From 2436bef58a3a80dce4760e6d493067a7fe2ad004 Mon Sep 17 00:00:00 2001 From: Hans Dembinski Date: Thu, 22 Aug 2024 12:05:31 +0200 Subject: [PATCH] support sum(w) < 0 in binned fits (#1022) This patch makes it possible to fit histograms with negative bin entries with `ExtendedBinnedNLL` and `BinnedNLL`. These occur, for example, when sweighted samples are histogrammed. This patch implements an extension of the Bohm-Zech approach for this case. The ideas behind this extension are compiled in a write-up that can be found in the documentation under "Studies: Fitting weighted histograms". The PR also fixes a mistake in `BinnedNLL`, which used the wrong cost function for weighted histograms, for ordinary histograms there is no change. This is a side result from thinking deeply about fitting weighted histograms. The old cost function lead to biased estimates, while the new one is now almost unbiased and performs even better than `ExtendedBinnedNLL` in application to weighted histograms. Other changes: - Automatic notebook stripping is implemented via pre-commit; so notebooks that run for a long time are not stripped - Switch to SVG plots in notebooks --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .pre-commit-config.yaml | 9 + bench/plot.ipynb | 5187 ++- doc/_static/interactive_demo.ipynb | 29 +- doc/notebooks/automatic_differentiation.ipynb | 3131 +- doc/notebooks/basic.ipynb | 9 +- doc/notebooks/binned_vs_unbinned.ipynb | 18214 +++++++---- doc/notebooks/conditional_variable.ipynb | 5613 +++- doc/notebooks/correlated_data.ipynb | 139 +- doc/notebooks/cost_function_benchmarks.ipynb | 6132 +++- doc/notebooks/cost_functions.ipynb | 15362 +-------- doc/notebooks/cython.ipynb | 375 - doc/notebooks/error_bands.ipynb | 172 +- doc/notebooks/external_minimizer.ipynb | 4 +- doc/notebooks/generic_least_squares.ipynb | 6 +- doc/notebooks/gof.ipynb | 61 +- doc/notebooks/hesse_and_minos.ipynb | 4998 ++- doc/notebooks/interactive.ipynb | 5 +- doc/notebooks/memory_layout.ipynb | 45 +- doc/notebooks/numba.ipynb | 1906 +- doc/notebooks/roofit.ipynb | 26 +- doc/notebooks/roofit/rf101_basics.ipynb | 5 +- .../roofit/rf109_chi2residpull.ipynb | 5 +- doc/notebooks/scipy_and_constraints.ipynb | 1206 +- doc/notebooks/simultaneous_fits.ipynb | 8 +- doc/notebooks/template_fits.ipynb | 8103 ++++- doc/notebooks/template_gof.ipynb | 7670 +++-- doc/notebooks/template_model_mix.ipynb | 627 +- doc/notebooks/unstable_fit.ipynb | 27214 +++++++++++++++- doc/notebooks/weighted_histograms.ipynb | 14076 ++++++++ doc/studies.rst | 1 + doc/tutorials.rst | 1 - noxfile.py | 20 +- pyproject.toml | 10 +- src/iminuit/cost.py | 392 +- tests/test_cost.py | 94 +- tests/test_without_numba.py | 4 +- 36 files changed, 92956 insertions(+), 27903 deletions(-) delete mode 100644 doc/notebooks/cython.ipynb create mode 100644 doc/notebooks/weighted_histograms.ipynb diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index d355ff39..cab5a003 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -45,6 +45,7 @@ repos: rev: v18.1.8 hooks: - id: clang-format + files: "src" # CMake formatting - repo: https://github.com/cheshirekow/cmake-format-precommit @@ -63,6 +64,14 @@ repos: additional_dependencies: [numpy] files: "src" +# Clear Jupyter notebook output and remove empty cells +# Override this by adding "keep_output": true to "metadata" block +- repo: https://github.com/kynan/nbstripout + rev: 0.7.1 + hooks: + - id: nbstripout + args: [--drop-empty-cells] + - repo: https://github.com/python-jsonschema/check-jsonschema rev: 0.29.1 hooks: diff --git a/bench/plot.ipynb b/bench/plot.ipynb index 40ed417b..312b191f 100644 --- a/bench/plot.ipynb +++ b/bench/plot.ipynb @@ -1,8 +1,8 @@ { - "cells": [ + "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -39,7 +39,1570 @@ }, { "data": { - "image/png": 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" ] @@ -59,7 +3151,2078 @@ }, { "data": { - "image/png": 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" ] @@ -69,6 +5232,7 @@ } ], "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import json\n", @@ -188,16 +5352,10 @@ " fname = title.replace(\" \", \"_\").replace(\".\", \"_\").replace(\"__\", \"_\").lower()\n", " plt.savefig(f\"{fname}.svg\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { + "keep_output": true, "kernelspec": { "display_name": "py39", "language": "python", @@ -213,9 +5371,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" - }, - "orig_nbformat": 4 + "version": "3.12.4" + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/doc/_static/interactive_demo.ipynb b/doc/_static/interactive_demo.ipynb index 9bad8399..01f12d39 100644 --- a/doc/_static/interactive_demo.ipynb +++ b/doc/_static/interactive_demo.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "cell_type": "markdown", "metadata": {}, @@ -9,24 +9,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9e09f787c7d94549a40efbc670e9eec7", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(Output(), VBox(children=(HBox(children=(Button(description='Fit', style=ButtonStyle()), ToggleB…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "from scipy.stats import norm\n", @@ -48,13 +33,6 @@ "\n", "m.interactive()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -75,7 +53,6 @@ "pygments_lexer": "python3", "version": "3.9.13" }, - "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "bdbf20ff2e92a3ae3002db8b02bd1dd1b287e934c884beb29a73dced9dbd0fa3" diff --git a/doc/notebooks/automatic_differentiation.ipynb b/doc/notebooks/automatic_differentiation.ipynb index f4af493e..56bca718 100644 --- a/doc/notebooks/automatic_differentiation.ipynb +++ b/doc/notebooks/automatic_differentiation.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "attachments": {}, "cell_type": "markdown", @@ -30,26 +30,21 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-21T10:26:37.436843Z", - "start_time": "2020-02-21T10:26:37.432080Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "JAX version 0.4.8\n", - "numba version 0.57.0\n" + "JAX version 0.4.31\n", + "numba version 0.60.0\n" ] } ], "source": [ "# !pip install jax jaxlib matplotlib numpy iminuit numba-stats\n", - "\n", + "%config InlineBackend.figure_formats = ['svg']\n", "import jax\n", "from jax import numpy as jnp # replacement for normal numpy\n", "from jax.scipy.special import erf # replacement for scipy.special.erf\n", @@ -77,13 +72,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-21T10:26:37.594856Z", - "start_time": "2020-02-21T10:26:37.585943Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# generate some toy data\n", @@ -117,13 +107,8 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-21T10:26:37.890967Z", - "start_time": "2020-02-21T10:26:37.886224Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "start_values = (1.5 * np.sum(n), 1.0, 2.0)\n", @@ -141,13 +126,8 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-21T10:26:38.532308Z", - "start_time": "2020-02-21T10:26:38.368563Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "data": { @@ -162,7 +142,7 @@ " \n", " \n", " EDM = 1.84e-08 (Goal: 0.0001) \n", - " \n", + " time = 0.4 sec \n", " \n", " \n", " Valid Minimum \n", @@ -183,7 +163,7 @@ "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 496.2 │ Nfcn = 66 │\n", - "│ EDM = 1.84e-08 (Goal: 0.0001) │ │\n", + "│ EDM = 1.84e-08 (Goal: 0.0001) │ time = 0.4 sec │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", @@ -193,7 +173,7 @@ "└──────────────────────────────────┴──────────────────────────────────────┘" ] }, - "execution_count": 24, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -205,13 +185,8 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-21T10:26:39.371830Z", - "start_time": "2020-02-21T10:26:38.797460Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "data": { @@ -226,7 +201,7 @@ " \n", " \n", " EDM = 1.84e-08 (Goal: 0.0001) \n", - " time = 0.2 sec \n", + " time = 1.2 sec \n", " \n", " \n", " Valid Minimum \n", @@ -247,7 +222,7 @@ "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 496.2 │ Nfcn = 26, Ngrad = 6 │\n", - "│ EDM = 1.84e-08 (Goal: 0.0001) │ time = 0.2 sec │\n", + "│ EDM = 1.84e-08 (Goal: 0.0001) │ time = 1.2 sec │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", @@ -257,7 +232,7 @@ "└──────────────────────────────────┴──────────────────────────────────────┘" ] }, - "execution_count": 25, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -269,13 +244,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-21T10:26:39.510553Z", - "start_time": "2020-02-21T10:26:39.373728Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "data": { @@ -290,7 +260,7 @@ " \n", " \n", " EDM = 1.84e-08 (Goal: 0.0001) \n", - " \n", + " time = 0.1 sec \n", " \n", " \n", " Valid Minimum \n", @@ -311,7 +281,7 @@ "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 496.2 │ Nfcn = 66 │\n", - "│ EDM = 1.84e-08 (Goal: 0.0001) │ │\n", + "│ EDM = 1.84e-08 (Goal: 0.0001) │ time = 0.1 sec │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", @@ -321,7 +291,7 @@ "└──────────────────────────────────┴──────────────────────────────────────┘" ] }, - "execution_count": 26, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -333,13 +303,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-21T10:26:40.573574Z", - "start_time": "2020-02-21T10:26:40.229476Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "data": { @@ -354,7 +319,7 @@ " \n", " \n", " EDM = 1.84e-08 (Goal: 0.0001) \n", - " time = 0.2 sec \n", + " time = 0.3 sec \n", " \n", " \n", " Valid Minimum \n", @@ -375,7 +340,7 @@ "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 496.2 │ Nfcn = 26, Ngrad = 6 │\n", - "│ EDM = 1.84e-08 (Goal: 0.0001) │ time = 0.2 sec │\n", + "│ EDM = 1.84e-08 (Goal: 0.0001) │ time = 0.3 sec │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", @@ -385,7 +350,7 @@ "└──────────────────────────────────┴──────────────────────────────────────┘" ] }, - "execution_count": 27, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -397,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -413,7 +378,7 @@ " \n", " \n", " EDM = 5.31e-05 (Goal: 0.0001) \n", - " time = 1.0 sec \n", + " time = 2.0 sec \n", " \n", " \n", " Valid Minimum \n", @@ -434,7 +399,7 @@ "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 496.2 │ Nfcn = 82 │\n", - "│ EDM = 5.31e-05 (Goal: 0.0001) │ time = 1.0 sec │\n", + "│ EDM = 5.31e-05 (Goal: 0.0001) │ time = 2.0 sec │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", @@ -444,7 +409,7 @@ "└──────────────────────────────────┴──────────────────────────────────────┘" ] }, - "execution_count": 28, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -469,13 +434,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-21T10:26:45.031931Z", - "start_time": "2020-02-21T10:26:40.674388Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "from timeit import timeit\n", @@ -498,17 +458,1145 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-21T10:26:45.142272Z", - "start_time": "2020-02-21T10:26:45.033451Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -563,13 +1651,8 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-21T10:27:38.715871Z", - "start_time": "2020-02-21T10:27:37.907690Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -618,14 +1701,14 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "30.4 ms ± 1.37 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" + "22.9 ms ± 2.51 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" ] } ], @@ -638,14 +1721,14 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "89 ms ± 28.6 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" + "35 ms ± 4.11 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" ] } ], @@ -668,14 +1751,14 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "187 µs ± 28.1 µs per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" + "107 μs ± 10.3 μs per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" ] } ], @@ -694,14 +1777,14 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "496 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" + "429 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" ] } ], @@ -754,13 +1837,8 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-21T10:25:43.510168Z", - "start_time": "2020-02-21T10:25:43.371319Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "# polynomial model\n", @@ -796,17 +1874,783 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-21T10:25:43.646212Z", - "start_time": "2020-02-21T10:25:43.512384Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -821,13 +2665,8 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-21T10:25:44.032210Z", - "start_time": "2020-02-21T10:25:43.648365Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "data": { @@ -835,7 +2674,7 @@ "Array(876.49545695, dtype=float64)" ] }, - "execution_count": 38, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -859,13 +2698,8 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-21T10:25:44.059729Z", - "start_time": "2020-02-21T10:25:44.034029Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "data": { @@ -995,7 +2829,7 @@ "└────┴──────────────────────┘" ] }, - "execution_count": 39, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -1007,17 +2841,1033 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-21T10:25:44.566228Z", - "start_time": "2020-02-21T10:25:44.065443Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -1049,6 +3899,7 @@ } ], "metadata": { + "keep_output": true, "kernelspec": { "display_name": "Python 3.8.14 ('venv': venv)", "language": "python", @@ -1063,8 +3914,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "python3", - "version": "3.10.8 (main, Oct 13 2022, 09:48:40) [Clang 14.0.0 (clang-1400.0.29.102)]" + "pygments_lexer": "ipython3", + "version": "3.12.4" }, "vscode": { "interpreter": { diff --git a/doc/notebooks/basic.ipynb b/doc/notebooks/basic.ipynb index 269e05c1..813b58f6 100644 --- a/doc/notebooks/basic.ipynb +++ b/doc/notebooks/basic.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "attachments": {}, "cell_type": "markdown", @@ -23,6 +23,7 @@ "outputs": [], "source": [ "# basic setup of the notebook\n", + "%config InlineBackend.figure_formats = ['svg']\n", "from matplotlib import pyplot as plt\n", "import numpy as np\n", "\n", @@ -164,7 +165,7 @@ "source": [ "try:\n", " Minuit(least_squares)\n", - "except:\n", + "except RuntimeError:\n", " import traceback\n", "\n", " traceback.print_exc()" @@ -178,7 +179,7 @@ "source": [ "try:\n", " Minuit(least_squares, a=0, b=0)\n", - "except:\n", + "except RuntimeError:\n", " import traceback\n", "\n", " traceback.print_exc()" @@ -1636,7 +1637,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.12.4" }, "nteract": { "version": "0.12.3" diff --git a/doc/notebooks/binned_vs_unbinned.ipynb b/doc/notebooks/binned_vs_unbinned.ipynb index bb35defd..97781492 100644 --- a/doc/notebooks/binned_vs_unbinned.ipynb +++ b/doc/notebooks/binned_vs_unbinned.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "cell_type": "markdown", "metadata": {}, @@ -21,10 +21,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", "import numpy as np\n", "from numba_stats import norm, expon\n", "import matplotlib.pyplot as plt\n", @@ -36,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -68,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -84,7 +85,7 @@ " \n", " \n", " EDM = 1.25e-05 (Goal: 0.0002) \n", - " time = 0.9 sec \n", + " time = 2.2 sec \n", " \n", " \n", " Valid Minimum \n", @@ -222,11 +223,11 @@ " \n", " \n", " \n", - " 2024-01-31T10:49:33.332534\n", + " 2024-08-22T10:58:24.216166\n", " image/svg+xml\n", " \n", " \n", - " Matplotlib v3.7.2, https://matplotlib.org/\n", + " Matplotlib v3.9.2, https://matplotlib.org/\n", " \n", " \n", " \n", @@ -254,7 +255,7 @@ "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", - " \n", + "L 198.286978 15.474462 \n", + "L 197.167706 17.243246 \n", + "L 196.048434 20.330999 \n", + "L 194.929162 24.678162 \n", + "L 193.80989 30.203084 \n", + "L 192.690617 36.804485 \n", + "L 191.571345 44.364446 \n", + "L 190.452073 52.751823 \n", + "L 185.974985 91.698893 \n", + "L 181.497897 132.471786 \n", + "L 179.259352 151.150013 \n", + "L 177.020808 167.838071 \n", + "L 175.901536 175.322455 \n", + "L 174.782264 182.202531 \n", + "L 173.662992 188.47131 \n", + "L 172.54372 194.133258 \n", + "L 170.305176 203.702207 \n", + "L 168.066632 211.112576 \n", + "L 165.828087 216.647108 \n", + "L 163.589543 220.622221 \n", + "L 161.350999 223.351987 \n", + "L 159.112455 225.12342 \n", + "L 156.873911 226.182492 \n", + "L 154.635366 226.728977 \n", + "L 150.158278 226.863494 \n", + "L 145.68119 226.329041 \n", + "L 136.727013 224.578711 \n", + "L 127.772836 222.55293 \n", + "L 109.864483 218.074094 \n", + "L 91.95613 212.999267 \n", + "L 74.047776 207.250034 \n", + "L 56.139423 200.736771 \n", + "z\n", + "\" clip-path=\"url(#p6c0fe9b107)\" style=\"fill: #1f77b4\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -440,7 +441,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -507,7 +508,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -523,7 +524,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -551,7 +552,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -583,7 +584,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -599,7 +600,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -615,7 +616,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -631,7 +632,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -649,12 +650,12 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -667,12 +668,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " 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- " \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", + "L 253.847644 241.257413 \n", + "\" clip-path=\"url(#p6c0fe9b107)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "L 259.578317 242.126205 \n", + "\" clip-path=\"url(#p6c0fe9b107)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "L 265.308991 242.771065 \n", + "\" clip-path=\"url(#p6c0fe9b107)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "L 271.039664 243.7248 \n", + "\" clip-path=\"url(#p6c0fe9b107)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "L 276.770337 244.005424 \n", + "\" clip-path=\"url(#p6c0fe9b107)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", + "\" clip-path=\"url(#p6c0fe9b107)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p6c0fe9b107)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "L 305.423702 245.9855 \n", + "\" clip-path=\"url(#p6c0fe9b107)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p6c0fe9b107)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p6c0fe9b107)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p6c0fe9b107)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", + "\" clip-path=\"url(#p6c0fe9b107)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\n", " \n", - " \n", + " \n", + "\" clip-path=\"url(#p6c0fe9b107)\" style=\"fill: none; stroke: #000000; stroke-width: 1.5\"/>\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", " \n", " \n", " \n", @@ -1023,7 +1024,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1034,7 +1035,7 @@ "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = -4.08e+06 │ Nfcn = 120 │\n", - "│ EDM = 1.25e-05 (Goal: 0.0002) │ time = 0.9 sec │\n", + "│ EDM = 1.25e-05 (Goal: 0.0002) │ time = 2.2 sec │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", @@ -1062,7 +1063,7 @@ "└───────┴───────────────────────────────────────────────────┘" ] }, - "execution_count": 15, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -1083,198 +1084,25 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "xm = np.linspace(np.min(pts), np.max(pts), 1000)\n", - "_, ym = density(xm, *m.values)\n", - "plt.hist(pts, bins=100, range=(0, 2), label=\"data\")\n", - "dx = 2 / 100\n", - "plt.plot(xm, ym * dx, label=\"fit\")\n", - "plt.legend()\n", - "plt.xlabel(\"x\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This fit is unbinned, the observed sample is binned here only for visualisation." - ] - }, - { - "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 190.9 (χ²/ndof = 1.0) Nfcn = 110
EDM = 2.17e-06 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 s 0.995 0.004 0
1 b 1.008 0.005 0
2 mu 999.3e-3 0.4e-3 0 2
3 sigma 99.22e-3 0.34e-3 0
4 tau 1.002 0.007 0
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s b mu sigma tau
s 1.32e-05 -0.005e-3 (-0.284) -0.04e-6 (-0.029) 0.30e-6 (0.248) -0.004e-3 (-0.161)
b -0.005e-3 (-0.284) 2.37e-05 0 -0.47e-6 (-0.286) 0.020e-3 (0.587)
mu -0.04e-6 (-0.029) 0 1.46e-07 -0.01e-6 (-0.046) -0.14e-6 (-0.054)
sigma 0.30e-6 (0.248) -0.47e-6 (-0.286) -0.01e-6 (-0.046) 1.12e-07 -0.37e-6 (-0.159)
tau -0.004e-3 (-0.161) 0.020e-3 (0.587) -0.14e-6 (-0.054) -0.37e-6 (-0.159) 4.77e-05
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" \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", @@ -1844,12 +2067,12 @@ " \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", @@ -1920,12 +2143,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1936,12 +2159,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1952,12 +2175,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1965,22 +2188,45 @@ " \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", @@ -1988,13 +2234,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2004,13 +2250,34 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2020,47 +2287,45 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2070,34 +2335,54 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "xm = np.linspace(np.min(pts), np.max(pts), 1000)\n", + "_, ym = density(xm, *m.values)\n", + "plt.hist(pts, bins=100, range=(0, 2), label=\"data\")\n", + "dx = 2 / 100\n", + "plt.plot(xm, ym * dx, label=\"fit\")\n", + "plt.legend()\n", + "plt.xlabel(\"x\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This fit is unbinned, the observed sample is binned here only for visualisation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Migrad
FCN = 190.9 (χ²/ndof = 1.0) Nfcn = 110
EDM = 2.17e-06 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
" + ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", @@ -2965,25 +2742,7 @@ "│ No parameters at limit │ Below call limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ s │ 0.995 │ 0.004 │ │ │ 0 │ │ │\n", - "│ 1 │ b │ 1.008 │ 0.005 │ │ │ 0 │ │ │\n", - "│ 2 │ mu │ 999.3e-3 │ 0.4e-3 │ │ │ 0 │ 2 │ │\n", - "│ 3 │ sigma │ 99.22e-3 │ 0.34e-3 │ │ │ 0 │ │ │\n", - "│ 4 │ tau │ 1.002 │ 0.007 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬───────────────────────────────────────────────────┐\n", - "│ │ s b mu sigma tau │\n", - "├───────┼───────────────────────────────────────────────────┤\n", - "│ s │ 1.32e-05 -0.005e-3 -0.04e-6 0.30e-6 -0.004e-3 │\n", - "│ b │ -0.005e-3 2.37e-05 0 -0.47e-6 0.020e-3 │\n", - "│ mu │ -0.04e-6 0 1.46e-07 -0.01e-6 -0.14e-6 │\n", - "│ sigma │ 0.30e-6 -0.47e-6 -0.01e-6 1.12e-07 -0.37e-6 │\n", - "│ tau │ -0.004e-3 0.020e-3 -0.14e-6 -0.37e-6 4.77e-05 │\n", - "└───────┴───────────────────────────────────────────────────┘" + "└──────────────────────────────────┴──────────────────────────────────────┘" ] }, "metadata": {}, @@ -3016,137 +2775,232 @@ " Hesse ok \n", " Covariance accurate \n", " \n", - "\n", + "
" + ], + "text/plain": [ + "┌─────────────────────────────────────────────────────────────────────────┐\n", + "│ Migrad │\n", + "├──────────────────────────────────┬──────────────────────────────────────┤\n", + "│ FCN = 87.19 (χ²/ndof = 0.9) │ Nfcn = 110 │\n", + "│ EDM = 2.41e-06 (Goal: 0.0002) │ │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ No parameters at limit │ Below call limit │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\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", - "
Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed Migrad
0 s 0.995 0.004 0 FCN = 36.34 (χ²/ndof = 0.8) Nfcn = 110
1 b 1.008 0.005 0 EDM = 1.7e-06 (Goal: 0.0002)
2 mu 999.3e-3 0.4e-3 0 2 Valid Minimum Below EDM threshold (goal x 10)
3 sigma 99.24e-3 0.34e-3 0 No parameters at limit Below call limit
4 tau 1.002 0.007 0 Hesse ok Covariance accurate
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" + ], + "text/plain": [ + "┌─────────────────────────────────────────────────────────────────────────┐\n", + "│ Migrad │\n", + "├──────────────────────────────────┬──────────────────────────────────────┤\n", + "│ FCN = 36.34 (χ²/ndof = 0.8) │ Nfcn = 110 │\n", + "│ EDM = 1.7e-06 (Goal: 0.0002) │ │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ No parameters at limit │ Below call limit │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\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", - "
s b mu sigma tau Migrad
s 1.32e-05 -0.005e-3 (-0.284) -0.04e-6 (-0.029) 0.30e-6 (0.249) -0.004e-3 (-0.161) FCN = 7.555 (χ²/ndof = 0.5) Nfcn = 102
b -0.005e-3 (-0.284) 2.37e-05 0 -0.47e-6 (-0.286) 0.020e-3 (0.587) EDM = 5.22e-05 (Goal: 0.0002)
mu -0.04e-6 (-0.029) 0 1.47e-07 -0.01e-6 (-0.046) -0.14e-6 (-0.054) Valid Minimum Below EDM threshold (goal x 10)
sigma 0.30e-6 (0.249) -0.47e-6 (-0.286) -0.01e-6 (-0.046) 1.13e-07 -0.37e-6 (-0.159) No parameters at limit Below call limit
tau -0.004e-3 (-0.161) 0.020e-3 (0.587) -0.14e-6 (-0.054) -0.37e-6 (-0.159) 4.78e-05 Hesse ok Covariance accurate
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Migrad
FCN = 2.084 (χ²/ndof = 0.4) Nfcn = 114
EDM = 4.66e-08 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
" + ], + "text/plain": [ + "┌─────────────────────────────────────────────────────────────────────────┐\n", + "│ Migrad │\n", + "├──────────────────────────────────┬──────────────────────────────────────┤\n", + "│ FCN = 2.084 (χ²/ndof = 0.4) │ Nfcn = 114 │\n", + "│ EDM = 4.66e-08 (Goal: 0.0002) │ │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ No parameters at limit │ Below call limit │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Migrad
FCN = 2.677e-06 Nfcn = 112
EDM = 2.67e-06 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
" + ], + "text/plain": [ + "┌─────────────────────────────────────────────────────────────────────────┐\n", + "│ Migrad │\n", + "├──────────────────────────────────┬──────────────────────────────────────┤\n", + "│ FCN = 2.677e-06 │ Nfcn = 112 │\n", + "│ EDM = 2.67e-06 (Goal: 0.0002) │ │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ No parameters at limit │ Below call limit │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-08-22T10:58:25.591219\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.2, https://matplotlib.org/\n", + " \n", + " \n", " \n", " \n", " \n", @@ -3155,242 +3009,848 @@ " \n", " \n", " \n", - 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"│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ s │ 0.995 │ 0.004 │ │ │ 0 │ │ │\n", - "│ 1 │ b │ 1.008 │ 0.005 │ │ │ 0 │ │ │\n", - "│ 2 │ mu │ 999.3e-3 │ 0.4e-3 │ │ │ 0 │ 2 │ │\n", - "│ 3 │ sigma │ 99.24e-3 │ 0.34e-3 │ │ │ 0 │ │ │\n", - "│ 4 │ tau │ 1.002 │ 0.007 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬───────────────────────────────────────────────────┐\n", - "│ │ s b mu sigma tau │\n", - "├───────┼───────────────────────────────────────────────────┤\n", - "│ s │ 1.32e-05 -0.005e-3 -0.04e-6 0.30e-6 -0.004e-3 │\n", - "│ b │ -0.005e-3 2.37e-05 0 -0.47e-6 0.020e-3 │\n", - "│ mu │ -0.04e-6 0 1.47e-07 -0.01e-6 -0.14e-6 │\n", - "│ sigma │ 0.30e-6 -0.47e-6 -0.01e-6 1.13e-07 -0.37e-6 │\n", - "│ tau │ -0.004e-3 0.020e-3 -0.14e-6 -0.37e-6 4.78e-05 │\n", - "└───────┴───────────────────────────────────────────────────┘" - 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Migrad
FCN = 36.34 (χ²/ndof = 0.8) Nfcn = 110
EDM = 1.7e-06 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 s 0.995 0.004 0
1 b 1.008 0.005 0
2 mu 999.3e-3 0.4e-3 0 2
3 sigma 99.20e-3 0.34e-3 0
4 tau 1.002 0.007 0
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s b mu sigma tau
s 1.32e-05 -0.005e-3 (-0.285) -0.04e-6 (-0.029) 0.31e-6 (0.250) -0.004e-3 (-0.162)
b -0.005e-3 (-0.285) 2.38e-05 0 -0.48e-6 (-0.287) 0.020e-3 (0.588)
mu -0.04e-6 (-0.029) 0 1.48e-07 -0.01e-6 (-0.046) -0.15e-6 (-0.055)
sigma 0.31e-6 (0.250) -0.48e-6 (-0.287) -0.01e-6 (-0.046) 1.16e-07 -0.38e-6 (-0.160)
tau -0.004e-3 (-0.162) 0.020e-3 (0.588) -0.15e-6 (-0.055) -0.38e-6 (-0.160) 4.79e-05
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" \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4748,31 +6035,15 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4780,15 +6051,15 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4796,15 +6067,15 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4812,15 +6083,15 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4828,15 +6099,15 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4845,83 +6116,81 @@ " \n", " \n", " \n", - 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"│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ s │ 0.995 │ 0.004 │ │ │ 0 │ │ │\n", - "│ 1 │ b │ 1.008 │ 0.005 │ │ │ 0 │ │ │\n", - "│ 2 │ mu │ 999.3e-3 │ 0.4e-3 │ │ │ 0 │ 2 │ │\n", - "│ 3 │ sigma │ 99.20e-3 │ 0.34e-3 │ │ │ 0 │ │ │\n", - "│ 4 │ tau │ 1.002 │ 0.007 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬───────────────────────────────────────────────────┐\n", - "│ │ s b mu sigma tau │\n", - "├───────┼───────────────────────────────────────────────────┤\n", - "│ s │ 1.32e-05 -0.005e-3 -0.04e-6 0.31e-6 -0.004e-3 │\n", - "│ b │ -0.005e-3 2.38e-05 0 -0.48e-6 0.020e-3 │\n", - "│ mu │ -0.04e-6 0 1.48e-07 -0.01e-6 -0.15e-6 │\n", - "│ sigma │ 0.31e-6 -0.48e-6 -0.01e-6 1.16e-07 -0.38e-6 │\n", - "│ tau │ -0.004e-3 0.020e-3 -0.15e-6 -0.38e-6 4.79e-05 │\n", - "└───────┴───────────────────────────────────────────────────┘" + "
" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "def integral(xe, s, b, mu, sigma, tau):\n", + " return s * n * norm.cdf(xe, mu, sigma) + b * n * expon.cdf(xe, 0, tau)\n", + "\n", + "\n", + "fig, ax = plt.subplots(3, 2, figsize=(10, 8), sharex=True, constrained_layout=True)\n", + "for axi, bins in zip(ax.flat, (200, 100, 50, 20, 10, 5)):\n", + " w, xe = np.histogram(pts, bins=bins, range=(0, 2))\n", + " c = ExtendedBinnedNLL(w, xe, integral)\n", + " m = fit(c)\n", + " display(m.fmin)\n", + " axi.stairs(w, xe, fill=True, label=\"data\")\n", + " axi.stairs(np.diff(integral(xe, *m.values)), xe, label=\"fit\")\n", + " axi.legend(title=f\"bins = {len(w)}\")\n", + " results[bins] = (np.array(m.values), np.array(m.errors), m.fmin.time)\n", + "fig.supxlabel(\"x\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 7.555 (χ²/ndof = 0.5) Nfcn = 102
EDM = 5.22e-05 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 s 0.995 0.004 0
1 b 1.008 0.005 0
2 mu 999.2e-3 0.4e-3 0 2
3 sigma 99.5e-3 0.4e-3 0
4 tau 1.002 0.007 0
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s b mu sigma tau
s 1.34e-05 -0.005e-3 (-0.297) -0.04e-6 (-0.030) 0.35e-6 (0.260) -0.004e-3 (-0.169)
b -0.005e-3 (-0.297) 2.42e-05 -0 -0.54e-6 (-0.298) 0.020e-3 (0.591)
mu -0.04e-6 (-0.030) -0 1.61e-07 -0.01e-6 (-0.048) -0.16e-6 (-0.059)
sigma 0.35e-6 (0.260) -0.54e-6 (-0.298) -0.01e-6 (-0.048) 1.37e-07 -0.43e-6 (-0.167)
tau -0.004e-3 (-0.169) 0.020e-3 (0.591) -0.16e-6 (-0.059) -0.43e-6 (-0.167) 4.83e-05
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "npar = len(results[np.inf][0])\n", + "\n", + "fig, ax = plt.subplots(npar, 2, sharex=True, figsize=(10, 10))\n", + "for j, (k, (v, e, _)) in enumerate(results.items()):\n", + " for i, (vi, ei) in enumerate(zip(v, e)):\n", + " c = f\"C{i}\"\n", + " ax[i, 0].errorbar(j, vi, ei, color=c, fmt=\"o\")\n", + " ax[i, 0].set_ylabel(par_names[i])\n", + " einf = results[np.inf][1][i]\n", + " ax[i, 1].plot(j, ei / einf, \"o\", color=c)\n", + "for i in range(npar):\n", + " ax[i, 1].set_ylim(0.95, 1.2)\n", + " ax[i, 1].axhline(1.05, ls=\"--\", color=\"0.5\")\n", + "plt.xticks(np.arange(7), [f\"{x}\" for x in results.keys()]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Shown on the left is the fitted value and its uncertainty estimate. Shown of the right is the relative size of the error bar of the binned fit compared to the unbinned fit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-08-22T10:58:26.797669\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.2, https://matplotlib.org/\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", + " \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", + " \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", + " \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", + " \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" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "x = np.arange(7)\n", + "y = [v[2] for v in results.values()]\n", + "plt.plot(x, y, \"o\")\n", + "for xi, yi in zip(x[1:], y[1:]):\n", + " plt.text(xi, yi * 1.2, f\"{y[0]/yi:.0f}x\", ha=\"center\")\n", + "plt.xticks(x, [f\"{x}\" for x in results.keys()])\n", + "plt.ylabel(\"time / sec\")\n", + "plt.semilogy();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now demonstrate that the binned fits and the unbinned fit are unbiased. We repeat the fit many times with independent random samples, the mean of the results minus the truth is the bias. In each iteration, the binned fits use the same data that the unbinned fit uses." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@joblib.delayed\n", + "def run(seed):\n", + " rng = np.random.default_rng(seed)\n", + " s = rng.normal(truth[2], truth[3], size=int(n * truth[0]))\n", + " b = rng.exponential(truth[4], size=int(n * truth[1]))\n", + " pts = np.append(s, b)\n", + " pts = pts[(pts > 0) & (pts < 2)]\n", + "\n", + " if bins == np.inf:\n", + " m = fit(ExtendedUnbinnedNLL(pts, density))\n", + " assert m.valid\n", + " else:\n", + " w, xe = np.histogram(pts, bins=bins, range=(0, 2))\n", + " m = fit(ExtendedBinnedNLL(w, xe, integral))\n", + " assert m.valid\n", + " return np.array(m.values)\n", + "\n", + "\n", + "results = {}\n", + "for bins in (np.inf, 200, 100, 50, 20, 10, 5):\n", + " results[bins] = joblib.Parallel(-1)(run(seed) for seed in range(100))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-08-22T11:00:24.676343\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.2, https://matplotlib.org/\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", + " \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", + " \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" + ], + "text/plain": [ + "
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"│ EDM = 5.22e-05 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ s │ 0.995 │ 0.004 │ │ │ 0 │ │ │\n", - "│ 1 │ b │ 1.008 │ 0.005 │ │ │ 0 │ │ │\n", - "│ 2 │ mu │ 999.2e-3 │ 0.4e-3 │ │ │ 0 │ 2 │ │\n", - "│ 3 │ sigma │ 99.5e-3 │ 0.4e-3 │ │ │ 0 │ │ │\n", - "│ 4 │ tau │ 1.002 │ 0.007 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬───────────────────────────────────────────────────┐\n", - "│ │ s b mu sigma tau │\n", - "├───────┼───────────────────────────────────────────────────┤\n", - "│ s │ 1.34e-05 -0.005e-3 -0.04e-6 0.35e-6 -0.004e-3 │\n", - "│ b │ -0.005e-3 2.42e-05 -0 -0.54e-6 0.020e-3 │\n", - "│ mu │ -0.04e-6 -0 1.61e-07 -0.01e-6 -0.16e-6 │\n", - "│ sigma │ 0.35e-6 -0.54e-6 -0.01e-6 1.37e-07 -0.43e-6 │\n", - "│ tau │ -0.004e-3 0.020e-3 -0.16e-6 -0.43e-6 4.83e-05 │\n", - "└───────┴───────────────────────────────────────────────────┘" + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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Migrad
FCN = 2.084 (χ²/ndof = 0.4) Nfcn = 114
EDM = 4.65e-08 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 s 0.995 0.004 0
1 b 1.007 0.005 0
2 mu 999.4e-3 0.5e-3 0 2
3 sigma 99.8e-3 0.7e-3 0
4 tau 1.003 0.007 0
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s b mu sigma tau
s 1.54e-05 -0.008e-3 (-0.397) -0.07e-6 (-0.042) 1.1e-6 (0.413) -0.007e-3 (-0.238)
b -0.008e-3 (-0.397) 2.9e-05 0.02e-6 (0.009) -1.8e-6 (-0.464) 0.024e-3 (0.624)
mu -0.07e-6 (-0.042) 0.02e-6 (0.009) 2.06e-07 -0.02e-6 (-0.071) -0.21e-6 (-0.063)
sigma 1.1e-6 (0.413) -1.8e-6 (-0.464) -0.02e-6 (-0.071) 4.95e-07 -1.4e-6 (-0.275)
tau -0.007e-3 (-0.238) 0.024e-3 (0.624) -0.21e-6 (-0.063) -1.4e-6 (-0.275) 5.22e-05
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" \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "┌─────────────────────────────────────────────────────────────────────────┐\n", - "│ Migrad │\n", - "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 2.084 (χ²/ndof = 0.4) │ Nfcn = 114 │\n", - "│ EDM = 4.65e-08 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ s │ 0.995 │ 0.004 │ │ │ 0 │ │ │\n", - "│ 1 │ b │ 1.007 │ 0.005 │ │ │ 0 │ │ │\n", - "│ 2 │ mu │ 999.4e-3 │ 0.5e-3 │ │ │ 0 │ 2 │ │\n", - "│ 3 │ sigma │ 99.8e-3 │ 0.7e-3 │ │ │ 0 │ │ │\n", - "│ 4 │ tau │ 1.003 │ 0.007 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬───────────────────────────────────────────────────┐\n", - "│ │ s b mu sigma tau │\n", - "├───────┼───────────────────────────────────────────────────┤\n", - "│ s │ 1.54e-05 -0.008e-3 -0.07e-6 1.1e-6 -0.007e-3 │\n", - "│ b │ -0.008e-3 2.9e-05 0.02e-6 -1.8e-6 0.024e-3 │\n", - "│ mu │ -0.07e-6 0.02e-6 2.06e-07 -0.02e-6 -0.21e-6 │\n", - "│ sigma │ 1.1e-6 -1.8e-6 -0.02e-6 4.95e-07 -1.4e-6 │\n", - "│ tau │ -0.007e-3 0.024e-3 -0.21e-6 -1.4e-6 5.22e-05 │\n", - "└───────┴───────────────────────────────────────────────────┘" + "
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Migrad
FCN = 2.677e-06 Nfcn = 112
EDM = 2.68e-06 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 s 0.996 0.005 0
1 b 1.006 0.007 0
2 mu 0.9998 0.0019 0 2
3 sigma 0.1001 0.0012 0
4 tau 1.002 0.008 0
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s b mu sigma tau
s 2.31e-05 -0.020e-3 (-0.599) -2e-6 (-0.173) 3.8e-6 (0.663) -0.014e-3 (-0.350)
b -0.020e-3 (-0.599) 4.64e-05 1e-6 (0.041) -5.5e-6 (-0.684) 0.04e-3 (0.673)
mu -2e-6 (-0.173) 1e-6 (0.041) 3.72e-06 -0.7e-6 (-0.291) -4e-6 (-0.259)
sigma 3.8e-6 (0.663) -5.5e-6 (-0.684) -0.7e-6 (-0.291) 1.4e-06 -3.7e-6 (-0.369)
tau -0.014e-3 (-0.350) 0.04e-3 (0.673) -4e-6 (-0.259) -3.7e-6 (-0.369) 7.03e-05
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(Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ s │ 0.996 │ 0.005 │ │ │ 0 │ │ │\n", - "│ 1 │ b │ 1.006 │ 0.007 │ │ │ 0 │ │ │\n", - "│ 2 │ mu │ 0.9998 │ 0.0019 │ │ │ 0 │ 2 │ │\n", - "│ 3 │ sigma │ 0.1001 │ 0.0012 │ │ │ 0 │ │ │\n", - "│ 4 │ tau │ 1.002 │ 0.008 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬───────────────────────────────────────────────────┐\n", - "│ │ s b mu sigma tau │\n", - "├───────┼───────────────────────────────────────────────────┤\n", - "│ s │ 2.31e-05 -0.020e-3 -2e-6 3.8e-6 -0.014e-3 │\n", - "│ b │ -0.020e-3 4.64e-05 1e-6 -5.5e-6 0.04e-3 │\n", - "│ mu │ -2e-6 1e-6 3.72e-06 -0.7e-6 -4e-6 │\n", - "│ sigma │ 3.8e-6 -5.5e-6 -0.7e-6 1.4e-06 -3.7e-6 │\n", - "│ tau │ -0.014e-3 0.04e-3 -4e-6 -3.7e-6 7.03e-05 │\n", - "└───────┴───────────────────────────────────────────────────┘" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def integral(xe, s, b, mu, sigma, tau):\n", - " return s * n * norm.cdf(xe, mu, sigma) + b * n * expon.cdf(xe, 0, tau)\n", - "\n", - "\n", - "fig, ax = plt.subplots(3, 2, figsize=(10, 8), sharex=True, constrained_layout=True)\n", - "for axi, bins in zip(ax.flat, (200, 100, 50, 20, 10, 5)):\n", - " w, xe = np.histogram(pts, bins=bins, range=(0, 2))\n", - " c = ExtendedBinnedNLL(w, xe, integral)\n", - " m = fit(c)\n", - " display(m.fmin)\n", - " axi.stairs(w, xe, fill=True, label=\"data\")\n", - " axi.stairs(np.diff(integral(xe, *m.values)), xe, label=\"fit\")\n", - " axi.legend(title=f\"bins = {len(w)}\")\n", - " results[bins] = (np.array(m.values), np.array(m.errors), m.fmin.time)\n", - "fig.supxlabel(\"x\");" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "npar = len(results[np.inf][0])\n", - "\n", - "fig, ax = plt.subplots(npar, 2, sharex=True, figsize=(14, 20))\n", - "for j, (k, (v, e, _)) in enumerate(results.items()):\n", - " for i, (vi, ei) in enumerate(zip(v, e)):\n", - " c = f\"C{i}\"\n", - " ax[i, 0].errorbar(j, vi, ei, color=c, fmt=\"o\")\n", - " ax[i, 0].set_ylabel(par_names[i])\n", - " einf = results[np.inf][1][i]\n", - " ax[i, 1].plot(j, ei / einf, \"o\", color=c)\n", - "for i in range(npar):\n", - " ax[i, 1].set_ylim(0.95, 1.2)\n", - " ax[i, 1].axhline(1.05, ls=\"--\", color=\"0.5\")\n", - "plt.xticks(np.arange(7), [f\"{x}\" for x in results.keys()]);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Shown on the left is the fitted value and its uncertainty estimate. Shown of the right is the relative size of the error bar of the binned fit compared to the unbinned fit." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "x = np.arange(7)\n", - "y = [v[2] for v in results.values()]\n", - "plt.plot(x, y, \"o\")\n", - "for xi, yi in zip(x[1:], y[1:]):\n", - " plt.text(xi, yi * 1.2, f\"{y[0]/yi:.0f}x\", ha=\"center\")\n", - "plt.xticks(x, [f\"{x}\" for x in results.keys()])\n", - "plt.ylabel(\"time / sec\")\n", - "plt.semilogy();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now demonstrate that the binned fits and the unbinned fit are unbiased. We repeat the fit many times with independent random samples, the mean of the results minus the truth is the bias. In each iteration, the binned fits use the same data that the unbinned fit uses." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "@joblib.delayed\n", - "def run(seed):\n", - " rng = np.random.default_rng(seed)\n", - " s = rng.normal(truth[2], truth[3], size=int(n * truth[0]))\n", - " b = rng.exponential(truth[4], size=int(n * truth[1]))\n", - " pts = np.append(s, b)\n", - " pts = pts[(pts > 0) & (pts < 2)]\n", - "\n", - " if bins == np.inf:\n", - " m = fit(ExtendedUnbinnedNLL(pts, density))\n", - " assert m.valid\n", - " else:\n", - " w, xe = np.histogram(pts, bins=bins, range=(0, 2))\n", - " m = fit(ExtendedBinnedNLL(w, xe, integral))\n", - " assert m.valid\n", - " return np.array(m.values)\n", - "\n", - "\n", - "results = {}\n", - "for bins in (np.inf, 200, 100, 50, 20, 10, 5):\n", - " results[bins] = joblib.Parallel(-1)(run(seed) for seed in range(100))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", - "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -7850,7 +14804,7 @@ "source": [ "ref = None\n", "for bin, values in results.items():\n", - " plt.figure()\n", + " plt.figure(figsize=(5, 3))\n", " m = np.mean(values, axis=0) - truth\n", " s = np.std(values, axis=0, ddof=1)\n", " plt.title(f\"{bin=}\")\n", @@ -7871,6 +14825,7 @@ "interpreter": { "hash": "bdbf20ff2e92a3ae3002db8b02bd1dd1b287e934c884beb29a73dced9dbd0fa3" }, + "keep_output": true, "kernelspec": { "display_name": "Python 3.8.12 ('venv': venv)", "language": "python", @@ -7886,9 +14841,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" - }, - "orig_nbformat": 4 + "version": "3.12.4" + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/doc/notebooks/conditional_variable.ipynb b/doc/notebooks/conditional_variable.ipynb index 9f3fb165..043bb0f5 100644 --- a/doc/notebooks/conditional_variable.ipynb +++ b/doc/notebooks/conditional_variable.ipynb @@ -1,8 +1,8 @@ { - "cells": [ + "cells": [ { "cell_type": "markdown", - "id": "naked-recruitment", + "id": "0", "metadata": {}, "source": [ "# Fit PDF with conditional variable\n", @@ -16,19 +16,20 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "technological-economy", + "execution_count": null, + "id": "1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "iminuit version 2.19.0\n" + "iminuit version 2.28.0\n" ] } ], "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", "import iminuit\n", "from iminuit.cost import UnbinnedNLL\n", "from iminuit import Minuit\n", @@ -44,8 +45,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "wicked-animal", + "execution_count": null, + "id": "2", "metadata": {}, "outputs": [], "source": [ @@ -68,7 +69,7 @@ }, { "cell_type": "markdown", - "id": "removable-forward", + "id": "3", "metadata": {}, "source": [ "The distribution in $x$ is more broad than the usual Gaussian because it is a convolution of many Gaussian distributions with varying means. We can visualise this by binning the data in $x$ and $y$." @@ -76,13 +77,2125 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "subjective-sleep", + "execution_count": null, + "id": "4", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -107,7 +2220,7 @@ }, { "cell_type": "markdown", - "id": "copyrighted-plenty", + "id": "5", "metadata": {}, "source": [ "## Fit with conditional variable\n", @@ -117,8 +2230,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "aware-fantasy", + "execution_count": null, + "id": "6", "metadata": {}, "outputs": [ { @@ -126,30 +2239,27 @@ "text/html": [ "
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Migrad Migrad
FCN = 1.93e+04 Nfcn = 130 FCN = 1.93e+04 Nfcn = 130
EDM = 2.25e-06 (Goal: 0.0002) EDM = 2.25e-06 (Goal: 0.0002) time = 0.1 sec
Valid Minimum No Parameters at limit Valid Minimum Below EDM threshold (goal x 10)
Below EDM threshold (goal x 10) Below call limit No parameters at limit Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced Covariance accurate
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a 0.000142 1.31e-08 5.73e-08 0e-6 0
b 1.31e-08 0e-6 4.76e-06 1.02e-08 0e-6
sigma 5.73e-08 1.02e-08 0 0e-6 7.08e-05
" @@ -228,14 +2338,14 @@ "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 1.93e+04 │ Nfcn = 130 │\n", - "│ EDM = 2.25e-06 (Goal: 0.0002) │ │\n", + "│ EDM = 2.25e-06 (Goal: 0.0002) │ time = 0.1 sec │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ No Parameters at limit │\n", + "│ No parameters at limit │ Below call limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Below EDM threshold (goal x 10) │ Below call limit │\n", - "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", - "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", - "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘\n", "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", @@ -246,13 +2356,13 @@ "┌───────┬────────────────────────────┐\n", "│ │ a b sigma │\n", "├───────┼────────────────────────────┤\n", - "│ a │ 0.000142 1.31e-08 5.73e-08 │\n", - "│ b │ 1.31e-08 4.76e-06 1.02e-08 │\n", - "│ sigma │ 5.73e-08 1.02e-08 7.08e-05 │\n", + "│ a │ 0.000142 0e-6 0 │\n", + "│ b │ 0e-6 4.76e-06 0e-6 │\n", + "│ sigma │ 0 0e-6 7.08e-05 │\n", "└───────┴────────────────────────────┘" ] }, - "execution_count": 4, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -274,13 +2384,1370 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "aquatic-belgium", + "execution_count": null, + "id": "7", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -305,7 +3772,7 @@ }, { "cell_type": "markdown", - "id": "integrated-listening", + "id": "8", "metadata": {}, "source": [ "## Fit without conditional variable\n", @@ -317,8 +3784,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "protecting-monte", + "execution_count": null, + "id": "9", "metadata": {}, "outputs": [ { @@ -326,30 +3793,27 @@ "text/html": [ "\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", "
Migrad Migrad
FCN = -8.774e+04 Nfcn = 95 FCN = -8.774e+04 Nfcn = 95
EDM = 1.33e-06 (Goal: 0.0002) time = 5.2 sec EDM = 1.33e-06 (Goal: 0.0002) time = 12.5 sec
Valid Minimum No Parameters at limit Valid Minimum Below EDM threshold (goal x 10)
Below EDM threshold (goal x 10) Below call limit No parameters at limit Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced Covariance accurate
\n", " \n", @@ -406,36 +3870,917 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - "
a 0.000839 4.35e-06 (0.030) -3.16e-05 (-0.027) 0.004e-3 (0.030) -0 (-0.027)
b 4.35e-06 (0.030) 0.004e-3 (0.030) 2.43e-05 -0.000141 (-0.718) -0.141e-3 (-0.718)
sigma -3.16e-05 (-0.027) -0.000141 (-0.718) -0 (-0.027) -0.141e-3 (-0.718) 0.0016
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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" ], "text/plain": [ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = -8.774e+04 │ Nfcn = 95 │\n", - "│ EDM = 1.33e-06 (Goal: 0.0002) │ time = 5.2 sec │\n", + "│ EDM = 1.33e-06 (Goal: 0.0002) │ time = 12.5 sec │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ No Parameters at limit │\n", + "│ No parameters at limit │ Below call limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Below EDM threshold (goal x 10) │ Below call limit │\n", - "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", - "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", - "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘\n", "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", @@ -446,13 +4791,13 @@ "┌───────┬───────────────────────────────┐\n", "│ │ a b sigma │\n", "├───────┼───────────────────────────────┤\n", - "│ a │ 0.000839 4.35e-06 -3.16e-05 │\n", - "│ b │ 4.35e-06 2.43e-05 -0.000141 │\n", - "│ sigma │ -3.16e-05 -0.000141 0.0016 │\n", + "│ a │ 0.000839 0.004e-3 -0 │\n", + "│ b │ 0.004e-3 2.43e-05 -0.141e-3 │\n", + "│ sigma │ -0 -0.141e-3 0.0016 │\n", "└───────┴───────────────────────────────┘" ] }, - "execution_count": 6, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -478,13 +4823,1114 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "julian-border", + "execution_count": null, + "id": "10", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -508,6 +5954,7 @@ } ], "metadata": { + "keep_output": true, "kernelspec": { "display_name": "Python 3.8.14 ('venv': venv)", "language": "python", @@ -523,7 +5970,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.12.4" }, "vscode": { "interpreter": { diff --git a/doc/notebooks/correlated_data.ipynb b/doc/notebooks/correlated_data.ipynb index cf2a9a38..d43c648f 100644 --- a/doc/notebooks/correlated_data.ipynb +++ b/doc/notebooks/correlated_data.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "cell_type": "markdown", "metadata": {}, @@ -15,10 +15,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", "from iminuit import Minuit\n", "import numpy as np\n", "import matplotlib.pyplot as plt" @@ -33,20 +34,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "value = [1.2, 1.5]\n", "error_sta = [0.3, 0.3]\n", @@ -71,100 +61,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 0.4737 (χ²/ndof = 0.5) Nfcn = 13
EDM = 1.46e-07 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 z 0.4 0.7 0 1
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z
z 0.737
" - ], - "text/plain": [ - "┌─────────────────────────────────────────────────────────────────────────┐\n", - "│ Migrad │\n", - "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 0.4737 (χ²/ndof = 0.5) │ Nfcn = 13 │\n", - "│ EDM = 1.46e-07 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ z │ 0.4 │ 0.7 │ │ │ 0 │ 1 │ │\n", - "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───┬───────┐\n", - "│ │ z │\n", - "├───┼───────┤\n", - "│ z │ 0.737 │\n", - "└───┴───────┘" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# construct covariance matrices\n", "cov_sta = np.diag(np.square(error_sta))\n", @@ -207,27 +106,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "total = 1.33 +/- 0.21(sta) + 0.16(sys)\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "z = m.values[0]\n", "jac = np.array([z, (1 - z)])\n", @@ -285,7 +166,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/doc/notebooks/cost_function_benchmarks.ipynb b/doc/notebooks/cost_function_benchmarks.ipynb index ba9e4260..f80eac88 100644 --- a/doc/notebooks/cost_function_benchmarks.ipynb +++ b/doc/notebooks/cost_function_benchmarks.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "cell_type": "markdown", "metadata": {}, @@ -26,10 +26,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from iminuit import Minuit\n", @@ -47,9 +48,20 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 2 need to re-try [(True, True), (True, True), (True, True), (False, False)]\n", + "10 9 need to re-try [(True, True), (True, True), (True, True), (False, False)]\n", + "10 57 need to re-try [(True, True), (True, True), (False, False), (False, False)]\n", + "10 85 need to re-try [(True, True), (True, True), (True, True), (False, True)]\n" + ] + } + ], "source": [ "n_tries = 100 # increase this to get less scattering\n", "\n", @@ -217,14 +229,2354 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "
" ] }, "metadata": {}, @@ -232,7 +2584,7 @@ } ], "source": [ - "fig, ax = plt.subplots(2, 2, figsize=(14, 8), sharex=True, sharey=True)\n", + "fig, ax = plt.subplots(2, 2, figsize=(9, 8), sharex=True, sharey=True)\n", "\n", "plt.sca(ax[0, 0])\n", "plt.title(\"Unbinned NLL\")\n", @@ -323,18 +2675,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10000 17 need to re-try [(True, True), (True, True), (False, False)]\n", - "10000 69 need to re-try [(True, True), (True, True), (False, False)]\n" - ] - } - ], + "outputs": [], "source": [ "n_tries = 100 # increase this to 500 to get less scattering\n", "\n", @@ -413,10 +2756,10 @@ "soft_l1 = []\n", "arctan = []\n", "\n", - "for l, s, a in joblib.Parallel(-1)(compute(n) for n in n_pts):\n", - " linear.append(l)\n", - " soft_l1.append(s)\n", - " arctan.append(a)\n", + "for li, so, ar in joblib.Parallel(-1)(compute(n) for n in n_pts):\n", + " linear.append(li)\n", + " soft_l1.append(so)\n", + " arctan.append(ar)\n", "\n", "linear = np.transpose(linear)\n", "soft_l1 = np.transpose(soft_l1)\n", @@ -425,14 +2768,3738 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "
" ] }, "metadata": {}, @@ -440,13 +6507,13 @@ } ], "source": [ - "fig, ax = plt.subplots(2, 3, figsize=(14, 8), sharex=True, sharey=False)\n", + "fig, ax = plt.subplots(2, 3, figsize=(9, 8), sharex=True, sharey=False)\n", "\n", "for k, (title, func) in enumerate(\n", " (\n", " (\"Least-squares\", linear),\n", - " (\"Least-squares with soft L1 norm\", soft_l1),\n", - " (\"Least-squares with arctan norm\", arctan),\n", + " (\"Least-squares\\nwith soft L1 norm\", soft_l1),\n", + " (\"Least-squares\\nwith arctan norm\", arctan),\n", " )\n", "):\n", " ax[0, k].set_title(title)\n", @@ -456,13 +6523,13 @@ " np.sqrt(n_pts) * func[0 + i],\n", " np.sqrt(n_pts * func[4 + i]),\n", " fmt=\"so\"[i],\n", - " label=f\"$\\sqrt{{n}}\\,\\Delta {x}$\",\n", + " label=rf\"$\\sqrt{{n}}\\,\\Delta {x}$\",\n", " )\n", " ax[1, k].plot(\n", " n_pts * 0.95 + 0.1 * i,\n", " func[2 + i] / func[4 + i],\n", " \"so\"[i],\n", - " label=f\"$\\sqrt{{n}}\\,\\Delta {x}$\",\n", + " label=rf\"$\\sqrt{{n}}\\,\\Delta {x}$\",\n", " )\n", " ax[0, k].legend()\n", "plt.semilogx()\n", @@ -486,6 +6553,7 @@ } ], "metadata": { + "keep_output": true, "kernelspec": { "display_name": "Python 3.8.14 ('venv': venv)", "language": "python", @@ -501,7 +6569,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.12.4" }, "vscode": { "interpreter": { diff --git a/doc/notebooks/cost_functions.ipynb b/doc/notebooks/cost_functions.ipynb index b150ee44..465ec9bf 100644 --- a/doc/notebooks/cost_functions.ipynb +++ b/doc/notebooks/cost_functions.ipynb @@ -1,8 +1,8 @@ { - "cells": [ + "cells": [ { "cell_type": "markdown", - "id": "negative-concord", + "id": "0", "metadata": {}, "source": [ "# Cost functions\n", @@ -24,11 +24,12 @@ }, { "cell_type": "code", - "execution_count": 76, - "id": "lucky-canvas", + "execution_count": null, + "id": "1", "metadata": {}, "outputs": [], "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", "from iminuit import cost, Minuit\n", "\n", "# faster than scipy.stats functions\n", @@ -42,7 +43,7 @@ }, { "cell_type": "markdown", - "id": "absent-missile", + "id": "2", "metadata": {}, "source": [ "We generate our data by sampling from a Gaussian peak and from exponential background in the range 0 to 2. The original data is then binned. One can fit the original or the binned data." @@ -50,21 +51,10 @@ }, { "cell_type": "code", - "execution_count": 77, - "id": "destroyed-fusion", + "execution_count": null, + "id": "3", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "xr = (0, 2) # xrange\n", "\n", @@ -85,7 +75,7 @@ }, { "cell_type": "markdown", - "id": "5c50cab3", + "id": "4", "metadata": {}, "source": [ "We also generate some 2D data to demonstrate multivariate fits. In this case, a Gaussian along axis 1 and independently an exponential along axis 2. In this case, the distributions are not restricted to some range in x and y." @@ -93,21 +83,10 @@ }, { "cell_type": "code", - "execution_count": 78, - "id": "b62cbb46", + "execution_count": null, + "id": "5", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Migrad
FCN = 768.1 Nfcn = 111
EDM = 3.76e-06 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 z 0.537 0.015 0 1
1 mu 0.996 0.004 0 2
2 sigma 0.1006 0.0035 0
3 tau 1.05 0.08 0
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z mu sigma tau
z 0.000231 -0.003e-3 (-0.052) 0.019e-3 (0.353) -0.24e-3 (-0.206)
mu -0.003e-3 (-0.052) 1.5e-05 -0.001e-3 (-0.069) -0.015e-3 (-0.053)
sigma 0.019e-3 (0.353) -0.001e-3 (-0.069) 1.25e-05 -0.043e-3 (-0.163)
tau -0.24e-3 (-0.206) -0.015e-3 (-0.053) -0.043e-3 (-0.163) 0.00568
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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], - "text/plain": [ - "┌─────────────────────────────────────────────────────────────────────────┐\n", - "│ Migrad │\n", - "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 768.1 │ Nfcn = 111 │\n", - "│ EDM = 3.76e-06 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ z │ 0.537 │ 0.015 │ │ │ 0 │ 1 │ │\n", - "│ 1 │ mu │ 0.996 │ 0.004 │ │ │ 0 │ 2 │ │\n", - "│ 2 │ sigma │ 0.1006 │ 0.0035 │ │ │ 0 │ │ │\n", - "│ 3 │ tau │ 1.05 │ 0.08 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬─────────────────────────────────────────┐\n", - "│ │ z mu sigma tau │\n", - "├───────┼─────────────────────────────────────────┤\n", - "│ z │ 0.000231 -0.003e-3 0.019e-3 -0.24e-3 │\n", - "│ mu │ -0.003e-3 1.5e-05 -0.001e-3 -0.015e-3 │\n", - "│ sigma │ 0.019e-3 -0.001e-3 1.25e-05 -0.043e-3 │\n", - "│ tau │ -0.24e-3 -0.015e-3 -0.043e-3 0.00568 │\n", - "└───────┴─────────────────────────────────────────┘" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def pdf(x, z, mu, sigma, tau):\n", " return z * truncnorm.pdf(x, *xr, mu, sigma) + (1 - z) * truncexpon.pdf(\n", @@ -1144,7 +148,7 @@ }, { "cell_type": "markdown", - "id": "fa50a81d", + "id": "8", "metadata": {}, "source": [ "If the gradient of the model is available, it can be passed to the cost function to enable the computation of its gradient, which Minuit then uses to potentially improve the minimization. We use a numerically computed gradient here obtained from the `jacobi` library. This is for demonstration purpose only and generally not recommended, since `jacobi` computes the gradient much more accurately than what is required for Minuit." @@ -1152,985 +156,10 @@ }, { "cell_type": "code", - "execution_count": 80, - "id": "8081f7f5", + "execution_count": null, + "id": "9", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 768.1 Nfcn = 87, Ngrad = 5
EDM = 3.31e-05 (Goal: 0.0002) time = 0.2 sec
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 z 0.537 0.015 0 1
1 mu 0.996 0.004 0 2
2 sigma 0.1006 0.0035 0
3 tau 1.05 0.08 0
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z mu sigma tau
z 0.000231 -0.003e-3 (-0.052) 0.019e-3 (0.353) -0.24e-3 (-0.206)
mu -0.003e-3 (-0.052) 1.5e-05 -0.001e-3 (-0.069) -0.015e-3 (-0.053)
sigma 0.019e-3 (0.353) -0.001e-3 (-0.069) 1.25e-05 -0.043e-3 (-0.163)
tau -0.24e-3 (-0.206) -0.015e-3 (-0.053) -0.043e-3 (-0.163) 0.00569
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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], - "text/plain": [ - "┌─────────────────────────────────────────────────────────────────────────┐\n", - "│ Migrad │\n", - "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 768.1 │ Nfcn = 87, Ngrad = 5 │\n", - "│ EDM = 3.31e-05 (Goal: 0.0002) │ time = 0.2 sec │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ z │ 0.537 │ 0.015 │ │ │ 0 │ 1 │ │\n", - "│ 1 │ mu │ 0.996 │ 0.004 │ │ │ 0 │ 2 │ │\n", - "│ 2 │ sigma │ 0.1006 │ 0.0035 │ │ │ 0 │ │ │\n", - "│ 3 │ tau │ 1.05 │ 0.08 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬─────────────────────────────────────────┐\n", - "│ │ z mu sigma tau │\n", - "├───────┼─────────────────────────────────────────┤\n", - "│ z │ 0.000231 -0.003e-3 0.019e-3 -0.24e-3 │\n", - "│ mu │ -0.003e-3 1.5e-05 -0.001e-3 -0.015e-3 │\n", - "│ sigma │ 0.019e-3 -0.001e-3 1.25e-05 -0.043e-3 │\n", - "│ tau │ -0.24e-3 -0.015e-3 -0.043e-3 0.00569 │\n", - "└───────┴─────────────────────────────────────────┘" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def grad(x, *par):\n", " return jacobi(lambda par: pdf(x, *par), par)[0].T\n", @@ -2147,7 +176,7 @@ }, { "cell_type": "markdown", - "id": "380b6ca6", + "id": "10", "metadata": {}, "source": [ "We can also fit a multivariate model to multivariate data. We pass model as a logpdf this time, which works well because the pdfs factorize." @@ -2155,143 +184,10 @@ }, { "cell_type": "code", - "execution_count": 81, - "id": "9da33a94", + "execution_count": null, + "id": "11", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 147.6 Nfcn = 134
EDM = 2.1e-06 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 mu 0.9946 0.0031
1 sigma 0.0986 0.0022 0
2 tau 0.972 0.031 0
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mu sigma tau
mu 9.73e-06 0e-6 -0e-6
sigma 0e-6 4.86e-06 -0e-6
tau -0e-6 -0e-6 0.000944
" - ], - "text/plain": [ - "┌─────────────────────────────────────────────────────────────────────────┐\n", - "│ Migrad │\n", - "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 147.6 │ Nfcn = 134 │\n", - "│ EDM = 2.1e-06 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ mu │ 0.9946 │ 0.0031 │ │ │ │ │ │\n", - "│ 1 │ sigma │ 0.0986 │ 0.0022 │ │ │ 0 │ │ │\n", - "│ 2 │ tau │ 0.972 │ 0.031 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬────────────────────────────┐\n", - "│ │ mu sigma tau │\n", - "├───────┼────────────────────────────┤\n", - "│ mu │ 9.73e-06 0e-6 -0e-6 │\n", - "│ sigma │ 0e-6 4.86e-06 -0e-6 │\n", - "│ tau │ -0e-6 -0e-6 0.000944 │\n", - "└───────┴────────────────────────────┘" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def logpdf(xy, mu, sigma, tau):\n", " x, y = xy\n", @@ -2306,7 +202,7 @@ }, { "cell_type": "markdown", - "id": "c272cd2b", + "id": "12", "metadata": {}, "source": [ "And we can also use a gradient as before." @@ -2314,143 +210,10 @@ }, { "cell_type": "code", - "execution_count": 84, - "id": "220d17c6", + "execution_count": null, + "id": "13", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 147.6 Nfcn = 81, Ngrad = 9
EDM = 3.73e-05 (Goal: 0.0002) time = 0.1 sec
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 mu 0.9946 0.0031
1 sigma 0.0986 0.0022 0
2 tau 0.972 0.031 0
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mu sigma tau
mu 9.73e-06 0e-6 -0e-6
sigma 0e-6 4.87e-06 -0e-6
tau -0e-6 -0e-6 0.000944
" - ], - "text/plain": [ - "┌─────────────────────────────────────────────────────────────────────────┐\n", - "│ Migrad │\n", - "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 147.6 │ Nfcn = 81, Ngrad = 9 │\n", - "│ EDM = 3.73e-05 (Goal: 0.0002) │ time = 0.1 sec │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ mu │ 0.9946 │ 0.0031 │ │ │ │ │ │\n", - "│ 1 │ sigma │ 0.0986 │ 0.0022 │ │ │ 0 │ │ │\n", - "│ 2 │ tau │ 0.972 │ 0.031 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬────────────────────────────┐\n", - "│ │ mu sigma tau │\n", - "├───────┼────────────────────────────┤\n", - "│ mu │ 9.73e-06 0e-6 -0e-6 │\n", - "│ sigma │ 0e-6 4.87e-06 -0e-6 │\n", - "│ tau │ -0e-6 -0e-6 0.000944 │\n", - "└───────┴────────────────────────────┘" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def grad(xy, *par):\n", " return jacobi(lambda p: logpdf(xy, *p), par)[0].T\n", @@ -2464,7 +227,7 @@ }, { "cell_type": "markdown", - "id": "introductory-watershed", + "id": "14", "metadata": {}, "source": [ "### Extended unbinned fit\n", @@ -2482,1010 +245,9 @@ { "cell_type": "code", "execution_count": null, - "id": "expanded-japanese", + "id": "15", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = -2.388e+04 Nfcn = 362
EDM = 2.8e-07 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 s 1.01e3 0.04e3 0
1 b 872 35 0
2 mu 0.996 0.004
3 sigma 0.1006 0.0035 0
4 tau 1.05 0.08 0
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s b mu sigma tau
s 1.36e+03 -0.4e3 (-0.272) -5.740e-3 (-0.040) 35.547e-3 (0.272) -0.440 (-0.158)
b -0.4e3 (-0.272) 1.22e+03 5.739e-3 (0.042) -35.543e-3 (-0.288) 0.440 (0.167)
mu -5.740e-3 (-0.040) 5.739e-3 (0.042) 1.5e-05 -0.001e-3 (-0.068) -0.015e-3 (-0.053)
sigma 35.547e-3 (0.272) -35.543e-3 (-0.288) -0.001e-3 (-0.068) 1.25e-05 -0.043e-3 (-0.161)
tau -0.440 (-0.158) 0.440 (0.167) -0.015e-3 (-0.053) -0.043e-3 (-0.161) 0.00568
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\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], - "text/plain": [ - "┌─────────────────────────────────────────────────────────────────────────┐\n", - "│ Migrad │\n", - "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = -2.388e+04 │ Nfcn = 362 │\n", - "│ EDM = 2.8e-07 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ s │ 1.01e3 │ 0.04e3 │ │ │ 0 │ │ │\n", - "│ 1 │ b │ 872 │ 35 │ │ │ 0 │ │ │\n", - "│ 2 │ mu │ 0.996 │ 0.004 │ │ │ │ │ │\n", - "│ 3 │ sigma │ 0.1006 │ 0.0035 │ │ │ 0 │ │ │\n", - "│ 4 │ tau │ 1.05 │ 0.08 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬────────────────────────────────────────────────────────┐\n", - "│ │ s b mu sigma tau │\n", - "├───────┼────────────────────────────────────────────────────────┤\n", - "│ s │ 1.36e+03 -0.4e3 -5.740e-3 35.547e-3 -0.440 │\n", - "│ b │ -0.4e3 1.22e+03 5.739e-3 -35.543e-3 0.440 │\n", - "│ mu │ -5.740e-3 5.739e-3 1.5e-05 -0.001e-3 -0.015e-3 │\n", - "│ sigma │ 35.547e-3 -35.543e-3 -0.001e-3 1.25e-05 -0.043e-3 │\n", - "│ tau │ -0.440 0.440 -0.015e-3 -0.043e-3 0.00568 │\n", - "└───────┴────────────────────────────────────────────────────────┘" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def density(x, s, b, mu, sigma, tau):\n", " return s + b, (\n", @@ -3502,7 +264,7 @@ }, { "cell_type": "markdown", - "id": "understood-monte", + "id": "16", "metadata": {}, "source": [ "The fitted values and the uncertainty estimates for the shape parameters are identical to the previous fit." @@ -3511,27 +273,16 @@ { "cell_type": "code", "execution_count": null, - "id": "governmental-hardware", + "id": "17", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "m.visualize()" ] }, { "cell_type": "markdown", - "id": "262567e0", + "id": "18", "metadata": {}, "source": [ "Once again, we fit 2D data, using the log-density mode." @@ -3540,166 +291,9 @@ { "cell_type": "code", "execution_count": null, - "id": "68ba4583", + "id": "19", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = -1.167e+04 Nfcn = 328
EDM = 4.42e-06 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 n 1000 32 0
1 mu 0.9946 0.0031
2 sigma 0.0986 0.0022 0
3 tau 0.972 0.031 0
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n mu sigma tau
n 1e+03 0e-6 0e-6 -0
mu 0e-6 9.73e-06 0e-6 (0.001) -0e-6
sigma 0e-6 0e-6 (0.001) 4.86e-06 -0e-6
tau -0 -0e-6 -0e-6 0.000944
" - ], - "text/plain": [ - "┌─────────────────────────────────────────────────────────────────────────┐\n", - "│ Migrad │\n", - "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = -1.167e+04 │ Nfcn = 328 │\n", - "│ EDM = 4.42e-06 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ n │ 1000 │ 32 │ │ │ 0 │ │ │\n", - "│ 1 │ mu │ 0.9946 │ 0.0031 │ │ │ │ │ │\n", - "│ 2 │ sigma │ 0.0986 │ 0.0022 │ │ │ 0 │ │ │\n", - "│ 3 │ tau │ 0.972 │ 0.031 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬─────────────────────────────────────┐\n", - "│ │ n mu sigma tau │\n", - "├───────┼─────────────────────────────────────┤\n", - "│ n │ 1e+03 0e-6 0e-6 -0 │\n", - "│ mu │ 0e-6 9.73e-06 0e-6 -0e-6 │\n", - "│ sigma │ 0e-6 0e-6 4.86e-06 -0e-6 │\n", - "│ tau │ -0 -0e-6 -0e-6 0.000944 │\n", - "└───────┴─────────────────────────────────────┘" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def logdensity(xy, n, mu, sigma, tau):\n", " x, y = xy\n", @@ -3714,7 +308,7 @@ }, { "cell_type": "markdown", - "id": "controlling-celebration", + "id": "20", "metadata": {}, "source": [ "### Binned Fit\n", @@ -3729,830 +323,9 @@ { "cell_type": "code", "execution_count": null, - "id": "robust-groove", + "id": "21", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 15.03 (χ²/ndof = 0.9) Nfcn = 270
EDM = 5.28e-06 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 z 0.540 0.015 0 1
1 mu 0.995 0.004
2 sigma 0.100 0.004 0.01
3 tau 1.05 0.08 0.01
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z mu sigma tau
z 0.000235 -0.004e-3 (-0.067) 0.020e-3 (0.354) -0.24e-3 (-0.209)
mu -0.004e-3 (-0.067) 1.63e-05 -0.001e-3 (-0.090) -0.015e-3 (-0.050)
sigma 0.020e-3 (0.354) -0.001e-3 (-0.090) 1.43e-05 -0.045e-3 (-0.160)
tau -0.24e-3 (-0.209) -0.015e-3 (-0.050) -0.045e-3 (-0.160) 0.00564
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"├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ z │ 0.540 │ 0.015 │ │ │ 0 │ 1 │ │\n", - "│ 1 │ mu │ 0.995 │ 0.004 │ │ │ │ │ │\n", - "│ 2 │ sigma │ 0.100 │ 0.004 │ │ │ 0.01 │ │ │\n", - "│ 3 │ tau │ 1.05 │ 0.08 │ │ │ 0.01 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬─────────────────────────────────────────┐\n", - "│ │ z mu sigma tau │\n", - "├───────┼─────────────────────────────────────────┤\n", - "│ z │ 0.000235 -0.004e-3 0.020e-3 -0.24e-3 │\n", - "│ mu │ -0.004e-3 1.63e-05 -0.001e-3 -0.015e-3 │\n", - "│ sigma │ 0.020e-3 -0.001e-3 1.43e-05 -0.045e-3 │\n", - "│ tau │ -0.24e-3 -0.015e-3 -0.045e-3 0.00564 │\n", - "└───────┴─────────────────────────────────────────┘" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def cdf(xe, z, mu, sigma, tau):\n", " return z * truncnorm.cdf(xe, *xr, mu, sigma) + (1 - z) * truncexpon.cdf(\n", @@ -4569,7 +342,7 @@ }, { "cell_type": "markdown", - "id": "2dc873af-e615-498a-be72-3e66720c53e1", + "id": "22", "metadata": {}, "source": [ "iminuit also shows the chi-square goodness-of-fit test statistic when the data are binned. It is calculated for free in the binned case.\n", @@ -4582,830 +355,9 @@ { "cell_type": "code", "execution_count": null, - "id": "838d5fcc-e2b2-4eb6-9831-205ca2753810", + "id": "23", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 15.65 (χ²/ndof = 1.0) Nfcn = 189
EDM = 1.07e-05 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 z 0.540 0.015 0 1
1 mu 0.995 0.004
2 sigma 0.104 0.004 0.01
3 tau 1.05 0.08 0.01
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z mu sigma tau
z 0.000235 -0.004e-3 (-0.067) 0.020e-3 (0.353) -0.24e-3 (-0.208)
mu -0.004e-3 (-0.067) 1.63e-05 -0.001e-3 (-0.090) -0.015e-3 (-0.050)
sigma 0.020e-3 (0.353) -0.001e-3 (-0.090) 1.31e-05 -0.043e-3 (-0.159)
tau -0.24e-3 (-0.208) -0.015e-3 (-0.050) -0.043e-3 (-0.159) 0.00568
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"├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ z │ 0.540 │ 0.015 │ │ │ 0 │ 1 │ │\n", - "│ 1 │ mu │ 0.995 │ 0.004 │ │ │ │ │ │\n", - "│ 2 │ sigma │ 0.104 │ 0.004 │ │ │ 0.01 │ │ │\n", - "│ 3 │ tau │ 1.05 │ 0.08 │ │ │ 0.01 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬─────────────────────────────────────────┐\n", - "│ │ z mu sigma tau │\n", - "├───────┼─────────────────────────────────────────┤\n", - "│ z │ 0.000235 -0.004e-3 0.020e-3 -0.24e-3 │\n", - "│ mu │ -0.004e-3 1.63e-05 -0.001e-3 -0.015e-3 │\n", - "│ sigma │ 0.020e-3 -0.001e-3 1.31e-05 -0.043e-3 │\n", - "│ tau │ -0.24e-3 -0.015e-3 -0.043e-3 0.00568 │\n", - "└───────┴─────────────────────────────────────────┘" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def pdf(x, z, mu, sigma, tau):\n", " return z * truncnorm.pdf(x, *xr, mu, sigma) + (1 - z) * truncexpon.pdf(\n", @@ -5429,7 +381,7 @@ }, { "cell_type": "markdown", - "id": "comparable-special", + "id": "24", "metadata": {}, "source": [ "The fitted values and the uncertainty estimates for $\\mu$ and $\\sigma$ are not identical to the unbinned fit, but very close. For practical purposes, the results are equivalent. This shows that the binning is fine enough to retain the essential information in the original data.\n", @@ -5440,830 +392,9 @@ { "cell_type": "code", "execution_count": null, - "id": "c26df624", + "id": "25", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 15.65 (χ²/ndof = 1.0) Nfcn = 189
EDM = 1.06e-05 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 z 0.540 0.015 0 1
1 mu 0.995 0.004
2 sigma 0.104 0.004 0.01
3 tau 1.05 0.08 0.01
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z mu sigma tau
z 0.000235 -0.004e-3 (-0.067) 0.020e-3 (0.353) -0.24e-3 (-0.208)
mu -0.004e-3 (-0.067) 1.63e-05 -0.001e-3 (-0.090) -0.015e-3 (-0.050)
sigma 0.020e-3 (0.353) -0.001e-3 (-0.090) 1.31e-05 -0.043e-3 (-0.159)
tau -0.24e-3 (-0.208) -0.015e-3 (-0.050) -0.043e-3 (-0.159) 0.00568
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"├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ z │ 0.540 │ 0.015 │ │ │ 0 │ 1 │ │\n", - "│ 1 │ mu │ 0.995 │ 0.004 │ │ │ │ │ │\n", - "│ 2 │ sigma │ 0.104 │ 0.004 │ │ │ 0.01 │ │ │\n", - "│ 3 │ tau │ 1.05 │ 0.08 │ │ │ 0.01 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬─────────────────────────────────────────┐\n", - "│ │ z mu sigma tau │\n", - "├───────┼─────────────────────────────────────────┤\n", - "│ z │ 0.000235 -0.004e-3 0.020e-3 -0.24e-3 │\n", - "│ mu │ -0.004e-3 1.63e-05 -0.001e-3 -0.015e-3 │\n", - "│ sigma │ 0.020e-3 -0.001e-3 1.31e-05 -0.043e-3 │\n", - "│ tau │ -0.24e-3 -0.015e-3 -0.043e-3 0.00568 │\n", - "└───────┴─────────────────────────────────────────┘" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "c = cost.BinnedNLL(n, xe, pdf, use_pdf=\"approximate\")\n", "m = Minuit(c, z=0.4, mu=0, sigma=0.2, tau=2)\n", @@ -6274,7 +405,7 @@ }, { "cell_type": "markdown", - "id": "275568f0", + "id": "26", "metadata": {}, "source": [ "Another option is to compute the cdf numerically with `use_pdf=\"numerical\"`, but this tends to be expensive and is only supported for 1D histograms." @@ -6283,830 +414,9 @@ { "cell_type": "code", "execution_count": null, - "id": "5a6fe4cc", + "id": "27", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 15.03 (χ²/ndof = 0.9) Nfcn = 270
EDM = 5.28e-06 (Goal: 0.0002) time = 2.0 sec
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 z 0.540 0.015 0 1
1 mu 0.995 0.004
2 sigma 0.100 0.004 0.01
3 tau 1.05 0.08 0.01
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z mu sigma tau
z 0.000235 -0.004e-3 (-0.067) 0.020e-3 (0.354) -0.24e-3 (-0.209)
mu -0.004e-3 (-0.067) 1.63e-05 -0.001e-3 (-0.090) -0.015e-3 (-0.050)
sigma 0.020e-3 (0.354) -0.001e-3 (-0.090) 1.43e-05 -0.045e-3 (-0.160)
tau -0.24e-3 (-0.209) -0.015e-3 (-0.050) -0.045e-3 (-0.160) 0.00564
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"├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ z │ 0.540 │ 0.015 │ │ │ 0 │ 1 │ │\n", - "│ 1 │ mu │ 0.995 │ 0.004 │ │ │ │ │ │\n", - "│ 2 │ sigma │ 0.100 │ 0.004 │ │ │ 0.01 │ │ │\n", - "│ 3 │ tau │ 1.05 │ 0.08 │ │ │ 0.01 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬─────────────────────────────────────────┐\n", - "│ │ z mu sigma tau │\n", - "├───────┼─────────────────────────────────────────┤\n", - "│ z │ 0.000235 -0.004e-3 0.020e-3 -0.24e-3 │\n", - "│ mu │ -0.004e-3 1.63e-05 -0.001e-3 -0.015e-3 │\n", - "│ sigma │ 0.020e-3 -0.001e-3 1.43e-05 -0.045e-3 │\n", - "│ tau │ -0.24e-3 -0.015e-3 -0.045e-3 0.00564 │\n", - "└───────┴─────────────────────────────────────────┘" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "c = cost.BinnedNLL(n, xe, pdf, use_pdf=\"numerical\")\n", "m = Minuit(c, z=0.4, mu=0, sigma=0.2, tau=2)\n", @@ -7117,7 +427,7 @@ }, { "cell_type": "markdown", - "id": "c7a06b88", + "id": "28", "metadata": {}, "source": [ "Fitting a multidimensional histogram is equally easy. Since the pdfs in this example factorise, the cdf of the 2D model is the product of the cdfs along each axis." @@ -7126,1021 +436,9 @@ { "cell_type": "code", "execution_count": null, - "id": "fad40fc9", + "id": "29", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 25.22 (χ²/ndof = 0.3) Nfcn = 206
EDM = 9.93e-06 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 mu 0.9932 0.0032
1 sigma 0.0984 0.0024 0
2 tau 0.940 0.033 0
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mu sigma tau
mu 1.05e-05 0e-6 -0
sigma 0e-6 5.68e-06 0e-6
tau -0 0e-6 0.0011
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"│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ mu │ 0.9932 │ 0.0032 │ │ │ │ │ │\n", - "│ 1 │ sigma │ 0.0984 │ 0.0024 │ │ │ 0 │ │ │\n", - "│ 2 │ tau │ 0.940 │ 0.033 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬────────────────────────────┐\n", - "│ │ mu sigma tau │\n", - "├───────┼────────────────────────────┤\n", - "│ mu │ 1.05e-05 0e-6 -0 │\n", - "│ sigma │ 0e-6 5.68e-06 0e-6 │\n", - "│ tau │ -0 0e-6 0.0011 │\n", - "└───────┴────────────────────────────┘" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def cdf(xe_ye, mu, sigma, tau):\n", " xe, ye = xe_ye\n", @@ -8156,7 +454,7 @@ { "attachments": {}, "cell_type": "markdown", - "id": "a6c8ae4e", + "id": "30", "metadata": {}, "source": [ "The automatically provided visualization for multidimensional data set is often not very pretty, but still helps to judge whether the fit is reasonable. There is no obvious way to draw higher dimensional data with error bars in comparison to a model, and so the automatic visualization shows all data bins as a single sequence. You can override the default visualization by calling `Minuit.visualize` with your own plotting function, or by assigning a plot function to the cost function `BinnedNLL` (monkey patching), or by deriving your own class from `BinnedNLL`." @@ -8164,7 +462,7 @@ }, { "cell_type": "markdown", - "id": "decent-treat", + "id": "31", "metadata": {}, "source": [ "### Extended binned maximum-likelihood fit\n", @@ -8177,856 +475,9 @@ { "cell_type": "code", "execution_count": null, - "id": "suitable-fetish", + "id": "32", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 15.03 (χ²/ndof = 1.0) Nfcn = 437
EDM = 9.97e-07 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 s 1.02e3 0.04e3 0
1 b 867 35 0
2 mu 0.995 0.004
3 sigma 0.100 0.004 0
4 tau 1.05 0.08 0
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s b mu sigma tau
s 1.38e+03 -0.4e3 (-0.280) -7.764e-3 (-0.052) 38.616e-3 (0.275) -0.452 (-0.162)
b -0.4e3 (-0.280) 1.23e+03 7.764e-3 (0.055) -38.615e-3 (-0.291) 0.452 (0.172)
mu -7.764e-3 (-0.052) 7.764e-3 (0.055) 1.63e-05 -0.001e-3 (-0.090) -0.015e-3 (-0.050)
sigma 38.616e-3 (0.275) -38.615e-3 (-0.291) -0.001e-3 (-0.090) 1.43e-05 -0.045e-3 (-0.160)
tau -0.452 (-0.162) 0.452 (0.172) -0.015e-3 (-0.050) -0.045e-3 (-0.160) 0.00564
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"├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ s │ 1.02e3 │ 0.04e3 │ │ │ 0 │ │ │\n", - "│ 1 │ b │ 867 │ 35 │ │ │ 0 │ │ │\n", - "│ 2 │ mu │ 0.995 │ 0.004 │ │ │ │ │ │\n", - "│ 3 │ sigma │ 0.100 │ 0.004 │ │ │ 0 │ │ │\n", - "│ 4 │ tau │ 1.05 │ 0.08 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬────────────────────────────────────────────────────────┐\n", - "│ │ s b mu sigma tau │\n", - "├───────┼────────────────────────────────────────────────────────┤\n", - "│ s │ 1.38e+03 -0.4e3 -7.764e-3 38.616e-3 -0.452 │\n", - "│ b │ -0.4e3 1.23e+03 7.764e-3 -38.615e-3 0.452 │\n", - "│ mu │ -7.764e-3 7.764e-3 1.63e-05 -0.001e-3 -0.015e-3 │\n", - "│ sigma │ 38.616e-3 -38.615e-3 -0.001e-3 1.43e-05 -0.045e-3 │\n", - "│ tau │ -0.452 0.452 -0.015e-3 -0.045e-3 0.00564 │\n", - "└───────┴────────────────────────────────────────────────────────┘" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def integral(xe, s, b, mu, sigma, tau):\n", " return s * truncnorm.cdf(xe, *xr, mu, sigma) + b * truncexpon.cdf(xe, *xr, 0, tau)\n", @@ -9040,7 +491,7 @@ }, { "cell_type": "markdown", - "id": "noticed-wireless", + "id": "33", "metadata": {}, "source": [ "Again, we can also fit multivariate data." @@ -9049,1045 +500,9 @@ { "cell_type": "code", "execution_count": null, - "id": "aeb53009", + "id": "34", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 24.64 (χ²/ndof = 0.3) Nfcn = 182
EDM = 5.24e-08 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 n 1.000e3 0.032e3 0
1 mu 0.9932 0.0032
2 sigma 0.0984 0.0024 0
3 tau 0.943 0.034 0
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n mu sigma tau
n 1e+03 0 -0e-6 0.0029
mu 0 1.05e-05 0e-6 -0
sigma -0e-6 0e-6 5.68e-06 0e-6
tau 0.0029 -0 0e-6 0.00113
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"│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ n │ 1.000e3 │ 0.032e3 │ │ │ 0 │ │ │\n", - "│ 1 │ mu │ 0.9932 │ 0.0032 │ │ │ │ │ │\n", - "│ 2 │ sigma │ 0.0984 │ 0.0024 │ │ │ 0 │ │ │\n", - "│ 3 │ tau │ 0.943 │ 0.034 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬─────────────────────────────────────┐\n", - "│ │ n mu sigma tau │\n", - "├───────┼─────────────────────────────────────┤\n", - "│ n │ 1e+03 0 -0e-6 0.0029 │\n", - "│ mu │ 0 1.05e-05 0e-6 -0 │\n", - "│ sigma │ -0e-6 0e-6 5.68e-06 0e-6 │\n", - "│ tau │ 0.0029 -0 0e-6 0.00113 │\n", - "└───────┴─────────────────────────────────────┘" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def integral(xe_ye, n, mu, sigma, tau):\n", " xe, ye = xe_ye\n", @@ -10102,7 +517,7 @@ }, { "cell_type": "markdown", - "id": "infectious-trash", + "id": "35", "metadata": {}, "source": [ "### Temporary masking\n", @@ -10115,832 +530,9 @@ { "cell_type": "code", "execution_count": null, - "id": "ruled-society", + "id": "36", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 6.623 (χ²/ndof = 0.8) Nfcn = 55
EDM = 3.75e-07 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 s 0.0 0.1 0 yes
1 b 870 40 0
2 mu 1.00 0.01 yes
3 sigma 0.200 0.002 0 yes
4 tau 1.02 0.08 0
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s b mu sigma tau
s 0 0 0 0 0.000
b 0 1.71e+03 0 0 0.950 (0.289)
mu 0 0 0 0 0.000
sigma 0 0 0 0 0.000
tau 0.000 0.950 (0.289) 0.000 0.000 0.00632
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" \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" - ], - "text/plain": [ - "┌─────────────────────────────────────────────────────────────────────────┐\n", - "│ Migrad │\n", - "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 6.623 (χ²/ndof = 0.8) │ Nfcn = 55 │\n", - "│ EDM = 3.75e-07 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ s │ 0.0 │ 0.1 │ │ │ 0 │ │ yes │\n", - "│ 1 │ b │ 870 │ 40 │ │ │ 0 │ │ │\n", - "│ 2 │ mu │ 1.00 │ 0.01 │ │ │ │ │ yes │\n", - "│ 3 │ sigma │ 0.200 │ 0.002 │ │ │ 0 │ │ yes │\n", - "│ 4 │ tau │ 1.02 │ 0.08 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬──────────────────────────────────────────────┐\n", - "│ │ s b mu sigma tau │\n", - "├───────┼──────────────────────────────────────────────┤\n", - "│ s │ 0 0 0 0 0.000 │\n", - "│ b │ 0 1.71e+03 0 0 0.950 │\n", - "│ mu │ 0 0 0 0 0.000 │\n", - "│ sigma │ 0 0 0 0 0.000 │\n", - "│ tau │ 0.000 0.950 0.000 0.000 0.00632 │\n", - "└───────┴──────────────────────────────────────────────┘" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def integral(xe, s, b, mu, sigma, tau):\n", " return s * truncnorm.cdf(xe, *xr, mu, sigma) + b * truncexpon.cdf(xe, *xr, 0, tau)\n", @@ -10962,7 +554,7 @@ }, { "cell_type": "markdown", - "id": "9424b64d", + "id": "37", "metadata": {}, "source": [ "We plot the intermediate result. Points which have been masked out are shown with open markers." @@ -10971,20 +563,9 @@ { "cell_type": "code", "execution_count": null, - "id": "happy-diabetes", + "id": "38", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "for ma, co in ((c.mask, \"k\"), (~c.mask, \"w\")):\n", " plt.errorbar(cx[ma], n[ma], n[ma] ** 0.5, fmt=\"o\", color=co, mec=\"k\", ecolor=\"k\")\n", @@ -10997,7 +578,7 @@ }, { "cell_type": "markdown", - "id": "heard-jurisdiction", + "id": "39", "metadata": {}, "source": [ "Now we fix the background and fit only the signal parameters." @@ -11006,856 +587,9 @@ { "cell_type": "code", "execution_count": null, - "id": "accredited-dispute", + "id": "40", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 15.03 (χ²/ndof = 0.9) Nfcn = 252
EDM = 6.92e-09 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 s 1.017e3 0.035e3 0
1 b 870 40 0 yes
2 mu 0.995 0.004
3 sigma 0.100 0.004 0
4 tau 1.05 0.07 0
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s b mu sigma tau
s 1.2e+03 0 -4.088e-3 (-0.030) 27.177e-3 (0.218) -0.485 (-0.196)
b 0 0 0 0 0.000
mu -4.088e-3 (-0.030) 0 1.55e-05 -0.001e-3 (-0.059) -0.013e-3 (-0.047)
sigma 27.177e-3 (0.218) 0 -0.001e-3 (-0.059) 1.3e-05 -0.032e-3 (-0.126)
tau -0.485 (-0.196) 0.000 -0.013e-3 (-0.047) -0.032e-3 (-0.126) 0.00509
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"├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ s │ 1.017e3 │ 0.035e3 │ │ │ 0 │ │ │\n", - "│ 1 │ b │ 870 │ 40 │ │ │ 0 │ │ yes │\n", - "│ 2 │ mu │ 0.995 │ 0.004 │ │ │ │ │ │\n", - "│ 3 │ sigma │ 0.100 │ 0.004 │ │ │ 0 │ │ │\n", - "│ 4 │ tau │ 1.05 │ 0.07 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬───────────────────────────────────────────────────┐\n", - "│ │ s b mu sigma tau │\n", - "├───────┼───────────────────────────────────────────────────┤\n", - "│ s │ 1.2e+03 0 -4.088e-3 27.177e-3 -0.485 │\n", - "│ b │ 0 0 0 0 0.000 │\n", - "│ mu │ -4.088e-3 0 1.55e-05 -0.001e-3 -0.013e-3 │\n", - "│ sigma │ 27.177e-3 0 -0.001e-3 1.3e-05 -0.032e-3 │\n", - "│ tau │ -0.485 0.000 -0.013e-3 -0.032e-3 0.00509 │\n", - "└───────┴───────────────────────────────────────────────────┘" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "c.mask = None # remove mask\n", "m.fixed = False # release all parameters\n", @@ -11866,7 +600,7 @@ }, { "cell_type": "markdown", - "id": "timely-afternoon", + "id": "41", "metadata": {}, "source": [ "Finally, we release all parameters and fit again to get the correct uncertainty estimates." @@ -11875,856 +609,9 @@ { "cell_type": "code", "execution_count": null, - "id": "recreational-pride", + "id": "42", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 15.03 (χ²/ndof = 1.0) Nfcn = 323
EDM = 3.22e-07 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 s 1.02e3 0.04e3 0
1 b 867 35 0
2 mu 0.995 0.004
3 sigma 0.100 0.004 0
4 tau 1.05 0.08 0
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s b mu sigma tau
s 1.38e+03 -0.4e3 (-0.280) -7.764e-3 (-0.052) 38.617e-3 (0.275) -0.452 (-0.162)
b -0.4e3 (-0.280) 1.23e+03 7.764e-3 (0.055) -38.616e-3 (-0.291) 0.452 (0.172)
mu -7.764e-3 (-0.052) 7.764e-3 (0.055) 1.63e-05 -0.001e-3 (-0.090) -0.015e-3 (-0.050)
sigma 38.617e-3 (0.275) -38.616e-3 (-0.291) -0.001e-3 (-0.090) 1.43e-05 -0.045e-3 (-0.160)
tau -0.452 (-0.162) 0.452 (0.172) -0.015e-3 (-0.050) -0.045e-3 (-0.160) 0.00564
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"├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ s │ 1.02e3 │ 0.04e3 │ │ │ 0 │ │ │\n", - "│ 1 │ b │ 867 │ 35 │ │ │ 0 │ │ │\n", - "│ 2 │ mu │ 0.995 │ 0.004 │ │ │ │ │ │\n", - "│ 3 │ sigma │ 0.100 │ 0.004 │ │ │ 0 │ │ │\n", - "│ 4 │ tau │ 1.05 │ 0.08 │ │ │ 0 │ │ │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬────────────────────────────────────────────────────────┐\n", - "│ │ s b mu sigma tau │\n", - "├───────┼────────────────────────────────────────────────────────┤\n", - "│ s │ 1.38e+03 -0.4e3 -7.764e-3 38.617e-3 -0.452 │\n", - "│ b │ -0.4e3 1.23e+03 7.764e-3 -38.616e-3 0.452 │\n", - "│ mu │ -7.764e-3 7.764e-3 1.63e-05 -0.001e-3 -0.015e-3 │\n", - "│ sigma │ 38.617e-3 -38.616e-3 -0.001e-3 1.43e-05 -0.045e-3 │\n", - "│ tau │ -0.452 0.452 -0.015e-3 -0.045e-3 0.00564 │\n", - "└───────┴────────────────────────────────────────────────────────┘" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "m.fixed = None\n", "m.migrad()" @@ -12732,7 +619,7 @@ }, { "cell_type": "markdown", - "id": "correct-notice", + "id": "43", "metadata": {}, "source": [ "We get the same result as before. Since this was an easy problem, we did not need these extra steps, but doing this can be helpful to fit lots of histograms without adjusting each fit manually." @@ -12740,7 +627,7 @@ }, { "cell_type": "markdown", - "id": "tough-europe", + "id": "44", "metadata": {}, "source": [ "### Weighted histograms\n", @@ -12750,7 +637,7 @@ }, { "cell_type": "markdown", - "id": "gothic-regular", + "id": "45", "metadata": {}, "source": [ "## Least-squares fits\n", @@ -12763,20 +650,9 @@ { "cell_type": "code", "execution_count": null, - "id": "packed-penguin", + "id": "46", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def model(x, a, b):\n", " return a + b * x**2\n", @@ -12798,711 +674,9 @@ { "cell_type": "code", "execution_count": null, - "id": "arabic-plant", + "id": "47", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 25.29 (χ²/ndof = 1.4) Nfcn = 29
EDM = 2.27e-22 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 a 0.99 0.04
1 b 2.04 0.15
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a b
a 0.00139 -0.0037 (-0.658)
b -0.0037 (-0.658) 0.0226
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(χ²/ndof = 1.4) │ Nfcn = 29 │\n", - "│ EDM = 2.27e-22 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ a │ 0.99 │ 0.04 │ │ │ │ │ │\n", - "│ 1 │ b │ 2.04 │ 0.15 │ │ │ │ │ │\n", - "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───┬─────────────────┐\n", - "│ │ a b │\n", - "├───┼─────────────────┤\n", - "│ a │ 0.00139 -0.0037 │\n", - "│ b │ -0.0037 0.0226 │\n", - "└───┴─────────────────┘" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "c = cost.LeastSquares(x, y, ye, model)\n", "m1 = Minuit(c, a=0, b=0)\n", @@ -13512,7 +686,7 @@ { "attachments": {}, "cell_type": "markdown", - "id": "7ad47416", + "id": "48", "metadata": {}, "source": [ "We can also plot the standard visualization manually and add further graphs to the figure." @@ -13521,20 +695,9 @@ { "cell_type": "code", "execution_count": null, - "id": "former-dominant", + "id": "49", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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XXyD9o7x4KvtxDPu+4tlVr59R3HG1kuraVPkKIyIiIlV02F7AyGkr2JudzxlhAbzt/RJepWZXDa3ya8fGxrJp06aS7cTERFJSUmpdEAGFERERkSrJK3Rw+0er2L7fTnioPx+M6on3hf+C4IhTzq5aUbV9qvzjNJpGRESkkoocTv716VpW7zpMiL83H952HuGhARB6DbS/DHwCrC7Ro6hlREREpBJM0+SxLzeycNNefL0N5ndfSXu/I3+doCBSaQojIiIilfC/Rdv4dMVuDAO+6byYqDUvwfRBUGC3urRKc5dVgRVGREREKmjWit28+v3vACR0XkabLe8UH+h1H/gGWlhZ5bnTqsAKIyIiIhXw/aa9/GfOegDeP2M1Z//xWvGBS5+G8+6wsLLKc7dVgRVGRERETmHN7sOM+XQNThNebL2OS3a+XHyg70PQe6y1xVWSO64KrDAiIiJyEtv35zJq+kryCp3cH/UHQ/e8VHyg5xjoN8Ha4qrAHVcFVhgREREpR2ZWHiOnreDw0UK6RYZyxw3XYYSdCT1ugwHPQjnrx7gzd1wVWPOMiIiIlGF/Tj43Tv2FtMPHiG5Uj2m3nEu9ID+4bQH4BntkEAH3XBVYLSMiIiL/cNhewM1Tl7Njv51BISkk9N5Fo6A/15jxD63Uwnfuxh1XBfbcqykiIuICWccKGT5tOVv35tA3MJXXzYk0/G4sbP3G6tKqhTuuClypMDJx4kTOPfdcgoODCQsLY8iQIWzduvWkz5k+fTqGYZR6+Pv7n1bRIiIirpCbX8StH6xgQ3o259fbwzTv5/EqzIXoGGjdz+ryqo27rQpcqTDy008/MXr0aH755RcWLlxIYWEhAwYMwG4/+axzISEhZGRklDx27dp1WkWLiEjtERgYiGmamKZJYKB1E4cdK3Bw+4crWbP7CF399zPD7wVs+VkQeS7c8Gmtm+bdnVYFrlQH1gULFpTanj59OmFhYaxevZo+ffqU+zzDMGjW7PRXLxQREXGF/CIHd32yml92HKK93yHiA1/A234AmnWBmz4Hv2CrS3QJd1kV+LT6jGRlZQHQsGHDk56Xm5tLy5YtiYqKYvDgwWzcuPGk5+fn55OdnV3qISIi4gqFDidjZq4l6ff9hPnk8VXIi/jaM6BxBxg+FwIaWF1irVflMOJ0Ohk3bhy9e/emc+fO5Z7XoUMHpk2bxpdffsknn3yC0+mkV69eJ51wZeLEiYSGhpY8oqKiqlqmiIhIuRxOk3GfrftzBV4vXh3RB/9ucdAgGkbMhcDGVpdYJxhmWfPBVsA999zDN998w88//0xkZGSFn1dYWMiZZ57JDTfcwDPPPFPmOfn5+eTn55dsZ2dnExUVRVZWFiEhIVUpV0REpBSn0+Tf8b/xxZo0fGwG7w7vwUVnhBUfPHYEAupbWR4AdrudoKAgoPguQ3X3qXH162dnZxMaGnrK7+8qTXo2ZswY5s2bR1JSUqWCCICPjw9nn30227ZtK/ccPz8//Pz8qlKaiIjIKZmmyWNfbuCLNWmEeOUxt9MSWv99tIwbBJG6pFK3aUzTZMyYMcyZM4cffviBVq1aVfoNHQ4H69evr9GZ3URERI4zTZNn529mxvLd+BsFLIp4m9a/vw9z7rS6tDqrUi0jo0ePZubMmXz55ZcEBweTmZkJQGhoKAEBxUOeRowYQfPmzZk4cSIATz/9NBdccAFt27blyJEjvPTSS+zatYvbb7+9mn8VERGRU3v5u995/+cUfCnkx8j3abJ/RfH07hfeb3VpdValwshbb70FQL9+/Urt/+CDD7jlllsA2L17N15/myb38OHD3HHHHWRmZtKgQQPOOeccli5dSseOHU+vchERkUp6/Yc/eP3HbdhwsLDFR4TvSwbvgOLhu83Psbq8OqvKHVhrUkU7wIiIiJRnavIOnp2/GQMn37WcSbu9iWDzhRs/gzYXW11emepKB1atTSMiIrXex7/s4tn5mwH4os03xUHEsMHQD902iNQlCiMiIlKrxa9O47G5GwC4p18bzr58FNRrBLHvwhmDLK5OoIpDe0VERDzB17/u4cH4XwG4pVc0Dw7sULwy7X3rwF+3/d2FwoiIiNRK323MZNxn63Ca8Frr5VzZvWlxEAEFETej2zQiIlLrLN66jzEz1+JwmrzWIomr9kzB+GgwZJW/FIlYRy0jIiJSqyzbfpC7Pl5NgcPJlOY/cNW+qcUHeo6B0MrNGi41Q2FERERqjdW7DjHqw5XkFzmZ3Ow7Bh+cXnzgokeg74OW1iblUxgREZFaYX1aFrdMW8nRgiJeDfuGIUc+KT5wyeMQ84C1xclJKYyIiIjH25KZzfBpy8nJL+KBpmu5JuvPIHLp09B7rLXFySkpjIiIiEfbti+Xm6cu58jRQs6Kqs+tt9wPCWuLJzPrNcbq8qQCFEZERMRj7Tpo56apv3AgN59O4SF8eNt5BAX4FK8142WzujypIIURERHxSHuOHOPG95azNzuPV0JmcXnbFgT4xxQfVBDxKJpnREREPM6+7DxufO8X0o8c5dXgGcQWfE3AyjcgfbXVpUkVqGVEREQ8ysHcfG6aupxdB3N5NfBjrin8FjDg6v9BZA+ry5MqUBgRERGPceRoAcPfX8G2fdlMrjedwY7vAQMGvwFn32R1eVJFCiMiIuIRDubmc/P7K9iacYQpAe9ztfNHMLxgyNvQbZjV5clpUBgRERG3ty87jxunLmfbvlwuCdzJVY7FxUEk9j3oEmd1eXKaFEZERMStpR85xk3v/cLOg0cJD/XnkdtvxUhrCD4B0DnW6vKkGiiMiIiI29p10F48fPdIDp3qm7x950VENawHTdQ/pDZRGBEREbe0bV8uN039hYPZdt4PfIve9Q7g7dsLqGd1aVLNNM+IiIi4nc0Z2Qx7ZxmHsu18GPQmfR3L8M7aBXs3WF2auIBaRkRExK38lnaEEdNWcPToUT4JfoPzC1eAzQ+GfVK83ozUOgojIiLiNlbvOsQt01ZSkH+UmSFv0KNgJXj7w/UzoG1/q8sTF1EYERERt7B0+wFu/3AVjoJjfBb6GmflrwbvALjhU2hzkdXliQspjIiIiOUWb93HXR+vJr/IyZWt/eh69CA468GNn0GrPlaXJy6mMCIiIpb6dmMmY2auodBh0v/MMCbd2B0vexfITocWF1hdntQAhREREbHMV7/u4f7P1uHnPMZ9rQ9y982X42PzgvpRxQ+pExRGRETEErNXpfLQF78RbObyZYP/EZ2xGWNrM+h4tdWluY3AwEBM07S6DJdTGBERkRr38bKdPPblRsI4zFf1X6bZsR3gHwohza0uTSygMCIiIjVqavIOnp2/mZZGJnODJ9Egbw8ENYPhCdC0k9XliQUURkREpMa8tugPXl74Ox2NncwOmkRQwSFo0AqGz4GGrawuTyyiMCIiIi5nmiaTvtvKGz9uJ9LYz5x6z+FXaIemXeDmLyC4qdUlioUURkRExKVM0+SZeZuZtiQFgJGXxeCXcz3s21w8oVlAfWsLFMspjIiIiMs4nSaPfrmBmct3Y+Dk6cFdGN4zGpwvgaMQfPytLlHcgMKIiIi4RJHDyYNf/EbCmnRu8/6G2yN2E3HeF8UHvWzFDxEURkRExAUKHU7GfbaO+b/t4d8+nzPaNhf2ARvnQtehFlcn7kZhREREqlV+kYPRM9byw+YMJvp8wA22RcUHLnkcusRZW5y4JYUREZFawG63ExQUBEBubi6BgYGW1HGswMGdH69i+R8ZvOH7Fpd7/QIYcOWr0ONWS2oS9+dldQEiIlI75OYXcev0Faz+I40P/F4uDiI2Xxg63dIgYrfbMQwDwzCw2+2W1SHlU8uIiIictqxjhdz6wQrW7D5Cd7+DXOCzHYxAuH4GtLnI6vLEzSmMiIjIaTlsL2D4tOVsSM8mNMCHJ267DlthB/ANgshzrC5PPIDCiIiIVNm+nDyGT11B4b6tXFiviP/cfhMdI0KAvlaXJh5EYURERKpkx/5cRn6wgtDDG5nl9yIhvjZsPpcAIVaXJh5GHVhFRKTSVu86zLVvLSXyyCpm+z1LA7KxNYyGeg2tLk08kFpGRESkUr7bmMm/Pl1LP+cvvO77Oj4UQas+cP1M8Au2ujzxQGoZERGRCvt42U7u/mQ1g81FvOX7v+IgcuZVcFO8gohUmVpGRETklJxOkxe/3crbP23nMq8VvOjzXvGB7iPgyslaZ0ZOi8KIiIicVEGRkwfjf2Xuuj0AdOkXh5m2HKPFBXDJE2AYFlconk5hREREypWdV8g9n6xm2bb92Ly8mBjblet6REHRl+DtZ3V5UksojIiISJkys/K45YMVpGbu4wO/14nu3JOWPa4oPqggItVIYURERE7w+94cbpm2AmdWOnP8J9GeXbB1Cxy6Exq2sro8qWUURkREpJRfdhzkzo9WEZX/Bx8GTKKxeRgCw+DGWQoi4hIKIyIiUmLeb3sY/9mvxJgrecPvdfzNfGhyJtw0G+q3sLo8qaUURkREBICpyTt4dv5mhtu+4ynfD/HChDYXw9Dp4B9qdXniAoGBgZimaXUZmvRMRKQ2cDgcJT8nJSWV2j4Vp9Pk6a838ez8zQB0axddHETOuRVunK0gIi6nMCIi4uESEhLo2LFjyfagQYOIjo4mISHhlM/NK3Qw5tM1TFuSAsCEy8/g2pHjYNT3cOWrYPNxVdkiJRRGREQ8WEJCAnFxcaSnp5fan56eTlxc3EkDyZGjBYx4fwVr1m9kmu8k3hkcwV1922AYBkSdq8nMpMZUKoxMnDiRc889l+DgYMLCwhgyZAhbt2495fM+//xzzjjjDPz9/enSpQuJiYlVLlhERIo5HA7Gjh1b5j3/4/vGjRtX5i2btMNHiXt7GfZda/jS73Eu9lrDwO3PubxmkbJUKoz89NNPjB49ml9++YWFCxdSWFjIgAEDsNvt5T5n6dKl3HDDDYwaNYq1a9cyZMgQhgwZwoYNG067eBGRuiw5OZm0tLRyj5umSWpqKsnJyaX2b9yTReybS2lxIInP/Z6mqXEYmpwBV7zs6pJFylSp0TQLFiwotT19+nTCwsJYvXo1ffr0KfM5U6ZM4bLLLuPf//43AM888wwLFy7k9ddf5+23365i2SIikpGRUenzkv/Yzz2frCGuaB6P+X6CDSe07gdDP4SA+q4pVOQUTqvPSFZWFgANGzYs95xly5bRv3//UvsGDhzIsmXLyn1Ofn4+2dnZpR4iIlJaeHh4pc77YnUaoz5YzgOO93nS56PiINJ9BNwUryAilqpyGHE6nYwbN47evXvTuXPncs/LzMykadOmpfY1bdqUzMzMcp8zceJEQkNDSx5RUVFVLVNEpNaKiYkhMjKyuMNpGQzDICoqigsvvJA3ftzGA5//io8zj4H1/uzr1/8puOp/GjEjlqtyGBk9ejQbNmxg1qxZ1VkPABMmTCArK6vkkZqaWu3vISLyd3a7HcMwMAzjpP3g3InNZmPKlCkAJwSS49svv/IKT3y9mZe+LQ4gN/fpRLO7v4Jhn8CF4yo8YsYTr494jirNwDpmzBjmzZtHUlISkZGRJz23WbNm7N27t9S+vXv30qxZs3Kf4+fnh5+fVoQUETmV2NhY4uPjue+++0oN742MjOS/k17hW3tLMrYkcYNtBx0GjeGW3n+uLdOwpUUVi5yoUi0jpmkyZswY5syZww8//ECrVqdeMKlnz54sWrSo1L6FCxfSs2fPylUqIuLBXNmyEBsby6ZNm0q2ExMTWbV+C5/tD8e5dQGzfZ/ieZ/3uSVse7W+r0h1qVTLyOjRo5k5cyZffvklwcHBJf0+QkNDCQgIAGDEiBE0b96ciRMnAjB27Fj69u3Lyy+/zBVXXMGsWbNYtWoV7777bjX/KiIidZfNZiv5ObrzuVz37nL6HUngMd+PsWEWj5iJ7GFdgSInUamWkbfeeousrCz69etHeHh4yeOzzz4rOWf37t2lhpH16tWLmTNn8u6779KtWzfi4+OZO3fuSTu9iohI1fiGt2f4tJWMzHrrzxEzpkbMiNurVMtIRVb2W7x48Qn7hg4dytChQyvzViIiUkmBnS4i6rI7eKHov/T3Xlu8s/9T0HuspnYXt1alDqwiIuI+ihxO/vvdNhpf+QADvX6mv20tprc/xjXvQKchVpcnckpaKE9ExIMdthdwywcr+Wh58bTw039OI/+CcRi3zFcQEY+hMCIi4qG2ZGZz9Rs/03DHl4T72Nk/53mykj+hKOZBdVb9m78vFJiUlFTmwoFiLYUREREP9M36DIa9mcRd2a/zP983+DbyA/L+WGp1WW4nISGBjh07lmwPGjSI6OhoEhISLKxK/klhRETEgzidJi9/t5UnZizifZ7iZu9FmBj4t7mQCowxqFMSEhKIi4srNRkcQHp6OnFxcQokbkRhRETEQ+TkFXLnx6tY+uN85vk9Qg+v3zH9QjBunE1hz3Eoi/zF4XAwduzYMkeBHt83btw43bJxEwojIiK4f7+CHftzGfL6zzT7fQazfJ8lzDgCYR0x7lwM7QdYXZ7bSU5OJi0trdzjpmmSmppKcnJyDVYl5VEYEZE6z937Ffy4ZR+D31hCxoFD3O2TiI/hgE7XwKiF0KiN1eW5pb9Pvlkd54lraZ4REanTjvcr+Gdz/vF+BfHx8cTGxlpSm2mavPXTdl76diumCT1ahlNvwKeQuQR6/UsTmZ1EeHh4tZ4nrmWYFZlW1WLZ2dmEhoaSlZVFSEiI1eWISC3hcDiIjo4utznfMAwiIyNJSUkptfZLVdjtdoKCggDIzc0lMDDwpOcfLSjiwfjfOLBhEREcwK/HzTx1dSd8vctu0K7s67u6fqsd/7dNT08vs99Idf7bSvkq+v2t2zQiUmfVZL+CyvRJST10lGvfXErTje/zic/zvOQ3lYnnF5YbRORENpuNKVOmAMXB4++Ob0+ePFlBxE3oky0idVZN9SuoTJ+UpdsPcN1ri7jr4EQe8/kEb8OJrUschJ15WjXURbGxscTHxxMREVFqf2RkpKW33+RE6jMiInVWTfQrqGifFNM0+XDpTqbPX8w071c407Yb08sbY+DzcN6d6h9SRbGxsfTv35/Q0FAAEhMTGTBggFpE3Iz6jIhIneXqfgUV7ZOy+fdtPPn1ZjLXJvKaz2vUN+yYgU0whn4I0b0r/b6u4Gl9Rv7Ok2v3dOozIiJyCq7uV1DRPikDH3qHz1en0dUrpTiIND8H486f3CaIiLiawoiI1Gmu7FdQ0b4mW1NSCQ3w4YIRz8KVr2Lc+g2ENq/y+7qCu08KJ55NYURE6rzY2Fg2bdpUsp2YmEhKSsppd3CsaF+TdpFN+GpMb2Lah0GP28Db77Tet7q5+6Rw4vkURkREoNStmD59+lRLB8eYmBgiIyNPuAV0nAFEhRj8cGk6LRu5Zz8GLTYnNUFhRETERU7aJ+XP/756Uzf8+0+o4coqRovNSU1RGBERcaHjfVKaNG1Wan9kiEH8f67m2v8th+CmFlV3clpsTmqK5hkREXEx7zYX0OPWx7k57VmwH6JpqC99R7+O7ZybrS7tpLTYnNQUhRERERcpdDiZ9O1W3knaQROC6d/CwVG7N2FjvsHWpqfV5Z2SFpuTmqIwIiLiAqmHjvLgzKUsS8sH4OpeXbj8ngNsP1RE2rNdLa6uYo53wD3VpHAxMTEWVCe1ifqMiIhUs3m/7eGpKW8yef8o4vxX8NZN3Rl/SRtW7yniSJ7V1VWcFpuTmqIwIiJSTY4VOPhP/Bp2zX6Id3mWpsYRngtfwuWdm536yW5Ki81JTdBtGhGRarAlM5tnPlnAA9n/pbv3NgCc3Ufid9kLHr/InRabE1dTGBEROQ2mafLJ8t2snD+Nt7zeJcTrKEU+wXgPeQ2vTtdYXV61ccWkcCLHKYyIiFTRkaMFPPTFb+zatJIFfpMBKIzogc/QadCgpbXFiXgQhRERESAwMLDMESPlWbnzEGM/XcuerDx8bC1ZH3UTnVs2w+eiCWDzcWGlIrWPwoiISCU4nCZv/vAHGT++DY5uRDdqwWs3dKdL88s9vm+IiFUURkREKigzK49HPk0iLv1F/uWzkh0hXQgb8z1BAf5Wlybi0RRGREQqYNHmvXwyexbPOqfQ3HYQp+FN6z43gJ+v1aWJeDyFERGRk8gvcvDfxI3UW/4/pnrHYzNMCkOj8Rk2HSLOtro8kVpBYUREpBw79ufyyIzF3HfwOXr6bALA0WUYPle+DH7BFlcnUnsojIiIlOGL1Wk89uUGHAWFNPLPpci7Ht5XvYqt2/VVer3KjtYRqUsURkTEY9jtdoKCggDIzc0lMDCw2t8jN7+IJxPWkPDrXpx4cUHrZjS49FO8QwKgUZtqfz8RURgRESmxPi2LF2d8zcP2F2nmfT5+Fz3IvRe1xealIbsirqQwIiJ1nmmavJ+8g+3fvc3btg8J9MqnXeBRfC98FRRERFxOYURE6rSDufk8/+lCrtr9Ird7/wpAYYsL8Y2bCn5BFlcnUjcojIhInbV0235++PRlniiaTojtGA4vX7wueQyfnqPBSwvBidQUhRERqXOKHE4mf/8HCYuXs8j3fQKMAo6Gdafe0HegSXuryxOpcxRGRKROSTt8lLGz1rF612GgEd82H8OgM0Kod+F9ag0RsYjCiIjUCaZpMi95FaE//Bsj/yqC/TrzfGwXrup2hdWlidR5CiMiUuulHrSz4JNJDDv0FiHGMVrUO4zt3qVENar+eUrE/WjCOfenMCIitZbTaZLw0wrCFj/IHcY6MGBvSBcib5qKt4KIiNtQGBERj+FwOEp+TkpKYsCAAdhsZffz2LEvhwWfvMLNWW8TYhylAB9yej5E00vHq29IFah1QVzJy+oCREQqIiEhgY4dO5ZsDxo0iOjoaBISEkqdV+Rw8s5P23n+tde5N/sVQoyj7A/tgve9S2g08N8KIiJuSC0jIuL2EhISiIuLO+Ev8/T0dOLi4oiPjyc2NpatmTk8GP8rv6ZlAV1YEXQh7btfRJNL7lcIEXFjCiMi4tYcDgdjx44t8xaBaZoYhsHYseM46N2IRqsmkVJwM8H+9Xnsyo6c2/1rDC81AIu4O4UREXFrycnJpKWllXvcNE3S0lKJ+OIarmjloEnjQNrfOZ2mIf41WKWInA79ySAi1cZut2MYBoZhYLfbq+U1MzIyKnRedm4ehxt05cIRTyqIiHgYtYyIiFsLDw+v0Hn1e4+kwZjXwKb/WxPxNGoZERG3FhMTQ2RkJIZhlHncAKIimjFg7OsKIiIeSmFERNyazWZjypQplDXDhQFgGEx+7Y1y5xsREfenMCIibi07r5CMowXcE9sLW3CjUscio6JKhvWKiOdSm6aIuK2f1m3h8FePco/je7w6m1ww8AlG3jsBigpITEw86QysIuI51DIiIm7ncG4es95+hi5zLmGIcyFehsn+1rFcOzgWigoA6NOnj4KISC1R6TCSlJTEVVddRUREBIZhMHfu3JOev3jx4pKhfn9/ZGZmVrVmEanFfk76nrRJF3J95iQaGrnsC2hD/s3zaTLiAwhsYnV5IuIClb5NY7fb6datG7fddlul7tNu3bqVkJCQku2wsLDKvrWI1GL7cvJ4Ys567t92H+290rETwJHz/4/mA8aCzcfq8kTEhSodRi6//HIuv/zySr9RWFgY9evXr/TzRKR2M50OEtak8fT8rWQdK8RuG8l/wlbR6qZXad4g0uryRKQG1FifkbPOOovw8HAuvfRSlixZUlNvKyI1yOFwlPyclJRUarsse7as4I//xrBpzn/JOlZI5+YhPDz6Hs4Y8zl+CiIidYbLw0h4eDhvv/02X3zxBV988QVRUVH069ePNWvWlPuc/Px8srOzSz1ExL0lJCTQsWPHku1BgwYRHR1NQkLCCefmZh1i5dt30vTTAbTP38hd3vN5eEAr5t7bm44RISecLyK1m2GWtRRmRZ9sGMyZM4chQ4ZU6nl9+/alRYsWfPzxx2Uef/LJJ3nqqadO2J+VlVWq34mIuIeEhATi4uJOWFn3+Kypx+cCcTqcrPz6bdqu+y+NOALA8oA+hA19mVat25/yfex2O0FBQQDk5uYSGBhYvb+IiFSr7OxsQkNDT/n9bck8I+eddx4///xzuccnTJjA+PHjS7azs7OJioqqidJEpJIcDgdjx449IYhA8Yq6hmEwbtw42rRqjm3+eM4v2gDAbiOCg32e47x+15Q71buI1A2WhJF169addPErPz8//Pz8arAiEamq5ORk0tLSyj1umiapqanc9tJn/NJuM8fwZX2bOznrukdp4R9Qg5WKiLuqdBjJzc1l27ZtJdspKSmsW7eOhg0b0qJFCyZMmEB6ejofffQRAJMnT6ZVq1Z06tSJvLw8pk6dyg8//MB3331Xfb+FiFgmIyOjQufttnuTEPUw/S+P5bzmbV1clYh4kkqHkVWrVnHRRReVbB+/nTJy5EimT59ORkYGu3fvLjleUFDAAw88QHp6OvXq1aNr1658//33pV5DRDzXyVo5/+7Fm/swbOiVp/VegYGBZd4OEhHPdlodWGtKRTvAiEjNczgcREdHk56eXmZQMChe0C4lJUXTt4vUMRX9/tbaNCJyWmw2G888P7G4s+o/jhmGAYbB5MmTFUREpFwKIyJSZQVFTt77aTvdt73KF9cF0DykdByJjIwsGdYrIlIeS0bTiIhnM51Ofty6j2fmbyHlgJ2Rtt7c1/EQ3914H52HjMcJJCYmMmDAALWIiMgpKYyISKWkbviZ7K8fYXZOP1Kc59E4yI8ul46jwVkT8S8CJ8Wd2vv06aMgIiIVojAiIhWSk76VXfET6Hx4EQDjfA7S8vxhjLm4HcH+f66qW2S3sEIR8VQKIyJyUo7svWyLf5zWuz+nMw6cpsEvwf2Jin2OCa07WF2eiNQCCiMiUq5tC94k4pen6cAxAFZ4n4NtwFP0Oi/G4spEpDZRGBGRE6QeOspz8zdzdPMRPvI9xgbakN7jYS65PA5vmwbhiUj1UhgRkWJOJ8d+jeen9Snc93tXCoqceBldmd52CoOH3EDnIK0XJSKuoTAi4kbsdjtBQUFA8TpQgYGBNfK+R7csInfeI4TlbqaXGUBg0av0aNOKx6/qyBnNNOuxiLiWwohIHWbftZYDcyfQ8vAy6gG5pj9f+F3Di9ecT/9urYpnUBURcTGFEZE6KHffLtLiH6L9vgW0xKTAtDHP9zIC+j/MiHO7YPNSCBGRmqMwIlKH5OQV8uHSnXybtJQ55rd4GSY/eMfguOgRBve84LRDiFbVFZGqUBgRqQNy92xm9Q8J3Lf9XLKOFQKNeSP0Drr17E/fPpeqJURELKUwIlKL2XeuJmP+87Tev4i+mDTJf5FGTTow9pJ2XNl1kEKIiLgFhRGRWujoH8ns/2YiLQ8toe2f+5Z4n8eES86kX+8YhRARcSsKIyK1SG7mNrI/vZ2IrLW0BBymwY8+MXjFjKdvTD+FEBFxSwojIrVATl4hHy3bxcdJm5nv3EYBNr7zvQT/vvdzUa+eCiEi4tYURkTciMPhKPk5KSmJAQMGYLPZyjm5kLw1n5K+PIG4g3dz+JgD8GJig//j0j59uPz8sxVCRMQjKIyIuImEhATuu+++ku1BgwYRGRnJlClTiI2N/evEwmPkr5hOQdJkgvMzaQP0KDib7U36qmOqiHgkw/SASQGys7MJDQ0lKyuLkBBNTS21T0JCAnFxcSfM0XF8BtT4+HhiB11C/i/v4VjyBvUKDwGw3wwlwW8IzfuP5vIe7RVCRMStVPT7W2FExGIOh4Po6GjS0tLKPG4YBs3Dm7H1Tqhn2gFIMxsT7x9H60vv5IrurRVCRMQtVfT7W7dpPJRVC6pJ9UtOTi43iACYpknangxmbW9NTHR94gOG0nHArfzr7JaVDiH63IiIO1IYEbFYRkZGhc57p+hqGg4Zz4NnRaolRERqFYUREYuFh4dX6Lznb7+aS7pHubgaEZGa52V1ASJ1mv0gPY4upnmIjfLaOgzDICoqin59+9RoaSIiNUVhRMQiG9KzePbL1QQsn8z/LvMt85zjo2kmT55c/nwjIiIeTrdpRGrK0UMUrZlB6vYN/N/RkazedRiAIO9rOHZuex7oXp+Z/3uWPXv2lDwlMjKSyZMnl55nRESkllEYEXG19NUcW/ouPpvn4O3Mp6VpsL/gfHxszRjUJZyYnq/QvUV9DMPgsXtuIjQ0FIDExMSTz8AqIlJLKIxIrePq4asVev2Co5gb4jm65F0CD64n4M/dG50t+dLnMob16sHQnu0JC/Ev9bS/B48+ffooiIhInaAwIlLN8godrP/6Xc5d/wSBQL7pzTznBaxpEssFfS7j/zqH4+ut7loiIscpjHioSi2oJq7lKILfv2G/3cHU/R34bGUq+Uej+Nw3mm/MXhzrdD2xMWdxbfNQqyvV50ZE3JL+PPNACQkJdOzYsWR70KBBREdHk5CQUC2vb7fbMQwDwzCw2+3V8pq1UbMgA+8lr5A/qRN8djOHvnqEd37azpGjhTRq0IAllyRw+8NTePz6vnR2gyDi6s+NiEhVqWXEw5S3oFp6ejpxcXHFC6pp5IXrFBVg2/Yds68LYkgHGz5LJwFwwAxhkbM7F7cN5YZe7bn4jDC3miVVnxsRcWdaKM+DVGRBtcjISFJSUk6r6d3T1y9xZf1HZt1F/S2zSrZXOtvzOQMJOjuWG3u3p21Y0Gm9vitqr6nPjYjIP2mhvFqoIguqpaamkpycTL9+/WqusNrINGHPGlj/BXtax5GQFszXv2YQsT+KF3zqM8/Rk1kH2zN02E08dkFrgv19rK64XPrciIi7UxjxIBVdUK2i50kZ9m+F9fEU/fY53kdSAJj7cyqTiq4HYJftbB5p+SkznhlN3s7PmDvpIQKrMYgEBgaecCvldOlzIyLuTmHEg1R0QbWKnid/KjgKK9+j6NfZeO/bABT/D+OY6cv3zu4sdXYmpl1jruoawcDOzfB2FvD+qHWWllwZ+tyIiLtTGPEgMTExREZGkp6eXuZfz8fv/cfExFhQnfuo0PDVogLw9iU3v4iF6/fSf9Ekgp3ZFJo2kpxd+crRk4PN+3Pp2W14tUs4TYL9Sp6anX3s1K/vRvS5ERF3p6G9HsRmszFlyhTgrwXUjtOCasVOOnw1Pwd+/QzHx9dif7UH936yinOeWcj98Zt4JX8w/ykcxYgGH/FH//f5978f45PRlzCyV3SpIOKJw2P1uRERd6fRNB4oISGB++67j/T09JJ9UVFR1bagmqeOpilv+KphACbMvj6EuA5/7b8i/zk2mq1o3TiQq7pFcFW3iJOOhin/9Yu/0N19eKyrPzciIv9U0e9vhREPdfyaQPUvqObK13aVUw5fBSJDDL7/Vzvmm735pd5FnHV2D67qFkGniJATWgwq/foeMjzWE/9tRcRzVfT7W7dpPJSrFlTzxNsQAMnzZ598+CqQmm3yrP9DxNwxiU8n3MyEQWfSuXnoKYMIVG54rDvTQnwi4o7UgVVKeNQsnYV5sGsJbPse/lhIxuJNFXra5W0C6BHdsNJvp+GxIiKuozAiQPFtiLFjx5Y52sI0TQzDYNy4cQwePNjav6bzc+CL2zFTkjAKj5bsDguqWCNfVYevanisiIjr6DaNAG56G6IwD7YtgnUzMU2TnQfsfLzmIAd2rMUoPMpesz6fFfXj7oJxPBDxPoENwv7srXoiwzCIioqq8vDV48Njy7ulc7qvLyJSl6llRAA3ug1xeCf8sRC2fV/S+nHMFsRlC5qw60gBAAu9bmG/WZ90v9b0PqMJF3cI47+dmrHoXB/i4uLAMEq18FTH8NXjw2Pj4uIwXPD6IiJ1mcKIAG5wG2L5uzhXvIvXwT9KdhnAXrM+i/PP4oj9ED62YLq3aMB57YZyYbsmdGkeWmpl3NjYWOLj408YvhoZGVktw1dd/foiInWVhvZ6qOqeC+T40NVTzdJ52kNXi/Ih4zdIW4F51k1szfLi5z8O0GjVq1yT9RFFpherzfb85OjGj86zcDTpyIXtwohp15jzWjUk0O/U+dnVw1c9eXisp84hIyKeSav21nLVvaCay25DZO+B1BWQthJSV2Bm/IrhyAfgoe8PMdveHYCWxlksNOqzuV4PzmrXkgvbNmZku8Y0DfGv0u9ynCuGr2p4rIhI9VIYcRFP/Av0tG9DFBWAowD8/pzFdOMc+PyWUqcYwCEziDXOduwtsOHv48X5rRoR0+5MLmzXmA5Ngys074eIiNQeCiNSSmxsLP3796/YbYi/t3qkrYQ963Be9Ci/thjOz38c4PctTiabBlvMFqxxtmOtsy1raUdweAcubNeEu9o15t2WDfDzVstCTanuFjURkeqgMCInOOltiNx98M2DkLoSsk8cCjxv4ULuy2/z55Yfi3ifhg0aENOuMZe0bcJjbRrRINDXxb+BiIh4EoUROZGziPaNvOgc5oXvj09BwxYc63EPmzOz2ZyWxbBN8/A2C3GYBlvNFqxxtmWNsx1rzHbsNJsR7O9NrzaNuLBdE2LaNqZlo3q69SIiIuVSGJFiP06EfRvhwB/UO7idrWP+7Pex6h22e7Xi0q9b4fyzdX+d7VbSzCb85myNl18wZ0aF0DkilH9FhNCpeQhtmwThbdN8eiIiUjEKI7WdaYL9ABzYCgd+h/2/F//sGwjDPmFfdh4b9mRx9oqZNDi2GyjuZHrU9GO7Gc6vzjasdHbAaULjIF86RYTSJGIU/SJC6dw8hKgG9fDyUquHiIhUncJIbeF0gH0/BDf7a9/skbBjMeQdOeF0uxFE32cWcsBePKvpzbZL8KWI7WYE25wR7D5SSP7enTx0140Mjm7MfyJCCQv284jbLeqkKSLiWSodRpKSknjppZdYvXo1GRkZzJkzhyFDhpz0OYsXL2b8+PFs3LiRqKgoHn30UW655ZYqllw9PHHoLQB7N8KhHZC7F3Iy4eD24haPg9swfYPYf89G9hzJI+PIMTrv3UdU3hGcGOwxm/C7M6I4bJjN2e4M54CZj5dh0KZJELkRI+kUEUr/5iFEh3rTvEkDAO7+5D+ec21qiMKOiEj1qnQYsdvtdOvWjdtuu61C01+npKRwxRVXcPfddzNjxgwWLVrE7bffTnh4OAMHDqxS0Z7A4XCU/JyUlHTyWToP7YDDu4pHquRmFv83J7M4cOTnwF0/AZCTV4gx/1GCdv9Q5ssUFDq45LmvyaEeAJ2MK3FyFTvMcPLxxdfmRYdmwXSKCGFw81D+ExHCmc1CCPAtXZfdbq+GK1A+jw2CIiLiEqc1HbxhGKdsGXnooYeYP38+GzZsKNl3/fXXc+TIERYsWFCh93HFdPDV8oVomuAsgsJjxZN9BTYGICEhgfvG3Et6xt6SUyObhDBl1IXEdg2BvCy4NfGv15lxHfzxbblvMzhkNjuyICe/iAe9Z9HTaxP7zVD2m/XZZYaxzWzONrM5aWYTMLwIC/Ynor4/4fUDaF4/gHZhQXRuHkrbsCB83KBjqcKIiEjd4DbTwS9btoz+/fuX2jdw4EDGjRvn6reukNu7+xQPXzWcUJRXvHbK8f8aBtz42V8nz70Xtv9Q+jzT+edBA544TPwXCVx33dATmvHT92cT90Ii8dcFEHumDy98uYrduQbpR/K47qAvPZyR7DPrs5/67DdDi3/+c3vTvjwK//ynesdnOF/XDyAi1J+I+gGE1/dncP0AwkMDiKjvT9MQf7cIHCIiIhXl8jCSmZlJ06ZNS+1r2rQp2dnZHDt2jICAgBOek5+fT35+fsl2dna2S2rzbhTJDd3z8Vn1TpnHHYY3LyZu5lihg2MFDobvTKGrPaOcVzPp9Mhctr5+d5n9CY7vuWWBjXmt7ufnZWkU4APAr9wA3ICvtxfN6wcQ/mfQiA71p1f9AO6pXxw0wkMDKrRQnIiIiCdxy2+2iRMn8tRTT7n8fRoPGs/3Yan8XrSXfHzIN32K/4tvyfZXSdspHuwKq4xrqceV5OHzt/OLzy3Am2O7N+DIOXDS98zJzuWQdwQje7Ynon5A8ePPVo2Ggb4eMVpFRESkOrk8jDRr1oy9e/eW2rd3715CQkLKbBUBmDBhAuPHjy/Zzs7OJioqqtprK8rex4f7z6DzmVdTz8+HAB8bAT42/H2L/xvqY+NOXxv+PsULugX4dCw+5899x38O+PP4gq8O8q9PT/2+wzoFc8MVHav99xEREfFELg8jPXv2JDExsdS+hQsX0rNnz3Kf4+fnh5+fn6tL48CXLwDwezV1ouzcNrpC54WHh5/2e4mIiNQWle7pmJuby7p161i3bh1QPHR33bp17N5dPHvnhAkTGDFiRMn5d999Nzt27ODBBx9ky5YtvPnmm8yePZv777+/en6DKvrn0Nu/b1dVTEwMkZGR5d5qMQyDqKgoYmJiTvu9REREaotKh5FVq1Zx9tlnc/bZZwMwfvx4zj77bB5//HEAMjIySoIJQKtWrZg/fz4LFy6kW7duvPzyy0ydOtXSOUYSEhLo2PGv2ySDBg0iOjqahISE03pdm83GlClTAE4IJMe3J0+eXP58IyIiInXQac0zUlOqc56RhIQE4uLiThjxcjwsxMfHV2gyt1O9x3333Ud6enrJvqioKCZPnnzar10bHP/3BEhMTDz5hHAiIuKxKvr9XacmpHA4HIwdO7bsobd/7hs3btxp37KJjY1l06ZNJduJiYmkpKQoiOC6VikREfFcdSqMJCcnk5aWVu5x0zRJTU0lOTn5tN/r73/p9+nTR3/581er1N9bjADS09OJi4tTIBERqaPqVBjJyChvwrKqnScVV1OtUiIi4nnqVBip6JBaDb2tfjXZKiUiIp6lToURDb21jlqlRESkPHUqjGjorXXUKiUiIuWpU2EEike6xMfHExERUWp/ZGRktQzrlbKpVUpERMpT58IIaOitFdQqJSIi5amTYQQ09NYKapUSEZGyuHyhvLoqMDCwzGGsdV1sbCz9+/fXDKwiIlKizraMiHXUKiUiIn+nMCIiIiKWUhgRERERSymMiIiIiKUURkRERMRSCiMiIiJiqTo7tFdDb0VERNxDnQ0jYh0FQRER+TvdphERERFLKYyIiIiIpRRGRERExFIKIyIiImIphRERERGxlMKIiIiIWEphRERERCylMCIiIiKWUhgRERERSymMiIiIiKUURkRERMRSCiMiIiJiKYURERERsZTCiIiIiFhKYUREREQs5W11ARVhmiYA2dnZFlciIiIiFXX8e/v493h5PCKM5OTkABAVFWVxJSIiIlJZOTk5hIaGlnvcME8VV9yA0+lkz549BAcHYxhGtb1udnY2UVFRpKamEhISUm2vK6XpOtccXeuaoetcM3Sda4Yrr7NpmuTk5BAREYGXV/k9QzyiZcTLy4vIyEiXvX5ISIg+6DVA17nm6FrXDF3nmqHrXDNcdZ1P1iJynDqwioiIiKUURkRERMRSdTqM+Pn58cQTT+Dn52d1KbWarnPN0bWuGbrONUPXuWa4w3X2iA6sIiIiUnvV6ZYRERERsZ7CiIiIiFhKYUREREQspTAiIiIilqr1YeSNN94gOjoaf39/zj//fFasWHHS8z///HPOOOMM/P396dKlC4mJiTVUqWerzHV+7733iImJoUGDBjRo0ID+/fuf8t9F/lLZz/Rxs2bNwjAMhgwZ4toCa4nKXucjR44wevRowsPD8fPzo3379vr/jwqo7HWePHkyHTp0ICAggKioKO6//37y8vJqqFrPlJSUxFVXXUVERASGYTB37txTPmfx4sV0794dPz8/2rZty/Tp011bpFmLzZo1y/T19TWnTZtmbty40bzjjjvM+vXrm3v37i3z/CVLlpg2m8188cUXzU2bNpmPPvqo6ePjY65fv76GK/cslb3ON954o/nGG2+Ya9euNTdv3mzecsstZmhoqJmWllbDlXueyl7r41JSUszmzZubMTEx5uDBg2umWA9W2eucn59v9ujRwxw0aJD5888/mykpKebixYvNdevW1XDlnqWy13nGjBmmn5+fOWPGDDMlJcX89ttvzfDwcPP++++v4co9S2JiovnII4+YCQkJJmDOmTPnpOfv2LHDrFevnjl+/Hhz06ZN5muvvWbabDZzwYIFLquxVoeR8847zxw9enTJtsPhMCMiIsyJEyeWef51111nXnHFFaX2nX/++eZdd93l0jo9XWWv8z8VFRWZwcHB5ocffuiqEmuNqlzroqIis1evXubUqVPNkSNHKoxUQGWv81tvvWW2bt3aLCgoqKkSa4XKXufRo0ebF198cal948ePN3v37u3SOmuTioSRBx980OzUqVOpfcOGDTMHDhzosrpq7W2agoICVq9eTf/+/Uv2eXl50b9/f5YtW1bmc5YtW1bqfICBAweWe75U7Tr/09GjRyksLKRhw4auKrNWqOq1fvrppwkLC2PUqFE1UabHq8p1/uqrr+jZsyejR4+madOmdO7cmeeffx6Hw1FTZXucqlznXr16sXr16pJbOTt27CAxMZFBgwbVSM11hRXfhR6xUF5VHDhwAIfDQdOmTUvtb9q0KVu2bCnzOZmZmWWen5mZ6bI6PV1VrvM/PfTQQ0RERJzw4ZfSqnKtf/75Z95//33WrVtXAxXWDlW5zjt27OCHH37gpptuIjExkW3btnHvvfdSWFjIE088URNle5yqXOcbb7yRAwcOcOGFF2KaJkVFRdx999385z//qYmS64zyvguzs7M5duwYAQEB1f6etbZlRDzDCy+8wKxZs5gzZw7+/v5Wl1Or5OTkMHz4cN577z0aN25sdTm1mtPpJCwsjHfffZdzzjmHYcOG8cgjj/D2229bXVqtsnjxYp5//nnefPNN1qxZQ0JCAvPnz+eZZ56xujQ5TbW2ZaRx48bYbDb27t1bav/evXtp1qxZmc9p1qxZpc6Xql3n4yZNmsQLL7zA999/T9euXV1ZZq1Q2Wu9fft2du7cyVVXXVWyz+l0AuDt7c3WrVtp06aNa4v2QFX5TIeHh+Pj44PNZivZd+aZZ5KZmUlBQQG+vr4urdkTVeU6P/bYYwwfPpzbb78dgC5dumC327nzzjt55JFH8PLS39fVobzvwpCQEJe0ikAtbhnx9fXlnHPOYdGiRSX7nE4nixYtomfPnmU+p2fPnqXOB1i4cGG550vVrjPAiy++yDPPPMOCBQvo0aNHTZTq8Sp7rc844wzWr1/PunXrSh5XX301F110EevWrSMqKqomy/cYVflM9+7dm23btpWEPYDff/+d8PBwBZFyVOU6Hz169ITAcTwAmlpmrdpY8l3osq6xbmDWrFmmn5+fOX36dHPTpk3mnXfeadavX9/MzMw0TdM0hw8fbj788MMl5y9ZssT09vY2J02aZG7evNl84oknNLS3Aip7nV944QXT19fXjI+PNzMyMkoeOTk5Vv0KHqOy1/qfNJqmYip7nXfv3m0GBwebY8aMMbdu3WrOmzfPDAsLM5999lmrfgWPUNnr/MQTT5jBwcHmp59+au7YscP87rvvzDZt2pjXXXedVb+CR8jJyTHXrl1rrl271gTMV155xVy7dq25a9cu0zRN8+GHHzaHDx9ecv7xob3//ve/zc2bN5tvvPGGhvaertdee81s0aKF6evra5533nnmL7/8UnKsb9++5siRI0udP3v2bLN9+/amr6+v2alTJ3P+/Pk1XLFnqsx1btmypQmc8HjiiSdqvnAPVNnP9N8pjFRcZa/z0qVLzfPPP9/08/MzW7dubT733HNmUVFRDVfteSpznQsLC80nn3zSbNOmjenv729GRUWZ9957r3n48OGaL9yD/Pjjj2X+f+7xazty5Eizb9++JzznrLPOMn19fc3WrVubH3zwgUtrNExTbVsiIiJinVrbZ0REREQ8g8KIiIiIWEphRERERCylMCIiIiKWUhgRERERSymMiIiIiKUURkRERMRSCiMiIiJiKYURERERsZTCiIiIiFhKYUREREQspTAiIiIilvp/1t8uAJJM2poAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "m1.visualize()\n", "plt.plot(c.x, model(c.x, *truth), ls=\"--\", label=\"truth\");" @@ -13542,7 +705,7 @@ }, { "cell_type": "markdown", - "id": "fa93b807", + "id": "50", "metadata": {}, "source": [ "We can also fit a multivariate model, in this case we fit a plane in 2D." @@ -13551,20 +714,9 @@ { "cell_type": "code", "execution_count": null, - "id": "c253cfa6", + "id": "51", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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P1LxnDczFLHmA5RgAFaAeAY5v/7FjHbHPaO1z7D8aDj//9rcJVFeJGN6vyK1ZXeXWwsNTSfEhIoQQ8vdAhUfH6hRntRBCCCGXg4YuE1fu7+mo8CCEEEI8RGcoPDzqrBZCCCGE/L1RiwchhBDiIUTGQWTtb7Vw5b5SocKDEEII8RDU1UIIIYQQ0oGoxYMQQgjxEAJ4CC60Cchz7fG2ocKDEEII8RDMxWM8GB3jQQghhJDWomM8CCGEEEI6ELV4EEIIIR5CYDwE5sIxHpfBRVCo8CCEEEI8hAgOogudESI8v/KgrhZCCCGESIZaPAghhBAP0RkOLqXCgxBCCPEQrh/jQV0thBBCCCE21OJBCCGEeAjrwaUuXCSOuloIIYQQ0lqii0Om01kthBBCCCEXoRYPQgghxEN0hoNLqfAghBBCPIQI/m8/gBgVHoQQQoiHEBgHwYUrzLpyX6nQMR6EEEIIkQy1eBBCCCEeQnDxrBaBuloIIYQQ0loi4yG6cHCpeBkcXEpdLS6wiCKMgkXuGA4Jooh6D84nMgajxQLmoV8UkTHUW8wem48xBpNo8uh8ZtHo0fmMggkiE+WO0izGGCyiCcxD8wGAKBrBmCB3DIcYM4Ixz90GdlbU4tEOO/Vn8dGxFOwqygYD0F0bjLk9R2BW98FQ8PLXcmkl+fggIwW/55+GyBi6+uowt/cwzOk1DGqFQu54OK4vxke79mHziSxYRBFhfr64ffgg3Jk4FD5qldzxcLqiDMsP7sUPWSdgEgQEenlhdt/BmD9oOHQaL7nj4byxFJuLfsTesr9gEk3w4r0xNmQirgqfDq1KJ3c8VJnL8WfJRuwv24Z6sRYqToNhQRMwIXQGAtShcsdDlbkW6/J+x6+Fu1BlqYGKU+LKsGG4OToJXX3C5Y4Hk1iPfefX40D5z6i2lIGHAr20YzE65BaEe8XLHQ8iMyHf8Dnyq76A0ZIPgEew90RE6xZA5zVE7nhgTEBVzReorP4YZstpABy8NeMR4P8AvL2ukDteizpDVwvHPPXniBsZDAbodDpUVlZCq9W26b5fnjqAZ/ZvgoLjbOdLNxxDPC26N/5vzAxZi4+fso/job82guPsz+fmAIyJiMHKSbfIWnzsPH0O96zZAAYGQWzMx3Mc+kaE4rM5N8NXrZYt38GiQtz+41qYBMFu/fEchxhtAL6fcRuCvH1ky1dYl4//nnwB9UIdRDT+EubBQ6vS4d+9nkOQOli2fBWmEryf9RSqLZVN8mkUPriv+0sI8+oqW75KUxX+nf429PXn7fIpwEPFK/HKwAfRSxsjWz6jUIsvzz2J4vrTYBftQHgowHEcbo5+DnF+Q2XLJzITjhTdhYr6VMBuB6cAwNA39H8I9Z0mUzpr0VFcdg9q6n5pmHPhrwKAgJCA16H1+0ebH9eVfUZbn+PDA8Pg7df+NoG6agvuGZrm1qyukv/n+WUkt7oCz+7fBMB+p84uTL/knsC67CPyhANQbqzDol0/WXfql9STDMBu/TmsPLFfnnAAjBYLHln3MwRRtCs6AGu3xjF9Cd7bsUemdNauqYVbfoTxkqIDsObLMVTglZQ/ZUpntSr7/SZFBwCIEGEwV+KrnBUyJbNal/dhk6IDsOYzCrVYm7tMpmRWn5zZ0KToAAABIkyiGa8eXylr18vOkq9QXH/GrugAABECRCZgQ96rsIgmmdIBeYZVzRQdACAAYDhR+jjMQqUMyayqar9BTd3PaNwqN7B2B5VWPAmzJVeOaOQiVHi0wZrT6eA5x+dI8+Dw2Un5duzfnz4Cs+i4oY0BWHViv2x97puOnYKh3ugwn8gY1h44ApNFnj7Zv/LOIb/a4PDgLIEx/JB1HJXGeomTWeXUnkVu3bkmO80GIkQcMxzCeWOpxMmsykzFOFmd7jRfft0Z5NedkTiZVZW5Bn+WpDnJx1BsLEN6eabEyawsognpFZvAHORjYKgXq3GiapfEyS48P2PIN3yOpkWHbQmIzISi6vVSxrJjqP4UzndrHKpqvpQqTrs0DCDmyuTpPD+hBzlWXuR0OFoRDJkVJRImsne8vNhpYQQAhbVVqDbL84vpRHEJlC10Q1UbTdAbqiVKZO/4+WIoWlh/ZlHEmYpyiRLZy6vNadVyBfXy/KLT17cuX2FdtnuDOJBXVwyhhdYMHjzO1hRIlMiewVIKk1jrdBkeShTXy1O4CawaJqHI6TIceFSbT0iUqCmT+QTgoHCzEmA0H5UqTrs0DJnuyuTpJEn43nvvITY2Fl5eXkhMTERqaqrDZSdMmACO45pM06dPty0zd+7cJrcnJye7/XV4KZTgW7jksIqX7/gJjUIJrhWXRFbJdIyHRqFs0oTc7HJKeY551iiUrToVTSPT+lPxrTvwVsXJc4Bua59XyclzDI+ab/lzxcCgasVy7tCa9cLAZFt/HFrz/nLgOY3bszh89hY/gzx4Tv4DxDs7txcea9euxaJFi7BkyRIcOHAAgwYNwtSpU1FcXNzs8uvWrUNhYaFtysjIgEKhwM0332y3XHJyst1yX3/9tbtfCiZ36eF0HHwFx2NK155uz+FIUtcEWJz8olNwHMZFxMJLIc+GdVLP+CbHdlyM44De4aEI8/eVMFWjSTHxLZZFEb5+6BUUIkmeS/X27w8ezoseL94b8X7yfAZjfHtDwzvfqPNQoIf/IIkS2Yv17YIgtfOzfhgYRgT1kyiRPX9lMEI1MYCTHw8MAnr4J0oX6iIK3gsBXqPgbLfBYEGw9yTpQl3CxysZcPodEeHjNUWqOO0ignN58nRuLzzeeustzJ8/H/PmzUPfvn2xfPly+Pj4YMWK5g+CCwoKQkREhG3asmULfHx8mhQeGo3GbrnAwEB3vxRc060PIn38m22Ot85huLu3PBsFALgyKh49dSEOuwtExnB//1ESp2o0ICocw7t1cZiPMeDecSPBtdDd4S6xukBMi+/ptLvqgaGjZDtryV+lxbiQiU5btZLCr4aal6tFQYMrQq9zeDsHDonBV8FX6S9hqkYKjset3a5yeDsPDleEDEGktzyFJcdxGBtyGxwdQ8GBRzefAYj0lu/HTTfdvXCcTwFfVU8Eect3ymqA/722NE0poFB0ga/PtVJGajPqanGRyWRCWloakpKSGp+Q55GUlISUlJRWPcann36KWbNmwdfX/lfw9u3bERYWhl69euG+++7D+fPnHT6G0WiEwWCwm9rDS6nCF5NmI8LbuuFUcBx4cODAQc0r8O7YG9EvKKJdj90RFDyPz5JuRbw26EI+HvyFjAqOx2ujr8aYyFjZ8nEch3dvvhb9IsNteXmOA89Zd6VPJI3HtL7ybVQB4M2JyRjbpRsAQHlh/TUUSg8MScQdfeX5td7gpq53YEjACAAXTrEEB/7C13hcyCQkR1wvZzxMCpuJkUHW73tjPusv0AG60Zgeeaec8TA98grcEm0tPnjO+u1VcNb1NyigJx7udbuc8dBHNx6Twu4CLqw567trXX+R3j1wY/TTsuYL9B6DnsEvg4MC1t0Hf+HfgLcqDgPCPwHHybfj06gHITz4Q3BQw1p88GhoAVEqohAV8o3Hd7U0jOPhyuTp3DqOR0FBAbp06YLdu3dj9OjRtvmPP/44/vzzT+zdu9fp/VNTU5GYmIi9e/di5MiRtvlr1qyBj48P4uLicPr0aTz11FPw8/NDSkoKFM30vz/33HN4/vnnm8xv73nOJkHA5rxMbC84DZMoYGBQJGbGD0CgRr7xHS4miCJ+zz+NzbknUS9Y0DsgFDcnDESYt5/c0QBYW15Szubg12MnUWM0IS44EDcN6Y8onWecc84Ywz59Pn44dRwVxnp00+pwa+8BiNW5v1WttbJrTiO1bBcM5koEqoMwOng8oryj5Y5lo687h/3l22Ewn4efUoehgVeiq0+C3LFs8uuKsVm/B0X15+Gv9MGEsOHoq42XrbXtUpXmYhwq/w1lpnxoeB/01o5DrO9gWXfqFzNZSlBY/R1qzCeh4LwQ7HMVgr2vBMfJP0AhAAhCGapqv4HRdAgcp4KPVxJ8vae14hiQ5kk5jseb+8e5PI7Hv4fv9OhxPDy68LjnnnuQkpKCw4cPO13uzJkz6N69O7Zu3YrJkyc3ud1oNMJoNNr+bzAYEB0d7dFvDCGEEM8gZeHx+r4rXC48Hh/xl0fv39xaPoeEhEChUKCoyP4UrKKiIkREOO+SqKmpwZo1a3DXXXe1+Dzx8fEICQlBVlZWs7drNBpotVq7iRBCCPE0oovdLJ1+HA+1Wo1hw4Zh27ZttnmiKGLbtm12LSDN+fbbb2E0GnHHHXe0+Dx5eXk4f/48IiMjXc5MCCGEEPdxe2m0aNEifPzxx1i9ejWOHz+O++67DzU1NZg3bx4AYM6cOVi8eHGT+3366aeYMWMGgoPtrztRXV2Nxx57DHv27EF2dja2bduG66+/HgkJCZg6daq7Xw4hhBDiNiLjXZ48ndsHdLj11ltRUlKCZ599Fnq9HoMHD8amTZsQHm49syEnJwf8JacnZmZmYufOndi8eXOTx1MoFDh8+DBWr16NiooKREVFYcqUKXjxxReh0cg3cA0hhBDiKgEcBBfG4nDlvlKhq9PS8R6EEEKckPLg0hdTJ8HLhYNL66steGbk7x69f5NnCEtCCCGENOFqdwl1tRBCCCGk1QS41l0idFwUt/H80ogQQgghfxvU4kEIIYR4COpqIYQQQohkXL3Q2+VwkTgqPAghhBAPwVy8tD27DE6n9fzSiBBCCCF/G9TiQQghhHgI6mohhBBCiGRExkFk7e8uceW+UvH80ogQQgghfxvU4kEIIYR4iIbL27tyf09HhQchhBDiIairhRBCCCGkA1GLByGEEOIhRPAQXWgTcOW+UvH8hIQQQkgnITDO5ckVr776KjiOw8MPP9wxL6gZVHgQQgghBPv27cOHH36IgQMHuvV5qPAghBBCPETDwaWuTO1RXV2N2bNn4+OPP0ZgYGAHvyp7VHgQQgghHoJduDpteyd2YeRSg8FgNxmNRqfP+8ADD2D69OlISkpy+2ukwoMQQgjxEAI4lycAiI6Ohk6ns01Lly51+Jxr1qzBgQMHnC7TkeisFkIIIeRvJjc3F1qt1vZ/jUbjcLl//etf2LJlC7y8vCTJRoUHIYQQ4iFE5togYCKz/tVqtXaFhyNpaWkoLi7G0KFDbfMEQcCOHTvw7rvvwmg0QqFQtDtPc6jwaIc6ixkbzx7D9vwzMIkCBgZHYlaPgQj38Zc7GgDAaLHgt8wsbD2VhXqzBb3CQnDLoAGIDtDJHQ0AYBFFbDt1GptOnEKN0YT44CDcMrg/4oOD5I4GABCZiJSSLGwqPIxKUy26+AThhuhh6KmNlDsaAIAxhn3nz+Kn/EMoM1Yj3EuH66OHYEBAV3Cc/KMWMsZwuuYM/irZhXJTGXQqHcaEjEZv/14ekQ8AThoK8GP+PhTUlUGr8sGUiMFIDOkBnvOM3ufThlJ8c/Ygzladh69Sg2nRfTApsieUvGfkO28sxp7z21BQnwM1r8EA3QgM1I2EklfJHQ0AUGk+j31l25BfexoKXoVe/kMwKGAc1Hzzv/o9ScOxGq7cvy0mT56MI0eO2M2bN28eevfujSeeeKLDiw4A4BhjrMMf1cMZDAbodDpUVla2qiK8WGZ5Ce7YuhYldTXgADAAPMeBB4f/jp2O6+P7uiVza+VVVGLOmu+RW1EJnuMgMgae48AYw7NXTcQdwwbLmq+0pgZzv16HzJJSKDgOAmO2v/+6YjQWjhsla74qcz0e2vcZDlXkXJSPh8BE3BYzGv/ue7WsO896wYxH96/BzpKTtlwNf6d3GYQXBt0AJd/xG4rWEpiAj8+sQMr5PeDBQ4Ro+ztINxALe9wPtYw7J8YY/nfiR3ybu9u23niOh8hEDAyIwX+HzIOfSprmZkfeObYDy47usH3+eHAQwdBbF4ZV42cj2MtX1nx/lWzC+vzVADgwiODAgYEhWB2G+7s/jSBNmKz5DpbvwLe574IBdvn8lQG4O34Jwr2i2/yYruwz2vocd/4xC2o/dbsfx1RtwuqJa1zKOmHCBAwePBhvv/12u3M44xnl82Wi1mzCHVvXoqy+FoC16AAAkTFYmIhHdv6E9NIC2fJZRBHz1q5DQaXBlqvhLwPw/JY/sONMtmz5GGO499uNyCo9DwAQLuRr+Pt/f6Xgh4zjsuUDgGcOfYsjFbkALs4nAgC+PpeCr7JTZMsGAK9m/ITdJacANOZq+PtL/iF8cPJ32bIBwLq8Ddhzfi8AQIRo9/dw5RF8ce4r2bIBwJpzO/Ft7m4AjetNvPA3oyIHL2SslS0bAGw4dxjLju4A0Pj5Ey9saU4ZSnDv7m8g52/F44aDWJe/CgwM7ML7yi7kKzeVYvmZpbb1KYecmpP4JvcdiBCb5Ku2GPDJmRdgEp2f3SE3EZzLk6ejwqMNfjh7DCV1NbYNwqU4Dvj46D6JUzX6I+sMsssrHObjOQ4fpsiX70B+AQ4V6h2vPwDLU1Jl27CerS7BjuJM24a+OatO74BFFCRM1ai0vgo/5B50mI8B+PJsCmotJmmDXVAv1GNz0Vbbhv5SDAx/leyEwWyQOJmVRRTwefZ2h7eLYPir5DhyakqkC3URxhjeP77L4W5DYAzp5/ORXpYvaa6LbS36AZyD3YYIESXGQhwzHJA4VaMdpRvBOViDDCKqLOU4VLFT4lRtI/fIpQCwfft2t7V2AFR4tMkf+WccfqgB64ZhW16WhIns/Xn6LBRO+oBFxpCam4d6s0XCVI3+PJ3tNB8DkFVaBn1VtXShLrKr5KTT9xcAzpuqkVVVJFEie3tKTzstigCgTjAjvTxHokT2TlVlwSQ6L3pEiDhaeUyiRPayqgtRbqpxugwHDimlmRIlsldYZ8DZqvNO32EFx+OPwlOSZbqYSTTiTM0JW0tCc3gocMxwUMJU9k4Y0mwtbM3hwOGEjIURsaKDS9vAJAoOf801sIjyNTOaBBFoRWuBWRTgJcNbbxKEVjUCmgR5WhQsogCOa3kVmpk8+cytbGlp7XIdzcJaV9CaW7lcR2vNeuFauZw7tD6fPNsYoVWfewZBpvcXaDkjA4PAzBKlaR+pDy6Vg+cn9CADgyPAOzmwkOc49AsKlzCRvf4RYbbjOprDAeiq08JP3f4Dl1zRPyK8xcJM56VBpFaes4P66KKcrj8AUPNKxPqGSpTIXt+AqBaX4QD01ka4P0wzuvl0a7HFCADifGPdH6YZsb5hUHHOD7wVwdBb20WiRPYifXTQtnBgq4WJ6B8oz/vrxXsjUBXidBkRIrp6x0mUqKko7zinn0EOPLp4d5cwUduJcHHIdDrG4+9lVo9BTt9SkTHM6zNMsjyXmtG/DzRKpdOMdw4fIttZGVf17I5Ab2+HxRvPcbh96CCo3XD6VmuMCI5HV58gp/mu6TIY/jKd9dBLG4kBAV2hcHDKp4LjMSG8N8K95TltOlgThMEBg8A72Kzw4JHg1x3RPl0lTmblr/JGctRQJ/k4RPsEY1iQPDsmNa/A7d2HgnfwDebBIUjjg6u69JY4mRXHcRgfOs3pjl3NazA8aLyEqeyNC5nutFWaAzAy2P1DghPnqPBogyhfLV4fczU4wG7j37ChuKl7f1wfJ9/ptFovL7x9/dVQ8DwUF+08uQvTxIQ4WU+n1SiVePfGa6BSNJ9vSJdI3D8mUbZ8PMfjjaG3wUehbrJz58Ah3i8M/+o9VaZ0Vq8MuQk6lbfd+gOsn8EILx2eHnCdTMms5sb+A0HqoCY7dx48/JR+WBB/t0zJrB7seTVi/UKb7NwVHA9vpQYvD5ot6+nSD/S9AoODu9i+Ew0UHA+1QoF3R98EtYynS18ROhV9tEMu/K8xIQ8ePBSYE/MQvBU+8oQDMChgHIYGXAkAdgUSDx4cONwU/QB0qmC54rUKc/GMFnYZtHjQOB7tOM85rTgPHx1LxR95Z2BhIvoFhmNen2G4Ib6fRwyQdFRfjBX70rA5MwsmQUBCcBDuGDYYNw/q7xEDEJ0+X4ZP96bh52OZqDOb0S0wALOHDsLtQwdCo5T/sKOC2nJ8cXYXfspPR43FiAgvLWZ2G4lZsaPgo5R/AKKS+ip8cWY3NuSmodJch2CNH27qNgK3x42CTi3fRr9Btbkam4u2YnvJDlSaK+Gn9MP40HGYGn4VAtQBcsdDjcWI73J2Y33eHhTXV8JX6YVpUUNxW8w4RHrLP4idUbDgq9Np+CJrP3JrKuCjVGF6dF/c1Ws04v3l32kKTMDe83/gr9JNKKrPh5JTYWDASEwMvQZdfGLljgeRiThYsQO7S39BQd1Z8JwCffyH4YrQ6xDj26tdjynlOB4zt94JlW/7u8PNNSZ8n7TarVldJUnh8d577+GNN96AXq/HoEGD8M4772DkyJHNLrtq1SrMmzfPbp5Go0F9fb3t/4wxLFmyBB9//DEqKiowduxYfPDBB+jRo0er8nTkh4gx5hHFhiOUzzWUzzWUzzWUzzUdlU/KwuOGLfNcLjzWX7XSowsPt//8Xbt2LRYtWoQlS5bgwIEDGDRoEKZOnYri4mKH99FqtSgsLLRN586ds7v99ddfx7Jly7B8+XLs3bsXvr6+mDp1ql1xIhVP/tIBlM9VlM81lM81lM81np6vs3J74fHWW29h/vz5mDdvHvr27Yvly5fDx8cHK1ascHgfjuMQERFhm8LDG88UYYzh7bffxtNPP43rr78eAwcOxGeffYaCggJs2LDB3S+HEEIIcRuXzmi5MHk6txYeJpMJaWlpSEpqPIqY53kkJSUhJcXx0NPV1dWIiYlBdHQ0rr/+ehw9etR229mzZ6HX6+0eU6fTITEx0eFjGo1GGAwGu4kQQgjxNDRkuotKS0shCIJdiwUAhIeHQ6/XN3ufXr16YcWKFfjhhx/wxRdfQBRFjBkzBnl5eQBgu19bHnPp0qXQ6XS2KTq67RcJIoQQQojr5D/F4RKjR4/GnDlzMHjwYFx55ZVYt24dQkND8eGHH7b7MRcvXozKykrblJub24GJCSGEkI7RGbpa3HruYkhICBQKBYqK7K9tUVRUhIiI1o2+p1KpMGTIEGRlWa+B0nC/oqIiREZG2j3m4MGDm30MjUYDjUb+0yAJIYQQZ1wtHi6HwsOtLR5qtRrDhg3Dtm3bbPNEUcS2bdswevToVj2GIAg4cuSIrciIi4tDRESE3WMaDAbs3bu31Y9JCCGEEHm4fbSmRYsW4c4778Tw4cMxcuRIvP3226ipqbGN1TFnzhx06dIFS5cuBQC88MILGDVqFBISElBRUYE33ngD586dw913W0c85DgODz/8MF566SX06NEDcXFxeOaZZxAVFYUZM2a4++UQQgghbtMZWjzcXnjceuutKCkpwbPPPgu9Xo/Bgwdj06ZNtoNDc3JywF80mmZ5eTnmz58PvV6PwMBADBs2DLt370bfvo1DkT/++OOoqanBggULUFFRgXHjxmHTpk3w8pLnGhqEEEJIR+gMhQcNme6hI7sRQgjxDFKOXHrVL/e4PHLplqs/9Oj9m/wXxiCEEEIIAIABLo3FcTm0JFDhQQghhHiIztDVQoUHIYQQ4iE6Q+HhcQOIEUIIIeTvi1o8CCGEEA/RGVo8qPAghBBCPERnKDyoq4UQQgghkqEWD0IIIcRDMMaBudBq4cp9pUKFByGEEOIhRHAujePhyn2lQl0thBBCCJEMtXgQQgghHqIzHFxKhQchhBDiITrDMR7U1UIIIYQQyVCLByGEEOIhqKuFEEIIIZLpDF0tVHgQQgghHoK52OJBhcffWFl1LfZm5cIsCOjXNRzdw4PljmTHUF+PXWdzUG+xoFdYCPqGh8kdyU6t0YSUkzmoMZoQGxqIAd0iwHGe84Uxmi3YnXUOlXX16Bqkw7CYLh6VzywK2FmYjfP1tYj08ceo8G5Q8J5zyJYgikjR50BfU40Qbx+MjYqBilfIHctGZCLSy8+hoK4cOpU3RgYnQKNQyR3LhjGGMzWnUVRfBC+FF/pq+8FL4SV3LDsZ5/XIrCiBt1KFsZGx0Kk9K19xfS7y605DwSnR3W8AfJU6uSORC6jwaCOj2YJXN27H+n1HYRFF2/zh8V3w8i1T0SVI3g+3RRTx3z924vP96TAJgm3+gMhwvHbNVCSEylsgiSLDR9v2YsXv+1FnMtvmdw8PxkuzpqB/twgZ01k3+F+kpOPdbSmoqjfa5kcH6fD8jCSM6t5NxnRW350+glfSfkeZsc42L8LHHy+MvApTonvKmMxq07mTeG7PVuhrq23zgry88Z8REzEzob+MyaxSz2dhacYG5NeV2eb5Kb1wT48k3NJttOwF5pnqM1iZ/QkK6wts89S8BldHTMf0yGtlz3eyogSP7vwZR8r0tnkaXoG5fYbjsSFXQilzAVxmKsL3ue8gu+aYbR4PBYYFTcb0qH9CxatlTNcyBoAx1+7v6TznJ9JlgDGGRz7/Cd+nZtgVHQBwMLsA/3h/Lc5X18qUzuqZX7Zixd40u6IDAI7pizHr87XIraiUKZnV27/sxHubUuyKDgA4W1yGee9/i8yCEpmSWa3cmYalP2+3KzoAIK+8EgtWrUdadr5Myay+PX0Y/979s13RAQBFtVW4Z/s6bM09JVMyq605Wbjv9w0ouqjoAICy+jo8+tcv+O5UhkzJrA6WncW/9q9CQV253fxqSz3+e/wnfJW9S6ZkVrm1uXgjcyn09YV2802iERsK1mFd/ncyJbPKrarATb9+gWPlRXbzjaKAj47uxVN7NsmUzKraXIGPsp5CTs0Ju/kiBOwv24I1594Ec2WvLoGGkUtdmTwdFR5tsO90HnacOAuxmQ+uIDKUVtXiy50HZUhmlVlciu8PH2224hUYQ43RhI9275M8VwN9RRVWb09r9jaRMZgFAe//liJxqkZV9UYs27q72dsYs2b8729/SZyqkUkQ8PL+35u9reE9fzHtd9k2rIwxvJC6zS7PpV7Z9wfMouDgVvdblvkrGGNgDhIuP7UF1ZZ6iVM12pD/PQQmOMy3Sf8LKkzlzd4mhXeP7EaNxQShmc8YA/BN1mGcrJDvx8Pu0p9QbamECLHJbQwMJ6r227WEEHlQ4dEGGw8cg4J3XE2KjOH7VPl+0f2Q4TyfwBg2HDnWpLVGKr8ezHR6uyAybD96BpW18mz4txzNgsnieKcoMob0nELklcnTavRX4VlUmByvGwbgXFU5Dp0vdLiMO6WXFiKnqtJpU2+ZsQ4787OlimQnt+Y8jlbmQXSS0Ciasb1Inh1TtaUahysPNbvTvNjesj0SJbJnFgVsOHO02aKjgYLj8f1p+baB+8u2gTlZfzwUOFi+XbpA7dBwVosrk6ejwqMNSgw1EETnvybLa+qc3u5OJdW1LXbwGQUBtSaTNIEuUVpVA95JYQRYd+4VMq3D0uoap4XbxcvJoaSudc/b2uU6Wklt6563WKZ8ZabqFpdRcDzOG6skSNNUtaXaYUtHA57jUWmWp/CtNZthbLG1isn2+QOAWsHg9HYRAqos8rUYtUbDOB6uTJ6OCo82CNP5tbhjCvH3kShNU2H+vi0u461Swlctz8FVYVo/iC0UbjzHIchPnnUY5u/bYmEJAKH+fhKkaSrcp3XPG+Hj7+YkzWttvtYu19FCNC2vF4GJCPPSSpCmKa3SH1wL/fMiExGgCpQokT1flRpeipbPR5Dr8wcAfsoAp7fz4KFVedYZiJ0RFR5tcMPwfk53TDzHYebIARImsnfDgL4tNINyuHFgP9lOu7x6aC+ntyt4DkkDEuDvrZEokb2r+vWAl8rxhpXnOAyP7YIugfLsmMZFxiJY47go4wB01wajf1C4dKEuMjAkAnHaQKe7zhAvH4yLipUqkp0uPkEYFBAD3klCb4UaV4b1lTBVIx+lL4YEDAXvZLPMgUNicKKEqRopeR4zuw+AwslZNQJjmNldvjOXhgclgXOy/kSIGBo4UcJEbceY65Ono8KjDYbERmHKgB5o7nun4DlEBPhj9tjBkudqkBASjNuHDmz2NgXHQefthXtGj5A4VaNQrR/uuar5jSbPcfBSKfFA8hiJUzXy1ajx6NQrmr2N5zgoeR7/Th4vcapGKl6BJSOSmr2Ng3Wn9NyIJNlOt+Q4Ds+Psj6/owRLEifLerrlv3pfDQXHOyw+HuyVDB+lPIUvAMzoMhMqXuWw+Lgm6jroVAHShrrIAwNGQ6f2clh8zOk1FN118rUojAm5BjpVcLPrjwOHAbqx6Obj/AeQ3OgYD2KH4zi8dvs0zLliGDTKxsGQOABje8bii/tvRYCvt3wBATw7dRIeumI0fNX2gyENj+6Cb+6chQitfM2gAHDflFF47Lorob2kVaNfdDg+e3AW4sODZEpmNXv0YLx4w1UIvqS7JyEsGKvuvgkDo+UdZ+S6uL54f/wMRF7SnB3rH4jVk2/BFVFxMiWzGt8lDiuvugkxWvvugChfLT6YeD2uje8jUzKr/gHR+GDk3Yj3sx9QL0jth2f6z8RN3UbJlMwqyjsKT/Z+GjG+sXbzfRS+mBV9O66NvF6eYBdE+WqxbtocjAiLtpvvq1TjkUHj8NzIq2RKZuWj9Mc9CUuR4D/Ybr6KU2NsyHW4udu/ZB8HhQAc8/STmt3AYDBAp9OhsrISWm37ms2r641IO5sPsyCib5cwRMnU/O5IndmMfTn5qLdY0DM0GLFB8vQLO2KyWLD/dD5qjSbEhgUiISJE7kh2LIKItHP5qKy1jlzaJzLUozZYImPYX5yH8/U1iPDRYnBIpEflY4whvbQQ+poqhHj7YlhYF/Aeli/TUICCunJoVd4YHBgLpQeNrAoA+XV50Nfr4a3wRg+/nlDxnjOyKgCcMZThZEUJvBQqJIZHw1vpWfnKTcUoqDsDBadErG9feCnaf+xYR+wzWvscfb5+Agqf9re6CbVGHL/tNbdmdRUVHh76xhBCCPEMUhYevb560uXCI/P2Vz16/0ZDphNCCCEewtUDRC+HpgQ6xoMQQgghkqEWD0IIIcRDWFs82n88FLV4XPDee+8hNjYWXl5eSExMRGpqqsNlP/74Y1xxxRUIDAxEYGAgkpKSmiw/d+5c6yl7F03JycnufhmEEEKIW9HptB1g7dq1WLRoEZYsWYIDBw5g0KBBmDp1KoqLi5tdfvv27bjtttvwxx9/ICUlBdHR0ZgyZQry8+2vCpqcnIzCwkLb9PXXX7v7pRBCCCHERW4vPN566y3Mnz8f8+bNQ9++fbF8+XL4+PhgxYoVzS7/5Zdf4v7778fgwYPRu3dvfPLJJxBFEdu2bbNbTqPRICIiwjYFBnrW6aKEEEJIW7EOmDydWwsPk8mEtLQ0JCU1jrbI8zySkpKQktK6y5/X1tbCbDYjKMh+YKnt27cjLCwMvXr1wn333Yfz5887fAyj0QiDwWA3EUIIIZ6GulpcVFpaCkEQEB5uf+2I8PBw6PX6Vj3GE088gaioKLviJTk5GZ999hm2bduG1157DX/++SemTZsGQWj+yolLly6FTqezTdHR0c0uRwghhBD38uizWl599VWsWbMG27dvh5eXl23+rFmzbP8eMGAABg4ciO7du2P79u2YPHlyk8dZvHgxFi1aZPu/wWCg4oMQQojncbW/5DLoa3Fri0dISAgUCgWKiors5hcVFSEiwvk1L9588028+uqr2Lx5MwYObP7CZw3i4+MREhKCrKysZm/XaDTQarV2EyGEEOJxXO1m6exdLWq1GsOGDbM7MLThQNHRo0c7vN/rr7+OF198EZs2bcLw4cNbfJ68vDycP38ekZGRHZKbEEIIkUNrLnvf0uTp3H5Wy6JFi/Dxxx9j9erVOH78OO677z7U1NRg3rx5AIA5c+Zg8eLFtuVfe+01PPPMM1ixYgViY2Oh1+uh1+tRXV0NAKiursZjjz2GPXv2IDs7G9u2bcP111+PhIQETJ061d0vhxBCCCEucPsxHrfeeitKSkrw7LPPQq/XY/Dgwdi0aZPtgNOcnBzwfGP988EHH8BkMuGmm26ye5wlS5bgueeeg0KhwOHDh7F69WpUVFQgKioKU6ZMwYsvvgiNpv0X1iGEEELk5uqZKZfDWS10dVo63oMQQogTUl6dNvbTZ8D7eLV8BwfE2npk3/WiR+/f6CJxhBBCCJGMR59OSwghhHQmrh4gejn0YVCLByGEEOIpJB4zfenSpRgxYgT8/f0RFhaGGTNmIDMzs2NeiwNUeBBCCCGd1J9//okHHngAe/bswZYtW2A2mzFlyhTU1NS47Tmpq4UQQgjxEFKf1bJp0ya7/69atQphYWFIS0vD+PHj253DGSo8CCGEEE/SAcdpXHoxVI1G06ohJyorKwGgyYVZOxJ1tRBCCCF/M9HR0XYXR126dGmL9xFFEQ8//DDGjh2L/v37uy0btXgQQgghHqKjulpyc3PtxvFoTWvHAw88gIyMDOzcubPdz98aVHgQQgghnqKDrk7b1guiLly4ED/99BN27NiBrl27uhCgZVR4EEIIIR6DuzC5cv/WY4zhwQcfxPr167F9+3bExcW58NytQ4UHIYQQ0kk98MAD+Oqrr/DDDz/A398fer0eAKDT6eDt7e2W56SDS11gEUSYzBa5YzgkiCKMJs/NJzIGo8UCT71ckCgy1Js9Nx9jDPUWs2fn8/T1J5ghMlHuKM1ijMEoWCB66PoDgHqLBYLomesPAEyiBRZRkDtG20g8gNgHH3yAyspKTJgwAZGRkbZp7dq1HfN6mkEtHu2wJ/McVm7dj72ZOWAA4sIDcfuVQzBzzAAoePlruSOnCvDZj/uw6+AZiIwhMkSLm6cMwc1TBkOlVMgdD8eKi7F8byp+O5UFiygizNcXdwwZjHlDh8JHrZI7Hs6WlOGTP/fhl/RMmAQBAT5euCVxIOZeMQw67/ZfvKmj5FVX4sNjKfj+dAbqBDP8VRrcmjAIC/qOQqi3r9zxUFJdg0937cd3BzNQbTTBW6XEDYP64e5xwxGlk/+iVQZzHb7M3oENuamoNNdCxSswJWIw5sRfiRjfULnjodZiwqqsPfjqzH6U1FdDyfGY2qUPFvQci94BEXLHg1GwYPXRg1h97ADyqg3gOQ6To+Nx36BEDAvvInc8CEzExry9+CZnF3JqS8ABGBHUA7NjJ2B4cILc8VrWQcd4tHpxGQpbujptG6/e983OQ3j5m9/B8xxE0brqOFjf66uG9MBrd14ta/GxdU8mnn3vF3AcIIiNby3HAcP7dsNbj90ga/HxV3Y27l63AYwxCBd99HiOQ9+wMHx1683wVatly3c4pxDzPvkOZkGwW388x6FbsA5f3DsLgb7uaX5sjVMVpbh58+eoNhvt1p+C4xDq5Yvvk+9ElK98O/eCSgNmfboG56tr7fPxHPzUanz1z1vRPTRYtnzlpmos2Lsc+bVlEHHx+uOh5pV4d/jd6BcQLVu+arMRc/76DMcr9E3y8RyHD0fPwtjw7rLlMwoW3LnpO+wpzLXbvyk4DgzAuxOvxfT4XnLFg8BEPHv4S2wvzrCbz3M8RCbi8T434vquiW1+XCmvThv9/nPgXfiBI9bVI/f+5+jqtH8Xeecr8cq3vwOAregAGgvMLQdP4cfU4zIks6qsqsPzyzdZd+qifT3JGLD/WA7WbjogUzrAaLHgoR9/hiCKdjslwNrtcqy4GO+k7JEpnbVr6tGvf4bJIjRZfyJjyC2rxJu/7JApndUjuzY2KToAQGAMJfU1eGrvrzIls1ry49YmRQdgLYKrjSY8vn6Tg3tKY9mJX1BQV263UwesOyyjYMbTh76WtevlvRN/4nilvtl8FlHEI6nfwyjI1326IiMNewvzmvyoFhgDYwyP/PkzKo31smQDgF8L0poUHQBs7+mbx9dDX1cuday2YZzrk4ejwqMN1u0+As7JEcM8B3y946CEiez9svMYLILgsKWNMeDbzemy9bn/evIkDEajw3wiY/j60GEYLfJsWHefykFBRZXDPnVBZPj50AlU1smzYT1yvhBHy4ua7NQbCIxhR8EZ5FVXSpzMKq+iEjtPn3Oa72hhMY4VFkuczKrSVIvN+kMQHBQWIhgK68ux73yWxMmsjIIFa88ecPj5Y2CoNNfjt3x5ftwwxrDq6IEmRZHtdgAmQcB3p5ru+KXyXe5up9toANiYnypRmvZpuDqtK5Ono8KjDTLzS5we6CUyIKvgvISJ7J3KKQHPOf/SFZVVobbOJFEie8eLS6BsoRuq2mSCvrpaokT2MvUlUPDO159ZEHGuRJ5fTMfLW95hMwCZFfLs2E8Wlbaqe/m4vsTtWZqTU1visOhowIPDqSq9RIns6esMqLE4/24qOR4nKuXJV2U2QV/r/LvJcxyOn5fn/QWA01WFYE4+hSIYTlUVSJiINIcKjzbQqJRoYb8u6/ETGpUSLQYEoJQpo0apdLpRsC2nkCufolVnEKhV8hyTrVG07nlbu1xH0yhbmU+mz5+ab/nAZQZAzcu0/lrxvAzyvb/qVhy7xoGT7fsLAKoW1iEPDppWfA5kJfFZLXKgwqMNruwf77QZS8FzmDRQvgO/xg2JhyA4/kXH8xxG9O8GjVqeDdfk7vFNjp24GMcBfUJDEe7nJ2GqRuN7OX9/ASBc54ce4fIcHDkuMg5KzvlX1k+lxvBQ94466MjQblHwbeGsJCXPY2z3GIkS2Uvwj0CIxt/pMgwMY0N7S5TIXri3P3poQ512FAhMxKRIeQ7e9FKqMCaqm9NWVQsTkRQj35kjV4T1hcLJd0QEw7jQvhImagc6xoNcbOqQXggP8Gu2Ob5hzj8mDpM21EVGDYpFfNdgh90Foshw57UjJU7VaGBEBEZ07QKFgw0XY8D9oxLBtaLVxh1iQgIwpX8PpxvWBRNGynbWUrCXD27rMdhpH/b8PonwUsrzi85bpcK80Y4//xwH3Dp8IAJ95DkrSMHxmBs/0eHtPDhMDh+ALj7uuyqnMxzH4f7e4x3+YFVwHEaGxGBAYJSkuS72wKBRDo8RU3AcegWG4Mqu7h/50pHbYsaDMdbsN4QHj3CvAEwMHyB5LmKPCo828FIr8fHCmxCms/4iV/AceI4Dx1m7WN6Ydw36RIfJlk/B83j78RvRLTLILh/PcVDwHJ6ePwXD+3WTLR/Hcfjg+uvQPyLclrchHwdg8ZXjcXWvnrLlA4CXbpqCUd2j7fI1FHILJozErYkD5YyHp4clYVo36y9eJceDB2f7hXd7jyFYOGCsnPFw3/hE3DLMumFv+Pw1FGrJfXviiSnj5YyHmdGjcGfcBAAXTlG9aP0ND+6Op/vfJF84AFd37YfH+yeBg/V4CR6crZWrf0AU3hl1i6z5xnWJwWtXJEPBXbRtuVCox+uC8FnyTS0eZ+ZOvbVd8eLAO6DileBgXX+KC7u5cC8d/m/ofGgUnt3VwjHXJ09H43i04zxns0XAtsNZ2HnsLMwWEX27heP6xL4IkHF8h4sJoohdB89iR1oWjCYLukeH4Nor+yM4QP7BpQDr2Su7z+Xg58xM1JjMiA8KxC0D+iPKQ845Z4zhQHY+fj6UicraenQN0uHG4f0RExIgdzSbQ6UFWH82A6X1tYjw8cfN3QeiV4D8g181yCwqxfr0oyiqqkawrw+uH9QHA6LkH/yqQU5NKX7M34+CujJoVT6YGjkYgwJiZGttu1RBbSW+zT6A7Ooy+Ck1SO7SB6PD4mXdqV+suLYaazOP4GR5KTRKJZJjemBidLxHDKAIAJWmGvxSkIYThjwoeQXGhPTGlWH9oeTbd/yJpON4vP2C6+N4PPysR4/jQYWHh74xhBBCPIOkhcf/XnS98HjkGY/ev3lGeUoIIYSQToGu1UIIIYR4Comv1SIHKjwIIYQQT9EJCg/qaiGEEEKIZKjFgxBCCPEUnaDFgwoPQgghxFO4OvoojVxKCCGEENKIWjwIIYQQD+Hq6KOXw8ilkrR4vPfee4iNjYWXlxcSExORmprqdPlvv/0WvXv3hpeXFwYMGIBffvnF7nbGGJ599llERkbC29sbSUlJOHXqlDtfAiGEEOJ+dHVa161duxaLFi3CkiVLcODAAQwaNAhTp05FcXFxs8vv3r0bt912G+666y4cPHgQM2bMwIwZM5CRkWFb5vXXX8eyZcuwfPly7N27F76+vpg6dSrq6+vd/XIIIYQQ4gK3D5memJiIESNG4N133wUAiKKI6OhoPPjgg3jyySebLH/rrbeipqYGP/30k23eqFGjMHjwYCxfvhyMMURFReHRRx/Fv//9bwBAZWUlwsPDsWrVKsyaNavFTDRkOiGEkNaScsj0bq+95PKQ6TlPPO3R+ze3tniYTCakpaUhKSmp8Ql5HklJSUhJSWn2PikpKXbLA8DUqVNty589exZ6vd5uGZ1Oh8TERIePaTQaYTAY7CZCCCHE03Bw8eq0cr+AVnBr4VFaWgpBEBAeHm43Pzw8HHq9vtn76PV6p8s3/G3LYy5duhQ6nc42RUdHt+v1EEIIIW7VcDqtK5OH6xSn0y5evBiVlZW2KTc3V+5IhBBCSKfk1tNpQ0JCoFAoUFRUZDe/qKgIERERzd4nIiLC6fINf4uKihAZGWm3zODBg5t9TI1GA41G096XQQghhEijE4xc6tYWD7VajWHDhmHbtm22eaIoYtu2bRg9enSz9xk9erTd8gCwZcsW2/JxcXGIiIiwW8ZgMGDv3r0OH5MQQgi5LHSC02ndPoDYokWLcOedd2L48OEYOXIk3n77bdTU1GDevHkAgDlz5qBLly5YunQpAOBf//oXrrzySvz3v//F9OnTsWbNGuzfvx8fffQRAIDjODz88MN46aWX0KNHD8TFxeGZZ55BVFQUZsyY4e6XQwghhBAXuL3wuPXWW1FSUoJnn30Wer0egwcPxqZNm2wHh+bk5IDnGxtexowZg6+++gpPP/00nnrqKfTo0QMbNmxA//79bcs8/vjjqKmpwYIFC1BRUYFx48Zh06ZN8PJq/ylIhBBCiNw6w8ilbh/HwxPROB6EEEJaS8pxPGJfehm8Cz+ixfp6ZD/9H4/ev3WKs1oIIYQQ4hnoInGEEEKIp+gEZ7VQ4UEIIYR4iM5wjAd1tRBCCCFEMtTiQQghhHgKV4c9vwyGTKfCgxBCCPEUdIwHIYQQQqTSGY7xoMKjHerrTNi+6Qj27cqC2WxBz75dMO2GoQgO84xzpk1mC/7cfRI792Sh3mhG99hQXDNlIKIiAuSOBgCwCCL+PHwa2w6cQk29CbERQbhhbH/ERgTJHQ0AIIoMe45k47c9J2CoqUdUqA7XXzkAPbuFyh0NAMAYQ+rZPGw8dBznq2sRofPDDUP6YWDXCHCc/M2sjDGkFxbiu6NHoa+qRqivD2b07YvErl09Ih8AHC0pxjfHjyDHUIlAL29c26M3ruwWC95D8p0uO4+vjx7B2Ypy+KnVuDqhJybHdYeS94zD8vJry7A+dz9OVenhrVBjQngfTArvB7XCM3Yp540V+L14J05X50DJKTE0sD/GhgyHRqGWOxoBDSDW5gFWsrOK8OS9n6H8fDU4jgNjDDzPgeM5/PuFGzBp2kA3pW6dwqJKPPLMWhToK8HzHETRmo8xhn8tmIwbpw+VNd95Qw3uW7YOWfmltnwKnoMgMtx37WjMv3qUrPmqa414+L/rcTirwJar4e+tVw3BotkTZN151pst+NeaH7HjVHaTfNcO7I1XbpgKpUK+nZNFFPHYr5uw8cQJKDgOAmO2vxPj4vDedddCo5Rv58QYw/N//YFVRw42yTc8IgorrrkRWpkvKLksNQX/27vblovnOIiMoXdwKD6fcRNCfHxkzbf2XAreOPYzOAAiGHhwEMHQxTsQy0fehSifQFnz/VWSindPrQbAIIKBAwcGhgCVFs/2+xeifaLa/JhSDiAW/+wrLg8gduaFp2gAsb+L+joTnrz3M1SW1wCwbsQA6y9kwSLi9afX4URGnmz5LIKIR5d8i6Jigy1Xw1/GgLc/3Ia9aWdly8cYwyMfbMTZwvN2+YQLfz/4MQW/pB6XLR8APPvhr8g4UwigMVfD37VbDmLN5oOyZQOAl37+HTuzzgFomu+nwyfw7h8psmUDgLd37caPJ04AAIQL34+Gv39mZ+P533+XLRsAfHooDauOWN/DS/MdLCrEo1t/lS0bAKw7cQz/27sbQGMu8cLfU2WlWPDzBsj5W3FXSSZeP/YT2IWdOgDbX319JR7YvwoCE2XLd7LqDN45tQoiRFsuduGvwVyNF48ug1EwyZavVVhjd0t7psvhGA8qPNrg918Po/x8tW2HeSme4/D9Z7slTtUoZd9p5BWU23ZEl+J5Dl9+v0fiVI0OnSlARrbeYT6OA1ZsSpVtw5pdUIad6Wccvr8A8NnPqbAI8mxYS6pqsP7gMduO6FIMwGcpB1FrMksb7IIakwmrDhxwuN0TGcN3GUdRWlsraa4GZkHABwf2ObxdYAxbsk/jTEWZhKkaMcbw3r49cNSeJjCGg/pCHNQXSprrYitP/wneQUKBicipKcXO4kyJUzX6sWArOAf5RIgoN1diV+l+iVORS1Hh0Qb7dp5y2swuCCL27pDvS7dn/xkonDSziyJDekYejEZ5dkw7M7KhcNJHzRhwprAMxRXVEqZqtPvw2Ra7Uc5X1uJ0XqlEieylnMlxWHQ0qDObcTCnQKJE9tIKClBnsThdRmAMu8+dkyiRvRPnS3G+znnRwwHYfi5bkjyXKqiuwpmKcqc/WBUch9+zz0iW6WJ1ggkHy8/ZWhKao+B47CyRbxuYVnYEIhz/MODA4UD5EQkTtUNrLnvf0uThPONIoMuE2SS0+Gtcrl/DAGC2CNa9dwssggg5erHNFgGtOTzCZBHcH6YZZsGar6VVaJYxX2uYWrlcR/P0fCax5eflOK7Vr6OjteZ5OY6DuRWvwx0sYuu2bXLlA9BiNw8Dg5k5L45l1wlOp6UWjzbo0TcKPO94z8nxHBJ6RUqYyF6vhAinv4g5AJHhOvh4y3Nkd99u4S0WZlofDSIC/SVKZK9PbLjTbhYAUKsUiI2U5+ybflHhLS7DAegTIc/ZN31Cwxx2E1xsQHjLr8MdEgKDoOYVTpcRGcOAMHnyRflrWzyw1SKKGBAqTz4/pQaRXgFOlxGZiD66th+82VHifKMddrUAAA8O3X1jJExEmkOFRxtMu3GY09uZyDDjdvnOypgyoS/UaqXjVgUOuOnaYbKdlTFxcHcE+Hk7PGWR5zjcPH4QVErnOwd3Gd6nG7qG6RwWlzzP4eqxfeHnI89ZD70jQjGoawQUDvIpeA4Te3dHhE6ewi1K649J3btD4eD9VXAchkZFoleoPIWRTuOFG3r1cZovVheA0V2iJU5mpVYocEf/QU6/H8He3pjSvYfEyaw4jsNtsWMc7tY5ABqFCldHDZEylp2roybaDiZtHofJ4eMky9MerhxY6uoYIFKhwqMNwiJ0WPTcDHAc7I6l4C7sCK66djAmThsgVzz4+3nhuceuBc/zdjunhu3Y6OHdccN0+TYKapUSbyy4BirlJflgzTgwPhJ3TUuULR/Pc3h14bXw1qia7Nw5jkN8VDAevOUKmdJZvTZzGgK8vZvsPHmOQ6TOH89dO1mmZFYvJU1GpL9/k52nguMQ6O2NN6dNkymZ1VNjr0RCYHCz+XxUKryffK2sp0s/OHIUhkREWr8TF81XcBzUCgXen3Yd1Ap5CnMAuCVmFMaF9gJwaT4ePMdj6eBZ8Fe1/1RQV40LGYHxodZtyMUtHzx4cOBwf8IcBGsCZEpHGtA4Hu04z/loeg6+/3w3UneehGAR0b13JGbcNgqTpw/0iAGSTp4uwtoN+7Ej5STMZgEx0cG48ZohmH7VQFnHeGhwVl+Gz7em4bf9mag3mdE1JAC3XDkIN40fCI1K/sOOCkoq8dVvafhl53HU1BsRHuSPGycOwi1XDYaPl/wDEBVXVWP17gNYd+AoKuvqEeLni1uGD8Ado4YgwEe+jX6D8ro6rD5wEGuOHEFpTQ0CvL1xc/9+mDd0KML8/OSOh2qTCauPHMSXGYdQWF0Ff7UGN/bqi7sGD0O0Vid3PBgtFnyZcQifHU5HTmUFfFQqXNujN+4eOhzdA+UfZM8iCvghLw1rzqUgu7oEKl6JSRF98Y+4ceilla+bpYHIRPxVkopfCv/A2ZpcKDgFhgUOwLVdktDLP75djynlOB7dn3oFChfG8RDq63H6Fc8ex4MKDxffGMaYRxQbjlA+11A+11A+11A+13RUPkkLj8UdUHgs9ezCQ/6fl5c5T/7SAZTPVZTPNZTPNZTPNZ6erzl0rRZCCCGESOsyKB5cIX+HPyGEEEI6DWrxIIQQQjxFJxhAjAoPQgghxEN0hmM8qKuFEEIIIZKhFg9CCCHEU1BXCyGEEEKkQl0thBBCCCEdiFo8CCGEEE9BXS2EEEIIkUwnKDyoq4UQQgghkqEWD0IIIcRD0MGlLiorK8Ps2bOh1WoREBCAu+66C9XV1U6Xf/DBB9GrVy94e3ujW7dueOihh1BZWWm3HMdxTaY1a9a486UQQggh7sc6YPJwbi08Zs+ejaNHj2LLli346aefsGPHDixYsMDh8gUFBSgoKMCbb76JjIwMrFq1Cps2bcJdd93VZNmVK1eisLDQNs2YMcONr4QQQgiRgEyFx3vvvYfY2Fh4eXkhMTERqamprr0OJ9zW1XL8+HFs2rQJ+/btw/DhwwEA77zzDq6++mq8+eabiIqKanKf/v374/vvv7f9v3v37nj55Zdxxx13wGKxQKlsjBsQEICIiAh3xSeEEEI6hbVr12LRokVYvnw5EhMT8fbbb2Pq1KnIzMxEWFhYhz+f21o8UlJSEBAQYCs6ACApKQk8z2Pv3r2tfpzKykpotVq7ogMAHnjgAYSEhGDkyJFYsWIFGHNc5hmNRhgMBruJEEII8TQNx3i4MrXVW2+9hfnz52PevHno27cvli9fDh8fH6xYsaLjXyDc2OKh1+ubVEpKpRJBQUHQ6/WteozS0lK8+OKLTbpnXnjhBUyaNAk+Pj7YvHkz7r//flRXV+Ohhx5q9nGWLl2K559/vn0vhBBCCJFKB51Oe+kPbI1GA41G02Rxk8mEtLQ0LF682DaP53kkJSUhJSXFhSCOtbnF48knn2z24M6LpxMnTrgczGAwYPr06ejbty+ee+45u9ueeeYZjB07FkOGDMETTzyBxx9/HG+88YbDx1q8eDEqKyttU25ursv5CCGEEE8VHR0NnU5nm5YuXdrscqWlpRAEAeHh4Xbzw8PDW91I0FZtbvF49NFHMXfuXKfLxMfHIyIiAsXFxXbzLRYLysrKWjw2o6qqCsnJyfD398f69euhUqmcLp+YmIgXX3wRRqOx2YrOUaVHCCGEeJKOOp02NzcXWq3WNt+T9oFtLjxCQ0MRGhra4nKjR49GRUUF0tLSMGzYMADA77//DlEUkZiY6PB+BoMBU6dOhUajwcaNG+Hl5dXic6WnpyMwMNCjViwhhBDSZh3U1aLVau0KD0dCQkKgUChQVFRkN7+oqMhtJ3C47eDSPn36IDk5GfPnz0dqaip27dqFhQsXYtasWbYzWvLz89G7d2/baTsGgwFTpkxBTU0NPv30UxgMBuj1euj1egiCAAD48ccf8cknnyAjIwNZWVn44IMP8Morr+DBBx9010shhBBC/pbUajWGDRuGbdu22eaJooht27Zh9OjRbnlOt45c+uWXX2LhwoWYPHkyeJ7HzJkzsWzZMtvtZrMZmZmZqK2tBQAcOHDAdsZLQkKC3WOdPXsWsbGxUKlUeO+99/DII4+AMYaEhATbEbmEEELIZU2Ga7UsWrQId955J4YPH46RI0fi7bffRk1NDebNm+dCEMfcWngEBQXhq6++cnh7bGys3WmwEyZMcHpaLAAkJycjOTm5wzISQgghnoK7MLly/7a69dZbUVJSgmeffRZ6vR6DBw/Gpk2bmhxw2lHoWi2EEEJIJ7dw4UIsXLhQkueiwoMQQgjxFDJ0tUiNCo92Kquuxd7TuTALAvp1DUf3sGC5I9mpNtThwJ7TMNWbEdczAt17R8odyU5tnQn7D2Wjts6E6C5B6NsjEhznSgNjx6q3WLAz9xwqjPXoptVhRGQXj8pnFgSknMnB+ZpaRGj9MTK2KxS8Wy+91CaCKGJPTi6KqqsR7OOLMTHRUCkUcseyEUWGA+fykVdWCZ2PF8YkxECj8pzNIWMMR87qkVNcDh8vNUb17gYfL7XcsexknC9CZlkJvJQqjIuKgU7T8hmIUjpVWYKMMj1UvAKjw2MQ7OUrd6RW6QxXp/Wcb9plwmi2YOmP27F+/1FYRNE2f3hcF7xy81R0CdLJFw6AYBGwctkW/PBlCsxmwTa/R78u+PdLMxHTvePH3W8LUWT47NsUfLkuFfVGs21+XHQwFj80DX16yFsgMcaw6vBB/C91Nwwmo21+N60Or06cgjFdu8mYzmp9+lG8vvkvlNfW2eZFaP3wzNWTMLl3dxmTWW0+mYXnt/6BoouuRB3k7Y3FE8fjhv59ZUxmtScrB0vWb0VeWeNVr/29NHjwqtG4ffRg2QvMjGw9nvvsN5wpLLPN81arMC95BO5KHil7vpPlpVi042ccOd94+qVGocC8vsPw2LDxUMpcAOdWV+Dfe37EvpLGgSKVHI9bug/CM0Ovgkbh4bu9TtDi4Tk/kS4DjDE8/OVP+H5fhl3RAQAHzxXgjuVrcb66VqZ0Vv/3wg/4fvVOu6IDAE6fKMCjd34EfV6Zg3tK48PPd+DTr3fZFR0AcC6/DA/+Zw2ysosd3FMaH6fvx/M7/7ArOgAg12DAnB+/x76CPJmSWa07eBSLN2y2KzoAoMhQjYVrNuL3zNMyJbPalnUaD2z4EcUXFR0AUFZXh8d++Q3rMo7KlMwq7WweFqxch/zySrv5VfVGvPLjdqzeeUCeYBeczCvB/Le+Qba+3G5+ncmM9zfuxrs/7JIpmVVOVQVm/vwljpXZf0+NgoAPj6Ri8a7fZEpmVVJXjZu3fIYDpfbfUwsTsSYrHQt3rm/xBAbiflR4tEHqmTzsOHEWYjMfXEFkKK2qxRe7DsqQzOrsST02bziA5r5XosBQV2PC2hU7pA92QXFpFdZs2NfsbaLIYLYIWPG1fBtWg9GI/+5t/vkZGETGsDTlL4lTNTJZBLy2ufn3r+Etf/W3HbJtWBljeGnbn3Z5LrX0j79gFgQHt7rfG7/+BcbQ7HcEAN7ZshvV9cbmb5TA+xt3wyKIzW5jAGD15v0oqahu9jYpvHdoD2rMJgjN5GMAvjl1BCfLS6UPdsGqk/tx3ljTbD4RDNsKTtm1hHisli5772y6DFDh0QYbDxyDgnfczCkyhnX7MiRMZG/bT+lQKBy/pYIgYuvGdAgWeTb8W/867vRcL1Fk2JV6GlXV9dKFusimM6dgdLJTFBnDAX0Bcg2VDpdxp12nz6GyzvG6YQByyipwJL/I4TLudKhQj9zKSqfbvvK6OuzKzpEs08XOlVbgSK7e4U4dAOrNFmw9miVhqkaVNfX4K+MMBNH53uPXfa5fC6s9zKKA9aePNrtTb6DgeHyfJd828JvT6S3nO3tEwkRtJ8fVaaVGhUcblBhqWtwolNXUOb3dncpLq8FaKHnNJgvqak0SJbJXVl4DvoX+aZExVBjkWYcltTVQtKL/vLi2RoI0TZVWt+55W7tcRyupaV03Y3GNPPnOt2K9KHgOpTJ1l1ZU1zlsiWnA8xzOG+TJV2s2Oy3MrRhK6uR5fwGgzOh82yEwESV18rUYESsqPNogXOfntMUDAEL8fSRK01RwmD9aGj5G46WCt68817QJCfKD2ELhxvMcAnXyrMNwXz+nv5Zsy/n4SZCmqTD/1j1vmFaefOF+rTtrINxPpvXXivUiiAzhMq2/IH/vlgtzkSE0QJ58vio1vFpxYGaEj78EaZoX0sKZKwqOlzVfq7jSzXKZdLdQ4dEGM4b3c9riwXMcbhoxQMJE9pKuGwJREB3ezit4TJkx1Gl3jDslje/jtC7ieQ7jR/WAn0yFUXJ8D3grHW9YeY7DyMiu6NqKCy+5w5ju3RDk4+3wdg5AfEgg+kXKc+bSgIhwxAUGOC19Q3x8MDZWnjODugbpMDQmyunO3VutwqS+8pwZ5O/jhQmDujv9ccNxQPLwXhKmaqTkedzUo7/TVkGBMczs0U/CVPZmdR/k9P0VmIiZcQMlTNR21NVC7AyNicKUAT3Q3OdawXOIDPDH7DGDJc/VoFt8GK65dWSzt/EKHv5ab9zyz/ESp2oUEuSHO29u/qJDPM/BS6PC3bePkzhVIz+1Gk+Obn798BwHJc/jqbHyrT+VQoH/TJvY7G0cAI7j8J9pE2U73ZLjODybNAkcxzksPp6ZPEHW0y0fu3o8FDzncOf0aPIV8NXIN17G/deNgUalBO+g+Lh7WiJCdPKNR/HAwFHQqb0cFh939hmC7jr5xjSa22sEIr39m83HAZjerQ+GhnSRPhixQ4VHG3Ach9dnTcOd44ZBo2wcDIkDMLZnLL6471YE+Dr+RSqF+xdfg3/cPwnePvYbz/5DYvC/LxYgNELecUbmzRqDhf+cCH8/+8GGenePwPtLb0dMV3kHYrtz4BC8PnEKQrztu3t6BgXj6xm3YHC4vOOMTB/QC2/fPB0Rl3QHxAQH4OM7bsDY7jEyJbO6Ii4Gn9w0AzGBAXbzo7T+ePf6azC9jzy/1hsM7BaJlfNvRkK4/ecs2M8HL82cgttGD5IpmVV8ZDBW/PtW9O1mf40MrY8G/775SiyYPkqmZFZRflqsv/YOjAjvajffV6XGoiFj8dyoJJmSWQVqfPDdVXfiioh4u+LXS6HE3b0T8dbo62QfB6VFnaCrhWOd8KRmg8EAnU6HyspKaNvZbF5db0Radj7MFhF9u4QhKlCe5ndH6utMyEjLhtFoQWxCGLrEhMgdyY7JbMGho3morTOhW5cgxHXzrHwWUURqQR4qjfWI1urQLyTMozZYoshwIDcf52vqEKH1w8AuER6VjzGGQ4V66KuqEeLrg6FdnHdxSI0xhuMFxcgrN0Dn7YVhsV2glKkL0pGs/FKcKy6Hn5caQxK6QO1BI6sCwJnKMpwsL4WXUonEiGh4K1VyR7KTV12Bo+VFUPEKjAiLhr+q/V24HbHPaO1zDPznK1Co2z8KrGCqx+EVT7k1q6uo8PDQN4YQQohnoMKjY3lWCU0IIYR0Zp1gyHQqPAghhBBPQYUHIYQQQqTSGa5O61lHUxFCCCHkb41aPAghhBBPQV0thBBCCJEKxxg4F042deW+UqGuFkIIIYRIhlo8CCGEEE9BXS2EEEIIkQqd1UIIIYQQ0oGoxYMQQgjxFNTVQgghhBCpUFcLIYQQQkgHohYPQgghxFNQVwshhBBCpNIZulqo8CCEEEI8RSdo8aBjPAghhBAiGbcWHmVlZZg9eza0Wi0CAgJw1113obq62ul9JkyYAI7j7KZ7773XbpmcnBxMnz4dPj4+CAsLw2OPPQaLxeLOl0IIIYRIoqG7pT3T5cCtXS2zZ89GYWEhtmzZArPZjHnz5mHBggX46quvnN5v/vz5eOGFF2z/9/Hxsf1bEARMnz4dERER2L17NwoLCzFnzhyoVCq88sorbnsthBBCiNsxZp1cub+Hc1vhcfz4cWzatAn79u3D8OHDAQDvvPMOrr76arz55puIiopyeF8fHx9EREQ0e9vmzZtx7NgxbN26FeHh4Rg8eDBefPFFPPHEE3juueegVqvd8noIIYQQ4jq3dbWkpKQgICDAVnQAQFJSEniex969e53e98svv0RISAj69++PxYsXo7a21u5xBwwYgPDwcNu8qVOnwmAw4OjRo80+ntFohMFgsJsIIYQQT+NKN8vl0t3ithYPvV6PsLAw+ydTKhEUFAS9Xu/wfrfffjtiYmIQFRWFw4cP44knnkBmZibWrVtne9yLiw4Atv87etylS5fi+eefd+XlEEIIIe7XCc5qaXPh8eSTT+K1115zuszx48fbHWjBggW2fw8YMACRkZGYPHkyTp8+je7du7frMRcvXoxFixbZ/m8wGBAdHd3ujIQQQghpnzYXHo8++ijmzp3rdJn4+HhERESguLjYbr7FYkFZWZnD4zeak5iYCADIyspC9+7dERERgdTUVLtlioqKAMDh42o0Gmg0mlY/JyGEECIHTrROrtzf07W58AgNDUVoaGiLy40ePRoVFRVIS0vDsGHDAAC///47RFG0FROtkZ6eDgCIjIy0Pe7LL7+M4uJiW1fOli1boNVq0bdv3za+GkIIIcSDdIKuFrcdXNqnTx8kJydj/vz5SE1Nxa5du7Bw4ULMmjXLdkZLfn4+evfubWvBOH36NF588UWkpaUhOzsbGzduxJw5czB+/HgMHDgQADBlyhT07dsX//jHP3Do0CH89ttvePrpp/HAAw9I3qohWASYTJ47foggiDAZzXLHcEgUGYxmC5iHnv4ligz1Js/NxxiDsc7k2fnqzR6dr95kgSh6bj6j2XPzAYDRYoEgeu5PbKNggcWD83VWbh3H48svv8TChQsxefJk8DyPmTNnYtmyZbbbzWYzMjMzbWetqNVqbN26FW+//TZqamoQHR2NmTNn4umnn7bdR6FQ4KeffsJ9992H0aNHw9fXF3feeafduB/udmBPFr5d8RfS954GY0B0XAhmzB6D5JnDoVDIPxjsscO5WLvqL6T+dRKiyBAeGYDrZyXiultHQqWSf5T8zNxirNi8D78fzIJFFBGq88Ut4wdh9qSh8Nao5I6H7KIyrNi6D7+mZcJkERDg64Wbxw7EnEnDoPXxkjseinJK8e3bv2LL17tgrDXBR+uNaXPG46Z/JSMwTCd3PJSdr8a3X+zGpo0HUVNjhMZLhSnTB+HWf4xFWIT8+Qy19Vi1PQ3f7T6Mitp6qJUKTBvSC/+cPAJxYUFyx0OtyYzP/zqANbsPoaSqBkqex1UDEnDXxJHoHdVya7O7GS0WfH4oHZ8dSkeewQCe4zApLh73Dh+BoU6GSZCKIIr46sQhrDyShjOV5eAAjOsSg3sHJ2Jslxi547WoM1yrhWOe+nPEjQwGA3Q6HSorK6HVatt035+/ScU7L20Ez3O2XyIcZx2z5Yop/fHka7fIWnz8uTkDS//zHTiOgyg0Vvocx2HQiDi8tGy2rMVHyrFzeOiDDWCMQbjolxzPcegdHYqPH74ZPl7yjcVyOLsQ89/9DmaL0CRfdIgOqx+ZhUA/b9nynTuRj0envoraqjq795dX8AgM0+LtrU8jtKt8O89ifSX+dfcKlJdXQxQa159CwcPHV43/fTgP3eLk23mWVddizrK1yDtfCfGiTZ+C56BWKvHJfTdhQEzrj0HraDX1Jsz78FucKChpko/nOLw3bwbG9JRv52m0WDBvw3rszcu1a9FXcBwYgGXTpuPqnj3ligdBFLFw24/YdPYkgMZeBwXHQWAMr1wxBbf3GdTmx3Vln9HW5xh53YtQqtr/A8dirkfqxmfcmtVV8v88v4zo88rw7ss/AoBd82fD9uGvzRnY9uNBOaIBAAwVtXhjyTowxux2SoC12fbQvrPY8PUemdIBRrMFT6z4GYIo2u3UAUBkDCdyS/DRL/LlE0QRj6/8GSaz0Gy+vPOVeGvDDpnSWb0+/+MmRQcAiIKIimID/u/h1TIls3r71Z+aFB2AtduvpsaI157fIE+wC978YQfyy+yLDgAQLnT7Pfb5z7J2bby/dU+TogOw5rOIIh778mcYzfJ17648eACp+XlNDiMQGANjDI/+9isq6+tlyQYA3588il/PnmxymIRwYX0+vXML8qoqZcnWWp1hHA8qPNrg1+/3g+Mc387xHH74Sr4d59af02Exiw4PLmKM4Yc1e2Xrc9964BSqao0OR/QVGcP3O4/AJNOGdc+JHBSWVzXZ6DcQRIZf007AUCvPhvXUwWycPpzTpOhoIAgi9m89gqKcUomTWekLKrB/z+kmRUcDUWA4daIQWZmFEiezqqypx6aDmU2KygYiYygoM2DPqRyJk1kZzRZ8t/eIw88fY0BlnRFbjpySOFnD8zOsTk93nA+ASRDw/bFj0ga7yKqjB+BkEw0AWHPisCRZiGNUeLTB6cxCp7+GmMiQnVUkYSJ7Z04WgeOdf+1KigyorTFKlMjeyfwSKFvohqquN6GowvmFBN0lM78EihbWn1kQkV1cLlEie2cyclteiAHZx/LcH6YZZ0+37rN/+pQ835HskrIWDzTkOQ6Z+SUSJbJXVFmNGqPJ6TJKBY/MQnnyVZlMKKpx/t3kOQ7HS+XJBwCZZSVOT+oQGcOx88VOlvAArAMmDyf/kYaXEY1GBY7jnLYYqFQKCRNd8twaZYvVPiBfRrVK2arWFo1Mx6BoVAqHv+bsl5Mnn9qrdQfeqmQ6QFetbt16ae1yHU2tbPl5GRg0cn0/lC0/L2MMmla8DndQt+LYNQ4cNAoZt4G8AoLguMWUBwcvhWfv9jrDwaXU4tEGiRN6O91x8goeoyfJN5bIqCt6QXDQDA8AvILDkMR4qGXaMV05IN5hMzdgPUi3Z5dQhOp8JUzV6Ip+8S1e2DE8wA8JkcHSBLrEkIl9oVA6/8r6+Huh36geEiWy129QN3j7OD8wWKnkMSwxXqJE9npGhSBU28JniwHj+8ZJE+gS4To/9IgIdtqdK4gME/rKs/68lCqMiY4G7ySghYlIim/fCNMdYUpsAhSc4++ICIakmAQJE5HmUOHRBldOHYCQcC345ir/C9/FmXPGShvqIsPHJCCme1jz+WDtY5819wqJUzXqFxOOoQldHHZnMAbcPW0kOGdbXjfqFhqAqwb3cLphvfuqkVDw8nxtAkK0mDb3SqfdaTMfTIbGW56zgry8VLjp9tEOb+c4DlfPGAatzkfCVI0UPI/5SSMd3s5zHKYM6omuwQHShboIx3G4Z3Kiw+JXwXMYEd8V/aPlO+vmvhGJDn98KTgOPYNDMD42VtpQF5k/cAQYWLMtvwqOQ5SfP66O7yV5rjZhzPXJw1Hh0QYaLxVe++SfCAmznqLEK3jwPAeO46BWKfGfN2choY9857ErFDxeefcf6BoTbPs/z3PgeA4KBY9FS2Zg8Eh5fi0B1g3rfxdciz7drBf1U/A8eM56miDHAY/cOB5XDZXvVDwAeGH2FCT2jLbL11Ao3T1lJG4eN1DOeLhn6W0Yd511JGCFkre+txdaQa7+5wTc9tg1csbD7fOuwNUzhgKwfv4aPnsAMH5SH9zzrylyxsOtYwfhrskjADSeotpQSCb2iMbzs+TNlzyoFx6dfgU4DrbvRkO+fl3D8faca2XNN7ZbNyy9agoUF7LxHAfFhUI9PjAIq264wWnh7m4DQiPwftJ1UCsU4AC7fJG+/vhy+q3wkqmrqrU6w1ktNI5HO85zNpst2L3tOPb9lQmzWUDPfl1w1fVDoQ2Q55fcpQRBROrOk9i9/QRMRjNiE8Ix9bohCArxlzsaAOupyKmZOdh84CRq6k2IDQ/EjDH9ERnkGeecM8Zw8Ew+ftmficraenQN1uGG0f3RLTRA7mg2mWlnsW3tblQUGxDSJRBTZo9DbN+ucseyOZtVhM0/H0JpsQEBQX646uqB6CljUX6pcyXlWL83A/llBmh9vDB9aG8MiYuSrbXtUoXlBnyfmoHs0nL4adSYMrAnRiV0A9/Cwc9SKampwTdHM5BZWgovpRJTExIwITZOttbAS5XX1+G7kxk4XKKHildgUrd4TI3rARXfvuNPpBzHY/S0F1wexyPl12c9ehwPKjw89I0hhBDiGSQtPJI7oPDY5NmFh2e3ORFCCCGdCJ3VQgghhBDSgajFgxBCCPEUIrNOrtzfw1HhQQghhHgKV0cf9fy6gwoPQgghxFNwcPEYjw5L4j50jAchhBBCJEMtHoQQQoincHX00ctghAwqPAghhBAPQafTEkIIIYR0IGrxIIQQQjxFJzirhVo8CCGEEA/BMeby5A7Z2dm46667EBcXB29vb3Tv3h1LliyByWRq82NRiwchhBBCnDpx4gREUcSHH36IhIQEZGRkYP78+aipqcGbb77ZpseiwoMQQgjxFOKFyZX7u0FycjKSk5Nt/4+Pj0dmZiY++OADKjwIIYSQy5Wr3SXu6mppTmVlJYKCgtp8Pyo8CCGEkL8Zg8Fg93+NRgONRtNhj5+VlYV33nmnza0dAB1cSgghhHgO1gETgOjoaOh0Otu0dOnSZp/uySefBMdxTqcTJ07Y3Sc/Px/Jycm4+eabMX/+/Da/RGrxIIQQQjxFB41cmpubC61Wa5vtqLXj0Ucfxdy5c50+ZHx8vO3fBQUFmDhxIsaMGYOPPvqoXRGp8CCEEEI8REeNXKrVau0KD0dCQ0MRGhraqsfOz8/HxIkTMWzYMKxcuRI8375OEyo8CCGEEOJUfn4+JkyYgJiYGLz55psoKSmx3RYREdGmx6LCgxBCCPEUHnqRuC1btiArKwtZWVno2rXrJU/Ztuekg0sJIYQQD8GJrk/uMHfuXDDGmp3aigoPQgghhEjGrYVHWVkZZs+eDa1Wi4CAANx1112orq52uHx2drbD03m+/fZb23LN3b5mzRp3vhRCCCHE/Rq6WlyZPJxbj/GYPXs2CgsLsWXLFpjNZsybNw8LFizAV1991ezy0dHRKCwstJv30Ucf4Y033sC0adPs5q9cudJu+NaAgIAOz08IIYRIqhNcndZthcfx48exadMm7Nu3D8OHDwcAvPPOO7j66qvx5ptvIioqqsl9FApFk6Nj169fj1tuuQV+fn528wMCAtp8JC0hhBBC5OW2rpaUlBQEBATYig4ASEpKAs/z2Lt3b6seIy0tDenp6bjrrrua3PbAAw8gJCQEI0eOxIoVK5we4GI0GmEwGOwmQgghxNO05rL3LU2ezm0tHnq9HmFhYfZPplQiKCgIer2+VY/x6aefok+fPhgzZozd/BdeeAGTJk2Cj48PNm/ejPvvvx/V1dV46KGHmn2cpUuX4vnnn2/fC2lGfa0Jf27Yj32/H4XFZEGPwTFIvn0MgiMCOuw5XGEyWbDzj+PYvSMTxnoz4hLCMO36oYiMCpQ7GgBAsAjYsyUDO39OR211Pbp2D8e020eja/dwuaMBAESRYf/e0/hj6zEYDHWIjArAtGsGo3sPz8jHGEN6eg62bs5AeXkNQsO0SE4egN59osBxnNzxwBjDkbN6bEw5iuKKagRrfTA9sQ+G9ejqEfkAIDO3GBt2HkV+aSV0fl5IHtELo/vGguc9I99ZfRnW7zyCc0Xl8PVSI2lYT4wfEA+lwjPOB8gvrcS6nUeQlVcKL40KEwd3x6TBCVCrPGOEhiJDNb5NO4KjBcVQKXlc2SMOV/fvBW+1Su5oLfPQ02k7EsfaeC7Mk08+iddee83pMsePH8e6deuwevVqZGZm2t0WFhaG559/Hvfdd5/Tx6irq0NkZCSeeeYZPProo06XffbZZ7Fy5Urk5uY2e7vRaITRaLT932AwIDo6GpWVla0a2e1i2ScK8NSt76C82ACO48AYA89z4HgOj/7fHEy8cUSbHq+j6Qsq8MSDn6Mwvxw8z0EUrfkYAx54NBnX3SRvvvISA566/X1knygEr+AhCqLt7z8evRq3PzxV1nw11fV46t9rcSwjz7b+FAoegiDihptH4L6HrpJ152k0mvHckvVI3XsaCgUHQWjMl5TUD48/eQ0UMu6cLIKIJat/w6/7TkDBcxBEZvs7rn8c3lhwDTQy7pwYY3hj7Xas+SO9Sb7B3aPwfw/OgL93x11Iqz0++nkPlv+UYsvFcxxExtCjSwg+eGgmgrQ+suZb80c63vjmD3Dche3LhXxdQnT48OGZiArRyZrvx8PH8eT638AYIDIGjrPui0P8fLDyzpnoERbS5sc0GAzQ6XTt2me09TkmDlsMpdKr3Y9jsdTjj7Slbs3qqjZvoR599FEcP37c6RQfH4+IiAgUFxfb3ddisaCsrKxVx2Z89913qK2txZw5c1pcNjExEXl5eXbFxcU0Go1t+NjWDiPbnPpaI5669R1UnreemdNQs4kig2AR8caDq5F5MLtdj90RBIuIxf/6AsX6Sluuhr+MMbz75q/Yl5IlWz7GGJ7/5yfIOVVkzSWIdn8//+8v+H39ftnyAcCrL2zEiWP5ABrXn3Ah3/pv92H9t/tkywYA7/zfZuzbdwYAIAj2+bZtO4pVK/+SLRsAfPDjbmzaZ72glNCw/i783X00G6+v/UO2bADw5dYDWPNHOoCm+Q6fLcSzKzfJFQ0A8NPeY1j+UwqAxlzihe3MmcLzeGT5xnaNm9BRdmWcxetr/7Du1C/Jpy8z4IFl6yGIbhpIohXScwvx+PebIIjMlqthdZXX1OGfq9ehzmSWLV+rMACiC5PnN3i0vfAIDQ1F7969nU5qtRqjR49GRUUF0tLSbPf9/fffIYoiEhMTW3yeTz/9FNddd12rxpBPT09HYGBgh17ytzl/rNuP8mKDbUd5KY7j8P0H29yawZk9u04iP7fMtiO6FM9zWPvZLolTNTq2/ywy0885WX/A2nc2y7ZhzTlXij27T9k2qM1Z88VuCBZ5NqxlZdX47bcjYA7yMQas+34f6upMEiezqq034es/Djrc7omMYWPKUZQZaiXN1cAsCFj1m+PCURQZ/jx0BueKyiVM1Ygxhk9/TYWj9jRBZDhythCHzxY6WML9VmzaB95Bi58gMpwrLsfOI2clTtVo5e40h91lAmMoqa7BLxmZzd7uKTrDMR5ua5Pt06cPkpOTMX/+fKSmpmLXrl1YuHAhZs2aZTujJT8/H71790ZqaqrdfbOysrBjxw7cfffdTR73xx9/xCeffIKMjAxkZWXhgw8+wCuvvIIHH3zQXS/FZt+2DHBO+oBFQUTqliNuz+FI6u4sp83soshw+OA5GOvlqfj3/X4MCqXjfIwBOaeKUHqhxUZq+/acbrEbpbysBmfPFDtdxl3S0rKdFkUAUF9vxtGMPIkS2Us/U4B6k8XpMoLIsPdEjkSJ7J3KK0VZVZ3TZTjO+qteDvryKpwrKnf6g1XBc7Lt2OtMZhzMyre1JDRHwfP4S6b1BwB/nDxjaylqDscB20/Kl69VGFwcx0PuF9Ayt3a2fvnll1i4cCEmT54Mnucxc+ZMLFu2zHa72WxGZmYmamvtfwGtWLECXbt2xZQpU5o8pkqlwnvvvYdHHnkEjDEkJCTgrbfewvz58935Uqx5TRaHvzYbWCyC23M4fG6zANaKT51FECFHL7bFLLTq+Aiz0fnOy13MZsHWH9zScnKwtPJ5zTJ9Blv7vCYPzseBg1mmFq3WPC/HcTALMn3+HLRU2mOyff4AOGztbcAYZFt/pJFbC4+goCCHg4UBQGxsbLPN6q+88gpeeeWVZu+TnJxsN3CYlHoMisGB7ccd/urkeA7d+0dLnKpRj96R2PrrIccLcEBEZAB8fNTShbpIwoDoFneefjofhEYFSBPoEj17RbbYoqBSK9AtJliiRPZ69mz52CiOAxIS5Dn7plfXsFYVbn1j5MkXHxkElVLhdMcoMoY+MWEOb3enyCB/+HtrUFXX/LFqgHXn36ebPOvPz0uNyCB/FJZVOVxGFJls+QCgT2QYjhUWO2yV4TkO/aI84+w0hzrBWS2ecW7WZWLa7LHWLbsDTGS4/u4J0gW6RNK0gVCrlQ4jcgBuuDVRtrMyxiQPhDbI12F3Fc9zuGbOOKjU8pz1MHhYLKK6BDjsI+Z5DlclD4CvX/uPOHdF94Rw9OkT5TTf6DE9EBoqz5HsEUH+GD8gHgoH+RQ8h4HxkejRpe1nFXQEfx8vTE/s4zAfz3PoFhaAEb3k+fGgUipw0/iBDo+h4DkOgX7emDQ4QeJkVhzH4bZJQ51uXzRqJaaP6iNprovNGTXEaVcQxwG3DBsgYaJ2cOXA0obJw1Hh0QahXQLxyFt3gOM4u2MpGnakSbeOkvV0Wj9/Lzz10kzwPG+f78KGInFsT1w3U758ao0ST3/4T6hUSvAXH4vCWTP2GRaHWQ9eJVs+nufwzEsz4e2thkJhv3XlOA4xcaGYf99kmdJZLf7PtdDqvJsUHzzPISxci0cWydMa2OCp2ycjPNC/yc6T5zkE+Hrjxbny5nvkpisQFxHUJJ+C5+CjUeH1BdfIerr0/KtHYUB8JDgOdgeZKngOKqUCbyy4BiqlQrZ8t04chHH94wDY/wZT8Bx4nserd0+X9XTkawb0xvWDrIXPxe+xgufAAXhlxhSEa/0c3JtIpc3jePwduHpO9rF9Z/D9B1uxb9tRCBYB3ftH4/q7J2DSTSM9YoCkrMxCfPfVHuzafhxms4BusSG47uaRmHbtEKcHd0olN6sI33/4O/788SCMtSZExobgmjnjMP2OsVB7yT/Aj76wAt+v3Ystm46gtsaE0DB/XDNjKGbMHAFvmbqpLnb+fDW++zYVm349jKqqOgQG+uKaa4fghhuHQ6v1ljseKqrr8PUfB7F+ZwbOG2qg8/PG9WP64fZJQxCqk3+jX1Nvwto/0vHdn4dRVF4FX28NrhnVB3ckDZV9DAoAMJot+G7HYXzzZzrySivhrVZh6vBe+EfSMMRGBMkdDxZBxIZdGVjzRzqy9WVQKRWYNCQBc64ahl7R8nRTXUwUGTYePo7P9x7E8cISKHkeE3rGYd7YYRgS3fRSHa0h5Tgek/s/DqWi/cWbRTBiW8brHj2OBxUeLr4xjDGPKDYcoXyuoXyuoXyuoXyu6ah8khYe/R5zvfA4+oZHFx7y//y9zHnylw6gfK6ifK6hfK6hfK7x9HydlWcMrE8IIYSQTnFWCxUehBBCiKfoBIUHdbUQQgghRDLU4kEIIYR4ChFweMGe1t7fw1HhQQghhHgIVy/0djlcJI4KD0IIIcRT0DEehBBCCCEdh1o8CCGEEE8hMoBzodWihQtdegIqPAghhBBPQV0thBBCCCEdh1o8CCGEEI/hYosHPL/FgwoPQgghxFNQVwshhBBCSMehFg9CCCHEU4gMLnWX0FkthBBCCGk1JlonV+7v4airhRBCCCGSoRYPQgghxFN0goNLqfAghBBCPAUd40EIIYQQyXSCFg86xoMQQgghkqEWD0IIIcRTMLjY4tFhSdyGCg9CCCHEU1BXCyGEEEJIx6EWD0IIIcRTiCIAFwYBEz1/ADEqPAghhBBP0Qm6WqjwaKeKEgPS/zwOi9mCnkPi0K13lNyR7FQb6nAgJQumegvieoajex/PyldXY8SBlCzU1RjRNTYUvQZ2BcdxcseyMdWbkLblMKrKqhEZH47+43p7VD6LRcC+w+dQbqhFWJA/hvSLhkLhOT2ngiDiwNFclJRVIUjni+EDukGpVMgdy0YUGY5k5KFQXwGtvzeGD4uFWu05m0PGGI5k63GupBw+GjVG9+4GH41a7lh2jucV45S+FF4qJUb17Aatt5fckexkFZ/HsfwiqJQKJMZHI8jXR+5I5AK3fdNefvll/Pzzz0hPT4darUZFRUWL92GMYcmSJfj4449RUVGBsWPH4oMPPkCPHj1sy5SVleHBBx/Ejz/+CJ7nMXPmTPzf//0f/Pz83PVS7JjqTfjg8S/x22d/QbAItvkDxvXCvz+cj4iYUElyOCJYBKx8ezN++DIFZlNjvh79uuDfr9yEmIQwGdMBoihizYfb8e0nf6K+zmybH5MQhkdevgm9BnSVMZ31M7jhnV+xesla1FTW2uZHdg/HIx/egyGTBsiYzuqX7Rl4d/WfqKyqs80LDfbDo3cl4YqRCTIms/pz7yn879NtKCmrts0L0Hpj4ZwJmDahn4zJrNIOZuO/b29CYWGlbZ6vrwb/vPMK3HD9UNkLzCPn9Hj2y99wRl9mm+etVuGuq0bg7ikjZc+XpS/Ff77+Dcfyim3z1EoFZl8xBA9NGwulzAVwXlklnlr3G/Zn59vmKXkeNw7rh8VXT4BG5TkFZrM6QYuH2z4hJpMJN998M+67775W3+f111/HsmXLsHz5cuzduxe+vr6YOnUq6uvrbcvMnj0bR48exZYtW/DTTz9hx44dWLBggTteQhOMMbx4x7v4ddWfdkUHABxLOYVFSS+hotggSRZH/u+5Dfh+1U67ogMATp8owKP/+BD6vDIH95TGyv9txufvbLUrOgAg90wJnrjzY5zNLJQpmdV3//0R7z+80q7oAAD92WIsTn4ZGTuPy5TM6uc/MvDyu5vsig4AKDlfjSdf34Cd+0/LlMxq5/7TeOqNH+yKDgCoMNThpXd/xS/bM2RKZnX4SC6eeOob6PX239OaGiPeeX8rvv1+n0zJrE7ml+CuZd8gu6jcbn6dyYx3f96NZT/ukimZVd75Ssx55xtkFpTYzTdZBKz6Yz9e+G6rTMmsSqtrMPvjtTiYU2A33yKK+G5/Bh5Z8zOYp++YReb65OHcVng8//zzeOSRRzBgQOt+ITLG8Pbbb+Ppp5/G9ddfj4EDB+Kzzz5DQUEBNmzYAAA4fvw4Nm3ahE8++QSJiYkYN24c3nnnHaxZswYFBQXOn6ADHNpxAqmbDoE188YKgojyYgM2fLDZ7TkcOXtSj83rDzRb8IoCQ12NCWs/2SF9sAtK9JX4fuVfzd4migxmswWfv7tN4lSNaiprsPLZNc3exkQGJor4+IkvJE7VyGwW8O7q7Q5v5zjgnVV/yLZhZYzh/1b+Dmc/yN9d/ScslxTtUlr+8R8QRThcRytW/4WaGqPEqRq9+/NuWAQRooN8q7btR3FldbO3SeHjbamoNZkgNLMNZADWpx5Flr5U+mAXfL77IMqqa5vNJzKG7ZlnkHZRSwiRh8d0Cp89exZ6vR5JSUm2eTqdDomJiUhJSQEApKSkICAgAMOHD7ctk5SUBJ7nsXfvXoePbTQaYTAY7Kb22Pr1TiiUjleZKIjY9Jl8O/ZtG9Od9vMLgoitPxxs0lojlT9/PgRnjcSiwLDnj+OoqqxzspT7/LUuFWaj2eHtoshwLOUkCs8WSZiqUeqhbBiq6x3ezhiQp6/A8Sy9hKkaHTulR0FRpdOW3sqqOqQePiddqIvk55fj+IlCp4WZ0WjBX7tOSpiqUWVNPXYcPdPsTvNiv+4/IVEie2ZBwE9px53mU/AcNu6Xr1Xw+7QMCE7eXwXPYcPBYxImajvGRJcnT+cxhYdeb91YhoeH280PDw+33abX6xEWZn+MglKpRFBQkG2Z5ixduhQ6nc42RUdHtytjmb4CgsX5m1pZWtWux+4I5eerwFoYts5ssqCu1iRRInvlpdXgeecfOSYyGMprJEpkr1xf0aoDNMv1Fe4P04zzFa1bL61drqOVtTafTO9va/IpFBzK5fr81dS12D3P8xxKq2qdL+QmtUYzTK340XK+Sp71BwDltc5/tAgiQ4mM+VqFudjN4uldSWhj4fHkk0+C4zin04kT8lTjzixevBiVlZW2KTc3t12PExIV5LTFAwACw3TteuyOEByqBZy2KQAaLxW8fTXSBLpEUJg/hBbOMed5DgHB0hwofKngqMAWC0vrckESpGkqJKh16yW0lct1tJAg31YtJ1++lp9XEBhCgv0lSNNUkJ83+BYOHBVFhjCdPOvPV6OGVysOzJQrHwCE+Dr/DCp4DhEy5muVhoNLXZk8XJsKj0cffRTHjx93OsXHx7crSEREBACgqMi+GbuoqMh2W0REBIqLi+1ut1gsKCsrsy3THI1GA61Waze1x5Q7rnC6Y+J5DtPmXtmux+4ISdcPgSg4yafgMeWGobKddjnxmkFOyyJewWPsVf3g6y/PaXnjbkyExsdxUcYreAwY3wfhMp25NHJgDAK03g5v5zggpksQesWHO1zGnXp3j0B0ZKDTYzyCdD4YPjBGulAXiYwMwIB+XcDzjgN6eakwbmwPh7e7k9bHCxMHdIfCST6OA6YN6yVhqkZKBY/rhvd1mk8QGa4b3lfCVPZuGt7fafEmiAwzhsp/ZlVn16Y9UGhoKHr37u10Uqvbd655XFwcIiIisG1b48GFBoMBe/fuxejRowEAo0ePRkVFBdLS0mzL/P777xBFEYmJie163rboN7oHrrhhRLOns/EKHqHRwbj+3qvcnsORbt3DcM2s5tcDr+Dhr/PGLXfLVxgFhWpx270Tm72N5zlovFT4x4NJzd4uBR9/b9z96uxmb+N4DgoljwWv/0PiVI2USgUe+efkZm/jOIADh4f/OUm20y05jsMjd00GB85h8fGvf06S9XTLexdMAs/zDouPe+6eAG9v+cbLWHjNGGhUSof5FkxNRIi2dS1L7jB/8khovb0cFh+3jR2EuDB5WgQB4B9jhiBC59dsPg5Acv+eGBwdKX2wthBF1ycP57YtQE5ODtLT05GTkwNBEJCeno709HRUVzcekd27d2+sX78egHWj9fDDD+Oll17Cxo0bceTIEcyZMwdRUVGYMWMGAKBPnz5ITk7G/PnzkZqail27dmHhwoWYNWsWoqLcP0AWx3F4csW9uPHBqVB7qS66ARh+1QD8b+vT0MrUTdDg/v9cg388MBnePvYbz/5DY/C/L+9BaIR8XUEAMPuByVjwxNXwu+SXe4/+XfDfL+9BdLy844zMWDgNj35yHwLD7ddTbL9ovPn7c+g9Up5fww2SxvXGS49ei7BLugO6RgTiv0/PxMhBsfIEuyBxcCze/M+N6BIRaDc/PMQfL/37OiSN7S1TMqu+faLwvzdmITYmxG5+YKAPHn90GmZcN1SmZFbxEcFY/fCt6Bdt32ql9dHg8RuvxD3Jo2RKZhUR6I8vHpqFoXFd7Ob7aFS4f+poPDmj+R8WUgnw8cZXC2ZhbEKMXeuql1KJueOG4bWbk2UfB6VFnaCrhWNuOvdu7ty5WL16dZP5f/zxByZMmGB9co7DypUrMXfuXACNA4h99NFHqKiowLhx4/D++++jZ8+etvuXlZVh4cKFdgOILVu2rE0DiBkMBuh0OlRWVra726XGUIeM3ZmwmCxIGByL8G4hLd9JQvV1JmTsz4bRaEZsj3B0ifGsfCaTBRn7z9pGLo3pIU/3gCOCRcCRv47bRi7tPjjWozZYoshw+ES+beTSvj0iPCofYwzHTulRXFaFIJ0PBvRy3sUhNcYYTmUVoVBfCa2/FwYO8KyRXwHgVEEpzpWUw89LjaHxXaD2sIGvskvKcVp/HhqVEsPiu8BbrWr5ThLKL6/E8cISqBQ8hsV0gZ9X+49t64h9RmufY7Lf7VBy7W91szATtlV/5dasrnJb4eHJpPgQEUII+XuQsvCY5DPL5cLj99o1Hr1/86wSmhBCCOnMGANaGBah5ft7Ns9qWySEEELI3xq1eBBCCCGeQmQA9/du8aDCgxBCCPEUjAFw4ZTYy6DwoK4WQgghhEiGWjwIIYQQD8FEBuZCV8vlcKIqFR6EEEKIp2AiXOtq8fyRS6nwIIQQQjxEZ2jxoGM8CCGEECKZTtni0VARGgwGmZMQQgjxdA37CilaEyzM6FJ3iQXmDkzjHp2y8KiqqgIAREdHy5yEEELI5aKqqgo6nXsutKlWqxEREYGd+l9cfqyIiIh2XyleCp3yWi2iKKKgoAD+/v4uXVjLYDAgOjoaubm5Hjsm/qUoszQos3Qux9yUWRodlZkxhqqqKkRFRYHn3XeEQn19PUwmk8uPo1ar4eXl1QGJ3KNTtnjwPI+uXbt22ONptdrL5ovYgDJLgzJL53LMTZml0RGZ3dXScTEvLy+PLhg6Ch1cSgghhBDJUOFBCCGEEMlQ4eECjUaDJUuWQKPRyB2l1SizNCizdC7H3JRZGpdj5s6gUx5cSgghhBB5UIsHIYQQQiRDhQchhBBCJEOFByGEEEIkQ4UHIYQQQiRDhYcTL7/8MsaMGQMfHx8EBAS06j6MMTz77LOIjIyEt7c3kpKScOrUKbtlysrKMHv2bGi1WgQEBOCuu+5CdXV1h+Vu6+NnZ2eD47hmp2+//da2XHO3r1mzRpbMADBhwoQmee699167ZXJycjB9+nT4+PggLCwMjz32GCwWiyyZy8rK8OCDD6JXr17w9vZGt27d8NBDD6GystJuuY5cz++99x5iY2Ph5eWFxMREpKamOl3+22+/Re/eveHl5YUBAwbgl1/sh29uzefbVW3J/PHHH+OKK65AYGAgAgMDkZSU1GT5uXPnNlmfycnJsmVetWpVkzyXDhrlaeu5ue8ax3GYPn26bRl3r+cdO3bg2muvRVRUFDiOw4YNG1q8z/bt2zF06FBoNBokJCRg1apVTZZp63eEdABGHHr22WfZW2+9xRYtWsR0Ol2r7vPqq68ynU7HNmzYwA4dOsSuu+46FhcXx+rq6mzLJCcns0GDBrE9e/awv/76iyUkJLDbbrutw3K39fEtFgsrLCy0m55//nnm5+fHqqqqbMsBYCtXrrRb7uLXJWVmxhi78sor2fz58+3yVFZW2r2u/v37s6SkJHbw4EH2yy+/sJCQELZ48WJZMh85coTdeOONbOPGjSwrK4tt27aN9ejRg82cOdNuuY5az2vWrGFqtZqtWLGCHT16lM2fP58FBASwoqKiZpfftWsXUygU7PXXX2fHjh1jTz/9NFOpVOzIkSO2ZVrz+XZFWzPffvvt7L333mMHDx5kx48fZ3PnzmU6nY7l5eXZlrnzzjtZcnKy3fosKyvrkLztybxy5Uqm1Wrt8uj1ertlPG09nz9/3i5vRkYGUygUbOXKlbZl3L2ef/nlF/af//yHrVu3jgFg69evd7r8mTNnmI+PD1u0aBE7duwYe+edd5hCoWCbNm36//buPaSp948D+MfbVprLZOali6TVspppxEyJBimmFQj9URaUXcgoowtmamCWBhlJ/RHdCK3+qESiMChLhPyjWFZmN7Vwsq6gYZd5K/Py/v7Rb+fnaVPnzpxGnxcEnc959uxzPnu284DPswlthloHZh888bDChQsXrJp49Pb2ws/PD8eOHRNi379/h1wux9WrVwEAtbW1ICI8fvxYaFNaWgonJyd8+vRJcq726j8sLAybNm0Sxax5s9vC1py1Wi127drV7/nbt2/D2dlZ9KF+5swZKBQKdHZ2jkjOfyouLoZMJkNXV5cQs1edNRoNUlJShOOenh4EBATgyJEjFtuvWrUKy5cvF8UiIiKwdetWANaNb0fn/Kfu7m54enri0qVLQiwpKQkJCQl2yc+SoeY82OfJ31DnEydOwNPTE21tbUJsuOvclzXvkX379mHOnDmi2OrVq7F06VLhWGodmG34Ty12ZDAYqLGxkWJiYoTY+PHjKSIignQ6HRER6XQ68vLyogULFghtYmJiyNnZmSorKyXnYI/+q6qq6NmzZ7R582azcykpKaRUKkmj0VBhYaFdfiZaSs6XL18mpVJJc+fOpczMTOro6BD1q1arydfXV4gtXbqUWlpaqKamZsRy7stoNJJCoSBXV/HPJkmt869fv6iqqko0Fp2dnSkmJkYYi5auqW97ot/1MrW3ZnxLYUvOf+ro6KCuri7y9vYWxSsqKmjixImkUqlo27Zt9OXLF8n5Ssm5ra2NAgMDacqUKZSQkCAaj39DnQsKCigxMZE8PDxE8eGqsy0GG8/2qAOzzT/5I3HDpbGxkYhIdKMzHZvONTY20sSJE0XnXV1dydvbW2gjNQep/RcUFFBISAhFRUWJ4jk5ObRkyRJyd3ensrIy2r59O7W1tdHOnTtHJOe1a9dSYGAgBQQE0IsXLyg9PZ3evHlD169fF/q19FqYzo1Ezn01NzdTbm4uJScni+L2qHNzczP19PRYvP7Xr1/3e02DjV1TrL82UtiS85/S09MpICBAdDOJi4ujlStX0rRp06ihoYH2799P8fHxpNPpyMXFxeE5q1QqKiwspNDQUDIajZSfn09RUVFUU1NDkydPHvV1fvToEb169YoKCgpE8eGssy36G88tLS3048cP+vbtm+Txxmzzz008MjIy6OjRowO2qauro1mzZjkoI+tYm7dUP378oCtXrlBWVpbZub6x8PBwam9vp2PHjvV7QxzunPvesNVqNfn7+1N0dDQ1NDRQcHCwTX06qs4tLS20fPlymj17Nh08eFB0bqh1Zr/l5eVRUVERVVRUiBZrJiYmCv9Xq9UUGhpKwcHBVFFRQdHR0Q7PMzIykiIjI4XjqKgoCgkJoXPnzlFubq7D8xmqgoICUqvVpNFoRPHRVmc2ev1zE4/U1FTasGHDgG2CgoJs6tvPz4+IiJqamsjf31+INzU1UVhYmNDm8+fPosd1d3fT169fhcdLydvW/k2uXbtGHR0dtH79+kHbRkREUG5uLnV2dlr8LQRH5dw3HyIivV5PwcHB5OfnZ7ZCvampiYio334dkXNrayvFxcWRp6cn3bhxg9zc3AZsP1idLVEqleTi4iJcr0lTU1O/+fn5+Q3Y3prxLYUtOZvk5+dTXl4elZeXU2ho6IBtg4KCSKlUkl6vl3xDlJKziZubG4WHh5Neryei0V3n9vZ2KioqopycnEGfx551tkV/41mhUNDYsWPJxcVF8mvHbDTSi0z+BkNdXJqfny/EjEajxcWlT548EdrcvXvX7otLbe1fq9Wa7bLoz+HDhzFhwgSbczWxV03u378PIsLz588B/H9xad8V6ufOnYNCocDPnz9HJGej0YiFCxdCq9Wivb3dqueytc4ajQY7duwQjnt6ejBp0qQBF5euWLFCFIuMjDRbXDrQ+JZqqDkDwNGjR6FQKKDT6ax6jg8fPsDJyQklJSWS8wVsy7mv7u5uqFQq7NmzB8DorTPw+7NQLpejubl50Oewd537IisXl86dO1cUW7NmjdniUimvHbMNTzwG8O7dO1RXVwtbS6urq1FdXS3aYqpSqXD9+nXhOC8vD15eXigpKcGLFy+QkJBgcTtteHg4Kisrcf/+fcyYMcPu22kH6v/jx49QqVSorKwUPa6+vh5OTk4oLS016/PmzZs4f/48Xr58ifr6epw+fRru7u44cODAiOSs1+uRk5ODJ0+ewGAwoKSkBEFBQVi8eLHwGNN22tjYWDx79gx37tyBj4+PXbfTDiVno9GIiIgIqNVq6PV60bbD7u5uAPatc1FREeRyOS5evIja2lokJyfDy8tL2OWzbt06ZGRkCO0fPHgAV1dX5Ofno66uDtnZ2Ra30w42vqUYas55eXmQyWS4du2aqJ6m92hrayv27t0LnU4Hg8GA8vJyzJ8/HzNmzJA8+bQ150OHDuHu3btoaGhAVVUVEhMTMWbMGNTU1IiuazTV2WTRokVYvXq1WdwRdW5tbRU+g4kIx48fR3V1Nd69ewcAyMjIwLp164T2pu20aWlpqKurw6lTpyxupx2oDmx48MRjAElJSSAis3/37t0T2tD/vnPBpLe3F1lZWfD19YVcLkd0dDTevHkj6vfLly9Ys2YNxo0bB4VCgY0bN4omM1IN1r/BYDC7DgDIzMzElClT0NPTY9ZnaWkpwsLCMG7cOHh4eGDevHk4e/asxbaOyPn9+/dYvHgxvL29IZfLMX36dKSlpYm+xwMA3r59i/j4eIwdOxZKpRKpqamirauOzPnevXsWxxMRwWAwALB/nU+ePImpU6dCJpNBo9Hg4cOHwjmtVoukpCRR++LiYsycORMymQxz5szBrVu3ROetGd9SDSXnwMBAi/XMzs4GAHR0dCA2NhY+Pj5wc3NDYGAgtmzZYvcby1By3r17t9DW19cXy5Ytw9OnT0X9jbY6A8Dr169BRCgrKzPryxF17u/9Y8ozKSkJWq3W7DFhYWGQyWQICgoSfVabDFQHNjycADvsh2SMMcYYswJ/jwdjjDHGHIYnHowxxhhzGJ54MMYYY8xheOLBGGOMMYfhiQdjjDHGHIYnHowxxhhzGJ54MMYYY8xheOLBGGOMMYfhiQdjjDHGHIYnHowxxhhzGJ54MMYYY8xheOLBGGOMMYf5D8xDL3FU7v3iAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def model2(x_y, a, bx, by):\n", " x, y = x_y\n", @@ -13593,142 +745,9 @@ { "cell_type": "code", "execution_count": null, - "id": "766174be", + "id": "52", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 71.61 (χ²/ndof = 0.7) Nfcn = 34
EDM = 5.66e-16 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 a 0.93 0.10
1 bx 1.87 0.16
2 by 2.93 0.16
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a bx by
a 0.01 0.000 0.000
bx 0.000 0.0245 0.000
by 0.000 0.000 0.0245
" - ], - "text/plain": [ - "┌─────────────────────────────────────────────────────────────────────────┐\n", - "│ Migrad │\n", - "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 71.61 (χ²/ndof = 0.7) │ Nfcn = 34 │\n", - "│ EDM = 5.66e-16 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ a │ 0.93 │ 0.10 │ │ │ │ │ │\n", - "│ 1 │ bx │ 1.87 │ 0.16 │ │ │ │ │ │\n", - "│ 2 │ by │ 2.93 │ 0.16 │ │ │ │ │ │\n", - "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌────┬──────────────────────┐\n", - "│ │ a bx by │\n", - "├────┼──────────────────────┤\n", - "│ a │ 0.01 0.000 0.000 │\n", - "│ bx │ 0.000 0.0245 0.000 │\n", - "│ by │ 0.000 0.000 0.0245 │\n", - "└────┴──────────────────────┘" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "c2 = cost.LeastSquares((x, y), Z, Zerr, model2)\n", "m2 = Minuit(c2, 0, 0, 0)\n", @@ -13737,7 +756,7 @@ }, { "cell_type": "markdown", - "id": "2f3f181e", + "id": "53", "metadata": {}, "source": [ "Multivariate fits are difficult to check by eye. Here we use color to indicate the function value.\n", @@ -13748,20 +767,9 @@ { "cell_type": "code", "execution_count": null, - "id": "bdf44a64", + "id": "54", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "xm = np.linspace(-1, 1, 100)\n", "ym = np.linspace(-1, 1, 100)\n", @@ -13776,7 +784,7 @@ }, { "cell_type": "markdown", - "id": "complete-howard", + "id": "55", "metadata": {}, "source": [ "### Robust least-squares\n", @@ -13792,711 +800,9 @@ { "cell_type": "code", "execution_count": null, - "id": "seasonal-singles", + "id": "56", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 364.9 (χ²/ndof = 20.3) Nfcn = 29
EDM = 9.85e-22 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 a 1.23 0.04
1 b 1.45 0.15
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a b
a 0.00139 -0.0037 (-0.658)
b -0.0037 (-0.658) 0.0226
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(χ²/ndof = 20.3) │ Nfcn = 29 │\n", - "│ EDM = 9.85e-22 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ a │ 1.23 │ 0.04 │ │ │ │ │ │\n", - "│ 1 │ b │ 1.45 │ 0.15 │ │ │ │ │ │\n", - "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───┬─────────────────┐\n", - "│ │ a b │\n", - "├───┼─────────────────┤\n", - "│ a │ 0.00139 -0.0037 │\n", - "│ b │ -0.0037 0.0226 │\n", - "└───┴─────────────────┘" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "c.y[3] = 3 # generate an outlier\n", "\n", @@ -14507,20 +813,9 @@ { "cell_type": "code", "execution_count": null, - "id": "available-organic", + "id": "57", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "m3.visualize()\n", "plt.plot(c.x, model(c.x, 1, 2), ls=\"--\", label=\"truth\");" @@ -14528,7 +823,7 @@ }, { "cell_type": "markdown", - "id": "helpful-train", + "id": "58", "metadata": {}, "source": [ "The result is distorted by the outlier. Note that the error did not increase! The size of the error computed by Minuit does **not** include mismodelling.\n", @@ -14539,711 +834,9 @@ { "cell_type": "code", "execution_count": null, - "id": "cheap-truth", + "id": "59", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 54.09 (χ²/ndof = 3.0) Nfcn = 69
EDM = 4.31e-06 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 a 1.00 0.05
1 b 2.04 0.23
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a b
a 0.00285 -0.0086 (-0.707)
b -0.0086 (-0.707) 0.0524
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(χ²/ndof = 3.0) │ Nfcn = 69 │\n", - "│ EDM = 4.31e-06 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ a │ 1.00 │ 0.05 │ │ │ │ │ │\n", - "│ 1 │ b │ 2.04 │ 0.23 │ │ │ │ │ │\n", - "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───┬─────────────────┐\n", - "│ │ a b │\n", - "├───┼─────────────────┤\n", - "│ a │ 0.00285 -0.0086 │\n", - "│ b │ -0.0086 0.0524 │\n", - "└───┴─────────────────┘" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "c.loss = \"soft_l1\"\n", "m3.migrad()" @@ -15252,20 +845,9 @@ { "cell_type": "code", "execution_count": null, - "id": "regulated-default", + "id": "60", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "m3.visualize()\n", "plt.plot(c.x, model(c.x, *truth), ls=\"--\", label=\"truth\");" @@ -15273,7 +855,7 @@ }, { "cell_type": "markdown", - "id": "attractive-porcelain", + "id": "61", "metadata": {}, "source": [ "The result is almost identical as in the previous case without an outlier.\n", @@ -15286,20 +868,9 @@ { "cell_type": "code", "execution_count": null, - "id": "1e9732ca", + "id": "62", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from resample.bootstrap import variance as bvar\n", "\n", @@ -15313,7 +884,7 @@ "\n", "berr = bvar(fit, c.x, c.y, c.yerror, size=1000, random_state=1) ** 0.5\n", "\n", - "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", + "fig, ax = plt.subplots(1, 2, figsize=(9, 4))\n", "for i, axi in enumerate(ax):\n", " axi.errorbar(0, m1.values[i], m1.errors[i], fmt=\"o\")\n", " axi.errorbar(1, m3.values[i], m3.errors[i], fmt=\"o\")\n", @@ -15323,7 +894,7 @@ }, { "cell_type": "markdown", - "id": "e4dad3f9", + "id": "63", "metadata": {}, "source": [ "In this case, Minuit's estimate is similar to the bootstrap estimate, but that is not generally true when the \"soft_l1\" loss is used.\n", @@ -15334,707 +905,9 @@ { "cell_type": "code", "execution_count": null, - "id": "indoor-wallet", + "id": "64", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 24.67 (χ²/ndof = 1.5) Nfcn = 29
EDM = 1.37e-22 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 a 0.98 0.04
1 b 2.07 0.15
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a b
a 0.00158 -0.0041 (-0.678)
b -0.0041 (-0.678) 0.0238
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Nfcn = 29 │\n", - "│ EDM = 1.37e-22 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ a │ 0.98 │ 0.04 │ │ │ │ │ │\n", - "│ 1 │ b │ 2.07 │ 0.15 │ │ │ │ │ │\n", - "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───┬─────────────────┐\n", - "│ │ a b │\n", - "├───┼─────────────────┤\n", - "│ a │ 0.00158 -0.0041 │\n", - "│ b │ -0.0041 0.0238 │\n", - "└───┴─────────────────┘" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "c.mask = np.arange(len(c.x)) != 3\n", "c.loss = \"linear\"\n", @@ -16044,7 +917,7 @@ }, { "cell_type": "markdown", - "id": "varied-rhythm", + "id": "65", "metadata": {}, "source": [ "Now the uncertainties are essentially the same as before adding the outlier." @@ -16053,22 +926,11 @@ { "cell_type": "code", "execution_count": null, - "id": "abaee0b1", + "id": "66", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Migrad
FCN = 1 Nfcn = 36
EDM = 2.38e-18 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
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y 0 1 0
z 0 0 1
" - ], - "text/plain": [ - "┌─────────────────────────────────────────────────────────────────────────┐\n", - "│ Migrad │\n", - "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 1 │ Nfcn = 36 │\n", - "│ EDM = 2.38e-18 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ x │ 1 │ 1 │ │ │ │ │ │\n", - "│ 1 │ y │ 2 │ 1 │ │ │ │ │ │\n", - "│ 2 │ z │ 3 │ 1 │ │ │ │ │ │\n", - "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───┬───────┐\n", - "│ │ x y z │\n", - "├───┼───────┤\n", - "│ x │ 1 0 0 │\n", - "│ y │ 0 1 0 │\n", - "│ z │ 0 0 1 │\n", - "└───┴───────┘" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = Minuit(cython_func, 1, 1, 1)\n", - "m.migrad()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the past, Cython would not create a proper signature for the function by default, which iminuit uses need to get the parameter names. This was improved at some point.\n", - "\n", - "If you encouter a function without a signature, you can tell Minuit explicitly about the names of the parameters with the keyword `name`. This can also be used to override the automatic detection and use other names." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = 1 Nfcn = 36
EDM = 9.78e-20 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 a 1 1
1 b 2 1
2 c 3 1
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a b c
a 1 0 0
b 0 1 0
c 0 0 1
" - ], - "text/plain": [ - "┌─────────────────────────────────────────────────────────────────────────┐\n", - "│ Migrad │\n", - "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 1 │ Nfcn = 36 │\n", - "│ EDM = 9.78e-20 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ a │ 1 │ 1 │ │ │ │ │ │\n", - "│ 1 │ b │ 2 │ 1 │ │ │ │ │ │\n", - "│ 2 │ c │ 3 │ 1 │ │ │ │ │ │\n", - "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───┬───────┐\n", - "│ │ a b c │\n", - "├───┼───────┤\n", - "│ a │ 1 0 0 │\n", - "│ b │ 0 1 0 │\n", - "│ c │ 0 0 1 │\n", - "└───┴───────┘" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = Minuit(cython_func, 1, 1, 1, name=(\"a\", \"b\", \"c\"))\n", - "m.migrad()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.13 ('venv': venv)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - }, - "vscode": { - "interpreter": { - "hash": "bdbf20ff2e92a3ae3002db8b02bd1dd1b287e934c884beb29a73dced9dbd0fa3" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/doc/notebooks/error_bands.ipynb b/doc/notebooks/error_bands.ipynb index 26c0c141..1659ccbd 100644 --- a/doc/notebooks/error_bands.ipynb +++ b/doc/notebooks/error_bands.ipynb @@ -1,8 +1,8 @@ { - "cells": [ + "cells": [ { "cell_type": "markdown", - "id": "frozen-raising", + "id": "0", "metadata": {}, "source": [ "# How to draw error bands\n", @@ -14,22 +14,12 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "white-dress", + "execution_count": null, + "id": "1", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", "import numpy as np\n", "from numba_stats import norm\n", "from iminuit import Minuit\n", @@ -70,14 +60,14 @@ "plt.legend(\n", " frameon=False,\n", " title=f\"$n = {m.values[0]:.2f} +/- {m.errors[0]:.2f}$\\n\"\n", - " f\"$\\mu = {m.values[1]:.2f} +/- {m.errors[1]:.2f}$\\n\"\n", - " f\"$\\sigma = {m.values[2]:.2f} +/- {m.errors[2]:.2f}$\",\n", + " f\"$\\\\mu = {m.values[1]:.2f} +/- {m.errors[1]:.2f}$\\n\"\n", + " f\"$\\\\sigma = {m.values[2]:.2f} +/- {m.errors[2]:.2f}$\",\n", ");" ] }, { "cell_type": "markdown", - "id": "realistic-trail", + "id": "2", "metadata": {}, "source": [ "We want to understand how uncertain the Gaussian curve is. Thus we want to draw a 1-sigma error band around the curve, which approximates the 68 % confidence interval." @@ -85,7 +75,7 @@ }, { "cell_type": "markdown", - "id": "polyphonic-patient", + "id": "3", "metadata": {}, "source": [ "## With error propagation\n", @@ -107,21 +97,10 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "technological-justice", + "execution_count": null, + "id": "4", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from jacobi import propagate\n", "\n", @@ -140,14 +119,14 @@ "plt.legend(\n", " frameon=False,\n", " title=f\"$n = {m.values[0]:.2f} +/- {m.errors[0]:.2f}$\\n\"\n", - " f\"$\\mu = {m.values[1]:.2f} +/- {m.errors[1]:.2f}$\\n\"\n", - " f\"$\\sigma = {m.values[2]:.2f} +/- {m.errors[2]:.2f}$\",\n", + " f\"$\\\\mu = {m.values[1]:.2f} +/- {m.errors[1]:.2f}$\\n\"\n", + " f\"$\\\\sigma = {m.values[2]:.2f} +/- {m.errors[2]:.2f}$\",\n", ");" ] }, { "cell_type": "markdown", - "id": "final-sensitivity", + "id": "5", "metadata": {}, "source": [ "Error propagation is relatively fast." @@ -155,18 +134,10 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "continent-astrology", + "execution_count": null, + "id": "6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.96 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1,000 loops each)\n" - ] - } - ], + "outputs": [], "source": [ "%%timeit -r 1 -n 1000\n", "propagate(lambda p: model(cx, p)[1], m.values, m.covariance)" @@ -174,7 +145,7 @@ }, { "cell_type": "markdown", - "id": "advance-flight", + "id": "7", "metadata": {}, "source": [ "## With the bootstrap\n", @@ -184,21 +155,10 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "duplicate-community", + "execution_count": null, + "id": "8", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "rng = np.random.default_rng(1)\n", "\n", @@ -219,14 +179,14 @@ "plt.legend(\n", " frameon=False,\n", " title=f\"$n = {m.values[0]:.2f} +/- {m.errors[0]:.2f}$\\n\"\n", - " f\"$\\mu = {m.values[1]:.2f} +/- {m.errors[1]:.2f}$\\n\"\n", - " f\"$\\sigma = {m.values[2]:.2f} +/- {m.errors[2]:.2f}$\",\n", + " f\"$\\\\mu = {m.values[1]:.2f} +/- {m.errors[1]:.2f}$\\n\"\n", + " f\"$\\\\sigma = {m.values[2]:.2f} +/- {m.errors[2]:.2f}$\",\n", ");" ] }, { "cell_type": "markdown", - "id": "greater-surge", + "id": "9", "metadata": {}, "source": [ "The result is visually indistinguishable from before, as it should be. If you worry about deviations between the two methods, read on." @@ -234,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "excess-parking", + "id": "10", "metadata": {}, "source": [ "In this example, computing the band from 1000 samples is slower than error propagation." @@ -242,18 +202,10 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "grave-month", + "execution_count": null, + "id": "11", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.72 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)\n" - ] - } - ], + "outputs": [], "source": [ "%%timeit -r 1 -n 100\n", "par_b = rng.multivariate_normal(m.values, m.covariance, size=1000)\n", @@ -263,7 +215,7 @@ }, { "cell_type": "markdown", - "id": "cordless-elder", + "id": "12", "metadata": {}, "source": [ "However, the calculation time scales linearly with the number of samples. One can simply draw fewer samples if the additional uncertainty is acceptable. If we draw only 50 samples, bootstrapping wins over numerical error propagation." @@ -271,18 +223,10 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "lucky-happening", + "execution_count": null, + "id": "13", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "258 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 1,000 loops each)\n" - ] - } - ], + "outputs": [], "source": [ "%%timeit -r 1 -n 1000\n", "rng = np.random.default_rng(1)\n", @@ -293,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "moral-keeping", + "id": "14", "metadata": {}, "source": [ "Let's see how the result looks, whether it deviates noticably." @@ -301,21 +245,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "brilliant-football", + "execution_count": null, + "id": "15", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# compute bootstrapped curves with 50 samples\n", "par_b = rng.multivariate_normal(m.values, m.covariance, size=50)\n", @@ -333,14 +266,14 @@ "plt.legend(\n", " frameon=False,\n", " title=f\"$n = {m.values[0]:.2f} +/- {m.errors[0]:.2f}$\\n\"\n", - " f\"$\\mu = {m.values[1]:.2f} +/- {m.errors[1]:.2f}$\\n\"\n", - " f\"$\\sigma = {m.values[2]:.2f} +/- {m.errors[2]:.2f}$\",\n", + " f\"$\\\\mu = {m.values[1]:.2f} +/- {m.errors[1]:.2f}$\\n\"\n", + " f\"$\\\\sigma = {m.values[2]:.2f} +/- {m.errors[2]:.2f}$\",\n", ");" ] }, { "cell_type": "markdown", - "id": "adequate-automation", + "id": "16", "metadata": {}, "source": [ "No, the result is still visually indistinguishable. This suggests that 50 samples can be enough for plotting.\n", @@ -350,21 +283,10 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "hindu-moderator", + "execution_count": null, + "id": "17", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n", "\n", @@ -390,7 +312,7 @@ }, { "cell_type": "markdown", - "id": "quiet-watch", + "id": "18", "metadata": {}, "source": [ "We see that the bootstrapped bands are a bit wider in the tails. This is caused by non-linearities that are neglected in error propagation." @@ -398,7 +320,7 @@ }, { "cell_type": "markdown", - "id": "following-campaign", + "id": "19", "metadata": {}, "source": [ "## Which is better? Error propagation or bootstrap?\n", @@ -426,7 +348,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.12.4" }, "vscode": { "interpreter": { diff --git a/doc/notebooks/external_minimizer.ipynb b/doc/notebooks/external_minimizer.ipynb index a712afc7..0a3d1552 100644 --- a/doc/notebooks/external_minimizer.ipynb +++ b/doc/notebooks/external_minimizer.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "cell_type": "markdown", "metadata": {}, @@ -119,7 +119,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.12.4" }, "vscode": { "interpreter": { diff --git a/doc/notebooks/generic_least_squares.ipynb b/doc/notebooks/generic_least_squares.ipynb index ed030fb6..4471c87f 100644 --- a/doc/notebooks/generic_least_squares.ipynb +++ b/doc/notebooks/generic_least_squares.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "cell_type": "markdown", "metadata": {}, @@ -87,7 +87,7 @@ "try:\n", " m = Minuit(lsq, a=0, b=0)\n", " m.errordef = Minuit.LEAST_SQUARES\n", - "except:\n", + "except RuntimeError:\n", " import traceback\n", "\n", " traceback.print_exc()" @@ -217,7 +217,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.12.4" }, "vscode": { "interpreter": { diff --git a/doc/notebooks/gof.ipynb b/doc/notebooks/gof.ipynb index 2e2553aa..026a322f 100644 --- a/doc/notebooks/gof.ipynb +++ b/doc/notebooks/gof.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "cell_type": "markdown", "metadata": {}, @@ -11,10 +11,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", "from iminuit import Minuit\n", "from iminuit.cost import BinnedNLL, ExtendedBinnedNLL\n", "import numpy as np\n", @@ -26,20 +27,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def generate(n, seed):\n", " rng = np.random.default_rng(seed)\n", @@ -55,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -86,7 +76,7 @@ " for mi in m:\n", " mi.migrad()\n", " if mi.valid:\n", - " pvalue = 1 - chi2(mi.fcn._fcn.ndata - mi.nfit).cdf(mi.fval)\n", + " pvalue = chi2(mi.fcn._fcn.ndata - mi.nfit).sf(mi.fval)\n", " r.append(pvalue)\n", " else:\n", " r.append(np.nan)\n", @@ -100,29 +90,29 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "fig, ax = plt.subplots(2, 4, figsize=(15, 6), constrained_layout=True)\n", + "fig, ax = plt.subplots(2, 4, figsize=(8, 4), constrained_layout=True)\n", "for i, (ni, vi) in enumerate(pvalues.items()):\n", " ax[0, i].hist(vi[:, 0])\n", " ax[1, i].hist(vi[:, 1])\n", - " ax[0, i].set_title(f\"n = {ni}, failed fits = {np.sum(np.isnan(vi[:, 0]))}\")\n", - " ax[1, i].set_title(f\"n = {ni}, failed fits = {np.sum(np.isnan(vi[:, 1]))}\")\n", + " ax[0, i].set_title(\n", + " f\"n = {ni}, failed fits = {np.sum(np.isnan(vi[:, 0]))}\", fontsize=\"small\"\n", + " )\n", + " ax[1, i].set_title(\n", + " f\"n = {ni}, failed fits = {np.sum(np.isnan(vi[:, 1]))}\", fontsize=\"small\"\n", + " )\n", "fig.supxlabel(\"pvalue\");" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The top row shows the p-values of the test statistic for `BinnedNLL` and the bottom row for `ExtendedBinnedNLL`. If the test statistic was perfectly chi-square-distributed, the p-value distribution should be uniform." + ] } ], "metadata": { @@ -144,9 +134,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8 (main, Oct 13 2022, 09:48:40) [Clang 14.0.0 (clang-1400.0.29.102)]" - }, - "orig_nbformat": 4 + "version": "3.12.4" + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/doc/notebooks/hesse_and_minos.ipynb b/doc/notebooks/hesse_and_minos.ipynb index dab6e147..5e875b5b 100644 --- a/doc/notebooks/hesse_and_minos.ipynb +++ b/doc/notebooks/hesse_and_minos.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "attachments": {}, "cell_type": "markdown", @@ -151,10 +151,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from scipy.stats import multivariate_normal, poisson\n", @@ -166,14 +167,2238 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] }, "metadata": {}, @@ -224,7 +2449,7 @@ " pcov[\"HESSE\"][i] = nh / nmc\n", " pcov[\"MINOS\"][i] = nm / nmc\n", "\n", - "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", + "fig, ax = plt.subplots(1, 2, figsize=(9, 4))\n", "\n", "plt.sca(ax[0])\n", "n = np.arange(101)\n", @@ -288,7 +2513,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -296,30 +2521,27 @@ "text/html": [ "\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", "
Migrad Migrad
FCN = 3.122 Nfcn = 168 FCN = 3.122 Nfcn = 168
EDM = 2.02e-05 (Goal: 0.0001) EDM = 2.02e-05 (Goal: 0.0001)
Valid Minimum No Parameters at limit Valid Minimum Below EDM threshold (goal x 10)
Below EDM threshold (goal x 10) Below call limit No parameters at limit Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced Covariance accurate
" ], @@ -330,12 +2552,12 @@ "│ FCN = 3.122 │ Nfcn = 168 │\n", "│ EDM = 2.02e-05 (Goal: 0.0001) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ No Parameters at limit │\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ No parameters at limit │ Below call limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Below EDM threshold (goal x 10) │ Below call limit │\n", - "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", - "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", - "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘" + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘" ] }, "metadata": {}, @@ -455,19 +2677,1218 @@ }, { "data": { + 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", - "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -505,7 +3926,7 @@ "display(m.fmin, m.params)\n", "display(m.merrors)\n", "\n", - "plt.figure(figsize=(14, 4))\n", + "plt.figure(figsize=(9, 4))\n", "\n", "plt.subplot(121, polar=True)\n", "plt.plot(d_phi, d_r, \".\")\n", @@ -514,12 +3935,12 @@ "m.draw_mncontour(\"cx\", \"cy\", size=100)\n", "plt.plot(m.values[\"cx\"], m.values[\"cy\"], \"+k\")\n", "plt.xlim(-0.1, 0.2)\n", - "plt.ylim(-0.1, 0.2)" + "plt.ylim(-0.1, 0.2);" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -604,14 +4025,1500 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "
" ] }, "metadata": {}, @@ -619,7 +5526,7 @@ } ], "source": [ - "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", + "fig, ax = plt.subplots(1, 2, figsize=(9, 4))\n", "plt.sca(ax[0])\n", "plt.fill_between(n_data, hl, hu, alpha=0.5, label=\"HESSE\")\n", "plt.fill_between(n_data, ml, mu, alpha=0.5, label=\"MINOS\")\n", @@ -641,6 +5548,7 @@ } ], "metadata": { + "keep_output": true, "kernelspec": { "display_name": "Python 3.8.14 ('venv': venv)", "language": "python", @@ -656,7 +5564,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8 (main, Oct 13 2022, 09:48:40) [Clang 14.0.0 (clang-1400.0.29.102)]" + "version": "3.12.4" }, "vscode": { "interpreter": { diff --git a/doc/notebooks/interactive.ipynb b/doc/notebooks/interactive.ipynb index e489227d..1e806405 100644 --- a/doc/notebooks/interactive.ipynb +++ b/doc/notebooks/interactive.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "attachments": {}, "cell_type": "markdown", @@ -18,6 +18,7 @@ "metadata": {}, "outputs": [], "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", "from iminuit import cost\n", "from iminuit import Minuit\n", "from numba_stats import norm, t, bernstein, truncexpon\n", @@ -392,7 +393,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.4" }, "vscode": { "interpreter": { diff --git a/doc/notebooks/memory_layout.ipynb b/doc/notebooks/memory_layout.ipynb index 9176b349..83c2113a 100644 --- a/doc/notebooks/memory_layout.ipynb +++ b/doc/notebooks/memory_layout.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "cell_type": "markdown", "metadata": {}, @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -54,17 +54,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.68 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" - ] - } - ], + "outputs": [], "source": [ "%%timeit -n 1 -r 1\n", "m = Minuit(cost1, x=0, y=0)\n", @@ -73,17 +65,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "470 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" - ] - } - ], + "outputs": [], "source": [ "%%timeit -n 1 -r 1\n", "m = Minuit(cost2, x=0, y=0)\n", @@ -92,17 +76,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "528 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" - ] - } - ], + "outputs": [], "source": [ "%%timeit -n 1 -r 1\n", "m = Minuit(cost3, x=0, y=0)\n", @@ -137,9 +113,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" - }, - "orig_nbformat": 4 + "version": "3.12.4" + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/doc/notebooks/numba.ipynb b/doc/notebooks/numba.ipynb index f8bf571b..f1ae721c 100644 --- a/doc/notebooks/numba.ipynb +++ b/doc/notebooks/numba.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "cell_type": "markdown", "metadata": {}, @@ -15,11 +15,12 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# !pip install matplotlib numpy numba scipy iminuit\n", + "%config InlineBackend.figure_formats = ['svg']\n", "from iminuit import Minuit\n", "import numpy as np\n", "import numba as nb\n", @@ -38,12 +39,1746 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -83,7 +1818,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -101,16 +1836,16 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "-103168.78482586428" + "np.float64(-103168.78482586428)" ] }, - "execution_count": 54, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -137,14 +1872,14 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "132 ms ± 2.15 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" + "327 ms ± 66.2 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" ] } ], @@ -165,16 +1900,23 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + }, { "data": { "text/plain": [ "-103168.78482586429" ] }, - "execution_count": 56, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -201,14 +1943,14 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "157 ms ± 3.75 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" + "432 ms ± 31.5 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" ] } ], @@ -226,16 +1968,16 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "475 µs ± 3.24 µs per loop (mean ± std. dev. of 3 runs, 100 loops each)\n", - "451 µs ± 5.18 µs per loop (mean ± std. dev. of 3 runs, 500 loops each)\n", - "66.3 µs ± 219 ns per loop (mean ± std. dev. of 3 runs, 1,000 loops each)\n" + "1.75 ms ± 24 μs per loop (mean ± std. dev. of 3 runs, 100 loops each)\n", + "1.32 ms ± 92.1 μs per loop (mean ± std. dev. of 3 runs, 500 loops each)\n", + "154 μs ± 9.82 μs per loop (mean ± std. dev. of 3 runs, 1,000 loops each)\n" ] } ], @@ -266,16 +2008,16 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "-103168.78482586429" + "np.float64(-103168.78482586428)" ] }, - "execution_count": 59, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -350,16 +2092,17 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "111 µs ± 18.5 µs per loop (mean ± std. dev. of 5 runs, 100 loops each)\n", - "56.9 µs ± 1.02 µs per loop (mean ± std. dev. of 5 runs, 500 loops each)\n", - "59.1 µs ± 929 ns per loop (mean ± std. dev. of 5 runs, 1,000 loops each)\n" + "The slowest run took 10.86 times longer than the fastest. This could mean that an intermediate result is being cached.\n", + "191 μs ± 152 μs per loop (mean ± std. dev. of 5 runs, 100 loops each)\n", + "45.6 μs ± 11.4 μs per loop (mean ± std. dev. of 5 runs, 500 loops each)\n", + "64.2 μs ± 23.9 μs per loop (mean ± std. dev. of 5 runs, 1,000 loops each)\n" ] } ], @@ -378,14 +2121,14 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "42.7 ms ± 1.59 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" + "55.5 ms ± 24.6 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" ] } ], @@ -407,15 +2150,15 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "63.1 µs ± 4.25 µs per loop (mean ± std. dev. of 5 runs, 100 loops each)\n", - "63.9 µs ± 1.08 µs per loop (mean ± std. dev. of 5 runs, 500 loops each)\n" + "151 μs ± 19.2 μs per loop (mean ± std. dev. of 5 runs, 100 loops each)\n", + "185 μs ± 31.9 μs per loop (mean ± std. dev. of 5 runs, 500 loops each)\n" ] } ], @@ -442,42 +2185,46 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "signature: (array(float64, 1d, C), float64, float64)\n", - "----------------------------------------------------\n", + "signature: (Array(float64, 1, 'C', False, aligned=True), float64, float64)\n", + "--------------------------------------------------------------------------\n", + "\t.section\t__TEXT,__text,regular,pure_instructions\n", + "\t.build_version macos, 14, 0\n", + "\t.section\t__TEXT,__literal8,8byte_literals\n", + "\t.p2align\t3\n", + "LCPI0_0:\n", + "\t.quad\t0x3ff0000000000000\n", + "LCPI0_1:\n", + "\t.quad\t0x3fd9884533d43651\n", + "\t.section\t__TEXT,__const\n", + "\t.p2align\t5\n", + "LCPI0_2:\n", + "\t.quad\t0\n", + "\t.quad\t8\n", + "\t.quad\t8\n", + "\t.quad\t8\n", "\t.section\t__TEXT,__text,regular,pure_instructions\n", - "\t.build_version macos, 12, 0\n", - "\t.globl\t__ZN8__main__8norm_pdfB3v76B146c8tJTC_2fWQAlbW1yBC0oR6GELEUMELYSPGrIQMVjAQniQcIXKQIMVwoOGKoQDDVQQR1NHAS2FQ9XgSs8w86AhbIsexNXqiUXJBeo6CupFwBRdnJ8MYibn55UUJSaXqNcC7QPGIsRqAA_3d_3dE5ArrayIdLi1E1C7mutable7alignedEdd\n", - "\t.p2align\t2\n", - "__ZN8__main__8norm_pdfB3v76B146c8tJTC_2fWQAlbW1yBC0oR6GELEUMELYSPGrIQMVjAQniQcIXKQIMVwoOGKoQDDVQQR1NHAS2FQ9XgSs8w86AhbIsexNXqiUXJBeo6CupFwBRdnJ8MYibn55UUJSaXqNcC7QPGIsRqAA_3d_3dE5ArrayIdLi1E1C7mutable7alignedEdd:\n", + "\t.globl\t__ZN8__main__8norm_pdfB3v22B150c8tJTC_2fWQAliW1xhDEoY6EEMEUOEMISPGsAQMVj4QniQ4IXKQEMXwoMGLoQDDVsQR1NHAS2hQ9XgStYw86ABbYse0tXqiUXJBeo6CurJ_2bXklRYnJJSB2ETCRF_2bcnq9cC7QNGJsRqAA_3d_3dE5ArrayIdLi1E1C7mutable7alignedEdd\n", + "\t.p2align\t4, 0x90\n", + "__ZN8__main__8norm_pdfB3v22B150c8tJTC_2fWQAliW1xhDEoY6EEMEUOEMISPGsAQMVj4QniQ4IXKQEMXwoMGLoQDDVsQR1NHAS2hQ9XgStYw86ABbYse0tXqiUXJBeo6CurJ_2bXklRYnJJSB2ETCRF_2bcnq9cC7QNGJsRqAA_3d_3dE5ArrayIdLi1E1C7mutable7alignedEdd:\n", "\t.cfi_startproc\n", - "\tstp\td9, d8, [sp, #-112]!\n", - "\tstp\tx28, x27, [sp, #16]\n", - "\tstp\tx26, x25, [sp, #32]\n", - "\tstp\tx24, x23, [sp, #48]\n", - "\tstp\tx22, x21, [sp, #64]\n", - "\tstp\tx20, x19, [sp, #80]\n", - "\tstp\tx29, x30, [sp, #96]\n", - "\tadd\tx29, sp, #96\n", - "\tsub\tsp, sp, #368\n", - "\tmov\tx19, sp\n", - "\t.cfi_def_cfa w29, 16\n", - "\t.cfi_offset w30, -8\n", - "\t.cfi_offset w29, -16\n", - "\t.cfi_offset w19, -24\n", - "\t.cfi_offset w20, -32\n", - "\t.cfi_offset w21, -40\n", - "\t.cfi_offset w22, -48\n", - "\t.cfi_offset w23, -56\n", - "\t.cfi_offset w24, -64\n", - "\t.cfi_offset w25, -72\n", - "\t.cfi_offset w26\n", + "\tpushq\t%rbp\n", + "\t.cfi_def_cfa_offset 16\n", + "\tpushq\t%r15\n", + "\t.cfi_def_cfa_offset 24\n", + "\tpushq\t%r14\n", + "\t.cfi_def_cfa_offset 32\n", + "\tpushq\t%r13\n", + "\t.cfi_def_cfa_offset 40\n", + "\tpushq\t%r12\n", + "\t.cfi_def_cfa_offset 48\n", + "\tpushq\t%r\n", "[...]\n" ] } @@ -503,34 +2250,32 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "signature: (array(float64, 1d, C), float64, float64)\n", - "----------------------------------------------------\n", + "signature: (Array(float64, 1, 'C', False, aligned=True), float64, float64)\n", + "--------------------------------------------------------------------------\n", "Instructions\n", - "add : 44\n", - "adds : 2\n", - "fmov : 3\n", - "fmov.2d : 1\n", - "fmul : 5\n", - "fmul.2d : 5\n", - "fsub : 1\n", - "fsub.2d : 2\n", - "madd : 6\n", - "mov : 108\n", - "mov.16b : 6\n", - "mov.d : 1\n", - "movi.16b : 5\n", - "movk : 3\n", - "mul : 3\n", - "smulh : 1\n", - "sub : 23\n", - "subs : 1\n" + "addq : 26\n", + "imulq : 10\n", + "movabsq : 105\n", + "movl : 23\n", + "movq : 305\n", + "movslq : 2\n", + "subq : 8\n", + "vmovapd : 17\n", + "vmovaps : 7\n", + "vmovsd : 28\n", + "vmovupd : 34\n", + "vmovups : 8\n", + "vmulpd : 19\n", + "vmulsd : 5\n", + "vsubpd : 10\n", + "vsubsd : 1\n" ] } ], @@ -572,6 +2317,7 @@ } ], "metadata": { + "keep_output": true, "kernelspec": { "display_name": "Python 3.8.13 ('venv': venv)", "language": "python", @@ -587,7 +2333,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.12.4" }, "vscode": { "interpreter": { diff --git a/doc/notebooks/roofit.ipynb b/doc/notebooks/roofit.ipynb index 7922fadc..e7dd8b93 100644 --- a/doc/notebooks/roofit.ipynb +++ b/doc/notebooks/roofit.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "attachments": {}, "cell_type": "markdown", @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -193,7 +193,7 @@ "└────┴───────────────────┘" ] }, - "execution_count": 4, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -230,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -337,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -449,7 +449,7 @@ "└───────┴───────────────────┘" ] }, - "execution_count": 6, + "execution_count": null, "metadata": {}, "output_type": "execute_result" }, @@ -505,7 +505,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -617,7 +617,7 @@ "└───────┴───────────────────┘" ] }, - "execution_count": 7, + "execution_count": null, "metadata": {}, "output_type": "execute_result" }, @@ -642,6 +642,7 @@ } ], "metadata": { + "keep_output": true, "kernelspec": { "display_name": "root", "language": "python", @@ -658,8 +659,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.0" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/doc/notebooks/roofit/rf101_basics.ipynb b/doc/notebooks/roofit/rf101_basics.ipynb index e17fc7f0..ba06b239 100644 --- a/doc/notebooks/roofit/rf101_basics.ipynb +++ b/doc/notebooks/roofit/rf101_basics.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "attachments": {}, "cell_type": "markdown", @@ -73,8 +73,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.9" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/doc/notebooks/roofit/rf109_chi2residpull.ipynb b/doc/notebooks/roofit/rf109_chi2residpull.ipynb index c1ffefe2..4a496711 100644 --- a/doc/notebooks/roofit/rf109_chi2residpull.ipynb +++ b/doc/notebooks/roofit/rf109_chi2residpull.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "attachments": {}, "cell_type": "markdown", @@ -94,8 +94,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.9" - }, - "orig_nbformat": 4 + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/doc/notebooks/scipy_and_constraints.ipynb b/doc/notebooks/scipy_and_constraints.ipynb index 7dd7918d..b3ce1c4c 100644 --- a/doc/notebooks/scipy_and_constraints.ipynb +++ b/doc/notebooks/scipy_and_constraints.ipynb @@ -1,8 +1,8 @@ { - "cells": [ + "cells": [ { "cell_type": "markdown", - "id": "ffdfe095", + "id": "0", "metadata": {}, "source": [ "# SciPy minimizers and constraints\n", @@ -21,11 +21,12 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "unnecessary-vermont", + "execution_count": null, + "id": "1", "metadata": {}, "outputs": [], "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", "from iminuit import Minuit\n", "from iminuit.cost import ExtendedUnbinnedNLL\n", "import numpy as np\n", @@ -36,7 +37,7 @@ }, { "cell_type": "markdown", - "id": "2bac1095", + "id": "2", "metadata": {}, "source": [ "The signal pdf is a Gaussian, the background is modelled with second degree Bernstein polynomials. We perform an extended maximum likelihood fit, where the full density model is given by the sum of the signal and background component." @@ -44,8 +45,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "equivalent-minnesota", + "execution_count": null, + "id": "3", "metadata": {}, "outputs": [], "source": [ @@ -63,7 +64,7 @@ }, { "cell_type": "markdown", - "id": "58a91a58", + "id": "4", "metadata": {}, "source": [ "In searches for rare decays, it is common to fit models like this to small simulated samples that contain only background, to calculate the distribution of some test statistic (usually the likelihood ratio of S+B and B-only hypotheses). Here, for simplicity, we use the signal amplitude itself as the test statistic.\n", @@ -73,1030 +74,10 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "abroad-census", + "execution_count": null, + "id": "5", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Migrad
FCN = -181.3 Nfcn = 96
EDM = 1.83e-06 (Goal: 0.0002)
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
\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", - "
Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 b0 16 17 0
1 b1 85 35 0
2 b2 16 16 0
3 sig -4.0 2.7
4 mu 0.500 0.005 yes
5 sigma 50.0e-3 0.5e-3 yes
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b0 b1 b2 sig mu sigma
b0 306 -0.41e3 (-0.676) 0.11e3 (0.387) 15 (0.319) 0 0
b1 -0.41e3 (-0.676) 1.21e+03 -0.37e3 (-0.645) -62 (-0.659) 0 0
b2 0.11e3 (0.387) -0.37e3 (-0.645) 266 13 (0.305) 0 0
sig 15 (0.319) -62 (-0.659) 13 (0.305) 7.28 0 0
mu 0 0 0 0 0 0
sigma 0 0 0 0 0 0
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" \n", - " \n", - "\n" - ], - "text/plain": [ - "┌─────────────────────────────────────────────────────────────────────────┐\n", - "│ Migrad │\n", - "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = -181.3 │ Nfcn = 96 │\n", - "│ EDM = 1.83e-06 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ No parameters at limit │ Below call limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Hesse ok │ Covariance accurate │\n", - "└──────────────────────────────────┴──────────────────────────────────────┘\n", - "┌───┬───────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼───────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ b0 │ 16 │ 17 │ │ │ 0 │ │ │\n", - "│ 1 │ b1 │ 85 │ 35 │ │ │ 0 │ │ │\n", - "│ 2 │ b2 │ 16 │ 16 │ │ │ 0 │ │ │\n", - "│ 3 │ sig │ -4.0 │ 2.7 │ │ │ │ │ │\n", - "│ 4 │ mu │ 0.500 │ 0.005 │ │ │ │ │ yes │\n", - "│ 5 │ sigma │ 50.0e-3 │ 0.5e-3 │ │ │ │ │ yes │\n", - "└───┴───────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌───────┬───────────────────────────────────────────────────────┐\n", - "│ │ b0 b1 b2 sig mu sigma │\n", - "├───────┼───────────────────────────────────────────────────────┤\n", - "│ b0 │ 306 -0.41e3 0.11e3 15 0 0 │\n", - "│ b1 │ -0.41e3 1.21e+03 -0.37e3 -62 0 0 │\n", - "│ b2 │ 0.11e3 -0.37e3 266 13 0 0 │\n", - "│ sig │ 15 -62 13 7.28 0 0 │\n", - "│ mu │ 0 0 0 0 0 0 │\n", - "│ sigma │ 0 0 0 0 0 0 │\n", - "└───────┴───────────────────────────────────────────────────────┘" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "rng = np.random.default_rng(2)\n", "x = rng.uniform(0, 1, size=35)\n", @@ -1112,7 +93,7 @@ }, { "cell_type": "markdown", - "id": "c1fac22f", + "id": "6", "metadata": {}, "source": [ "In this example, the signal amplitude came out negative. This happens if the background has an underfluctuation where the signal is expected. This is not an issue if the sum of signal and background density is still positive everywhere where we evaluate it. As long as the total density is positive, individual components are allowed to be negative.\n", @@ -1122,7 +103,7 @@ }, { "cell_type": "markdown", - "id": "4ecf9073", + "id": "7", "metadata": {}, "source": [ "## Migrad fit on toys\n", @@ -1132,8 +113,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "id": "following-bruce", + "execution_count": null, + "id": "8", "metadata": {}, "outputs": [], "source": [ @@ -1154,21 +135,10 @@ }, { "cell_type": "code", - "execution_count": 27, - "id": "bd848895", + "execution_count": null, + "id": "9", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "nfailed = sum(not m.valid for m in fits)\n", "plt.title(f\"{nfailed} fits failed ({nfailed / len(fits) * 100:.0f} %)\")\n", @@ -1178,7 +148,7 @@ }, { "cell_type": "markdown", - "id": "432b611e", + "id": "10", "metadata": {}, "source": [ "The distribution of the signal amplitude looks fairly gaussian which is good, but the fit failed to converge in a few cases due to the problem just described. Simply discarding these cases is not acceptable, it would distort conclusions drawn from the distribution of the test statistic, which is commonly needed to set limits or to compute the p-value for an observed amplitude.\n", @@ -1190,7 +160,7 @@ }, { "cell_type": "markdown", - "id": "d364cb83", + "id": "11", "metadata": {}, "source": [ "## SciPy constrained fit on toys \n", @@ -1200,8 +170,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "id": "young-ocean", + "execution_count": null, + "id": "12", "metadata": {}, "outputs": [], "source": [ @@ -1225,21 +195,10 @@ }, { "cell_type": "code", - "execution_count": 29, - "id": "0ce87a47", + "execution_count": null, + "id": "13", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "nfailed = sum(not m.valid for m in fits_constrained)\n", "plt.title(\n", @@ -1265,7 +224,7 @@ }, { "cell_type": "markdown", - "id": "881008cc", + "id": "14", "metadata": {}, "source": [ "There are no failures this time. \n", @@ -1279,7 +238,7 @@ }, { "cell_type": "markdown", - "id": "f04e7c57", + "id": "15", "metadata": {}, "source": [ "## Bonus: unconstrained SciPy fit\n", @@ -1289,18 +248,10 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "a60a75f7", + "execution_count": null, + "id": "16", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "18 failed\n" - ] - } - ], + "outputs": [], "source": [ "@joblib.delayed\n", "def compute(itry):\n", @@ -1311,43 +262,36 @@ " m.limits[\"b0\", \"b1\", \"b2\"] = (0, None)\n", " m.fixed[\"mu\", \"sigma\"] = True\n", " m.scipy()\n", - " return m.values[\"sig\"] if m.valid else np.nan\n", - "\n", + " return m\n", "\n", - "sigs_bfgs = joblib.Parallel(-1)(compute(i) for i in range(200))\n", "\n", - "print(np.sum(np.isnan(sigs_bfgs)), \"failed\")" + "fits_bfgs = joblib.Parallel(-1)(compute(i) for i in range(200))" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "589bf6b1", + "execution_count": null, + "id": "17", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "plt.title(f\"{np.sum(np.isnan(sigs_bfgs))} BFGS fits failed\")\n", - "plt.hist(sigs_migrad, alpha=0.5, bins=10, range=(-10, 10), label=\"Migrad\")\n", - "plt.hist(sigs_constrained, alpha=0.5, bins=10, range=(-10, 10), label=\"SciPy[SLSQS]\")\n", - "plt.hist(sigs_bfgs, bins=10, range=(-10, 10), fill=False, label=\"SciPy[L-BFGS-B]\")\n", + "nfailed = sum(not m.valid for m in fits_bfgs)\n", + "plt.title(f\"{nfailed} BFGS fits failed ({nfailed / len(fits_bfgs) * 100:.0f} %)\")\n", + "for f in (fits, fits_constrained, fits_bfgs):\n", + " plt.hist(\n", + " [m.values[\"sig\"] for m in f],\n", + " alpha=0.5,\n", + " bins=10,\n", + " range=(-10, 10),\n", + " label=f[0].fmin.algorithm,\n", + " )\n", "plt.xlabel(\"sig\")\n", "plt.legend();" ] }, { "cell_type": "markdown", - "id": "933fcadf", + "id": "18", "metadata": {}, "source": [ "In this example, the BFGS method actually failed less than Migrad, but it still fails in some cases, while the constrained fit did not fail at all." @@ -1355,7 +299,7 @@ }, { "cell_type": "markdown", - "id": "0a2de7c9", + "id": "19", "metadata": {}, "source": [ "## Speed comparison\n", @@ -1367,8 +311,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "84dfd255", + "execution_count": null, + "id": "20", "metadata": {}, "outputs": [], "source": [ @@ -1377,66 +321,40 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "6e6c7b2b", + "execution_count": null, + "id": "21", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "8.09 ms ± 2.77 ms per loop (mean ± std. dev. of 7 runs, 3 loops each)\n" - ] - } - ], + "outputs": [], "source": [ "%timeit -n3 m.reset(); m.migrad()" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "bafa5a40", + "execution_count": null, + "id": "22", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20.7 ms ± 9.33 ms per loop (mean ± std. dev. of 7 runs, 3 loops each)\n" - ] - } - ], + "outputs": [], "source": [ "%timeit -n3 m.reset(); m.scipy()" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "05a2b917", + "execution_count": null, + "id": "23", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "38.8 ms ± 7.17 ms per loop (mean ± std. dev. of 7 runs, 3 loops each)\n" - ] - } - ], + "outputs": [], "source": [ "%timeit -n3 m.reset(); m.scipy(constraints=NonlinearConstraint(lambda *par: model(x, *par)[1], 0, np.inf))" ] }, { "cell_type": "markdown", - "id": "365a71bf", + "id": "24", "metadata": {}, "source": [ - "Migrad is the fastest, followed by the L-BFGS-B method. The constrained fit is much slower.\n", - "\n", - "The constrained fit is much slower, since it has to do more work.\n", + "Migrad is the fastest, followed by the L-BFGS-B method. The constrained fit is much slower, since it has to do more work.\n", "\n", "Migrad is quite fast because of its smart stopping criterion. Migrad stops the fit as soon as the improvement of the fitted parameters become small compared to their uncertainties. Migrad was explicitly designed for statistical fits, where the cost function is a log-likelihood or least-squares function. Since it assumes that, it can stops the fit as soon as the parameter improvements become negligible compared to the parameter uncertainty, which is given by the inverse of its internal approximation of the Hessian matrix.\n", "\n", @@ -1460,7 +378,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.4" }, "vscode": { "interpreter": { diff --git a/doc/notebooks/simultaneous_fits.ipynb b/doc/notebooks/simultaneous_fits.ipynb index 0b6622a7..dfb8d31a 100644 --- a/doc/notebooks/simultaneous_fits.ipynb +++ b/doc/notebooks/simultaneous_fits.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "cell_type": "markdown", "metadata": {}, @@ -15,6 +15,7 @@ "metadata": {}, "outputs": [], "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", "from iminuit import Minuit\n", "from iminuit.cost import UnbinnedNLL\n", "from iminuit.util import describe\n", @@ -76,7 +77,6 @@ "source": [ "def plot(cost, xe, minuit, ax, **style):\n", " signature = describe(cost)\n", - " data = cost.data\n", "\n", " values = minuit.values[signature]\n", " errors = minuit.errors[signature]\n", @@ -98,7 +98,7 @@ "source": [ "m = Minuit(lh, μ_1=1, μ_2=2, σ=1)\n", "\n", - "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n", + "fig, ax = plt.subplots(1, 2, figsize=(9, 4))\n", "\n", "hists = [np.histogram(lhi.data, bins=50) for lhi in lh]\n", "\n", @@ -140,7 +140,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.12.4" }, "vscode": { "interpreter": { diff --git a/doc/notebooks/template_fits.ipynb b/doc/notebooks/template_fits.ipynb index cdbd2009..3e617aac 100644 --- a/doc/notebooks/template_fits.ipynb +++ b/doc/notebooks/template_fits.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "attachments": {}, "cell_type": "markdown", @@ -16,10 +16,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", "from iminuit import Minuit\n", "from iminuit.cost import poisson_chi2, Template\n", "import numpy as np\n", @@ -37,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -52,7 +53,7 @@ "\n", "rng = np.random.default_rng(1)\n", "truth = 750, 250\n", - "xe, n, t = generate(rng, 100, truth, 15)" + "xe, n, t = generate(rng, 500, truth, 15)" ] }, { @@ -64,12 +65,780 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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Migrad Migrad
FCN = 4.338e+04 (chi2/ndof = 3336.6) Nfcn = 110 FCN = -991.9 (χ²/ndof = -76.3) Nfcn = 119
EDM = 3.77e-06 (Goal: 0.0002) EDM = 0.000125 (Goal: 0.0002)
Valid Minimum No Parameters at limit Valid Minimum Below EDM threshold (goal x 10)
Below EDM threshold (goal x 10) Below call limit No parameters at limit Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced Covariance accurate
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0 x0 761 30 782 31 0
1 x1 193 19 201 20 0
x0 933 -172 (-0.294) 985 -0.2e3 (-0.322)
x1 -172 (-0.294) 365 -0.2e3 (-0.322) 404
" ], @@ -189,30 +955,30 @@ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 4.338e+04 (chi2/ndof = 3336.6)│ Nfcn = 110 │\n", - "│ EDM = 3.77e-06 (Goal: 0.0002) │ │\n", + "│ FCN = -991.9 (χ²/ndof = -76.3) │ Nfcn = 119 │\n", + "│ EDM = 0.000125 (Goal: 0.0002) │ │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ No Parameters at limit │\n", + "│ No parameters at limit │ Below call limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Below EDM threshold (goal x 10) │ Below call limit │\n", - "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", - "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", - "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘\n", "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ x0 │ 761 │ 30 │ │ │ 0 │ │ │\n", - "│ 1 │ x1 │ 193 │ 19 │ │ │ 0 │ │ │\n", + "│ 0 │ x0 │ 782 │ 31 │ │ │ 0 │ │ │\n", + "│ 1 │ x1 │ 201 │ 20 │ │ │ 0 │ │ │\n", "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌────┬───────────┐\n", - "│ │ x0 x1 │\n", - "├────┼───────────┤\n", - "│ x0 │ 933 -172 │\n", - "│ x1 │ -172 365 │\n", - "└────┴───────────┘" + "┌────┬───────────────┐\n", + "│ │ x0 x1 │\n", + "├────┼───────────────┤\n", + "│ x0 │ 985 -0.2e3 │\n", + "│ x1 │ -0.2e3 404 │\n", + "└────┴───────────────┘" ] }, - "execution_count": 4, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -247,7 +1013,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -286,7 +1052,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -294,17 +1060,17 @@ "output_type": "stream", "text": [ "fit\n", - " b 761 +- 31\n", - " s 193 +- 19\n", - " correlation -0.29\n", + " b 782 +- 31\n", + " s 201 +- 20\n", + " correlation -0.32\n", "bootstrap\n", - " b 761 +- 36\n", - " s 193 +- 36\n", - " correlation -0.98\n", + " b 782 +- 18\n", + " s 201 +- 18\n", + " correlation -1.00\n", "fit+bootstrap\n", - " b 761 +- 47\n", - " s 193 +- 40\n", - " correlation -0.75\n" + " b 782 +- 36\n", + " s 201 +- 27\n", + " correlation -0.53\n" ] } ], @@ -342,7 +1108,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -350,30 +1116,27 @@ "text/html": [ "\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", "
Migrad Migrad
FCN = 18.27 (chi2/ndof = 1.4) Nfcn = 3464 FCN = -2138 (χ²/ndof = -164.5) Nfcn = 1859
EDM = 6.02e-05 (Goal: 0.0002) EDM = 1.87e-05 (Goal: 0.0002)
Valid Minimum SOME Parameters at limit Valid Minimum Below EDM threshold (goal x 10)
Below EDM threshold (goal x 10) Below call limit SOME parameters at limit Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced Covariance accurate
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" \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", - " \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", @@ -1427,96 +2190,64 @@ " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1529,196 +2260,228 @@ " \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", + " \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", - " \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", - " \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", @@ -1730,30 +2493,30 @@ " \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", @@ -1765,30 +2528,30 @@ " \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", @@ -1801,19 +2564,16 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1821,8 +2581,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1844,147 +2607,836 @@ " \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", - "
0 x0 800 50 780 40 0
1 x1 190 40 199 26 0
2 x2 9.0 1.4 47 5 0
3 x3 8.5 1.3 50 5 0
4 x4 9.0 1.4 48 5 0
5 x5 7.4 1.2 43 4 0
6 x6 8.4 1.3 43 4 0
7 x7 6.4 1.1 39 4 0
8 x8 9.2 1.7 42 5 0
9 x9 4.8 2.3 29 5 0
10 x10 7.0 1.5 33 5 0
11 x11 5.5 1.0 28 4 0
12 x12 5.1 0.9 24.6 3.3 0
13 x13 4.6 0.9 21.5 3.0 0
14 x14 4.7 0.9 22.7 3.1 0
15 x15 4.3 0.8 23.1 3.2 0
16 x16 3.2 0.7 21.9 3.1 0 20 x20 0.0 0.5 0.4 0
21 x21 0.0 0.4 1.0 0.9 0
22 x22 2.1 1.5 7.8 2.7 0
23 x23 19 4 109 10 0
24 x24 54 7 255 16 0
25 x25 23 4 111 10 0
26 x26 1.1 1.0 13 4 0 31 x31 0.0 0.5 0.4 0
x0 2.24e+03 -1.45e+03 (-0.756) -14.6 (-0.226) -13.8 (-0.222) -14.6 (-0.226) -12 (-0.214) -13.6 (-0.221) -5.25 (-0.100) 23.6 (0.289) 68.9 (0.644) 23.2 (0.320) -6.29 (-0.134) -8.3 (-0.191) -7.42 (-0.184) -7.59 (-0.185) -7.06 (-0.181) -5.12 (-0.161) 2.82e-08 1.26e+03 -0.5e3 (-0.516) -17 (-0.104) -18 (-0.106) -18 (-0.105) -16 (-0.100) -15 (-0.096) -10 (-0.065) 40 (0.213) 60 (0.331) 37 (0.221) -2 (-0.012) -9 (-0.078) -8 (-0.073) -8 (-0.075) -8 (-0.075) -8 (-0.074) -0 -0 -1.04e-06 -2.67e-06 -0 -5.71 (-0.081) -26.2 (-0.131) 29.8 (0.089) 5.32 (0.026) -3.16 (-0.062) -0 -0.5 (-0.013) -3 (-0.028) -0.03e3 (-0.070) 0.05e3 (0.085) -0.01e3 (-0.034) -6 (-0.050) -0 -0 -0 -0 -0 -2.52e-05
x1 -1.45e+03 (-0.756) 1.64e+03 14.6 (0.264) 13.8 (0.260) 14.6 (0.264) 12 (0.250) 13.6 (0.259) 5.25 (0.117) -23.6 (-0.338) -68.9 (-0.754) -23.2 (-0.375) 6.29 (0.157) 8.3 (0.223) 7.42 (0.215) 7.59 (0.217) 7.06 (0.212) 5.12 (0.188) -2.72e-08 -0.5e3 (-0.516) 675 17 (0.142) 18 (0.145) 18 (0.143) 16 (0.137) 15 (0.131) 10 (0.088) -40 (-0.292) -60 (-0.453) -37 (-0.302) 2 (0.016) 9 (0.106) 8 (0.100) 8 (0.102) 8 (0.103) 8 (0.100) 0 0 0 0 1.03e-06 2.66e-06 0.5 (0.018) 3 (0.039) 0.03e3 (0.095) -0.05e3 (-0.116) 0.01e3 (0.047) 6 (0.068) 0 5.71 (0.095) 26.2 (0.153) -29.8 (-0.104) -5.32 (-0.030) 3.16 (0.073) 0 0 0 0 2.52e-05
x2 -14.6 (-0.226) 14.6 (0.264) 1.88 0.842 (0.470) 0.895 (0.477) 0.734 (0.452) 0.831 (0.468) 0.584 (0.384) 0.525 (0.222) -0.302 (-0.098) 0.345 (0.165) 0.522 (0.384) 0.507 (0.403) 0.453 (0.389) 0.464 (0.392) 0.432 (0.382) 0.313 (0.340) 2.37e-09 -17 (-0.104) 17 (0.142) 22 3 (0.150) 3 (0.148) 3 (0.141) 3 (0.140) 3 (0.126) 1 (0.036) -1 (-0.024) 0 (0.022) 2 (0.093) 2 (0.110) 1 (0.103) 2 (0.106) 2 (0.107) 1 (0.104) -0 0 0 0 9.87e-09 2.43e-08 0 (0.004) 0 (0.008) 1 (0.019) -2 (-0.023) 0 (0.009) 0 (0.014) 0 0.0578 (0.028) 0.264 (0.046) -0.3 (-0.031) -0.0538 (-0.009) 0.0319 (0.022) 0 0 0 0 2.62e-07
x3 -13.8 (-0.222) 13.8 (0.260) 0.842 (0.470) 1.71 0.842 (0.470) 0.69 (0.445) 0.781 (0.460) 0.549 (0.378) 0.494 (0.218) -0.284 (-0.096) 0.324 (0.162) 0.491 (0.378) 0.477 (0.397) 0.426 (0.382) 0.436 (0.385) 0.406 (0.376) 0.294 (0.334) -1.08e-10 -18 (-0.106) 18 (0.145) 3 (0.150) 23.3 3 (0.152) 3 (0.144) 3 (0.143) 3 (0.128) 1 (0.037) -1 (-0.025) 1 (0.022) 2 (0.095) 2 (0.112) 2 (0.105) 2 (0.108) 2 (0.109) 2 (0.106) 0 -0 8.99e-09 2.28e-08 0 0.0543 (0.028) 0.248 (0.045) -0.282 (-0.031) -0.0505 (-0.009) 0.03 (0.021) 0 0.0 (0.004) 0 (0.008) 1 (0.020) -2 (-0.024) 0 (0.010) 0 (0.014) 0 0 0 0 0 2.48e-07
x4 -14.6 (-0.226) 14.6 (0.264) 0.895 (0.477) 0.842 (0.470) 1.88 0.734 (0.452) 0.831 (0.468) 0.584 (0.384) 0.525 (0.222) -0.302 (-0.098) 0.345 (0.165) 0.522 (0.384) 0.507 (0.403) 0.453 (0.389) 0.464 (0.392) 0.432 (0.382) 0.313 (0.340) -1.07e-10 -18 (-0.105) 18 (0.143) 3 (0.148) 3 (0.152) 22.7 3 (0.143) 3 (0.141) 3 (0.127) 1 (0.037) -1 (-0.024) 0 (0.022) 2 (0.094) 2 (0.111) 2 (0.104) 2 (0.107) 2 (0.108) 2 (0.105) 0 0 -7.03e-08 2.45e-08 -0 0 0.0 (0.004) 0 (0.008) 1 (0.019) -2 (-0.024) 0 (0.010) 0 (0.014) 0 0.0578 (0.028) 0.264 (0.046) -0.3 (-0.031) -0.0537 (-0.009) 0.0319 (0.022) 0 0 0 0 2.62e-07
x5 -12 (-0.214) 12 (0.250) 0.734 (0.452) 0.69 (0.445) 0.734 (0.452) 1.4 0.681 (0.443) 0.478 (0.364) 0.431 (0.210) -0.248 (-0.092) 0.283 (0.156) 0.428 (0.364) 0.416 (0.382) 0.372 (0.368) 0.38 (0.371) 0.354 (0.362) 0.256 (0.322) -9.87e-11 -16 (-0.100) 16 (0.137) 3 (0.141) 3 (0.144) 3 (0.143) 19.9 3 (0.135) 2 (0.121) 1 (0.035) -1 (-0.023) 0 (0.021) 1 (0.090) 2 (0.106) 1 (0.099) 1 (0.102) 1 (0.103) 1 (0.100) 0 7.25e-09 -1.65e-07 0 0.0473 (0.027) 0.216 (0.043) -0.246 (-0.029) -0.0441 (-0.009) 0.0262 (0.021) 0 -0 0.0 (0.004) 0 (0.008) 1 (0.018) -2 (-0.022) 0 (0.009) 0 (0.013) 0 0 0 0 0 2.16e-07
x6 -13.6 (-0.221) 13.6 (0.259) 0.831 (0.468) 0.781 (0.460) 0.831 (0.468) 0.681 (0.443) 1.68 0.541 (0.376) 0.487 (0.218) -0.28 (-0.096) 0.32 (0.162) 0.484 (0.377) 0.471 (0.396) 0.421 (0.381) 0.431 (0.384) 0.401 (0.375) 0.29 (0.333) -1.17e-10 -15 (-0.096) 15 (0.131) 3 (0.140) 3 (0.143) 3 (0.141) 3 (0.135) 19.9 2 (0.120) 1 (0.036) -0 (-0.021) 0 (0.022) 1 (0.089) 2 (0.105) 1 (0.098) 1 (0.101) 1 (0.102) 1 (0.099) 0 0 0 0 -0.1 (-0.031) 0 (0.007) 1 (0.019) -1 (-0.021) 0 (0.010) 0 (0.013) 0 8.8e-09 2.29e-08 -0 0.0536 (0.028) 0.245 (0.045) -0.278 (-0.030) -0.0499 (-0.009) 0.0296 (0.021) 0 0 0 0 2.43e-07
x7 -5.25 (-0.100) 5.25 (0.117) 0.584 (0.384) 0.549 (0.378) 0.584 (0.384) 0.478 (0.364) 0.541 (0.376) 1.23 0.421 (0.220) -0.0401 (-0.016) 0.295 (0.174) 0.346 (0.314) 0.331 (0.325) 0.295 (0.313) 0.302 (0.315) 0.281 (0.307) 0.204 (0.273) -1.31e-11 -10 (-0.065) 10 (0.088) 3 (0.126) 3 (0.128) 3 (0.127) 2 (0.121) 2 (0.120) 18.1 1 (0.042) -0 (-0.006) 1 (0.029) 1 (0.082) 1 (0.094) 1 (0.088) 1 (0.090) 1 (0.091) 1 (0.089) 0 0 0 5.87e-09 1.39e-08 0 -0.424 (-0.258) 0.207 (0.044) 0.116 (0.015) 0.0813 (0.017) 0.0193 (0.016) 0.0 (0.003) -1 (-0.086) 1 (0.019) -1 (-0.008) 1 (0.012) 0 (0.012) 0 0 0 0 0 1.4e-07
x8 23.6 (0.289) -23.6 (-0.338) 0.525 (0.222) 0.494 (0.218) 0.525 (0.222) 0.431 (0.210) 0.487 (0.218) 0.421 (0.220) 2.99 0.999 (0.256) 0.732 (0.277) 0.348 (0.203) 0.298 (0.188) 0.266 (0.181) 0.272 (0.182) 0.253 (0.178) 0.184 (0.158) 4.8e-11 40 (0.213) -40 (-0.292) 1 (0.036) 1 (0.037) 1 (0.037) 1 (0.035) 1 (0.036) 1 (0.042) 27.6 4 (0.137) 3 (0.113) 1 (0.048) 0 (0.027) 0 (0.025) 0 (0.026) 0 (0.026) 0 (0.026) -0 0 1.44e-09 -1.43e-09 0 0.0195 (0.008) -3.03 (-0.416) 2.16 (0.177) 0.842 (0.112) 0.00754 (0.004) 0 0.0 (0.001) 0 (0.006) -12 (-0.217) 9 (0.109) 2 (0.046) 0 (0.004) -0 -0 0 0 0 -7.48e-08
x9 68.9 (0.644) -68.9 (-0.754) -0.302 (-0.098) -0.284 (-0.096) -0.302 (-0.098) -0.248 (-0.092) -0.28 (-0.096) -0.0401 (-0.016) 0.999 (0.256) 5.1 0.934 (0.271) -0.0941 (-0.042) -0.171 (-0.083) -0.153 (-0.079) -0.157 (-0.080) -0.146 (-0.078) -0.106 (-0.070) 1.73e-10 0 -1.09e-08 -3.64e-08 0 -0.048 (-0.014) 0.432 (0.045) -2.1 (-0.132) 1.75 (0.178) -0.0329 (-0.014) 0 60 (0.331) -60 (-0.453) -1 (-0.024) -1 (-0.025) -1 (-0.024) -1 (-0.023) -0 (-0.021) -0 (-0.006) 4 (0.137) 26.2 3 (0.138) 0 (0.017) -0 (-0.018) -0 (-0.017) -0 (-0.017) -0 (-0.017) -0 (-0.017) -0 0 -0 -0 -0.0 (-0.000) 0 (0.004) 2 (0.036) -5 (-0.060) 3 (0.056) -0 (-0.002) -0 -0 -0 0 0 -5.36e-07
x10 23.2 (0.320) -23.2 (-0.375) 0.345 (0.165) 0.324 (0.162) 0.345 (0.165) 0.283 (0.156) 0.32 (0.162) 0.295 (0.174) 0.732 (0.277) 0.934 (0.271) 2.33 0.238 (0.157) 0.195 (0.139) 0.175 (0.134) 0.179 (0.135) 0.166 (0.132) 0.12 (0.117) 1.13e-12 0 4.71e-11 -4.1e-09 37 (0.221) -37 (-0.302) 0 (0.022) 1 (0.022) 0 (0.022) 0 (0.021) 0 (0.022) 1 (0.029) 3 (0.113) 3 (0.138) 22.2 1 (0.038) 0 (0.016) 0 (0.015) 0 (0.016) 0 (0.016) 0 (0.015) -0 0 0.00938 (0.004) 0.336 (0.052) 1.97 (0.183) -2.32 (-0.349) 0.0023 0 0 0.0 (0.001) 0 (0.005) 2 (0.032) 8 (0.110) -10 (-0.204) 0 (0.003) -0 -0 -0 0 0 -1.01e-07
x11 -6.29 (-0.134) 6.29 (0.157) 0.522 (0.384) 0.491 (0.378) 0.522 (0.384) 0.428 (0.364) 0.484 (0.377) 0.346 (0.314) 0.348 (0.203) -0.0941 (-0.042) 0.238 (0.157) 0.984 0.296 (0.325) 0.264 (0.313) 0.271 (0.315) 0.252 (0.307) 0.182 (0.273) -9.79e-11 -2 (-0.012) 2 (0.016) 2 (0.093) 2 (0.095) 2 (0.094) 1 (0.090) 1 (0.089) 1 (0.082) 1 (0.048) 0 (0.017) 1 (0.038) 13 1 (0.070) 1 (0.066) 1 (0.067) 1 (0.068) 1 (0.066) 0 0 0 5.47e-09 1.33e-08 0 0.0327 (0.022) 0.172 (0.041) -0.0118 (-0.002) 0.0295 (0.007) -0.223 (-0.211) 0 (0.002) 0 (0.006) 1 (0.019) 1 (0.011) 1 (0.016) -2 (-0.150) 0 0 0 0 0 1.37e-07
x12 -8.3 (-0.191) 8.3 (0.223) 0.507 (0.403) 0.477 (0.397) 0.507 (0.403) 0.416 (0.382) 0.471 (0.396) 0.331 (0.325) 0.298 (0.188) -0.171 (-0.083) 0.195 (0.139) 0.296 (0.325) 0.843 0.257 (0.328) 0.263 (0.331) 0.245 (0.323) 0.177 (0.287) -7.11e-11 0 4.86e-09 1.39e-08 0 0.0327 (0.024) 0.149 (0.039) -0.17 (-0.026) -0.0305 (-0.008) 0.0181 -9 (-0.078) 9 (0.106) 2 (0.110) 2 (0.112) 2 (0.111) 2 (0.106) 2 (0.105) 1 (0.094) 0 (0.027) -0 (-0.018) 0 (0.016) 1 (0.070) 10.7 1 (0.077) 1 (0.079) 1 (0.080) 1 (0.078) 0 0 0 0 0.0 (0.003) 0 (0.006) 0 (0.014) -1 (-0.017) 0 (0.007) 0 (0.010) -0 0 0 0 1.5e-07 0
x13 -7.42 (-0.184) 7.42 (0.215) 0.453 (0.389) 0.426 (0.382) 0.453 (0.389) 0.372 (0.368) 0.421 (0.381) 0.295 (0.313) 0.266 (0.181) -0.153 (-0.079) 0.175 (0.134) 0.264 (0.313) 0.257 (0.328) 0.726 0.235 (0.319) 0.219 (0.311) 0.158 (0.277) -6.73e-11 0 4.78e-09 1.24e-08 0 0.0292 (0.023) 0.133 (0.037) -0.152 (-0.025) -0.0272 (-0.007) 0.0162 (0.018) -8 (-0.073) 8 (0.100) 1 (0.103) 2 (0.105) 2 (0.104) 1 (0.099) 1 (0.098) 1 (0.088) 0 (0.025) -0 (-0.017) 0 (0.015) 1 (0.066) 1 (0.077) 9.19 1 (0.074) 1 (0.075) 1 (0.073) 0 0 0 0 0.0 (0.003) 0 (0.005) 0 (0.013) -1 (-0.016) 0 (0.007) 0 (0.010) 0 -0 0 0 1.34e-07 0
x14 -7.59 (-0.185) 7.59 (0.217) 0.464 (0.392) 0.436 (0.385) 0.464 (0.392) 0.38 (0.371) 0.431 (0.384) 0.302 (0.315) 0.272 (0.182) -0.157 (-0.080) 0.179 (0.135) 0.271 (0.315) 0.263 (0.331) 0.235 (0.319) 0.749 0.224 (0.313) 0.162 (0.279) -5.43e-11 0 4.48e-09 1.29e-08 0 0.0299 (0.023) 0.137 (0.037) -0.155 (-0.025) -0.0279 (-0.007) 0.0165 (0.018) -8 (-0.075) 8 (0.102) 2 (0.106) 2 (0.108) 2 (0.107) 1 (0.102) 1 (0.101) 1 (0.090) 0 (0.026) -0 (-0.017) 0 (0.016) 1 (0.067) 1 (0.079) 1 (0.074) 9.75 1 (0.077) 1 (0.075) 0 0 0 0 0.0 (0.003) 0 (0.006) 0 (0.014) -1 (-0.017) 0 (0.007) 0 (0.010) 0 0 -0 0 1.37e-07 0
x15 -7.06 (-0.181) 7.06 (0.212) 0.432 (0.382) 0.406 (0.376) 0.432 (0.382) 0.354 (0.362) 0.401 (0.375) 0.281 (0.307) 0.253 (0.178) -0.146 (-0.078) 0.166 (0.132) 0.252 (0.307) 0.245 (0.323) 0.219 (0.311) 0.224 (0.313) 0.681 0.151 (0.272) -5.74e-11 -8 (-0.075) 8 (0.103) 2 (0.107) 2 (0.109) 2 (0.108) 1 (0.103) 1 (0.102) 1 (0.091) 0 (0.026) -0 (-0.017) 0 (0.016) 1 (0.068) 1 (0.080) 1 (0.075) 1 (0.077) 9.93 1 (0.076) 0 0 0 4.48e-09 1.21e-08 0 0.0278 (0.023) 0.127 (0.037) -0.145 (-0.025) -0.0259 (-0.007) 0.0154 (0.017) 0.0 (0.003) 0 (0.006) 0 (0.014) -1 (-0.017) 0 (0.007) 0 (0.010) 0 0 0 -0 1.27e-07 0
x16 -5.12 (-0.161) 5.12 (0.188) 0.313 (0.340) 0.294 (0.334) 0.313 (0.340) 0.256 (0.322) 0.29 (0.333) 0.204 (0.273) 0.184 (0.158) -0.106 (-0.070) 0.12 (0.117) 0.182 (0.273) 0.177 (0.287) 0.158 (0.277) 0.162 (0.279) 0.151 (0.272) 0.452 -7.57e-11 -8 (-0.074) 8 (0.100) 1 (0.104) 2 (0.106) 2 (0.105) 1 (0.100) 1 (0.099) 1 (0.089) 0 (0.026) -0 (-0.017) 0 (0.015) 1 (0.066) 1 (0.078) 1 (0.073) 1 (0.075) 1 (0.076) 9.37 0 0 0 3.32e-09 9.14e-09 0 0.0202 0.0921 (0.033) -0.105 (-0.022) -0.0188 (-0.006) 0.0111 (0.016) 0.0 (0.003) 0 (0.006) 0 (0.014) -1 (-0.017) 0 (0.007) 0 (0.010) 0 0 0 0 -1.25e-06 -0
x17 2.82e-08 -2.72e-08 2.37e-09 -1.08e-10 -1.07e-10 -9.87e-11 -1.17e-10 -1.31e-11 4.8e-11 1.73e-10 1.13e-12 -9.79e-11 -7.11e-11 -6.73e-11 -5.43e-11 -5.74e-11 -7.57e-11 4.96e-09 -0 0 -1.06e-15 4.24e-14 -0 -6.61e-11 3.35e-10 8.66e-10 4.04e-10 6.92e-12 0 0 0 0 0 -0 -0 -0 -1.87e-13 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 -0 0 0 0 0 0 0
x18 0 0 0 -0 -0 0 0 0 0 0 -0 -0 0 -0 0
x19 -1.04e-06 1.03e-06 9.87e-09 8.99e-09 -7.03e-08 7.25e-09 8.8e-09 5.87e-09 1.44e-09 -1.09e-08 4.71e-11 5.47e-09 4.86e-09 4.78e-09 4.48e-09 4.48e-09 3.32e-09 -1.06e-15 0 5.45e-07 -1.29e-13 0 6.39e-10 2.48e-09 -1.99e-08 -6.18e-09 -2.06e-10 0 0 0 0 -8.77e-13
x20 -2.67e-06 2.66e-06 2.43e-08 2.28e-08 2.45e-08 -1.65e-07 2.29e-08 1.39e-08 -1.43e-09 -3.64e-08 -4.1e-09 1.33e-08 1.39e-08 1.24e-08 1.29e-08 1.21e-08 9.14e-09 4.24e-14 -0 -1.29e-13 6.59e-07 -0 1.99e-09 5.74e-10 -7.53e-08 -2.41e-08 1.21e-09 x19 -0 0 0 0 -0 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 6.71e-13 0 0 0 0 0 0 0
x21 x20 -0 0 0 0 0 -0 0 0 0 -0 0 0 0 0 -0 -0 0 0 0 0 -0 0 0 0 0 0 0 -0 -0 -0 -0 0 0
x22 -5.71 (-0.081) 5.71 (0.095) 0.0578 (0.028) 0.0543 (0.028) 0.0578 (0.028) 0.0473 (0.027) 0.0536 (0.028) -0.424 (-0.258) 0.0195 (0.008) -0.048 (-0.014) 0.00938 (0.004) 0.0327 (0.022) 0.0327 (0.024) 0.0292 (0.023) 0.0299 (0.023) 0.0278 (0.023) 0.0202 -6.61e-11 x21 -0.5 (-0.013) 0.5 (0.018) 0 (0.004) 0.0 (0.004) 0.0 (0.004) 0.0 (0.004) -0.1 (-0.031) 0.0 (0.003) 0.0 (0.001) -0.0 (-0.000) 0.0 (0.001) 0 (0.002) 0.0 (0.003) 0.0 (0.003) 0.0 (0.003) 0.0 (0.003) 0.0 (0.003) 0 0 0 6.39e-10 1.99e-09 0 2.19 0.0106 (0.002) -0.0762 (-0.007) -0.0247 (-0.004) 0.00233 0.99 0.0 0.0 (0.001) -0.0 (-0.000) 0.0 (0.000) 0.0 0 0 0 0 0 2.55e-08
x23 -26.2 (-0.131) 26.2 (0.153) 0.264 (0.046) 0.248 (0.045) 0.264 (0.046) 0.216 (0.043) 0.245 (0.045) 0.207 (0.044) -3.03 (-0.416) 0.432 (0.045) 0.336 (0.052) 0.172 (0.041) 0.149 (0.039) 0.133 (0.037) 0.137 (0.037) 0.127 (0.037) 0.0921 (0.033) 3.35e-10 x22 -3 (-0.028) 3 (0.039) 0 (0.008) 0 (0.008) 0 (0.008) 0 (0.008) 0 (0.007) -1 (-0.086) 0 (0.006) 0 (0.004) 0 (0.005) 0 (0.006) 0 (0.006) 0 (0.005) 0 (0.006) 0 (0.006) 0 (0.006) 0 0 0 2.48e-09 5.74e-10 0 0.0106 (0.002) 17.8 0.947 (0.032) 0.371 (0.020) 0.00444 (0.001) 0.0 7.53 0 (0.002) 0 (0.003) 0 (0.002) 0 (0.001) 0 0 0 0 0 -1.79e-08
x24 29.8 (0.089) -29.8 (-0.104) -0.3 (-0.031) -0.282 (-0.031) -0.3 (-0.031) -0.246 (-0.029) -0.278 (-0.030) 0.116 (0.015) 2.16 (0.177) -2.1 (-0.132) 1.97 (0.183) -0.0118 (-0.002) -0.17 (-0.026) -0.152 (-0.025) -0.155 (-0.025) -0.145 (-0.025) -0.105 (-0.022) 8.66e-10 x23 -0.03e3 (-0.070) 0.03e3 (0.095) 1 (0.019) 1 (0.020) 1 (0.019) 1 (0.018) 1 (0.019) 1 (0.019) -12 (-0.217) 2 (0.036) 2 (0.032) 1 (0.019) 0 (0.014) 0 (0.013) 0 (0.014) 0 (0.014) 0 (0.014) 0 0 0 -1.99e-08 -7.53e-08 0 -0.0762 (-0.007) 0.947 (0.032) 49.8 3.46 (0.113) -0.0548 (-0.007) 0.0 (0.001) 0 (0.002) 103 0 (0.028) 0 (0.013) 0 (0.002) 0 0 0 0 0 -9.96e-07
x25 5.32 (0.026) -5.32 (-0.030) -0.0538 (-0.009) -0.0505 (-0.009) -0.0537 (-0.009) -0.0441 (-0.009) -0.0499 (-0.009) 0.0813 (0.017) 0.842 (0.112) 1.75 (0.178) -2.32 (-0.349) 0.0295 (0.007) -0.0305 (-0.008) -0.0272 (-0.007) -0.0279 (-0.007) -0.0259 (-0.007) -0.0188 (-0.006) 4.04e-10 x24 0.05e3 (0.085) -0.05e3 (-0.116) -2 (-0.023) -2 (-0.024) -2 (-0.024) -2 (-0.022) -1 (-0.021) -1 (-0.008) 9 (0.109) -5 (-0.060) 8 (0.110) 1 (0.011) -1 (-0.017) -1 (-0.016) -1 (-0.017) -1 (-0.017) -1 (-0.017) -0 0 -0 -0 -0.0 (-0.000) 0 (0.003) 0 (0.028) 244 0.01e3 (0.045) -0 (-0.002) -0 -0 -0 -0 0 -6.18e-09 -2.41e-08
x25 -0.01e3 (-0.034) 0.01e3 (0.047) 0 (0.009) 0 (0.010) 0 (0.010) 0 (0.009) 0 (0.010) 1 (0.012) 2 (0.046) 3 (0.056) -10 (-0.204) 1 (0.016) 0 (0.007) 0 (0.007) 0 (0.007) 0 (0.007) 0 (0.007) -0 0 -0.0247 (-0.004) 0.371 (0.020) 3.46 (0.113) 18.9 -0.0184 (-0.004) 0 0 0.0 (0.000) 0 (0.002) 0 (0.013) 0.01e3 (0.045) 103 0 (0.001) -0 -0 -0 0 0 -3.5e-07
x26 -3.16 (-0.062) 3.16 (0.073) 0.0319 (0.022) 0.03 (0.021) 0.0319 (0.022) 0.0262 (0.021) 0.0296 (0.021) 0.0193 (0.016) 0.00754 (0.004) -0.0329 (-0.014) 0.0023 -0.223 (-0.211) 0.0181 0.0162 (0.018) 0.0165 (0.018) 0.0154 (0.017) 0.0111 (0.016) 6.92e-12 -6 (-0.050) 6 (0.068) 0 (0.014) 0 (0.014) 0 (0.014) 0 (0.013) 0 (0.013) 0 (0.012) 0 (0.004) -0 (-0.002) 0 (0.003) -2 (-0.150) 0 (0.010) 0 (0.010) 0 (0.010) 0 (0.010) 0 (0.010) 0 0 0 -2.06e-10 1.21e-09 0 0.00233 0.00444 (0.001) -0.0548 (-0.007) -0.0184 (-0.004) 1.14 0.0 0 (0.001) 0 (0.002) -0 (-0.002) 0 (0.001) 13.2 0 0 0 0 0 1.54e-08
x27 0 0 0 -0 -0 -0 0 -0 0 0 0 0 0 -0 0 0 0 0 0 -0 0 -0 -0 0 0 0 0 0 0 0 -0 -0 -0 0
x28 0 0 0 -0 -0 -0 0 0 -0 0 0 0 0 0 -0 0 0 0 0 -0 0 -0 -0 0 0 0 0 0 0 -0 0 -0 -0 0
x29 0 0 0 0 0 -0 -0 0 0 0 -0 0 0 -0 0 0 -0 -0 0 0 0 0 -0 -0 0 0 0 0 -0 0 0
0 -0 0 -0 -0 0 -0 -0 0 0 0 0 0 0 -0 -0 -0 0 0 0 0 0 0 0
x31 -2.52e-05 2.52e-05 2.62e-07 2.48e-07 2.62e-07 2.16e-07 2.43e-07 1.4e-07 -7.48e-08 -5.36e-07 -1.01e-07 1.37e-07 1.5e-07 1.34e-07 1.37e-07 1.27e-07 -1.25e-06 -1.87e-13 0 -8.77e-13 6.71e-13 0 2.55e-08 -1.79e-08 -9.96e-07 -3.5e-07 1.54e-08 0 0 0 0 5.19e-06 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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"├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ SOME Parameters at limit │\n", + "│ SOME parameters at limit │ Below call limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Below EDM threshold (goal x 10) │ Below call limit │\n", - "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", - "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", - "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘\n", "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ x0 │ 800 │ 50 │ │ │ 0 │ │ │\n", - "│ 1 │ x1 │ 190 │ 40 │ │ │ 0 │ │ │\n", - "│ 2 │ x2 │ 9.0 │ 1.4 │ │ │ 0 │ │ │\n", - "│ 3 │ x3 │ 8.5 │ 1.3 │ │ │ 0 │ │ │\n", - "│ 4 │ x4 │ 9.0 │ 1.4 │ │ │ 0 │ │ │\n", - "│ 5 │ x5 │ 7.4 │ 1.2 │ │ │ 0 │ │ │\n", - "│ 6 │ x6 │ 8.4 │ 1.3 │ │ │ 0 │ │ │\n", - "│ 7 │ x7 │ 6.4 │ 1.1 │ │ │ 0 │ │ │\n", - "│ 8 │ x8 │ 9.2 │ 1.7 │ │ │ 0 │ │ │\n", - "│ 9 │ x9 │ 4.8 │ 2.3 │ │ │ 0 │ │ │\n", - "│ 10│ x10 │ 7.0 │ 1.5 │ │ │ 0 │ │ │\n", - "│ 11│ x11 │ 5.5 │ 1.0 │ │ │ 0 │ │ │\n", - "│ 12│ x12 │ 5.1 │ 0.9 │ │ │ 0 │ │ │\n", - "│ 13│ x13 │ 4.6 │ 0.9 │ │ │ 0 │ │ │\n", - "│ 14│ x14 │ 4.7 │ 0.9 │ │ │ 0 │ │ │\n", - "│ 15│ x15 │ 4.3 │ 0.8 │ │ │ 0 │ │ │\n", - "│ 16│ x16 │ 3.2 │ 0.7 │ │ │ 0 │ │ │\n", + "│ 0 │ x0 │ 780 │ 40 │ │ │ 0 │ │ │\n", + "│ 1 │ x1 │ 199 │ 26 │ │ │ 0 │ │ │\n", + "│ 2 │ x2 │ 47 │ 5 │ │ │ 0 │ │ │\n", + "│ 3 │ x3 │ 50 │ 5 │ │ │ 0 │ │ │\n", + "│ 4 │ x4 │ 48 │ 5 │ │ │ 0 │ │ │\n", + "│ 5 │ x5 │ 43 │ 4 │ │ │ 0 │ │ │\n", + "│ 6 │ x6 │ 43 │ 4 │ │ │ 0 │ │ │\n", + "│ 7 │ x7 │ 39 │ 4 │ │ │ 0 │ │ │\n", + "│ 8 │ x8 │ 42 │ 5 │ │ │ 0 │ │ │\n", + "│ 9 │ x9 │ 29 │ 5 │ │ │ 0 │ │ │\n", + "│ 10│ x10 │ 33 │ 5 │ │ │ 0 │ │ │\n", + "│ 11│ x11 │ 28 │ 4 │ │ │ 0 │ │ │\n", + "│ 12│ x12 │ 24.6 │ 3.3 │ │ │ 0 │ │ │\n", + "│ 13│ x13 │ 21.5 │ 3.0 │ │ │ 0 │ │ │\n", + "│ 14│ x14 │ 22.7 │ 3.1 │ │ │ 0 │ │ │\n", + "│ 15│ x15 │ 23.1 │ 3.2 │ │ │ 0 │ │ │\n", + "│ 16│ x16 │ 21.9 │ 3.1 │ │ │ 0 │ │ │\n", "│ 17│ x17 │ 0.0 │ 0.4 │ │ │ 0 │ │ │\n", "│ 18│ x18 │ 0.0 │ 0.4 │ │ │ 0 │ │ │\n", "│ 19│ x19 │ 0.0 │ 0.4 │ │ │ 0 │ │ │\n", - "│ 20│ x20 │ 0.0 │ 0.5 │ │ │ 0 │ │ │\n", - "│ 21│ x21 │ 0.0 │ 0.4 │ │ │ 0 │ │ │\n", - "│ 22│ x22 │ 2.1 │ 1.5 │ │ │ 0 │ │ │\n", - "│ 23│ x23 │ 19 │ 4 │ │ │ 0 │ │ │\n", - "│ 24│ x24 │ 54 │ 7 │ │ │ 0 │ │ │\n", - "│ 25│ x25 │ 23 │ 4 │ │ │ 0 │ │ │\n", - "│ 26│ x26 │ 1.1 │ 1.0 │ │ │ 0 │ │ │\n", + "│ 20│ x20 │ 0.0 │ 0.4 │ │ │ 0 │ │ │\n", + "│ 21│ x21 │ 1.0 │ 0.9 │ │ │ 0 │ │ │\n", + "│ 22│ x22 │ 7.8 │ 2.7 │ │ │ 0 │ │ │\n", + "│ 23│ x23 │ 109 │ 10 │ │ │ 0 │ │ │\n", + "│ 24│ x24 │ 255 │ 16 │ │ │ 0 │ │ │\n", + "│ 25│ x25 │ 111 │ 10 │ │ │ 0 │ │ │\n", + "│ 26│ x26 │ 13 │ 4 │ │ │ 0 │ │ │\n", "│ 27│ x27 │ 0.0 │ 0.4 │ │ │ 0 │ │ │\n", "│ 28│ x28 │ 0.0 │ 0.4 │ │ │ 0 │ │ │\n", "│ 29│ x29 │ 0.0 │ 0.4 │ │ │ 0 │ │ │\n", "│ 30│ x30 │ 0.0 │ 0.4 │ │ │ 0 │ │ │\n", - "│ 31│ x31 │ 0.0 │ 0.5 │ │ │ 0 │ │ │\n", + "│ 31│ x31 │ 0.0 │ 0.4 │ │ │ 0 │ │ │\n", "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌─────┬─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n", - "│ │ x0 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x24 x25 x26 x27 x28 x29 x30 x31 │\n", - "├─────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n", - "│ x0 │ 2.24e+03 -1.45e+03 -14.6 -13.8 -14.6 -12 -13.6 -5.25 23.6 68.9 23.2 -6.29 -8.3 -7.42 -7.59 -7.06 -5.12 2.82e-08 -0 -1.04e-06 -2.67e-06 -0 -5.71 -26.2 29.8 5.32 -3.16 -0 -0 -0 -0 -2.52e-05 │\n", - "│ x1 │ -1.45e+03 1.64e+03 14.6 13.8 14.6 12 13.6 5.25 -23.6 -68.9 -23.2 6.29 8.3 7.42 7.59 7.06 5.12 -2.72e-08 0 1.03e-06 2.66e-06 0 5.71 26.2 -29.8 -5.32 3.16 0 0 0 0 2.52e-05 │\n", - "│ x2 │ -14.6 14.6 1.88 0.842 0.895 0.734 0.831 0.584 0.525 -0.302 0.345 0.522 0.507 0.453 0.464 0.432 0.313 2.37e-09 0 9.87e-09 2.43e-08 0 0.0578 0.264 -0.3 -0.0538 0.0319 0 0 0 0 2.62e-07 │\n", - "│ x3 │ -13.8 13.8 0.842 1.71 0.842 0.69 0.781 0.549 0.494 -0.284 0.324 0.491 0.477 0.426 0.436 0.406 0.294 -1.08e-10 -0 8.99e-09 2.28e-08 0 0.0543 0.248 -0.282 -0.0505 0.03 0 0 0 0 2.48e-07 │\n", - "│ x4 │ -14.6 14.6 0.895 0.842 1.88 0.734 0.831 0.584 0.525 -0.302 0.345 0.522 0.507 0.453 0.464 0.432 0.313 -1.07e-10 0 -7.03e-08 2.45e-08 0 0.0578 0.264 -0.3 -0.0537 0.0319 0 0 0 0 2.62e-07 │\n", - "│ x5 │ -12 12 0.734 0.69 0.734 1.4 0.681 0.478 0.431 -0.248 0.283 0.428 0.416 0.372 0.38 0.354 0.256 -9.87e-11 0 7.25e-09 -1.65e-07 0 0.0473 0.216 -0.246 -0.0441 0.0262 0 0 0 0 2.16e-07 │\n", - "│ x6 │ -13.6 13.6 0.831 0.781 0.831 0.681 1.68 0.541 0.487 -0.28 0.32 0.484 0.471 0.421 0.431 0.401 0.29 -1.17e-10 0 8.8e-09 2.29e-08 -0 0.0536 0.245 -0.278 -0.0499 0.0296 0 0 0 0 2.43e-07 │\n", - "│ x7 │ -5.25 5.25 0.584 0.549 0.584 0.478 0.541 1.23 0.421 -0.0401 0.295 0.346 0.331 0.295 0.302 0.281 0.204 -1.31e-11 0 5.87e-09 1.39e-08 0 -0.424 0.207 0.116 0.0813 0.0193 0 0 0 0 1.4e-07 │\n", - "│ x8 │ 23.6 -23.6 0.525 0.494 0.525 0.431 0.487 0.421 2.99 0.999 0.732 0.348 0.298 0.266 0.272 0.253 0.184 4.8e-11 0 1.44e-09 -1.43e-09 0 0.0195 -3.03 2.16 0.842 0.00754 0 0 0 0 -7.48e-08 │\n", - "│ x9 │ 68.9 -68.9 -0.302 -0.284 -0.302 -0.248 -0.28 -0.0401 0.999 5.1 0.934 -0.0941 -0.171 -0.153 -0.157 -0.146 -0.106 1.73e-10 0 -1.09e-08 -3.64e-08 0 -0.048 0.432 -2.1 1.75 -0.0329 0 0 0 0 -5.36e-07 │\n", - "│ x10 │ 23.2 -23.2 0.345 0.324 0.345 0.283 0.32 0.295 0.732 0.934 2.33 0.238 0.195 0.175 0.179 0.166 0.12 1.13e-12 0 4.71e-11 -4.1e-09 0 0.00938 0.336 1.97 -2.32 0.0023 0 0 0 0 -1.01e-07 │\n", - "│ x11 │ -6.29 6.29 0.522 0.491 0.522 0.428 0.484 0.346 0.348 -0.0941 0.238 0.984 0.296 0.264 0.271 0.252 0.182 -9.79e-11 0 5.47e-09 1.33e-08 0 0.0327 0.172 -0.0118 0.0295 -0.223 0 0 0 0 1.37e-07 │\n", - "│ x12 │ -8.3 8.3 0.507 0.477 0.507 0.416 0.471 0.331 0.298 -0.171 0.195 0.296 0.843 0.257 0.263 0.245 0.177 -7.11e-11 0 4.86e-09 1.39e-08 0 0.0327 0.149 -0.17 -0.0305 0.0181 -0 0 0 0 1.5e-07 │\n", - "│ x13 │ -7.42 7.42 0.453 0.426 0.453 0.372 0.421 0.295 0.266 -0.153 0.175 0.264 0.257 0.726 0.235 0.219 0.158 -6.73e-11 0 4.78e-09 1.24e-08 0 0.0292 0.133 -0.152 -0.0272 0.0162 0 -0 0 0 1.34e-07 │\n", - "│ x14 │ -7.59 7.59 0.464 0.436 0.464 0.38 0.431 0.302 0.272 -0.157 0.179 0.271 0.263 0.235 0.749 0.224 0.162 -5.43e-11 0 4.48e-09 1.29e-08 0 0.0299 0.137 -0.155 -0.0279 0.0165 0 0 -0 0 1.37e-07 │\n", - "│ x15 │ -7.06 7.06 0.432 0.406 0.432 0.354 0.401 0.281 0.253 -0.146 0.166 0.252 0.245 0.219 0.224 0.681 0.151 -5.74e-11 0 4.48e-09 1.21e-08 0 0.0278 0.127 -0.145 -0.0259 0.0154 0 0 0 -0 1.27e-07 │\n", - "│ x16 │ -5.12 5.12 0.313 0.294 0.313 0.256 0.29 0.204 0.184 -0.106 0.12 0.182 0.177 0.158 0.162 0.151 0.452 -7.57e-11 0 3.32e-09 9.14e-09 0 0.0202 0.0921 -0.105 -0.0188 0.0111 0 0 0 0 -1.25e-06 │\n", - "│ x17 │ 2.82e-08 -2.72e-08 2.37e-09 -1.08e-10 -1.07e-10 -9.87e-11 -1.17e-10 -1.31e-11 4.8e-11 1.73e-10 1.13e-12 -9.79e-11 -7.11e-11 -6.73e-11 -5.43e-11 -5.74e-11 -7.57e-11 4.96e-09 0 -1.06e-15 4.24e-14 -0 -6.61e-11 3.35e-10 8.66e-10 4.04e-10 6.92e-12 0 0 -0 -0 -1.87e-13 │\n", - "│ x18 │ -0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 -0 0 0 0 0 0 -0 -0 0 -0 0 │\n", - "│ x19 │ -1.04e-06 1.03e-06 9.87e-09 8.99e-09 -7.03e-08 7.25e-09 8.8e-09 5.87e-09 1.44e-09 -1.09e-08 4.71e-11 5.47e-09 4.86e-09 4.78e-09 4.48e-09 4.48e-09 3.32e-09 -1.06e-15 0 5.45e-07 -1.29e-13 0 6.39e-10 2.48e-09 -1.99e-08 -6.18e-09 -2.06e-10 0 0 0 0 -8.77e-13 │\n", - "│ x20 │ -2.67e-06 2.66e-06 2.43e-08 2.28e-08 2.45e-08 -1.65e-07 2.29e-08 1.39e-08 -1.43e-09 -3.64e-08 -4.1e-09 1.33e-08 1.39e-08 1.24e-08 1.29e-08 1.21e-08 9.14e-09 4.24e-14 -0 -1.29e-13 6.59e-07 -0 1.99e-09 5.74e-10 -7.53e-08 -2.41e-08 1.21e-09 -0 -0 -0 -0 6.71e-13 │\n", - "│ x21 │ -0 0 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 -0 -0 0 -0 0 0 0 0 0 0 -0 -0 -0 -0 0 │\n", - "│ x22 │ -5.71 5.71 0.0578 0.0543 0.0578 0.0473 0.0536 -0.424 0.0195 -0.048 0.00938 0.0327 0.0327 0.0292 0.0299 0.0278 0.0202 -6.61e-11 0 6.39e-10 1.99e-09 0 2.19 0.0106 -0.0762 -0.0247 0.00233 0 0 0 0 2.55e-08 │\n", - "│ x23 │ -26.2 26.2 0.264 0.248 0.264 0.216 0.245 0.207 -3.03 0.432 0.336 0.172 0.149 0.133 0.137 0.127 0.0921 3.35e-10 0 2.48e-09 5.74e-10 0 0.0106 17.8 0.947 0.371 0.00444 0 0 0 0 -1.79e-08 │\n", - "│ x24 │ 29.8 -29.8 -0.3 -0.282 -0.3 -0.246 -0.278 0.116 2.16 -2.1 1.97 -0.0118 -0.17 -0.152 -0.155 -0.145 -0.105 8.66e-10 0 -1.99e-08 -7.53e-08 0 -0.0762 0.947 49.8 3.46 -0.0548 0 0 0 0 -9.96e-07 │\n", - "│ x25 │ 5.32 -5.32 -0.0538 -0.0505 -0.0537 -0.0441 -0.0499 0.0813 0.842 1.75 -2.32 0.0295 -0.0305 -0.0272 -0.0279 -0.0259 -0.0188 4.04e-10 0 -6.18e-09 -2.41e-08 0 -0.0247 0.371 3.46 18.9 -0.0184 0 0 0 0 -3.5e-07 │\n", - "│ x26 │ -3.16 3.16 0.0319 0.03 0.0319 0.0262 0.0296 0.0193 0.00754 -0.0329 0.0023 -0.223 0.0181 0.0162 0.0165 0.0154 0.0111 6.92e-12 0 -2.06e-10 1.21e-09 0 0.00233 0.00444 -0.0548 -0.0184 1.14 0 0 0 0 1.54e-08 │\n", - "│ x27 │ -0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 -0 0 -0 -0 0 0 0 0 0 0 -0 -0 -0 0 │\n", - "│ x28 │ -0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 -0 0 -0 -0 0 0 0 0 0 -0 0 -0 -0 0 │\n", - "│ x29 │ -0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 -0 0 0 -0 -0 0 0 0 0 0 -0 -0 0 -0 0 │\n", - "│ x30 │ -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 -0 -0 0 -0 -0 0 0 0 0 0 -0 -0 -0 0 0 │\n", - "│ x31 │ -2.52e-05 2.52e-05 2.62e-07 2.48e-07 2.62e-07 2.16e-07 2.43e-07 1.4e-07 -7.48e-08 -5.36e-07 -1.01e-07 1.37e-07 1.5e-07 1.34e-07 1.37e-07 1.27e-07 -1.25e-06 -1.87e-13 0 -8.77e-13 6.71e-13 0 2.55e-08 -1.79e-08 -9.96e-07 -3.5e-07 1.54e-08 0 0 0 0 5.19e-06 │\n", - "└─────┴─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘" + "┌─────┬─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐\n", + "│ │ x0 x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x24 x25 x26 x27 x28 x29 x30 x31 │\n", + "├─────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤\n", + "│ x0 │ 1.26e+03 -0.5e3 -17 -18 -18 -16 -15 -10 40 60 37 -2 -9 -8 -8 -8 -8 -0 -0 -0 -0 -0.5 -3 -0.03e3 0.05e3 -0.01e3 -6 -0 -0 -0 -0 -0 │\n", + "│ x1 │ -0.5e3 675 17 18 18 16 15 10 -40 -60 -37 2 9 8 8 8 8 0 0 0 0 0.5 3 0.03e3 -0.05e3 0.01e3 6 0 0 0 0 0 │\n", + "│ x2 │ -17 17 22 3 3 3 3 3 1 -1 0 2 2 1 2 2 1 -0 0 0 0 0 0 1 -2 0 0 0 0 0 0 0 │\n", + "│ x3 │ -18 18 3 23.3 3 3 3 3 1 -1 1 2 2 2 2 2 2 0 -0 0 0 0.0 0 1 -2 0 0 0 0 0 0 0 │\n", + "│ x4 │ -18 18 3 3 22.7 3 3 3 1 -1 0 2 2 2 2 2 2 0 0 -0 0 0.0 0 1 -2 0 0 0 0 0 0 0 │\n", + "│ x5 │ -16 16 3 3 3 19.9 3 2 1 -1 0 1 2 1 1 1 1 0 0 0 -0 0.0 0 1 -2 0 0 0 0 0 0 0 │\n", + "│ x6 │ -15 15 3 3 3 3 19.9 2 1 -0 0 1 2 1 1 1 1 0 0 0 0 -0.1 0 1 -1 0 0 0 0 0 0 0 │\n", + "│ x7 │ -10 10 3 3 3 2 2 18.1 1 -0 1 1 1 1 1 1 1 0 0 0 0 0.0 -1 1 -1 1 0 0 0 0 0 0 │\n", + "│ x8 │ 40 -40 1 1 1 1 1 1 27.6 4 3 1 0 0 0 0 0 -0 0 0 0 0.0 0 -12 9 2 0 -0 -0 0 0 0 │\n", + "│ x9 │ 60 -60 -1 -1 -1 -1 -0 -0 4 26.2 3 0 -0 -0 -0 -0 -0 -0 0 -0 -0 -0.0 0 2 -5 3 -0 -0 -0 -0 0 0 │\n", + "│ x10 │ 37 -37 0 1 0 0 0 1 3 3 22.2 1 0 0 0 0 0 -0 0 0 0 0.0 0 2 8 -10 0 -0 -0 -0 0 0 │\n", + "│ x11 │ -2 2 2 2 2 1 1 1 1 0 1 13 1 1 1 1 1 0 0 0 0 0 0 1 1 1 -2 0 0 0 0 0 │\n", + "│ x12 │ -9 9 2 2 2 2 2 1 0 -0 0 1 10.7 1 1 1 1 0 0 0 0 0.0 0 0 -1 0 0 -0 0 0 0 0 │\n", + "│ x13 │ -8 8 1 2 2 1 1 1 0 -0 0 1 1 9.19 1 1 1 0 0 0 0 0.0 0 0 -1 0 0 0 -0 0 0 0 │\n", + "│ x14 │ -8 8 2 2 2 1 1 1 0 -0 0 1 1 1 9.75 1 1 0 0 0 0 0.0 0 0 -1 0 0 0 0 -0 0 0 │\n", + "│ x15 │ -8 8 2 2 2 1 1 1 0 -0 0 1 1 1 1 9.93 1 0 0 0 0 0.0 0 0 -1 0 0 0 0 0 -0 0 │\n", + "│ x16 │ -8 8 1 2 2 1 1 1 0 -0 0 1 1 1 1 1 9.37 0 0 0 0 0.0 0 0 -1 0 0 0 0 0 0 -0 │\n", + "│ x17 │ -0 0 -0 0 0 0 0 0 -0 -0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 -0 0 0 0 0 0 0 │\n", + "│ x18 │ -0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 │\n", + "│ x19 │ -0 0 0 0 -0 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 │\n", + "│ x20 │ -0 0 0 0 0 -0 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 │\n", + "│ x21 │ -0.5 0.5 0 0.0 0.0 0.0 -0.1 0.0 0.0 -0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 0.99 0.0 0.0 -0.0 0.0 0.0 0 0 0 0 0 │\n", + "│ x22 │ -3 3 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 7.53 0 0 0 0 0 0 0 0 0 │\n", + "│ x23 │ -0.03e3 0.03e3 1 1 1 1 1 1 -12 2 2 1 0 0 0 0 0 0 0 0 0 0.0 0 103 0 0 0 0 0 0 0 0 │\n", + "│ x24 │ 0.05e3 -0.05e3 -2 -2 -2 -2 -1 -1 9 -5 8 1 -1 -1 -1 -1 -1 -0 0 -0 -0 -0.0 0 0 244 0.01e3 -0 -0 -0 -0 -0 0 │\n", + "│ x25 │ -0.01e3 0.01e3 0 0 0 0 0 1 2 3 -10 1 0 0 0 0 0 -0 0 0 0 0.0 0 0 0.01e3 103 0 -0 -0 -0 0 0 │\n", + "│ x26 │ -6 6 0 0 0 0 0 0 0 -0 0 -2 0 0 0 0 0 0 0 0 0 0.0 0 0 -0 0 13.2 0 0 0 0 0 │\n", + "│ x27 │ -0 0 0 0 0 0 0 0 -0 -0 -0 0 -0 0 0 0 0 0 0 0 0 0 0 0 -0 -0 0 0 0 0 0 0 │\n", + "│ x28 │ -0 0 0 0 0 0 0 0 -0 -0 -0 0 0 -0 0 0 0 0 0 0 0 0 0 0 -0 -0 0 0 0 0 0 0 │\n", + "│ x29 │ -0 0 0 0 0 0 0 0 0 -0 -0 0 0 0 -0 0 0 0 0 0 0 0 0 0 -0 -0 0 0 0 0 0 0 │\n", + "│ x30 │ -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 │\n", + "│ x31 │ -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 │\n", + "└─────┴─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘" ] }, - "execution_count": 7, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -2051,12 +3503,675 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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Migrad Migrad
FCN = 18.27 (chi2/ndof = 1.4) Nfcn = 60 FCN = -2140 (χ²/ndof = -164.6) Nfcn = 55
EDM = 2.94e-08 (Goal: 0.0002) EDM = 3.28e-05 (Goal: 0.0002)
Valid Minimum No Parameters at limit Valid Minimum Below EDM threshold (goal x 10)
Below EDM threshold (goal x 10) Below call limit No parameters at limit Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced Covariance accurate
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0 x0 800 50 784 35 0
1 x1 190 40 199 26 0
x0 2.24e+03 -1.45e+03 (-0.756) 1.22e+03 -0.5e3 (-0.506)
x1 -1.45e+03 (-0.756) 1.64e+03 -0.5e3 (-0.506) 665
" ], @@ -2171,30 +4283,30 @@ "┌─────────────────────────────────────────────────────────────────────────┐\n", "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 18.27 (chi2/ndof = 1.4) │ Nfcn = 60 │\n", - "│ EDM = 2.94e-08 (Goal: 0.0002) │ │\n", + "│ FCN = -2140 (χ²/ndof = -164.6) │ Nfcn = 55 │\n", + "│ EDM = 3.28e-05 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ No Parameters at limit │\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Below EDM threshold (goal x 10) │ Below call limit │\n", - "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", - "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", - "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", + "│ No parameters at limit │ Below call limit │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘\n", "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ x0 │ 800 │ 50 │ │ │ 0 │ │ │\n", - "│ 1 │ x1 │ 190 │ 40 │ │ │ 0 │ │ │\n", + "│ 0 │ x0 │ 784 │ 35 │ │ │ 0 │ │ │\n", + "│ 1 │ x1 │ 199 │ 26 │ │ │ 0 │ │ │\n", "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌────┬─────────────────────┐\n", - "│ │ x0 x1 │\n", - "├────┼─────────────────────┤\n", - "│ x0 │ 2.24e+03 -1.45e+03 │\n", - "│ x1 │ -1.45e+03 1.64e+03 │\n", - "└────┴─────────────────────┘" + "┌────┬───────────────────┐\n", + "│ │ x0 x1 │\n", + "├────┼───────────────────┤\n", + "│ x0 │ 1.22e+03 -0.5e3 │\n", + "│ x1 │ -0.5e3 665 │\n", + "└────┴───────────────────┘" ] }, - "execution_count": 9, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -2256,14 +4368,9 @@ "All three methods are implemented in the built-in `Template` cost function (see documentation for details)." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2271,30 +4378,27 @@ "text/html": [ "\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", "
Migrad Migrad
FCN = 11.52 (chi2/ndof = 0.9) Nfcn = 48 FCN = 8.447 (χ²/ndof = 0.6) Nfcn = 46
EDM = 6.05e-05 (Goal: 0.0002) EDM = 7.7e-06 (Goal: 0.0002)
Valid Minimum No Parameters at limit Valid Minimum Below EDM threshold (goal x 10)
Below EDM threshold (goal x 10) Below call limit No parameters at limit Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced Covariance accurate
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0 x0 0.86e3 0.11e3 790 50 0
1 x1 190 40 197 28 0
x0 1.16e+04 -1.56e+03 (-0.330) 2.56e+03 -0.5e3 (-0.358)
x1 -1.56e+03 (-0.330) 1.94e+03
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"│ FCN = 11.52 (chi2/ndof = 0.9) │ Nfcn = 48 │\n", - "│ EDM = 6.05e-05 (Goal: 0.0002) │ │\n", + "│ FCN = 8.447 (χ²/ndof = 0.6) │ Nfcn = 46 │\n", + "│ EDM = 7.7e-06 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ No Parameters at limit │\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Below EDM threshold (goal x 10) │ Below call limit │\n", - "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", - "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", - "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", + "│ No parameters at limit │ Below call limit │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘\n", "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ x0 │ 0.86e3 │ 0.11e3 │ │ │ 0 │ │ │\n", - "│ 1 │ x1 │ 190 │ 40 │ │ │ 0 │ │ │\n", + "│ 0 │ x0 │ 790 │ 50 │ │ │ 0 │ │ │\n", + "│ 1 │ x1 │ 197 │ 28 │ │ │ 0 │ │ │\n", "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌────┬─────────────────────┐\n", - "│ │ x0 x1 │\n", - "├────┼─────────────────────┤\n", - "│ x0 │ 1.16e+04 -1.56e+03 │\n", - "│ x1 │ -1.56e+03 1.94e+03 │\n", - "└────┴─────────────────────┘" + "┌────┬───────────────────┐\n", + "│ │ x0 x1 │\n", + "├────┼───────────────────┤\n", + "│ x0 │ 2.56e+03 -0.5e3 │\n", + "│ x1 │ -0.5e3 764 │\n", + "└────┴───────────────────┘" ] }, - "execution_count": 10, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -2390,7 +5250,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2398,30 +5258,27 @@ "text/html": [ "\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", "
Migrad Migrad
FCN = 61.24 Nfcn = 43 FCN = 54.95 Nfcn = 46
EDM = 1.03e-05 (Goal: 0.0001) EDM = 5.98e-07 (Goal: 0.0001)
Valid Minimum No Parameters at limit Valid Minimum Below EDM threshold (goal x 10)
Below EDM threshold (goal x 10) Below call limit No parameters at limit Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced Covariance accurate
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0 x0 660 70 760 50 0
1 x1 210 40 203 27 0
x0 5.25e+03 -838 (-0.290) 2.25e+03 -0.4e3 (-0.341)
x1 -838 (-0.290) 1.59e+03
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"│ FCN = 61.24 │ Nfcn = 43 │\n", - "│ EDM = 1.03e-05 (Goal: 0.0001) │ │\n", + "│ FCN = 54.95 │ Nfcn = 46 │\n", + "│ EDM = 5.98e-07 (Goal: 0.0001) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ No Parameters at limit │\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Below EDM threshold (goal x 10) │ Below call limit │\n", - "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", - "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", - "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", + "│ No parameters at limit │ Below call limit │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘\n", "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ x0 │ 660 │ 70 │ │ │ 0 │ │ │\n", - "│ 1 │ x1 │ 210 │ 40 │ │ │ 0 │ │ │\n", + "│ 0 │ x0 │ 760 │ 50 │ │ │ 0 │ │ │\n", + "│ 1 │ x1 │ 203 │ 27 │ │ │ 0 │ │ │\n", "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", "┌────┬───────────────────┐\n", "│ │ x0 x1 │\n", "├────┼───────────────────┤\n", - "│ x0 │ 5.25e+03 -838 │\n", - "│ x1 │ -838 1.59e+03 │\n", + "│ x0 │ 2.25e+03 -0.4e3 │\n", + "│ x1 │ -0.4e3 739 │\n", "└────┴───────────────────┘" ] }, - "execution_count": 11, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -2517,7 +6130,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2525,30 +6138,27 @@ "text/html": [ "\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", "
Migrad Migrad
FCN = 11.42 (chi2/ndof = 0.9) Nfcn = 47 FCN = 8.503 (χ²/ndof = 0.7) Nfcn = 45
EDM = 1.85e-06 (Goal: 0.0002) EDM = 9.17e-06 (Goal: 0.0002)
Valid Minimum No Parameters at limit Valid Minimum Below EDM threshold (goal x 10)
Below EDM threshold (goal x 10) Below call limit No parameters at limit Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced Covariance accurate
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0 x0 760 90 780 50 0
1 x1 190 40 199 28 0
x0 8.08e+03 -1.26e+03 (-0.329) 2.45e+03 -0.5e3 (-0.351)
x1 -1.26e+03 (-0.329) 1.81e+03
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"├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ No Parameters at limit │\n", + "│ No parameters at limit │ Below call limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Below EDM threshold (goal x 10) │ Below call limit │\n", - "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", - "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", - "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘\n", "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ x0 │ 760 │ 90 │ │ │ 0 │ │ │\n", - "│ 1 │ x1 │ 190 │ 40 │ │ │ 0 │ │ │\n", + "│ 0 │ x0 │ 780 │ 50 │ │ │ 0 │ │ │\n", + "│ 1 │ x1 │ 199 │ 28 │ │ │ 0 │ │ │\n", "└───┴──────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌────┬─────────────────────┐\n", - "│ │ x0 x1 │\n", - "├────┼─────────────────────┤\n", - "│ x0 │ 8.08e+03 -1.26e+03 │\n", - "│ x1 │ -1.26e+03 1.81e+03 │\n", - "└────┴─────────────────────┘" + "┌────┬───────────────────┐\n", + "│ │ x0 x1 │\n", + "├────┼───────────────────┤\n", + "│ x0 │ 2.45e+03 -0.5e3 │\n", + "│ x1 │ -0.5e3 756 │\n", + "└────┴───────────────────┘" ] }, - "execution_count": 12, + "execution_count": null, "metadata": {}, "output_type": "execute_result" } @@ -2644,7 +7010,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2652,21 +7018,21 @@ "output_type": "stream", "text": [ "full fit\n", - " x0 796 +- 47\n", - " x1 187 +- 40\n", - " correlation -0.76\n", + " x0 784 +- 35\n", + " x1 199 +- 26\n", + " correlation -0.52\n", "T(JSC)\n", - " x0 858 +- 108\n", - " x1 185 +- 44\n", - " correlation -0.33\n", + " x0 793 +- 51\n", + " x1 197 +- 28\n", + " correlation -0.36\n", "T(ASY)\n", - " x0 660 +- 72\n", - " x1 205 +- 40\n", - " correlation -0.29\n", + " x0 760 +- 47\n", + " x1 203 +- 27\n", + " correlation -0.34\n", "T(DA)\n", - " x0 762 +- 90\n", - " x1 194 +- 43\n", - " correlation -0.33\n" + " x0 784 +- 49\n", + " x1 199 +- 27\n", + " correlation -0.35\n" ] } ], @@ -2691,12 +7057,1533 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -2733,6 +8620,7 @@ } ], "metadata": { + "keep_output": true, "kernelspec": { "display_name": "Python 3.8.12 ('venv': venv)", "language": "python", @@ -2748,9 +8636,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8 (main, Oct 13 2022, 09:48:40) [Clang 14.0.0 (clang-1400.0.29.102)]" + "version": "3.12.4" }, - "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "bdbf20ff2e92a3ae3002db8b02bd1dd1b287e934c884beb29a73dced9dbd0fa3" diff --git a/doc/notebooks/template_gof.ipynb b/doc/notebooks/template_gof.ipynb index fe6bde2f..4bc52403 100644 --- a/doc/notebooks/template_gof.ipynb +++ b/doc/notebooks/template_gof.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "attachments": {}, "cell_type": "markdown", @@ -14,10 +14,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", "from iminuit import cost, Minuit\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", @@ -27,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -35,30 +36,27 @@ "text/html": [ "\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", "
Migrad Migrad
FCN = 13.87 (χ²/ndof = 0.8) Nfcn = 101 FCN = 13.87 (χ²/ndof = 0.8) Nfcn = 101
EDM = 2.91e-08 (Goal: 0.0002) EDM = 2.91e-08 (Goal: 0.0002) time = 0.2 sec
Valid Minimum No Parameters at limit Valid Minimum Below EDM threshold (goal x 10)
Below EDM threshold (goal x 10) Below call limit No parameters at limit Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced Covariance accurate
\n", " \n", @@ -113,16 +111,16 @@ "
\n", "\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " 2023-04-27T12:51:54.476322\n", + " 2024-08-22T11:44:15.234319\n", " image/svg+xml\n", " \n", " \n", - " Matplotlib v3.7.1, https://matplotlib.org/\n", + " Matplotlib v3.9.2, https://matplotlib.org/\n", " \n", " \n", " \n", @@ -133,211 +131,211 @@ " \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", @@ -461,12 +459,12 @@ " \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", @@ -537,12 +535,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -553,12 +551,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -569,12 +567,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -587,17 +585,17 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -605,12 +603,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -620,12 +618,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -635,12 +633,12 @@ " \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", + " \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" @@ -851,14 +849,14 @@ "│ Migrad │\n", "├──────────────────────────────────┬──────────────────────────────────────┤\n", "│ FCN = 13.87 (χ²/ndof = 0.8) │ Nfcn = 101 │\n", - "│ EDM = 2.91e-08 (Goal: 0.0002) │ │\n", + "│ EDM = 2.91e-08 (Goal: 0.0002) │ time = 0.2 sec │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ No Parameters at limit │\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Below EDM threshold (goal x 10) │ Below call limit │\n", - "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", - "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", - "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", + "│ No parameters at limit │ Below call limit │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘\n", "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", @@ -881,30 +879,27 @@ "text/html": [ "\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", "
Migrad Migrad
FCN = 201.8 (χ²/ndof = 1.0) Nfcn = 100 FCN = 201.8 (χ²/ndof = 1.0) Nfcn = 100
EDM = 2.37e-05 (Goal: 0.0002) EDM = 2.37e-05 (Goal: 0.0002)
Valid Minimum No Parameters at limit Valid Minimum Below EDM threshold (goal x 10)
Below EDM threshold (goal x 10) Below call limit No parameters at limit Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced Covariance accurate
\n", " \n", @@ -959,16 +954,16 @@ "
\n", "\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " 2023-04-27T12:51:54.631153\n", + " 2024-08-22T11:44:15.435767\n", " image/svg+xml\n", " \n", " \n", - " Matplotlib v3.7.1, https://matplotlib.org/\n", + " Matplotlib v3.9.2, https://matplotlib.org/\n", " \n", " \n", " \n", @@ -979,1548 +974,1548 @@ " \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", @@ -2644,12 +2639,12 @@ " \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", @@ -2720,12 +2715,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2736,12 +2731,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2752,12 +2747,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2770,17 +2765,17 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2788,12 +2783,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2802,12 +2797,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2816,12 +2811,12 @@ " \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", @@ -2913,12 +2908,12 @@ " \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", - " \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", - 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\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", - " \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", - " \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", + " \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", + " \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" @@ -3785,12 +3780,12 @@ "│ FCN = 201.8 (χ²/ndof = 1.0) │ Nfcn = 100 │\n", "│ EDM = 2.37e-05 (Goal: 0.0002) │ │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ No Parameters at limit │\n", + "│ Valid Minimum │ Below EDM threshold (goal x 10) │\n", + "├──────────────────────────────────┼──────────────────────────────────────┤\n", + "│ No parameters at limit │ Below call limit │\n", "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Below EDM threshold (goal x 10) │ Below call limit │\n", - "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", - "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", - "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", + "│ Hesse ok │ Covariance accurate │\n", + "└──────────────────────────────────┴──────────────────────────────────────┘\n", "┌───┬──────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", "├───┼──────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", @@ -3859,75 +3854,2264 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for bins in data:\n", - " plt.figure()\n", - " plt.title(f\"bins = {bins}\")\n", - " plt.hist(\n", - " chi2(bins - 2).cdf(data[bins]), bins=10, range=(0, 1), label=\"cost function\"\n", - " )\n", - " plt.hist(\n", - " chi2(bins - 2).cdf(data2[bins]),\n", - " bins=10,\n", - " range=(0, 1),\n", - " alpha=0.5,\n", - " label=\"sum of pulls squared\",\n", - " )\n", - " plt.legend()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When 20 bins are used, the number of counts per bin is large enough so that both test statistics are chi-square distributed. When 200 bins are used with samples of the same size, the density in some bins drops low enough so that we are not in the asymptotic limit and see deviations from the theoretical chi-square distribution. These deviations are larger for the sum of pulls squared." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py310", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - }, - "orig_nbformat": 4 + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-08-22T11:44:26.393355\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.2, https://matplotlib.org/\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", + " \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", + " \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", + " \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" + ], + "text/plain": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for bins in data:\n", + " plt.figure()\n", + " plt.title(f\"bins = {bins}\")\n", + " plt.hist(\n", + " chi2(bins - 2).cdf(data[bins]), bins=10, range=(0, 1), label=\"cost function\"\n", + " )\n", + " plt.hist(\n", + " chi2(bins - 2).cdf(data2[bins]),\n", + " bins=10,\n", + " range=(0, 1),\n", + " alpha=0.5,\n", + " label=\"sum of pulls squared\",\n", + " )\n", + " plt.legend()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When 20 bins are used, the number of counts per bin is large enough so that both test statistics are chi-square distributed. When 200 bins are used with samples of the same size, the density in some bins drops low enough so that we are not in the asymptotic limit and see deviations from the theoretical chi-square distribution. These deviations are larger for the sum of pulls squared." + ] + } + ], + "metadata": { + "keep_output": true, + "kernelspec": { + "display_name": "py310", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/doc/notebooks/template_model_mix.ipynb b/doc/notebooks/template_model_mix.ipynb index abc5046d..60d41f68 100644 --- a/doc/notebooks/template_model_mix.ipynb +++ b/doc/notebooks/template_model_mix.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "attachments": {}, "cell_type": "markdown", @@ -14,10 +14,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", "from iminuit import Minuit\n", "from iminuit.cost import Template, ExtendedBinnedNLL\n", "from numba_stats import norm, truncexpon\n", @@ -35,20 +36,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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Migrad
FCN = 87.01 (chi2/ndof = 0.9) Nfcn = 194
EDM = 4.93e-05 (Goal: 0.0002)
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 x0_n 990 40 0
1 x0_mu 0.4951 0.0020 0 1
2 x0_sigma 0.0484 0.0018 0
3 x1 630 40 0
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x0_n x0_mu x0_sigma x1
x0_n 1.43e+03 0.000324 (0.004) 0.0185 (0.274) -441 (-0.284)
x0_mu 0.000324 (0.004) 3.87e-06 -5.26e-08 (-0.015) -0.000347 (-0.004)
x0_sigma 0.0185 (0.274) -5.26e-08 (-0.015) 3.21e-06 -0.0185 (-0.251)
x1 -441 (-0.284) -0.000347 (-0.004) -0.0185 (-0.251) 1.69e+03
" - ], - "text/plain": [ - "┌─────────────────────────────────────────────────────────────────────────┐\n", - "│ Migrad │\n", - "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 87.01 (chi2/ndof = 0.9) │ Nfcn = 194 │\n", - "│ EDM = 4.93e-05 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ No Parameters at limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Below EDM threshold (goal x 10) │ Below call limit │\n", - "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", - "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", - "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", - "┌───┬──────────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼──────────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ x0_n │ 990 │ 40 │ │ │ 0 │ │ │\n", - "│ 1 │ x0_mu │ 0.4951 │ 0.0020 │ │ │ 0 │ 1 │ │\n", - "│ 2 │ x0_sigma │ 0.0484 │ 0.0018 │ │ │ 0 │ │ │\n", - "│ 3 │ x1 │ 630 │ 40 │ │ │ 0 │ │ │\n", - "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌──────────┬─────────────────────────────────────────┐\n", - "│ │ x0_n x0_mu x0_sigma x1 │\n", - "├──────────┼─────────────────────────────────────────┤\n", - "│ x0_n │ 1.43e+03 0.000324 0.0185 -441 │\n", - "│ x0_mu │ 0.000324 3.87e-06 -5.26e-08 -0.000347 │\n", - "│ x0_sigma │ 0.0185 -5.26e-08 3.21e-06 -0.0185 │\n", - "│ x1 │ -441 -0.000347 -0.0185 1.69e+03 │\n", - "└──────────┴─────────────────────────────────────────┘" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# signal model: scaled cdf of a normal distribution\n", "def signal(xe, n, mu, sigma):\n", @@ -260,26 +90,6 @@ "m.migrad()" ] }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "m.visualize()" - ] - }, { "attachments": {}, "cell_type": "markdown", @@ -302,195 +112,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\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", - "
Migrad
FCN = 92.87 (chi2/ndof = 1.0) Nfcn = 190
EDM = 1.67e-05 (Goal: 0.0002)
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 x0_n 990 40 0
1 x0_mu 0.4964 0.0019 0 1
2 x0_sigma 0.0487 0.0016 0
3 x1_n 629 29 0
4 x1_mu 0.97 0.14 0
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x0_n x0_mu x0_sigma x1_n x1_mu
x0_n 1.3e+03 0.000424 (0.006) 0.0123 (0.209) -264 (-0.252) -0.369 (-0.076)
x0_mu 0.000424 (0.006) 3.44e-06 -9.8e-08 (-0.032) 0.000293 (0.005) -1.64e-05 (-0.065)
x0_sigma 0.0123 (0.209) -9.8e-08 (-0.032) 2.64e-06 -0.0129 (-0.271) -1.64e-05 (-0.075)
x1_n -264 (-0.252) 0.000293 (0.005) -0.0129 (-0.271) 851 0.337 (0.085)
x1_mu -0.369 (-0.076) -1.64e-05 (-0.065) -1.64e-05 (-0.075) 0.337 (0.085) 0.0183
" - ], - "text/plain": [ - "┌─────────────────────────────────────────────────────────────────────────┐\n", - "│ Migrad │\n", - "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 92.87 (chi2/ndof = 1.0) │ Nfcn = 190 │\n", - "│ EDM = 1.67e-05 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ No Parameters at limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Below EDM threshold (goal x 10) │ Below call limit │\n", - "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", - "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", - "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", - "┌───┬──────────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼──────────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ x0_n │ 990 │ 40 │ │ │ 0 │ │ │\n", - "│ 1 │ x0_mu │ 0.4964 │ 0.0019 │ │ │ 0 │ 1 │ │\n", - "│ 2 │ x0_sigma │ 0.0487 │ 0.0016 │ │ │ 0 │ │ │\n", - "│ 3 │ x1_n │ 629 │ 29 │ │ │ 0 │ │ │\n", - "│ 4 │ x1_mu │ 0.97 │ 0.14 │ │ │ 0 │ │ │\n", - "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌──────────┬───────────────────────────────────────────────────┐\n", - "│ │ x0_n x0_mu x0_sigma x1_n x1_mu │\n", - "├──────────┼───────────────────────────────────────────────────┤\n", - "│ x0_n │ 1.3e+03 0.000424 0.0123 -264 -0.369 │\n", - "│ x0_mu │ 0.000424 3.44e-06 -9.8e-08 0.000293 -1.64e-05 │\n", - "│ x0_sigma │ 0.0123 -9.8e-08 2.64e-06 -0.0129 -1.64e-05 │\n", - "│ x1_n │ -264 0.000293 -0.0129 851 0.337 │\n", - "│ x1_mu │ -0.369 -1.64e-05 -1.64e-05 0.337 0.0183 │\n", - "└──────────┴───────────────────────────────────────────────────┘" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# signal model: scaled cdf of a normal distribution\n", "def signal(xe, n, mu, sigma):\n", @@ -510,26 +134,6 @@ "m.migrad()" ] }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "m.visualize()" - ] - }, { "attachments": {}, "cell_type": "markdown", @@ -540,195 +144,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\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", - "
Migrad
FCN = 92.87 (chi2/ndof = 1.0) Nfcn = 190
EDM = 1.67e-05 (Goal: 0.0002)
Valid Minimum No Parameters at limit
Below EDM threshold (goal x 10) Below call limit
Covariance Hesse ok Accurate Pos. def. Not forced
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 x0_n 990 40 0
1 x0_mu 0.4964 0.0019 0 1
2 x0_sigma 0.0487 0.0016 0
3 x1_n 629 29 0
4 x1_mu 0.97 0.14 0
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x0_n x0_mu x0_sigma x1_n x1_mu
x0_n 1.3e+03 0.000424 (0.006) 0.0123 (0.209) -264 (-0.252) -0.369 (-0.076)
x0_mu 0.000424 (0.006) 3.44e-06 -9.8e-08 (-0.032) 0.000293 (0.005) -1.64e-05 (-0.065)
x0_sigma 0.0123 (0.209) -9.8e-08 (-0.032) 2.64e-06 -0.0129 (-0.271) -1.64e-05 (-0.075)
x1_n -264 (-0.252) 0.000293 (0.005) -0.0129 (-0.271) 851 0.337 (0.085)
x1_mu -0.369 (-0.076) -1.64e-05 (-0.065) -1.64e-05 (-0.075) 0.337 (0.085) 0.0183
" - ], - "text/plain": [ - "┌─────────────────────────────────────────────────────────────────────────┐\n", - "│ Migrad │\n", - "├──────────────────────────────────┬──────────────────────────────────────┤\n", - "│ FCN = 92.87 (chi2/ndof = 1.0) │ Nfcn = 190 │\n", - "│ EDM = 1.67e-05 (Goal: 0.0002) │ │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Valid Minimum │ No Parameters at limit │\n", - "├──────────────────────────────────┼──────────────────────────────────────┤\n", - "│ Below EDM threshold (goal x 10) │ Below call limit │\n", - "├───────────────┬──────────────────┼───────────┬─────────────┬────────────┤\n", - "│ Covariance │ Hesse ok │ Accurate │ Pos. def. │ Not forced │\n", - "└───────────────┴──────────────────┴───────────┴─────────────┴────────────┘\n", - "┌───┬──────────┬───────────┬───────────┬────────────┬────────────┬─────────┬─────────┬───────┐\n", - "│ │ Name │ Value │ Hesse Err │ Minos Err- │ Minos Err+ │ Limit- │ Limit+ │ Fixed │\n", - "├───┼──────────┼───────────┼───────────┼────────────┼────────────┼─────────┼─────────┼───────┤\n", - "│ 0 │ x0_n │ 990 │ 40 │ │ │ 0 │ │ │\n", - "│ 1 │ x0_mu │ 0.4964 │ 0.0019 │ │ │ 0 │ 1 │ │\n", - "│ 2 │ x0_sigma │ 0.0487 │ 0.0016 │ │ │ 0 │ │ │\n", - "│ 3 │ x1_n │ 629 │ 29 │ │ │ 0 │ │ │\n", - "│ 4 │ x1_mu │ 0.97 │ 0.14 │ │ │ 0 │ │ │\n", - "└───┴──────────┴───────────┴───────────┴────────────┴────────────┴─────────┴─────────┴───────┘\n", - "┌──────────┬───────────────────────────────────────────────────┐\n", - "│ │ x0_n x0_mu x0_sigma x1_n x1_mu │\n", - "├──────────┼───────────────────────────────────────────────────┤\n", - "│ x0_n │ 1.3e+03 0.000424 0.0123 -264 -0.369 │\n", - "│ x0_mu │ 0.000424 3.44e-06 -9.8e-08 0.000293 -1.64e-05 │\n", - "│ x0_sigma │ 0.0123 -9.8e-08 2.64e-06 -0.0129 -1.64e-05 │\n", - "│ x1_n │ -264 0.000293 -0.0129 851 0.337 │\n", - "│ x1_mu │ -0.369 -1.64e-05 -1.64e-05 0.337 0.0183 │\n", - "└──────────┴───────────────────────────────────────────────────┘" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def total(xe, x0_n, x0_mu, x0_sigma, x1_n, x1_mu):\n", " return signal(xe, x0_n, x0_mu, x0_sigma) + background(xe, x1_n, x1_mu)\n", @@ -740,26 +158,6 @@ "m.limits[\"x0_mu\"] = (0, 1)\n", "m.migrad()" ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "m.visualize()" - ] } ], "metadata": { @@ -778,9 +176,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.12.4" }, - "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "bdbf20ff2e92a3ae3002db8b02bd1dd1b287e934c884beb29a73dced9dbd0fa3" diff --git a/doc/notebooks/unstable_fit.ipynb b/doc/notebooks/unstable_fit.ipynb index c6a1ce2a..8f156ae4 100644 --- a/doc/notebooks/unstable_fit.ipynb +++ b/doc/notebooks/unstable_fit.ipynb @@ -1,5 +1,5 @@ { - "cells": [ + "cells": [ { "cell_type": "markdown", "metadata": {}, @@ -29,10 +29,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", "import numpy as np\n", "from iminuit.cost import LeastSquares\n", "from iminuit import Minuit\n", @@ -42,14 +43,1791 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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2oGF4eHgY3bp1S/azRInH5rFjxyxl1apVS3Y/jh071ihcuLDx119/WZWPGDHCyJ8/v3Hy5EnDMO5uc0mGm5ubcf78eatp79y5Y9y6dcuq7MqVK4aXl5fxwgsvWMouXLiQ7DFmGIbRrFkz45FHHjFu3rxpKUtISDACAwONSpUqWcoS40jPa+PGjZZ6W7duNTp16mR89tlnxnfffWeEhoYaDz30kOHs7Gzs27cvxW2ZyM/Pz+jVq1eS8iZNmlht16eeesqoVq1aqvPq06ePUbJkSePixYtW5d26dTPc3d2T/A2k5Ntvv7Wcg1Jz/zYfNWqUIcno16+fpezOnTtG6dKlDQcHB+ODDz6wlF+5csUoVKiQ1bon/r36+PgYMTExlvKvv/7akGRMnz7dUtarVy+rv/ktW7YYkoxFixZZxbh27dok5QcOHDAcHR2NF1980bhy5Yrh4+Nj1K1b17h9+3aS9UvP695zQ+I6JPc6duxYus87icdj4ryvXLliSDImTZqUzJ646+rVq0bRokWNvn37WpWfPXvWcHd3T1J+v8S/7/vPaQ0bNjQcHByMl156yVKWuF/vPUbTuw/Onz9vODo6Gm3atLFazltvvWVISvaYuPdvLrnjODQ01HBwcDBOnDhhKevVq5chyWq7GoZh1K5d2wgICLC8P3r0qCHJmDFjRpL5NmnSxJBkfPnll5ayW7duGd7e3kanTp0sZamdPxJj/+677wxJxtSpU5MsJzmJ85w0aZJx584do1KlSkbNmjUt2yzx7+3ChQtJ6j788MNGq1atrMoyczwnCg0NtZqmWbNmlnN4SoYOHWpIMg4dOpSu9UXuxB0t5Ghdu3bVkCFDtHr1arVs2VKrV69O150s6e6VvPXr1+vpp5+2ukpasWJFtWrVSt9//32SOk2aNFHVqlWTlN971SrxTkqjRo303//+11L+448/Srr7EPi9hgwZkqR7k6R0dYGQpKJFi2rnzp06ffp0qld7U9OrV68kV97uvWIXHx+vqKgoFSlSRJUrV7Z0S0xNeHi4jhw5orfffluXLl2y+qxZs2b66quvlJCQIMMw0r0f3N3d9dRTT+m///2vQkND5eDgoPj4eC1dulQdOnRQ4cKFLdMuWrQoSVeZ5JQvX97y/9QeKnd2dk7X/NJr2bJlatSokYoVK2Z1JbV58+b64IMPtHnzZqt++506dUpyh+/eZ8ISEhIUFRWlhIQE1a1bN1376PLly/rll180ZswYXb16VVevXrV8FhwcrFGjRikyMlI+Pj7y9vZOd1ebe+96BAYGWnWLad++vTp37qwaNWpo5MiRlu63D6po0aL6999/tXv37iSDLEh3/55WrFihrl27yjAMq20eHBysJUuWaN++fekaUa1o0aKSpNWrV6tmzZrJdnFKzYsvvmj5f/78+VW3bl39+++/6tOnj9UyKleurH/++SdJ/Z49e1pd8e/cubNKliypH3/8Mcn5JdGyZcvk7u6uJ5980mrdAwICVKRIEW3cuNFy56969eoaPXq0Ro4cqd9//10XL17Uzz//nGRgj/QeD9WqVUtS9u6771rugCfy9vbO9HmnUKFCcnR0VFhYmPr06ZPss5Tr1q1TVFSUunfvbrUN8ufPrwYNGlh1uU1Nnz59rLo0N2jQQNu3b7faf4n7de/evZay9O6D9evXKy4uToMGDbJazpAhQ6wGV0htWySKjY3VjRs3FBgYKMMwtH//fpUpU8Zq+pdeesnqfaNGjfTVV19Z3ieev1N6PrVIkSJWd/odHR1Vv379ZI/dfv36qUuXLlZlieeLmJgYScrQ3axEiXe1evXqpZUrV1rdqUrO/edd6cGO5+7du6tu3bq6cOGCVq9erXPnzqXaXiQkJGjJkiWqXbu2/P3907Vc5E4kWsjRPDw81Lx5cy1evFjXr19XfHx8uh+wPn/+vG7cuJHsyGcpjYZWrly5ZMtXr16t999/X+Hh4bp165al/N5G8sSJE8qXL58qVKhgVbdy5crpijclEydOVK9eveTr66uAgAC1bt1aPXv2tEog0pLceiUkJGj69OmaPXu2jh07ZtUN76GHHkpznkeOHJF0N4lLSXR0tG7evJmh/dCzZ08tXbpUW7ZsUePGjbV+/XqdO3dOzz//vNV0mRmCuFChQil2zbl582aq3UAy6siRI/r9999T7KZ6/vx5q/cpHXtffPGFpkyZosOHD1s9JJ7S9Pc6evSoDMPQO++8o3feeSfFOHx8fOTs7KzmzZunOc/0qFixop566il98803io+PN2UAkeHDh2v9+vWqX7++KlasqBYtWqhHjx6W4+DChQuKiorSvHnzNG/evGTncf82T0mTJk3UqVMnjR49WlOnTlVQUJA6dOigHj16JDva6f3u/6Lr7u4uZ2dnlShRIkn5/RcpJCUZCtrBwUEVK1ZM9ffbjhw5oujoaHl6eib7+f3rPmzYMC1ZskS7du3S+PHjk73A9CDHwyOPPJJs/cyed5ycnDRhwgQNHTpUXl5eevTRR9W2bVv17NlT3t7ekv53TnriiSeSnYebm1u6Yk9u/0mSr69vkvJ7n71K7z44ceKEpKT72cPDI9XBeBKdPHlS7777rlatWpXk2a/o6Gir987OzknOQcWKFUtST0r54l/p0qWTPNNcrFgx/f7770mmrVSpUorHTeL2v/eCT0Y8++yzGjt2rMaMGZPswBP3MgwjScwPcjz7+fnJz89P0t2kq1+/fmrevLkiIiKSbTc2bdqkyMhIvfbaa5leJnIHEi3keD169FDfvn119uxZtWrVynK1OSskd8LcsmWL2rdvr8aNG2v27NkqWbKkChYsqPnz5yd7p8psXbt2VaNGjfTtt9/q559/1qRJkzRhwgR98803SfrHpyS59Ro/frzeeecdvfDCCxo7dqyKFy+ufPnyaciQIVaDNaQkcZpJkyalOOx7kSJFdPPmzXTFmCg4OFheXl5auHChGjdurIULF8rb2ztJI3nhwoV0PaNVpEgRyzNrJUuWVHx8vM6fP2/1ZSguLk6XLl3K9B3D5CQkJOjJJ5/Um2++meznDz/8sNX75PbRwoULFRISog4dOmjYsGHy9PRU/vz5FRoaqr///jtdMUjSG2+8keJAIonJbnx8vC5cuJDmPKW7z9qkNSKir6+v4uLiFBsbm+oX3JSGa74/QfP391dERIRWr16ttWvXasWKFZo9e7beffddjR492rKuzz33XIrJf+Jza2lxcHDQ8uXLtWPHDn3//ff66aef9MILL2jKlCnasWNHmiOZJZdYppRspvfOdloSEhLk6empRYsWJfv5/V+2//nnH0ticuDAgWTrnD17Nl3Ldnd3T/dFigc57wwZMkTt2rXTypUr9dNPP+mdd95RaGiofvnlF9WuXdtS/6uvvrIkX/dK71D8Ke2r5Mrv3X8Z3QeZER8fryeffFKXL1/W8OHDVaVKFRUuXFiRkZEKCQlJsg3Tc5EjMcFNLvlKbR4ZPXarVKkiKeXjLS2Jd7VCQkL03XffpTrtlStXkiSyZh7PnTt31ieffKLNmzcne25dtGiR8uXLp+7du6drmci9SLSQ4z399NPq37+/duzYoaVLl6a7nqenp5ydnXX06NEknyVXlpIVK1bI2dlZP/30k9XV7Pnz51tN5+fnp4SEBP39999Wd7EiIiLSvayUlCxZUq+88opeeeUVnT9/XnXq1NG4ceMsiVZavy2SnOXLl6tp06b67LPPrMqjoqKsrrynNO/EO3dubm6pXinM6H7Inz+/evTooQULFmjChAlauXKl+vbtm6Sxr1evnuXKcGpGjRplGRkuMSHcs2eP1W+97NmzRwkJCZn6nbDUts+1a9ce6Crq8uXLVb58eX3zzTdWyxk1alS6Yki861mwYME04zh16lS67pJJd0dvTOv3uv755x85OzunmZQUK1ZMUVFRScpPnDiR5K5t4cKF9cwzz+iZZ55RXFycOnbsqHHjxmnkyJGWkT7j4+NNuzP36KOP6tFHH9W4ceO0ePFiPfvss1qyZIlV18CskJgAJTIMQ0ePHk01UaxQoYLWr1+vxx57LM0viQkJCQoJCZGbm5ulu1rnzp2TDDJQsmTJdMU7f/78dP+OXHrPOympUKGChg4dqqFDh+rIkSOqVauWpkyZooULF1rOSZ6enqYdAxmR3n2QeGfkyJEjVsf4hQsXUkx2Eh04cEB//fWXvvjiC6tBOB5khL0yZcqoUKFCOnbsWKbnkR4PP/ywKleurO+++07Tp0/P1NDrzz33nN5//32NHj1a7du3T3aaO3fu6NSpU0k+N/N4Tuw2eP8dROnu6MMrVqxQUFCQqRfvYJ9ItJDjFSlSRHPmzNHx48cz9Bsf+fPnV/PmzbVy5Uqr55uOHj2qNWvWZGg+ic8KJTp+/LhWrlxpNV2rVq301ltv6aOPPtKsWbMs5fePLpfo8OHDcnFxSdJN5V7x8fG6du2apeuKdPdLRKlSpay6MBYuXDjZE35a63X/Fclly5YpMjLSqktf4nNR938ZDggIUIUKFTR58mT16NEjSaN54cIFeXh4ZGo/PP/885o6dar69++va9euJTsSYGae0XriiSdUvHhxzZkzxyrRmjNnjlxcXFIcITI1hQsXTjZR6Nq1q9577z399NNPSa54Jj6XktYV9sTk8t5uMDt37tT27dutjpvEURXvj8PT01NBQUH6+OOPNWjQoCRfNBL3kaRMP6N17zwS/fbbb1q1apVatWqV5u+1VahQQVu2bFFcXJzlLtnq1at16tQpq3136dIlq65ljo6Oqlq1qtasWaPbt2/L2dlZnTp1svx4972jVqYUZ0quXLmiokWLWiWwiUn4vX93WeXLL7/UyJEjLc+yLF++XGfOnEn1B7W7du2q2bNna+zYsUme87lz546uXbtm6Q3w4Ycfatu2bVq1apXatGmjsLAwvfzyy2rcuLFVsvMgz7SkJL3nnftdv349yW9KVahQQa6urpZ9EhwcLDc3N40fP15NmzZN8mxdRo6BzEjvPmjevLkKFiyoGTNmqEWLFpbjLKW24l73nhMSGYah6dOnZzruggULqm7dutqzZ0+m55Feo0ePVrdu3Swj9N5/Dvz5558VFxeXZIj3RPfe1UrJoUOHdPPmzSRDqmfmeE7pmPnss8/k4OCgOnXqJPnsxx9/VFRUFL+dBUkkWrATqT0HlJr33ntPP//8sx577DG9/PLLio+P18yZM1W9enWFh4enax5t2rTRhx9+qJYtW6pHjx46f/68Zs2apYoVK1r1Ua9Vq5a6d++u2bNnKzo6WoGBgdqwYUOKd8/SM7z71atXVbp0aXXu3Fk1a9ZUkSJFtH79eu3evdvqN3gCAgK0dOlSvf7666pXr56KFCmSZlLatm1bjRkzRr1791ZgYKAOHDigRYsWJbmLUKFCBRUtWlRz586Vq6urChcurAYNGqhcuXL69NNP1apVK1WrVk29e/eWj4+PIiMjtXHjRrm5uVkGusjofqhdu7aqV6+uZcuWyd/fP9nGLLPPaI0dO1YDBgxQly5dFBwcrC1btmjhwoUaN26cihcvnuF5BgQEaM6cOXr//fdVsWJFeXp66oknntCwYcO0atUqtW3bViEhIQoICFBsbKwOHDig5cuX6/jx42lewW/btq2++eYbPf3002rTpo2OHTumuXPnqmrVqrp27ZrVelWtWlVLly7Vww8/rOLFi6t69eqqXr26Zs2apccff1yPPPKI+vbtq/Lly+vcuXPavn27/v33X8vvF2X2Ga1nnnlGhQoVUmBgoDw9PXXo0CHNmzdPLi4u+uCDD9Ks/+KLL2r58uVq2bKlunbtqr///tvq7kSiFi1ayNvbW4899pi8vLz0559/aubMmWrTpo0lIfnggw+0ceNGNWjQQH379lXVqlV1+fJl7du3T+vXr0/38PVffPGFZs+eraeffloVKlTQ1atX9cknn8jNzc0qQc8qxYsX1+OPP67evXvr3LlzmjZtmipWrKi+ffumWKdJkybq37+/QkNDFR4erhYtWliG1F62bJmmT5+uzp07688//9Q777yjkJAQyzliwYIFqlWrll555RV9/fXXlnlmxV2h9J537vfXX3+pWbNm6tq1q6pWraoCBQro22+/1blz59StWzdJd++uz5kzR88//7zq1Kmjbt26ycPDQydPntQPP/ygxx57TDNnzjR9nRKldx8k/pZVaGio2rZtq9atW2v//v1as2ZNmueEKlWqqEKFCnrjjTcUGRkpNzc3rVixIs07YWl56qmn9J///EcxMTHpfpYtM5555hkdOHBA48aN0/79+9W9e3f5+fnp0qVLWrt2rTZs2JBml/zEZ7VSasPXrVsnFxcXPfnkk1blmTmex40bp61bt6ply5YqU6aMLl++rBUrVmj37t0aNGhQshcHFi1aJCcnJ3Xq1CnDy0MulO3jHAJpuHd499SkZ3h3wzCMDRs2GLVr1zYcHR2NChUqGJ9++qkxdOhQw9nZOUndlIZR/+yzz4xKlSoZTk5ORpUqVYz58+dbhpa9140bN4zBgwcbDz30kFG4cGGjXbt2xqlTpzI9vPutW7eMYcOGGTVr1jRcXV2NwoULGzVr1jRmz55tNd21a9eMHj16GEWLFjUkWYZ9ThwaeNmyZUnmffPmTWPo0KFGyZIljUKFChmPPfaYsX379iTDahvG3WF5q1atahmO/d7hb/fv32907NjReOihhwwnJyfDz8/P6Nq1q7FhwwareaR3PySaOHGiIckYP358qtsoM+bNm2dUrlzZEsvUqVOthllOSXLDu589e9Zo06aN4erqmmSfXr161Rg5cqRRsWJFw9HR0ShRooQRGBhoTJ482YiLizMMw3oI4/slJCQY48ePN/z8/AwnJyejdu3axurVq5MM7W0YhrFt2zYjICDAcHR0THK8/f3330bPnj0Nb29vo2DBgoaPj4/Rtm1bY/ny5RnabsmZPn26Ub9+faN48eJGgQIFjJIlSxrPPfecceTIkXTPY8qUKYaPj4/h5ORkPPbYY8aePXuSHIcff/yx0bhxY8txVqFCBWPYsGFGdHS01bzOnTtnDBgwwPD19TUKFixoeHt7G82aNTPmzZuX7nj27dtndO/e3ShTpozh5ORkeHp6Gm3btjX27NljNd392zml4aZ79eplFC5cOMlymjRpYjVkfeLf63//+19j5MiRhqenp1GoUCGjTZs2VsN2J84zuZ90mDdvnhEQEGAUKlTIcHV1NR555BHjzTffNE6fPm3cuXPHqFevnlG6dGkjKirKqt706dMNScbSpUvTu5mSldo5xzDSf965f3j3ixcvGgMGDDCqVKliFC5c2HB3dzcaNGhgfP3118nGEBwcbLi7uxvOzs5GhQoVjJCQkCT7734ptT0Z3a+p7YNE8fHxxujRoy3bISgoyPjjjz+S/NxBcsO7Hzp0yGjevLlRpEgRo0SJEkbfvn2N3377Lcm5OaX4kmu7zp07ZxQoUMD46quvrMrvP0bvnfe9x19q57HkbNiwwXjqqacMT09Po0CBAoaHh4fRrl0747vvvkvXPBP3VXL7pUGDBsZzzz2XrjjS8vPPPxtt27Y1SpUqZRQsWNBwdXU1HnvsMWP+/PnJthnR0dGGs7Oz0bFjR1OWD/vnYBgmPYkL2JEOHTro4MGDSZ6FQPZKbT9Mnz5dr732mo4fP55q90ogtwgLC1PTpk21bNmydI+uCpilT58++uuvv7RlyxZbh5Jp4eHhqlOnjvbt25epZ24Bs6XeeR7IBe5/jufIkSP68ccf03yYH+bKyH4wDEOfffaZmjRpQpIFANlg1KhR2r17t7Zu3WrrUDLtgw8+UOfOnUmykGPwjBZyvfLlyyskJETly5fXiRMnNGfOHDk6OqY47DayRnr2Q2xsrFatWqWNGzfqwIEDaQ7hC2RUWj8L4OjomKln9QB7V6ZMmQz/HEdOs2TJEluHAFgh0UKu17JlS/33v//V2bNn5eTkpIYNG2r8+PFJfmMDWSs9++HChQvq0aOHihYtqrfeeivF4XuBzErrZwHSGqAGAID04hktAECesXXr1lR/FqBYsWIKCAjIxogAALkViRYAAAAAmIzBMAAAAADAZCRaAAAAAGAyEi0AAAAAMBmJFgAAAACYjEQLAAAAAExGogUAAAAAJiPRAgAAAACTkWgBAAAAgMlItAAAAADAZCRaAAAAAGAyEi0AAAAAMBmJFgAAAACYjEQLAAAAAExGogUAAAAAJiPRAgAAAACTkWgBAJDN5syZoxo1asjNzU1ubm5q2LCh1qxZY+uwAAAmsqtEKzQ0VPXq1ZOrq6s8PT3VoUMHRUREpFpnwYIFcnBwsHo5OztnU8QAACRVunRpffDBB9q7d6/27NmjJ554Qk899ZQOHjxo69AAACaxq0Rr06ZNGjBggHbs2KF169bp9u3batGihWJjY1Ot5+bmpjNnzlheJ06cyKaIAQBIql27dmrdurUqVaqkhx9+WOPGjVORIkW0Y8cOW4cGADBJAVsHkBFr1661er9gwQJ5enpq7969aty4cYr1HBwc5O3tne7l3Lp1S7du3bK8T0hI0OXLl/XQQw/JwcEh44EDADLFMAxdvXpVpUqVUr58dnVtMN3i4+O1bNkyxcbGqmHDhilOR9sEADlDetsmu0q07hcdHS1JKl68eKrTXbt2TX5+fkpISFCdOnU0fvx4VatWLcXpQ0NDNXr0aFNjBQBk3qlTp1S6dGlbh2GqAwcOqGHDhrp586aKFCmib7/9VlWrVk1xetomAMhZ0mqbHAzDMLIxHtMkJCSoffv2ioqK0q+//pridNu3b9eRI0dUo0YNRUdHa/Lkydq8ebMOHjyY4oa5/6phdHS0ypQpo1OnTsnNzc30dQGQ9a7H3VH9cRskSbv+00wujnZ9nSnPiImJka+vr6KiouTu7m7rcEwVFxenkydPKjo6WsuXL9enn36qTZs2pZhs0TYBuQvtkv1Kb9tkt3t0wIAB+uOPP1JNsiSpYcOGVl0xAgMD5e/vr48//lhjx45Nto6Tk5OcnJySlCeODgXA/hSIu6N8Ti6S7v4t06DZl9zYNc7R0VEVK1aUJAUEBGj37t2aPn26Pv7442Snp20CchfaJfuXVttkl3t04MCBWr16tTZv3pzhriQFCxZU7dq1dfTo0SyKDgCAjEtISLC6YwUAsG92lWgZhqFBgwbp22+/VVhYmMqVK5fhecTHx+vAgQNq3bp1FkQIAEDaRo4cqVatWqlMmTK6evWqFi9erLCwMP3000+2Dg0AYBK7SrQGDBigxYsX67vvvpOrq6vOnj0rSXJ3d1ehQoUkST179pSPj49CQ0MlSWPGjNGjjz6qihUrKioqSpMmTdKJEyf04osv2mw9AAB52/nz59WzZ0+dOXNG7u7uqlGjhn766Sc9+eSTtg4NAGASu0q05syZI0kKCgqyKp8/f75CQkIkSSdPnrQaZvHKlSvq27evzp49q2LFiikgIEDbtm1LdWQnAACy0meffWbrEAAAWcyuEq30DJAYFhZm9X7q1KmaOnVqFkUEAAAAAEnlzl9/BAAAAAAbItECAAAAAJORaAEAAACAyUi0AAAAAMBkJFoAAAAAYDISLQAAAAAwGYkWAAAAAJiMRAsAAAAATEaiBQAAAAAmI9ECAAAAAJORaAEAAACAyUi0AAAAAMBkJFoAAAAAYDISLQAAAAAwGYkWAAAAAJiMRAsAAAAATEaiBQAAAAAmI9ECAAAAAJORaAEAAACAyUi0AAAAAMBkJFoAAAAAYDISLQAAAAAwGYkWAAAAAJiMRAsAAAAATEaiBQAAAAAmI9ECAAAAAJORaAEAAACAyUi0AAAAANhOXKz0nvvdV1ysraMxDYkWAAAAAJiMRAsAAAAATEaiBQAAAAAmI9ECAAAAAJORaAEAAACAyUi0AAAAAMBkJFoAAAAAYDISLQAAAAAwGYkWAAAAAJiMRAsAAAAATEaiBQAAAAAmI9ECgLwkLlZ6z/3uKy7W1tEAAJBr2VWiFRoaqnr16snV1VWenp7q0KGDIiIi0qy3bNkyValSRc7OznrkkUf0448/ZkO0AAAkL7PtGQDAfthVorVp0yYNGDBAO3bs0Lp163T79m21aNFCsbEpX5Xdtm2bunfvrj59+mj//v3q0KGDOnTooD/++CMbIwcA4H8y054BAOxLAVsHkBFr1661er9gwQJ5enpq7969aty4cbJ1pk+frpYtW2rYsGGSpLFjx2rdunWaOXOm5s6dm2ydW7du6datW5b3MTExJq0BAACZa89omwDAvtjVHa37RUdHS5KKFy+e4jTbt29X8+bNrcqCg4O1ffv2FOuEhobK3d3d8vL19TUnYAAAkpGe9oy2CQDsi90mWgkJCRoyZIgee+wxVa9ePcXpzp49Ky8vL6syLy8vnT17NsU6I0eOVHR0tOV16tQp0+IGAOBe6W3PaJsAwL7YVdfBew0YMEB//PGHfv31V9Pn7eTkJCcnJ9PnCwDA/dLbntE2AYB9sctEa+DAgVq9erU2b96s0qVLpzqtt7e3zp07Z1V27tw5eXt7Z2WIAACkKSPtGQDAvthV10HDMDRw4EB9++23+uWXX1SuXLk06zRs2FAbNmywKlu3bp0aNmyYVWECAJCqzLRnAAD7Yld3tAYMGKDFixfru+++k6urq+U5K3d3dxUqVEiS1LNnT/n4+Cg0NFSS9Oqrr6pJkyaaMmWK2rRpoyVLlmjPnj2aN2+ezdYDAJC3pac9AwDYN7u6ozVnzhxFR0crKChIJUuWtLyWLl1qmebkyZM6c+aM5X1gYKAWL16sefPmqWbNmlq+fLlWrlyZ6gPHAABkpfS0ZwAA+2ZXd7QMw0hzmrCwsCRlXbp0UZcuXbIgIgAAMi497RkAwL7Z1R0tAAAAALAHJFoAAAAAYDISLQAAAAAwGYkWAAAAAJiMRAsAAAAATEaiBQAAAAAmI9ECAAAAAJORaAEAAACAyUi0AAAAAMBkJFoAAAAAYDISLQAAAAAwGYkWAAAA8ACux91R2RE/qOyIH3Q97o6tw0EOQaIFAAAAACYj0QIAAAAAk5FoAQAAAIDJSLQAAAAAwGQkWgAAAABgMhItAHaFkZ0AAIA9INECAAAAAJORaAEAAACwK/bQw4VECwAAAABMRqIFAAAAACYj0QIAAAAAk5FoAYC9iouV3nO/+4qLzdJF2UNfeAAAchISLQAAAAAwGYkWAAAAAJiMRAsAAACAFbqMPzgSLQAAAAB5QnYmkCRaAAAAQC7G3SnbINECAAAAAJORaAFADsDVRgAAchcSLQBAliB5BADkZSRaAGAikgsAACCRaAEAAACA6Ui0AAAAAMBkJFoAAAAAYDISLQAAAAAwGYkWAAAAAJiMRAsAAAAATEaiBQAAAAAms7tEa/PmzWrXrp1KlSolBwcHrVy5MtXpw8LC5ODgkOR19uzZ7AkYAID7ZLQtAwDYH7tLtGJjY1WzZk3NmjUrQ/UiIiJ05swZy8vT0zOLIgQAIHWZbcsAAPajgK0DyKhWrVqpVatWGa7n6empokWLmh8QAAAZlNm2DABgP+zujlZm1apVSyVLltSTTz6prVu3pjrtrVu3FBMTY/UCAMCWaJsA2IW4WOk997uvuFhbR2NTuT7RKlmypObOnasVK1ZoxYoV8vX1VVBQkPbt25dindDQULm7u1tevr6+2RgxAABJ0TYBgH3J9YlW5cqV1b9/fwUEBCgwMFCff/65AgMDNXXq1BTrjBw5UtHR0ZbXqVOnsjFiAACSom0CAPtid89omaF+/fr69ddfU/zcyclJTk5O2RgRAACpo20CAPuS6+9oJSc8PFwlS5a0dRgAAAAAcim7u6N17do1HT161PL+2LFjCg8PV/HixVWmTBmNHDlSkZGR+vLLLyVJ06ZNU7ly5VStWjXdvHlTn376qX755Rf9/PPPtloFAEAel1ZbBgCwf3aXaO3Zs0dNmza1vH/99dclSb169dKCBQt05swZnTx50vJ5XFychg4dqsjISLm4uKhGjRpav3691TwAAMhOabVlAAD7Z3eJVlBQkAzDSPHz+xuoN998U2+++WYWRwUAQPql1ZYBAOxfnnxGCwAAAACyEokWAAAAAJiMRAsAAAAATEaiBQAAAAAmI9ECAAAAAJORaAEAAACAyUi0AAAAAMBkJFoAAAAAYDISLQAAAAAwGYkWACBPiY+P18qVK3X16lVbhwIAyMVItAAAeUr+/PnVvXt3XbhwwdahAAByMRItAECeU69ePR07dszWYQAAcjESLQBAnjNo0CC99dZbOnXqlK1DAQDkUgVsHQAAANntmWeekSRVq1ZN7du3V1BQkGrXrq1HHnlEjo6ONo4OAJAbkGgBAPKcY8eO6bffflN4eLh+++03hYaG6vjx4ypQoIAqV66s33//3dYhAgDsHIkWACDP8fPzk5+fn9q3b28pu3r1qsLDw0myAACmINECAECSq6urGjVqpEaNGtk6FABALsBgGACAPOfOnTsaN26cGjZsqDp16qhXr15at26drcMCAOQiJFoAgDxnxIgRmj17tpo1a6YOHTro1q1batu2rXr37i3DMGwdHgAgF6DrIACk4HrcHVV99ydJ0qExwXJx5JSZWyxevFhLlixR48aNLWXHjh1T27ZtNXnyZA0bNsyG0QEAcgPuaAEA8pzY2FiVLl3aqqxcuXKaMWOG5s2bZ6OoAAC5CYkWACDPefzxx/XFF18kKS9XrpxOnz5tg4gAALkNiVYWux53R2VH/KCyI37Q9bg7OapObl0W8eXu+AAzTJgwQdOmTdPgwYN15MgRSdLt27c1Y8YMVa1a1cbRAQByAx44AADkOdWrV1dYWJj69eunmTNnytHRUfHx8SpatKhWrlxp6/AAALkAiRYAIM8JDAzU2rVrtXv3bkVEROjgwYNydXVVgwYN5ObmZuvwAAC5AF0HAQB5zo4dO3Tz5k1JUuXKldWxY0c9+eSTkqThw4fbMjQAQC5BogUAyDM6d+6sDz74QA4ODjp//nySz2NjYzV58mQbRAYAyG3oOggAyDPKlCmj1atXyzAM1axZUw899JBq1qypmjVrqlatWoqIiFDJkiVtHSYAIBcg0QIAe5UQ/7//n9gmVXhCypffdvHYgQ8//FCS5OjoqK1bt+r06dPav3+/wsPD9e233yohIUETJ060cZQAgNyARAsA7NGhVdKaN//3flFnya2U1HKCVLW97eKyE7GxsSpYsKAk6amnnrJxNACQftfj7qjquz9Jkg6NCZaLI1/ncyqe0QIAe3NolfR1T+nqGevymDN3yw+tsk1cdmT37t36448/bB0GACAXI9ECAHuSEC+tHS7JSObD/y9bO8K6WyGSGDBggHbu3Jmk/O+//9bVq1dtEBEAILch0QIAe3JimxRzOpUJDCkm8u50SFFERISCgoKSlK9fv17du3fP/oAAALkOiRYA2JNr58ydLo9yc3PTlStXkpQ3atRIO3bssEFEAIDchkQLAOxJES9zp8ujWrZsmezvZeXLl09xcXE2iAgAkNuQaAGAPfELvDu6oBxSmMBBcvO5Ox1SNHbsWG3atEmdOnXSgQMHJEk3b97UhAkTVKNGDRtHBwDIDUi0AMCe5Mt/dwh3SUmTrf9/3/IDfk8rDb6+vtqxY4du3LihmjVrqlChQnJ1ddX333+vSZMm2To8AEAuwMD7AGBvqraXun5593e07h3i3a3U3SSL39FKFz8/P/344486efKkwsPDVbBgQTVo0EDFixe3dWgAkLfcO1LuiW1ShSdyxQVDEi0AsEdV20vlg6QPfO++f3Z5rmmYskN8fLw+/fRTRUREqHTp0qpZs6Zq1apFkgUA2e3QqrsXDhMt6vz/Fw4n2P2Fw0x3Hbxy5YqWLl2qDz/8UB9++KGWLFmS7AhOeV18wv9+62bXsctW721dJ7cui/iIL6/EZ5VU+QVmaZKVndsiOwwaNEjvvvuuzp07pxEjRqh169by9PRUmTJl1L69fTfs6RGfYGj735f0XXiktv99Kd37MzvqEB/x2Wt8iXJie5Fjz+GHVklf97TunSFJMWfulh9aZXp82dk2ORiGkeG5f/bZZ5o0aZJat26tUqVKSZIiIyO1du1avfHGG+rTp4/pgSbavHmzJk2apL179+rMmTP69ttv1aFDh1TrhIWF6fXXX9fBgwfl6+urt99+WyEhIeleZkxMjNzd3RUdHS03N7d011v7xxmNWnVQ52JuWcpKujtrVLuqalm9pE3r5NZlER/x5ZX4JElxsdL4u+dgvXVaciyc8rQPUCc7t8X9Mnv+TYu3t7e++OILBQcHy9XVVdu2bdOmTZs0ZswYPfPMM5oxY4Zpy0rNrFmzNGnSJJ09e1Y1a9bUjBkzVL9+/XTVfZC2afT3h3Qm+qalLD37MzvqEB/x2Wt8Obm9yPZzeHrbmYR4aVr1VH4b0uHuna0hB5JcSMz29vY+6T3/ZirRqly5svbt26fCha033LVr11SnTh399ddfGZ1luq1Zs0Zbt25VQECAOnbsmGaidezYMVWvXl0vvfSSXnzxRW3YsEFDhgzRDz/8oODg4HQtMzON2do/zujlhft0/8ZNfHR9znN1kuzQ7KqTW5dFfMSXV+KzyEyidTMmQ90Ns3NbJCerEq0iRYrozz//lK+vr4oXL66tW7fK399fU6dO1enTp7NlQIylS5eqZ8+emjt3rho0aKBp06Zp2bJlioiIkKenZ5r1aZuIj/iILyfFZ5HetunYFumLtinPJ1Gv1VK5Rg8cny3apkx1HXRwcNDVq1eTlF+9elUODikNOWyOVq1a6f3339fTTz+drunnzp2rcuXKacqUKfL399fAgQPVuXNnTZ06NctijE8wNPr7Q0l2pCRL2ejvD1ndqsyuOrl1WcRHfHklvgdyaJU06567JYs6372amELXjOzcFtmtfPnyOn367lVUHx8fRUZGSpLatWunhQsXZksMH374ofr27avevXuratWqmjt3rlxcXPT5559nyfJy498D8REf8eWM+DLl2rkMT2c37e3/y1SiNXnyZDVp0kSdOnXS4MGDNXjwYHXs2FFBQUGaMmWKqQE+qO3bt6t58+ZWZcHBwdq+fXuKdW7duqWYmBirV0bsOnbZ6pbz/QxJZ6Jvatexy9leJ7cui/iIL6/El2mZ6Aefndsiu3Xs2FFr1qyRJDVp0sSS3Bw6dEg3btzI8uXHxcVp7969Vu1Tvnz51Lx58xTbJ9om4iM+4sup8WVKEa8MT2cX7e09MjXqYO3atfXLL7/o5MmTliuCpUqVUv369ZU/f84a8ers2bPy8rLekV5eXoqJidGNGzdUqFChJHVCQ0M1evToTC/z/NWUd2RK02VXndy6LOIjvuSmy43xZUpCvLR2uP533e5ehiQHae0IqUobq26E2bktssObb76pMWPGyNnZWe+9955Veb169eTh4aGYmJgsfc440cWLFxUfH59s+3T48OFk69A2mbMs4iO+5KYjvszXyTS/wLvPYMWcUfLt0/8/o+UX+MDx2aptytAdra1bt6pcuXIqU6aMypQpow4dOmjnzp168skn1bBhwxyXZGXWyJEjFR0dbXmdOnUqQ/U9XZ0zPF121cmtyyI+4ktuutwYX6ac2JbKw8aSZEgxkXeny8RyzdgW2WHatGmKjo6WJIWEhOj69euSpDJlyujgwYOaOHGili1bplmzZmV7bOlB22TOsoiP+JKbjvgyXyfT8uW/O4S7pP89KSXr9y0/sLoAmOPb2/tkKNHq37+//P39tXv3bkVERGjSpEnasGGD6tSpY+nfntN4e3vr3DnrPqDnzp2Tm5tbsnezJMnJyUlubm5Wr4yoX664Sro7JzlkEjno7ggn9csVz/Y6uXVZxEd8eSW+TMlEP3gpe7dFdihVqpTCw8MlSV999ZWuXbtm+axEiRLq3bu32rdvn+XPGicuL3/+/Mm2T97e3snWoW0iPuIjvpwaX6ZVbS91/VJyve+851bqbvl9v6OV49vb+2Qo0fr77781bdo01alTRxUrVlTPnj21Z88e1a5dW0OGDDE1MLM0bNhQGzZssCpbt26dGjZsmGXLzJ/PQaPaVZWUYn6uUe2qKn8+h2yvk1uXRXzEl1fiy5RM9IPPbHzZtk6ZMHToULVr106NGt0dvWrRokXatWtXtjyTdT9HR0cFBARYtU8JCQnasGFDlrVPufHvgfiIj/hyRnwPpGp7acCu/71/dvndId2T+bHiHN/e3idDiZa/v7/Onz9vVebg4KAxY8Zo7dq1pgaWkmvXrik8PNxyVfLYsWMKDw/XyZMnJd3tWtGzZ0/L9C+99JL++ecfvfnmmzp8+LBmz56tr7/+Wq+99lqWxtmyeknNea6OPN2crMq93Z1THD4yu+rk1mURH/HllfgyLLEffGrX8tx8rPrBP0h82bJOmTBo0CDt2bNHLVu2lGEYmjVrlgIDA+Xm5iZ/f39169ZNH3zwgWWQjKz2+uuv65NPPtEXX3yhP//8Uy+//LJiY2PVu3fvLFtm4r7xdrfuHpOe/ZnVdYiP+Ow5vpzaXtjNOfzenxnxC0z1Z0dydHt7nwz9jtZHH32k+fPna9WqVfL19bWU79ixQ506dcqW7oNhYWFq2rRpkvJevXppwYIFCgkJ0fHjxxUWFmZV57XXXtOhQ4dUunRpvfPOO9nyg8WSdPXmbT3y3s+SpAW966lRJY80s+XsqpNbl0V8xJdX4svQ72gljjooyfqh4/9fRjJdNB40vsxui3tl1e9oVapUSdu3b1fhwoX1+++/Wy7ghYeH648//kj2J0yywsyZMy0/WFyrVi199NFHatCgQbrqPsi2iU8wtOvYZZ2/elOerne7y6S1b7KrDvERnz3Gl9Pbi2w9h2fmNx4zUSdb29v7pPf8m6FRBxO7B1aqVEkdO3ZUrVq1FB8fr4ULF2rixIkZCjCzgoKClFpuuGDBgmTr7N+/PwujStm9Oy69f+DZVSe3Lov4iC+vxJchif3g17xpPcS7W6m7DxunkmRlNr4sX6cHcOTIEcv/GzRoYJXcZOD64wMbOHCgBg4cmG3LS5Q/n4MaVngoR9bJzmURX/bXyc5lZXd8iXJie5HbzuFSDm5v75GhROvMmTMKDw/Xb7/9pvDwcC1YsEBHjhyRg4ODJk6cqDVr1qhGjRqqUaOGWrZsmVUxAwAyo2p7qXyQ9MH/90h4drlU4YlUu2jkRdkxGAYA5FoJ8f/7/4ltebqdyVCi5eXlpeDgYAUHB1vKbt68qQMHDlgSsFWrVmn8+PGKiooyO1YAwIPKQD94AAAy5NCquz0nEi3q/P89Jyak2XMiN8rUDxbfy9nZWfXq1VO9evXMiAcAAACAvbE8C3xf9+uYM3fL03gWODfK0KiDAAAAAGAlIV5aO1xJkizpf2VrR1h3K8wDSLQAAHnK+fPnNXny5GQ/mz59uk6fPp3NEQGAnTuxTYpJ7dxpSDGRd6fLQ0i0AAB5yqVLlzRlyhQNGDDAqnzYsGF6//33deHCBRtFBgB26to5c6fLJR74GS0AAOyJv7+/wsLC1KxZM924cUOffvqphgwZoq+//lobNmxQjRo1bB0iANiXIl7mTpdLkGgBAPKcypUra9OmTWrWrJkqV66s69eva+PGjfL397d1aABgf/wC744uGHNGyT+n5XD3c7/A7I7Mpug6CADIkypUqKCGDRvq77//Vr169VS5cmVbhwQAaXJxLKDjH7TR8Q/ayMUxh9wzyZf/7hDukqT7f4vw/9+3/CDP/aQIiRYAIM8xDEPPPfecduzYoU2bNikiIkJdu3bVnTt3bB0aANinqu3vDuHu6m1d7lYqTw7tLpFoAQDymDt37uiZZ57Rzp07tXnzZjVq1EhhYWGKiIhQx44dFRcXZ+sQAcA+VW0vDdj1v/fPLpeGHMiTSZZEogUAyGN27dqlI0eOaMuWLfL19ZUkeXl5aePGjTp79qy2bNli4wgBwI7d2z3QLzDPdRe8Vw7p2AkAQPYIDAzUvn375OBg/RxBiRIltHPnziTlAABkBne0AAB5TkrJFEkWAMAsJFoAAAAAYDK6DmaxxCE4c2Kd3Los4sv+Otm9LAAAgJyOO1oAAAAAYDISLQAAAAAwGV0HASAFdG3M23bu3KkGDRrYOgwAgJ3ijhYAAMno0qWLrUMAANgx7mgBAPKsrl27JltuGIYuX76czdEAAHITEi0AQJ61fv16ffXVVypSpIhVuWEY2rx5s42iAgDkBiRaAIA8KygoSK6urmrcuHGSz2rUqGGDiAAAuQWJFgAgz5oxY0aKn61bty4bIwEA5DYMhgEAyHO2bt2qcuXKqUyZMipTpoy8vLw0fPhwxcTE2Do0AEAuQaIFAMhz+vfvL39/f+3evVsRERGaNGmS1q9frzp16igyMtLW4QEAcgESLQBAnvP3339r2rRpqlOnjipWrKiePXtqz549ql27toYMGWLr8AAAuQCJFgAgz/H399f58+etyhwcHDRmzBitXbvWRlEBAHITEi0AQJ4TEhKiQYMG6dSpU1bl0dHRcnNzs1FUAIDchFEHAQB5TmL3wEqVKqljx46qVauW4uPjtXDhQk2cONG2wQEAcgUSLQBAnnPmzBmFh4frt99+U3h4uBYsWKAjR47IwcFBEydO1Jo1a1SjRg3VqFFDLVu2tHW4AAA7RKIFAMhzvLy8FBwcrODgYEvZzZs3deDAAUsCtmrVKo0fP15RUVG2CxQAYLdItAAAkOTs7Kx69eqpXr16tg4FAJALMBgGAAAAAJiMRAsAAAAATEaiBQAAAAAmI9ECAAAAAJORaAEAAACAyUi0AAAAAMBkJFoAAAAAYDISLQAAAAAwmV0mWrNmzVLZsmXl7OysBg0aaNeuXSlOu2DBAjk4OFi9nJ2dszFaAACsjRs3ToGBgXJxcVHRokVtHQ4AIAvYXaK1dOlSvf766xo1apT27dunmjVrKjg4WOfPn0+xjpubm86cOWN5nThxIhsjBgDAWlxcnLp06aKXX37Z1qEAALJIAVsHkFEffvih+vbtq969e0uS5s6dqx9++EGff/65RowYkWwdBwcHeXt7p3sZt27d0q1btyzvY2JiHixoAADuMXr0aEl3e12kF20TANgXu7qjFRcXp71796p58+aWsnz58ql58+bavn17ivWuXbsmPz8/+fr66qmnntLBgwdTXU5oaKjc3d0tL19fX9PWAQBM41hYei/67suxsK2jQRajbQIA+2JXidbFixcVHx8vLy8vq3IvLy+dPXs22TqVK1fW559/ru+++04LFy5UQkKCAgMD9e+//6a4nJEjRyo6OtryOnXqlKnrAQBARtE2AYB9satEKzMaNmyonj17qlatWmrSpIm++eYbeXh46OOPP06xjpOTk9zc3KxeAACkZsSIEUkGX7r/dfjw4UzPn7YJAOyLXT2jVaJECeXPn1/nzp2zKj937ly6n8EqWLCgateuraNHj2ZFiACAPGro0KEKCQlJdZry5ctnTzAAAJuzq0TL0dFRAQEB2rBhgzp06CBJSkhI0IYNGzRw4MB0zSM+Pl4HDhxQ69atszBSAEBe4+HhIQ8PD1uHAQDIIewq0ZKk119/Xb169VLdunVVv359TZs2TbGxsZZRCHv27CkfHx+FhoZKksaMGaNHH31UFStWVFRUlCZNmqQTJ07oxRdftOVqAADysJMnT+ry5cs6efKk4uPjFR4eLkmqWLGiihQpYtvgAACmsLtE65lnntGFCxf07rvv6uzZs6pVq5bWrl1rGSDj5MmTypfvf4+eXblyRX379tXZs2dVrFgxBQQEaNu2bapataqtVgEAkMe9++67+uKLLyzva9euLUnauHGjgoKCbBQVAMBMdpdoSdLAgQNT7CoYFhZm9X7q1KmaOnVqNkQFAED6LFiwIEO/oQUAsD+5ftRBAAAAAMhuJFoAAAAAYDISLQAAAAAwGYkWAAAAAJjMLgfDAICcysWxgI5/0MbWYQAAABsj0QIAZAmSTgBAXkaiBQA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UixYtij3id99992nx4sXy8/NT8+bNtXnzZq1du1Y333yzw7Xff//9mjBhgjZs2KBu3bo5/D7FKen+V9h7PPbYY9ZL+F9v586dOnv2rO6//36b5Y5+RysxMVGJiYmScsNyenq69YIfnTp1srn32KxZs3T+/HlroP/qq6+s95gbMWKE9ZTEI0eOaPHixZKkHTt2SJL1PUNCQvJd0AQAXMZ1FzwEAPf0yy+/GEOGDDEaNmxoeHl5GTVq1DDuuOMOY+bMmdbLlefJzMw0pk+fbrRt29aoVq2aUbVqVaNNmzbGjBkzjKysrHzvff1lvj08PIy6desaAwYMMA4ePFhgPZ07dzZatGhR4Gt5l/Eu7JGYmGjcfPPNRseOHYvc5kaNGhmtW7cu9PW8y7tPmTKlwPVffxnxvMunb9++Pd/47t27G35+foaPj4/RuHFjIzY21tixY4fNuM8++8wICwszvL29jebNmxuff/65MXDgwGIv737u3Dlj0KBBRu3atY3q1asb3bt3N/bv32+EhITYXJq8qPokGevXr7dZHhERYQwePNhmWd7l3X/77bcCt/3QoUPWZddeir049u5/hb3ngQMHDE9PzwL/Xl5++WUjODjY5pL2NyLvZ1DQ49q/l7x6Cxt77c+qqH3ansvHA4CzWAyjBN/GBQAA+SxevFjDhg3T0aNHVbNmTVeX45DMzEw1bNhQY8aM0XPPPefqcgDA7fEdLQAAbtCf//xnBQcHa/bs2a4uxWELFy5U5cqV8933CwDgGI5oAQAAAIDJOKIFAAAAACYjaAEAAACAyQhaAAAAAGAyghYAAAAAmIygBQAAAAAmI2gBAAAAgMkIWgAAAABgMoIWAAAAAJiMoAUAAAAAJiNoAQAAAIDJCFoAAAAAYDKCFgAAAACYjKAFAAAAACYjaAEAAACAyQhaAAAAAGAyghYAAAAAmIygBQAAAAAmI2gBAAAAgMkIWgAAONmcOXMUEREhX19f+fr6KjIyUqtXr3Z1WQAAE7lV0IqLi1O7du1Uo0YN1a1bV3379lVycnKRcxYtWiSLxWLz8PHxcVLFAADk16BBA7355pvauXOnduzYobvuukv333+/9u3b5+rSAAAmcaugtWHDBg0bNkxbtmzRmjVrdOXKFXXr1k3p6elFzvP19dWJEyesjyNHjjipYgAA8uvdu7fuvfdeNWnSRH/605/0xhtvqHr16tqyZYurSwMAmKSSqwsoifj4eJvnixYtUt26dbVz50516tSp0HkWi0UBAQF2ryczM1OZmZnW5zk5OTp79qxuvvlmWSyWkhcOAHCIYRi6cOGCAgMD5eHhVp8N2i07O1srVqxQenq6IiMjCx1HbwKAssHe3uRWQet6qampkqRatWoVOe7ixYsKCQlRTk6O2rRpo4kTJ6pFixaFjo+Li9P48eNNrRUA4Lhjx46pQYMGri7DVD/++KMiIyOVkZGh6tWra+XKlWrevHmh4+lNAFC2FNebLIZhGE6sxzQ5OTnq06ePzp8/r++//77QcZs3b9aBAwcUERGh1NRUTZ06VYmJidq3b1+hP5jrPzVMTU1VcHCwjh07Jl9fX9O3BShLLmVdVfs31kmStv31blX1svPzmKx0aVrT3D+/kCx5VSulClGRpKWlKSgoSOfPn5efn5+ryzFVVlaWjh49qtTUVH366adasGCBNmzYUGjYojehInOoN9GXUErs7U1ue0Rr2LBh2rt3b5EhS5IiIyNtTsWIiopSWFiY5s2bpwkTJhQ4x9vbW97e3vmW510dCijPKmVdlYd3VUm5+7z9QctT8v7f6Uu+vjQ0mKo8nhrn5eWlW265RZLUtm1bbd++XW+//bbmzZtX4Hh6Eyoyh3oTfQmlrLje5JZBa/jw4fr666+VmJhY4lNJKleurNatW+vgwYOlVB0AACWXk5Njc8QKAODe3CpoGYahESNGaOXKlUpISFCjRo1K/B7Z2dn68ccfde+995ZChQAAFG/s2LHq2bOngoODdeHCBS1btkwJCQn617/+5erSAAAmcaugNWzYMC1btkxffvmlatSooZMnT0qS/Pz8VKVKFUlSTEyM6tevr7i4OEnS66+/rttvv1233HKLzp8/rylTpujIkSN64oknXLYdAICK7fTp04qJidGJEyfk5+eniIgI/etf/9I999zj6tIAACZxq6A1Z84cSVJ0dLTN8oULFyo2NlaSdPToUZvLLJ47d05DhgzRyZMnddNNN6lt27batGlTkVd2AgCgNL3//vuuLgEAUMrcKmjZc4HEhIQEm+fTp0/X9OnTS6kiAAAAAMivfN79EQAAAABciKAFAAAAACYjaAEAAACAyQhaAAAAAGAyghYAAAAAmIygBQAAAAAmI2gBAAAAgMkIWgAAAABgMoIWAAAAAJiMoAUAAAAAJiNoAQAAAIDJCFoAAAAAYDKCFgAAAACYjKAFAAAAACYjaAEAAACAyQhaAAAAAGAyghYAAAAAmIygBQAAAAAmI2gBAAAAgMkIWgAAAABgMoIWAAAAAJiMoAUAAAAAJiNoAQAAAIDJCFoAAAAAYDKCFgAAAAD3kpUuveaX+8hKd3U1BSJoAQAAAIDJCFoAAAAAYDKCFgAAAACYjKAFAAAAACYjaAEAAACAyQhaAAAAAGAyghYAAAAAmIygBQAAAAAmI2gBAAAAgMkIWgAAAABgMoIWAAAAAJiMoAUAAAAAJiNoAQAAAIDJCFoAAAAAYDKCFgAAAACYzK2CVlxcnNq1a6caNWqobt266tu3r5KTk4udt2LFCjVr1kw+Pj669dZb9c9//tMJ1QIAUDBH+xkAwH24VdDasGGDhg0bpi1btmjNmjW6cuWKunXrpvT09ELnbNq0SQMGDNDgwYO1e/du9e3bV3379tXevXudWDkAAH9wpJ8BANxLJVcXUBLx8fE2zxctWqS6detq586d6tSpU4Fz3n77bfXo0UMvvfSSJGnChAlas2aNZs2apblz5xY4JzMzU5mZmdbnaWlpJm0BAACO9TN6EwC4F7c6onW91NRUSVKtWrUKHbN582Z17drVZln37t21efPmQufExcXJz8/P+ggKCjKnYAAACmBPP6M3AYB7cduglZOTo5EjR+qOO+5QeHh4oeNOnjwpf39/m2X+/v46efJkoXPGjh2r1NRU6+PYsWOm1Q0AwLXs7Wf0JgBwL2516uC1hg0bpr179+r77783/b29vb3l7e1t+vsCAHA9e/sZvQkA3ItbBq3hw4fr66+/VmJioho0aFDk2ICAAJ06dcpm2alTpxQQEFCaJQIAUKyS9DMAgHtxq1MHDcPQ8OHDtXLlSn333Xdq1KhRsXMiIyO1bt06m2Vr1qxRZGRkaZUJAECRHOlnAAD34lZHtIYNG6Zly5bpyy+/VI0aNazfs/Lz81OVKlUkSTExMapfv77i4uIkSc8995w6d+6sadOmqVevXvr444+1Y8cOzZ8/32XbAQCo2OzpZwAA9+ZWR7TmzJmj1NRURUdHq169etbH8uXLrWOOHj2qEydOWJ9HRUVp2bJlmj9/vlq2bKlPP/1UX3zxRZFfOAYAoDTZ088AAO7NrY5oGYZR7JiEhIR8y/r376/+/fuXQkUAAJScPf0MAODe3OqIFgAAAAC4A4IWAAAAAJiMoAUAAAAAJiNoAQAAAIDJCFoAAAAAYDKCFgAAAACYjKAFAAAAACYjaAEAAACAyQhaAAAAAGAyghYAAAAAmIygBQAAAAAmI2gBAAAAgMkIWgAAAABgMoIWAAAAAJiMoAUAAAAAJiNoAQAAAIDJCFoAAAAAYDKCFgAAAADXyUqXXvPLfWSlu7oa0xC0AAAAAMBkBC0AAAAAMBlBCwAAAABMRtACAAAAAJMRtAAAAADAZAQtAAAAoBy7lHVVDcd8o4ZjvtGlrKuuLqfCIGgBAAAAboLQ5D4IWgAAAICTEZjKP4IWAAAAgArBmQGXoAUAJuITSgAAIBG0AKBQhCYAgD3oFygIQQtAhUATBADYg34BsxC0AAClgn+sAAAqMoIWAAAAAJiMoAXArXCUBACA0ke/vXEELQAuwy9xAABQXhG0AAAAAMBkBC0AAAAAMBlBCwDKAE6jBACgfCFoAQAAADCF0z44zMn+489HNtk+LyMIWgBQkWSlS6/55T6y0l1dDQAAJffTKml2+z+eL31ImhGeu7wMIWgBAAAAcA8/rZI+iZEunLBdnnYid3kZCltuF7QSExPVu3dvBQYGymKx6IsvvihyfEJCgiwWS77HyZMnnVMwAADXKWkvAwAo9/TA+JclGQW8+L9l8WPKzGmEbhe00tPT1bJlS82ePbtE85KTk3XixAnro27duqVUIQA4BxfQcF+O9jIAqNCObJLSjhcxwJDSUnLHlQGVXF1ASfXs2VM9e/Ys8by6deuqZs2a5hcEAEAJOdrLAKBCu3jK3HGlzO2OaDmqVatWqlevnu655x5t3LixyLGZmZlKS0uzeQAA4Er0JgAVXnV/c8eVsnIftOrVq6e5c+fqs88+02effaagoCBFR0dr165dhc6Ji4uTn5+f9REUFOTEigEAyI/eBKDCC4mSfAMlWQoZYJF86+eOKwPKfdBq2rSpnnzySbVt21ZRUVH64IMPFBUVpenTpxc6Z+zYsUpNTbU+jh075sSKAQDIj94EoMLz8JR6TPrfk+vD1v+e93gzd1wZ4Hbf0TJD+/bt9f333xf6ure3t7y9vZ1YEQAARaM3AYCk5n2khz+SVo+2vcS7b2BuyGrex3W1XadCBq2kpCTVq1fP1WUAAAAAKKnmfaTQaOnN/51C/edPpcZ3lZkjWXncLmhdvHhRBw8etD4/dOiQkpKSVKtWLQUHB2vs2LFKSUnRRx99JEmaMWOGGjVqpBYtWigjI0MLFizQd999p2+//dZVmwAAqOCK62UAgGJcG6pCospcyJLcMGjt2LFDXbp0sT4fNWqUJGngwIFatGiRTpw4oaNHj1pfz8rK0gsvvKCUlBRVrVpVERERWrt2rc17AADgTMX1MgCA+3O7oBUdHS3DKOhu0Lmub1CjR4/W6NGjS7kqAADsV1wvAwC4v3J/1UEAAAAAcDaCFgAAAACYjKAFAAAAACYjaAEAAACAyQhaAAAAAGAyghYAAAAAmIygBQAAAAAmI2gBAAAAgMkIWgAAAABgMoIWAKBCyc7O1hdffKELFy64uhQAQDlG0AIAVCienp4aMGCAfvvtN1eXAgAoxwhaAIAKp127djp06JCrywAAlGMELQBAhTNixAi98sorOnbsmKtLAQCUU5VcXQAAAM72yCOPSJJatGihPn36KDo6Wq1bt9att94qLy8vF1cHACgPCFoAgArn0KFD2rNnj5KSkrRnzx7FxcXp8OHDqlSpkpo2baoffvjB1SUCANwcQQsAUOGEhIQoJCREffr0sS67cOGCkpKSCFkAAFMQtAAAkFSjRg117NhRHTt2dHUpAIBygIthAAAqnKtXr+qNN95QZGSk2rRpo4EDB2rNmjWuLgsAUI4QtAAAFc6YMWP07rvv6u6771bfvn2VmZmp++67T4MGDZJhGK4uDwBQDnDqIACgwlm2bJk+/vhjderUybrs0KFDuu+++zR16lS99NJLLqwOAFAecEQLAFDhpKenq0GDBjbLGjVqpJkzZ2r+/PkuqgoAUJ4QtAAAFc6dd96pDz/8MN/yRo0a6fjx4y6oCABQ3hC0StmlrKtqOOYbNRzzjS5lXS1Tc8rrupxZn7LSpdf8ch9Z6aU3x5n1ARXApEmTNGPGDD377LM6cOCAJOnKlSuaOXOmmjdv7uLqAADlAd/RAgBUOOHh4UpISNDQoUM1a9YseXl5KTs7WzVr1tQXX3zh6vIAAOUAQQsAUOFERUUpPj5e27dvV3Jysvbt26caNWqoQ4cO8vX1dXV5AIBygFMHAQAVzpYtW5SRkSFJatq0qfr166d77rlHkvTyyy+7sjQAQDlB0AIAVBgPPfSQ3nzzTVksFp0+fTrf6+np6Zo6daoLKgMAlDecOggAKF5WujQxMPfPrxyXvKq5th4HBQcH6+uvv5ZhGGrZsqVuvvlmtWzZUi1btlSrVq2UnJysevXqubpMAEA5QNACAFQYb731liTJy8tLGzdu1PHjx7V7924lJSVp5cqVysnJ0eTJk11cJQCgPCBoAQAqnPT0dFWuXFmSdP/997u4GgBAecR3tAAAFc727du1d+9eV5cBAJCknOw//nxkk+1zN0bQAgBUOMOGDdPWrVvzLf/111914cIFF1QEABXUT6uk2e3/eL70IWlGeO5yN0fQAgBUOMnJyYqOjs63fO3atRowYIDzCwKAiuinVdInMdKFE7bL007kLnfzsEXQAgBUOL6+vjp37ly+5R07dtSWLVtcUBEAVDA52VL8y5KMAl7837L4MW59GiFBCwBQ4fTo0aPA+2V5eHgoKyvLBRUBQAVzZJOUdryIAYaUlpI7zk0RtAAAFc6ECRO0YcMGPfjgg/rxxx8lSRkZGZo0aZIiIiJcXB0AVAAXT5k7rgzi8u4AgAonKChIW7Zs0dNPP62WLVvK29tbV69elZ+fn7766itXlwcA5V91f3PHlUEELQBAhRQSEqJ//vOfOnr0qJKSklS5cmV16NBBtWrVcnVpAFD+hURJvoG5F74o8HtaltzXQ6KcXZlpCFoAgAonOztbCxYsUHJysho0aKCWLVuqVatWhCwAuEHZOX+Epm2Hzqpjkzry9LDkH+jhKfWYlHt1QVlkG7b+N77Hm7nj3JTD39E6d+6cli9frrfeektvvfWWPv744wKv4FTRXb+zXfvc1XPK67qcWZ9DN9hz8KZ8zqrP0Z+fs9bF/uc+yvI2jRgxQq+++qpOnTqlMWPG6N5771XdunUVHBysPn36uLq8UpedY2jzr7/ry6QUbf71d7v3bWfMob4br0852dKhf0s/fpr7X3t7UwnnOFxfRpr0ml/u48Ca0utNTuy3Zb1fOGtO/N4T6vrWBuvz2IXbdeek7xS/90TBE5r3kR7+SKoRYLvcNzB3eXPzfx87szdZDMMo8bu///77mjJliu69914FBgZKklJSUhQfH68XX3xRgwcPNr3QPImJiZoyZYp27typEydOaOXKlerbt2+RcxISEjRq1Cjt27dPQUFB+tvf/qbY2Fi715mWliY/Pz+lpqbK19fX7nnxe09o3Kp9OpWWaV1Wz89H43o3V4/wei6dU17X5cz69NMqafVo23s/+AbmfjpT2C8GR+Y4sT5Hf37OWhf7n60LGVd062vfSpIWDWpX+KeG18pKlybm/t7WK8clr2pFj3dwnsP70nUc/f1bnICAAH344Yfq3r27atSooU2bNmnDhg16/fXX9cgjj2jmzJmmrasos2fP1pQpU3Ty5Em1bNlSM2fOVPv27YufqBvrTeO/+kknUjOsy+zZt50xh/puvD79tCr3ktnXXs3Nnt5Uwjk3VJ8zepMT+21Z7xfOnPP0kl35TgLM60pzHmtT+LZlpElvBuX++c+fSo3vKv5IlgP9zNm9yaGg1bRpU+3atUvVqtlu0MWLF9WmTRv98ssvJX1Lu61evVobN25U27Zt1a9fv2KD1qFDhxQeHq6nnnpKTzzxhNatW6eRI0fqm2++Uffu3e1apyPNzJGdzVlzyuu6nFmf9QZ7hc0q6FMYR+Y4sT6Hf0E6aV3sf/nnOdQsnBC0bqjZXqe0glb16tX1888/KygoSLVq1dLGjRsVFham6dOn6/jx45oyZYpp6yrM8uXLFRMTo7lz56pDhw6aMWOGVqxYoeTkZNWtW7fY+fQm6svHSb2prPcLZ/Zb9r9c2TmG7pz0nU3wvn5ugJ+Pvn/5roI/EHSkNznwAaCze5NDpw5aLBZduHAh3/ILFy7IYinm09Qb1LNnT/3973/XAw88YNf4uXPnqlGjRpo2bZrCwsI0fPhwPfTQQ5o+fXqp1ZidY2j8Vz8Vdfs1jf/qJ5tDlc6aU17X5cz6HLrBnoM35XNWfY7+/Jy1LvY/2xF5zeLakCVJJ1Mz9PSSXYWfouEEDu9LThYaGqrjx3M/va9fv75SUlIkSb1799aSJUucUsNbb72lIUOGaNCgQWrevLnmzp2rqlWr6oMPPiiV9ZXH/x+o7xpO6k1lvV84s9+y//1h26GzhYasvLknUjO07dDZQseUJlf1JoeC1tSpU9W5c2c9+OCDevbZZ/Xss8+qX79+io6O1rRp00wt8EZt3rxZXbt2tVnWvXt3bd68udA5mZmZSktLs3mUhCM7m7PmlNd1ObM+h26w5+BN+ZxVn8O/IJ20Lva/P+aV9SBT1pttnn79+mn16tWSpM6dO1vDzU8//aTLly+X+vqzsrK0c+dOm/7k4eGhrl27Ftqf6E3UV1R9zupNZb1fOLPfsv/94fSFwuc4Ms5srupNDl11sHXr1vruu+909OhR6yeCgYGBat++vTw9y9aVQU6ePCl/f9vr7/v7+ystLU2XL19WlSpV8s2Ji4vT+PHjHV6nIzubs+aU13U5sz6HbrDn4E35nFWfw78gnbQu9r8/xpWkWUQ2vtmu9zdTWW62o0eP1uuvvy4fHx+99tprNsvbtWunOnXqKC0trVS/Z5znzJkzys7OLrA/7d+/v8A59CZz1lVe63NWbyrr/cKZ/Zb97w91a/jYNc/ecWZzVW8q0RGtjRs3qlGjRgoODlZwcLD69u2rrVu36p577lFkZGSZC1mOGjt2rFJTU62PY8eOlWi+Izubs+aU13U5sz6HbrDn4E35nFWfw78gnbQu9r8/xpXlICOV7WY7Y8YMpaamSpJiY2N16dIlSVJwcLD27dunyZMna8WKFZo9e7bTa7MHvcmcdZXX+pzVm8p6v3Bmv2X/+0P7RrVUz89HhX2ByKLc7xG3b+SaW2i4qjeVKGg9+eSTCgsL0/bt25WcnKwpU6Zo3bp1atOmjfX89rImICBAp07Zfmpx6tQp+fr6Fng0S5K8vb3l6+tr8ygJR3Y2Z80pr+tyZn3WG+wVNcu3vu0N9hyZ48T6HP4F6aR1sf/9Ma8sBxmpbDfbwMBAJSUlSZIWL16sixcvWl+rXbu2Bg0apD59+pT6d43z1ufp6VlgfwoICChwDr2J+oqqz1m9qaz3C2f2W/a/P3h6WDSud3PrmOvnSNK43s2LvzJuKXFVbypR0Pr11181Y8YMtWnTRrfccotiYmK0Y8cOtW7dWiNHjjS1MLNERkZq3bp1NsvWrFmjyMjIUlunIzubs+aU13U5sz7rDfaKmnX9DfYcmePE+hz+BemkdbH//fHqDTcLB+8rY6+y3GxfeOEF9e7dWx07dpQkLV26VNu2bXPKd7Ku5+XlpbZt29r0p5ycHK1bt67U+lN5/P+B+q7hpN5U1vuFM/st+5+tHuH1NOexNqrr622zPMDPp0RX9CsNrupNJQpaYWFhOn36tM0yi8Wi119/XfHx8aYWVpiLFy8qKSnJ+qnkoUOHlJSUpKNHj0rKPbUiJibGOv6pp57Sf/7zH40ePVr79+/Xu+++q08++UTPP/98qdbpyM7mrDnldV3OrM+hG+w5eFM+Z9Xn8C9IJ62L/S/XDTWLn1ZJs6+5R9PSh6QZ4bnLTVRWm+2IESO0Y8cO9ejRQ4ZhaPbs2YqKipKvr6/CwsL06KOP6s0337ReJKO0jRo1Su+9954+/PBD/fzzz3r66aeVnp6uQYMGldo68/5uAvxsj3jas2+X9hzqu/H6rL+Pfa973Z7eVII5N1xfafcmJ/bbstwvnF1f3ty1ozpbny8a1E7fv3yXS0NWHlf0phLdR+udd97RwoULtWrVKgUFBVmXb9myRQ8++KBTTh9MSEhQly5d8i0fOHCgFi1apNjYWB0+fFgJCQk2c55//nn99NNPatCggf7v//7PKTcslhy7oaiz5pTXdTmzPodusOfIHCfW5+jPz1nrYv/LVeL7aDl4XxkrB+5x4vC+dI3Suo9WkyZNtHnzZlWrVk0//PCD9QO8pKQk7d27t8BbmJSGWbNmWW9Y3KpVK73zzjvq0KGDXXNv5GeTnWNo26GzOn0hQ3Vr5B4BLe7vxllzqO/G61NOdu4R64uncr+LFBJVfJ9xYI4z63Naj3ZwXWW5Xzi7vktZV9X81X9Jkn56vbuqetlx7T0n3EcrjzN7U4muOph3emCTJk3Ur18/tWrVStnZ2VqyZIkmT55cogIdFR0draKy4aJFiwqcs3v37lKsqnDXn/Jjz1+ks+aU13U5sz6bX9j2NDJH5zixPkd/fs5aF/tfrh7h9XTHLbXtaxbF3lfGkntfmWa97N4f7eHwvuQEBw4csP65Q4cONuGmBJ8/3rDhw4dr+PDhTltfHk8PS4mvSumsOc5cV3mtTx6eUqOOpT7HmfU5rUc7uK6y3C+cXV9Z58ztKlHQOnHihJKSkrRnzx4lJSVp0aJFOnDggCwWiyZPnqzVq1crIiJCERER6tGjR2nVDABQCZpFSe4rU9J/nJVDzrgYBgCg/CtR0PL391f37t3VvXt367KMjAz9+OOP1gC2atUqTZw4UefPnze7VgCAIxy8rwwAAHCcQzcsvpaPj4/atWundu3amVEPAMBsDt5XBgAAOK5EVx0EALghB+8rAwAAHEfQAoDyzsH7ypRXp0+f1tSpUwt87e2339bx40V9nw0AAPsQtACgInDwvjLl0e+//65p06Zp2LBhNstfeukl/f3vf9dvv/3mosoAAOXJDX9HCwDgJpr3kUKjHbqvTHkSFhamhIQE3X333bp8+bIWLFigkSNH6pNPPtG6desUERHh6hIBAOUAQQsAKhIH7ytT3jRt2lQbNmzQ3XffraZNm+rSpUtav369wsLCXF0aAKCc4NRBAECF1LhxY0VGRurXX39Vu3bt1LRpU1eXBACwl1c16bXU3IdXNVdXUyCCFgCgwjEMQ4899pi2bNmiDRs2KDk5WQ8//LCuXr3q6tIAAOUEQQsAUKFcvXpVjzzyiLZu3arExER17NhRCQkJSk5OVr9+/ZSVleXqEgEA5QBBCwBQoWzbtk0HDhzQv//9bwUF5V4YxN/fX+vXr9fJkyf173//28UVAgDKAy6GAQCoUKKiorRr1y5ZLLb3FKtdu7a2bt2abzkAAI7giBYAoMIpLEwRsgAAZiFoAQAAAIDJOHWwlFX1qqTDb/Yqk3PK67qcWZ/10qKlPUdOrA8AAAA3jCNaAAAAAGAyghYAAAAAmIygBQBAAbZu3erqEgAAboygBQBAAfr37+/qEgAAboyLYQAAKqyHH364wOWGYejs2bNOrgYAUJ4QtAAAFdbatWu1ePFiVa9e3Wa5YRhKTEx0UVUAgPKAoAUAqLCio6NVo0YNderUKd9rERERLqgIAFBeELQAABXWzJkzC31tzZo1TqwEAFDecDEMAECFs3HjRjVq1EjBwcEKDg6Wv7+/Xn75ZaWlpbm6NABAOUHQAgBUOE8++aTCwsK0fft2JScna8qUKVq7dq3atGmjlJQUV5cHACgHCFoAgArn119/1YwZM9SmTRvdcsstiomJ0Y4dO9S6dWuNHDnS1eUBAMoBghYAoMIJCwvT6dOnbZZZLBa9/vrrio+Pd1FVAIDyhKAFAKhwYmNjNWLECB07dsxmeWpqqnx9fV1UFQCgPOGqgwCACifv9MAmTZqoX79+atWqlbKzs7VkyRJNnjzZtcUBAMoFghYAoMI5ceKEkpKStGfPHiUlJWnRokU6cOCALBaLJk+erNWrVysiIkIRERHq0aOHq8sFALghghYAoMLx9/dX9+7d1b17d+uyjIwM/fjjj9YAtmrVKk2cOFHnz593XaEAALdF0AIAQJKPj4/atWundu3auboUAEA5wMUwAAAAAMBkBC0AAAAAMBlBCwAAAABMRtACABQvJ/uPPx/ZZPscAADkQ9ACABTtp1XS7PZ/PF/6kDQjPHc5AAAoEEELAFC4n1ZJn8RIF07YLk87kbucsAUAQIEIWgCAguVkS/EvSzIKePF/y+LHcBohAAAFIGgBAAp2ZJOUdryIAYaUlpI7DgAA2HDLoDV79mw1bNhQPj4+6tChg7Zt21bo2EWLFslisdg8fHx8nFgtALipi6fMHQerN954Q1FRUapatapq1qzp6nIAAKXA7YLW8uXLNWrUKI0bN067du1Sy5Yt1b17d50+fbrQOb6+vjpx4oT1ceTIESdWDABuqrq/ueNglZWVpf79++vpp592dSkAgFJSydUFlNRbb72lIUOGaNCgQZKkuXPn6ptvvtEHH3ygMWPGFDjHYrEoICDA7nVkZmYqMzPT+jwtLe3GigYAdxQSJfkG5l74osDvaVlyXw+JcnZlbm/8+PGScs+6sBe9CQDci1sd0crKytLOnTvVtWtX6zIPDw917dpVmzdvLnTexYsXFRISoqCgIN1///3at29fkeuJi4uTn5+f9REUFGTaNgCA2/DwlHpM+t8Ty3Uv/u95jzdzx6HU0ZsAwL24VdA6c+aMsrOz5e9ve5qKv7+/Tp48WeCcpk2b6oMPPtCXX36pJUuWKCcnR1FRUfrvf/9b6HrGjh2r1NRU6+PYsWOmbgcAuI3mfaSHP5JqXHdWgG9g7vLmfVxTVwVEbwIA9+J2pw6WVGRkpCIjI63Po6KiFBYWpnnz5mnChAkFzvH29pa3t7ezSgSAsq15Hyk0Wnrzf0dQ/vyp1PgujmRdZ8yYMZo0aVKRY37++Wc1a9bMofenNwGAe3GroFW7dm15enrq1CnbK1ydOnXK7u9gVa5cWa1bt9bBgwdLo0QAKJ+uDVUhUYSsArzwwguKjY0tckxoaKhzigEAuJxbBS0vLy+1bdtW69atU9++fSVJOTk5WrdunYYPH27Xe2RnZ+vHH3/UvffeW4qVAgAqmjp16qhOnTquLgMAUEa4VdCSpFGjRmngwIG67bbb1L59e82YMUPp6enWqxDGxMSofv36iouLkyS9/vrruv3223XLLbfo/PnzmjJlio4cOaInnnjClZsBADesqlclHX6zl6vLgAOOHj2qs2fP6ujRo8rOzlZSUpIk6ZZbblH16tVdWxwA3AB60x/cLmg98sgj+u233/Tqq6/q5MmTatWqleLj460XyDh69Kg8PP64xse5c+c0ZMgQnTx5UjfddJPatm2rTZs2qXnz5q7aBABABffqq6/qww8/tD5v3bq1JGn9+vWKjo52UVUAADO5XdCSpOHDhxd6qmBCQoLN8+nTp2v69OlOqAoAAPssWrSoRPfQAgC4H7e6vDsAAAAAuAO3PKIFAOUN57QDACosr2rSa6mursJ0HNECAAAAAJNxRAuAy3AUBwAAlFcELQBuhXAGAEDpo9/eOE4dBAAAAACTcUQLAFAq+DQUAFCREbQAVAj8ox8AYA/6BcxC0AKAQtBsAQD2oF+gIAQtADARzRYAAEgELQAAAAAVhDM/ECVoAQAAAE7GGRDlH0ELAAAAcBOOBDRCnWtwHy0AAAAAMBlHtACgIvGqJr2W6uoqAAAo9ziiBQAAAAAmI2gBAAAAgMkIWgAAAABgMoIWAAAAAJiMoAUAAAAAJiNoAQAAAIDJCFoAAAAAYDKCFgAAAACYjKAFAAAAACYjaAEAAACAyQhaAAAAAGAyghYAAAAAmIygBQAAAAAmI2gBAAAAgMkIWgAAAABgMoIWAAAAAJiMoAUAAAAAJiNoAQAAAIDJCFoAAAAAYDKCFgAAAACYjKAFAAAAACYjaAEAAACAyQhaAAAAAGAyghYAAAAAmIygBQAAAAAmc8ugNXv2bDVs2FA+Pj7q0KGDtm3bVuT4FStWqFmzZvLx8dGtt96qf/7zn06qFAAAW4cPH9bgwYPVqFEjValSRY0bN9a4ceOUlZXl6tIAACZyu6C1fPlyjRo1SuPGjdOuXbvUsmVLde/eXadPny5w/KZNmzRgwAANHjxYu3fvVt++fdW3b1/t3bvXyZUDACDt379fOTk5mjdvnvbt26fp06dr7ty5euWVV1xdGgDARG4XtN566y0NGTJEgwYNUvPmzTV37lxVrVpVH3zwQYHj3377bfXo0UMvvfSSwsLCNGHCBLVp00azZs1ycuUAAEg9evTQwoUL1a1bN4WGhqpPnz568cUX9fnnn7u6NACAidwqaGVlZWnnzp3q2rWrdZmHh4e6du2qzZs3Fzhn8+bNNuMlqXv37oWOl6TMzEylpaXZPAAAKC2pqamqVatWkWPoTQDgXtwqaJ05c0bZ2dny9/e3We7v76+TJ08WOOfkyZMlGi9JcXFx8vPzsz6CgoJuvHgAAApw8OBBzZw5U08++WSR4+hNAOBe3CpoOcvYsWOVmppqfRw7dszVJQEAyrgxY8bIYrEU+di/f7/NnJSUFPXo0UP9+/fXkCFDinx/ehMAuJdKri6gJGrXri1PT0+dOnXKZvmpU6cUEBBQ4JyAgIASjZckb29veXt733jBAIAK44UXXlBsbGyRY0JDQ61/Pn78uLp06aKoqCjNnz+/2PenNwGAe3GroOXl5aW2bdtq3bp16tu3ryQpJydH69at0/DhwwucExkZqXXr1mnkyJHWZWvWrFFkZKQTKgYAVBR16tRRnTp17BqbkpKiLl26qG3btlq4cKE8PDjBBADKG7cKWpI0atQoDRw4ULfddpvat2+vGTNmKD09XYMGDZIkxcTEqH79+oqLi5MkPffcc+rcubOmTZumXr166eOPP9aOHTvs+vQQAACzpaSkKDo6WiEhIZo6dap+++0362tFnW0BAHAvbhe0HnnkEf3222969dVXdfLkSbVq1Urx8fHWC14cPXrU5pPBqKgoLVu2TH/729/0yiuvqEmTJvriiy8UHh7uqk0AAFRga9as0cGDB3Xw4EE1aNDA5jXDMFxUFQDAbG4XtCRp+PDhhZ4qmJCQkG9Z//791b9//1KuCgCA4sXGxhb7XS4AgPvjpHAAAAAAMBlBCwAAAABM5panDgIAnMyrmvRaqqurAADAbXBECwAAAABMRtACAAAAAJMRtAAAAADAZAQtAAAAADAZQQsAAAAATEbQAgAAAACTEbQ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" ] }, "metadata": {}, @@ -266,7 +27405,7 @@ " err = np.array(err)\n", " nfcn = np.array(nfcn)\n", "\n", - " fig, ax = plt.subplots(1, 2, figsize=(10, 4), sharex=True)\n", + " fig, ax = plt.subplots(1, 2, figsize=(9, 3), sharex=True)\n", " plt.suptitle(f\"{minimizer} median(nFCN)={np.mean(nfcn):.0f}\")\n", " for v in (True, False):\n", " m = valid == v\n", @@ -296,6 +27435,7 @@ } ], "metadata": { + "keep_output": true, "kernelspec": { "display_name": "py311", "language": "python", @@ -311,7 +27451,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/doc/notebooks/weighted_histograms.ipynb b/doc/notebooks/weighted_histograms.ipynb new file mode 100644 index 00000000..3f0db66e --- /dev/null +++ b/doc/notebooks/weighted_histograms.ipynb @@ -0,0 +1,14076 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fitting weighted histograms\n", + "\n", + "If histograms are filled with weighted data, we need to construct cost functions that handle this case correctly. Particularly troublesome is when bins contain a negative sum of weights, which can occur randomly if weights are partially negative, like sweights.\n", + "\n", + "Bohm and Zech showed a way to fit weighted histograms, but their approach formally only works if the sum of weights is non-negative. Here, we discuss the ideas behind the cost functions that iminuit uses for weighted histograms, and how to generalize the Bohm-Zech approach to bin entries that are negative. This work is based and references the following papers:\n", + "\n", + "- [1] Baker & Cousins, NIM 221 (1984) 437-442\n", + "- [2] Bohm and Zech, NIMA 748 (2014) 1-6\n", + "- [3] H. Dembinski, M. Schmelling, R. Waldi, NIMA 940 (2019) 135-141.\n", + "\n", + "## Baker-Cousins transform\n", + "\n", + "The maximum-likelihood method is widely very successful, and its qualities shine in particular in the application to Poisson-distributed data, because maximizing the likelihood yields an unbiased estimate of models fitted to this data [3], while naive least-squares does not unless one uses a special iterative scheme.\n", + "\n", + "In constrast to least-squares methods, maximizing the likelihood does not yield a chi-square distributed minimum value, that can be used as a goodness-of-fit test statistic. Baker and Cousins [1] showed that the log-likelihood for Poisson distributed data can be replaced by a delta-log-likelihood, which has this property and is otherwise equivalent. One essentially adds a constant to the log-likelihood, which does not change the location of the minimum. For a single bin in a histogram, one can derive the following statistic, which is also known as the Cash statistic:\n", + "\n", + "$$\n", + "\\ell_\\text{poisson}(n; \\mu) = 2 [n (\\ln(n) - \\ln(\\mu)) - \\mu + n]\n", + "$$\n", + "\n", + "It can be minimized to obtain an unbiased estimate for $\\mu$, and the minimum value is asymptotically chi-square distributed and serves as a goodness-of-fit test statistic. The case $n = 0$ is handled by formally extending the integer realm to the real realm, and realizing that $x \\ln x \\to 0$ for $x\\to 0$. In other words, in case of $n = 0$, $n \\ln n$ has to be replaced by 0.\n", + "\n", + "Furthermore, this form is beneficial for numerical computation on a computer, because near the minimum we have $\\mu \\approx n$ and $\\ell_\\text{poisson}(n;\\mu) \\approx 0$. This means that the sum $\\sum_k \\ell_\\text{poisson}(n_k;\\mu_k)$ over $k$ bins grows slowly and adds terms of similar size, which is ideal from the point of view of accuracy in floating point arithmetic.\n", + "\n", + "As shown by Baker & Cousins, a similar statistic with the same nice properties can be derived from the log-likelihood for multinomially-distributed data:\n", + "\n", + "$$\n", + "\\ell_\\text{multinomial}(n; \\mu) = 2 [n (\\ln(n) - \\ln(\\mu))]\n", + "$$\n", + "\n", + "We further note that $\\ell_\\text{multinomial}$ and $\\ell_\\text{poisson}$ are equivalent for multinomially-distributed data. Because $\\sum_k \\mu_k = \\sum_k n_k$ is always guaranteed, if the sum goes over all $k$ bins, the terms $n_k - \\mu_k$ always sum up to zero, so they can be removed altogether.\n", + "\n", + "## Bohm-Zech transform\n", + "\n", + "Bohm and Zech proposed the scaled Poisson distribution (SPD) as an approximate way to handle sums of weights instead of Poisson counts. This approach also works for multinomially distributed data, as we will see later. The idea of the Bohm and Zech is to use the likelihood for Poisson distributed data also for weighted data. They match the first and second moment of the compound Poisson distribution for weighted data with a single Poisson distribution through a scaling factor $s$, that is multiplied with the prediction and the observation.\n", + "\n", + "The scaling factor is computed as $s = \\sum_i w_i / \\sum_i w_i^2$, where $w_i$ are the weights in the current bin. Instead of the Baker & Cousins transformed log-likelihood $\\ell(n; \\mu)$ for Poisson-distributed data, where $n$ is the observed count and $\\mu$ is the expectation, we now compute $\\ell(s w; s \\mu)$ with $w = \\sum_i w_i$. This can be further simplified:\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\ell_\\text{poisson}(s w; s \\mu) &= 2 [(s w) (\\ln(s w) - \\ln(s \\mu)) - s \\mu + s w] \\\\\n", + "&= 2 s [w (ln(w) - ln(\\mu)) - \\mu + w] \\\\\n", + "&= s \\, \\ell_\\text{poisson}(w; \\mu)\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "Eventually, we find that the normal delta-log-likelihood gets scaled by the factor $s$. Note that we did transformations here that are only allowed for $s > 0$ and $w > 0$, otherwise the logarithms are not defined. The case $w = 0$ can be included in the same way as $n = 0$ by replacing $w \\ln w$ with 0.\n", + "\n", + "### Handling $s=0$\n", + "\n", + "Often, $w = 0$ also implies $\\sum w_i^2 = 0$. In that case $s$ becomes undefined. There is no elegant solution for this, because we need to know the true value of $s$ to perform a correct scaling, but we cannot get it empirically, as there is no data.\n", + "\n", + "One might consider setting $s$ to 0 or 1, but these choices lead to problems. Using $s=0$ implies that empty bins cannot pull the prediction $\\mu$ down, which would result in $\\mu$ values that are overestimated. Similarly, $s=1$ may introduce a too strong pull if the average value of $s$ for the other bins is much smaller than 1, or too little if that average is way larger than 1. We cannot simply use the average value of $s$ either, because $s$ may vary systematically from bin-to-bin. This variation may not even be predictable.\n", + "\n", + "In iminuit, we use the median of $s$ values from bins with entries, which will reduce the bias in at least some scenarios, but in general, practitioners should avoid empty bins in weighted histograms altogether.\n", + "\n", + "### Multinomially-distributed weighted data\n", + "\n", + "While it is irrelvant whether we use $\\ell_\\text{multinomial}$ or $\\ell_\\text{poisson}$ for ordinary multinomially-distributed data, the situation becomes different when weights are involved. If we consider weighted data and apply the Bohm-Zech transform, we find that $\\ell_\\text{multinomial}(s w; s \\mu) \\neq \\ell_\\text{poisson}(s w; s \\mu)$. This is because $\\sum_k s_k (w_k - \\mu_k) \\neq 0$ in general, even if $\\sum_k (w_k - \\mu_k) = 0$ holds. Numerical experiments confirm that $\\ell_\\text{multinomial}(s w; s \\mu)$ yields biased results, the correct cost function for weighted multinomial data is therefore $\\ell_\\text{poisson}(s w; s \\mu)$.\n", + "\n", + "### Extension to datasets with negative sums of weights\n", + "\n", + "The Bohm-Zech formula is only applicable if $w = \\sum_i w_i \\ge 0$ (with the extra condition that we\n", + "discussed), but formally fails if $w < 0$. Since $\\sum_i w_i^2$ is always non-negative, $w < 0$ implies $s < 0$.\n", + "\n", + "Our extension of $\\ell_\\text{poisson}(s w; s \\mu)$ to this case is to use $s = |\\sum_i w_i| / \\sum_i w_i^2$ and replace $w \\ln (w)$ with 0 for $w \\le 0$.\n", + "\n", + "This solution works, because it has the same gradient as a sum of squared studentized residuals $\\sum_k s_k (w_k - \\mu_k)^2/\\mu'_k$, where $\\mu'_k$ approaches $\\mu_k$ in successive iterations, but is fixed during the gradient computation [3]. We know from the Gauss-Markov-Aitken theorem that the minimum of this quadratic function yields an unbiased estimate of $\\mu_k$, if there are no additional constraints on $\\mu_k$. Here, we have the constraint $\\mu_k > 0$, so this won't be perfectly unbiased for very small $\\mu_k$, but otherwise. Since the quadratic function and the original function have the same gradient, the minima of both functions are the same, and the original function also yields an unbiased or a low-bias estimate for very small $\\mu_k$.\n", + "\n", + "We mention the sum of squared studentized residuals, because it provides better intuitive insight. For example, it is clear that $s$ should be positive, since it acts as a modifier of the variance, effectively replacing $\\mu'$ with $\\mu'/s$. A negative $s$ cannot fulfill this purpose. Also, a negative $s_k$ would allow us to reduce the sum by making the disagreement between $w_k$ and $\\mu_k$ larger, which is contradictory.\n", + "\n", + "The gradient is not affected by the particular choice of replacing $w \\ln(w)$ with 0 for $w < 0$, any other constant would also do, since this term drops out in the computation of the gradient. Our choice is motivated by the goal to keep the function minimum approximately chi-square distributed, although that property generally dissolves when negative weights are involved. The delta-log-likelihood compares the expectation from the model with the expectation from the so-called saturated model, but the definition of the latter breaks down for $w < 0$. Our ad hoc choices cannot fix that. The function minimum value can even become negative.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's visualize variations of $\\ell_\\text{poisson}$ for a signle bin and a couple of $w$ values, with $w^2 = 1$.\n", + "\n", + "* `l1`: This is the chosen extension of $\\ell_\\text{poisson}$ as discussed above, with $s = |\\sum_i w_i| / \\sum_i w_i^2$, and $w \\ln w$ replaced by 0 for $w < 0$.\n", + "* `l2`: Like `l1`, but we replace $w \\ln w$ with $w \\ln|w|$.\n", + "* `l3`: Like `l2`, but we use $s = \\sum_i w_i / \\sum_i w_i^2$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-08-22T11:48:56.446730\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%config InlineBackend.figure_formats = ['svg']\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "def log_or_zero(x):\n", + " x = np.atleast_1d(x).copy()\n", + " ma = x > 0\n", + " x[ma] = np.log(x[ma])\n", + " x[~ma] = 0\n", + " return x\n", + "\n", + "\n", + "def l1(w, w2, mu):\n", + " s = np.abs(w) / w2\n", + " return 2 * s * (w * (log_or_zero(w) - log_or_zero(mu)) + mu - w)\n", + "\n", + "\n", + "def l2(w, w2, mu):\n", + " s = np.abs(w) / w2\n", + " return 2 * s * (w * log_or_zero(np.abs(w)) - w * log_or_zero(mu) + mu - w)\n", + "\n", + "\n", + "def l3(w, w2, mu):\n", + " s = w / w2\n", + " return 2 * s * (w * (log_or_zero(np.abs(w)) - log_or_zero(mu)) + mu - w)\n", + "\n", + "\n", + "w2 = 1\n", + "fig, ax = plt.subplots(1, 3, figsize=(9, 3))\n", + "for i, (axi, w) in enumerate(zip(ax, (-10, -5, 5))):\n", + " mu = np.geomspace(1e-3, 10, 100)\n", + " color = f\"C{i}\"\n", + " plt.sca(axi)\n", + " plt.plot(mu, l1(w, w2, mu), color=color, label=f\"l1 w={w}\")\n", + " plt.plot(mu, l2(w, w2, mu), color=color, ls=\":\", label=f\"l2 w={w}\")\n", + " plt.plot(mu, l3(w, w2, mu), color=color, ls=\"--\", label=f\"l3 w={w}\")\n", + " plt.ylim(-1000, 1000)\n", + " plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All cost functions only differ for $w < 0$. `l3` is contradictory, since it prefers an infinite value of $\\mu$ for $w < 0$. Both `l1` and `l2` prefer $\\mu = 0$, which is correct. The vertical offset between `l1` and `l2` grows as $w$ becomes more negative. For both `l1` and `l2`, the function value at the minimum can become arbitrarily negative, something that never happens for $w > 0$ and should not happen for a chi-square-distributed variable. For $w < 0$, we cannot ensure this property, and hence function minimum value is no longer chi-square distributed. It can still qualitatively used as a GoF test statistic, as we will see below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Toy experiments\n", + "\n", + "We illustrate these ideas with toy experiments. We generate data from an exponential distribution whose samples are weighted with a normal distribution. To make the toy more interesting, the width of the normal distribution is a function of the value realised by the exponential distribution. We then form weighted histograms from these data. The average weight per bin is constant, but the variance of the weights increases." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-08-22T11:48:57.444014\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.2, https://matplotlib.org/\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", + " \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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from iminuit.cost import BinnedNLL, ExtendedBinnedNLL\n", + "from iminuit import Minuit\n", + "from scipy.stats import expon, norm, chi2\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import boost_histogram as bh\n", + "from joblib import Parallel, delayed\n", + "\n", + "rng = np.random.default_rng(1)\n", + "\n", + "npoints = 100000\n", + "bins = 20\n", + "\n", + "x = expon.rvs(size=npoints, random_state=rng)\n", + "w = norm.rvs(0.1, x, size=len(x), random_state=rng)\n", + "\n", + "h = bh.Histogram(\n", + " bh.axis.Regular(bins, np.min(x), np.max(x)), storage=bh.storage.Weight()\n", + ")\n", + "h.fill(x, weight=w)\n", + "\n", + "plt.stairs(h.values(), h.axes[0].edges)\n", + "ma = h.values() > 0\n", + "plt.errorbar(\n", + " h.axes[0].centers[ma], h.values()[ma], h.variances()[ma] ** 0.5, fmt=\"o\", color=\"C0\"\n", + ")\n", + "plt.errorbar(\n", + " h.axes[0].centers[~ma],\n", + " h.values()[~ma],\n", + " h.variances()[~ma] ** 0.5,\n", + " fmt=\"s\",\n", + " color=\"C3\",\n", + ")\n", + "plt.axhline(0, ls=\":\", color=\"0.5\", zorder=0)\n", + "xm = np.linspace(0, h.axes[0].edges[-1])\n", + "plt.plot(xm, expon.pdf(xm) * npoints * np.mean(w) * h.axes[0].widths[0]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The line shows the true density, and the data points show the outcome of the sample. Error bars indicate the statistical uncertainty. Bins with negative sums of weights use square markers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We fit this histogram with iminuit's builtin cost functions, which use the ideas discussed above, to obtain an estimate of the slope $\\lambda$ of the exponential." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "xe = h.axes[0].edges\n", + "n = h.values()\n", + "vn = h.variances()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Migrad
FCN = 22.93 (χ²/ndof = 1.3) Nfcn = 40
EDM = 4.77e-05 (Goal: 0.0002) time = 0.3 sec
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 n 10.9e3 0.4e3 0
1 lambd 1.16 0.06 0
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n lambd
n 2.01e+05 26.722 (0.927)
lambd 26.722 (0.927) 0.00413
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Migrad
FCN = 32.6 (χ²/ndof = 1.7) Nfcn = 13
EDM = 1.18e-06 (Goal: 0.0002) time = 0.1 sec
Valid Minimum Below EDM threshold (goal x 10)
No parameters at limit Below call limit
Hesse ok Covariance accurate
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Name Value Hesse Error Minos Error- Minos Error+ Limit- Limit+ Fixed
0 lambd 0.996 0.021 0
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lambd
lambd 0.000457
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The estimates obtained with `ExtendedBinnedNLL` and `BinnedNLL` differ, and we note that the uncertainty of $\\lambda$ is smaller for `BinnedNLL`. That is a consequence of fitting weighted data. For ordinary data the estimates are equal.\n", + "\n", + "To see whether these estimates are biased, we repeat the toy experiment many times with independent data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def run(seed):\n", + " rng = np.random.default_rng(seed)\n", + " # we also randomize the sample size\n", + " x = expon.rvs(size=rng.poisson(npoints), random_state=rng)\n", + " w = norm.rvs(0.1, x, size=len(x), random_state=rng)\n", + "\n", + " h = bh.Histogram(\n", + " bh.axis.Regular(bins, np.min(x), np.max(x)), storage=bh.storage.Weight()\n", + " )\n", + " h.fill(x, weight=w)\n", + " xe = h.axes[0].edges\n", + " n = h.values()\n", + " vn = h.variances()\n", + " data = np.transpose((n, vn))\n", + " ntot = np.sum(n)\n", + "\n", + " m1 = Minuit(ExtendedBinnedNLL(data, xe, model1), ntot, 0.6)\n", + " m1.limits[0] = (0, None)\n", + " m1.limits[1] = (0, None)\n", + " m1.migrad()\n", + "\n", + " m2 = Minuit(BinnedNLL(data, xe, model2), 0.6)\n", + " m2.limits = (0, None)\n", + " m2.migrad()\n", + "\n", + " return (\n", + " ntot,\n", + " m1.valid,\n", + " m1.values[0],\n", + " m1.values[1],\n", + " m1.fval,\n", + " m2.valid,\n", + " m2.values[0],\n", + " m2.fval,\n", + " )\n", + "\n", + "\n", + "result = Parallel(n_jobs=8)(delayed(run)(seed) for seed in range(1000))\n", + "ntot, valid1, ntot1, lambd1, minval1, valid2, lambd2, minval2 = np.transpose(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-08-22T11:49:23.618867\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.2, https://matplotlib.org/\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", + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.title(\"$\\\\lambda$ estimate\")\n", + "plt.hist(\n", + " lambd1,\n", + " bins=20,\n", + " alpha=0.5,\n", + " label=f\"ExtendedBinnedNLL\\nvalid={np.mean(valid1)*100:.0f}%\\nmean = {np.mean(lambd1):.2f}\\nstd.dev. = {np.std(lambd1):.2f}\",\n", + ")\n", + "plt.hist(\n", + " lambd2,\n", + " bins=20,\n", + " alpha=0.5,\n", + " label=f\"BinnedNLL\\nvalid={np.mean(valid2)*100:.0f}%\\nmean = {np.mean(lambd2):.2f}\\nstd.dev. = {np.std(lambd2):.2f}\",\n", + ")\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "plt.hist(ntot1 / ntot - 1)\n", + "plt.title(\n", + " f\"ExtendedBinnedNLL: $n_\\\\mathrm{{tot}}$ estimate\\n(mean-truth)/truth = {np.mean(ntot1) / np.mean(ntot) - 1:.3f}\"\n", + ")\n", + "plt.xlabel(\"(estimate - truth) / truth\")\n", + "plt.xlim(-0.2, 0.2);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, we find that the estimates of $\\lambda$ have low bias, although they are not perfectly unbiased.\n", + "\n", + "The variance of $\\lambda$ is smaller for `BinnedNLL`. If you are not interested in the amplitude of the distribution, it is therefore better to use `BinnedNLL` for weighted histograms. Note that `BinnedNLL` and `ExtendedBinnedNLL` internally use the same cost function for weighted data, so the difference in precision originates from the additional information used in `BinnedNLL`, that the sample is complete, there are no further events in other bins.\n", + "\n", + "The second plot shows the estimate of the total sum of weights with `ExtendedBinnedNLL`, which is distributed around the true value. We observe a small upward bias of 2%.\n", + "\n", + "We claim above that estimates for our chosen cost function are unbiased, while these results show small bias. There are two reasons for that. i) The cost Bohm-Zech approach does not guarantee unbiasedness anymore, since we must estimate $s$ from the information in the bin, and the empirical estimate for $s$ that we use is not an unbiased estimate of the true value, because it is a non-linear function of the weights. ii) The unbiasedness was claimed for the bin expectations and linear functions thereof. The model parameter $\\lambda$ is not a linear function of the bin expectations, therefore it is also not guaranteed to be unbiased. We will come back to this point below.\n", + "\n", + "We further said that the minimum value is no longer chi-square distributed when bins with negative sums of weights are fitted, so let's look at the actual distribution of the minimum value of the cost function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-08-22T11:49:24.062732\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.2, https://matplotlib.org/\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", + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", + "\n", + "plt.sca(ax[0])\n", + "plt.hist(minval1, bins=50, density=True)\n", + "x = np.linspace(0, 100)\n", + "plt.plot(x, chi2(bins - 2).pdf(x))\n", + "plt.title(\n", + " f\"ExtendedBinnedNLL minimum value\\nndf = {bins-2} mean = {np.mean(minval1):.2f} median = {np.median(minval1):.2f}\"\n", + ")\n", + "\n", + "plt.sca(ax[1])\n", + "plt.hist(minval2, bins=50, density=True)\n", + "x = np.linspace(0, 100)\n", + "plt.plot(x, chi2(bins - 2).pdf(x))\n", + "plt.title(\n", + " f\"BinnedNLL minimum value\\nndf = {bins-2} mean = {np.mean(minval2):.2f} median = {np.median(minval2):.2f}\"\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We find that these distributions are broader than the asymptotical chi-square distribution and contain negative values, as expected. One can qualitatively still use very large values compared ot the expected mean as evidence for a bad fit, however, since bins with $w < 0$ only reduce the function minimum value." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For reference, it is interesting to run the toy experiment again with a very narrow weight distribution. We expect that the bias largely disappears, as our original toy experiment with its broad weight distribution was designed to be challenging. For this run, we draw the weights from a narrow normal distribution with constant width of 0.01." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def run(seed):\n", + " rng = np.random.default_rng(seed)\n", + " # we also randomize the sample size\n", + " x = expon.rvs(size=rng.poisson(npoints), random_state=rng)\n", + " w = rng.normal(0.1, 0.01, size=len(x))\n", + "\n", + " h = bh.Histogram(\n", + " bh.axis.Regular(bins, np.min(x), np.max(x)), storage=bh.storage.Weight()\n", + " )\n", + " h.fill(x, weight=w)\n", + " xe = h.axes[0].edges\n", + " n = h.values()\n", + " vn = h.variances()\n", + " data = np.transpose((n, vn))\n", + " ntot = np.sum(n)\n", + "\n", + " m1 = Minuit(ExtendedBinnedNLL(data, xe, model1), ntot, 0.6)\n", + " m1.limits[0] = (0, None)\n", + " m1.limits[1] = (0, None)\n", + " m1.migrad()\n", + "\n", + " m2 = Minuit(BinnedNLL(data, xe, model2), 0.6)\n", + " m2.limits = (0, None)\n", + " m2.migrad()\n", + "\n", + " return (\n", + " ntot,\n", + " m1.valid,\n", + " m1.values[0],\n", + " m1.values[1],\n", + " m1.fval,\n", + " m2.valid,\n", + " m2.values[0],\n", + " m2.fval,\n", + " )\n", + "\n", + "\n", + "result = Parallel(n_jobs=8)(delayed(run)(seed) for seed in range(1000))\n", + "ntot, valid1, ntot1, lambd1, minval1, valid2, lambd2, minval2 = np.transpose(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-08-22T11:49:37.371002\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.2, https://matplotlib.org/\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", + " 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.title(\"$\\\\lambda$ estimate\")\n", + "plt.hist(\n", + " lambd1,\n", + " bins=20,\n", + " alpha=0.5,\n", + " label=f\"ExtendedBinnedNLL\\nvalid={np.mean(valid1)*100:.0f}%\\nmean = {np.mean(lambd1):.2f}\\nstd.dev. = {np.std(lambd1):.3f}\",\n", + ")\n", + "plt.hist(\n", + " lambd2,\n", + " bins=20,\n", + " alpha=0.5,\n", + " label=f\"BinnedNLL\\nvalid={np.mean(valid2)*100:.0f}%\\nmean = {np.mean(lambd2):.2f}\\nstd.dev. = {np.std(lambd2):.3f}\",\n", + ")\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "plt.hist(ntot1 / ntot - 1, bins=20)\n", + "plt.title(\n", + " f\"ExtendedBinnedNLL: $n_\\\\mathrm{{tot}}$ estimate\\n(mean-truth)/truth = {np.mean(ntot1) / np.mean(ntot) - 1:.3f}\"\n", + ")\n", + "plt.xlabel(\"(estimate - truth) / truth\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now find negligible bias for $\\lambda$ and $n_\\text{tot}$. The precision is also greatly improved, since the weight distribution is so narrow that it can effectively be neglected as a source of additional uncertainty to the underlying Poisson process.\n", + "\n", + "The estimates from `ExtendedBinnedNLL` and `BinnedNLL` for $\\lambda$ become equal in this case (and their precision), because the ordinary likelihood for unweighted samples factorizes into a part for $n_\\text{tot}$ and another for $\\lambda$. This means that the estimates for $n_\\text{tot}$ and $\\lambda$ are independent. This factorization is broken for weighted histograms in general, and only restored here, because the weight variance is negligible.\n", + "\n", + "Finally, we have another look at the function minimum values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-08-22T11:49:37.930557\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.2, https://matplotlib.org/\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", + " 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", + "\n", + "plt.sca(ax[0])\n", + "plt.hist(minval1, bins=50, density=True)\n", + "x = np.linspace(0, 100)\n", + "plt.plot(x, chi2(bins - 2).pdf(x))\n", + "plt.title(\n", + " f\"ExtendedBinnedNLL minimum value\\nndf = {bins-2} mean = {np.mean(minval1):.2f} median = {np.median(minval1):.2f}\"\n", + ")\n", + "\n", + "plt.sca(ax[1])\n", + "plt.hist(minval2, bins=50, density=True)\n", + "x = np.linspace(0, 100)\n", + "plt.plot(x, chi2(bins - 2).pdf(x))\n", + "plt.title(\n", + " f\"BinnedNLL minimum value\\nndf = {bins-2} mean = {np.mean(minval2):.2f} median = {np.median(minval2):.2f}\"\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The minimum values now follow the theoretical chi-square distribution very well, since bins with a negative sum of weights do not occur anymore." + ] + } + ], + "metadata": { + "keep_output": true, + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/studies.rst b/doc/studies.rst index 6c612a95..cda4eba5 100644 --- a/doc/studies.rst +++ b/doc/studies.rst @@ -11,6 +11,7 @@ The following studies explore different aspects of the library, including its pe :maxdepth: 1 notebooks/binned_vs_unbinned + notebooks/weighted_histograms notebooks/hesse_and_minos notebooks/numba notebooks/automatic_differentiation diff --git a/doc/tutorials.rst b/doc/tutorials.rst index 69e4d301..b63ed28a 100644 --- a/doc/tutorials.rst +++ b/doc/tutorials.rst @@ -25,7 +25,6 @@ Important for most users are only the first two entries. notebooks/roofit notebooks/external_minimizer notebooks/generic_least_squares - notebooks/cython RooFit tutorials ---------------- diff --git a/noxfile.py b/noxfile.py index 8769d0a1..f797a2a5 100644 --- a/noxfile.py +++ b/noxfile.py @@ -28,7 +28,7 @@ @nox.session(reuse_venv=True) def test(session: nox.Session) -> None: """Run all tests.""" - session.install("-e.[test]") + session.install("--only-binary=:all:", "-e.[test]") extra_args = session.posargs if session.posargs else ("-n=auto",) session.run("pytest", *extra_args, env=ENV) @@ -36,7 +36,7 @@ def test(session: nox.Session) -> None: @nox.session(python=MINIMUM_PYTHON, venv_backend="uv") def mintest(session: nox.Session) -> None: """Run tests on the minimum python version.""" - session.install("-e.", "--resolution=lowest-direct") + session.install("--only-binary=:all:", "-e.", "--resolution=lowest-direct") session.install("pytest", "pytest-xdist") extra_args = session.posargs if session.posargs else ("-n=auto",) session.run("pytest", *extra_args) @@ -45,7 +45,15 @@ def mintest(session: nox.Session) -> None: @nox.session(python=LATEST_PYTHON) def maxtest(session: nox.Session) -> None: """Run the unit and regular tests.""" - session.install("-e.", "scipy", "matplotlib", "pytest", "pytest-xdist", "--pre") + session.install( + "--only-binary=:all:", + "-e.", + "scipy", + "matplotlib", + "pytest", + "pytest-xdist", + "--pre", + ) extra_args = session.posargs if session.posargs else ("-n=auto",) session.run("pytest", *extra_args, env=ENV) @@ -62,7 +70,7 @@ def pypy(session: nox.Session) -> None: @nox.session(python="3.12", venv_backend="uv", reuse_venv=True) def cov(session: nox.Session) -> None: """Run covage and place in 'htmlcov' directory.""" - session.install("-e.[test,doc]") + session.install("--only-binary=:all:", "-e.[test,doc]") session.run("coverage", "run", "-m", "pytest", env=ENV) session.run("coverage", "html", "-d", "build/htmlcov") session.run("coverage", "report", "-m") @@ -72,7 +80,7 @@ def cov(session: nox.Session) -> None: @nox.session(python="3.11", reuse_venv=True) def doc(session: nox.Session) -> None: """Build html documentation.""" - session.install("-e.[test,doc]") + session.install("--only-binary=:all:", "-e.[test,doc]") # link check session.run( @@ -89,7 +97,7 @@ def doc(session: nox.Session) -> None: @nox.session(python="3.11", reuse_venv=True) def linkcheck(session: nox.Session) -> None: """Check all links in the documentation.""" - session.install("-e.[test,doc]") + session.install("--only-binary=:all:", "-e.[test,doc]") # link check session.run( diff --git a/pyproject.toml b/pyproject.toml index 677fe945..6d3aaf39 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,9 +6,7 @@ build-backend = "scikit_build_core.build" name = "iminuit" description = "Jupyter-friendly Python frontend for MINUIT2 in C++" version = "2.28.0" -maintainers = [ - { name = "Hans Dembinski", email = "hans.dembinski@gmail.com" }, -] +maintainers = [{ name = "Hans Dembinski", email = "hans.dembinski@gmail.com" }] readme = "README.rst" requires-python = ">=3.9" license = { file = "LICENSE" } @@ -41,11 +39,11 @@ documentation = "https://scikit-hep.org/iminuit" [project.optional-dependencies] test = [ "coverage", - "cython", # ipywidgets 8.0.5 and 8.0.6 are broken # see https://github.com/jupyter-widgets/ipywidgets/issues/3731 "ipywidgets", - "ipykernel", # needed by ipywidgets >= 8.0.6 + # needed by ipywidgets >= 8.0.6 + "ipykernel", "joblib", "jacobi", "matplotlib", @@ -106,6 +104,8 @@ pydocstyle.convention = "numpy" [tool.ruff.lint.per-file-ignores] "test_*.py" = ["B", "D"] +"*.ipynb" = ["D"] +"automatic_differentiation.ipynb" = ["F821"] ".ci/*.py" = ["D"] "bench/*.py" = ["D"] "doc/*.py" = ["D"] diff --git a/src/iminuit/cost.py b/src/iminuit/cost.py index 21444f8e..b8277502 100644 --- a/src/iminuit/cost.py +++ b/src/iminuit/cost.py @@ -106,19 +106,18 @@ class documentation for details. Any, Iterable, Optional, - overload, TypeVar, Callable, cast, ) import warnings -from ._deprecated import deprecated_parameter, deprecated +from ._deprecated import deprecated_parameter __all__ = [ "CHISQUARE", "NEGATIVE_LOG_LIKELIHOOD", "chi2", - "multinominal_chi2", + "multinomial_chi2", "poisson_chi2", "template_chi2_jsc", "template_chi2_da", @@ -142,13 +141,30 @@ class documentation for details. _TINY_FLOAT = np.finfo(float).tiny -def _safe_log(x): - # guard against x = 0 - return np.log(x + _TINY_FLOAT) +def log_or_zero(x): + """ + Evaluate to log(x) for x > 0 and to 0 otherwise. + + Parameters + ---------- + x : array + Argument. + + Returns + ------- + array + Elementwise contains log(x) for x > 0 and zero otherwise. + """ + # return 0 for x <= 0 + r = np.zeros_like(x) + ma = x > 0 + r[ma] = np.log(x[ma]) + return r def _unbinned_nll(x): - return -np.sum(_safe_log(x)) + # sorting makes sum more accurate, protect against x = 0 + return -np.sum(np.sort(np.log(x + _TINY_FLOAT))) def _z_squared(y, ye, ym): @@ -162,71 +178,6 @@ def _replace_none(x, replacement): return x -@deprecated("The class is deprecated and will be removed without replacement") -class BohmZechTransform: - """ - Apply Bohm-Zech transform. - - See Bohm and Zech, NIMA 748 (2014) 1-6. - - :meta private: - """ - - __slots__ = "_obs", "_scale" - - _obs: NDArray - _scale: NDArray - - def __init__(self, val: ArrayLike, var: ArrayLike): - """ - Initialize transformer with data value and variance. - - Parameters - ---------- - val : array-like - Observed values. - var : array-like - Estimated variance of observed values. - """ - val, var = np.atleast_1d(val, var) - - self._scale = np.ones_like(val) - np.divide(val, var, out=self._scale, where=var > 0) - self._obs = val * self._scale - - @overload - def __call__( - self, val: ArrayLike - ) -> Tuple[NDArray, NDArray]: ... # pragma: no cover - - @overload - def __call__( - self, val: ArrayLike, var: ArrayLike - ) -> Tuple[NDArray, NDArray, NDArray]: ... # pragma: no cover - - def __call__(self, val, var=None): - """ - Return precomputed scaled data and scaled prediction. - - Parameters - ---------- - val : array-like - Predicted values. - var : array-like, optional - Predicted variance. - - Returns - ------- - (obs, pred) or (obs, pred, pred_var) - """ - val = np.atleast_1d(val) - s = self._scale - if var is None: - return self._obs, val * s - var = np.atleast_1d(var) - return self._obs, val * s, var * s**2 - - def chi2(y: ArrayLike, ye: ArrayLike, ym: ArrayLike) -> float: """ Compute (potentially) chi2-distributed cost. @@ -237,24 +188,47 @@ def chi2(y: ArrayLike, ye: ArrayLike, ym: ArrayLike) -> float: Parameters ---------- - y : array-like + y : array-like with shape (N,) Observed values. - ye : array-like + ye : array-like with shape (N,) Uncertainties of values. - ym : array-like + ym : array-like with shape (N,) Expected values. Returns ------- float - Const function value. + Value of cost function. """ y, ye, ym = np.atleast_1d(y, ye, ym) + assert y.ndim == 1 return np.sum(_z_squared(y, ye, ym)) -def _chi2_grad(y: NDArray, ye: NDArray, ym: NDArray, ymg: NDArray) -> NDArray: - return -2 * np.sum((y - ym) * ymg * ye**-2, axis=1) +def _chi2_grad(y: ArrayLike, ye: ArrayLike, ym: ArrayLike, gym: ArrayLike) -> NDArray: + """ + Compute gradient of :func:`chi2`. + + Parameters + ---------- + y : array-like with shape (N,) + Observed values. + ye : array-like with shape (N,) + Uncertainties of values. + ym : array-like with shape (N,) + Expected values. + gym : array-like with shape (K, N) + Gradient of ym with respect to K model parameters. + + Returns + ------- + array with shape (K,) + Gradient of cost function with respect to model parameters. + """ + y, ye, ym, gym = np.atleast_1d(y, ye, ym, gym) + assert y.ndim == 1 + assert gym.ndim == 2 + return -2 * np.sum((y - ym) * gym * ye**-2, axis=1) def _soft_l1_cost(y: NDArray, ye: NDArray, ym: NDArray) -> float: @@ -262,15 +236,16 @@ def _soft_l1_cost(y: NDArray, ye: NDArray, ym: NDArray) -> float: return 2 * np.sum(np.sqrt(1 + z_sqr) - 1) -def _soft_l1_cost_grad(y: NDArray, ye: NDArray, ym: NDArray, ymg: NDArray) -> NDArray: +def _soft_l1_cost_grad(y: NDArray, ye: NDArray, ym: NDArray, gym: NDArray) -> NDArray: inv_ye = 1 / ye z = (y - ym) * inv_ye - return -2 * np.sum(z * ymg * inv_ye * (1 + z**2) ** -0.5, axis=1) + f = (1 + z**2) ** -0.5 + return -2 * np.sum(z * inv_ye * f * gym, axis=tuple(range(1, gym.ndim))) -def multinominal_chi2(n: ArrayLike, mu: ArrayLike) -> float: +def poisson_chi2(n: ArrayLike, mu: ArrayLike) -> float: """ - Compute asymptotically chi2-distributed cost for binomially-distributed data. + Compute asymptotically chi2-distributed cost for Poisson-distributed data. See Baker & Cousins, NIM 221 (1984) 437-442. @@ -288,21 +263,21 @@ def multinominal_chi2(n: ArrayLike, mu: ArrayLike) -> float: Notes ----- - The implementation makes the result asymptotically chi2-distributed and - keeps the sum small near the minimum, which helps to maximise the numerical - accuracy for Minuit. + The implementation makes the result asymptotically chi2-distributed, + which helps to maximise the numerical accuracy for Minuit. """ n, mu = np.atleast_1d(n, mu) - return 2 * np.sum(n * (_safe_log(n) - _safe_log(mu))) + return 2 * np.sum(n * (log_or_zero(n) - log_or_zero(mu)) + mu - n) -def _multinominal_chi2_grad(n: NDArray, mu: NDArray, gmu: NDArray) -> NDArray: - return -2 * np.sum(n * gmu / mu, axis=tuple(range(1, gmu.ndim))) +def _poisson_chi2_grad(n: NDArray, mu: NDArray, gmu: NDArray) -> NDArray: + assert gmu.ndim == 2 + return 2 * np.sum((1.0 - n / mu) * gmu, axis=1) -def poisson_chi2(n: ArrayLike, mu: ArrayLike) -> float: +def multinomial_chi2(n: ArrayLike, mu: ArrayLike) -> float: """ - Compute asymptotically chi2-distributed cost for Poisson-distributed data. + Compute asymptotically chi2-distributed cost for multinomially-distributed data. See Baker & Cousins, NIM 221 (1984) 437-442. @@ -324,11 +299,12 @@ def poisson_chi2(n: ArrayLike, mu: ArrayLike) -> float: which helps to maximise the numerical accuracy for Minuit. """ n, mu = np.atleast_1d(n, mu) - return 2 * np.sum(mu - n + n * (_safe_log(n) - _safe_log(mu))) + return 2 * np.sum(n * (log_or_zero(n) - log_or_zero(mu))) -def _poisson_chi2_grad(n: NDArray, mu: NDArray, gmu: NDArray) -> NDArray: - return 2 * np.sum(gmu * (1.0 - n / mu), axis=tuple(range(1, gmu.ndim))) +def _multinomial_chi2_grad(n: NDArray, mu: NDArray, gmu: NDArray) -> NDArray: + assert gmu.ndim == 2 + return -2 * np.sum(n / mu * gmu, axis=1) def template_chi2_jsc(n: ArrayLike, mu: ArrayLike, mu_var: ArrayLike) -> float: @@ -443,23 +419,21 @@ def template_nll_asy(n: ArrayLike, mu: ArrayLike, mu_var: ArrayLike) -> float: # precision. Fall back to plain numpy for float128 which is not currently supported # by numba. try: - from numba import njit as jit + from numba import njit from numba.extending import overload as nb_overload - @nb_overload(_safe_log, inline="always") - def _ol_safe_log(x): - return _safe_log # pragma: no cover + jit = njit(nogil=True, cache=True, error_model="numpy") + + @nb_overload(log_or_zero, inline="always") + def _ol_log_or_zero(x): + return log_or_zero # pragma: no cover @nb_overload(_z_squared, inline="always") def _ol_z_squared(y, ye, ym): return _z_squared # pragma: no cover _unbinned_nll_np = _unbinned_nll - _unbinned_nll_nb = jit( - nogil=True, - cache=True, - error_model="numpy", - )(_unbinned_nll_np) + _unbinned_nll_nb = jit(_unbinned_nll_np) def _unbinned_nll(x): if x.dtype in (np.float32, np.float64): @@ -467,28 +441,20 @@ def _unbinned_nll(x): # fallback to numpy for float128 return _unbinned_nll_np(x) - _multinominal_chi2_np = multinominal_chi2 - _multinominal_chi2_nb = jit( - nogil=True, - cache=True, - error_model="numpy", - )(_multinominal_chi2_np) + _multinomial_chi2_np = multinomial_chi2 + _multinomial_chi2_nb = jit(_multinomial_chi2_np) - def multinominal_chi2(n: ArrayLike, mu: ArrayLike) -> float: # noqa + def multinomial_chi2(n: ArrayLike, mu: ArrayLike) -> float: # noqa n, mu = np.atleast_1d(n, mu) if mu.dtype in (np.float32, np.float64): - return _multinominal_chi2_nb(n, mu) + return _multinomial_chi2_nb(n, mu) # fallback to numpy for float128 - return _multinominal_chi2_np(n, mu) + return _multinomial_chi2_np(n, mu) - multinominal_chi2.__doc__ = _multinominal_chi2_np.__doc__ + multinomial_chi2.__doc__ = _multinomial_chi2_np.__doc__ _poisson_chi2_np = poisson_chi2 - _poisson_chi2_nb = jit( - nogil=True, - cache=True, - error_model="numpy", - )(_poisson_chi2_np) + _poisson_chi2_nb = jit(_poisson_chi2_np) def poisson_chi2(n: ArrayLike, mu: ArrayLike) -> float: # noqa n, mu = np.atleast_1d(n, mu) @@ -500,11 +466,7 @@ def poisson_chi2(n: ArrayLike, mu: ArrayLike) -> float: # noqa poisson_chi2.__doc__ = _poisson_chi2_np.__doc__ _chi2_np = chi2 - _chi2_nb = jit( - nogil=True, - cache=True, - error_model="numpy", - )(_chi2_np) + _chi2_nb = jit(_chi2_np) def chi2(y: ArrayLike, ye: ArrayLike, ym: ArrayLike) -> float: # noqa y, ye, ym = np.atleast_1d(y, ye, ym) @@ -516,11 +478,7 @@ def chi2(y: ArrayLike, ye: ArrayLike, ym: ArrayLike) -> float: # noqa chi2.__doc__ = _chi2_np.__doc__ _soft_l1_cost_np = _soft_l1_cost - _soft_l1_cost_nb = jit( - nogil=True, - cache=True, - error_model="numpy", - )(_soft_l1_cost_np) + _soft_l1_cost_nb = jit(_soft_l1_cost_np) def _soft_l1_cost(y: NDArray, ye: NDArray, ym: NDArray) -> float: if ym.dtype in (np.float32, np.float64): @@ -1294,17 +1252,62 @@ class BinnedCost(MaskedCostWithPulls): Base class for binned cost functions to support histograms filled with weights. Histograms filled with weights are supported by applying the Bohm-Zech transform. - See Bohm and Zech, NIMA 748 (2014) 1-6. + + The Bohm-Zech approach was further generalized to handle sums of weights which are + negative. See Baker & Cousins, NIM 221 (1984) 437-442; Bohm and Zech, NIMA 748 + (2014) 1-6; H. Dembinski, M. Schmelling, R. Waldi, Nucl.Instrum.Meth.A 940 (2019) + 135-141. + + Bohm and Zech use the scaled Poisson distribution (SPD) as an approximate way to + handle sums of weights instead of Poisson counts. This approach also works for + multinomial distributions. The idea of the Bohm and Zech is to use the likelihood + for Poisson distributed data also for weighted data. They show that one can match + the first and second moment of the compound Poisson distribution for weighted data + with a single Poisson distribution with a scaling factor s, that is multiplied with + the predicted expectation and the observation. + + This scaling factor is computed as s = sum(wi) / sum(wi**2), wi are the weights in + the current bin. Instead of the Baker & Cousins transformed log-likelihood + l(n; mu) for Poisson-distributed data, where n is the observed count and mu is the + expectation, we now compute l(sum(w) * s; mu * s), this can be further simplified: + + l(w * s, mu * s) = 2 * [(w * s) * (log(w * s) - log(mu * s)) - s * mu + s * w] + = 2 * s * [w * (log(w) - log(mu)) - mu + w] + = s * l(w, mu) + + For multinomially-distributed data and s = 1, sum(w-mu) = 0, which is why these + terms can be omitted in the standard calculation without weights, but in case of + weighted counts, sum(s * (w - m)) != 0 and the terms must be kept. + + The original formulas from Bohm and Zech are only applicable if w >= 0 (with the + extra condition that w * log(w) evaluates to 0 for w = 0). One can generalize the + formula to w < 0, which is relevant in practice for example in fits of sweighted + samples, by computing s = abs(sum(wi)) / sum(wi ** 2) and replacing w * log(w) with + 0 for w <= 0. + + This works, because this extension has the right gradient. The gradient should be + equal to hat of the quadratic function s * (w - mu)**2/mu', where mu'=mu but fixed + during the gradient computation, see D. Dembinski, M. Schmelling, R. Waldi. The + minimum of this quadratic function yields an unbiased estimate of mu, even if some w + are negative. Since the quadratic function and the original function have the same + gradient, the minima of both functions are the same, and the original function also + yields an unbiased estimate. + + The gradient is not affected by the particular choice of how to handle w * log(w) + with w < 0, since this term drops out in the computation of the gradient. Other + choices are possible. Our goal was to select an option which keeps the function + minimum approximately chi-square distributed, although that property tends to + dissolve when negative weights are involved. The minimum can even become negative. :meta private: """ - __slots__ = "_xe", "_ndim", "_bohm_zech_scale", "_bohm_zech_n" + __slots__ = "_xe", "_ndim", "_bohm_zech_n", "_bohm_zech_s" _xe: Union[NDArray, Tuple[NDArray, ...]] _ndim: int - _bohm_zech_scale: Optional[NDArray] - _bohm_zech_n: Optional[NDArray] + _bohm_zech_n: NDArray + _bohm_zech_s: Optional[NDArray] n = MaskedCost.data @@ -1346,9 +1349,9 @@ def __init__( "xe must be longer by one element along each dimension" ) - self._bohm_zech_scale = None - self._bohm_zech_n = None - self._set_bohm_zech(n, is_weighted) + # _bohm_zech_s will be set properly when init of base class + # is called, which in turn calls our _update_cache() override + self._bohm_zech_s = np.zeros(0) if is_weighted else None super().__init__(parameters, n, verbose) def prediction( @@ -1419,7 +1422,7 @@ def _pred( def _n_err(self) -> Tuple[NDArray, NDArray]: d = self.data - if self._bohm_zech_scale is None: + if self._bohm_zech_s is None: n = d.copy() err = d**0.5 else: @@ -1438,46 +1441,48 @@ def _pulls(self, args: Sequence[float]) -> NDArray: n, ne = self._n_err() return (n - mu) / ne - def _set_bohm_zech(self, n: NDArray, is_weighted: bool): - if not is_weighted: - return - val = n[..., 0] - var = n[..., 1] - self._bohm_zech_scale = np.ones_like(val) - np.divide(val, var, out=self._bohm_zech_scale, where=var > 0) - self._bohm_zech_n = val * self._bohm_zech_scale - def _update_cache(self): super()._update_cache() - self._set_bohm_zech(self._masked, self._bohm_zech_scale is not None) + n = self._masked + if self._bohm_zech_s is not None: + val = n[..., 0] + var = n[..., 1] + s = np.zeros_like(val) + ma = var > 0 + s[ma] = np.abs(val[ma]) / var[ma] + # Use median of s from bins with entries to bins which have zero entries. + # This is arbitrary, but still better than other arbitrary choices. + s[~ma] = np.median(s[ma]) + self._bohm_zech_s = s + self._bohm_zech_n = val * s + else: + self._bohm_zech_n = n - @overload - def _transformed( - self, val: NDArray - ) -> Tuple[NDArray, NDArray]: ... # pragma: no cover + def _transformed(self, val: NDArray) -> Tuple[NDArray, NDArray]: + s = self._bohm_zech_s + ma = self.mask + if ma is not None: + val = val[ma] + n = self._bohm_zech_n + if s is None: + return n, val + return n, val * s - @overload - def _transformed( + def _transformed2( self, val: NDArray, var: NDArray - ) -> Tuple[NDArray, NDArray, NDArray]: ... # pragma: no cover - - def _transformed(self, val, var=None): - s = self._bohm_zech_scale + ) -> Tuple[NDArray, NDArray, NDArray]: + s = self._bohm_zech_s ma = self.mask if ma is not None: val = val[ma] - if var is not None: - var = var[ma] + var = var[ma] + n = self._bohm_zech_n if s is None: - if var is None: - return self._masked, val - return self._masked, val, var - if var is None: - return self._bohm_zech_n, val * s - return self._bohm_zech_n, val * s, var * s**2 + return n, val, var + return n, val * s, var * s**2 def _counts(self): - if self._bohm_zech_scale is None: + if self._bohm_zech_s is None: return self._masked return self._masked[..., 0] @@ -1813,9 +1818,9 @@ def _pred(self, args: Sequence[float]) -> Tuple[NDArray, NDArray]: def _value(self, args: Sequence[float]) -> float: mu, mu_var = self._pred(args) - n, mu, mu_var = self._transformed(mu, mu_var) + n, mu, mu_var = self._transformed2(mu, mu_var) ma = mu > 0 - return self._impl(n[ma], mu[ma], mu_var[ma]) + return self._impl(n[ma].reshape(-1), mu[ma].reshape(-1), mu_var[ma].reshape(-1)) def _grad(self, args: Sequence[float]) -> NDArray: raise NotImplementedError # pragma: no cover @@ -1891,10 +1896,20 @@ class BinnedNLL(BinnedCostWithModel): The cost function has a minimum value that is asymptotically chi2-distributed. It is constructed from the log-likelihood assuming a multivariate-normal distribution and - using the saturated model as a reference. + using the saturated model as a reference, see :func:`multinomial_chi2` for details. + + When this class is used with weighted data, we use the Bohm-Zech transform for + Poisson-distributed data and the :func:`poisson_chi2` cost function, because + :func:`multinomial_chi2` yields biased results for weighted data. The + reasoning for this choice is that :func:`multinomial_chi2` and :func:`poisson_chi2` + yield the same result for a model which predicts probabilities and expected counts + are computed by multiplying the probability with the total number of counts. Thus we + can derive :func:`multinomial_chi2` as a special case of :func:`poisson_chi2` in + case of unweighted data, but this mathematical equivalence is gone when data are + weighted. The correct cost function is then :func:`poisson_chi2`. """ - __slots__ = () + __slots__ = ("_chi2",) @property def cdf(self): @@ -1955,6 +1970,10 @@ def __init__( same length as there are model parameters. Default is None. """ super().__init__(n, xe, cdf, verbose, grad, use_pdf, name) + if self._bohm_zech_s is None: + self._chi2 = multinomial_chi2 + else: + self._chi2 = poisson_chi2 def _pred(self, args: Sequence[float]) -> NDArray: # must return array of full length, mask not applied yet @@ -1969,7 +1988,7 @@ def _pred(self, args: Sequence[float]) -> NDArray: def _value(self, args: Sequence[float]) -> float: mu = self._pred(args) n, mu = self._transformed(mu) - return multinominal_chi2(n, mu) + return self._chi2(n.reshape(-1), mu.reshape(-1)) def _grad(self, args: Sequence[float]) -> NDArray: # pg and p must be arrays of full length, mask not applied yet @@ -1978,20 +1997,27 @@ def _grad(self, args: Sequence[float]) -> NDArray: ma = self.mask # normalise probability of remaining bins if ma is not None: - scale = np.sum(p[ma]) - pg = pg / scale - p * np.sum(pg[:, ma]) / scale**2 - p /= scale + psum = np.sum(p[ma]) + pg = pg / psum - p * np.sum(pg[:, ma]) / psum**2 + p /= psum # scale probabilities with total number of entries of unmasked bins in histogram - scale = np.sum(self._counts()) - mu = p * scale - gmu = pg * scale + n = self._counts() + ntot = np.sum(n) + mu = p * ntot + gmu = pg * ntot ma = self.mask if ma is not None: mu = mu[ma] gmu = gmu[:, ma] - # don't need to scale mu and gmu, because scale factor cancels - n = self._masked if self._bohm_zech_scale is None else self._bohm_zech_n - return _multinominal_chi2_grad(n, mu, gmu) + n = n.reshape(-1) + mu = mu.reshape(-1) + gmu = gmu.reshape(gmu.shape[0], -1) + s = self._bohm_zech_s + if s is None: + return _multinomial_chi2_grad(n, mu, gmu) + # use original n and mu because Bohm-Zech scale factor cancels + s = s.reshape(-1) + return _poisson_chi2_grad(n, mu, s * gmu) class ExtendedBinnedNLL(BinnedCostWithModel): @@ -2075,7 +2101,7 @@ def __init__( def _value(self, args: Sequence[float]) -> float: mu = self._pred(args) n, mu = self._transformed(mu) - return poisson_chi2(n, mu) + return poisson_chi2(n.reshape(-1), mu.reshape(-1)) def _grad(self, args: Sequence[float]) -> NDArray: mu = self._pred(args) @@ -2084,11 +2110,14 @@ def _grad(self, args: Sequence[float]) -> NDArray: if ma is not None: mu = mu[ma] gmu = gmu[:, ma] - n = self._counts() - s = self._bohm_zech_scale + mu = mu.reshape(-1) + gmu = gmu.reshape(gmu.shape[0], -1) + n = self._counts().reshape(-1) + s = self._bohm_zech_s if s is None: return _poisson_chi2_grad(n, mu, gmu) # use original n and mu because Bohm-Zech scale factor cancels + s = s.reshape(-1) return _poisson_chi2_grad(n, mu, s * gmu) @@ -2554,6 +2583,7 @@ def _model_parameters(model, name): "BarlowBeestonLite": ("Template", Template), "barlow_beeston_lite_chi2_jsc": ("template_chi2_jsc", template_chi2_jsc), "barlow_beeston_lite_chi2_hpd": ("template_chi2_da", template_chi2_da), + "multinominal_chi2": ("multinomial_chi2", multinomial_chi2), } diff --git a/tests/test_cost.py b/tests/test_cost.py index 30512aa0..6ffcf335 100644 --- a/tests/test_cost.py +++ b/tests/test_cost.py @@ -13,7 +13,7 @@ Constant, NormalConstraint, Template, - multinominal_chi2, + multinomial_chi2, PerformanceWarning, ) from iminuit.util import describe @@ -54,6 +54,11 @@ def expon_cdf(x, a): return -np.expm1(-x / a) +def scaled_expon_cdf(x, n, a): + with np.errstate(over="ignore"): + return n * -np.expm1(-x / a) + + def numerical_cost_gradient(fcn): jacobi = pytest.importorskip("jacobi").jacobi return lambda *args: jacobi(lambda p: fcn(*p), args)[0] @@ -650,6 +655,32 @@ def test_BinnedNLL_weighted(use_grad): assert m2.ngrad == 0 +@pytest.mark.parametrize("use_grad", (False, True)) +def test_BinnedNLL_negative_weights(use_grad): + n = [48.57881659, 7.39393075, -1.6026582, 2.56719152, 1.03007896] + vn = [242.13921373, 46.13228182, 15.26273872, 6.26334277, 1.52360132] + xe = [0.0, 1.41453733, 2.82907465, 4.24361198, 5.6581493, 7.07268663] + + w = np.transpose((n, vn)) + c = BinnedNLL(w, xe, expon_cdf, grad=numerical_model_gradient(expon_cdf)) + + if use_grad: + ref = numerical_cost_gradient(c) + assert_allclose(c.grad(1), ref(1)) + assert_allclose(c.grad(12), ref(12)) + + m2 = Minuit(c, 1, grad=use_grad) + m2.limits = (0, None) + m2.migrad() + assert m2.valid + assert m2.values[0] == pytest.approx(1.3, abs=0.05) + + if use_grad: + assert m2.ngrad > 0 + else: + assert m2.ngrad == 0 + + def test_BinnedNLL_name(binned): mle, nx, xe = binned @@ -958,6 +989,36 @@ def test_ExtendedBinnedNLL_weighted(use_grad): assert m2.ngrad == 0 +@pytest.mark.parametrize("use_grad", (False, True)) +def test_ExtendedBinnedNLL_negative_weights(use_grad): + n = [48.57881659, 7.39393075, -1.6026582, 2.56719152, 1.03007896] + vn = [242.13921373, 46.13228182, 15.26273872, 6.26334277, 1.52360132] + xe = [0.0, 1.41453733, 2.82907465, 4.24361198, 5.6581493, 7.07268663] + + w = np.transpose((n, vn)) + c = ExtendedBinnedNLL( + w, xe, scaled_expon_cdf, grad=numerical_model_gradient(scaled_expon_cdf) + ) + + # if use_grad: + # ref = numerical_cost_gradient(c) + # assert_allclose(c.grad(1, 0.1), ref(1, 0.1)) + # assert_allclose(c.grad(1, 1), ref(1, 1)) + # assert_allclose(c.grad(2, 12), ref(2, 12)) + + m2 = Minuit(c, 50, 1, grad=use_grad) + m2.limits = (0, None) + m2.migrad() + assert m2.valid + assert m2.values[0] == pytest.approx(65, abs=1) + assert m2.values[1] == pytest.approx(1.3, abs=0.05) + + if use_grad: + assert m2.ngrad > 0 + else: + assert m2.ngrad == 0 + + def test_ExtendedBinnedNLL_bad_input(): with pytest.raises(ValueError): ExtendedBinnedNLL([1], [1], lambda x, a: 0) @@ -1739,16 +1800,16 @@ def test_NormalConstraint_bad_input_6(): dtypes_to_test.append(np.float128) -def test_multinominal_chi2(): +def test_multinomial_chi2(): zero = np.array(0) one = np.array(1) - assert multinominal_chi2(zero, zero) == 0 - assert multinominal_chi2(zero, one) == 0 - assert multinominal_chi2(one, zero) == pytest.approx(1416, abs=1) + assert multinomial_chi2(zero, zero) == 0 + assert multinomial_chi2(zero, one) == 0 + assert multinomial_chi2(one, zero) == 0 n = np.array([(0.0, 0.0)]) - assert_allclose(multinominal_chi2(n, zero), 0) - assert_allclose(multinominal_chi2(n, one), 0) + assert_allclose(multinomial_chi2(n, zero), 0) + assert_allclose(multinomial_chi2(n, one), 0) @pytest.mark.skipif( @@ -2109,22 +2170,3 @@ def cdf(xye, a): match=(r"output of model has shape \(11,\), but \(12,\) is required"), ): c(1) - - -def test_BohmZechTransform(): - from iminuit.cost import BohmZechTransform - - with pytest.warns(FutureWarning): - val = np.array([1.0, 2.0]) - var = np.array([3.0, 4.0]) - tr = BohmZechTransform(val, var) - s = val / var - mu = np.array([2.0, 2.0]) - ns, mus = tr(mu) - assert_allclose(ns, val * s) - assert_allclose(mus, mu * s) - var2 = mu**2 - ns, mus, vars = tr(mu, var2) - assert_allclose(ns, val * s) - assert_allclose(mus, mu * s) - assert_allclose(vars, var2 * s**2) diff --git a/tests/test_without_numba.py b/tests/test_without_numba.py index 674f0283..54970abf 100644 --- a/tests/test_without_numba.py +++ b/tests/test_without_numba.py @@ -13,10 +13,10 @@ def npa(*args, **kwargs): @pytest.mark.parametrize( "func,args", ( - ("_safe_log", (npa(1.2, 0),)), + ("log_or_zero", (npa(1.2, 0),)), ("_z_squared", (npa(1.2, 0), npa(1.2, 1.0), npa(1.2, 1))), ("_unbinned_nll", (npa(1.2, 0),)), - ("multinominal_chi2", (npa(1, 0), npa(1.2, 0))), + ("multinomial_chi2", (npa(1, 0), npa(1.2, 0))), ("chi2", (npa(1.2, 0), npa(1.2, 1.0), npa(1.2, 0.1))), ("poisson_chi2", (npa(1, 0), npa(1.2, 0.1))), ("_soft_l1_cost", (npa(1.2, 0), npa(1.2, 0.1), npa(1.0, 1.0))),