $\\ell_F$ and renormalization $\\ell_R$ \n", + "coupling $a$, the factorization $\\ell_F = \\log(\\mu_F^2/Q^2)$ and renormalization $\\ell_R=\\log(\\mu_R^2/Q^2)$ \n", "logs are shown. For instance, the first index shows that the grid \n", "contains a leading-order (LO) which has the coupling $a_s^2$." ] @@ -448,6 +444,16 @@ "#### Observable binning" ] }, + { + "cell_type": "markdown", + "id": "c7696983-778e-4d20-9c74-bb7e210cb530", + "metadata": {}, + "source": [ + "Grids often correspond to an dataset, which correspond to an observable measured at several kinematic configurations, or bins.\n", + "The bins might be just 1 dimensional, as is the case here with rapidity, or for more complicated measurements higher dimensional.\n", + "Each bin is characterized by its left and right border." + ] + }, { "cell_type": "code", "execution_count": 9, @@ -455,29 +461,41 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bin configurations for index 0:\n", - "shape: (18, 2)\n", - "┌───────────┬────────────┐\n", - "│ left bins ┆ right bins │\n", - "│ --- ┆ --- │\n", - "│ f64 ┆ f64 │\n", - "╞═══════════╪════════════╡\n", - "│ 2.0 ┆ 2.125 │\n", - "│ 2.125 ┆ 2.25 │\n", - "│ 2.25 ┆ 2.375 │\n", - "│ 2.375 ┆ 2.5 │\n", - "│ 2.5 ┆ 2.625 │\n", - "│ … ┆ … │\n", - "│ 3.625 ┆ 3.75 │\n", - "│ 3.75 ┆ 3.875 │\n", - "│ 3.875 ┆ 4.0 │\n", - "│ 4.0 ┆ 4.25 │\n", - "│ 4.25 ┆ 4.5 │\n", - "└───────────┴────────────┘\n" - ] + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "shape: (18, 2)\n", + "┌────────────┬─────────────┐\n", + "│ dim 0 left ┆ dim 0 right │\n", + "│ --- ┆ --- │\n", + "│ f64 ┆ f64 │\n", + "╞════════════╪═════════════╡\n", + "│ 2.0 ┆ 2.125 │\n", + "│ 2.125 ┆ 2.25 │\n", + "│ 2.25 ┆ 2.375 │\n", + "│ 2.375 ┆ 2.5 │\n", + "│ 2.5 ┆ 2.625 │\n", + "│ … ┆ … │\n", + "│ 3.625 ┆ 3.75 │\n", + "│ 3.75 ┆ 3.875 │\n", + "│ 3.875 ┆ 4.0 │\n", + "│ 4.0 ┆ 4.25 │\n", + "│ 4.25 ┆ 4.5 │\n", + "└────────────┴─────────────┘" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -486,11 +504,13 @@ "\n", "# Each element of bins is an object with a left and right limit and\n", "# an associated bin normalization.\n", - "for idx, bin_dim in enumerate(range(bin_dims)):\n", - " print(f\"Bin configurations for index {idx}:\")\n", - " bin_left = grid.bin_left(0)\n", - " bin_right = grid.bin_right(0)\n", - " print(pl.DataFrame({\"left bins\": bin_left, \"right bins\": bin_right}))" + "df = pl.DataFrame({})\n", + "for bin_dim in range(bin_dims):\n", + " df = pl.concat([df,pl.DataFrame({\n", + " f\"dim {bin_dim} left\": grid.bin_left(bin_dim),\n", + " f\"dim {bin_dim} right\": grid.bin_right(bin_dim),\n", + " })],how=\"vertical\",)\n", + "df" ] }, { @@ -516,33 +536,46 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape: (18, 2)\n", - "┌───────┬───────────────────┐\n", - "│ index ┆ bin normalization │\n", - "│ --- ┆ --- │\n", - "│ u32 ┆ f64 │\n", - "╞═══════╪═══════════════════╡\n", - "│ 0 ┆ 0.125 │\n", - "│ 1 ┆ 0.125 │\n", - "│ 2 ┆ 0.125 │\n", - "│ 3 ┆ 0.125 │\n", - "│ 4 ┆ 0.125 │\n", - "│ … ┆ … │\n", - "│ 13 ┆ 0.125 │\n", - "│ 14 ┆ 0.125 │\n", - "│ 15 ┆ 0.125 │\n", - "│ 16 ┆ 0.25 │\n", - "│ 17 ┆ 0.25 │\n", - "└───────┴───────────────────┘\n" - ] + "data": { + "text/html": [ + "
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indexbin normalization
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" + ], + "text/plain": [ + "shape: (18, 2)\n", + "┌───────┬───────────────────┐\n", + "│ index ┆ bin normalization │\n", + "│ --- ┆ --- │\n", + "│ u32 ┆ f64 │\n", + "╞═══════╪═══════════════════╡\n", + "│ 0 ┆ 0.125 │\n", + "│ 1 ┆ 0.125 │\n", + "│ 2 ┆ 0.125 │\n", + "│ 3 ┆ 0.125 │\n", + "│ 4 ┆ 0.125 │\n", + "│ … ┆ … │\n", + "│ 13 ┆ 0.125 │\n", + "│ 14 ┆ 0.125 │\n", + "│ 15 ┆ 0.125 │\n", + "│ 16 ┆ 0.25 │\n", + "│ 17 ┆ 0.25 │\n", + "└───────┴───────────────────┘" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "df_bins = pl.DataFrame({\"bin normalization\": grid.bin_normalizations()})\n", - "print(df_bins.with_row_index())" + "df_bins.with_row_index()" ] }, { @@ -598,43 +631,88 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape: (18, 2)\n", - "┌───────┬───────────────────┐\n", - "│ index ┆ bin normalization │\n", - "│ --- ┆ --- │\n", - "│ u32 ┆ f64 │\n", - "╞═══════╪═══════════════════╡\n", - "│ 0 ┆ 1.0 │\n", - "│ 1 ┆ 1.0 │\n", - "│ 2 ┆ 1.0 │\n", - "│ 3 ┆ 1.0 │\n", - "│ 4 ┆ 1.0 │\n", - "│ … ┆ … │\n", - "│ 13 ┆ 1.0 │\n", - "│ 14 ┆ 1.0 │\n", - "│ 15 ┆ 1.0 │\n", - "│ 16 ┆ 1.0 │\n", - "│ 17 ┆ 1.0 │\n", - "└───────┴───────────────────┘\n" - ] + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "shape: (18, 2)\n", + "┌───────┬───────────────────┐\n", + "│ index ┆ bin normalization │\n", + "│ --- ┆ --- │\n", + "│ u32 ┆ f64 │\n", + "╞═══════╪═══════════════════╡\n", + "│ 0 ┆ 1.0 │\n", + "│ 1 ┆ 1.0 │\n", + "│ 2 ┆ 1.0 │\n", + "│ 3 ┆ 1.0 │\n", + "│ 4 ┆ 1.0 │\n", + "│ … ┆ … │\n", + "│ 13 ┆ 1.0 │\n", + "│ 14 ┆ 1.0 │\n", + "│ 15 ┆ 1.0 │\n", + "│ 16 ┆ 1.0 │\n", + "│ 17 ┆ 1.0 │\n", + "└───────┴───────────────────┘" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "# Load our modified grids\n", "grid_nrm = pineappl.grid.Grid.read(\"./LHCB_DY_8TEV_custom_normalizations.pineappl.lz4\")\n", "df_nbins = pl.DataFrame({\"bin normalization\": grid_nrm.bin_normalizations()})\n", - "print(df_nbins.with_row_index())" + "df_nbins.with_row_index()" + ] + }, + { + "cell_type": "markdown", + "id": "243c36ed-2adf-44e4-bd96-0b391815bc3c", + "metadata": {}, + "source": [ + "## Fast-Kernel (FK) tables" + ] + }, + { + "cell_type": "markdown", + "id": "61bbee09-dd78-4a9d-9830-8eb417545f72", + "metadata": {}, + "source": [ + "Fast-kernel (FK) tables are a special kind of grid, which contain the\n", + "Evolution Kernel Operators ([EKO](https://eko.readthedocs.io/))\n", + "which encode the DGLAP evolution equations. Furthermore, FK tables do not\n", + "have perturbative orders while PineAPPL grids do.\n", + "\n", + "You can load them as well, using the FK interface:" + ] + }, + { + "cell_type": "markdown", + "id": "866767df-261e-46c9-9038-acaa0ac7c214", + "metadata": {}, + "source": [ + "```python\n", + "# Loading a FK table\n", + "fk = pineappl.fk_table.FkTable.read(\"path/to/fktable.pineappl.lz4\")\n", + "```" ] } ], "metadata": { "kernelspec": { - "display_name": "nnpdf", + "display_name": "PineAPPL", "language": "python", - "name": "nnpdf" + "name": "pineappl" }, "language_info": { "codemirror_mode": { @@ -646,7 +724,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.6" } }, "nbformat": 4, diff --git a/pineappl_py/pyproject.toml b/pineappl_py/pyproject.toml index ff4f9d84..f8e19cb3 100644 --- a/pineappl_py/pyproject.toml +++ b/pineappl_py/pyproject.toml @@ -26,10 +26,11 @@ dependencies = ["numpy>=1.16.0,<2.0.0"] [project.optional-dependencies] cli = ["pineappl-cli"] docs = [ - "sphinx>=6.2.1", + "sphinx>=7.0.0", "sphinx_rtd_theme>=1.2.2", "nbsphinx>=0.8.8", "ipykernel>=6.13.0", + "polars>=1.8.0" ] test = ["pytest", "pytest-cov"]