diff --git a/.flake8 b/.flake8 index 04c6bd8ba..65119c5bd 100644 --- a/.flake8 +++ b/.flake8 @@ -6,6 +6,14 @@ per-file-ignores = # Imported but unused - */__init__.py:F401 + */__init__.py:F401,D400,D205 # Print and asserts - test/*:T001,S101 + test/*:T001,S101,D + # Ignore errors in module docstring + pypesto/logging.py:D400,D205,D107 + pypesto/problem.py:D400,D205,D107 + pypesto/result.py:D400,D205,D107 + pypesto/version.py:D + # ignore D100='Missing docstring in public module', + # D105='Missing docstring in magic method' and D107='Missing docstring in __init__'. + *:D100,D105,D107 diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index ab1a55fb3..3540a902a 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -16,7 +16,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.9] + python-version: [3.7] steps: - name: Check out repository @@ -50,7 +50,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.9] + python-version: [3.7] steps: - name: Check out repository @@ -84,7 +84,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.9] + python-version: [3.7] steps: - name: Check out repository @@ -180,7 +180,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.9] + python-version: [3.7] steps: - name: Check out repository @@ -208,7 +208,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [3.9] + python-version: [3.7] steps: - name: Check out repository diff --git a/CHANGELOG.rst b/CHANGELOG.rst index bd5fb0999..6d4474ea6 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -6,6 +6,21 @@ Release notes .......... +0.2.9 (2021-11-03) +------------------ + +* General: + * Automatically save results (#749) + * Update all docstrings to numpy standard (#750) + * Add Google Colab and nbviewer links to all notebooks for online + execution (#758) + * Option to not save hess and sres in result (#760) + * Set minimum supported python version to 3.7 (#755) + +* Visualization: + * Parameterize start index in optimized model fit (#744) + + 0.2.8 (2021-10-28) ------------------ diff --git a/CITATION.cff b/CITATION.cff index 44094b1a0..d6aadccdc 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -64,7 +64,7 @@ authors: given-names: "Stephan" orcid: "https://orcid.org/0000-0001-9524-6633" - - family-names = "Hasenauer" + family-names: "Hasenauer" given-names: "Jan" orcid: "https://orcid.org/0000-0002-4935-3312" title: "pyPESTO - Parameter EStimation TOolbox for python" diff --git a/README.md b/README.md index c791b6b7a..7a010d2d4 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,6 @@ parameter estimation. [![PyPI](https://badge.fury.io/py/pypesto.svg)](https://badge.fury.io/py/pypesto) [![CI](https://github.com/ICB-DCM/pyPESTO/workflows/CI/badge.svg)](https://github.com/ICB-DCM/pyPESTO/actions) [![Coverage](https://codecov.io/gh/ICB-DCM/pyPESTO/branch/master/graph/badge.svg)](https://codecov.io/gh/ICB-DCM/pyPESTO) -[![Quality](https://api.codacy.com/project/badge/Grade/134432ddad0e464b8494587ff370f661)](https://www.codacy.com/app/dweindl/pyPESTO?utm_source=github.com&utm_medium=referral&utm_content=ICB-DCM/pyPESTO&utm_campaign=Badge_Grade) [![Documentation](https://readthedocs.org/projects/pypesto/badge/?version=latest)](https://pypesto.readthedocs.io) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2553546.svg)](https://doi.org/10.5281/zenodo.2553546) diff --git a/doc/_static/colab-badge.svg b/doc/_static/colab-badge.svg new file mode 100644 index 000000000..e5830d533 --- /dev/null +++ b/doc/_static/colab-badge.svg @@ -0,0 +1 @@ + Open in ColabOpen in Colab diff --git a/doc/_static/nbviewer-badge.svg b/doc/_static/nbviewer-badge.svg new file mode 100644 index 000000000..d9d26af9b --- /dev/null +++ b/doc/_static/nbviewer-badge.svg @@ -0,0 +1,186 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Open in nbviewer + Open in nbviewer + + + + + + + + + + + + + + diff --git a/doc/conf.py b/doc/conf.py index 928dab5c9..99e84f5bd 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -110,6 +110,19 @@ # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False +# Add notebooks prolog to Google Colab and nbviewer +nbsphinx_prolog = r""" +{% set docname = 'github/icb-dcm/pypesto/blob/main/doc/' + env.doc2path(env.docname, base=None) %} +.. raw:: html + +
+ + Open in Colab + + Open in nbviewer +
+ +""" # -- Options for HTML output ---------------------------------------------- @@ -127,7 +140,9 @@ # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -# html_static_path = ['_static'] +html_static_path = ['_static'] + +# Favicon html_favicon = "logo/logo_favicon.png" # Custom sidebar templates, must be a dictionary that maps document names diff --git a/doc/example.rst b/doc/example.rst index 4451a5dc9..fbd15cf07 100644 --- a/doc/example.rst +++ b/doc/example.rst @@ -1,7 +1,23 @@ Examples ======== -The following examples cover typical use cases and should help get a better idea of how to use this package: +We provide a collection of example notebooks to get a better idea of how to +use pyPESTO, and illustrate core features. + +The notebooks can be run locally with an installation of jupyter +(``pip install jupyter``), or online on Google Colab or nbviewer, following +the links at the top of each notebook. +At least an installation of pyPESTO is required, which can be performed by + +.. code:: sh + + # install if not done yet + !pip install pypesto --quiet + +Potentially, further dependencies may be required. + +The following examples cover typical use cases and should help get a better +idea of how to use this package: .. toctree:: :maxdepth: 1 diff --git a/doc/example/amici_import.ipynb b/doc/example/amici_import.ipynb index 408d2064c..73f5451f7 100644 --- a/doc/example/amici_import.ipynb +++ b/doc/example/amici_import.ipynb @@ -9,6 +9,17 @@ "This is an example using the \"boehm_ProteomeRes2014.xml\" model to demonstrate and test SBML import and AMICI Python interface." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# install if not done yet\n", + "# !apt install libatlas-base-dev swig\n", + "# %pip install pypesto[amici] --quiet" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -566,7 +577,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -580,7 +591,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/doc/example/conversion_reaction.ipynb b/doc/example/conversion_reaction.ipynb index 979c41499..a145884b8 100644 --- a/doc/example/conversion_reaction.ipynb +++ b/doc/example/conversion_reaction.ipynb @@ -8,6 +8,17 @@ "===================" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# install if not done yet\n", + "# !apt install libatlas-base-dev swig\n", + "# %pip install pypesto[amici] --quiet" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -503,7 +514,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -517,7 +528,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/doc/example/fixed_parameters.ipynb b/doc/example/fixed_parameters.ipynb index a23b4bdae..f0b621ad0 100644 --- a/doc/example/fixed_parameters.ipynb +++ b/doc/example/fixed_parameters.ipynb @@ -17,6 +17,16 @@ "of them to specified values." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# install if not done yet\n", + "# %pip install pypesto --quiet" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -430,7 +440,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -444,7 +454,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/doc/example/hdf5_storage.ipynb b/doc/example/hdf5_storage.ipynb index 3e44202f6..e3dea71f6 100644 --- a/doc/example/hdf5_storage.ipynb +++ b/doc/example/hdf5_storage.ipynb @@ -7,6 +7,16 @@ "# Save and load results as HDF5 files" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# install if not done yet\n", + "# %pip install pypesto --quiet" + ] + }, { "cell_type": "code", "execution_count": 24, @@ -118,7 +128,9 @@ "outputs": [ { "data": { - "text/plain": "" + "text/plain": [ + "" + ] }, "execution_count": 27, "metadata": {}, @@ -126,8 +138,10 @@ }, { "data": { - "text/plain": "
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9ezd33XWXeytKkjSPiuyz+B7gLcAzgDuAZwGfAy4p+kUi4j3AAZn5kj0NGBFrgeuAp2TmzfVzpwBvBpYB52bmhzJzG+DCNpK0ALm3oiRJ869Iz2J3Zl4BPB74Yr1g6y76BSLiROAl05z/k0mvD42IhnFFEXE88G3gqEnnDgbOAR4FPAA4IyLuWzSPJKmzVKtV+vv7WbVqFcuWLSs7jiRJi0ahYjEijgOeDHwzIu5HbUXUOUXEftQKu3dMOb8UuDAizoyIQ4ArgOOmecTpwJnA7ZPOnQRclZl3ZuZO4PPAcwrm2RIR1YioAr8pco8kqVyDg4MMDw/bqyhJ0jwrUiyeA1wEfKLeq/hlakNAi/gX4CygMvlkZo4CJwMvpzbE9C2Z+a2pN2fmaZl57ZTTE4vYTNgGbC4SJjO3ZGZXZnYBhxf8HiRJJZrYW3HdunVzv1mSJDXNnHMWM/NS4NJJp47MzDm3poiI04BbM/PKiHjJDG+rAl0U7Kmsm25lg/E9uF+S1CHGx8fp7+9n3bp1dHcXngEhSZKaYM6exYg4KCK+GhH/EREbqG2nsbHAs58HnBwRNwJnA0+LiHPrz+wBLgc+ADwC2FKf21jEVmp7Kk7YyD2HqUqSFgj3VpQkqTxFVkP9MPBF4BXAncCN1LapePJsN2Xm4yde13sWH5uZ/7N+bSQinp+ZN9WvnwD8rmDmK6gVlwcAO4FnA2cUvFeS1EEqlQo9PT2sWrWq7CiSJC06ReYsHpaZHwPGM3MkM98AHLqvX3iiUKy/viUzBwvet5XaPMirqRWuF2XmDfuaR5LUXkZGRhgYGHBvRUmSSlKkZ3E8Iu4uKiNiDcWKzLtl5gXABXuU7J73Hzbl+CJqi+5Ikhao/v5+wL0VJUkqS5Gi71LgQmBdRPw1cBXwuZamkiQtatVqlUqlQm9vL8uXLy87jiRJi9KcxWJmvgO4DPg+8Hjgo9QWrJEkqSWGhobYtWuXvYqSJJWoyDBUqA35/BEwAvwqM6utiyRJWuwqlQpdXV309fWVHUWSpEWryNYZxwO/Br5KbSXSX0bE/VsdTJK0OE3srbh27Vr3VpQkqURF5iy+Hzg9M++dmYcArwXOa20sSdJiNTAwwNjYmENQJUkqWZFicVlmfnPiIDO/DLjhlSSpJSqVCkuXLmXNmjVlR5EkaVErUiz+MCKeM3EQEU+iNn9RkqSmGh0dZWBggL6+PvdWlCSpZDMucBMRA0AV6Ab+KiIqwBiwP/D/z088SdJi0t/fT7VadQiqJEltYLbVUO83bykkSaI2BHXlypWsXLmy7CiSJC16MxaLmfnb+QwiSVrcdu3axdDQEJs2bSo7iiRJoticRUmSWs69FSVJai8Wi5Kk0lWrVSqVCmvWrGHp0tlmSEiSpPlS6CdyRKwGHgz0AN/LzIGWppIkLSoDAwOMjo66sI0kSW1kzp7FiHgo8B/A+4H3Ab+NiEe0OpgkafGoVCp0d3e7t6IkSW2kyDDUfwROzcxjM/MY4DnUikZJkvbZ2NgYO3bsoK+vjyVLnB0hSVK7KPJTeW1mXj1xkJlXAb2tiyRJWkzcW1GSpPZUpFgcj4h7TxxExGHAWMsSSZIWlUqlwvLly91bUZKkNlNkgZuzgesj4or68cnAy1sXSZK0WAwPDzM4OMhBBx1EV1dX2XEkSdIkc/YsZuYXgccB1wHXA4/NzC+0OJckaRGoVCoADkGVJKkNzdmzGBGXZeYTgV9OOnd9Zj6spckkSQva5L0Ve3p6yo4jSZKmmLFYjIjPA0cBfxQRP510qQcYb3UwSdLCtnPnTkZGRti4cWPZUSRJ0jRm61l8HXAY8DHglZPOjwI/b2EmSdIiUKlUWLJkCWvXri07iiRJmsaMxWJm3gzcHBGRmfYkSpKaZmxsjO3bt7u3oiRJbazIAjcWipKkptq+fTvj4+MubCNJUhvzz7mSpHk3sbdib29v2VEkSdIMLBYlSfNq9+7d7Ny5k76+PvdWlCSpjRXZOmM18C7gaODPgXcCr83Mu1qcTZK0ALm3oiRJnWHOYhH4ALAN2ADsAtYCHwVOaWGuPRYRhwDvBgaAazLzwpIjSZKmqFar9Pf3s2rVKpYtW1Z2HEmSNIsiw1AfmJlnASOZOQicChzb0lR753TgHzPzDOCFZYeRJDUaHBxkeHjYXkVJkjpAkWJxbMpxN9COK6QeBGytv56aWZLUBib2Vly3bl3ZUSRJ0hyKFIv/FhHvAlZGxBOAS4GrWxtrr9wKbKq/duEeSWoz4+Pj9Pf3s27dOrq7u8uOI0mS5lCkqHoDcBewHTgH+Cnwt60MtZc+DrwmIj4BnF92GEnSPe3YscO9FSVJ6iBFFrg5ITPfDrx9Tx8eEWcDzwGqwCcy83178Yy1wHXAUzLz5vq5U4A3A8uAczPzQ5m5jdp8SklSG6pUKvT09LBq1aqyo0iSpAKK9CxuiYjfRMRbIuLgog+OiMcAJwDHAA8BXhkRMen6n0x6fWhENOzMHBHHA98Gjpp07mBqPZyPAh4AnBER9y2YaUtEVCOiCvym6PciSdo3IyMjDAwMsH79evdWlCSpQ8xZLGbmw4EnAauB6yPiKxHx9AL3XQM8LjNHgQOp9WLuBIiIpcCFEXFmfcuLK4DjpnnM6cCZwO2Tzp0EXJWZd2bmTuDz1Hov55SZWzKzKzO7gMOL3CNJ2nfurShJUucptBBMZt6UmW8AngXsD3y24H0jEfE24BfAldRXK60XkCcDL6c2xPQtmfmtae4/LTOvnXJ6E7V9HydsAzYXySNJmn/VapVKpUJvby/Lly8vO44kSSpozmIxIg6MiNdExE+AC4DPsQfFWWa+FTgAOIRaT+FkVaAL6Cn6vPr7p2rHrTwkScDQ0JB7K0qS1IGKLHDzK2rbZbw8M79T9MERcTSwIjNvzMzBiLiU2vxFIqIHuBz4APB14KqI2JaZVxZ49Fbg0ZOON3LPYaqSpDZSqVTo6uqir6+v7CiSJGkPFCkWD8nMHXvx7COAt0XEo6j1ID6d+pYW9eGpz8/MmwAi4gTgdwWfewW1RXcOoDYH8tnAGXuRT5LUYhN7K65du9a9FSVJ6jAzFosR8bnMfC7wnfrqofeQmcfM9uDM/Fp9NdMfA2PAFzLzs5Ou3zTp9S1FA2fm1og4C7ia2tYZH8/MG4reL0maPwMDA4yNjTkEVZKkDjRbz+K76p9fsbcPr89XfOve3j/pOYdNOb4IuGhfnytJaq1KpcLSpUtZs2ZN2VEkSdIemrFYzMwf1l++KDP/avK1iPgCcE0rg0mSOtvo6CgDAwPsv//+7q0oSVIHmm0Y6nnAwcCj6/MDJ/QAR7c6mCSps/X391OtVh2CKklSh5ptGOongPsBDwC+MOn8KPDdVoaSJHW+SqXCypUrWbFiRdlRJEnSXphtGOoPgB9ExBWZeds8ZpIkdbihoSGGhobYtGlT2VEkSdJeKrR1RkR8CFgNdAHdwOGZeWhLk0mSOpZ7K0qS1PmWFHjPx4HrgLXAhcAO7jksVZKku1WrVfr7+1mzZg1Llxb5m6QkSWpHRYrFama+C/gW8Evgz4E/bWUoSVLnGhgYYHR01IVtJEnqcEWKxYH6518D98vMXdSGokqS1KBSqdDd3e3eipIkdbgi44NuiIhLgLcAX42Io4Cx1saSJHWisbExduzYwX777ceSJUX+HilJktpVkZ/krwbOzcz/qL9eApzSwkySpA7l3oqSJC0cM/YsRsSDJh3urh9vAz4DrGp1MElS56lUKqxYsYKVK1eWHUWSJO2j2YahzrbiaRU4oslZJEkdbHh4mMHBQTZu3EhXV1fZcSRJ0j6asVjMzMPnM4gkqbNVKhUA91aUJGmBmHOBm4hYDfwD8MfUts14J/DazLyrxdkkSR2iWq1SqVRYs2YNPT09ZceRJElNUGSBmw8A24ENwC5gLfDRVoaSJHWWnTt3MjIy4sI2kiQtIEWKxQdm5lnASGYOAqcCx7Y0lSSpo0zsrbh27dqyo0iSpCYpUixO3VOxGxhvQRZJUgcaGxtj+/btrFu3zr0VJUlaQIr8VP+3iHgXsDIingBcClzd2liSpE6xfft2xsfHHYIqSdICU6RYfANwF7V5i+cAPwX+tpWhJEmdo1KpsHz5cnp7e8uOIkmSmmjO1VCBszPzTcDbWx1GktRZdu/ezc6dO9mwYYN7K0qStMAU6Vl8SstTSJI60sTeig5BlSRp4SnSs/h/I+KbwLepDUcFIDPf17JUkqS2V61W6e/vZ/Xq1SxbtqzsOJIkqcmKFIt31j8fPulctQVZJEkdZHBwkOHhYQ488MCyo0iSpBaYsViMiOWZOZyZfznXe1oTTZLUziqVCkuWLHFvRUmSFqjZ5ix+KSKeFREN74mIroj4c+DLrYsmSWpX4+Pj9Pf3s27dOrq7u8uOI0mSWmC2YajPAd4FvC8irgL+E+gGjgAeB3wd+POWJ5QktZ0dO3a4t6IkSQvcjMViZt4FnBkR7wSeDhwNjAM/AN6cmVvnJ6Ikqd1UKhV6enpYtWpV2VEkSVKLzLnATWbeBnxoHrJIkjrAyMgIAwMDHHjgge6tKEnSAlZkn0VJku7m3oqSJC0OFouSpMKq1SqVSoXe3l6WL19edhxJktRCFouSpMKGhoYYHh62V1GSpEVgzjmL9a0zXgvcD3hF/ePdmTnW4mySpDZTqVTo6uqir6+v7CiSJKnF5iwWgfcABwAPBbqAPwM2An/TwlySpDYzsbfi2rVr3VtRkqRFoMgw1BOBlwC7MnMHcDLw+FaGkiS1n4GBAcbGxhyCKknSIlGkWBzJzPGJg8wcBkZbF0mS1I4qlQpLly5lzZo1ZUeRJEnzoMgw1J9FxJlAd0QE8BrgxpamkiS1ldHRUQYGBth///3dW1GSpEWiSM/iq4AHARuA7wCrgVe3MJMkqc309/dTrVYdgipJ0iIyZ89ifZ7iX81DFklSm6pUKqxcuZIVK1aUHUWSJM2TIltnfGC685npaqiStAgMDQ0xNDTEpk2byo4iSZLmUZFhqL+f9DEAPByotjKUJKl9uLeiJEmLU5FhqG+bfBwR7wC+0rJEkqS2Ua1W6e/vZ82aNSxdWmRNNEmStFAU6Vm8h8zcCRzcgiySpDYzMDDA6OioC9tIkrQI7emcxS7gwcBNLUskSWoblUqF7u5u91aUJGkRKjKm6PeTXleBzwAXtiaOJKldjI2NsWPHDvbbbz+WLNnjgSiSJKnD7fGcxXYVEYcA76a2CM81mWlBK0n7wL0VJUla3GYsFiNigOlXPe0Cqpm5tmWp9s7pwD9m5g8i4uvY+ylJ+6RSqbBixQpWrlxZdhRJklSC2XoW7zdvKZrjIGBr/fVYmUEkqdMNDw8zODjIxo0b6erqKjuOJEkqwYzFYmb+duJ1RDwQWE2tV7EbOBL4WMvT7ZlbgU3ANvZilVdJ0h9UKhUA91aUJGkRK7Ia6seApwMrqfXcHQl8m/YrFj8OvDcidgHnlx1GkjpVtVqlUqmwZs0aenp6yo4jSZJKUmQ11McDhwMfBs4GNgNvKPLwiHgr8Nz64Vcz8/V7GjAi1gLXAU/JzJvr504B3gwsA87NzA9l5jbg1D19viTpnnbu3MnIyAgbN24sO4okSSpRkeGa2zJzJ/BL4P6ZeQ2w/1w3RcRJwMnAA4FjgQdHxDMnXf+TSa8PjYjeaZ5xPLVezKMmnTsYOAd4FPAA4IyIuG+B74OI2BIR1YioAr8pco8kLTYTeyuuXdtu65hJkqT5VKRY3B0Rfwr8AviziFhHgWKR2tzB12bm7swcAW4CDgWIiKXAhRFxZn3LiyuA46Z5xunAmcDtk86dBFyVmXfWi9jPA88pkIfM3JKZXZnZRa23VJI0ydjYGNu3b2fdunXurShJ0iJX5DeBNwB/DXyNWi/h74D/b66bMvPnmXk9QETcB3he/Rlk5ii1XseXUxti+pbM/NY0zzgtM6+dcnpiEZsJ26gNjZUk7aPt27czPj7u3oqSJKnQnMX+zJyYC3h8RPRlZn/RL1AfbvpV4HWZ+aspl6vUVljdkxUUplvDfXwP7pckzaBSqbB8+XJ6extmBkiSpEWmSLF4eUT8htpqo5/bw0LxkcAXgFdn5mcnne8BLgc+AHwduCoitmXmlQUeuxV49KTjjdxzmKokaS/s3r2bnTt3smHDBvdWlCRJhYah3ht4J/Bk4DcR8cGIeMBcN9XnIn4ROGVyoQhQn8P4/Mz8aGbeApwAfLdg5iuAEyPigPqiOM+mVnBKkvbBxN6KDkGVJElQoGcxM8eBy4DLIuJo4ALgZUD3HLe+DlgBvC8iJs59JDM/Un/uTZO+xi1FA2fm1og4C7ia2tYZH8/MG4reL0lqNLG34urVq1m2bFnZcSRJUhuYs1isr1z6NOAlwPHAJdRWKZ1VZr4KeNU+5pt41mFTji8CLmrGsyVJMDg4yO7du9mwYUPZUSRJUpsoMmdxG/AzanMW/zwzh1sbSZI03yqVCkuWLHFvRUmSdLcixeIjplnFVJK0QIyPj9Pf38+6devo7p5rhoEkSVos5lzgxkJRkha2HTt2uLeiJElqUGQ1VEnSAlapVFi2bBmrVq0qO4okSWojFouStIiNjIwwMDBAX1+feytKkqR7KDJnkYh4DLAfcPdvEpl5aatCSZLmh3srSpKkmRTZOuPTwAnAfwLV+ukqYLEoSR1sYm/F3t5eli9fXnYcSZLUZor0LD4aODoz72p1GEnS/BkaGmJ4eJiDDz647CiSJKkNFZmzeIuFoiQtPJVKha6uLvr6+sqOIkmS2lCRnsXvRMRngS8DQxMnnbMoSZ1rYm/FtWvXureiJEmaVpFi8eH1z6dNOuecRUnqYAMDA4yNjbmwjSRJmtGcxWJmPg4gIpYCXZk50vJUkqSWqlQqLF26lDVr1pQdRZIktak55yxGxIERcRmwE9gVEVdFxKbWR5MktcLo6CgDAwOsX7/evRUlSdKMiixw80HgemADcCBwLXBeK0NJklqnv7+farXqEFRJkjSrInMWj8rM5046fmtE/LxVgSRJrVWpVFi5ciUrVqwoO4okSWpjRXoWeyLi7t8oIqKX2gI3kqQOMzQ0xNDQkL2KkiRpTkV6Fj8LXBERn6wf/yXw+dZFkiS1insrSpKkooqshvr2iLgN+DNqPZEXAJ9ocS5JUpNVq1X6+/tZs2YNS5cW+VuhJElazGb8bSEi1mbmjojYD/hS/WPCeuDOVoeTJDXPwMAAo6OjDkGVJEmFzPan5W8BDwJ+xz3nKHbVj7tbF0uS1GyVSoXu7m73VpQkSYXMWCxm5oMm3pOZ45Ov1XsbJUkdYmxsjB07drDffvuxZEmRtc0kSdJiV+Q3hh9Mc+7bzQ4iSWod91aUJEl7arY5i1cCDwV6I2LHlHt+1OpgkqTmqVQqrFixgpUrV5YdRZIkdYjZ5iw+E9gPOJ/adhkTRoFtrQwlSWqe4eFhBgcH2bhxI11dXWXHkSRJHWLGYaiZuSMzbwaeAZySmb+tX/pbwD9NS1KHqFQqAO6tKEmS9kiROYufBO5Vf91PbSXUj7UqkCSpearVKpVKhTVr1tDT01N2HEmS1EGK7Mp8n8x8NkBmbgf+Z0T8pLWxJEnNsHPnTkZGRti4cWPZUSRJUocp0rPYExFrJw4iYjW1vRYlSW1uYm/FtWvXzv1mSZKkSYr0LH4a+F5E/Cu1IajPojY0VZLUxsbGxti+fTt9fX3urShJkvbYnL89ZOY7gTcA64DVwOsz832tDiZJ2jfbt29nfHzcvRUlSdJeKdKzCPB14Frqw08jYr/MvLNlqSRJ+6xSqbB8+XJ6e3vLjiJJkjrQnD2LEfEqYDvwO+C/J32WJLWp3bt3s3PnTvr6+txbUZIk7ZUiPYt/AzwyM3/U6jCSpOaY2FvRIaiSJGlvFVnxYJuFoiR1jom9FVevXs2yZcvKjiNJkjpUkZ7FyyPiZcD/AYYmTjpnUZLa0+DgILt372bDhg1lR5EkSR2sSLH4RmA58KFJ56pAd0sSSZL2SaVSYcmSJe6tKEmS9smcxWJmrpyPIJKkfTc+Pk5/fz/r1q2ju9u/6UmSpL03Z7EYEc+a7nxmXtr8OJKkfbFjxw73VpQkSU1RZBjqKye9XgYcA1wDWCxKUpupVCosW7aMVatWlR1FkiR1uCLDUB83+Tgi/gh4Z8sSSZL2ysjICAMDAxx44IHurShJkvZZka0z7iEzfw0c3YIskqR94N6KkiSpmfZ0zmIX8BBgtGWJJEl7bGJvxd7eXpYvX152HEmStADs6ZzFKvDfwItbE0eStDeGhoYYHh7m4IMPLjuKJElaIGYsFiPiE5n5V8B5mfm5ecwkSdpDlUqFrq4u+vr6yo4iSZIWiNl6Fk+KiEcAb4uIX1Ebgnq3zPxRS5NJkgpxb0VJktQKsxWLHwU+A2ymcZuMKnBEq0JJkoobGBhgbGzMhW0kSVJTzVgsZuY5wDkR8dnMfP48ZpIk7YFKpcLSpUtZvXp12VEkSdICMufWGRaKktS+RkdHGRgYYP369e6tKEmSmmqP91mUJLWP/v5+qtWqQ1AlSVLTWSxKUgerVCqsXLmSFStWlB1FkiQtMBaLktShhoaGGBoasldRkiS1xGz7LP6G2qqn08pMV0OVpBK5t6IkSWql2bbOeE7988uB3dS20hgF/hJY1uJckqRZVKtV+vv7Wbt2LUuXzvafckmSpL0z29YZPwSIiPtl5vGTLr0mIm5oeTJJ0owGBgYYHR11CKokSWqZInMW+yLigImDiNgErG1dJEnSXNxbUZIktVqRsUv/BPx7RHwD6AJOBl7fylCSpJmNjY2xY8cO7nWve7FkieuUSZKk1pjzt4zMPA94AvAT4MfASZn56VYHkyRNz70VJUnSfCj6J+n7APtRW+Tm/q2LI0maS6VSYcWKFe6tKEmSWmrOYjEi3gi8DHgusAJ4a0S8pdXBJEmNhoeHGRwcZP369XR1dZUdR5IkLWBFehafDzwJ2JmZvwceBpzS0lSSpGlVKhUA91aUJEktV6RYHMnM4YmDzOwHRlqWSJI0rWq1SqVSYc2aNfT09JQdR5IkLXBFVkO9NSKeDFQjYjnwOuC3rY0lSZrqrrvuYmRkhI0bN5YdRZIkLQJFisVXAJ8BjgF2AtfjMFRJmneVSoXu7m7WrnWrW0mS1HpFhqF2ZeaJwFpgfWY+CnAXaEmaRxN7K65bt869FSVJ0ryYsWcxIvarv/xaRDwW6KI2FHUD8CVq22lIkubB9u3bGR8fd29FSZI0b2Ybhnox8Pj6699POj8K/O+WJZIkNahUKixfvpze3t6yo0iSpEVixmIxM58AEBHnZ+b/mL9IkqTJdu/ezc6dO9mwYYN7K0qSpHlTZOJLdeqJiPhCC7JIkqYxsbeiQ1AlSdJ8mm3O4nnAwcCjI+KASZeWAdHqYJKkP+ytuHr1apYtW1Z2HEmStIjMNmfxE8D9gAcAk3sSR4HvtjKUJKlmcHCQ3bt3s2HDhrKjSJKkRWa2OYs/AH4QEVdk5m0R0ZeZ/fMXTZJUqVRYsmSJeytKkqR5N1vP4oTeiPg50BcRxwFXAM/MzF+2NpokLW7j4+P09/ezbt06uru7y44jSZIWmSIL3HwQeDXwX5m5Ffhn4KOtDCVJcm9FSZJUriLF4r0y8/KJg8z8MOB4KElqsUqlwrJly1i1alXZUSRJ0iJUaOuMiFhBfQuNiDgIcDyUJLXQyMgId911F319fe6tKEmSSlGkWPww8A3gwIh4J3B9/ZwkqUXcW1GSJJVtzgVuMvP8iPhP4MlAD3BGZn6z5ckkaZGa2Fuxt7eX5cuXlx1HkiQtUkV6FgF+nJlvoLbf4kER0dPCTJK0qA0NDTE8PGyvoiRJKtWcxWJEnA38S0QcCnwReAlwXmtjSdLiValU6Orqoq+vr+wokiRpESvSs/gk4DTg2cDFmXkC8ICWppKkRcq9FSVJUrsoNAw1MweBk4Cr6qecRCNJLTAwMMDY2JhDUCVJUumKFIu/j4gPAw8BroiIfwBub20sSVqcKpUKS5cuZfXq1WVHkSRJi9yMxWJ9jiLAi6gVh0+u9zBWgRfPQzZJWlRGR0cZGBhg/fr17q0oSZJKN9vWGZdS6018b2a+cOJkZr6p5akkaRHq7++nWq06BFWSJLWF2YrFvoj4KHByRHxg6sXM/JvWxSouIg4B3g0MANdk5oUlR5KkvVKpVFi5ciUrVqwoO4okSdKscxZfANwGjAO/n+ajXZwO/GNmngG8cK43S1I7GhoaYmhoyF5FSZLUNmbrWXx/Zh4fES/IzLfNW6I9dxCwtf56rMwgkrS33FtRkiS1m9l6FjdExN8BKyPiNVM/5itgAbcCm+qvC20F0rYuvBAOOwyWLKl9vtARtfvMNm0u27P5LryQ6r3vzcbNm/njJz6RpZdcUnYiSZIkYPaexdOB5wO9wP2nXKu2LNGe+zjw3ojYBZxfdpi9duGFcMYZMDhYO/7tb2vHAKeeWl6uTmabNpft2Xz1Nu2qt+nSrVttU0mS1Da6qtXZ676IeF1mvnee8kz+umuB64CnZObN9XOnAG8GlgHnZuaHCj6rD+ibcnozcO2VV17J5s2bm5R6Hxx2WO2X7ylGNm3it9dcM/95FoB7P+Yx9NzeuCWobbp3bM/mm6lNufe94eab5z2PJElaXG677TZOPPFEgMMnaq7JZutZJCI2AYdFxI+p9SZeB/xDZt7WgqyTv+7xwMeAoyadOxg4B3gwMAxcFxFXZ+YvCjzy1cBbWxC1eW65ZdrTS7dtY8mSzh5dW5al27bNeN423XO2Z/PN1KYz/fdAkiRpPs1YLNa3pPgu8K/AW4DlwOOAGyLi4ZnZ2A3WPKcDZwKfmXTuJOCqzLyznu/zwHOAsws875+AC6ac2wxcu69Bm+bQQ6ftWew69FCOOOKIEgItALZpc9mezTdDm3LoofOfRZIkaYrZehb/HnhTZk4u2L4QET+sX2vZNhWZeRpAREw+vQmY/Gf4bcBxBZ/XD/RPPjfl2eU755x7zgcD6O2tndfesU2by/ZsPttUkiS1sdnGjj1oSqEIQGZ+koJFWpN1TXNufN5TtMqpp8JHP1qbq9TVVfv80Y+6yMW+sE2by/ZsPttUkiS1sdl6FqcrziYMNztIAVuBR0863ghMszJEBzv1VH9JbDbbtLlsz+azTSVJUpuarWdxtL7AzT3Uz5VRLF4BnBgRB0REL/Bs4Osl5JAkSZKkBW+2YvEjwCfrW1gAEBEHUlt05sOtDjZVZm4FzgKuBm4ELsrMG+Y7hyRJkiQtBjMOQ83Mj0TEkcDWiPgF0APcB/hAfd5iy2XmYVOOLwIumo+vLUmSJEmL2az7LGbm6yLiXOD4+qnrM3NhzROUJEmSJDWYtViEu4d/XjoPWSRJkiRJbWK2OYuSJEmSpEXKYlGSJEmS1MBiUZIkSZLUwGJRkiRJktTAYlGSJEmS1GDO1VAXkojYAry17BySJEmS1O4WVc9iZm7JzK7M7AIOLzuPJEmSJLWrRVUsSpIkSZKKsViUJEmSJDWwWJQkSZIkNbBYlCRJkiQ1sFiUJEmSJDWwWJQkSZIkNbBYlCRJkiQ1sFiUJEmSJDWwWJQkSZIkNbBYlCRJkiQ1sFiUJEmSJDWwWJQkSZIkNbBYlCRJkiQ1sFiUJEmSJDWwWJQkSZIkNVhadoD5FBFbgLeWnUOSJEmS2t2i6lnMzC2Z2ZWZXcDhZeeRJEmSpHa1qIpFSZIkSVIxFouSJEmSpAYWi5IkSZKkBhaLkiRJkqQGFouSJEmSpAYWi5IkSZKkBhaLkiRJkqQGFouSJEmSpAYWi5IkSZKkBhaLkiRJkqQGFouSJEmSpAYWi5IkSZKkBhaLkiRJkqQGFouSJEmSpAZLyw5Qom6AO+64o+wckiRJkjTvJtVC3dNdX8zF4kaAU089tewckiRJklSmjcCvp55czMXi94FHA9uAsZKzTLYZuJZatttKzrJQ2KbNZXs2n23afLZpc9mezWebNpft2Xy2aXO1a3t2UysUvz/dxUVbLGbmMPDtsnNMFRETL2/LzJtLjLJg2KbNZXs2n23afLZpc9mezWebNpft2Xy2aXO1eXs29ChOcIEbSZIkSVIDi0VJkiRJUgOLRUmSJElSA4vF9tMPvK3+Wc3Rj23aTP3Yns3Wj23abP3Yps3Uj+3ZbP3Yps3Uj+3ZbP3Yps3UTwe2Z1e1Wi07gyRJkiSpzdizKEmSJElqYLEoSZIkSWqwaPdZbFcRcQrwZmAZcG5mfqjkSB0vItYC1wFPacN9bTpORLwVeG798KuZ+foy83S6iDgbeA5QBT6Rme8rOdKCERHvAQ7IzJeUnaWTRcRVwAZgpH7qrzPzeyVG6ngR8VRgC7AK+EZmvqrcRJ0tIk4DXjHp1OHAZzLzFTPcojlExAuAN9UPL8vM15WZZyGIiDcCfwkMA5dk5jklRyrEnsU2EhEHA+cAjwIeAJwREfctN1Vni4jjgW8DR5WdZSGIiJOAk4EHAscCD46IZ5YaqoNFxGOAE4BjgIcAr4xJu/Zq70XEicBLys7R6SKiCzgaeEBmHlv/sFDcBxFxBPAR4OnA/YEHRcQTy03V2TLz4xP/PoFTgf+iVoxrL0REL/AB4DHUfh99dP3nv/ZSvf1OAR5K7Xeo4yPiWeWmKsZisb2cBFyVmXdm5k7g89R6HLT3TgfOBG4vO8gCsQ14bWbuzswR4Cbg0JIzdazMvAZ4XGaOAgdSG+2xs9xUnS8i9qP2h7d3lJ1lAQhqvd6XRcRPIsKemn33TGq9CrfV/zv6PMACvHnOA/4uM39XdpAO1k2tRlgF9NQ/hkpN1PkeSG0UwY7MHAO+Djyj3EjFWCy2l03UfhmfsA3YXFKWBSEzT8vMa8vOsVBk5s8z83qAiLgPtV9yvlZuqs6WmSMR8TbgF8CVwNaSIy0E/wKcBVTKDrIArKf27/IZwInASyPi8aUm6nxHAt0R8Y2I+Anwcvy32hT13puVmfmvZWfpZJk5ALwF+CW1n0k3U5vOo733I+AJEbFfRKwAngYcVHKmQiwW20vXNOfG5z2FNIeI+BPgcuB1mfmrsvN0usx8K3AAcAi13nDtpfrcpVsz88qysywEmfndzHxRZu6s99R8AnhS2bk63FJqI4leADwMOA54camJFo6/Bpz3vY8i4hjgfwD3BjYCY4BzFvdB/WfSBcC3qPUqfhvYXWKkwiwW28tW7vlXho04fFJtJiIeSa2n4Y2Z+amy83SyiDg6Io4FyMxB4FJq8xe1954HnBwRNwJnA0+LiHPLjdS5IuJR9fmfE7r4w0I32jt3AFdk5n9n5hDwRWoFo/ZBRCyjNsfu/5SdZQF4AnBlZv5XZg5TK3IeW2qiDhcRa4BLM/OYzHwsMAj8utxUxbgaanu5AtgSEQdQm7f0bOCMciNJfxARh1D7xeZ5mXlVyXEWgiOAt0XEo6jNC3s6cH65kTpbZt49RDIiXgI8NjP/Z3mJOl4fcHZEPILavKUXAy8tNVHn+wrwqYjoAwaAJ1L776r2zTHAf9TXfNC++Qnw7ohYRa2oeSrw/XIjdbzDgU9HxEOozQU9jQ4ZSWTPYhvJzK3U5tlcDdwIXJSZN5QaSrqn1wErgPdFxI31D39x3EuZ+TVqcz5/DPwQuC4zP1tuKukPMvMrwFf5w7/R8zPzu+Wm6mz11WTfTW0Y2i+A3wKfLDXUwnAEcFvZIRaCzPwmcDG1/8//lNofiv6h1FAdLjN/CnyBWnveAHwgM79TbqpiuqrVatkZJEmSJEltxp5FSZIkSVIDi0VJkiRJUgOLRUmSJElSA4tFSZIkSVIDi0VJkiRJUgP3WZQkta361iwvo7Z0exX4EXBWZt5S8P67gPtl5s0tyvcz4BWZ+a0mPOtw4L2Z+eyI2AR8PjMfsQf3vxToy0yXuJckNYXFoiSpLUXEe4EHAE/JzFsjYgnwAuC7EXF8Zi60PdXuDQRAZt4OFC4U6/d8pBWhJEmLl8WiJKntRMRm4KXAIZlZAcjMceDTEfFg4E3AmRFxM/A94Bjg74DfAf9MrRfy+0yabhERTwXeDCwDBoHXZeZ3I2IL8HBgI/DTzHxBRJwFPLt+/83AyzPz9oi4L3A+0Av8Elg1S/7zgMOALuBTmfmeiDgMuAb4FrVCuAt4BXAd8HHg4Ij4BvDXwM8yc3U93x/VPzbVv99vAi8GDgden5kX19+3P/Au4MuT4hwEjGTmIRFxMPBB4FBqvbWfzcx31HNdC9xUz/yYzNw26fv5FnAncHT9+3o28MHM/Pyk6x/MzM9HxC5qG3g/vp73/Zn5T9O1kySpvTlnUZLUjo4HbpooFKe4AnjUpOOfZeYfA18F/hV4bWY+ELgaWAkQEfcB3gE8qX7tDODSiJgo9u4NPKheKL4IuD9wXGYeC3yNWiEHcCHwscw8Bnh//b7pXAhcnZn3Bx4JvCAinl+/dijwjfqz3whcQu3n8WnArzPzCdM871HAE4E/plaE3Tcz/5Raofm2yW/MzFsz89j6858J7AJOrV/+DHB+Zj4YOA44KSKeW7+2GXh7Zh41uVCcpJKZ983Mf57he56wHPhdZj4SeA7wDxGxYo57JEltyGJRktSuemY4v5xaz+GEa+uf70+tB+1KgMy8GBioX3s8tZ7DKyPiRmrF3DhwZP369Zk5Wn/9FOBhwA/q730lEBFxL2o9mJ+uP/87wM+mhqsXoI8EPlR/33bgAmrFHtSKrovq1y4DxurPnc0Vmbk9M4eA24Gv18//GthvuhsiYn/gMuBNmflv9VyPAd5e/76up1a4Hlu/ZRT47iwZrp3l2lRfqn/+EbX/vabtgZUktTeHoUqS2tH1wH0i4qDMvGPKtcdRG7Y54a765yq1YZ2TTRSA3cCVmfm8iQsRcQi1wuuZk54x8d53ZeZ59fctB9bzhwJ18tcYpdGSaXIs4Q/F79R7llArGGczPOV4ZLY3R0Qv8BVqw18vrp/urud6RGYO1t+3P7Wex/2B4UkF83Qmt9HUtl425b1DAJlZjQhobA9JUgewZ1GS1HYycyvwAeDi+jw7ACLiL6nNl3vXNLf9O9AVEU+qv/dp1Io8gKuAkyPi6Pq1JwE/BaYbHvkN4LSIWFs/Phv4TGbeCfyQ2nBRIuJB1Hozp2YfoFbsnll/3zrgRcDl9bccEBF/Vr/2VGqF379TKyJn6k0tLCKWAp8DbszMd07KtaOe6zX19/UB3wGevhdf5r+Bh9Sf80fM3TMqSepA9ixKktpSZr4pIv4K+FJ9ztty4Abg4Zn522nePxIRzwA+EhHvAG4E/qt+7ecRcQbw2YjoolaYPS0zd9Z7vib7OHAwcH1EVIFbgJfUr/0F8MmIeBnwn9QWhJnOqcCH6sXtMmrDXi+gNsdxF/DCiHgXtR64Z2TmWET8HBiLiBuA503/2EL+HHgytWG0P+YPvXpPAk4BPhgR/17PdXFmXlhf4GZP/D3wqYh4MrWFfv5tH/JKktpUV7VanftdkiRpn9WLsp9l5uqys0iSNBeHoUqSJEmSGtizKEmSJElqYM+iJEmSJKmBxaIkSZIkqYHFoiRJkiSpgcWiJEmSJKmBxaIkSZIkqYHFoiRJkiSpwf8DEHaUS9yQoMIAAAAASUVORK5CYII=\n" + "image/png": 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\n", 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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -168,21 +214,26 @@ "source": [ "# plot profiles\n", "pypesto.visualize.profiles(result)" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 29, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 29, + }, "outputs": [ { "data": { - "text/plain": "" + "text/plain": [ + "" + ] }, "execution_count": 29, "metadata": {}, @@ -190,8 +241,10 @@ }, { "data": { - "text/plain": "
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\n" 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -202,13 +255,7 @@ "source": [ "# plot samples\n", "pypesto.visualize.sampling_fval_traces(result)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", @@ -264,13 +311,13 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "data": { - "text/plain": "" + "text/plain": [ + "" + ] }, "execution_count": 32, "metadata": {}, @@ -278,8 +325,10 @@ }, { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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+ "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -301,7 +350,28 @@ "outputs": [ { "data": { - "text/plain": "[,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ,\n ]" + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] }, "execution_count": 33, "metadata": {}, @@ -309,8 +379,10 @@ }, { "data": { - "text/plain": "
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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -324,10 +396,21 @@ { "cell_type": "code", "execution_count": 34, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": "" + "text/plain": [ + "" + ] }, "execution_count": 34, "metadata": {}, @@ -335,8 +418,10 @@ }, { "data": { - "text/plain": "
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\n" + "image/png": 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njqX5o/99lL7eWpCspSLJqXSeGy5dwYmheXasa8CwLCTPIxSUefLoJFbF4//+0Ea2rKrnQH+K9vo4pg/npquRybeeGGLr6kYaayIYhrW0iVyzvY0TQwtMzRU5N2cR1CXKRmXp8X975yoyBZMbtjXzR5/ZwU2Xdy5tRiG9Sh7XX9rJg4dGmczkLirqfO3mNDub5dnjabIFg9/95FZu2Nbyjv7fLtTyXJCD7t5/Csdx+f1Pb3/HOQqz4rwuIrkg0R1+ZZrx2Ww1Cvjcm0cBR05N8sAzI2/4A3+jCOLNSOaCdPhGlu6fFgs5gyePppjJlKpW95i2VHSrqRKp85Lep25ed1Ft1AUZ9O++d4pbruh60+jnnW72Pws+dsMa/vo39/CxG9a84eNTs1kWCzb/vH+AxYLN1C+wk8MvOjK48J0cTf30+bL3EsturrfB/GIRARFVgoCkYvtQNCxiYRXfBxmJgfE5NvTUc2pskRWtQVxX5JWRNHu31rFvaxuGDSNTefSAQGd9iMkFg5akTmquwJ17enjm+BQbVtShKxa2L3JkYJa9fa2s7Kjl2//8Il3NUVobInzihjWUyhU0XcSwq46gkK6gShLffmKEHesa0PUA//zDAe7cvYJrtzVjVaC5IcKVm1sxLAvThr/9/ius7khy7Y42EiGJ8fkiluuxp6+VxYKB7bh0tyb5n985wZrOJN9/apg1nRFGpgp0tYS5fMM2FopljpyaY3VHiMZElC/dN0AsHOBj169mb18Lmghg8tCRKQ72p/i1W9ctbUYvnp5ib18r6cUyN13ezshUiZYalT/89HZWtCUu+vxNHw70p2ipDVI0baK69Lb/Z/c8eIrR6RwdjTFUBdZ21nKgP8WVm5sRBYHZTIGGt3FPvZG76YJE19sa5dXRBR55ofoeb+acWsgZ1MVCHOx/5U3n4XQ0xV/XZ+0nnXg/6c56I0v3T4NkTOeqra08eTTFrk1NdDTGuWlXByXD5eFDY1gVl0/eVH3vC1Hga19zwdn3Vu6+j92whlt2d70nRPJm1uu3OrYoy0vR7sH+FDdc1v627/Ne2MbfiZPu7RyCPw0uSJULeZNjQzP/ouMyliOTt0EsrPPymepER8sHHwjrKrMLJVzfwsdiTWcdrluNTnxPRQtIXLe1kQAqwZBEpmCxY32CvlUNVGyR+54axvRhYq7M0YEZ2urDqIoFqJQqAiuaIlzSW0tEFdjT18r2tfUEZAhpEsmYTjCgMpRKE9IEKpaDJ1js3tzKy2fnGUkt0NMWZ2Aiy2NHppjK5Hj25RRPHUtx2boWcgWDz962nnS2TEdjmJ7WWuriQZKRINPpAq4rEA3rpGYL3LizE9dz+L1Pb0bTVNobIgxOFPjju49wejTHXVd28czxNMWyxYf2dbNlVT1/e+8JDvRPYvrQXBNdMgaYtstVfa3ctLODS9c3s3ZFnO6WGCvbEzz+4gTPnZilYNnMZi5OLGuCzYf2ddPbkeSvvn2cR468vnhyOl1YSuBfuILet7WdA/0pLl3XwncPDPJv7lyHIIn84ZcP8/Dz42/5f36BNCRRAN9bigq0gMxdV/dy+1W9HOz/8Xv8pOR0wdX1+NEx5nKlN4264Me5hYeeG74oqd5QEwZeX48yc/7zebdEcgGfvGkdf/kfdi+59C7prePQiWmSUY1nj08vRXZv9Zq3w9sRyTvpov1uuyc01oT54FVd/O6ntvLBq7re1nr9buWhidfkyd5JLuntHII/DV4rVSajGrIc+Lk4BN8plsnkbRBQJHrbawELTbCwbAtdATUgI4oq+TLoqkA4ICKICrmChYSI6cuYPki+TF0SfEclbzg8f2qSu/b2ogmQiATYu60V03LwUbGBTNakuyNI/2CanGFx5eZa1nfXEAqqiDg4noUmQU9bDaqsomsBBkZL1EQldm1qYjxdJqyJXLqhibGZHK21cVJzBT58zUoEyaK7NUZA8Lj+sk7qYyFGpjLMZ8uk8yWaayP81XeP43kOjufRUKNyxeZm5rIVBscWcFyWEvZPHk1h+tWixj/52ku0NkSWrgS7moNoAuRLJrfs7mTH+ka+8tBpejuiaKrCyeEF/vbeV/h3f/E0Dz53jl++tofOljj7nx0hV7SWNPaZTJHJTIU1HbVv2ijzaw+/yksDM0sJ/Km5PFf1tfL40XH29rXywqlJeloTNMbDb5qX+UloAZlP3LCam3Z1MJ+zLnKj3byri+6WqhR14T162mL82q3raK6L8vjhETIFg1PD8zz47Bh/f98pNnUl3/AHfiG3UBfXMCoOE7NFhicWL3K7NdaE+dwda/mr37iSz92x9h3Xo/yk1PJG0suFyMKsOPzTD06xc2MTHY0h/uNHL3nDqGNsJnvR/e9UzrlATK99/o+eH2YhZ/Kj54ff8DVmxXnH1usL9184/oXbmVyFL933Cplc5S3X927s1PB6Yni7vNzbOQQnZrPvKL/02oumC1Lln//7XUsR9L/U3KVlmettoAVkJMHBsFVkBSIBcPCoj1bllplMET0AgYBKe72O50k4gO9baKKKLfu4tsrozAKNNSE6mqMk41BwLNoaQjRGNHasb6BouigSPHdiirv2rOSytbU4DkxlDHpaVeYWSrTWBvH8qvRTMipVsqiLYVZs1nc2MJWZIaLL9LYkUAT43AfWsli02L25Dcdz8V2JungQy/PRFBFdhrE5kzUdEVprw3zpe6fYsbaeXNmlMRmmPqni2AKCKPDI2TF8Hz5+XS9NDVEUwSWfr+ZQPN9naCLLv7lzHQ3x8PnRLw6KJLKmM8mffuWlqhkgEeEf7nuV3tYo/+njm9EDKkbFIhzW6D8zwIrmGM8cn2FTt0ldPEwirpOMhvjG46fZ09fKdLrAJ2/owfV9zIrDQt5gcCLLR65dxXeePMoHdrVz5EyaE0Pz3HFlN1dsaWNsOktHU5zZ2ew7lgDmF0tsWVXP869M84OnR1nIm7TWqksyiBaQ+dTN65iZzSJIEtGQTLZU4ez4LNMLJkbF56Uzs0sFnydGFi4ikvnFEp7n82T/BLu3tNDXW0c6X2JkMsu5KYGHD40t1Xzs297O2bE8/3Dfq/z2Rze/IynmJ6WW+586Szys8fKZOW67cuUbfsevv6yT42fniEV0vnj3kdfJND95zHdaGHnPg6cwTBtNVZaef8OONqYyJt94dIg9fa3MzuVoeI3sc0Fi/L3PbF36DPf0teI57hueqyz6OJ7AwGiGNZ01HOhP8Tuf7Lto434rWfDdTFP9SWK4elsLbQ3xtywWvuAQvPA+r83HffPRAcqmy9MvT3L7ni4u39j8htLehc/9pl0dF0mV74dWQ8uRydtgJlPE8SV0xcb1q12EZcDxZYZnc3S3hJEVCV2ykBUZTRUwTZdv/OgcAuBTQVEsWupiJIIq9dEQrq0ioRLXVUwHAgERw3SYzRp0tcTQFAtdU0lGVNa06kylS1Qcj3zRomw4TM/ncR1AEKm4Hisawli2TW04QNnysH3oPzOL5cP3nzlHKGChaQ664qMpEoIgcf/TIzhYbF1Zy8yCie9Xq92vu3QFEU2iMaljmB6vjs5hmBY372pEkgTm8xbDEwuoARXb9djYneTaba1YFYeB0Rx/+OXDGBUL05eJBFUefWGMPX2tfOYDq9AVuH3PCu7c28ULr6b5gy8f5vCradrqY9xxZQ+LhQpbehIMjOd54NlzfOfx0xTLFs21ERTRZ/eWZp58aY77D57j2ZdGGZ/J0dsWZ3I2y56+VrauauJgf4qhiRz9p2cvquNoOP9D/+LndvDha1ddVAvzWknngqxy5NQMZqXqvvvCXetIpa3XySDPvTrLxGyJXMnhoUOjBESVoYksJ4fn6WyKMT6T4zc/toVP3bxuyW1z/1Nn2f/cKOm8ySPPj/Pk0Ql80aOlMUrZdJds3smoxm9+ZDMzc1WZ6yP7unjxdNXu/bWHX33Tos/XJmFNyyb1DhPRe/va+cCu7je0YL/uyv0dXslfuHq+qq/touebrsd0usCvf3Aj0+kCldeMVLogMS7kTb7+yBmiIZm7ruohGpJp+AkyuBC57NxQ3aB3rm/mQH+KzsYw+Bc7AN9OFvxp5aE3s46/Ve3Qhfd5rUNwYi7H2EyWxUKFp1+eRFdlFguVN5T2xmay1McV/suv7mBNa4Krt7bwl/9hN3v6LjalvPa7/Yss7Fwmk7dBY00YPJ9FU0ASLAQfDFtEFqA5GUMWVSo2GLZKAJCxQPS548oeTp5Lg6vi+hKGUXWBHXw5he1ZlM0KtbEAlm3zvSeHCUdEGpNhtICCaatokoNhg+mqPPjcGDFdIhlWOTORpTERZWwuS09rgmhIRFUDzCwUaW2qFjsWyiYVFzzP4peuWoVhq3i2ynefOkdTQqNSsYhHdDKLHqIisr6zpmof1iVyBYOwruILMDCapretBtt28WyVruY4humysj3J6HSeZETn6WOTHD0zx2XrWpY2ov/+1WMEBCiULa6/rJMVjTrZokvetFjdkUQP/NgK/NyJaZ49lmJwIkNtXCUZDTE0kaX/9BzbVjZy5GyaG7bVsmtjK03JKBFN5uRwhnUraimUHAIKTC9YJCMyusLSD/zWK3ou2sAuVNCfmyrwg2eGOPhSiqOvTnHP/lN85/EzjKSyF8kqL7w6TTCgUBsN0NNae9GxUrNZTg3PEw+p2J7D0YFZrtnegqS4bF1TR1NthBdfnWXn+mY2dNcvyVbHh+aIhzUO9qf4/pOD7Olr5YZL22iO65SKFk+/PMneS9o4dGKaD+7txPPh2RPVq/LVHXWcGJrn2u1t1EYDTM7nGBpbvOi7OjaTRVdYIqNrt3cynzUvSkRLsvSm8lR3W+Ii2e5C8r2jMc5nblnNX35hN5+9bQ2aYL1lHugClpL2/RMXyz+NcZprI/z1vSdoro3Q1hDHrDiYFYeyYXPX1b0koxq37eomIIu01YZZ3ZF83fE9p0oYR89UDR2HXpni9j0r+NxtPTx/Ks3YTI4/+/wW9m798Wb7Vl0Kfhp5aCFnvI6AUvP5JTK7ZHUds29Sd+Sd584LMpkm2CQiAXZvaVmqFXsjaa+jMc5CweH40BxHB9OcGp7nBz/hFLxQJPzNRwd+4YWdy2TyNpiYzaKrCgs5EwEVRQBd8TB9EGQLXXFJ58rV2hPfx7BVogGRolGhsymJIDmIyCQSGsWyxba19VRskZqwj+ULCKJIfSKELinIEgh4FA2LmZxD2bDQJIedGxqIR4MYNjz47BiLhTI9rUkCgWpF/uhUlvb6BILgcuWWVjxPIBnVuHv/MEFVQFcAqVpr4gAVC5pqNGJRnYnpPBFN4oWBNOvaE4SDIrbr8PRLKTb21vHy2VnikSDHhmZQJIF8yWRsOsdstoztWVx/aQcBWWShYLCnr5WP39DLf/3cDizXZXaxREKHzsbkeeupyg+fHcHF4prtbXzkmh7+y6f7eP6VKbpaEuSLFiXDoqctzs6NTYRCClu6Ezx0JM03Hj9NOGBh2C4fu64Hy4b7nx7hkpVN9J+ZY6HgcHRogXhY4s8+v53hyYWlDe83PrSJh49M8ugLYyiKSMlwyeZNzIqHYdmoqsIfffkw03PV+olNvTV0NMbI5Mps6Kqjs/ViLTyoqZwYmqOjSSesKty+u4vGZJhi2aG3NcFwapE7dnfx1LEU0+k8Dz8/yobuWu4/OES2aLKnr5XBVJ7tq2vZ3FvHsZEMRdNm37Y2Hj0yxuUbm9jQ1cB3Dwyi6wpbV9aiKz6beuqQZTAdj6dfnuGLd7940dVrIqIzkSmzoSvBH31mB6JsMTaTX1r7r9+1gYefH3/LRPMN21rY3FPHU8dSnB5NA/D0y2OMTBbZ/9wwTYkI331qkhu3NfCHn97Oh69d9YbHuUBYn7xpHb+0b9VFG++FTffCxcQPnh7iG4+c5puPnuHE4DTbepP82ed3IMoCFdvh5eE0f/bVly5a8+OHR3jo8ART6QINyRArO6J8/vYN1MWDVOzqxcqdu7t56Mg8v/8Ph/negTNLUecLr6R+JvvuPQ+e4gdPD/GdJ04zOpVj6HwxcGtddCn/9xdff5nH+icxK85Fxoonj5yjYrsXRZA5UyIWlNjX10JzQn/TrgpjM1kGJ7J0tyc52J9iVXvd6yLEC7cXC5WlWp+Tw/Nv2jHhvcQymbwNYmEdSbbJlSp4PtjnIxNNsJmcszFsiURYx7AhIAjIioVhi4T1AHO5AgFkPBwEH770wCmSUQ3bdrF8lXOpBVTVZ+f6ejxgKl2iIRmiaNqEwiKeB4Yt09sRRpJEAgr8+l0bCIckoiEPXfRAsgkGFWYW80sbrCz71IQ1rtzShITFouEwOmWhiD6W7RHSVSbTBqWKRVtjBNOH2qhMuliV12azFj3tScKKwPY1zdieRX1NCF9wuHFnF994dIioJlE2YHbBoKk2wkLeYlNXnLLpMziZIyA5JKI6hi0zMpVhb18rLw9O01QTxjShuUZnZXuCiivwy9d0UTJtQnqAoakc115Sw7bVdSgyxCNBDvan2NSdxLBVTo8u0tYQ49S5ebataQAqfHBPDwf7UwxPLLJYdCnbAi8OzNFWp/NbH7uEeLhK5Ou6ahF8v9q4sq+N7x0c4trtnUtXgtFQtZD0V29ZB76HriucGlvgewfOXLQZBnWFbWuaCAVUglqAXMlgPlvm3FSBolG5qD5HliS2rmng5HCaq7e14no+azsT/N6vbCccVklEVepiYc5Nl3jh1Aybe+q55YoudAWu2NREXVxD1wIsGi4H+lNs6q1fim5+0uEVDamEdYkzY1kePTLOwf40FdshEVH4tdvWURsNvqU89c1HBzgzXWQyXWDtiiSu4zI2k6W5Jkq2YBCL6Dx2ZIxwUOW7T43zZvO+38gZ9bWHB/j9fzzM1x4eoLUuuhQB/fsPbqL/9BxBXWZFo47nCzx0ZJKMUcGqOKxsr3mdmy01n6epPsqhE9Nctq6aL/iH+17l8SMTvHxmjnypGjmFgz+OgLWAsmQPHxjNvevCvoWcweh0jss3NZEMBxiZLvLFf/4xqa/trppFSqaNIgt889EzS90KZmezpPMWp0YynJnIsKevlfbGaiHvXNbm64+dQdcVPnXzOv783+9ainguSJodjXF62+IMj1cvlPJF43UJ/wu3E5EA12xvZ+2KBGs6a96yY8J7hWUyeRsEFImn+mdpiOtIAiiCj66A5So0JhVcLBJhBUGuylivjBSZXiwSC8q01sYwbHBcEUWA23d3ERBcJARUwaK9KYTvSoRCKrmCRcX2iYVEWuvD4MgEQyqz2SKKoFIXF5CAlpogAUnF8VVKtoRjS7TU6kSCOrEQXLW1hdpoiIa4yvhsGcNROTE8x+pWlcaaMNPpIroCXc0RJqbyiIIPWGzubSCkKsxnDc6OLRDW5Sopqi6OK9CciPDymUVmF6tX76tX1HFyeJ7auErF8ehpi1UlqtQiq1s1bEfl9NgCugJNtWE2difpW9nE4ESWuZxJT0cE3xfIFQxEZOJhldaGMD6QKUskIzonhubRlWoUs25FPSgV7ryyG8Oy+c6TI2xbVcuzJ9IMTixyVV8re7a2kc6WyBVNPri3l68+Msjv/N0hdMXiyktaODeVZSpTteo+2T/BlVuamckW2NvXyp17upjLlfjVW1ZxbHCefdvayJccvvP4IPmSw+RsNZH//YOD/ME/HiKiOlg2nByZpaclySMvTLCQs0hEdJ47Mc0dV3awujOBYdlLG1pLTZQXT83y6ugiX/znF8ksGpgVH8/3ONifYnymwNd+dAbbcTB92Nxdy/BkkZfOTnNiOM2evlZePjNL2aywb3sbl6yq45rtbUsOr9lMkYLhkclbbOyp4cjALI21UQ6+PIUiC6Tz5dfJUxfkpQu6/fcPDvGha3pJRlVePJ0mn7OYyuS5eWdXNRF+SSvFskUsorH/0OhFPdZSs9mlfEpnY5ht6+q4Z/8pvvX42ddVxN9yRRd7tjRzbHie7WvqCGsKnY1JmuuqdvK5jElNTGd4MnvRmhtrIrTWRckVDW7c2YmkSGTy1WhvY28doaDK4OQCN25ruUj2NCs2e/ta2dvX9o5dfRdyD6+NYpIxnQ/u7SWTM9+wzc+FXMq6FUk6Gi9+3PSrYx3iYY1njk9zSU8tO9Y18pffPsYlvbU010b4k6/0c/f+U0sRyU/2sfvYDWvZvKqej1y7ipGZEms7o/zRZ3awtjPKTKbIUGqRj123klMjGa67tJ0vfHjLL2zA3PuGTPr7+7nzzju59dZb+eQnP8nk5OvrCaamptiyZQu33nort956K5/5zGd+7usqGzYbuutIxhUEH44MZsibFqpkEVBUTg7nkABNUJAVh/amGHMLBuAiiQ7FskVA9jF9WCiUcZBQdYWJjI8iqsxnDTQBkETyxTJaQEUBXN+mUDSJhTUqtotC1bNvebCQK6EJIMk+rm+jKDK5sonvqnQ1xplZKGL60Nmo4/oWV66vx7RVgrrAZKaMYcORgVnWrKhjbsGgUpEQBLj/6WEM22X7mkYiIYGAYmHYMmE9gGFZ9LbFwfe5YVsLqgTX9DUzMpknqIhokkC+ZPGrN6/DslUCEhw9PQ+AFgjwV985wdEz0/S2xZnPlvFsmXsPDFJyfCo+nJ3Ikgxp1CdDTM3lcXyLumSIh46k2dyTRFcgnXGIhlWyZZvdW1oI6Sr1cYU9W9rY1J2gIRLghu0dLBQMgprIr9y8ir/77SswfRVVFvnRCykefHaMhrjKTbs6qYsHeei5MeKR6jEefn6MQEDl+wfPUbG9i8YvC8D9B4d44ZVptq9rwCVAKpNnc08j3z0wyNY1DTx2JIVVqfC7v7KJgukyPV9g6DzRXbejjclMng/urdaotNQGiYZUymWLkC4ubZifu2PtkhRlVHxKZYt1nXUMTWQJ6RLru+sQBIGy6TA6XaBkOuRLFhNzOSquiygK9J+eIzWX5/bdXXz/4BD/9o41qLLMX3/3JKoEf/jp7dxwWTsPHzrH3ftP8cf/+wVmMyVWtkbYsrIePGGpRuieR04jigIRTajaoY+Mc/POrqW81kLeZCZd5DtPnOaxI5P88OmRJalnfLrIk/0pnjs+tdS255bdK/j+wSEGRjPUxoNksmXmchVEwcVyLFzHYU9fKw8/P0rRqPD9g+cIBgT+8NM7uOWKriXiS0Z0JufymIbN3EKJ2liA+piOIvrkyx6//feHefjI5FJEeefeqtQWfs2gubfK99y9/xT7nxl5wyhrXXcddXGd6bn80v/bH352M7JQfd18tsQnb1qJYTkXEaGmyIgieJ5Ld2uCoalFnjgyga7KBAISQ6lF/sMvbebS9Q3MzuWYyRRf1815JlOkvTHO7EKRZFTi1Gie+58ZIhkJ0VgTpqc1wdd+dJae1gR1idDrOib8PF1f7xsy+a3f+i3+63/9rzzwwAN84AMf4I//+I9f95yTJ0/ygQ98gAceeIAHHniAL3/5y7+QtR0ZmAVXpuJWQ86oprJoibi+R208CIDlgoyMadhs7E6QLzscObNAICDhI+H7LttWN2NWfPIlm7qoho1FXUKi5FhENYW+3nrMikfRdolqCk0JmcGJRVRVwjyvKZTLFnJA4SuPnUUQfOyKgGU5OI6Hi0VdUuXcdJVs2huT/PP+QQxbRpAsmpMxpudLCIpDbTxExXVoqYsxMpVn/3OjbF3dyLMvT6HIHo6t4FPtQ6YIoAcDWJaH6/nMZi0EwHREDr86SzSqMjCRJVc2URQBSbHIWw4fv66qpx8fnuf2PSvYtrqJ2miAsKaQLVpcuaWZpniQYsGiMRlE8D3Cukw6X0EUVBrO14b8zfdOYfqgB2QqTgXXcahPqOiKxVzW5vGj4xwbXuS3//4w0WiA5rooRwdmGZ0ukS97iHgE1OqGrWsys1mLV0cWuffAEB+7bjWPvZhieqHAFZuasO2qRDKXLZ7vSRVh25oGMgWLBw+NcuvubuoSIWwfkmEVT3ToPp8n+S+f20xABgkFSRRob4xxz0NnUSS4eWcX/3T/AGMzWT60r5tdm5uwHZv+wQxPHJ0kEVH41I2raU5EONCfwnE98mWLGy5dwb0HB7njyh6eemmK+cUiCAKHTkyzkDfJl0zuOzDEVx8aIOC7qIrIR/Z101gTIVcyuKqvBUlUOTk8z96+VgYnc5Qth1iketX//MkZ9m5p5fEjE6TmDQ6fmsG0XeZz1fO/bEM9QxMFTk8WOD40z94t1Sv+nrY4favr6WmLsbevbWmQWl1cY/WKGg72p6iN6uzpa0WWRIKaxH/7/A72XtKGJPhMpovEdIWbd3ZRKlusbKvlf373FCFNpVS2+JWb1yAJ8Gu3raFc8flvXz3KD56pXqWrgkVqNk9NXKdoVogFFXo74gQVl10bW18XebyWMOoSoTd1bl1w9Z0+l+ZAf4o9W1rfVBacz5ksFCusaAzxZ5/fxlMvpTmbynN8aJ6tq+p4+uVZ7j0wzOrOBH/yby/jUzevIxbRqI2qdDUlqi42x+faHW3cvrebZ49PcePlHRwfTnOwP8XDL6awTIe+1fUkoxrX7WjjW4+eWap56miM016fIHdefvzi3UcYGlu4qPHo5Gx2ySjwWtns54X3BZlUKhW+8IUvsHr1agBWrVrF9PT065538uRJzp49yx133MEnPvEJzpw583NfWzKm84HLWhAkC02GWFDF9KFiubx8eo6WWgXTh8FUFsuBoCYiSzKqorKqNYYakJF9+NL3ThFUbAyzQmo+h6I4SL6KiIoqq0wvlDBsABtJgpIFpifz0pl5JMCqWJg+zC6UiIV8etvipObKiJKAqqi01UcJKeCeLyQ0ffB8iw/u7UFSLBBUDr48wa6NLWiIbOmpQRR8Fgpl8H12bWrh6OkZVrXHCagq87kilu1TsFwWixYiAvUJlVWtOv2Dab5zcARNtvnwvl4qFYeiUSWmqXSR6bRDXJMYmsph2PD8yRnaGyJkiwbtLVEUWebeg4Ns6alFV8CoeNQlgxQqLqokIoqQzpWYzRa5aVcHN13ejiJYTGeKxLQAI5NFwsEApq9yfGh+6Ud/42VtSILA1Hyejd2NHOhPMZfOc2Ysi++KTKcL/Ns7NjA+k6O9IcyG7jpCusjWNQ2EgyrNdVG++vAwV/XVgiByw7YW/uPHLyGoCYxN59m5sYmTwwt0NmlMzhcIhVQefT5FQ0Ll9iu78R0VWVJxPJe1HXGGUwt8aF83LgKHB6b40L5urlhfx4buWpqTEfSAyoH+FD86PMHRgTm6WmNLyf7LNzUzNpPFtCp0NMZ44ZVpPn7DamYXLTrrI3z+jrX89W9cyZ17VjKVLlRt08dnqQv79LbX8MTRcVa311KfDPPo4VF2bWzFtGw6GmP82ddfYmQiw1WXtNK3up4DL6f44N7equttocxTL00yMVdi66pa1q2oJnnDWoBfvXkNs9kypg+vDKfpbo5w25UrePj5c4gifOGXNlKu+Hzn8bPs7WvFqJiEzk8MlSSBloY4yZjOro2tPPjsGN947Cxl0+aGS1ew/9AIt+3uYiiVJhxUWSyYHB5I03Q+QgrrytLGXnFVXER8HxJRnYl5g689dAbDlSibFrec7359y+7ON4w8LuRvnj0+sSRhfeuxAb71+FlmZ7OcPp+TePbE5BtGMfmSxZe+9wqnhjI01OgYtsiB/hQHjlal02Q0yHMnpuluifO1h08zMlmt9L/3ibPMZAxms2XuuLKH1FyBravr+cHTI1y+sYH6aIipdIG+1Q0c6E/x1MspWuqD/NK+Xjb0/NhRqCpwamgWXbGW5MeFvMn+QyOs6azBdh3+rzs38vjRSX7jfzzNPQ+e+oXUobwvyCQQCHDrrbcC4Hkef/M3f8O+ffte9zxVVbntttu47777+MxnPsO/+3f/jkrl9dWt+XyeVCp10d/MzMy7Xl8FiYBYJZHGRBBFcKiLaly2phHBV9EEsCoeAQmiQZVC2UZXfIJBlZmFEo4Auzc1YToKoiTS2xzDrIjIIuQNC8+HsCqjKz6yqCKJVSuxKLh87LpVDM/kmZqvymErmpJ4rkpvS5zaaIj9z50jqNgEZQ/fV5EE+J1PbMYXLBxHQhIEXh0townQWh8iHBQwfZGgHsDxIV+0QBQBj+7WBAFFQhE8mhJhFMXl+HCa/rMZrIqD44HlVJOanY0hLF9hYt7gu08OIYkQUqC9VuPVc1lypseDz44xOJHm3961Csf1ODmyiOf4eK7NL+/rpWR5GDbkS9X1RXQJQRLZ0JnAB144OcPl6+sJqgr3PTVJZ2OcouVxoD/FYy+Mowrw0WtX8cjhUT5+fS87NzVjORb5sk0oYPHL1/bQ3ZYgEJCwKhXaG6IcPjXLB6/qJVcyWNkewTCrclYsJDM1n+eKTY1oosr4bJHnT8/x3IlpLl3bzMhUkVLZ4pbLWnEqMg89N4YmwKXrGxlKFfif3znBxHyOuVyJoCyBIPH1R4fobo3TGFfZvbEJo+ICMtmihaDAsaEZ9vS1cusVK/jsLetJRHXGZrJ0NQe5c1cHuZLLi6fnWbsizg3b26iNqXQ1R1BVjzNjef7gy4fJF03uuLKHpmSU9R1V59uxwRluu6KLew8OMrdQJBnXzif0WxhKLfKHv7qZ0ZkCw1NZeloj/Jvbe1FVuGlXBzde1smaFXE8D3QtwH1PDbFvextDk4uENJXV7VGGJxZZ01nD8FSeqfkyuhZgLlMmGdc52J8ipCrcsK2F8VkD03TYsrqeX7p6NbPpAvc/dZaFQjVxPJkuEwoGeHV0jtp4kOakTl0iAr5HUyLCiaF5pjJ5btndyXWXtrNvWxuNNSEczyFXKBPRZZpqggynFlnVkUSXfAYmshTO57oKpdfX4lzoOtC3qpbBiTy//w+HOXxyipLhcviVGUwfvn/wHC+fmWNVewJVgT/49A5U5ceJ8IcOjbB7Swt5w0YSFfLFqpNxTVcN8bDGdw8McuPOTmYXiuza1MSJ4QynRzM89uIE9z01QrFkIEkuTTVh7n96hI9d34sviIzPLnLHlT3ce2CQq7a28vJQeulcRiazS8R21eYWVEXhyWNpJMVeuj8e0fnUzetY3VaD68KTR6vNO9WA9Ibtcd5r/MLJ5OGHH2b37t0X/X3qU58CqhHKb/7mb+I4Dp/73Ode99pf//Vf58Mf/jAAV155JcFgkJGRkdc975577uHqq6++6O+jH/3ou1rvQs5A9KHiVzv9xmMq4/MVHB9MD3SlGjEs5MuIAjg+ZEtlTEcglzNIRGUkYFNvHS4WekAmpKuoiojrQVCV8bCoiahYeOgKeG6ViB46NIkkS/zwmVGaa0LM5ywc30JWbGLhAPmSye1XdGChYPoSFRfARlNUhkcNQrqArinEghIWFhs6k3hutTpfFG1UWaGjMY7vVX9kB/tTrGiOYtgezxxP4bkyqbkyNXGNqCbjCT66DJ+7Yy0bextwXQ8Rl33bWultizA8V8B0ZY4PzVMqV7hldyeZkgO+Sn0sxGMvTiACXa01qGqA7z89hK5Ad3sNhu1x8KVpTMtBV1W+9dgg67uSGLbHgZdSXLG5mX/64SucmVhgb18rfWsaKLs2yViA+pogqzuSeD5Mp6vNIOdysGZFEtPyaK6JogVUZhbKPPXyJNGgRzSk09sYZTBV1b0fPZxCFAU2dCYxbAiqCvGwxrGz88iiQzQoEw6q2J7AbLbAXXt7+e5TI2iqxIqmIH/0mR1kCyY9rdV6CFmCX7l5Fa7tkjcdKo5AIqJh+vDIC2PIPtTGw6xqDxMKKjx/apoDR8fRBJ+xWQPDhon5EpIocOpclooH6axJQzyMSDWi2dST5NhQhpfPzmJaFpGQxtGBWRYLDjVxhZXtCZIxlUvXNWK5oAY8eloTCH7VjPHSmXnCQQUZleGJPKos89TLk7TWhelpiVIqV9i+pp72+iAnhzOYtkXFlrjnobPMZorcemUXjzw/TlgTuevKLizT4rO3raG5PoTpQNl0sV0ISA7ffHSAqUyZeFjjgWfOsbojwp99fgdnxjNkSy6X9NZgVDy+e2AQXxDpPzvNr9y8hh8+M0q+5PDNR88iywL/+dMbCCkyZdMnEdU4PbbAXXt7GZrIYjjCkmvrtQnn1xbuRUMqe/taufG8vPZfPtdHfULn6Zcn6VtViyL47O1rJVesMJstY9nwF996ibp4CC0gs5AzePjQOJ0NOjvWNvDKyDynxrPsvaSWnesaKJataj4ktcAH9/RQNl3GpnO4jnPeSJJkfWcN+DILeYuZhTJNiQjDEwsgiKTzZTZ012JVPPZt+3Hvt/sOnuOWK7r40/9rJ6YPR05PV3vt5Vza64P81scuob0+yOxslv7BNP9r/yvctKuDX71tDdvXNHJmPP2u9r+fBr9wMrnhhht4+umnL/q7++67KZVK/Oqv/iqO4/ClL30JRVFe99qvfvWrLC7+uFDL931k+fUdYT75yU/yxBNPXPT39a9//V2t1/U8xucLaAIYtk9AcJlfLCEAmlhts6IJcHVfK6ZfzS80J2NoMozNF1EDKhLVBpEVV0KSKwiyhSKAKllIks/Dz1UlvbJRTdQbdoVwwOe6y1qoWBafv3MVmi6gaTKTcxaWI+ALDkE9gCypqFSJLlewyBV9dMVDkqFo+CB6NNdG8WwVwwFdVZlO24QCKrIAQQ20QDWh+7uf3IwkC4gS9LbFOT06z64Njbx8Zg7TgVKxwuBMnuZEhIBUNSes6ooQVBUUUeUf7nsVTbL41VvWo+syoiChygII1X5m+7a3EY2oBAIuLk61IPT82suWTXdLhIWCie1aXLOtlbpEEFWS+fDVvZyZyPKxa1cRDQXoag6zY10d02mDYtmhpT6EIlSPUxdVaEhGyZcNMlmLfMlmZrFAxbW4cUcbv/nhzbiOyvxiVa65/+kRDNOlLqFSNquFomfG5ylbNtmiyWdvXY0kyGxZVc/xoXkU0aY2FqBgWrTWR0nN5MjkHe555FW6mpMUDItSxWd2oUhHQ5xAIEA8pCJL8MNnz2GaFru3tPKVH53BtCo0J2MEFAHD8jAqLiVbIDVXRFdctq+pp+b81b5lu4xM5TDP94bb09fKbVdWpSnb9tE1Fde3uH13F6fOZXBdifqYytnxAn/5rWPMZcvIYpWETo/Nky0YfOSaXlY1hzB9qEsE2f/cKNvXNnBiaJ62miB506K3I8l3nhzhjj09HBtaXHJIiZKAJFRHVDfVhcgULV4aXKC1Jko8rJEplAgHZW7cUQcolE2X509Oky2a3LCznYGxAsUKHD41R0uNRlBTOT2Woac1QUgT2LWuiUrF5UP7epdknA2dSZ48msawq52ka+M64zNF5rMldqyrp2RYS10LklGN6y9r50cvjF9UuDeaynJkYJZsoczqjjhPHp3lz7/5Mp+5ZTWRsMYffLmf9oYg//XzfTTVRjgyMMuuTY2k8yY/fGYQWRb5zQ9vprMpzkKhwhNHJxEFOPBSmkdfnEBTFU6PZrjp0g6SsQBnxxdY3VnDvQeH0TWJjT0JTB+eOzlF/+k5NvfU8eyJFBu6Gmitj1KxbNrqgzx3YorvPTm0dC4XOjU31lR/bzs316MqFnnL5Z6Hz/I7f3eIex4+i+nD0YFZOptirGpJcGYsz0OHRqiPhX/u0cn7QuaCagK+o6ODv/qrvyIQCLzhc44cOcK9994LwIsvvojneXR1db3uedFolNbW1ov+Ghsb39W66hIhwoEAhgOKImDYEnpAQhJgIFVEV2wmMmV8wLYtShUbSbHImg6beurQBChVPHwgIIoIqMioLBg2hq0i+DJ9qxswHVA1v7qxGh4AqigR1lV0UUVEwsOjpSGIj4dpuUzN5QkoFqZfDb8FEWrDCoYj0lwbJR6UKRZcJMFlaiFHsWxRKFs01SpUfDg+OIfjC3S1RnA9n6Cm4touji3QVh/G9QVqEiLXbe9AkyEUUhkaz5HOl7FcC00NIHoBLNtd2mRARVUEFFEkky1zycoGnjqaBiw2dCWrrOoqpLMVupsjzBUsDBu+8dggqzuTiIKAGlC4pCdJQJawKja6pqIrAqIAsiyxULCwbAfbhvqoSlMiSKZsA9UeaQdemqC1Lo7leJQrNkFVIaapJMIqtXGVbNGkqTZMqWhx+cYmxmaydDbGaawJ0392mtb6OA8+O8b4dAHPk7CAkcksm3rqqLgKgYDKM8dmSGfL9HbWMpUucOm6BhTNxzAsKk6F1to4uaLFXK5IU1InV7LYtqaBHz43QSIksbqzhnBQRlIsWmrDCL7HpWsbUBSHXZubsXzobNII6yJ7+1p5ZWS+WndkumQKFpu6k4TOE/Tuza0Mjs/zzPE0azp1br2ik4W8SWdzgoP9KVzXJ6xL6ErVXPDNx0foaYtSqXgYtowmWMxmStx0eTvRcIC+dTUADE4UKOQttq5pIBoUCGnVfnTruxL0tsWZSpeIhzWaa6JEwiqCUB3PEA0qTM6XWdkWw7JVFFlkIV/m2h1NCLDUdPPowBQbe2voak7y/KlJru5rYXVnjLLpY/keyahKQKqe/8dv6F0aoqYrPjft6iA1m2fnhia+//Qonc1x/sd3TtLdGGVfXzNf/LUd3HR515Il+flXpplKFzg2NFPNkYU0muqjlMoWv/OJjbQ3xJZyM7MLZcamy3zrsTP80r5ezIrPM8emWNcV5fsHhqiPq/QPzpOIBLh1dxdaoDpB9aqtbUzOl9jUU0tNRGZirsgnblrDcGqRPVuawfcIKDKmYXHZ+nr29rVybGieukTVdWlXHLqaa/jaj4aqSobn05jQX9epORJSkXwF21VxXJ+rtrZyyao6bt7VQT5XvaB45tgUkXDVzFBfE+TImVkePzr2rvbAd4r3BZm8+uqrPPHEE7z00kvcdttt3HrrrXz2s58F4Jvf/CZ/9Vd/BcDv/u7vcujQIW6++Wb+9E//lL/4i79AFH++pzCVLqBqEosFCxkXJIve1iQeDlbFxfQVfF+gYvtMzBqIggwEmJwrgACzWQPX9zjy6jSu7yEKgAAJXUCULQQB5hcLINkYhoDpQ0tCw3AERDwqto9pQ77scnQgTaXiklmooIgqLw+m8XwVUZAoWB71MQXTF9Bkj4gm4vo+ectGFiRioSBmxePeg8Noooom2GzsTSALAr5fnYnuOB56QGVsKoflwOaeCIu5akW+6UBAhN1b2sgWKrxwMk0wABXbRVZkLB+u3FJbtR2/OoNZcdi3owVJdOhsCvLwkTS1MYn9z41iORai4BPSFRRZZP+hET51Uy/5UoWyUcFxRQxbJBlSkGSF8ZlFtq9qQFIkQppMPKwhCwoCHqYNiiRRNmxMpypLhoMqw5MZKrZNRJdpSWhAdTqm7cPZyRzZQpmgpoLv8bnbNjKTybOQK1Moe4iCxy27O7l5Vzezi0UUH+JRlaAmoMmgCS5XXtLEzg0NaAJ86JpuXM8nvVhhZKqApqjMLOYJhST0gIwoS9z/zDl8z6ejMUQgoHJiaJ6O+ij5gsBCrkx9UqdsVZCRSYYDlMsOqqLS2RzHtGzWd9UznSmSiAS555EzFM0K4NHbGmVqIUd9TYSpdAHLVmmujTI2k6VYrnD7nhXs2tyEIgcwbJXh1CL/6WObeeHUPLmyzcmRWQxbxXIgGQvy+IsTYAco29CcDDA8U2DH6lqOD2fZ0JNkIm2gBhRWNMXIFyvM5UqYZgXBBxD40v2nSEZ0IkGVmmgQs2JVC0ZXJMlkbe47eI5i2eKDV3XRVBdi19oEQ5NViceyBZKRIMeH5jEtn5LpED5v/y6bPrOL1ZqgguXT0Rjmq48M8tLpOW6/sosXTk5zyxUdvDy8wFMvT4IPIxNZdm9pob0xwk0723nx1DQBRebqbbUkdBHbcrh0fSOHXkkznam61z56bTd7N7cwmSlz2YZGaiLV/6v/5xPrmJq3efK8uWUha1IXUxHxWNEYYU9fK47r8uTRFCvbkhi2RF0yTEiV+NC+lSSjOpetayKdq5AvuwTkaoHsZ25cQ00syD8+cIZYFEqmxc6NTcwuFPndT25mbXfyDTs4FwybxYLBD58ZYWV7lE/euJZLeuv4+x+coiGh86Gru3Fdi5svX7E0p6ZkuEuDz34eeF+Qydq1azlz5gwPPvjgku33n/7pnwD4yEc+whe+8AUAGhoa+Od//mf279/P9773vSX3188TzbURQqpMtmRi2BJf+t4ZHN+iYAjUxTQ0oUJ9jc5stkxbYxhJcnFdgbPjOQpFi1hU57kTM2xeWY8WkJAB3wfTkfnH+wdRBIuuliS4CpMzufNymoAuVyvpfbFaJFks2wgCBFWVsllBVzzu2teFIni4bjXnYSPi+xbpvI3hVhAEgbiuYDqA55HJl9nbV73qXTQEfE9BxqdSsYjpErIsVluvCKBJNgJVV01HQ5zFfBkBh6ACK5oj1MZCzCxaqIpELCKiChaCr1IsW7iegCTL4EosFhw6mhOMzeQAhbaGCIWSS7HkcOTVOcam81y+oRlZUPnhs+fY0J3Ax0JXqhHL2bF5JtMGpg+u4xLSZaJBBcuxWN0aQ1dAEC1Oj2URJJvJTAk1IKLrCu11MbIFE9MXKNmQL5oUSxZRPUBNPEjFtaiJa4QUC9cXiIQCNNVoREIq+ZJDqWzRkAjzwsAMa1vjXLKyiR8+P04Fn7pYEFUJsFi0sSoCjTVhaiI6nc0xRMVhMFVkbCrP8GSekcksuze3smNdDb0tCcqGxUeu6cUBSpbHlt56XM9HDcDLQ2ls20MLqJhOhYDA+UJBm9uv7OHZEyku29DI8GSR//zll0hGFJKREPmyzSdu6GVkcoGpdLX+pf/MHOtX1CIJIsFABcuxuP6ydoIhlQefHePUcIZ82WWxYGBWbAJy1bwhyBbzuRKr2uvY/+wo0bBKU20Q34OxuTyy6FEXU2ltiPC3976CIFOd4CnC1jUNnBmfJ1s0KZYtZhYNgkGVjuYY0bDK//2hTYiSSEdTlOaaKIYd4KuPDHL5xjpGZ7NMpfPcuaeH1GyWZETFlyzWdtbTXq/RXBvhhm21RFSRqFatF3E8H9d1WdUeY1V7AuV8B+H9z4/xzcfPENQkvnDXRtrqIwRkkdlFi+Nnshi2QDKuUp8IUjJc/v6+U+zdWks8EqRoVSP/suGiqrCppw7bVvnewarspAlw/WWdNCRCLBQrLJYrrFsRRxRdbtndSTgkk0pnyWRLjE7nUQM+89kypl11WtbEghw5PcUHrujEsD1ODKXZvLIO11H5u++f4tK1tdxyRSfPvZK+qMZl/DwR/Oj5EURRQA2IfOr6NZwey/MnXzmKJInctruL+54eZjZr8cd3H8Nxf1wvdbA/Vb2A+jnhfUEm73fIikxzjU6hbPHBq6p9oSTRIRkNYfoBNAFqkwrlsstCzmZwfIHrd3Ty1PEZBMFGVyVsxyeAj48FgoMuw/U7OzFslfueGkKTbRqSYUxsPMHC9FU8wSKTNQEQBZ9YSEGVbTqakni+i2tLGK7I0Ngi8YiO4/gIgsLwdIFHn59CxiFv2FRcm5CuEg1rrGmNUy5bJHQZq+KSLtg8cWSaomWjCFUVqqc9hOV5VHxobw7hYFGX1PFxGZkrEA441CY0To0uEhBB8GUMW8V2LeSAxOBElrGpBXTFZmA8W716v7oXD5cVTVFCQZVXRtKs7EzwyOEJEjGNVDrLbVesQEZCRMV0VHzBIhLRuHR9DcW8BQKUSg4tSZ1i2QME0sUKjq0yOJHFdxWS0WrSvKs+Sr5U7fOlCZCazxEJafzTDwcI6iIBRWJ20aajPophVxPSDfEwZ8Zz2I6H67icncwS0xwioWriXJQq7FjbQACZQrnCgZcmSJcszoxmWMiVqVQcaoIKc2mLc9MFUvMlOhtC5Eo2vU0KAgFq49rS96pYdnjq5XFMGzb2RBF8lbPjWc7NFCmWLSQCZEsWlYpLOlfCMG0sB1Y0xTh0Ypo1nUmmMhZHB2bQVZmKLfHVRwarZKp4XL6pnrMTC7i+R8VXef7kHBOzJWShmle4ZnsHiiSyWCizoStOc20YTVERfRXH89AVi9uv7MTHozkZ4fhghh88PYrni7w6usB8zuT6y9pZyFf447uPIYtw47YWWurj4HskYjA6XeI7j58lFpSJBCViYY2nj08xMLrAVCa/lEvzPZnjgxkeeGaUFU0KXc015AoVxiZLBBSLguHy2NFJXjyTpWy7KLLCcyemuXxjtZ1KT5POmfFFdm2q2sSfOz7FyvYk3c1hzkws4rgejTXhatugvMP9zwzhOjA6U/hxyxVBZSpTwDJtdq5vXJKlDvSnmMzk2NhTbbZp+pBeLOL4PqosMzi2SFutiiyohHUVQYLBVImaiMahkzN4voBZcTl1rlrrc/jUJL2tCZqSUR56/hzrumpY256gbFrctrsLx4HGZNUSfeXmZq7f1sLd+0/xe39/mP5TM9XoXANFVoiF1SV5bv9z5yiZFT5yzSoO9qfobY2iqz5X/UTe5eeFZTJ5B3AdB01VUFWFRERhsWAjICMoNpoApmcjeQFeObeAoojEI9Viv0QkgO8q1f5eUtWSa9oqvi/jAG11IWzXYkVzjGJFIqBKHHhxhlyumkz2XAU1ILNouIiiSECCw6cXCSkWFVdBDbgokktdTYiQAmWzgueI7H92lD19bRi2zNGBGQKBqkukKRGiZENACZAzHVzPoSYicen6RkYmC9hYWB7gqghUjQWyH+A7j5/D8wQsW6WtviqXlAyTTSuSGLaPKoCsWCiKyqET01y9rYXm+iiGrdBcU92IHdvDdSXCukLJsLn5ik4m54qs76pFlGyaklFqwiqGDS+dnUaXwXUU2uojyILK/3pogEgoQDQmYdhQl6z2Q7Msb6mIzvEsggGJ63ZURyUH9QCW6bFQtJnJlM5vjiuYWTCoVGwCikQiqlalGttGV+ADV7aykK9w9dZ2JudKGLZKMqxVK9VllbpY1SIeDktsWlmD4HuYFQ8BEV0LYJ+vOdq9sYEbtrVQEwvjOi6GrfLwC+eQBY+gWnW3PfriBNdu6+DBQyP4rsrIZJqbd3aRKxhkyxauZ3NyJMNLg2kCqoxV8QhqEqoMH9jVyfHBedLZElvXNBLVFU4MzXD9Ze00JaJ896lRNFVldKZEvlBBxGXPljb0gMSDh6a5ZlsttmPRt6aWruYaJCFA2XQZSqXxcOmqj/DQkTR9PQlEQaRkWciSwDXb21jIlxAliXLJYkNXDXalwk27OljZluD5gTTnphZYv6Ie160m/GczBiWjgmlBtlDm9ssb8TxIBDVMBy5bW4cm+VyztZVfu2UNjx5JMzKVIREJsKIpie+ryOfH8G5f1cBLg2nyZZudG5uojalkCwaGrfDICxOIYnX8dFBTaEwGaaiNkIzofOvxQWYyRTZ0NXCwP8UTRyc5N7XAzEJpyVpr2FAbD5IzTXRVYs+WNhynanb4x/tPs2tjLf/pI5tRBQdRlCiaNltWR9h7SRse1a7VqgJGySEakimaLtdtb8cwPOriIe59coRXhtPs3lzP4GSO+VyRyzc0ElJlYmGN//fbJ0mEA0QjKscGp5emgy6UnSVXV7Zk0toQw7BlFEVc6ipdNGx2bWymuTZERJP4+PW93La7i8eOzDKYWuQ3PrzpHU/IfLdYJpN3gOaGONOZEmCjiAohVSKgCpTKMJc1cT3QFdjQlSQWVmivDWH5PmFNRJVtklEFx3WQzruarIqPDKhi1bq7ojnCmfEFSobDlVtaMV2b+ZxFxbbJlUxSs7nq/PjmJN1NMUBFk13wZUxbpFypupBqwirDkwtcs6MVNeCgKw43Xt6J4El4flXCKpYsPCpoqogWUHGQSIQ1GpIhXF/l1ZF5RMVCk6rOsky+zLXbOpBEF02xcG2VqYXqBMdISKVoVLCxKJsyuuSjKBK9zTF8F3TFp7U2jutWOxaXTYeSabFYtMAVeeT5cVzHRURBCbjVZpmKy5rzky0RbWzHxfYs/s3ta1AFAXwZXXGpVHxePD2F6TjMLFpsWJHkbKpETUKnuS6KIFt4fgVFdXBdj+a6agSyojFGMhxgIVdhMVvCE1z+9t5XqI0GGZzOIhLA9jwqjsXODU0Uyxam6/LqWBbDhqLloQkWuBLJmMrZiQIvDswSC2tMpfMoAgQCMo21YQxbZDZbYtfGalO+UFDFtEVOjswylyuxqaeGB58/R1NdCCSL1sYEU5kc8YhOa0OUSEBhXWe1aPCVwQyZfJnNPUlGpgsIgr80w2VwIoPjuKztrMP3YSiVpiauEzg/zfOqre1kcw6ZfJn1XTV0N4dxfZUXB9JkchVKpkWpXEEQHBZKDiBRtHyODsxi2BKLeZN7nxyuyl1JleHJEuvaNFRVwnN9QiGN5towIU2luVYlk3d44dQUmlRtJ3LXvpUIgoxhVChVPExf5dnj00RCKpOZHONzRXKGC7KEHlCZThcYnzMpV3ymMrmqDb9i8/HrezF9ODue48VXp+lqDiMDLXVBdMXm9t1d+L5AW53O9Ze2cf/TI2gCNNXqXLahkWRMw7SrRY2/+bFLqu9RtgmeL6zMFk1qggqNyRj5okUqk8fz7OpMlat7GJwoULbhb75/mvqkztxCGc9WeeZ4Csd3mciUCWsqQV1hfsGgsTZITTQI+PSfrg5L276uDsOSGJrIEg9prGiJUXE8ZrNFPnJNN4IokFksUSh7NMbDHB2YvahtS7ZkIQc8PMEiNV8gHAqwqiPKf/p4H/UJjfqkDFSbiYZ1lSMDs3Q1x/nLbx3nqw+9+nPdJ5fJ5B1gYi6H4zh4nsQzx1PEIzqLuQp4HooiULEFXKokkc3bVX3f9VnTUceCAfWxEONTJTI5AJugKrBYtvEBswLRkMbcokE0qBBQBGJBhVBYJhCAhkQIRRGIBFUqlk0kqOICJ0dzCEC5VCERkpEUB8MB24GVLQkkVDxfJhSQSc0XUBXImRYLRYOAIuN5Po5v4/gwMZ9DC4AqgIfI2VEDyxcwyhb1NTqqLFKxJQxXRlcszo4XMSyLcsUiEICyISMFHGw81nUmMOyqn//ESAZdscgXfIYnFwjqAsGgysH+CV4ZTfOZW1aj6wqZnEmlUnU2vXg6Q1CXqKAyn62gyhKmCQGpet6aYDFXcED0WNtRQ0u9wtnJRcKhAK8MpxGBucUivqPi+RK+oxIMqpQMB01xsGyX1oYoakCmtzOC6Et8aF83+ZJFRNfRBBvP81DPWzwDqsxizmA6XebY0AyCaFO2VcanF7FtOD2a4bINjbi+Q0OyGoU110WYnC8iyBa5fAXTtnhpaIG+nmS1mFVTGZ3Osbo1SlhXWdkSp1KR0XWHtvoEqbk8ulitYxpMZfj49b1sXFnD6s4a9IBKS0MEUZQolS3mciXaG+OEdZUz42kEAQKaRlNS5/DADLGQAlhEQioziyaW5TAxbyDgIUkisZDKn339GAXLZDZtsq4zydGBaQJqhUvXN2JYFpPpErdc3kUsGKCno9rF1zif6I+GVEzTIRZSODkyS8P5djDHBjP4yCQiCkFFxLKqcmFjUkET4OM3rMS0LZLREM11ESzbxXU8Zhbz7N58QaqaZHiyxELeYHq+xMT5wt3rd3QQCgYwTI+x+QKqrJK3PHradVTJIxIO8INnRrliUyNj8wWCikP3+SLff37wDIWSg+/5VXtxIoQk+OzZ3MTxoQyWD7mCQSCg8s1Hh4iq1YLk7z4xRCKq8cTRcX7l5l5cH1wPfMEiGFQAiaHJPK+MpJlZKKKqCvL5cRV6UGHPJa2cHs2wfXUzYdWnrSHK08emERD59hNnGZst0t2cIBpWSERDHOhPcejkJNvWNDCYyrOmM8YffHo7LXUheprrcByZzGKZ9Z01/MN9r/If/sfTFC0HXJXvPXWOii2wWLD45WtX8tT5UduPH5n4uQ7LWiaTd4C2+hiNiSg/fG4EXVdwfIvJ+SK+KKAFVBbyJUzbx8dhOl1GEzxEyeORw6NMzxUJKBY5o1INgW0FH4eAolCu+KSzxfPt0ZMcOT1LNm8RCqn4nogsqIxO5oiGdBx8fJHqVbkNuVIFWfAIh1RsGywbHM9iTWeSaFilULaqA7IEqE+GaIxpnJsqoqkyIHFyKI3nCfiuhRZQ0PVqYq4mppKIaJQMCz2oIvoCoaCMoFj4nohxXrp46tgMoigR1lRMyyEkKqTmDXzfR1MskKoTEsu2SiysMZ8zKRUcNAFuvryTx15M0V4XW7pCS81lcW0VEZ8z44sEgNHpIp7vowRkzk0vYFTAcKojjMulqtPHd9RqW5i8wSeu6WZu0WB2waBsWuiaS75kMbNYoDmpkzcgEPB54ZUZBB9EX+Xk8Bxbemo4ObJIyTIBBU8A3xXY1FtHTJPYdL67a3drlaSDio+syBimy3WXthMPysTCOrpWnXdTF9V49PA4vqMSCYvgV+eEJGIapiMwOV+iqznO6VSWzSuThEPw6mgG31NBsrjhshWYftU9t7G7kXWdcYYmCvzZ116iZFokIwpmxeb6HZ3Ytst3nxysNsLMO6TmCqxo0ljRFCcR0WhMhggpAXTFQg9UbddjMzk8z2VVW5ygJnHTrg5CmoqHQDIMri/gu9WpkaNzZdZ2xZEEaIxruI7LXVd1oSsWqbkcCwWDumTVABDSA2gyfPCqLtZ115Ip2Eylq+66eCyArjiEAypZw6YhHgZB5tCJaQKajwDYtossKXzv4BAfvKqLLavreOrlKQqGy/U7OunrSWL6FrGwyqbuJN87OMyzJ+YxKxZRVcW0RAZSBVprQ1y+sZnLNzRzbDCL4UnkSiZTmTwfu34VB/pT3HdwmE/euJJVbSEQRLJlh1i42nlC1wMcG5zmVz+wDtNR+dEL47TUBglrCpdvaECWVR57YZyuxupwvLXtCTQBGpMauza2MD5T5GB/dT67YYucHJxnoWiweWUds9kipl2NTJ47McXwRIa+NfVs6q6lULKYzxrM56rSm+1V+wLetKuTgdEcf/S/X2RgNMf4bJZTI2k8JNSAtNTK3nOrMmIoqHJyZJZYWKGrUVuKavb0tWKYr+8Y8l5hmUzeAcZmsjxyeJTaeIh1K+IMjORZ0RhhcDyLrri01McolSu01IapS+qcHM2jCjLNdRGePjaN66ucGEyjKSqC7GDbMg8dGmFiLkdTMkpzrYbrQktdGMN2EQUoGxV0xWHliiSFUoXx6TyO42PYEqLssXNdLYYtoig+kaDK8bNpXj67yMxCnom5LImIiu1ZBLAIBkQMG+azJqpUJbN1KxoYm8oi+Cq10SCaAFMLBnWxIFpAoGKfz9v4oCgS2ZxPsWiiSlUNecuqWhK6jONDJKhg2iKZXIUnX0phOyr4ATQtQLFskS1YXLquicWyiWFDJmdy6fpGdAU29tawWDBR1UD1dncDCNW8iCiCIPhMzedZKNqIkosuV+hojPPDQ+eqPceUClvXNjGTLmP6Eobj0VwbRNNFfEdl//Pn6K7XiARVvvn4WfBkaqIBIiGVs+PzbOmtxbIFSuXqVbJhw9RciVdG5vB8n4LlMZkuElRlZLk6aMywBZrroogytNTo9LTVMjmbRxJAwkeQXT6wqwtVsQgGVJ5/Nc13nxphdsEkWzTRAhK10SDNdRGaayIIftWiLIkOpTI4bgVNcCgZNnPZPJYtMjCa4YYdHZwcySAhEtZkVM2mrT7Kh6/qpXzeKTQxW2IkVUSToaMhzl995wQOHmVbob0hxrMnJvnIvlUsFlwSEZ0fvTDOxp4EsZBGMqxg2dVGnI5foactjoyH74uU7aqUOjpTYmVHgkVD4sn+yfPSkEA8qLCiKYHhQFdTnIP9KfJmdYLgzGIBy/IxfRnTgf7T86RzJbJFg5UdMRQUwrpKbUJDFgWu2dZKd2uC2ojEjTs7OXB0HFG2aUjoHDqxQEixiIU0NnTXMDGXp2yB6UOuWCEeVHEcgasuaeKHz42wbW0txZLHP90/wOhkgXhYZU9fa1UGCmookooWUAhrMpt7GpmYL5LJlVi3oo5oSCWdK/Phfd30diT54XPnCIaqucTejgQ1MY10rjqZ1PShPhnm4MspYuEAv3xtD601ISTFQQvIpOZKdDVF+MrDZzHtagfu3/7lzSyWHUqGix6QiUVUkmGdqbkSpmXTWhvi9t2dGKa7lIzfs7kFRRC4/+lRZjNlsnmLVR1RrtnWysmRNMeGF9naW8Pm3noiYRXLlnEdl9/71DZcx8Wstsn4uWCZTN4BGpJhLllVRzpbQldUSqaLIEmUDIcjZzNoAoiySMX1GZ3KsVA0Kds+vS0RWuoiCAJ87PpeBMlicKzqYKmJ67Q1hNFkG9eXaK2X0VSRtpogg+OLVBwPw6m2YmmpDfPgc2OoAQld8VAEAcuWmczkmMkYeFi01IcJazK1sTD3Pz2KD3iuTNmtdgjwRQtJEqlNqsxmTJ4+NsGGFbXoioUvVVvCIAhki2VCuspivoTp++QKFrMLZSRBoCGhkzMchlOLtNdVN15BsCkYFWzPIhKU+Ni+FeSKFoLg8fDz5zAdh7/7/kkk2cWsVG3OnU1xxqbzlB1Y1ZZgMl2kobaafPew6GrWKVvVKCtTsBEFn9WdSfL5Cg4BhicXaWuIkl70UfDRBI9VHVFkAZIhlZp4CNFXGEql6WmLs2iIHBua4UNXd1MoW/StqkdXLEJBHYdqLunGnSvQ1eq/u5oi9K1qYLFgMpUukcmZ1IZFIrrPQCqLrtiYZZvGiEJAUXny6Dg+1TyYYQsoKNRGFUpWtSVLrmCwtquGqXSBuazBtrWNaAq4rouiuJgVi809jXz14SGCqsTMosWZKQPD9miqi6IrHuu7a5nLGmzoqeVsKsuZ8TypOZtUOkckrPLAM0Ps7WvlszevRZJFcpWqBLanrxXfl5hdLPPIC6NsXVtPQBFJzRUwLIu2hhCeJ/HKyCy1cZ1s0aS7JYbvCrx0Zo6O5jiLuQrnpnKIikVQE/FcePVcmrv29jK7UMZGoGQ5BBWP0Zks56az3L5nBVPzBcK6TEwLEFEFNAEWiybPnZwhkzVIRvTq4K2cia6AJHhMzBdoq4twcmge25XRFLju0nYkScUCVEXGQUWTLTb2JOhbVU9Ql873dpNpSuoYFQtNlVnZHqcmrjM2nWPf9jb2bWvl5HCahoTKVX1thFSZoVQas2IzvVBCV6CjKUJNLMTBl1J4voUkiqxsr2E6XWDXpiY0wcLwfWrCAYoli8HJHCXTQhNgYibHnr4GomGVkK5Wa7OQWdtZg+fBkYE57riyi9RsgWsuaSakq0vdli3bwhdcTgzP4eLT0xpnaKo6BOzeA4N84Zc2IkoiDzwzRCptsHNjE0cG5ghFRGzHp6kmyP5nx0jN5hmazvN4/xQPPjuCUbGQZYkv3n0EWZZ+qtHEPy2WyeQdoGK7tNZFuPWKbo6enqWtPsjYdI51nTWcGF5gJmdyYmgefJf7nx7l7OgiPhUGJrJce2k9ChCQVYplEcOq5ko29CRRRBnLVfBcC9dVaautOpTueegsyYhOxbaYyxroqsjqzhoWixVMX+TsZA7Xt1CVAJGQjCSpNCbCNNZEcDyH//CRtTi+TViXEHyJbLGat8hkDXy/2oyyt7Pa1qHiq+SLEBCqw4ASkRCqXGF1WxLP9QgHVf75wdNEo9U2+EMTC9xwWTvlis9CvoTnKhRNB1lUaa6JUHEV5rMlPE+kpyVKIqJxzfY2PFuuti5xwKy4fGBnJ65nEw4GWL+iFhmZU+fmKRarn5UvSmgyuHZ17G+uWKEmruP48I1Hh5AEH8O2sW0VBwFNqw4YS4Zk5haKFMoW8/mq9dc732pfV1UEQTw/LbNaNyDhMDpfxHMdHjucwgEak0E8oLEmzLefGKS3I4JhSzi2CohMZFzC4QAeAmbForMpTHtjDMPx8AULVQItoFIsmSBZ3H5lF5btsr47RlCVKJYsRueLlA0HTZTwhWqPt+1rGhGRkQQPSfAxba/qFvNFVFlkfVeM02OLJEIaouCTL1p4vkBAcljZHieoS8RiKp2NEWxL4MHnxuio10CAeFhlx7omdE1Ckyu01UV4cSDNyrYa7n9qiFUdtVRcsJzqlXRAVtiyso5DJ6aoicOuTS14tkpHXYiXB+dxXJBE2Nhdi2lBbSxEwfZZ1aoT1KrtZ37w9CiNSZ2JdJH+oQxF2+GxI+PcdVUPDbURssUKU5k80ajGUydmMCyRuniQp45PMTyZQ5Qs6uJBVtSFq/VXho3redi2j4+CKEiks2XmFkuARd6wMW2QpQBjc3nWrEigAoosogUkKjY8diRFS22YB54dJhKBibRVbSyZrI6SiAZkXMdBVRUkUaYpoTGbLfLBvb0010RxUHl1OENYV4nGJBayJi8PZvB8n7a6MLYj0RDX+eGzI5hOtSuDEnBoqtHY2FtDR6NOKm1RsmFkKoMowof2dVMyfERB4JLeRoKqwvefHqE2rjKbLrCqI0EyojOfLdHRFOcbj54mqMlsXV2PJCrYFY9oWOXmXR3s7msloFTlw6p7T2U+W+L//vBm5rOltxwG9rNimUzeIcqWzavn0rQ3hWmpiXLvgXNYts0H9/YiCgKyJOF4Ajfv6mBddx0SKpLgg6vyWH8Kw7RIzeXZ2FOPgMdCvoIqWeTLFf7wfx1DkhwqtsTozAK371lxvm28TFAVyRsOgu/RVR+q2nUlEUFUyOQMFEmmbNnIskciHKBYruDZASRBwai4iJKF5XgEZIHethiyAGFNoSaiVWUswLRcREALSNhOhcWygGH7vDKcRlccPnvLWnxXxnZt1nXXUhcLMTadJRkNoUnQUqsSVDzS+TKibNHblsS2KyRjOlrAQZKqbrddm5rwfBujYqHrCoqokM0bVHwLDw/P99B0BdN2UGWXuZxFUJc4OTJLJlvGcTzSC9WOszvXN1EXCzGfK2HbAoLgMzpbpGxDLBxAFKGnLUZDrUJIV6mL6SBa6JqMJlTdMB+/bhVFE468Oke+7CDLEqIPCB627aGrEvu2tSOJMpZjoSsumioS1CVODaexbAE1EKClPoppVPDwEXyV2UULo2LRlAziuuB7AhOzJQKyiiJLnE0tkgxrxCNBDFukYgOoGBUbXbFprY1TmwhSLJl4fvWKfufGRurjEc6MLZIpGNy8s4toRCeiqVi2SFdLgrHpPAEBKrZAPBLgik2NmHY1ukznDYKqiCYLCKjEwxoH+lMEFdi3tY2Q5oPg0JQMMzK5SMW1eeJIip2b6nFslYXz0YNlCzzy/DjHBzPnaypEJtN5fGzKJYeKo5IrVlAFi9/8yGYiQY2mughNtRE8D1a2JXHsCo7rUSybhAMBNNHFcjxKRoX5bJmu5gh9axr4oy+fpK4mULWAexbHhzPURlUE0cfyRQIBkZsv7yISDGB6KodfmeXxo+PMLBZQZQXDqiboN66IMzixWCXstQ2cTWWr83wsiUMnpjkzlidfqmDYULA8/tf+ARoSKpIsUbZBUxQQXaYyecyKz4OHxpEUB0GQ6WqN4zguqUy5WhIQkIgEZe7auxLPqcpvvhtgVUucTM5GFtXzFf4Wnc1JHjucoqs1SkNCBV9koWjQ1Rzho9f3UhPVeOlsBi2g8PypSW7d1U3/6Rm2rW6grU5DDUgogstC0USSPS5ZVcfkXJGyWeELH9zA+HwOXYHaeIj/91vHqI2H0H6OO/4ymbwDLOTKSKJIOmsxv2hQMi0+dFUP2aKFpvlMzBXw8REEj+6WGA8dGqXiWPStbKJQsti6pg7Pg6ZkGEEUqCCSL5gYtsp8zuCXr+lmdKaML1pkix5NNWFcW+Xplycplh3yRQOEat7DBVrro2SyBg01QV4ZyZDUZSzbR1EEaqNhTgzPYds+MwsGnqNSE9WRZJnm+ggS1ZYNoljVTjOLBo3JIDnTors1gu0ArsdiwWLTygbOzZk0xDU0ycIqewi+yEPPn6OjOYKoWBQtH3ylOuJXC+B4VSkjIKskY0EcW6UxEQJcdm+qY3y2RGttlAeeGcbFIqgFUBWRsmGzcWUDxwZnGBhdQBZUuhqjFMoOri+waWUcSRYJ6iI3bKvFw6dScYmFNc6MzyMgUDJtXM8iFtJRAwqFQgVVVDAqDkg2vqPiCxVKNnzvwBjJiI6Ayw2XreCFV+c40J+ibHtYFQFJFhmbyRMLSXiOwMmRRWazDmFVYXw6z8qOOLoCPi62ZSOKIoLgoSs+sbiKJMmYPgio/K/9p2iq0bAcl6Y6nURMRwvIZAsGqmJhVKojn+99cgTDVtAVl4gm094Qx/UqmD7ky9Webx+7fjWSXK1pqIloPHpkDE0RCWsSt17RTcUHx6kOL2s/32/sK4+cISBLZIs2ll3tr2V7Fh/a143pQ0ddiIIBsihTsR1EWUZA5vKNzaiBavGo6/oUX9NkMh5R0BWIBhWa68IoooKmKUymc1yyNoFpq0SiKo8fGWd6vkDF8UBw6WqK0NtWy8RsjvHZMtGIimFLuI7LyZEMXY1RVrYl8T24c08HolB1ENpOgMcOpwjqAYZTi+cT3iEsy6YhriMKLhu6k8iyRMmwCQcVXhqYZSpjYPsSt13ZjemrFMoO02mDE0PzRFSR6y9rY/clTQR1SGXyRFSRXZuaqEuEyecsZrNFZjJF8gWHYtkmHHC5fXcX5YqLjEVNVOW67W2cmylg+7D/uXPYrkdtXMOwXDQBsmUT04HHj4yDaPGhq1dyYmQBcNm5sQnRF6k4ApZt4Xo+Q6kcTYkIDzxzjoZkkEzWwLJhoVBiy6o6Lt+cpL0xyYH+FJmCy8C5BYrFaieMZ49PM5exCAdVvvyD0xjnc2kXKuBN7+e3Ty6TydvArDjMZsrc/8wwV29tJxnV+cojZ6hL6tQlQgycy/P9p0YR8XFcgVzB5PKNTciyiq74xOPVnlB5wyYSVhlJZZlfKBGL6KQyOToaNJLREB2NEVxb4bEj49Qnqz/UlZ0J6hI6DckIqgK+ZLGQt3B9m0RER/AE6hIaxQoYhoOMg65UramCWP3yyoqFIIJhOizmDQwX/vreU8TUao7CFyCTLaGqCq4rUzIdJKVaW+A5Pi31VddZyVYJ6iovDkxREwuCIFAoiDx7cgpNhmzRJKCKiJ5K/+k0ZsUiX6jgYtHcqDKXd3BcmZpYEEWuXi2FZJWYLiEiUyg7BAS4ZFUDoYCErFhUbKjYDqs7YlQsmQefG0ULBKj4Kt87OEwsIhPTJBaKDgF8SoaLLFYr8VPzeRrrwpQtH8N0MS2BsmmBpxKQRe7a20PFsQirKgf6J0hEAly3ow0l4DM2k2U2UyJXrBDUFCTJZmNXdb54NCIzMl1EFDzKNogIxGI6LiB4CkPTBWQBfK/qXAsq1bbvtXGds+M5XFdEEkV8wUKUwCFAXTTEi6en2NvXiiBZGH6V6FUVZKma8PUdj5mMgw8osgBAsWyxa2MLlg+j03lyRRPXswjIKqZtsVgos5Ar87HrV1EfC3HvwSFcv5ofi6rqksnCsOGR50dRAKPi4rkek+k8ve1RAgIs5D3iUY2wojK5YNDbFuGX9q5kLmciApVKNVfywDNDyLKMb593jwnQUh+lYjt01mpEAzK1sWoEVhMPUSxbKLJPybTYsrKe51+ZIRZR8bHRAgIbumtwHA/DVgkGBD60r4f7Dg7R3VpD3rQwLIfaqIrtehRLHu0NMbIFg4V8Bd93uXxjMysa4yzmLCQBBMFiXWeM/tNzXL6hgZzpEtICtNZEkUWVymuT075PQKt2vl7dGaEuGWRkMs/J0TzJqIquqPzd9wc5N52n4kN3U5Sx6Rx7trRQKLtMLxRIxgJ4PnzjkbMYpsV1O9rpP5MjGQ1QNlwKZYct3QliuoAmu6TzNt95YoimmhCiVC2wffzFCUzLIRKSCSgK61fE0QSVU+fmuWV3J1bF4UP7VhKP6MQ0ia1rGnhxYIZ8qdr/TFOsdzSm+L3AMpm8DeYWiyiyTFdLjFzRZCFvsG9bO9949AwzmSJhXebOvV30tCbIFkxCwQBBXSKkwEAqh2urhDSFRFjBdW2+/ugQ4aCKJMHEbBmR6pRFxxYYnV7gzj0rUMRq+/3JuSKIIsWixVWbW/j/2PvzILvO9LwT/J39nHvvufvNvLlnIjOxEyABcAH3tcQq1r5ILpcleVHbDs+41Y6we6xot1sth6YnHBNhOdxu2T1aSlWlkmonS8WlyCoS3ACSIBYSALHlvt3MvPt69nPmjwOzR6GO6Qp5qK4Y64u4EYjADeTJvMjv/b73fZ7f0++BIIm0Oj431mqk0hIjOZNz1ytkUhptW4jNi2mDH72xzJMnp6m34fpyDUOLsxgMKeQXH58jIL6hJBSRtKmiCiK24zCcTWJqMrIqoSgCgaehqj6dnk1CgWP7SrFevevx0tlVhrIG9Y6DrokIUYDlenQHcWstEjwGtgi+wqsXYxNbxpRotB1mx2L8vOWBHwhkUjrNXnwCLuYSKGg4gUMmoaDLGm9d3kTXFERJQAC++OgUgS/gAr4fUe97rG63ERQHQ4WxkkGjMyCpuQhChOsE1LouquKgCB6iJGIacRTz1HCSfRNZxoZS2G5AMZ3gynKLQzM5UkkFRdYwDOIZSCjxwOEyiqSRUHwUWUIXfYyEiK44jA4ZBPjoBlxaaeBEAYdnMthuwKHZJL2+g6GKRIFGNqmiIrBR79K3I8yECIIGvooXRVxfbSKJESEeGVNF1xRsy2E0l2KraXP6UoXpIZ0fvLJAMZfAMBQGgxil0hlEvHxuC8sJSKdkFMXnyFyJetsjCBzsAM5c3qJjO7iBw2N3TNB1Qr7x4+s4XsBg4HFg3OTstV0EYpSMHUGr79HqOFQ7Axp9j0uLVfq2R0rX0RSFoUySS0s72JFGrefS7loEkUC14+JEEgM3YmW7R1qT+fg9M6iCSDIlYnke/+xvHaFn+1xf61HKJ/HCiLNXt/EjBxEYG0oyVjKxPQdV1fjxWytxOqkQkdAVdFXkk/fNQhRR63gMvIB0SsEwAEHg+lKP5UqPuw4Oc9/hES4uVMmZCj4OjhcwVc7GdIWcgZmMJdRTRZXXLtR46a01Pv/QHkRBIpVQkQV46I5xEmp8SClmErz49jopQ+PyYpVCxmCr0ScSQv7OUwdpDeICKRKxuNXhvttKpJMC6VQCkLB9iZFikr/1sXlypkEUaAznkxzfP8Rjd05BCNdWm2hyfAh0PZ+58SzppE4UBXi+8yF+/tjeIRAiJofTOFE8M/lvfunoX89M/s9ek8NZBDEioauAQCGtc/HmLkfmS5SyBpu1AdmURjqpMV1KISJy5tI2laaD4wYYikel3kPXNExN4QsPz9DqOXQHHu98sIMvOpgJlc16h7nxIqOlNBCw2bTYqfexbYfVahfLA9sLcZ2QzWqPAzNDKFIsVT2+b4RO16E78NGVGGj46PEJuj0HIYRSwcQwfKbLWewoQhbjEC9VVYhEEQGZkDjrRFMcnAB6PZt2L25rRKGCokpcWa2jKQpPnpwkl1W469AI+bTKc2+vgiAiihquG/KJ+8fJmwYJTaPTtwgihwePjOIiI0YSb3+wQ+FWUFQkOlRbfertHlEk8O7VHbKp+O/eu1knlVB5f3GH22ZL2I6HLvj4oYMqaYSRx069z0a1hyyL/NLj81xb6hFEGoqosVPv4wQaSUOhZTnxz9vTsDwFWXBxPInFzToH9xRodBzWtzu0Oh6ZlMZ4USMkiLNoIljc6JM0ZK6uVDGTGqriYHmx2q7lwPpmF9vT8FyRlcoA0FjY7KILCpqqsLbdIwo03r66SymTZKvRRlZiUcOpcxucmC+yfyKPIMQeHUkQKBfSXF6s0/fiaOPdWo9sQmXgODi+z/6pLLWOz/x4hmurDQaWyzNvLNL3Ygjg3skM6aRCq+dRawbcd1uRgeWxthsTDn7lyX2Ioooma6RSGrutLrfvLZHUJbww4u3rDX78zgZBCEIYk6ZHSjoTw7GnYXOnSyGbZLKYYLvZY99krDybGsriuLEx8JFjE9yxL0MhDbbtY9suYRggyyKt3i2Apyfz+sUthEhmaaNFJiWzU+tBJHH7/DAvnNnEjkKur9YZG0qgKjKqGPDJ+6cxlABBkNBlh4XNFiEuDx8d4U9fvEG3b2O7AaqkYeoyEyMZXnpnnetrTRwPBgOPrGlw8XoTSRJIqfHv08RQkqwhE4URdiTFJk03JIhAUkQMXUKIPITI58D0EBeu15Flhy89Mo8fOtxzaJTVzTarlR5eJJIyZIZzBiOlFHMTOYYzGqqiIUQaL59bw46gO3DQECikk/T7NtutHuvbHfZNpkmpLpYb0BvE+0nPcjhxcITNnTZbtQ6+C1lDIwwdTh4uc2x/nlxaZ3krbgcWs0l+51vvxTMT+aPb8v+6mPwM6459ZQa2i6ZKfO2FG+RMg7XtLsO5JJIokVBk1nba2BE8e2aZX7hrir7tsL7T58XzVUbzCZK3HOrD+RTphMb7N6v83U8e4IU3txAFWNmMJcNC5OOHIroq8pkH9nBjvYPrwTvXtmh1LVRVRBKi+BfRigd8KTXESGhEUYgT+aiaTFKPWWJXVmvIAqhomEmNVsejnDPjIlVpkU0ohKFLp+9hOw5uoHF1eZeZ8QxeAAMPwsjn6nKdTMog8AM6fY/Il1jabDKUT/FLT+yh0XNwbQfdkNEUjYWNJrbnUy4kUMS42KrE4WGPnZgkqcftG1FUOftBFU1REcWIw7NFnj29jC743HN4FIQISZZZ2GzwwNGxmEkkaYQRRMjs1Ad8+t4pBCEgCEMUJZaJyopDIZOg07Vpdm1EQWTvRA4vcHB8h9ZAwFAc+k6IH8EPXlviwTvGKWQ0+o7H3HgRGQ1DjaOQR4sJEEWyaYNIdHCi2EC607IRQkglNWTFQdXBNGL458PHx/CAZt/l5Xc30CSH2TGTAIeJYROVuM30q0/NsljpxlJRT0MSNKoNh65l0+x5KILCVrXLcDGF5QlEIQxlU4wPp3n7gx32Txd59+ou67t9Ds0U8UOfZseN4aKqwrCpk0hKqJLG+FAaU5P5szPrJHWNZsfC8T0St8gG+yczZM0EpWyCK0s1/u5TB2JareTRdgRStxIoLcdlKKczVjCwPYG17R5DmRQeEaou8dqFTfJpma7lEvkabqQhqREZU8MPoN7pc3U1hoB2+w57xjJokoOuSYzk07R6Hus7HTTN5/G7Jrl0s0ZvELJvNIsQSvihRBgKNK2YDWd5Gn/4o+u8cWEXTxDYN5kjmzIIggBDcWgOPHp9l1/9xF5+7dMHsV2HJ+6aoNqyyKZUrq402Ok4gI/r+DhRRNbU0YWAh4+PM5TXEAVodixEPDxBZn66yFtXNhEEgVZPYLveQRY1KvUea9UBU8NJZAEEYLfZp9GyWN/pUMgnCcSYjHDf7cP0evGMy45AViJSSZ3N3W6cWyIKWJ7KS++sc9tchnoPsulYfXlweojd5oC+F8/cwkBjOKvS6rmogsfte8us1vo4jsdv/MpxHMcjEoWPbJ/862LyM64vPLKPfEbjY3dN8vrFLSq1PgPHp9ro40UhF2/UYtXNbaNkTJVsSuOR4xMsbbaoNGwsT2al0kBVBLQE3HPbKOmkRialYRoqhqHw6uVtFEXDkECSRQQhYnzIZLveY2BHlPNJND3+T6SIsfJDFyLsUKLaGjBRUNisWiRvOb8FIeT4/jJJPb4a+6HLcEbDdl1UWeOdq7vUWw5RJNIdeMiqTBD6tAcBIOKFEf2BQ9+K2G05DBcMnDAknVRA8kkndX56dg0xksknZSRF5oOlGlIEV5abyIj0rICV7Rau5+ASX8Ut28H2HHZbDkIocN/RMtdW6iQMje7AYqiQ4MpqHwlQpRBDl9kzkqVoKqiKh+XEjmnHddk3kyVpaLiuwJXlBookUOt6hIGG6/o4fsRoQWOr2ok3BDEm8+YSMSNq33RMFT5xYBjbCQgDkdcubMbKLyVWoTmhS0aX0dWQ4WyKP3lxGQWNvi/Q6FoIYsRkMclWLeKDxSaeHwARhhZAFPGtl27ya5/eT60Dc2NZ3riwS4hI24p5ZIQar17cwPEdFMXHD0IypkYupbOx20WXA/wQtusDegMHJJFrK7u4TsD9R0c5d73Cl5/YS0oTuWd/EWsQ4gcBaUMmCAOcCBRJxlB8dlt9RgoJRksJDMVBkUWCUMD2NbIpiWtrbSRCBrbL3/v4Hm5stDi+dwQvBFONUAVAclmu9BkdSgMC69U2j985iaZC4Et8/9QCh/YUcT2JMIxo92x0AQJfIIhgpmwyXc4TRRHbbZtkQokjjQONmVGTD5arnDiQ5/d+eBUBDUEIKBdMXnpnHY+In7y7RrvnIMmQM2LszMp2gydPTnJoNoeIT7loUKl2qbUtPDRCLySXU8ikdDIJjVxSIwjhT168wZ6xLJW6zfs3q1iejKpoeK6PIcus1+IgsodvG+HyUp3xUopGN0QiVtnNT+QJ/ABJjJgezVHtDKg0BhzfO8RMOYWIT2fgUM6l0fV4o9eFkMXVLrIMCSXG2aRMDdtyEBBQFYeTh0bIJDVGhtJEYhxqNpJL43s+717tcvpKBcd3uHP/KEPZFNvNDjutLvuni0wOm7jIWI4bp3bqCv/T185h6ArlgvmR7ZF/XUx+xnV9eYeXz23gBT5ffHSWR45PUO/aPHHXFGc/2OHwnjy7XYfFjRajhSSXV5pcW6nxi4/O0+7Fp5C9E3mWttqEjsTA8nn+9AqFtEourVNIG8yOZpCkACcQYq+BKFHK6Dx57zS+H1DvOXCrNZQwNKZKCgEhuhgiCPFJPZPQiQKNb7x4nflymlbPJiAm4iZ1sG/JWQ0l5PMPzbHZ6JMxFEQRJAH+4EfXmCzHrbZsUiGViPNTynkN2wPPDZEkAV2U0RSBQ3vyCAIosspg4HJ4LkvPdnjk2ARmUuM7P71J3jTwQwg8jZ16j5ypcfZaHUGAIHIwEwL5jE5/4DBayDJWTHFjvYksgO3JZBMy7y02CAA/UAgCF8tzMFQNx4Hl7SaZpMqxuRJToybruz1CwWekmEJXRcJQ5q79oxTSCVIqzI3n8YJbVGQROnaI43gEgk9KF3nsznGCQMWOApoDB1FQGXgRm1WbczcqfO7hWRQhom95zIyn2ahauCEkdZFXL2yxst3FjgQiX8PzI+45XGZqOEMmFd9y7jk8AmHI+4u7GIqPE7r84uN7qXc8Wt2Iq8s1EByyhswnTs5g+xKSKMZSbUMlcH32jBfQdYWtapuxYopiSiKZ1Ki0HNZ2W7R7LlNlEwExjkGQHOxIJqmq9OyA6eEMdqQhqyKiEOBFHrfPldmp94kEkenxOF7BUAUMJSD0RLxQwUfAssRY+TZwcHwIQoHVSoeEHuePTA7Ht59AcBg4Hi+d38TyIQxgcbNJFMU3x3tvGyWpC6gqnLu2ExMERJnxUhLfl/jy47NIAoShROMW7n6l0iaXidE/KVmKkyJFHy8QkCWB4WyKrz2/wPF9RRAEErpCFIGmy0ihghAJ6HKclbNRbXP3oTLV1oBGZ8ADx8ax/TjLJaFJLFRamCmJ9xcbWB4sbvUo5hMIgOV6H8Jc776thBcIXF6s8+O31jgwkccwJBxPpGnFbbhLSzvsNvocnY3JFUlDgkhhcbPO3/v0fnQBJElBxmfgxDHH3391gXqrTxRqBH6AoQSkUwbPnl4hm9TIGhrtnk3/VrHyvQAEyKQVRAQuLTVw/ZDpcoL/8e/d/dfU4J+HZbs+hqZh2QF/9voqY8Ukz51eIaEpvHJunYePjTMylKbVczl5eIQrizXKOZ1vvxyH2CxvtXnxnS28COZG03ywWsP1PMaGksyN5wgih7EhjULW4PULFQaOg+0KqCIoQogmxq2hl95eQ0Lh7Ad1en0Hx9MIkViruyR1BSeC584sYyg++6cL2BGx+TEQsKMI39NY3WnHevlIZLXSZKKUxCceOuqKxL6pPIYk0B0EaEoc9UokcnC6yNJ6C0OHlC7TtnzeurLDnuEUXcvn5nqdkbyGImjIksJLZ1fZbvX40sNzIMZQvACHoWwKN4KN3R7fe22RpY0+YqQxNpzG9iKqnQHjpRQfPzmNHcHyZp1CLsnZqzsIQL1jIcsqqqyhKfHQ3fEi/MhBUWR8T8R2PSRRJowisqaG4ztsNbuEYUDfjbi0VKfZtbB8ECKF77xyg5OHR7i+3MYKHRwnIIhcJEEkEkQUARKKQKU+YN9kARGBviewsN4k9EUqtR66BJom8qVH50knNMIgwHIdVnc6lDIafcen1rJwo4hsAmzb477Dw1heHK2cN1XSCQVBCilkU4iRRtPy6QwsgsjjmdeX+MKjM+y0ekiKjCRH9AYOta7HUD6B5UlMDqeBiNvnhyllDQxDxjQUROK8GV2IyKV1ah2HnCmgCxGOHaDJGrIMju/wmQfmuHB9J0bS92Nnft+T8D0fTfIII/jWT27w8PFxipkkW/U2pbTGwI0PLKtbLnfMl0glNNa2HdKmxslDQxiyEw+0y1mqrR5RpOEHLrKoERFRyiaxXedD+GOt1WPvVBExAk2RGMrGMQ5pQ+X+g8PstgZxxozi4kYCM2WTIIRzNyqMFk0UMb7NlnNJljZbGKpIpdlns97F8uKWZKVu89jxAtOlFF98dIrF9Satnsd4Ucb1RN67WUdE5r2FOOzryFwOkZB626LTDegPXAxDRRE0Bk7A3sks+bSBaSr4bsBGvYMmKrx1uUK5ZFLIqEBErTNgopTlg+Uaq7s2k6UMuy0bRYtjlBOayLW1BncdGKLdc6k0usiyhBX5BDj8nU/tY61qYfsxeQMhRt+v7gxY2mzhuwKNtoMiCtw2k2e5MuB/+P3/LWTro1p/XUx+hrVT79IZuB/SN4kEHrxj9JZZUOLKcpVmZ0BKl3nl/DqNnsvkmME/+PxBZBluny+QTshcW65j6BofLDdJpzT2jKRZ3u6gSPHAuN93OH6wTKvn8+7VHewAAkSqLQdF8XjynmkuL+1w1/5R3ri8zXazw8AOaHRszn6wTb/v8LeemGKz6dEfOMgCmBrIRDz/5mrs6zBken2Hbs9hspxGUeSYeOy79OyAsZKBmdK4shgHPtmBxmRJRNNERosJPE/EckJ26l0+ee80XhTno8+MZbE8EV0GUYp45Pg4P3pjhUxKx3EjREHk4o0GmhTRt1weOzHOw8fHKQ+lcXwHiYiEJlBvDdAkD0EMkASHfDbmhn3uwT04Afwv37+CZbl0+y67zRBVFrl9j4kgaLQ7FggOJw/k8byAqysNwhAkQeUP/+w6nhuSVKM4Wzur44ce9c6AT98/y6XFKlNDGcRAQzNEam2PjWof34eQEDuKZyHpRBxx9u61LQ7tGcJxXR66ffhDmbWuikyVU7y/UOf6epu5sSST5RQXb1ZRdQlVELA9mT/96SJuJHJxcYdae0CtOUCRIZVQKaQNthptUkZAOZfmudPrfP6hPQhILG50IQppt+MWlaEqLG20QHRw3ZCXzq4REOD5EWEgkDFjCbCkOLQsFy9wGMkrCMgs7vbImfHXElGQZBEvcrnr0AiK4FDtxA5+x3VIpmQsT8ELAqbKGSaKGoYSMjucIWloqIqCG0IUBbh+EAMNbZdac0A+reNFGvtnUqQUl7mpAp4fIslSnOwZSLy3UEVTNTwfvMBhomTS7zt4AiyuNxBFgZQuExFheTBaSqMLsLZtIwlSPJSXBXKmzkhBR5Li2Zgk+VxZboMQ4ochkihgKCGuq3FkNsvZa+0YCxRqmAmNUjaBgIamRNyxr4ipS/zSY3NcW28xXkjz5nsVPD/E0AUaPY+IiHbfQZGg7wQc219AxSORUEhpKilN5L6jZTIJjfGhDBGQ0CRUCdZ2ety1v0ij42AmdTqdAEOJPUULG232T+fJpw22axYPHC2Cr3Fttc9ozqRS66LLEQPbpdOPLQO3zeSZG49jnhHhyN4smqLQ6lr8t185Rqtrsbbd+sj2yb8uJv8Hy3Z9en2PSn3Ag3eMkU/rrFc7FLM6zY7D2as7VGo25WyKRtdlomySSSmIaIzmTBY2OqRNnYfvmOCbLy7gBw4nDgzx7Z8soCgyyxsxNkISI25uthGFkGprwNG5Ep2eje3Bj8+uQqQgShH7JouoSkTOVMmbSV58e5Ug8Hnw2AjbTQvHj+Nvn7xnGtt36LoRhqZwfH+Zc9eblAtJ9k0Ncer8JoIo88zriygR+H58+pwdyWG7PtMjKXRNZafZxw41ri7V2G0N8PyQPSMZ9o/n6DseISGdgc9OvUenb2F5cHmhRj6jx9ksrkMQhqQMhbsPjjAIfEIvIm/qZA2V7d0OKUOjY7kYqoaZUhl4CkQStiuRSSnYEWzsdjEklydPThCJAjkzQpYFhrNJbC/GfadTBqoUo/JFUWIoZ9C3HRB9PvvgHlIJDR+RA5M5LE9AUxT6to+qChzZW6JtWTiegyorGKpMPqVzc73OwkYHEZ/58Sz1tkVCVTgwWYAoQNUFgkjCx0GIApa2euw2+sxPZOn0PHabcZDS8labtK7iRjHZdXYsntVoisLceIazV3fRZA3bCVjfabK6PSCINFa3GxycybGw2cVxAgaWj+NFRIAfhLx8bp1SPkmzFYEQ8ImT06giJA2ZrcaAnu2iKQKOq6DJGq+e30GUFDp2QCZl8OO3V5geMtEEHxmFdz+oEUU+XqQxOZyi2xdY2GghEtMZdNlnz1iKuckCjb6HA7S6DkQB3YGDIIo0OhatrsP8RA5JlFjebKII0S1PSwy1XNvpoMgSb1ypYMgC//UXDyCJEemExpvv7xJGYGYkqnWHnVZMSCgXUowOpTGUCN+LIw/CUKAziG9NQ/kEo6U033xxgdcvNjh9qYYfyuw0+lSbNqos8q2fLDLwRG6s11BljT3lDJuNPoYSUMok8Nw4zTKKfMaLJh4B67s9cqnY+3XH3mF6ls+3X16knEugiCL1zoC8adDtWpSzKSxPoT/wEAWBlu2jKjJpXUISIKHD1bUWVhDy5D0zJHWN5UoLQwmJQuh6DoIo8OkHZ+LiJ4kcnM4gCbFvLQwDDCXiyXumaNshxYxBJqFR79lcXm7QGXi8fH4TACnSaPUGZE2Df/3H58maBpPl7Ee2V/51MfkZ1ndPLdAZOCR0ibsODiOKAif2DzOcS3D/0RFGSwl0TaTVs9k7kUUENCFCU8DQJCI/AsXl4ePj+IFEt2/z1L2TXF9r8LG7phDRiMT4xHx9tY3rh2RNjYEXghjwmQdmEQRotmxq3QHtvsv8eAbb8Tl2oMRu04FI4sZqC0P2+OUn9wIenidz6vwGASHFjE6jZbNd7/PSOys8ec8MP3lnlXIhhSDA1m7c8653BixutRkfynDmcoVsUkcRHRKGSjmfIJ/WMJMSfQ/Gimk0QUCRBabGEgzcEEMJOTAzxE6tH4cTqRqqJKHLIQHgeQILm20SWkQ+o+OGEEYesgAD26GUTlBrddHEiL7l0+55qAI8cdcYlqeyZySN7fh4nobtRaTTsR+l77iIkkMkyBgSbFQ7TA5nCEORl89W2DuVQlPAdiMSRgyBJIKJIZM3LlbImTKjwwZhKKIJkNQFQgGmyhmurbRw3JjD5UcR1VafRtdCUSUkZGQhYmPHRhY0LtzYodlzkSWRQtYgbxps7rS5/+gItuewvtOhlDcoFxL82Zk1ZsomhhpDCQ0lQFdiGN/e8Qyh4DFdzjM+lKbW6pPUJB46NoYfRhQzSTIpnQeOjtK3XMyUwgtn1smmVLxI4qfvrlNt9lnd6jCwHDQlotLs8sido+hCSBD41Ft9pkbTeIGIg0y9Y3HngQJJI54nSYKERMT8RBZVgFImiedrlHJJgiBClCU812Npu0XP9lnd6bJ/VEOWBEbzKdZ2u1TqPfZPD2H5ApYXxzUMbA9FEpEQ6AxCXECUNWQEFCFOhAwEwJd59q0VyjkVMRJptAdEfkDXd0kZGkEk0mjbnL++S4RCMW3Q6PT45SfnOXl0hFfObbCy3eWB28oU00mKGZUnT06yVe9QzKXQpTjC4as/uspaLcbFaLrC9bU6gqCgKSK2DboiMFXO0HVCbq7XsdyAx+8cR1YDBCJGSiar2y0UVUFW4ttFyogVjGfer6CLAm4UEeIjElOKNVnEdlwMBdwgZL1uk0qqvPlejedPryBJAn4QklTiwDAvdFiu9jm2N0fbDm9J7yV0RSKV0Bgrptk/mWVzt0s+rRP6EQPHoZBJfZjS+Mq5jf8yfCZPP/00999/P5/5zGf4zGc+w7/5N//mL7yn0+nw9//+3+fjH/84X/nKV6hWqx/5c+mqzIN3jHPhepXxoRR37C/Rs3xeOb/BqQvr7BnLkDMNVnY6GKrEVn3A/GSe7766jBM6+B6YSY31SqwKiaKI2+fKjBRNfvj6KtVOnyCCSnXAxk6P772yxJn3t7myVCOhSqxsdkjqEVEEsxN5wlDAULX4lB3GwVtDWYXdZp9HT0wACrIoE0YynZ7N3HgGXRBAdHji7kn8IKSUT+B6LrfvHaLRttisW7z+3jaG4pBO6rx7dRfb9rh9rsRGtYPraewfT+P7sT/F1GQEwSGlgu0LuG5A5KmkDIVBGGCqAn035AsPz+BHDuW8Rr3nsbHbQ5RiAq7lRbS6DkPZBGGgMJw3ubLapDNwGMqa2H7EaqVDMWMg4uB5IoYSF/ScaeBHDmZKQhMDPBdUUeWN9+tEBHQdB1EUMDWPenfA/UdHkASVSt0iEn22Gz3Gy1k2ax0CfB68Y4h3PqihoLHdGtDsxz6WNy5ssdPo88RdU5w6v0nPitiqDthuOmzV+gwGPgk5VvZEADh89oFZsimdayt1FDFWg02PFpgoZVBEjf/X01exnYhT5ze48+AwISGWGzJaNOl5cHmxiiRDIZ3ga392E0WJ/RlfeHg2dtmLAbIUGyhr7QEHpnIM51OsbDXJmjoCsdz44/dMAZBJ6aQNWK70GC2ZSJEUk5LVOI54vJBEk2M5bKXeRxJVggj6XkCn51AwNXRFo2uH+CEgeVy4vhvHUNs+Hyy3mJ/IUcwmcLwQP9IYK6VJ3xqSz41lSCqxj8JQQtZrFm9f3qVcSiAIAemkjO/FtAA7gvcWG/QGDhox3+zxO0fp2pBNazhugKYqmLLCbscCHAxD5s1L24TAbqvP7HCG9apFEMQpj5ocMVpOoUo+qqzgeiH/69NXyZs6TiAiyR6/+tRelre71AYxFuf0pR28KGSr3kNR4bY9wxiaTFITMU2DieEkY4U0kacQEVMvzKSOLEGEDzjISkin73DPbaMxVkeAIIjberbrIeBTa8dCgKGsxqWlJjfXaqiKyFTZREJkY6eHosl0+w6rlT6FnIrnyZy7tk2n56AIDj3Hww8dUqrLSDGB64eMDyUo53QSmkbfcv5cnklCVz6yvfLnpphcunSJf/7P/znPPPMMzzzzDP/kn/yTv/Ce3/md3+HEiRM8//zzfOlLX+K3f/u3/0qe7ZHjk9x1aITfe+YDzIRCGMKr5ze5+/AoA9sjYcj82esrjA+bfPeni0SRTzKhEQUakiLy03fXSOtqbMBbqtHoWlRqXT55/zTff2WJ/sChmDF44OgYx/cPcdehYb7z8hILG01EUcDzZeoth97AYb6cYuA4bOy0SScVHMfj4PQQoijR6MYmsL7lxLkZmsJoycTyBEJfRlMCUprK9HAaQ5MJo4AHjo5Saw+4Y1+R7Q4YmsCXH5/HCSLMpMarFyr0LAcnEkkYCs1mPPhs9SL6noAXeaRTOrbrkNAVhECh78G3f3KT+Ykc7X6I40NC0/jpu5ts7lhEeEiSwLX1FmZC5fTlTZBcDu8pkk0ZOJ6DLInMjeeQBLADDR+HpiWBJNLp22zVbBKqSMcWME0NN4jR8pu7FhtVi6F0EsvTiCIBUSZ2wycUtqoWl5daBG5Ao+Pi2iGyoOH7AYubDXoDFwQwFJ/7bx+l0bGpNHscnS+BIBCFIZcXa5w8NMbXXriGHcixxt/3OXOlQTkbo2WO7i+g6TJty8NxY0ik5caRsbbr8Tcem8dMqPgBfLBSRRAibM/n8GyJ/sDDCxxmx7JYjkMqqdG3faIIXnprg4ShxKmFRYV0MsIPfOYmigzsADdwAAddlfnhayuIYiz7LedTt/w3AmIUsdVoY5oa9U6frbqPJAuMlZK8daWCLgQIUgxf9IHddp/OIJ4vRaHCvbcXqLUcShkJBFjf6TAzkiKfNvAC4khoAcq5JOl0zJTrOwFNy+OOvRluny8hyRGruwNmymlCQaAfxBj3fZMlBl6IHWjICozmTJ47vYIkwuiQyWatjeWJPH96GU1QOTiV4PMP7aHedZkfTWF5UKl1WdnqMjuWYnwojSpK3NzqsVO3mRxK8MjxcS7e3MbxfAgUxgtpdEWksttFFDyOzBfwfRgvpanULJa26tTbFiIhGV0jDCNExac7cOJDja4wktMZL6ZR0eg5KtdW2jR6FqHvIwkuUShwaWGXtXqfyaEUXiSTz2q8c22L2dE811bqzIwV2NjtMlJM0rMcUrrC6fe3QICZ0Sy2JeD4DncdHMWLQixf48Zqk2rbx440vChiZtSk2Y2zZ2QlIogiAj/gv/vVEwR+gKbKH9k++XNVTJ5++mk+/elP80//6T+l3W7/hfecOnWKT33qUwB88pOf5LXXXsPzvL+S5/v0A7P8T/+Xe5mfyHPq/CZ3Hyzzg1MLNDo2u40+X3pknj/80VXuPTJCdxDy/kKVRsfCtl0kWaLv2nywUidlqLy/WONbP1mk3bU5fmCIhc0OjY6DF/jMjMamxl/9xF5a/Vhff2lxBzOlIUkiPSfitfe2ef29bUQ5ou/6IDlkkhLb9QGS4NEc+ORTBu98sIPleqzXYrd1EMVO9kxKxfEiUpqCmVIYOBGTwyYD2yeIInRNxdAkVrYazE9kSdxKEOwPPLJmAlVwUAWBpOIgCiKGClv1Ae1ufG0PI497DpcRBSjnRBA8NusdZkZMVipdFFFDQuLIXAHf90glNC5e62BoIs2ug6ZoBEJAQleo1AcIkkPoS7i2x+Z2lzCCsYJJuxfw4tsr8exB1lhYb5JLKYiiwEqlE7ujRQFNkbm01CSpy/huyO178yztdBgfNunaAUgOdx8axdBlOn0PL4CVqkvSkDgwU+SPfnSdIPRJanDs4BB3HyzRsxweumMUXXLo2w6zYwVSukjPVYjCiMiTcR2fSBBJaCpvXa3jhwLH9+dwg4iUoeF5Lhu7XY7tHcJM6HBLPvveQh1dV7hjrkCzG7JVa7FTsxCEiEeOT/Ctl25gKD4aKp6noioKa7stDkynsR0RO9L44xev8xu/cjs5UyOIPOpdByuIaHUcQkHAciI0wWGqnEdVBLoDH9+HmdFMHMCGSj4dI/89P0SVY+bb9bU6QqCR1ARcT2HPSBoATRBwvYC13RbdnoPlRWiqhBhoBDiossCVpQZdKyKb0hEihZxpoCgSjhNwfblL0/IxFIcgCpEkH0WIN9v7jozg+AGEMFJMc2lph1/+xF5sTyBEo9Wz2Kp2cTyJpa06n39ojvXdPmOlFESQ1CUmSln+8LkPmBxJo6lifPCRQJE9tppd9k8VKOWS/OlLy8yNZlBkkaKpoElyPBMLIqxAZOB65DMGthNgmjKDgUg6peET30LtCF45v07P9hjKJtmqddmu+yA4BJGAocjoisxg4CBFsG+ywKWlHW7fW6La6vGp+2cZyae4tNQmnVbZN5WhlFG5uFBlfadNfxCSVAU8P2LgORyZL9wiQ0Cr5aBIKj85u0ajO6DedlA1GUEU+e0/ehdBFGNo7Ee0fm6KSalU4h//43/MM888w8jICL/1W7/1F96zu7tLqVQCQJZlUqkUjUbjL7yv0+mwsbHx517b29v/Wc/31R9d4Tf+/WneubLJfUdG2Gn2ODpX4vpqkwMzOQauz9G5Eu8vVMmnRY7OlfgPT19hfMik1upTTKd49cIWubSCLEncfahMJqWTUGVqbZdz13bJmjp7xrJcXq4zPpTm/YUqAyvOr16pNKnUemzWO9xzsMyxfUW6XY+RfJLtmocsy2RTOv/j778Xy5JfucHMaJqkqhAG4Hrxyc91AmzHozNwyKcTtDouz51eoZwz6AxcegOPSHIJw5AfnVknn1ZJqrCw1UaWBXZaXRxPQ5TjLJQoihgtpcmkdH7vR1fiE5GoEAQBEiGep4Eo8eLbG8yPp/jEXWNIioMqOoRhgOUF/PD1FebHTDpdjyDyaXQtXFtgq94mbarUGiGyLJJOGYRhRBj6bNa7rG63+fg9U2w0bbYbXc7dbBCEIn075FsvL2B5Eq7tfJhU1+o4qJpIOWeSTsbMtHJO57f/8BKKGM+Abqy12K11yZka567uUDA1Hjk2ymgpiZnQcJyAmfEcp6/ssnciT9dWeP29CoYSt0OurzVw/AhNiZgfz1OptlEVAcsNyJkRBCraLQyOqsikEgp+JHD26hZJQ0NWBPaMpRGiEF3XePtKBcuBNy9vISMgyRF7J3NYXoxiWdqqU2sPKJoGE6UM3YGP5Xg8ec8kiqQiKwHvLzbpdKxbZkrwozBuZXoaO61ePH/wicGZCRlNidBFH0ny0YWAybLJdsOi3fcYy6fQJYeEHhcJMxXn2HgRZJMypYyB7YVxHr0YoCsOsqxx+v0Kh2byLG20MZR4GN9sWyxuNJAlkRCRF99ew/Y1FtZaNLoBXuAwsCNG8ipJRcRMKWR0CUNTIJRZ3GqgCCF3HxzBdkNC0WHPaIEry1UOzhQAH0WIcKMQQwliSrIX8cKZNUYLKVw34vSVOhldxfd9cmmFY/uG0TWBKIIIcEMfx/UYyiYQBIdqa4CuCHR6Aa4L2aSCHwSoRLjwYQrj4T0ZdCNi73SRzsBlYEnMT2ZIGRpZU0OWZQxDQSamO7teSLmYIKnHOT66KhB6IRNDaZxI4IevrfDCW+uIQqzuHMqq7FT7iEgoikjbDgiBty5v8sSdk7z1QZVSRmBzu/PhrDehS4wPZ/+z9sH/b+uvvJg8//zzPPjgg3/u9bf/9t/m3//7f8/Ro0cRBIFf+7Vf47XXXvuZ/j1R/Ivfwh/90R/x2GOP/bnXV77ylb/0M2/XeyxsNPm1Tx9kopTh6kqdk4dHWdhoMlI0+fpzN7ix2sRMyjx2fAIRjdXtNv/1F25DUUXuv22UF99Z5RfumiJvJjh3fZdiTiMMQyaGTdpdi7sPjrC+3aHeshkvJekPPB68fZwfv7PKTsvme6eWEYGN3QE7zR7losnabpeB43Njvc3yZgtRjHjy5ARJRWS0GKfV1TsOrh/eyswISCdj+m9SV9ltddms9fjKL8xR6zp896eLDGwfwVdJagpffmIfrh+y2biVq+74FDMpOj0LQ40zLlQpjgUdyss8dGyUdj9ux/3CnWNYnsDqTgvPjXjirjHyaY0zV+t0eiK2rzGwfHIpg9tmCyR0jVRSZSSfwEyIDGyP0XyGSs2imDHo9lwEOf5ehrImYShw8WYNVZFImwKqrPLJk5Ns7nYY2C73HhlBVhzaVkCr5/DkyUkEOS4koegyUkrQ73vIksjnHpqm1XNQZJ+5iSxz48X/bdOSIoayOhEiPTtAU30UAaaGE9Q6AxA9Pnf/FH0vbo3lMwZjpQRLO714XlLO0+46HNubx/c0DCUgoWv03QhRkhgrxhJXWZZQFZ+B7VHOpggCmUanzyPHJriyVGOynMEBLi800BSRSAzp9RxGS1m6fZ9kSgbJwUworGy2Gc6maPcGyIIaQ0Atn1bXJpfWEBC4bTaPofiMl1L4OPhhhO2GyDJ0XJedVkAUSXhIuG7IqQsbSEIUm1g9jd1Wj4ENAnEwE4JPKZtC1zQKOZG5iSJff/4m9i1/y6N3TqIrGodmYtd8y/L5YKXFnQfKGLpMLq1w721lVMmnO/Bo9VwUKYYUjhbThAh4nk/PjZgeyfLyu2sUswnaA49QgIMzBZY34rTEUi6B6wW3oIgCqiDiRSLzoykaLYsvPrqHQkYjpSsM5wyMlMTydo9W2yOhi+i6Rm8QI06urrSptWwMJcJxZbZrAxRFoZBO0OrZ+GEcbe0EApbjYvswnNUxVY3Al+n2XYbSBoIQkDE0zt2oEAoBlhOiKqAbChKwfyqHJslYdtymTGgKqqqx2+jjuTH598l7xri63sAPQmRRZnokNgyXcybX15ps13ukUwZ37DMpZjW8SONbP13C9wMev2uCR4+O/KX3wJ9l/ZUXk49//OO89tprf+717/7dv+OrX/3qh++JoghZ/ou9vaGhIWq1GgC+79Pr9chms3/hfb/6q7/KT3/60z/3+uM//uO/9DOXCymOzBepNPo0ug6fun8PZy5v8dkHZjl1boMryw0ePTHJD19b4dT5DZAc7jk0zLs3a3zt2WsUcxoH9+SRpf80TJ2j1nKoNGJWz6fu34MggpnSaHYdrq81afRsnju9QiFtYNkeX3h4lhCR66tN3rqyg66KKLLMuWu7KLJI1tTImHHW+cATsB2Pp+6bQZEFrizV2albLG4PeOHtFQTRoVLrUsqaZJNxbsZr5+MbV7mQBDFAkDwSusTcaIYghJ4V8D9/9xLtnk06ZdAbONQ7FtWWRTqpEXgKw3mD9d0uhbSM5Yn4gU+94yHLPvMjGWRJo9YaEPghru9RSCc5/f4mM6MpDMVDiAJEUULVNEQlVk9tN/pIYoRhqES+xlA2iaLEM6HPPzTHcqWJImqoCmxU+7x1tYYqxZt9EGmMD5lcXqgzNpSi1XeRFZ9OO6DfC0gmVdZ22+ybLJLQ47zs7XoPUYoHoxMjJu22y9x0nvWdHroOIhoD22ffRIGErtLqeNiRgOs5WL7EcDbBRNkgCOJ5Q89y0JMyuqbFprxIwlACBpZLp+vguHEo00w5i0RMRFbkkGp7gJnUWN1u8uQ905w6t0GzZeH6EZqq4PgB7YFHEPiMlpKogkQUqgRBwJ7JDJ1+bP6TpQjL9oCIUiYVB0ZVuuwZy/LqpRoD26fVDXBdl7ypoUoauqQRCRFBEPfcG12Xh+4Yp5AxaPcc+paDpirkTY3tRtzaFJBxPI/V7RZRqGGoEU/cPUGtPcBBwNBilZNInJf+0jtr6KqEH4ETuEwNmRTTCTxk7jlcJqUp9C2Hv/HEHIW0huOBpmpUW11aPZvDc8XY8xOBgoPnekyVs6zVegznU4RhGN8sAoeOFRBEAl4goSoC9bbLZrWLJsHYsI4QyLS68Wzq/YUmuhBgJOObX6XW5cCePLWey/W1Op+7fxpFiEgo8UwoFAKWN5tIIlxdbmN78ezQ8uDN9zeptSw0QyIini9NDmcRI4lKrQNRiKpGKJrE6nYbP4pQZI2XzteodwYkFJ8IEVlUIAo5MDWE54eIiki376ArYLk+A8tlvJQklVC4e18eQg1djudWj54Yw/Ph//7Vd3nx3NZfeg/8WdbPRZsrkUjwe7/3e7z33nsAfOMb3+CJJ574C+976KGHePrppwF47rnnOHHiBIryF9UJ6XSa8fHxP/cql8t/6efbrvfQZAnHjXPNTV2mlE3y4tlVHjk+TlJX2G52eer+KT770Cw/emOLqZEsixtN7thX4p2rNURBZGQoFQ+rbZucqTI3mmKkmESS4buv3CRjquyfzvL5h+f5/qkl7r1tBEmCKBLZbvRZ2+4wN5Hl8w/O0uxYCCKcvbbL5HCSUjbJTrVHOiFTb/fQNYWrK3WShkI+bVDKGsyWTfZOZrBtqDRsNmstpkYyXLhRYaKcxnY8ggjeubJDFKicOr+Jqsp0+g6nzm0wXU6Rz+gMbIdLKw32jGXoOT47jT5bjTYzwxnGSikcT+K500uomkIQxgNuy4NKvcsTd08iSSCKEpVmhzv2DTNRSuMhI4oy1cYg5jh5IdfW2xiqBIJAozsgEhwEIcJyVDZ2euQSEY4HughJXSOlCzx+5yiH5oqs7gywBg4JWeLk4RGefWOZ2SGV7bpD3jSo1HtEUUi372M7DgnFYWmzweMnJlnfjd3cUSTw+ntb+L5P3/bQEG/d8gI0JaDRHjCUS6ELPkEoIMkx00oXFAopHceL8MKIyJeoNy00VcNxHSxP4usvXmenZXFtrYkoR4zlNUBCkmTCSMT24gGKJCqkEwGfe3iGclblyGyeKAzodD1USSRnamSTCpYbokgCr79XIfQFvDAGUQrEEuVGy0ZQHExNRJZjJRYCRGFEUleYG9GIIG5BSVDO6Gw1BjieR7tnI0Q+jhfwzZ9cJ2Fo6CrIeDiey9W1Jp4X8btPX0ESRRTRRyLOOXn32i6+5xMGKi7g+g4Q8MjxCS4t1rAGDrWGi+PFgV1R5LFT7zGWV1na7hBGIn0nwAldtptdpobS1LsOhhLHHycSMkGksdu2MDUPXVOxLJdCNoEqRISRiOf5hFFsLBzOpnjz/QpD2XhepooaguTFQhfP4cB0hp4rkRBi9tYXH52n0bIppmKVmh3BD99YxSFEkhxsz2duvBgTHsKQlUoPSfC5tLTDUM5gOJ+Mh/gbDRqtmJDd6jiYSQ2QsezYjCkIAlu1Hr2BQymrc9f+UTZqHo4fE5b3T+fpWw5HZwsoYvwzWdnt0um5JJLQ7NpsVgdYnkCl3mFmLEvTcTkyW/jQbP2Ts+t0+s5feh/8P1o/F8VEkiR+53d+h9/8zd/k4x//OFeuXOGf/bN/BsC//bf/lj/5kz8B4Nd//de5ePEiTz31FN/85jf5l//yX/6VPF+5kGK0lKTZcbi53iYUBF45t8GZSztcXanzr/7B3RzbN0I5n+R7pxa4fW+B1UqLLz8+jyhIvH+zym5rwG6tT6trMVnO8c4HO0yPZsmkFaoNi8N7ilxZqLG528V2fT59/xTJhMRU2eS5MyscnSvw+IlJ3rq8jeuHGLpKozXgiTvHqbc9NnbbLGz1yJg6xVva8vmJPGldZs+4yeWVBm07YHIkjevB5cVafFpCYm4iH/N9irHbfG48z5+9uUghoxMGPq2+w9/82BzTY1kcN+BbLy9ybaXJTt3iWy/dpFiQSSg6mgSqJvH+4g4HZ3LoAqR0CSfwURWfasui23dY3+3h+R5bNQvfDRFlGNghb17aZOB63NzuIEoC4yWTd69Wub7WxHYj+hbc2GiT0ASSCY26BTlTZ6HSwlBgz2iB0ZwJgcSpcxsoSrzJfPfUTT738B7cQMH1I/qWw3AhgSgKzIxmeeaN5Tj1smWRTMh4vg+AoYo8fuckb72/jW17WJ7AK+c2SegaIhLDOQNR9Gk7AlEUEXgSrheHZimqTFIR4o1YdPBD6A8cFta7+KHDgekCp85vcGTPEN9/ZTGOGIjAdV3c0MO2Q95faqJrEkGokdJV7EjizUu77BlNs7rdI2NKCFFsGvzhG0t0Bw6Pn5jgJ++sUcxqtG2wbI8L16o8cfcYHyz2sSNI6CoZU+fIdA5NVbiyVMcKNM5c3mK9buESYEegSdBse7eyM4q8fXmbT92/h5ffXQNkui6MF9L8+O11FEXgyFyJrKlz6mIVOxJZ2ury1H3T1Ds+W/UuYuTz3Jl1FAEIQ/7mL+zjP/7wKtmUjus72DZookK94+IEIhnTwHUdmj2bG6ttuj0XN4K8qbO63SZtGkiRREBELp2IP8NGn2zKYK3S5uZ2F88JcfwQWdJwA5AUj3/0uUO4gUMkxsbP66tdNna7+IHIUC5FJPlYtxIpG22boZx+C8gJuuAwWU7heRGepzHoh1xc2CYSPcaHTHJpnbeu1jmwJ8vcZBzpO5RVuW12iFpnwPG5LDe3WqQTChE+11eruEFEMZNgp+5gJCSGcknWqy02al1s26XdixMYd1oWiiqiqRJBFLCwEXtKZDTKRRNdFeOsF0Wl1hxwc7WN4/ofmq0fvGPsI90nfy6KCcCJEyf4wQ9+wPPPP8/v/u7vYpox3fLXf/3X+fKXvwxANpvlP/yH/8Czzz7Ln/7pnzI+Pv5X9ny3zQ1x7toufdvj6nKdh4+PM1k2eeLOSSbLWXRVxvNCjsyVGC3ELtxq22Zho8HfeHwvz76xSs/26Qw8nnljkfuPjrDd6CGEEkN5A1GCieEsHyw3CaOA6dEM2zWLanPA3QeHsZyQF95e4Z7DZQQEZEFi/1SekWKK3WaPrz53k8mhBMubLRzf4ZefnCeZEOg7ISP5NM2uSxj5hAF855Wb/PKT+9jY7WE5Dt2Bw8kj8c3NjuJb0vRomrFSgu+/tkwmobBvohAPsXsueyeyHJzJsVHrceeBYQJPI5VQuFlpIUQh7UHA3HiOruczOmSyWunx5uUq+yfzvPjOOrfNZpElhf3TOdwoRFdlTF1kfrxAwYxdxevbbfwwYG4iy8JGj1JaxfVDXn53k7Vqn5mRJLbt8/Rri0iigh3GGA5DgVCMPQYIDq2+w7F9JUqZJEHksbzZQdc1ZBHev1nF930eun2c9Vqbh24fY2G9BYhEQBDExNe7Do5QzCbYbnYYH0qgKTH9OBQgEmQ8P+JPf3KTnuWQzxjUOzbNbtwqSxs6QqTxzgcVGgOXvKkycAUmh3T+8ecOY+oCGdNAUmKBRDIp4zgCF27uMjqcIiJEECIu3NxFE3xGizq5pMrCRhtD1QgFSJsaH7tnCjeICAUwdBXXc7i+1gBJYLSUwvNF2j2bKAyxHA/Pd25RmuM5iyGFzI0VMJMqtutTbQwYK6QQBJGV7T4hsXJoq9ZhOJ/k+lodU1VYq7Z56I5RJCBnKgyZMvPjORzHwdBubcgdm5ff3cByBb7wyB7qHR8tIWIm5Diwq2PT6Lq0rfhndmmxxsDxef/GLp4Xe6m++/IS6VSM9l/b6TBVThOEDpYTcPq9LZpdm0hwSCY1LNehY3lIgoioSlxfa9EfeLi+82E7FmRsy0eXRUYLST7zwCzPvLEY884aNp7v0PcCLt6oIkkSgeTT6A7oujLjwyYLa22CyKFruYDMV390k6GMwXA2iSyKiGhs1wdsVjvomkan77C6M8COJPwQTp3fxPUFDu0ZxvNCEobMT86uoQgyhiGQNQ0c16eQTQAiV5cbpAyVgeUjqyGlbJJqc4Ami1SaXWw74NBMkfYgxA8jZEUiqctYXoiuitx1cBhd/Wi3+5+bYvLzvr770xs8dMcoh2by/PjtdW6uNrjrYJmvv3Cdbzx/FYB901m+/LF92E5sFLq8WGekkGKn3uOR4+MUc/HQrphNxv3WUGAw8PB9gVrL4Xuv3OS+28aot1wUKWK50ubug6McmCmwVe1SyBrMjZm8cWkLMymTTIh0Bw73Hh7lF+4eZ2okzbOn10CArhVSqTqIUhSjqSfT7DZtIkIeODqCIIm8cGaNP/izawxlkngelLIJljcaHJguMF4yubne4eThcowTkeNch0Ja5/3FGsf3lalU+5hJGUly2G71yGWSfPPFm4zkdZpdG8sO+OaPr9+SZyps7HbYP5XFjyQW1utkEyqN9oB+38VHIGuqXFmpMj2UYv/0ENmUwXa9xy+cLOMGEd/6yU0+ed80v//Dq4wNJ2l2XO48WObNSxUsy0HXYzjiHz9/g9mxFNWWx4kDWQ7OFNludNhteYyXDKrtHvm0wfhQGl2HILqF3I9gpJhkcaONE8XemE7PwXZ8xooJyrk0oyWTWldAF2IDZRgF7DT7PHA0bknqEqQTMf9MUxwiIUJVQsaHU+QSKiOFNI1WH1mRubbRYuAJ9AcOILHTcfA8iUxC5hP3zJBL6RSyCdwAvvj4DBt1l8N7criBwH1HRoiA3aaFBBQSEklDZjDwmJ8w8UOJ6aE0shDjUJJGyLF9w1RbDsV0AlBo9h3eX6hRqducu1lnNK9Rb1nYjkCj6+IJIAqQM1VWKwNGswbH9pa5vFyn3oo3/pShMlJM8dqlLU7uz2B5IsN5lUbHo5RWEARuyajHqHdsHDd2h0fEUuF0UkaMwFBVOj2Hds/mV586wAtvrfDkySnSSQ1dgf/qswcwNRXLg6XNNgIC9U6I6/scmR3i7AfbXF7qUC4YBH7cYvOjENtyGcrGAWmyqMaRAn5EWvfpuQEDLyST1JDlkDv2DWFoEts1m3/9zUuEUcjjJybJpHQWV1oUsgYbu32WNttMjySJIpVMQueDlRrH9pVQFAFV8jB0hZWtBq4dsm+yhCQF6JrCtZU6ohCiygIfu3sKWZJQlJjC3e3Z/P1PHWS91qHWclipdDixf4RXz2/geyGPHp8gpUvkcyquDZIc8dAd4wQh5EwDIh9NC3B9H88PSBsqza7DWqXNHXMFnjo5zR3zBdJJ7SPbI/+6mPwMa7vei1lCioQsiTx6YpzDc0Vefnc9NrXVu3z1R1f47373LVY2W6iKSuAHfPLePQRBSLPvkU8rjBZ0XD9kftxkopyi1bNZ2e0gqi45U2XvZJ4w8mj1HPwg5OhciXrHotm1mBzKMFIwWNzsYibU2NAoyIShwPnrOxyYLhAG8NkH9+CHIueubVPKJvjTl24SBRHloknS0PjeK4vMjOb4YKnGw8fHGR9Kst3soaqQMRSShkGra7Hb7DNeShAEIbqm8K/+4AIP3R4n9Z28rYyiRHz8nhkuL9YR0bi53iVwQmbHc4wOmXz1uWtkUxpffnwvh6eyjBZMCtkkhWySIHKYHsthOT6bVQvdUKjVLRRF5K6DozghSEpArd1nz1iG0Fdodwexc971ePyucVwPRAmuLNU4OJ3FDWI5Z7NrMVnOUMwmGC/paIJMrW0xNpwhZchMDGdIKgqi6LHbjInFI3kdQ1XQhQBNjSWo/Z6D60WMDKXxQqi17Vt4HIF0SuSVSw1CX+Li9Sb9vs2BPVkMVeP5d9ZR1YDp0QTr9YCtaof1ms3d+0uYSZXFzTpDBZO50TjKN6HAnQeGCDyZrdoAQfLwI3jp7CqKHIcx/fC1RQQ/DqUSpXhYXMzqqAKs7nSJcLA8kcsLNS4u1BktpFnZapFNa6iyxNH5IrVWgKpBEAT85OwaW7UOy1ttThwo88q5DUZLaQJgcauLKkfsGTPRcLi50UJTRPZP5rAjOHttm72TGR67c4LtVpdCTkGXBQ7PFLA8jZWdJq4Hw7kU0+NZROIe/dSoTialc/ZqBT9wiHyB7768xLkPdhkbjqXkh2dLpJIyaUPhseMTCCI4XsjA85kYThOJAqcurPOZB+biIqNKXLxRpdru85kHZpkfS7Ow1iGV0OgNXPIJDVmSODht4gR+/Ltk+Tiuj+1pXF+uI8kRigiKpBD4EYgiz55e4W8/tRdRUKh2elQaXYZLJueuVpkcTvGDU8uoiobrumgq3HmgzFA+iUCAKChMlpJsNx0iYmy8jIQohvyjLx6mO4jIpXQyuoAmhuw2XATB47bpJJmMzusXt8kkdQxFxrJdPnbPFF/98VXcwCed1Ag9ke+8ssDqVpdSTqE3cEjoIvl0ku26x1qlQ0SIFwScOJTj8EyRd67V+B9+/23euVr7SPfJvy4mP8MqF1J8/qE5XruwheMGmAkJWRY+xBR85oFZzl7d4bbZIi+8tUKrZyHJEtfXqtx1aBhFEqh3PP74xzc4MJ5jYaPLf/8f3yYII+45PIoYaSxvtRkpGAzlUxyYzKOpGq+c2+DFd9b4zk8X0Y14yPzqhS0mhlKcvbqL7fg0OhbTo1m++8pNGt0YGFnZ7fKZB2bZrHbYN5Wj2fMIvICCqTJSNFlYr5HUNPaMJPnFR+do91wUIeTd67tEhHzy/mnOX69RzCYoZJO8+X6Fzz4whe/LXF5pcGA6w2qlj+PFKp9G16LW6nNts8nR2TyOHRdCWXBY2ekQEqOyf/j6IilN5P0bbWQlQhRFXr2wyfmrO2zUOuw2+rieTxj4fPulRfIpjU7fZ3GjxnQ5TzGjoisCuipzZbGKZXs8fuckIwUT1/OBiHLOJKkL1FoDfE/G9iRev7iFKoAmiwiiTzatcWm5S0KTWa+20DQNz/cZeBKuDcM5g8WtNo7rslPrYiZkbq43aQw8XjizjufC1aUmr1/cIJ1SuW3PMBpxm21uMoPvyTGYT5SodXyWNpo4noAuC3TtiFwCvAj2zpg4EZQKKpoSoMpwY6VDhE8+bSCEEStbPcaGTFQlQBBihlkupyCKIXbkMDOcxvM0bqzXODJXpjdw2Nhts3+qiCCEOF5Id+BSbQwgjAUBM6MmU2WDySETQQp48uQkG7udGKMycBGQGdgBzYHEC2fW6PU9bB8EwSOhq2iKTBRC2lBxbZF8SiOhaXT7DhEShhab9PpWQIiGoUoIgcYLZ5Y5vq+MFwrIasjjd01wz+FhbFsmndQY2B71jsuNtTpuGPD9Vxbp9z26XR9NgMiPyGcSGHp4S2IdcWzfMJtVC1Hx0VWNV86vYygOH7t7GkmRePr1RVxPwXfioDJFiUhoCobicNfBIXp9HycMCIKIsWKCyA/58hOzlAtJNqrtWLKuyGQMlbmJNElF5Im7JkgoIQld4+Z6CzMpY2oS19Y62BGkkwqWE/DqxQq1fogAJGQZEZHtWpf942m6rkCASCIhIAoalqcxGDg8eGwMgoi58RyRIJJMiPzS43PU2ha2G/CD1xZ4/M4p1rZ7aIJExw7o9n1OXVjHNGT2T+fJJXVypkHoaaRT2n95bK6f97V/psijJ8aYGctQyCa5stjg3atxKNZrFza588AwlxZrZE2D8eE0o3kVM2VwY62Noausbbe5bbaAKIkffriVap9n31xhqzbgwHQRP4hY3GyxutOk149bZVu1Po8cGyPwRV46u8pnH5wB4LMPzPJnb67y/Jl1Li3WODpfxHLcW/3sDu/drFJpxOymatOi0bVRJJG17TY7LY+ebeP5IW4YYLkBtifF13xVRhFlbp8v8oNXF3H9kE/cO838ZIGXz61TSKvomsbbH1RIGCobu11Ond/kM/fPcmWxQSalc3W1jq6JuIFGNqXjBPDtn1znEyenSOgaz765xnZ1AELEo8fHKRdTnNg3TIjAdmOAKMrk0wm6A59rK3UmR/IMbIcjs0VunyvzozdWOL6vTMbUWdnuACGphIbl+tiuw/23jdLoOESSg6R4HJjK0+45tHouQSjR7jns1C2ypkEkyCiEhFE8IA9v/Xl+Is+r7+0gIrCx2+HkoTFCL2T/VJb1ap+pcppjB4ZotPtIciz5vXCzycRwmiAMWa+2MNMap85tcOeBERQlYuCFWLaLG8pogocQqqzv9JBEFR+RQjrB2atVBAHSKYVq22XvVJqkLtEd+IiCyuJGGzESkUSwbIlMSr8FKFRJKg7H9g/xk3c3UOQIQYgzPPJpDVWNlWLFtMpoIYUmawz8gMAT2DNi0h24hJHDx+6a5pk3FpHEuF331P1TPHJ8HFmK+P7Lq8wMm8yUU2iSgyzJVOpd7Ai2mm0SCZVqs4fnqTiexItvr+J5sFG12Kz3OLwnT9/ykQSJMIg/h/1TBTZ2W+i6T61t0e3ZzE0WGFgeuqpw+koFURRoWw6NvsWhWZOlrQE/eGOdKFRodvucOr9J6Mq4vsMXHpnD8jQkJcD3An7hnmkcz0FXNWptBzGS0SSfnhvLipOaTLXp4vguOdOg2XeZHslh2QHbdYsz71dIpwxWd9qMD6UY+JDQFdpWhKHA3skssijy9rUaS5tddCE+KFxeqvGlR+fYbVosbHewQnj1/AYT5QSWB53ugL7t4NgRruvTGzg0eg75lEIYRbihh6aEeEFIKWMwVkhzba3J/ESW0aLOPYdHsDx4+3KFP3npBggi2aRC3hQRooikCpLsoEj+n2Nz6ar0ke2RP1Mx+c/xaPz/y7Jdn2bX5a3L26xuxRLdfZM5Li81OL5/6M9V/4SucGi2RLPjUsonUMSILz48Tymb4PX3Nj/8cD9x7x7efL/C1aUGhYyCLEGlbnF0rsy5GzXSSZm7Dg7z3kKVnWaPkaJJp+9y9tou33nlJrPjae6/fYS3Lu8wP57jynKDVFLgnQ92eOb1Zc5d3SWX1lmpdPj2y4u4QcCXHpvn1LkNDk8XiBBptj0EBJa3mixX2gRhyCvn1xktppgdy1Jv9ZkeTrHd6HJ4tkgmpaML8Kn79hCKLtOjaZ48Oc3N9QZzE1kqtQ5eELFd66NLMaeq23OYG89RzCT56btrfPahGcoFk+WNNvmMipmQcSNYqbRJaCq1tkU6KbPd6PGlR+bp9lw6lvdhCNaDd4zSaA/IpjRcx8fyAjzXx/UidF3Bi2BmOMP5ay1sN2KkaKBpGu2+iwAIosjL727wozeWMFQJK4yYLafo2B450+C9G1V0VWJ2NM2eiSw7TZtu3yGViGNU3726w8GZLJXdHrfNlbAcGV2IKOUSBH4IQkjK0BGIXdeG5lFvuni+x+HZIrro0HdldhtdPN9HEAJ8P+I7r9zktrkiURTTDG6sNsilDIqZBCESjuuiqBJ+6BCFMm9c3KLTd9ju2uw0+liexkhB4eFj4yhEdC0fEZFy3uQ7Ly/i2HHqX8JQaQ48CimdH7+9Ss9yufvgGK4r8+71LY4fGMayfEJ80gmVtd0eAyfk+MEyqibx3FurWJ6GKPqEEViWQymdYm2nze3zwwxsh42dDp+4bwZEn7FSgrMfbNOzQn7n2xcRRZ/teo+RYgIv8tkzWqBngSYrJBIqnheSS+ukkyrVlo1hqHihwHDWQIrUD+MbthsdhvMmT56cwNAkNEVDREBTHHzXR9cUytkET7++GqNOPB9VEbAjmUq9zbX1FrIiUcoYLG328AIHRY49QyVT5ehcgUfuHKHTdxgfyrBVH/DGe5s4rsvCZpNG30GWFIrpRHxYnCvQd2G90uFvPrGPckZnKKMhSyKq6FDKGUiCxkatRT6dRJE0Xr2wiWHIvH2tynghzdp2n0xK553LO7HAwhOxnZCE4mK5PvOTOVxX4LnTy+iKw2TZ5IsPzzM3YWK5AkKkkTRltho2ERqVhkveVPiHnz1E3lRQ/nf8e/+/Wj9TMflP0tz/ktezby6RTak8eXKK19+rEIURR+cK/MPPH2a0lOLBO8YoF5J8+sE9tLoWPcvj6deWaLb6bNVtXjm/Trtnc/LwCGvbbf7+Zw/heA6fuHeaVy9sYtkhmaTOC2fWeOmdVRRZRFdlXn53g4PTBcJQ4NS5DV69sMX0iMlUOYMqCfQHPkfmCjR7FnceGOK1C1XuOzpCPq3zwO3jiILIcD7J0bkSgiAiCiFPnpxElmWefm2JrmUznNfZbbmcubSN64UEkcBypcnt8wW+9ZNFkgmVn57dZGWrFbePopDVShPB1/C9ANcL+PbLSzTbFhNlk+NzRU4cKLNSHTA7kgERbMej0RmgazJDGZ2B5aIZKvmsge36VKo99k/n0NTY2S6KAjNjOS4v1WgPLEq52NC5stVHkyVy6Rj/8uCxcVRZ4oV3Vqm3BgQ+1BoWmi7R7nm8d6OB68cZENmkShQGbOx2+PSD0/yNj+0lm9SoNm1cYKSgYTsOn3logosLVcbKCXw/pNl16bsOqzst+gOHO+aLFPIKjZ4LIRh6RAiYhsy11QaNrsvqTgdRlDg+l+Paqk1AiGGoOE5Iy449Ep4vMDWc5sZKG0US2TeVp9uzkYiL8AO3jyOpIaoi8uO3VtBVjYHlQijT6VkkEwq273H28i57R/OxDyfSKOcMBp6IIAQMbBfX9Xj0xBgLmx08PyAMXfoDl2bX4b7bxkgnDWrtLstbLfp2xGghGXtgVnp0B14six74FFIKfSvgU/fNoCshkqhx+lKFKystInx6lkdGl+g5PrIskksoGJKMQMSn7p/lpbNrfPHRGRRZY227T8pQaPd8HN9BFRTOXdvBDyKurTa4vtLijfcq3DaXx4tcNClCUdRYeXi4TMoQGcqZvPH+FqWcQbXZxws8bm428UINQ9UQJY8Ah/uOlrm53WFuIsNuw0IX/HhQH0Yot+YaqaTKwIahbIqfnN2k64RsVQdoosa3X7nJdqOHKovsncxxdO8w3/rJIpIo0+q4GErAf/WpebYbFi++s0ImqWEmYuNiOqWRNxPc2LAYuRX41e4HGIqLpPgcmMoiRJDQY1Brz3YJIof7joyDANdWaqSSMnakcmgyRyEhsLLdJGsaXFvvMzlkkk1p5NIJNDXCCxwCXyJlxMq3xa0OAyfg/I0alhOQzxgf2R75MxWTmZkZ/sW/+Bc8/fTTvPjiix++/ktZnb5DEIS8cm6TSwtVPn5yip++u8H/85sX+A/fvxw7z5MyJw+X+eFrSyxvdanUBjx4xyjlUuz5+PHb6/QsjzOXKxyYydPquXz12euMl2KK6fpOl5GSwcPHx6k0+oyVEhyYyvG5h/ZQzKqMFAyePDnJlx6bo9aKoZGKInPhRpW58Sz5dILRYprz13fJmip/95P7OTidi1HsSYVcWuFHbyyzVR3geiHfPXWTp+6djNU92QSHpvN85Yl9rFd7ZFIKKV3j5mabX3p8lq1afBNrdD1+em4TWfA4MJtjudJkqJBCViQ+cd8EZkqn3nawg5DXLm6wUe0ycDx8L+IX7p6ka/kUMipvf7CDqsp4nk/RNCjnTS7eqNFsuwyZGo/dOYYqi4RexPEDw2SSOrYNhi7StULeX6wiKRFb1R4CIAkin7hvGscNcDwPVZWodx32jJq0ex6lvE5jEJA1VRKaxlgxRSmr4zghoRjS6tiIkY8sKsiqhBBqnLm0jSop/PD1JSaGEowNpWO3tSwxMpTGtgReOLPGtdUmnicTRAJ9x+fZ02sU0wneu1mj1YkNiqmkzNdeuA6RgGHItPsxAmRtp01CERnOJWn3HAppleP7h1ivdgnCCEl2kUIVM6lyz21lDAX2T+XRFIliJoXlBAzldR45NsEzby7S6UfstrokDI2z17aIQonhXILrGy0OzRT53qlFJAFubnQpZZKcOr+OmVJ4+0ps4vvmiwucmC8giwKrlR5hFGGoIlMjGYr5mIfXsxwMVaHvRLi+wxN3TpHSRCoNh3RKp2uHFNIJOj2HIALbgx+8uoKkBDx+5yTjQ2k0KeTobB5dU+JkTVVldbvNw8fGefaNZW6bzXPHvgL/8HMHmBzKICGhSBpvX9lisdImpcsc2lOKYxoMhZGSQT6dJAgFZkcySEKIF4IYxSfzcs5EkRU0WaDZs6k0fQQ0Xnp7g+ffqWB5EtmkxtOvL8ZZ8LeP8YPXFkinVCrNPn/nkweQJZHJkkHxlvT7b35sDt0IQBTYbLi4noJpSByYyWPoAAG1zgBRgFbPYqyUom85GAmFdtfB8lQ0ZBw/jPH8moQkCsiixDd+vIiuBEgCzE0W6HRddAFubHZxkbFsn3RKZmY8Ty4jst3q0R84RJFIu+eRVIhtABE8+8Yq1caAX7h7kkd+HnAqrVaL1dVVvve97/H1r3+dr3/963zjG9/4SB/s52mpisRL72xwYDrP8laXhC7zhUfnyKd1vvTYPLoq8+kH5j50mv7Bn33AySNj7BlN8/K76zxyfJxyIYmqSJTyBn0r4OlXl7h9b4mEHsdqappMpWYxWtD5lScP8vs/vMZ//7++jR8G5EyDZ95YxnFDbNfn/YU6x/cO8fSry3z2wWlOX9oiYURs1Tr80mN7ySYUtuoDXjm/yW/+3tuYCZlyPomZjJHsL7+7wZWlJnPjOXRV4Zs/vk69M0BSYGAHVGoDshmdM5e22TOW5ZVzW7x2K9tj/1QOO9IIA5mFzR6XFqps7XSYHc2hK7C206XTt3nizileOLNOOqXQthycIKDds9k3UeBj90yystli33iC7sDBtj2euGuK77y8gA+ookit7bJVb7NSaaMb8WnXc+Mi9ctPztPq+PzwtRVCfILAx7EDpscyfLDSRBYjsobMxLDOsb1DCKFMszNAFEWaHRvTjMOELtzcodP1aXRd3EBi4Phx0Jjo8CtPHaDRcZgZTdPueRAFFNMGW7tdKrsdHDfuRb9yYRNdiU/lUeDzi4/Pce7aNl98eJ5QiDAUh1xK5cm7J3BcB88O2Kp2KWV1JoYz9NyIb718E0SB2XETQRR5470KU8NxFHFCgcAPGc6mqPU9Wn0XPwqJhLhYCoLMS2dX+cJDswRBxJWVDs3OgCNzJXabfTK3RAwvnFnm/qMjJAyNZ0+vUW31GC2a7DYtHjk2QYjDb/zK7WiqQG/g8/aVbf70pZvsm0rjBx5BpPHahU3GiiZXlndpdl10VWVho8HMaJ56y0YgIpQ8IsHlyOww2w2bds/mv/nFo2zt9pkaTtLu2Sxv90nqGi+9vcrACfC9iFcubNLq2zxx5yQDJ0QQJIrpJOmEgu8FCLJHOhUHtG01LN54b4urq03CMKLXDVjYqLFZ7YIAji/gOA6q4mMoMr7v0u1ZqKrK5FCGbErnpXdWePTEGMfmS6zstMiacRJoMacRhXBs3zApQyRvaqQUiULKIEDCslyiMGB2LIfvqfhhiCKJ9G2H/ZM5NFVCljQMTaNvudhOiKbECrxcyqDeGnB9rYWkxNLqufFYBXjheg1REDg4neOpe2cgEhGiWLyxWGljR1BvDVBu5RpdW2ni+wFipPHyuxsMFVRkAYqZFD0XTl/apesE/O1P7MVM6fyrPzzL82c3P9J98mcqJv+pgPzhH/4hf/AHf8DXv/51vva1r32kD/bztNpdm3uPjHB1pcFnH9zDHXuLfPqBWf7jbzzGJ+/fQ6Ntoasy/+gLR/h//F/v4x994Qi6KnPf0XHGh0zyaYX/9ivHUGSZYjbBqXMbDG7151+/uMknTs6wsN7i+TMrVOo2azttHj4+zmcf2sNwzuCZ15e480CZ9xaqeH7AQ3eMIivw6Qem6Aw8Hr9zgtWKRbPjUC6pjJUzNLsu2ZTE/+0rx5geTjM1YvKrTx3i8FSWJ09O8uiJCRY3mtiux5H5IsP5FBu7fRbWW7y/UGdxvcUXHp7j+6cWODJf4ME7RhnOJ/G8ANt26PUd5sZNnju9xgtvrZM2RO49PMbadp+krnJlqcZT903i+nBjrcPWThdZlnjuzDLdnsvSdh8/UOMM80qTIHA5cWCYVt+lOfBY225zcHqI7768xHvXqlRqXWzP5xMnp/F9kdVKiyfumqDXD+gMXBDikKIPlhu4PuiaiiZrZE2VrVoLxw3p9N14puIHCKLLgalivAGGEZVaN05PlETOXG6QTSgEgc/0SIalSpfvvbKMH4RMDqUJwojxgk7OVPgHnz6A7clYdkApa6IrMoeniyQSIQsbbSxPo9YeIIkStiegGRK1pk29adO3HOqdAUfmiyiSSFbXWNzo8JkHZ2P2kgADD/7kJzfYbfURo5Bu36PR9vB8mBqJ24VH9hbQNYW8aXDq3Aadvo2hyQRRbL48dW6Dk7eNYugSkhjy1L2TXFlpk01JFEyVSr1Lrw+GqqEqGs+9tcxjd06QMVUUUeOtK7sIgsex/SU8z2WslInzPxp97j40xvW1GqOlBOW8xm7dwRrAwHEIQh9NE0gZCqP5DJquYLkRf/ziTQwl4N7Do+SSCo2exZce28vzp9d45+o2WUOm3XUQ5NgnlTQ0BGT2TeUIEckkFbKmyucfnqPRcXn+rRU0TWFyKE21aYMYgqDQd0HXxbg96IRcW6pi6hEeDhNlE8sJkDWJy4sN2j2XO2YTRIGAJMPAtpFlhY2dDpYXZ844bohhxHy49xZ22Ki2iaIAUY5hj7YbYTk+N9aatHo2Q4UUja5FvWPxzZduEgQB9bbLI8dHCTwND49MQqHnOhzbV0QURQaWz2jBwAsFPGRGCknu3p9BFBweOzGJG0C7Z3Fwusg3XriOGzj84qPzBJ5Mx/JvwVvbHN87xFaty8RwhlP/H/Pc/9Mz4Ov1Or/2a7/G7bffzpEjR/iVX/kVdnZ2PrKH+nlbXhhy+v0KSUPmuTOreEEIxCmMP3r9JjfWGrx9eYOrKw3+9dfOcXUlxuI32haCAPWOxx89/wG5lMo7Vyofbubnr2+jawqXlnbZP53j0w/M8sq5DXYaFmvbbY7vG2aslOHIXIlXL2zwxYfn2KrF85hGx2OkkGKl0kVTZVa3uvTsgGdf36Cy2+HBI2VcH85d3+HCQp1vvHCNbzz/AZYHA8tnY7eDH0bkUioA19YajBeSzE9kefjYGD9+e51T59aZHE6T1FWWK12urzX5+MkZZEnj3eu7JHWFuw4OU23ZnLlSRVUEepbDRq3H3ESO8ZLJq+c3MRMywwWT6aEUQ7kE6zs9Dk1ncH2fKytVmr2Ad67WUMSIyu6AKAi577ZRLi5s8+kHp9k7lecHp5ZZWG/z6oUNwjDk9vky3YFHJAiMlUzOXKqQNXW++Mg8CxsNXMdFESAkZHIoiyQKbNd71LsWvZ6PbYMkQsZUODJbYKacZiRnktRlhrJxQl0hY7C20+au/UPMT+bYaVpoukylbmF5Ir2+y247Hs7nTJ1Xzq9TSBskkxpCqCEJEaHokEkk+MFrS+zWeiysN7lj7xB+FHHuepWtWp9941lMXcaO4OnXlhAin5WdAYMwoN4Z8OAdYwgCaJrG5m4PURJQpfhr/PGPbzCWNwkCj1pnwG/8yu1Ybshuw6acUxGEmJicS6ncWG2xtttnbjyH43gcmCmRMjSePbNGOqnhBR6dnsWjx8fp9GzuOjhMpdln32SeQT92UuuaxoUbu6QSGuev1+l0BxSyKcrZFLKkMZRLgijyztVdhrIpcok4uMxyHSq3iM6fvH+alV2b9WqPsXKGvJngg+UacxNZHjo+Sdv20XWZThdsx0eTPdo9l4s3apx+f4vTl7Yp5xPU2wPmRlPcc2iYhKaxXu0zWkywVunS7Fqsb3dZWGvjBC4Hp00OzKaxfYmNyoCRnMHsqMnaVpP7bxvj/LUalqfx47fXsN2Q+Ykim7UejucTiQ4Hpwt8+6c32Gl1sR2fTNLg9394jZGiQRCCooi8+M4q0yMp6l0bWRLw7YBcyuDsB7t87uFZzJTGxYVdJoayGEpEvxciErCw3mbfVB5RFijkVBwvpNbpowk+I4UEjqehouH4Du1enIb67OkVhvMprix3kWURL3DJGjIDJ0AUBdKmgqHL3Fiv/zk111A+9ZHtkz9TMfmt3/otbr/9dk6fPs3p06c5ceIEv/mbv/mRPdTP21LCkHuPjNDsONx7ZAQlikNaG22Latvld79/mUI68aGi6833K6xut5BlERC4tlJndjyHrsgUs0lcL+S1C3HA1ivnNggCAcf1+eHrizx8fJwzV7bjzOmeQ63dRxTh0EwBWYZ94yYPHh1FFkW6fZe7Dg5Tbw0QxTg+9+Mnpzh1YSvOqAhCcmmDd6/u8JkHZrm53uLUhXUKWY3HTkwyUkix23IIQ3jhzDqSInHu+i6KHPHI8XFSCYX7j46ystXmtj05nrxngnpnQH/gIEsS11dbpFMqR+YKpJIK7y02+PzD8/zwtRXOX9shnZLJpXU8L0SRQoppjSCKGDg+k+UskiRxbO8I11bqqKrEbXMlTl3cIKlr/OC1JWptj70TWTRZ5JefnEeURA5M5elYHueu7fDW5W16fY/1nQ4PHBnl0mKdvu1zbO8I3z61iOWDLMXtK9vxmR43GQxc6u0+miqzstPh2kqTKBLww1jqubHbYXY0z2sXd9isdulZAcWMSqsbpz82uhYzY2kuL+3w2PFxwghURcQLHO45NMIb723Q6AxwA4fb58pcvN5CFEMePTFGLmPwnZeXuLZa58ylCp+5f5bhfJJGxyIg5j7dd2QERYolxdeX27xyfoO94yZmUmFps0EmraFKEX07Rsd8/OQ0N9ca+KHA+Rt1dEVjYsik2upjqBqBr1E0Na6vN3ngaJndxoBO3+HgnhzXVxp0+lbcgvMcFjc7pFMGCVXh+IFhZElifaeNroiIksILb63T6TkcmiliKA4TQwkyZoKsqXFxYRvbcej2HN58b5M9Y2mqrR6WB24g0er5CMBMOU0xY+CGEZmUwtZ2G9d1mR/Pxby4KOTc1V1s26fRcVnd7dMYCHzjhevceSBHOZ/gqZNT+H5AMZNgciTD+FAGM6nQ6cfZ8KuVHp2+x3AuxfpODz8QEFCJQo33F3dw/ZB0VqaU15mfLHJjs0mjYyErDnsnczS6Fs3ugIyucXB6iM1dF1WRGSuZbO32SRgKL51d4+Hj4yiCwG69j2mIPH7PGKIgkUlI1Ds2vhDx/ybvv8PkPK/7bvwzvfe2szvbOxZ90RvRCYIQm0iR6rSUxEWS4/zsvI5sK3Gk2LIV2XGJI8u2JEoiRVEAO0iAJIBdorcFsAAW2/vsTu+9//54FiMilGW+tvg6js91Pdc1u/PMPPe0+9z3Od9y9PwUm3rs2IwKFCJY3W5FJMmRrYgQSWDYnUAtl7IQSLLgi1Mui0imC1h0GkpIKSw5p2YKcPaGD5lCTCaTZ8eaWroa9Zy4MsfobIjbU1FB6douWBKk0gVB5LIkIpcr8OXP9JLLfbhGgh8omczMzPDFL34RvV6PyWTi13/915mbm/tQB/Z/Uqi0KhwmBb/+xEocJgUqrYCIyBaK1S3k5EKYnUu9kS0rnfzZj29w/OI0EjE8vkuA40plYvoH3Lx9aY4da+oYnYuys9eFSiml3qHHbtIwMhPi8V1tPLarjXAijUIuxaCWsKzRzNRiAotRwytnJlEpJVwbC1AulXE5BC6CTivj6qiPA5uamPNH6GmyEo5nBFZ8sUxbvZF4uojNoMYXTiwhjgQ+wcEtTbzUP8HaTjveUI5isYRRp+Lo2SmUCsGq2GZUIxdLkSskeEJJdq+zolZKOLi1gXiqyPNvj1EqFnl0ZzMNTh3FUhmdWkpjjQ65VFAODsWyJDNFoYwjFdSANy+vRYJIIEfuaOXWlI913Q6GpkMkM0VUShn1NXpqTErKwMxiDMTw2M5mbEY1hWIFvUbBqasLJFI5ro152NTjQCLNsxBMMO6O0NFgQlSWolUrMBnUzHgSOK1qTg0skMzliKXyzIfTDIwGKFNgx+o6Utkyl+/4UCoEvalwMotZp8TtS9LeYCWVK2PRKcnnS5y7FSSdy7Kqw0YwlgNkVEQ56qw6pFIxHfVGtGoZu9e5qK/Rs2VlLUf6x9Gr5DQ4DWRyJV7oX2RzjxWVDHb2upBIxGxcXksuL6JchEiygFopI1+CC0NekrkiyWQOu1nDYiBNm0uHP5pALpPiNGuRykrIJSWsZjXHL85TY9HS5jJj1iuwW1Qs7zBSKIrJ5CpURFJsJg3heAqlUsr4XJTOOj1r22tQqeS8emaCVW1WsqUiEomYTEGB2agik82x6I/T1WRlMZjh1mSIPRtrEYsk/ODYGHJZEY8/jtMqKOiWyvCjt0ex62W01RmWvNtzSMUiDm5twh8SegoGrZI5b4wmh4ZFf5xtq5yIKgrmfHEsJhVOi45jF2YIJzKolBUUigrLW3WoZCW2raolm8uh08j4yPYW9BoJkWQGpbjChk4rnlCWTLJCJJZDKs/TUmtk20oXybSYhho1R05OYTfqmFgMk8rkqDFrWQglqXdoUCnlZLNFVnfYWN5sJF8QYzaoqVQkyCQyRqYDrGq3Uy5XiCWzGHUqaqw68jlBEt8XyXF9JIZSBCY9WLUqGpx6Lg15yeRLiEQglUsoVXJk8hWC4SwatQKVLMeu3nrmPEnkChn1NjX5fIW96xuosWp4/cwM84E4Wjk0O3WCk2exzNBUkBqrGm84jdOqRvnPbdtbLBbJ5X4qXZzJZBCJRB/aoP5PC0G6QYpKJcVh0lT1bWqtOnb1uljXbSO35D39hceW0z/gZnWbjWg8T7FUYd4fY1evi74BoRmvVsrQqaVIxGLqLCpanQZe6p9Er5GhkEmZ8yUoFsuIxBL6rs7T3WRf8jrJc6RvHKdVx9RClPs3NrB9bT3hRJZyRUS5LKA3Xnl3khqznkQmvcSAzuIOJFArJbS7DCjkUno7nLx2eoZTVxZorjUyPBOkzqYjly8wOhdh51oXfQNu9m5oWFJI9hCICk16k0rK5hU1eIJlro8GyObK9A+4WdVmJp0TDL+MWiVT7gT5YpnFoGBaFIynuW9tHTtW11KqVLgy4kOtkPLG+Rl6uxwo5VIy+SINDiMjMyEObm7i2eOjiKQFkpkijU4Tp68vsqrdilQsobPRRDCepalOR76U45EdLbz07hRalQxPKMsb5zzEEjmOnJoilliCraZzSxwHFWa9ko/ubMNu0DA+H8ETTPHYzjbODPq5MxMgmy/w0Z1tpDIF9FoZA8N+4skcb1+a59SVOdLpAoFYBr1WSd+Am//+3E0og0gM0wshbk/FKZSLxJJFTl11c/m2F6NOkMBYCAieIuFEmmRKUIUdGPHzu39zhfN3vLTWaWmpE4zTJPIi2UIFi0Eond2ZCVNr1XB5JECuVKZUKfPuDTc1JjWVihj1kobT1GKCEhIMWgkf29vGrCeBL5RAIpFQLomQiGQYdYJacDiRRgxkChUMSlAqJei1Ss7eXCCcSGM1ajBoFUwvxNBrBI0rk1bJj06Mo1RK0alK1Nk06LVyZMh5sX+CTctryJfE2Axq0tkiSoWcI33jrO6wYVDLqVREGLRKxFIxU544iWSBjgYjTU49ahlsX+NEJVdweyrMijYzJXI8tLWZcDQNlNmxqhajVkkmWyIQziMRKQgmiqjkYkGVQCgMEE+UkMmlZIqQKUiYcEfQqBVURGIKORkqlQREFWZ9CY6eneWBLfWIJDlWttr4xo9ukM/nmfcnaXPpEVFGJpcgFoPVoCadzRFNZIgm81y6vUggViCTq1BvNyBCRDKdQyKGaW+cbAGuDvvobjQwuhijXFBwYzJIIl3g4W2tiBAhEUEsmUUjE2DgEjGkCwVyFTnpTJ6OJj2FQhm9RonJqGTBH8ekUfHwjia0KhnRbBGZtMxcIIZWIeGT+9uIJAp87+gw4UThn1+C/uDBgzz99NMcPnyYw4cP87nPfY7777//QxvU/4mxe30TAyMB/uLwzaqwI8DTh3p4+tAyzt1cxGnV8Vcv3WZXr4sbEwG2rnRy/MIc3zs6yqxXYNB2Npn4nc+u5Y1zs1wa8rIQzPC3R4fYtqqWK8N+HtjcyMf3d9N3bZ5oMkt3kwmJTPBycJiVdDaaUUhF7O6tJxBJM7MY4/iFWUqVMmKxsKLN5EskM3mmFtOEohlWtjp4cFsb+zY0sbrNzO2pEANjHnb1ulAphR9Xd5OVs4Me6m1qfulgN6GEwMCnIgg8rlvm4PjFecJLarhOs54jfeM8sbudwbEAu3pdPLyjjXeuzDLvS5DLF0jni0jEImqtGnyJHLNeoWSiUorxh1MkMsJKbMtKJ3emBG7Jj98ZJ1Mo0N1k4fytRdZ3O5h2pxmaCuKPJnn6YAdGrQK7WU46XcbtjyOXSDnSN43NoGRTTw2NTiNXhn1s6akhmiywfU0tYgkEk4K/uz+coYKEZLJEJJFBIi2yqs1BKlvi1mQAuUzMilYHkVgOi16GVCxlMZDgkZ2tzPqT7F7nEiROlBKef3sChUxAdq3rtjHljWHSymlvMGPQqdCpFPjDCXq7bWjVSqwGNRXE3JoMIQJG5+JMLsbxR1Ls21DPjtV1nB70IpWKESEhky0TCpegUkKnUmDWKzFoFLTUGTl+YQ6zUUmxJOKxHW2IJRVC8QwlURGZSEytRU8skycSKxBP5Imlc8jkUvquzuMPp1CKwB9L8tC2FkplEUatkoERP0Uk1No0lMghlUqolEUoZTDri+OwaFkIxEjmK2QyRT6ytYlwLE+uoGB6McKyFhP5Yo7eTjs1RoEbVBbBX714m3Aiw0e2NyGXislVRBzpGyebL5DP5bGYlEy4I+TzZRocWnxRQfxSKSuzf1MT6WyF8zeDlEUV1GqhoW63yJCJyxiUcmZ9CVTSMkadAk8ozUIohjee5cqQh0AsRaVYwRcRFjWfPtDJ8HQAhUwEogJSylgMcuRSKbvW1mPQKIjFy4jEIp7Y3UIolWdZg4lkuoBCLkWjkNLVaMYdTFAqgcWgov+aG6VcxuYVNl4+PY5GLvTy1nfbWQxlMGrlyGUlfunBZWg1Ci4N+ZDJhN1TpVImlslSZ9dyZyYKFRH5MqTyBXKFMrlsmXxeUIMu5UXkiwVODsxRKBbZu6ERhQTa6o2UyiIC0TRSmYIJd4I6s4pMjmr1RAD+5D+0OfIDJZMvfOELPP7445w7d44zZ87w2GOP8cUvfvFDG9T/iRFP5ThxWRB2fOfy3D0ZvrHGyEPbWqpN+ivDPn7ns+vpabWxa6n5tWd9A5Fkgb9+8RbhWJ6NPTU8urOVqyM+epotnB1c5NGdLWxeWUc8leO109NMuWN4Qln+x/N3cFnltNcbcfuT9F1bJJstMONL8e1XbnFoSxNvnJ3l8m0fa1otPLazFZFIxKw3hkat4Gvfu8wr747xUt8Es/4UfQNunnljDIUM/sNTq5n3p0mls/zqoz1kChXGF6LMeWPU21SoVDLqbSpWtVvYv6GBn5yYoCzO4QnH6WqyQBkQiRmeCaGVi9ix2sXL/dNk8xWGp8OIEWE1qoinCrTWmThzw4tYXKHRqUOvkRFPFwWDJo2cvgE3KrmE4ZkYi8EEnz6wjHM3PUwvxjl5ZYG//Mktam0C38Np0nP+9gIquQS1XMzqdiu3p8MsBJKolfCJ/R3MB5JEE3nUCokgvZ4tIBZV2LGmniOnxtBrZLx2ZpZiWYxSLkKjlFBBRHeDienFCGs7HKjkCiY9Uc7f8qFWyvCFsywEEixrNjLribFlpZP5QJbORj2fPbCM66MB9BoF0XgBiUSEuALLmuw4zTou3F4kEE1TKBZ5Ync7I7Nh9FoZaqUMkUhCrVXFwIifjcscjMzEef3spMC0jqeZ8SXQqGUcPiXwHxQyEQ9ua8SoUaLTSklksiTTZWa8KQZHonjDGY6em2LKHcNuUqNSiFErZfzkxAQKhQSbUU0sW8IXynJ9zL/kZphjWbOZTK6CL5ghX5RQqVQ40jfBsmYrr52e4Y2z04TiOcTiPIuhBDazigabjsVQnK4mO6WSiDISiqUSLXVmwvE8crGI9d0OvKE0tRY9br8gO9LdZMGkVVFBTCiSYd+GRu7MhJBIRCyEUpy/uUAgWaCQE8zIVjRbUchEFAtlLAYVpaIMiURBPFcilihweyZGsVQhGMtQKosp5Stk8hVsBjVnby6gUgo2CUq5jKujAWwGDYl0BW8kRzCaR6eWcbhvHLNWRSSRxe1PUGPWEovnUasUFMvwUv8UyWyOSDyD2aBCLBXzyukp1nY7yBZK3BgPs6zJighQKcQYdAqUMhE2o5pQooRWJSVfLPHI9jbiWai36zFqFQxPx7h024tOLccXTlEqlagxasmVChy7OI1KUSIYT6FUFlDJZAJKVKfm4m0PgUSem2MBJJIK4VgWpQh6WoxkClBeWgzebcCLlsBDH0b83GSSTCYBgWeyZ88efv/3f5+vfvWr7Nu3j1gs9qEN6v/E0GsU7NvQgFmvZN+GhvdJOW9d7WLbUpN+wzIHNpMGEHYuX/+1zXQ2mKorhLcuz9Bar+f4xVke2t7CpSEfrXVGfvjmKK+enqxea9/GRgYnAhzYWM/oXIajZ6dpcGjZubaOGpuW8zc9rGixEk3leHBbIz2tVl4+M8X4fJRjF2d4Yk8H/QNuUtkCNWYNfQNuTlyeq/Z2cgVBdbapRsPGHidmnQqjVmDhv/LuNL5wBqlITDZfopwXfDB297p486yAVz+4vhbtUhJw+1NcHQ8gllTYv6GeTD7PyjY7r5yexh/OEE9m8UUz5IslFgM55FI5UqmUy3f8qJQSwrE0e9bX88Tedq4O+7CbNLzYL8j+F4pw35o6tq6qJRzLURGV8UUT7F3fQDJTxBvN0lhr5J3L85y76aFUktDgUKNSSmhwaNAoBRLj21fmaKjV4g7EaKgxcHnYw2O7Wphyxzh3a5H2ehPBaA6jVsmZQS+FSpFIMkuDQ8/aThtvnJ2iw6XDadXxv166jUwqZv96Bzajhu+9Psq1cR8f39tOKlMiUywy54kTTmZYjMTJZPM8tL2FN87N4bSoiMSzPLm3nWA0x/WxIIV8jkgyzyf2t7G+x87UQoRGpyCVIZVKKZUqDI75qLXq0KvkHOmbpKfFwo/fGSWVyjM6n8BsUGDSyUlm8kjFYhwWDTajCsQ5ArEceo2U9d0OHCYNwXiOOW+MWosGjVpBLJknEM8hlUA6k0erlC3tgOvYt95FLJMXJiWDAoVMQq4gwWpUky9U0GrkvNg3hUYGs54YxUKJljo9ImkJEWV0WgV2k4IVLQZE4hwPb2vl7B0P9621EoimUCgkROJ5zt30sKnHisuhpFgqIZFKyGQL3JgMEoyVeHfQTTJTplIuEIylQFJEJCkQjWcRiUWcHvSQzpUwaJWIxGUKlDh9fRF/NE1XoxFPIIlGqSAczfDU/e0gLmDUS1DIJWTyefyRFF2NZuYDCcoVMXq1gjcuTFPv0HP+phuTVsG6bgczizFmPCnGZ0PE0lm2r3Ji1ilQSKWUyxVyhTwlIJYocPaGB7tRg0IhYd4Xp1Ao4QumyBcLyCUSjHoJgWiWi7e9lCtiAtEUOrVMKMGVczQ7DOzbUE+uICUYzVEsKNBrhJL38EwIiUSMVlfBZtQgQUx3owUAl1WNSJojm61g0sl4+mAXJp0MlUb5oc2RPzeZfPrTnwZg06ZNbN68uXrc/ftfW3zqgW7+8rd28qkHut933zNHh7gxEeDfPLSMzz7Yc899tTY9ALt6XbTVG2irN+IPZ+hsMHHq6hyHtjYx74+zu9dF/7V5svkin3qgmzqbjo/ubMNp03Fl2EutVcf5W15qrRrimQIHtzThsKg5fmEetUJe9aPftqKG7WtcTLoj7F0qnRw9Ny1cv8HE+ZseOhqMDM+E8IfT1Dv0nLu1SCYveKsf2NzA1lW1Qi1cq+LQ9naShRLHLsxxazLE7vX1zCzESeYhGM9Woc4/OTlFuVxhz9pa/OEcF28vcv+mRm5OBtFpFBw5OcnWVU5MOgXTi2Fy+QIuuxq9WkY0VeL6mB+DSsKjO1o4c2ORjnozerWMoekQ5UqFS7e9DM+GaKgxE4zmkMlKgndLOodSLqoaluVyeVLZMul0keXNVla1Onipf4rtq10UC/Dyu9M0OzX0dtXQ6TLQVmdi8wo7894kJp0cuaxAe70Rs1ZKrlikUCxgNijZsKwGm0lD/4AblUxCMJ7jzkyS62NeHrmvGW84SyghTEpKmYShqRCRZB61TM7V0SCFUpHPP7wMq16LQiZFXBa8QjobDLTUWjBrhQWKUirhke2tqJRypPIKUilsWG6io8FMMp1jzhent9OGqFxmTaeNRLaIXiNlyh3FZlDQu8yG0QC1Ng0quZRKSbD2FYskLAYT5IolysUSABIJaFQCGU8plVFj0aFWybh4x0NFXKFQAKdVyw/fGMWkk7KhuwaLQYVGJaXGpEEsriCVFdmwzEEwXUCrkiASgd2kwe1NolbKyeZKDE2HmfKkuTEapVgqYtGrKBVkGLUqXu6fZEOnHadVhVSqgLIcuVRCU42ebL6CXi3FaVHTUqfHF0rR5rLyV0duk0lXqIhEyBXS6nff7Y2RzReot+uoVODxXc14wxla68wMjAYYX4ghl4sp5ipkM2XC0SJufxK7SVBF8IbTnLu5iF4tRamUsG9DAyaDmJZ6E7l8jhUtJuwmHVeGfTQ7TahkMkG+v1AiVyyiVclZ3mwjlS3x/ePDSKUSSpUC3lCKTL6ETq0QrHglkEgUkFRkHOmb4OCWJhBVOHZxnuZaA9OLUbzhHGJxAbVSCqIcWpVEIDvmc1iNCkQicFlVUFBgNMj4Sd84SrlI6A1lIRStoFSKyeVLKJVScvnShzo//txk8vLLLwNw5MgRhoeHq8fIyAjDw8M/76H/10Yy8/6aYyAilI4m5mP83Wt3CERS99z/zNEhfusvzqLTSPnNT66lXIYTl+c5fWMBu0nNph4Hq9psnBpws2GZg3xB+NDNBhXlShlPIMGhbc2olSL+48fXEogmaavVs2WFA6VUzLZVToKRFLuX/OhzxTL5XJGzgx7qbGoGRvyMzEaRS0XU27Xs2+iiyanjid0CjPfVdyeRSiQE4gVqrRqKxQpDUyESqSJf+mY/g2M+kukCezfUs7pdUOCtsWg5dmEatz9Oe52e09cXkIhFjM8n8MfyDIz4uTMdoW/AzRO7WigWK2xZ6eTSLS+hRJZsoYIvlMGsU6BRCYmQigiVQkEonmFXr4vlLTpi6QIHNjVy5sYiqWyB8fko4VgGsUTC7ckEyVQBbzhDKlPk6rCPfevqEUsk5LMl5AoJi+EE7lCc+9bWIpFUmFyI8fiuNg6fmuLEFTevnJlm3h+nXJFy4Y6P6cUYnlABp1mJXim45SWzJaJxwTt+IZDgwOYGPnmwm3JZ8FNJ5yqYdHKUMgnZbJFGhw6FTMwTu9pZDGYwapUMTgSwGlQ4DGqOXZxGrRSDWIwvlEImFaOSFWlt0JPMFokm8hzuG0erkpFKlqgxaaGoQC4Ws2WlE38kS2u9jll/CqNGgdsbx25Uc308iDec5fwNH8WiAC+2GJRk80VcNXpGZ6M8vK2VRCqL06bFatAgl0kJR7MYdUoO942zGIhTLhe4b00tN0ZD+KJJPMEkW1Y6MekFvow3nCQYzTDjiVAuiXilf5ZVbRbiySy1Vj1WnYKh6RCNDiMauQSxpMzD21p5qX+SMhX8UeG8GU+YWX+cfetdIAWDToVUUkIqK+Ky6zg96CGVztHRYEYmguZaA3O+FPFkhsd3t6BSyEgkSviXzOdWtJopVUTUWDRQElNj1lJn0xNL5PBEEjy2s42b40E0ailvLmmduf0pRFSIJ9OMzsWptah5ak8niUyBcrGC2aCkmJdgVEuZ86Ux69Uc7hvnVx9dzuG+cUKxNP5IknypzDuX3AQiGWSKCnO+GE/taxeEX2VSpheTFIslVLIyyXQJbzjPm5cEAcr7Vrs4fcONXi3lY3vbKJcq3J6KYDcqyOQkWDUKvOE8WpWS0ZkUYrEUTyBFNl/GqFdTkRapNWu5b40LkUiMN5IkGM8SiKQIxLJkcmX++qXbZHJl5LJ/ZtXgu37s/9rjmaNDfPmvzvPM0aF7/m8zaaq9kV29rmqJC+DqHU+Vf/La6RkUUgmSpUZ5vUOLxaCi7/oCfQNuiqUy6WyJL32zn2ePDZPNF7lwy0skmceiE1ztwsksdpOaUwNujp6b5fKIn0KhREUk5sZEgC8+vpKGGj2xVI7Hd7Xz/NsTrGq3VR0iv/PaHVKZEkfPzrAQSLCr10W2UEKrliIRCWZEJ67M0+w0VMd95Y6PaDJLg11NfY0BvVbggSgVMjyBFDaLir0b6jmwpZH+ATe3p4Ks67Zj1isFDopWxbJmM231BkwGBalUDo1SwqblDmxGFdFklp29LvLFEplCgTfPz3Ft1I9EpODE5XnuTAXZu6GeT+5v54m9bfQNzJHLF6izqtm2qhaFVILbl2Bdt4N3rs6DCNzBBIl0HjEi/vaVYexGFQatisHxIA01Mvaub+DSkJeBkQCheBalqMKWHhuP72zlxniIHxwfJ1UAo1bJgi9JvUNPoSxob2XzZS4MLiIWw4El9QKzVo5KKcEdTCGWiOi/toA3nMRqFEh7//ZQDzqNgsVQArtFTSpb4kj/ODvW1GEzqrk0EqGYF1Fj0jDrTbBlRQ25fIGXTk8ytRAhmc6RL5eJxDKsaLVQLEg4N7iIw6gCkRhvKMWDW5tZCCRpdGiRycoc2tLC5EKUZKZAPJHl3esLnLu5yPquWnK5EiajlKvDXpqcBiYWY3Q1WVgMpCiVoN5u4LXTM3zv6CgOk5YH1tfRVadn97o6NEoFMomUZ94cY8Efw2ZSo1ZKqDHp8EfSAidi0MNCOIFOo2QhkOHyHQ+bltfQ4DAyNhcnGM/Q5hL6ME1OHWIJlEtFsvkK8USZCXeEOqsauUyK2xdlIZSmWCwzOhehJBLR4jIyF4gRiWeorzEw643x4JZmEpk85SLIZWXy+SIv9o+zf0MTxSJMLUQ4sKkJkVjM+mU1xJNZcsUiq9qt2M06ooksFpMKjVrGrYkgFp0CbyCOOxhDr5UhFks4f9PN8lYrerWcVe1W1GoFM4tJxuYjHNzSxORCjLPXffzg2Dh2o6C1l8qVicRznLiySLYi5q1Lc/giSVa0WqgARp2UtZ12kukCvnAauUzC9tW1lCoSJLIKmSKYdSq0Kilj7gQLvjgWg4p0pog/nEQmEjTTLHrBHlgqlvDCyTHMehXFYvkeRfO7C9UPIz5QMuns7OT1119ncXGRaDRaPf41xWIwcc+H4gnEyeaL1fufPtTDN399G08f+mmJyxdKcmc6XG2A3U00YrEYk1bGrz6yQjDAuij0MbauquX0kr7XO5fnyBdK7Fzr4tj5OcQo8EVy/PkLg2iUQs302qif5U0Wdq2tp3/ATSZb4vZ0mN/6i7MUyxVaXUa2LsnA9HbaeWJPO5+8v6Pau3mpf5qHtrfwbx9Zzsv90/zRDwY4dXWe3b0uFoIJdq9zsardQpNTz/XRAA6LlmKhhEYpYl23g+tjATobzChlMmRSEXKJmJ29LgYnglgMShprdLTW6Xmpb4L/9eJNxIBSLsNsUKNTq3D701SAN87NEU1k2Lqylou3vHz2QAdrO+30Xxeg1Ps3NSGTQDQlmIH5IlnkUhHDc1EqlSI1Vg1qpZyrwz5WtFjwR9JIZBLK5TI2k4Ida+o4cWWeRCrLk3vaKRUUnLgyx5519exYU0eD3cCFkQDdTWYkEimlMhzY3MC8L4paKcFkUCCRglom5cl97Uy6I0ikEibmo2hVItrqjSjlCkZmItyaDJLKFNi6qo4fvT3BzEICRFLUKhnpdI6FgKBv9uaFGZpqDXiDAilVqZTiicRBLEKrlVFj1fH82xNs6K7hubcnWAxnOHNjgdoaDYvBJEq5iM8caEerVuANp7EalHgCadZ129GopSwGMsz74gxORKgzKwnGsuzfWM/GnhpOXp3DYJRAWcyG5U4qCGCJ1jotWrWSTKHCmRtudva6KJUrLAZTGE1aLo/6SaSKvHlhhky2yP4NDTisepQKMeWSGHcgSq1NR1mc4/Gdbbz67jQiWZ5gTDBwK5VKnL8pJLQfvz1GmRy7el2kM2VuT4axm7XM+5NMLMS4NhrEqJXx0rtTtDfYeOP8DFajnI/taccfThEIp5BJJULvYi7E3vUNKMSCfIw/niZfhqujfla0WoREXCgx400zMhOmUCgBFVL5Ius6TRRKFZSSAqs77FTKUCwUOLilGbEI5gNZFoMpShUxs5449Q49BrWMwQkfrXUGSqUKkUSWN8/P0zcwz4NbG2mq03P/xnoSmSI3JwIUimUGRvy0uwzIRUU+uquFifkYKrmEZK7MpSE/HfV61nU5yOdLyMWQL5bRqWHaHUcsFRZfSpWI1lotDTUGDBoJD2500d1sQQrE00UyuRKH+yYplIrsXOti2pvg7ODiPfPPP7tt78mTJ/mP//E/snv3bjZt2vQL75mEQiEefvjh6rF7927WrFnzvvMWFxdZs2ZN9bzPf/7zv7Ax/ENxl1MiNODrGRgN8stfP8nRs1PVc967IwG4PRnk5FU3l257eWJ3G0/t7ySeynF91I9OJSeQyFR/sBqVhEe2N1Wb/A/taKZYLLNxeS0PbmskXxEgfsVSmSlPFIdZyX1ra1GrJRy7OM3OXhcf3y80r1e0Wnn32iKlcpmnD/Xwh7+6mf2bmzi0rYXORiM7e13sWOPkv/7bdfRdm2PGk6iKUa5qs3NnJsTKNhuX7/j4zAPdXB720uoy8ezxEVLZPBKRlFqLkn/3cA/lSolcocSZGx5C0RwmnYzebjsiETjMauoduqpETCyZRSUXZE1MOgWvnJ6CCmxd6SSayPPWxVm2rXYiV8joG3DzYv8UChn4Q0nWdjroH3DTZDdQZ1XR2WDl4m0vkwtxyuUS6Yyg7bWi1YJeo0QpE7GqzUGlIqFUqrB5eS06pYxCqYw7GOUjW5tortVw/qYHu0WFXiUhkyvz+tlJWmo1OC1qfnJqCrtBg0YpZ3AswGIoidOs5mN7BRLqhVs+/uT5m7isKjL5HG31Rp7a186zb43iCQrlMKNeSSKV4/jFGQKxLHKZGJUcHrmvjbM3PEwuxjHrVOTzJSx6DaVyBbVcxkt94zy4tQGVUszju1sIxTPU2bVIkFEqQ4UCcqmCdC6Hw6ymyWngxyfGUcrFlBGhVUmRySVMe2IUKiIy+TL91xbRqmWkcyXKeSnhWJ5wPE2DQ8PjO9uxGDW8e8ONTCSivV6QXHlgcyPz/iRefxKXXcfgRIC1nXbG3FGujvoIRVN01JuQyUX4ozn8YcFDvtYu44ld7ZSLclpq9KTSOXQaOVaTmk3La+jtsqOQKWir1+GyqiiXwRdMkMkWWdZkYnmrma4GK6vabMhlZbqbzcgQI5OIKZZFqBQyHEZBkbvZaSSWyIJMxIVbXiiXKJYqSKVimpx6PJEU18f81FpV7FzjIhBNYzcqiSSyqGQK7kyHyRZkvNg/QSqbJ50vEU3myRQgEs9SqYiYWYhSY9ZQrlRAJKbGoiOdzVGplGivNwiukT0OzFoVoWiWnhYLL5wYY3WHjXROQFQd2NhAulhiMZDm4fvacDkM+GIZ9m9oRKsUmv3rumu4POoTqgRZMWJRhXJBQb6Yp1Sq0FpvQKcUo1LIuDwWIJbKE0kWsBs13JoMsnVlLQqZjGKpiFkr48HNzVUTvyvDvveV4H+R8YGSya1btxgZGbnn+EX2TCwWC6+++iqvvvoqL7/8MnV1dXz1q1/9meP4yEc+Uj33O9/5zi9sDB8knj7Uw9d+eSOP7+ng8MlxwvEsh0+O37NDuRvhWIYfn5hg19o6VrZbkYgFu1m9RkGby8QPjo/iDabQqCQc2NQAQK3dUG3yJ9MF/sOfneb7bwxxYFMzINirfu7BTsQiCZlskfISVFGpkKFRioincqzvdiCVVPgPH1tdTW6XhnzVxOdyGGl2arAZNURjReKpIt997Q5GnYzf+6V1vHNljg3LaugfcDPnTfCDY8PVL2RbnRGTXsUb52Yw6BQYNApsJjUNNUbWdztY3WGmVCovybPMcfziLGduCCuj/RvrKRTLFIplMvkSuVyOh7Y3oteCWiWhp8XMwa2N5IsVXuyfqK6mHBYNP3pngou3BF7MlREPTbUGjl2Y5pEdLQSied6+NE+xLBDCGhxaPIEEjXYjxVIBXzSFWinh1mQQiVTCS/2TqOVKdFoZdrOGrStrUYqgpc5CWVzGYlTjsus5f8vDoztayOZziMRw+oYHuVTMzckgFIUyZVu9gSd2t9PeZOHWdJTOeh25fJmVbTauDPspFCucG1ykUBKscr3hDJFEAZlUzqQ7wpN7Wqi36xmc8GEzq7g54SeWyFAoV+hustDsNOC06LCbtdwYC5DMlDh1dQ67SUUsWeHC0AKFktB/kytEPHpfE3KpjKn5CCNzUeosGj61r4tiuYJYLPB5rgx7MesV3JryMeuPYdAomHBHCcRSePxxfvXRbmQKKa+dncJp0zI4HiCdK+CPJnD7Ezy5p42J+SiLwQSP3tfGO1fcWHVyQfiz1sDfvjaEXi3m7GCIGa9QhgrFc1y84yOTLTG9EKOzwcD+DfXkChVePzNDNFNkaCqEWCKhudaASi6ju9HE4ISX3i4bC4EsyxpMZItQqZSQioWmfLEscKFmfAmSuRIzi0L/Zdqbwe2NY9AquDzkI5bKc3BrM1KJhP7rbpwmPTKJDINWSa5SZmg6yp0ZP6vabEhEFUSI+P6bw8RTWbascJLOFPnu0VEUchHheIZ4UlgYjbtTWPQqzHoV054EtTYtR89PIRHDQiDBoW1NnLri5tj5aWotStRqKaKygncuz3P62gIyeZlKuYRRq+BI/7hQer3tocFh5PCpCXTKMrU2A2JZkUpZIM3mCxUyBWh06QnG8gyO+pHLxUhkRTYvd2LQyPEGE6xst2DQKXj7yizruh3cngqzvtvxvgXvLzI+UDIpl8t85zvf4T/9p/9EMpnk29/+NqXSh1N7e/HFF1GpVHzkIx953323bt1ibGyMxx57jM985jOMjo7+zOeIx+O43e57Dq/X+08e2/ffGOIr377Ei6fGeGJP+z0S9P97mA0qHthcDyIRl2578UUyAHhDyWq57PZkCIlYhE4tRyL+qaJAMpPn1FXhnFNX3RSLZTzBLKtbzMQzZY6enebYhTnkUlFVdqS3o5ZwPE80kcGgU/G1Z67wzNEhsvni+xJfa72JvgE3oUSmWvJ6uX8atVLG/g0NHL84x6p2G2a9kg09TlZ1Oti3wUWLy4AYER0NBibm41wfF8y0Bsf9WPRSIoksTpsGpVxcTQZWk5KmGh2tdQYcZg1Om47XTs/w4xMThGJ5yiUFb5yd5fiFOaRi4ce6ss3GzSVk3JaVLnasqSOayrO6zcKGZU5kUtCoFaQyWVRKCY/saOX1szO01hlJF/Iks0W8kRTeUJqx2TjJdJ4ndrcTiGZ5aHsz074IeoWcsbkI67vtZCuQLRRY9CXpaNDTf81NT4uVd67MMeVJoJKLeXJfB3VLfYTb00HqLEp6O+1cHfYzOhXCZlChkMt5+d1Jmp0advU2cPLKPAatnBqjhkangXL5pwSyS0M+DFoVb5yfoafFxqwnhlwq5+pwgL6rc/Q0G5n2xFCrJPjDST6xv5P+ATcrWq14Q2k0GjEttQYUMhEHNjdQKBboqDfz5oVptq50USyV0arkSMUQSxd455KbWU+M1jojWrUUu0WLy6bFH8lQKotIZYrki2VUcgXJdJ7lrVYi8SwOs4Y9a11oNSqOnJpiMZjivjVOnBYto7MRntzTDpQIJXKMuqPsWFNLsSBlYj7K62dnyeRL9F93c3BLE+F4jkQ6j1IpYcGfQiODDT0OArEsbS4Tp666KRXKKKVFwoks+RKYdEqGp0MoVVLcwTg1Zi3FMrx8eppKRUowmiKbF8pWmXyRljoTV4d9uOw6Lt7ysLPXxawnTjZbotGhxWJUo5IV0SnF1NpUSCpiVrdZKZRErG23sqzZxtlbXtZ1O/j2a3cwasUY9XI+sb8Ns1bFlh4TG5Y50aoVTLojhJI5AtE0u3udVMolHBYNTosWs15OPFWgt8vO6g4bcrlMaM7LhYVIqVTCF8rTVKMnk8uxqt3KnC9BjUVNMptj3/oGMgUpIoq4/Rl0igrpbIVEKo8/lqJSEOEJJti2UjCHK5elXLjtwRtOYTFpEFekWA0atCphnF96YiX3r6/7J8+BPy8+UDL5xje+wejoKIODg1QqFc6cOcPXv/71X/hgSqUS3/rWt/jN3/zNn3m/QqHgkUce4aWXXuLzn/88X/jCF8jn34+u+v73v8+ePXvuOT75yU/+k8YWjmWqE/zbl+bZssJZlaD/+2L7akGSJJUtML0YIxBJUWPRVstljy+JIn77ldu8dnoGXyDO998Y4r8/O1A9Z/c6F2aDitY6IxWJhDfPz7Cq3YZUIsZpEbS8VrbZ8MZSdDaYOLS1pTphLQYTBCLJ93mvaBRydvW6ePvyHLuW4LSfPtCJQi4lEElX5fY/tb+DzctrAIgmCvzw2CiJTIHGGhNatZhossR3Xh/mznSIFS0OakwaDCo5Rq2CmxMBHtreQDpT4kdvj+EJppd87xPs7HXx+J52hmdCBGIpHtrRxCfv76CtzoBJr2ZkJsRTe9txWjTEIknq7Vru31DH9YkQNyfDnL7mwWFSYDNpGZmNkcmX2LLSyeRClPnFJDUmFS+cHMexxK0JRjNIZGX+7tUhtGoZbXVm5Aopr52ZRa+R03dlnmS6hFIuRy2TsbrDhi+Uob3BxIwnxbWRgODkVyjw2YMdGPUKmmqNjM9HGRjxky9WSKTz3JkJ8ciOViKJIi+/O8GjO5tZ02lnMRxj1hOrKhS81D/JE7vaefXMJFtWOrl4y8PKVjtQpK3eiFalQKdR8s5lN5mMILVi0MjY2evi/G2h8Z9KlmmyC836WpuGwyenyeUL7FjjIpPN0d1k5uj5KfQ6JeFYlm2rnEwvJrAZ1OTzZcSAVqPAF0phN6lxWrVcGvJTqhQZno3QXKOhb2ABpUyMN5rm8m1hYh6fj9HgEF772cFFpFIxJaTYDRreODtLJJ5lPhClvd7I1pW1ROMZPrarnWAsy7Qnxqf3d2JQKbk66qeIiHiqyN+9MkRHo55fe3gZJRFkS1LsJg0SkZjzN91sWO7k5f5JaixaxGJIpPM4rToGRrzsXutixpPg3WuLAASjKT55oJOyqExjjSC//vC2Vs7ccDPujlKulMgUpMwF06QzQn+iUCqyrNmAUilh0h2hsUaDQSPjwMZ6/LEihWKZWpuOm9NB8iUF3nCcWDLLg1ubuT0eRq8WmvMzvjQquZhMLo/DpOfOVASnVUVzrYEFf4Jn3xrDH0rjMCnYv7GJ546PUqqASqHAZdNx/MIc6WyRQhFePj2FSiaw3v3hDOmChGgiI/Rx/EkMqiK1Vh391+eJJnIUikW2r6rj6nAAfzhNoZhnIRjHZlLS4NDzl4dvMjAe/ifNgf9QfKBkcuHCBf7oj/4IhUKBTqfju9/9LufOnftHXfDYsWPs2LHjnuPpp58G4MyZMzQ3N9PZ2fkzH/ulL32Jp556CoD77rsPtVrN1NTU+8777Gc/y8mTJ+85/qk+9maDSpjY3zPB392RuAPxn/kYm0lTlciY8SS4NCTI9j99qIc//uIWetps96DAZHIZp64KEOPBiQBf/7XNVc6KzSQI3r3XV2XzShdbV9YJdr5X3WTzeUIJARm1eYWDeoeel/unef3MNE8/2M2hbS08d/wOzx4fIRBN8W8+spynD/Wwe10dwXiWd6/Ns6u3nvM3PTQ5tbhDaX7rL84yNBHg3E0P3U1mYqk84/NBVrcKpTCtSsbxC3MMix2gbQAAm4pJREFUTftRKmVMe5McPjVJb6cNrUbF8EyIQ1ua6Ls2z7bVLpQKORs7raQyRR7f1c7IbBSJSMJL705hMSgJhJM8sLmBOzPRqqHP/k1NKFUC1PX2RIC9G11YjGouDXlZ3WZlyh1Fr5Hy1L52Zv1pAtEMH9nWzFuX5vj0gXaWt1mQiSTsWV9PKlVakqJP8/jOVqYXI1REYkqVIqNzEV44NU6hWKRULhOIZCiXSzy6q5Wh6SgKuZyOBiO+SI6BES8d9UZ6u+zMeuMoZCLkMglOi5oTV+bYu74es0Hwrqk1G/jR2xNEYzk0Kgk9zRYKpRLrumqY88bYtNzJzUk/q9tqOH19Ebc/wZ3pAI/tbEEhU6CRS0AEnY16Pr6vA7FIzNRihExRRI1FyzuX5tjQXUOhBAq5iAIijp6dxqAThBjzxRKX7/hY321HLsuRL5ZRSKRkswU29NSQSOdZDCS4f3M9pZKUE5fnKZTg4JYGtq2u46U+wU8nlshwaGsTmWyetnojB7c0ML0Yo1CocPmOkGxWtFmQSWVIxBXu31yPL5pDKpfw9qU59q5rQKGQcaR/nO0raigv9QF7O61E4znevLLA9RE/SkmOYqGE1aRiwzInkWSOnmYzgUiafEFQE7467KPJacRuUeMwq3l8V8vSDnces1ZBKlVmeCbEsjYL4WSGA5uaePX0DBdueimWcsx441Qq4EvkKJVKFEpibk0G6b++SCRRYDGQRKOW4Q+lABESUQWzXoFCUqSpxoRRq2B8LsqlIa8ATClVuDrsQyyWUGczcPLqHBuX1ZDMlOgbmKejwcyGZQ5S+TJalZRXz0wIUi2RDL5oEvcS5LxChWQ6x7puB+OeKKWKwJ6f88d49L4WFAoZo7MRshUFfQNuAtEsU54ExVKJQCzF9lU1WIwq5FI5f/vKMGq5lHM3PT+3JP+Lig+UTKRSKWLxT0+VywX28j8mHnjgAU6fPn3P8cwzzwBw4sQJDh48+Pc+9oc//CGRSKT6d6VS+Znj0Ov1uFyue46ampp/1HjfG599sIf/8Rs77iElPnN0iN/9Xxd4/u3hnymi9vieDgZG/O/7MGssOgCe2t/J139tM08f6rknYa1ut1XJjgBHz05xeyrM/etr+crn1vHorna8oSQP7WjlsV1tTHsEtdgfvzNGvU3Fx/d2MbG0cvaGUrx2dopzg27G5qLcmgyxGMjwjeeuEY5liKeKHDs/SzCWw2ZWs3Wlk7VdDq4O+9iyvBZfOMWDW5oYHA/w+plpIskCqYyAxDHqZOzqdXF5OEipUiCdK7Cu20EuX+birUW6miycv73IE7vakcskfPf1O/zt0RFmvHFGZ0N0Npp447xQoro54RcUlO33Gvp4fFHmfXEe391CrV1HKl3kx++M4bRqyBbyiMQiSuUyWqWMi7e9FEsVqJSxm9S01RtJpIvE0jlEIjh3y4NKKezOtBoZPzw+zoIvjt2kwqyX015volKB21Mhtiy3s6zZxOR8lN29ThZDMbK5CqViiWwBbk4G2bW6lhWtVvRaBYViheGZII/vaiMSz+H2CmMuVXLs21DPhp4a3r22SDpTIp0roFKIaKwxcHZwkXS2gj+WEKwOEnliqRKRRJZEJsdcIMOdqaCgKVapcH3Mz7ImBxPzYaQi6GmxEE/nSGZzKGUyooksDrOWSXcEuVyGx59kwzIHKoWUdE7OO5fn+e6bdzBplRRLQt9tzpeh1qRDJCmxq9fFyavzrOywEk3leXBrA4lsDqNOxTeevY5MLrC513XWcGXYj1wmYk2Hg2A0RTieJRBNYzOq8AbTNDu1HD45xoObm3AHkgzPBmmpM6JQyBABD+1o4uCWZmoswnjXdNgpVBRMLETQKCRMLETwBpN01Jv5zusjyGRiHGYl+za4KFEhkhBkjsQSCb1ddravriUQS6HXSvjY7jZMGgXfenGIQDTJgc0NdDdbOHvTz33La7gxHmTBl2Rlq523L87Q7DTwxK52UukcazrtuOwaOhtMrGw2cXsmQlu9gURWxLg7yg+Oj1V3mt5gnHAszSM7Wnj97Az5Yg6JVIJGLWZuSdJILRNjMyrocMpordPT1Wii0WHEE84wvRCj1qqhwaHDrJMTSRawGWQoZQp8wQR6hQy9SkmuACOzIR67rw25SLj2A5sbGZoOMjEfJxzP0d5gRKeQopAL5c+KSMTGnhrWdtrY2PNPnwN/XnygZNLR0cFzzz1HqVRiamqK//yf//Pfu3v4p8SNGzdYt27d33v/lStXOHLkCACXL1+mXC7T0vL3l5k+jDAbVNXb7kD8Z/JD3ht9V+fp7bL/zP7K0bNTfOmb/YzORav/+1kJK5svcvH2Igadii//9WWujfp5/u1hXuqf5MW+UdLZAg0OAVZ5YHMTL5yc4srIIu1LK2ezXsmnDnRxpG+CriYTm5bXEI5nuW+NUEOViSv8l89tRCauUCyWeWCDC6VUzH1ra5ErhKazRFLh/k2NlMoVVAop4WSe+9fX8PSDgn5WJlckliigVUpZ125lXZeNB7c0MzIToqvJwhsXpjlycozeLjvJTAGTVo5WI6dSobrbkklkTC0kWAzG74EzViQSbk5EaKk14gun+dHbY6zttBOJ55nzphh3x3n3moc7U2Ee3NKEy67Dolcjl4vJFypMzEeZmE9w4vI8o3MRFHIZh0+NI5NI2NnrIpEpcHsyTCiW58Z4ALtJxb4NLhodRppdegrFMvFknkIRfnJqjD3rGjh/00Nng5mKRMT1MT8quQyxGNK5Mk6rmncuz+MNpQjF8vzpj4dQyiUUikXWdztoqddTY9LiDQulSJtZ8ML5xrODbOi08rufWUf/gJsfvzPBYihDJptneauNSKKAWCTi0Z2tyBUVAnFBRDGSyDO1mECnUvDCiTEkYkE5eHmLGZmigMuu48Z4AJVCQiYryKLEknmUUmFy2rrSybg7zEIoTj5XxGKQ8+DmJkRlES+eGqO1xkAqU6RvwI3NqMQfSVFr1dF3bZ7uZoHEGoil2LWmnrODHuxmDWqVnOMXZygUSjTXGrk+7sdlU5MrCCW20bkQi6EUy5qMeMIpkpkcj2xv5fqYf8nzpoJWreDSkJ/XTs+QyRTYstJJLFkiGs/jtGiQSSScuDzPzl4X71yaZXWHmXAiS61FTyJZRqGU4PbFeWhHE06zHrtRhU4txmJU4o3mePf6ItOeFN5wgv0b6vGE01wc8rB7Qz1KmZhyWczR89OolQpujgeRiKT441n80Qybl9eQzpYRU6G93izs9mQitq504vYlWddh5dZUmMd3Csg/uUJMjVnD0GyWbE5ES62eWV8UlVzEhmV1hOJ5XjszRVONoIzd5jLjCcUpFEVEslk8oRi5XAGNUs7UQpjZQJKeZiNyuYgHtzQz70+jkMsIhHOoVXIklGms0fHO5TlUCikzngR6jfyfX4L+d3/3dxkaGiIUCvHxj3+cdDrN7/3e7/3CBzM/P/++HcTzzz/Pn//5n1fHcf78eQ4dOsQf//Ef8yd/8if37Jj+vw6XTc+un8EPubtDyeaFFfTpGws01ujYu6Gh+thsvsirp6fobjJz4dYik+4Q4ZjQpH9vwgLB0fFT93dVV+tWo5p0tsSl2160agXHL8wSjud489wsb5yd5tMPtC+pwMrYvtLJ5x/qprerhtXtNhYCqXvGqlbJKJZF/NfvXsJmUnH6xjzn7wQYnotWG8b71jdy5NQUL/VPsKbDxvh8hEQ6RyhRJFsQDJ1S2SJ2o5YXTkzyO39zkT/98SD1FhVPLHm57F3XwK3JIC1OPb2dNlQqGQ6zmh8eG0EpFzSgamxqzg56GJtLkMsV+MrnNvD0oR5qrTpqrSqmFqNsWeGkzqYjHMswNB3CalDismp4YncLNVY1795w4w0nkcrFvHttkXlvjJXtFnLFEg/taGLv+gaGJsP0dtVw4uosGpWEzxzsQr0ER6YiQiYTIxGLWIwkKBfFOG06zHoVfQNunFYdt6d8PLm7DZFYxKkrc+xd34BGJSWTKaJVSimXhGsd3NpM34CbdpcBlUJMplDm3E0PErGI0zcWaHXpeOy+Nm5NBOntsrNtZQ1XRoP8wQ+usmtJzUAhk/DA5mYCMUH11RtKISrB9GKc1W1m9DoxJp2cPb0ulCroarJweyKIzaCgxqLm9LUQU4txHtrejFQKJoOCZqeGb/zKOuLZEi/3T+P2xfnVR1fw2pkZlDIFR05N8Yc/GODZt0bYv6GBsgSuj4fYvc7F5w51MbmQZDGYYO+6BsplGJwMAqBTi1nVZkOnkaFRyHhseyvpfAmVQkKtRUMgmmPCHefty/NC2S+eRaNS8NbFWcKxAm9cmGbLilquDnux6OXIZWW6mkw8tbeVscUoDpMCvVZCe4Mek15OJpvDblIjEcFH72un0Wng+IU53ro4g80k4850hGiqQFudgcmFECJRhQ1dtdjNgifQwS1NXBrycHMigkqhYCGQZvMKJ/PeBBa9mpf6x2l3mYkk0qzptHFnJohKKkWrkrKixcKcL0ZHvZFgNMcbZ2eZXkwy4Y4IPCqNACx569IMu3pdKJZsldUqCdfHvShkMt69vsisL43bH0MuEfHQ9hZuTfl4fFczJnWZULzI+HwErUKOP1qgWBFKYMlcmfF5AVJ++voi5256WNtpIZsvkMzmUMjzhNIlztxY5LH7Wui/5q7+3v/Zy1xTU1P84R/+IefPn+fSpUt885vf5M6dO7/wwQwODqJQ3Euq+fjHP86///f/HgCHw8H3vvc9jh49yosvvkhXV9cvfAz/b+PpQz338EPeKwKplEt5Yk+7YG/bU3PPqkApl/KJ+zvI5oX68+kb3ioU+G7cTUrPHrtDPvdT9U8RVBPCa6eneHRnM/dvahA0tVbWYNGpiSTyvHpmlh8cH+WHx8aIp3J89sEePneop4pE+zcP9+AL/xRd5rTplmTIvZh0cqAirPquzFYnt1qbigc3NZLJl7gyEuRHxyeZcEd4+mAXF4YWqmN8fHcLx64s0LdEPLw1GaS7ycLhvgl62swkUzl84TTruh2cHfRSa9PiCSR5ZEfLkmGWk5ffHa++B/V2HUdOTXHqyhxmvYJam5pHd7TQN7CAzSinVIa+a25WtdkYn48xMhPmVx7uIZoukEgVefviPF31Jm5MBHn3+gJXhjwsa7IyMR/FH8lgNwhs5WSmQKEowmnRIpdIEUuKeAIJ0tk8D2xq4uqwj1lvmjqHoNG1qsNO/zU3gUiasze9jLsT/PGzN9jQLSTdnb0u7CYlPS1WvKEUW1Y6eePcNPf1OghE8hy/OEOjU49WJeOR7W30DbiJJLKE4xn+8Fc2MudLMDobYN4nABf++pVhkrkCd6YiSMRSinkp04sxSqUS47NJPMEEbS4jDouGOpueSFzQ8jKq5aSyFV7smySayOKNFZlaCPPQjibqHHr+/CeDPLGrncsjApR7VbuF9gYz18b8iEuwvNlEo0NDpiBYWBu1agYn/diNClQKGVa9ikCsQDSRIRhJ88qZSfR6OZWKmKNnZ7AvgSEGRvz0dtmRSsTotCqKpSKPbG/ljfMzrOt2MjDqpd5uwGpU8+N3Jmmwa2iuMy65dwYoVQoY1Aou3PRhM2oYmQ3T02xicDLIsXMzHNjcgFgqoVAQ88bZWQLhNAatgp5WG0qFnHQuRyqdY2WbnYERL3vX17N3fT3heAqXTcWMN04slSeTz1Fr1RFNZJArZCjlMjzBDGqVhEl3jEAkTWejmRsTIaLJTNV24pEdgmyMQioQXw06FbPeGApRhWQmh04ho6nGwEIgXhVi1aoV+CJpTDoZJr2GxVCWdEEQUF3Xace49ByhaJr13bWUyzDriXNnOkhXkwmpREK9Vcv10QBKuZRyRYFUVKaz0YjTrqlWJ3q77ERi6Q9tLvy5yeTOnTsMDQ3x27/929XbQ0NDDA4Ofig7k3+p8V5+yMEtTffcd2hby9+L+trV28Dju9rvgYyeuuomHMvw7LFhvvTNfkZmgozPR3nuxGhV/TOWzL4nedUTTRR4/u0xNEoZjTV6jvSPYzUq2LLSSTguIHnuJji1SlYd06Q7hlhUqCYATyBBNJnl4e2tnLq6wKQ7RlONjsf3tFc5NltX1lFAYLLfFdezmzQ88+YIdpMGUaXM7z29nlaXkb4BN6evexieCfHA1qYqsk0mFkyRXu6f5vqon8d3ttLq0lEoi/CFktiMGv70xzewGQUjMm8oyZkbC9WS1MpmCxt6nLxxYYY961x0NlqZcCfYsMyJWAT71zdyeciHTq1AKZdVJ+iB0QDNTh3ruu2s7LDxzpU5HrmvjfH5OMVKecmQq4H+gXlkIjH1FhW3J6OUyhU6GgzkCgW2rXKiUsp47q1RDmxuIJvNo1TIOHfLw+N72hgcD9BUoyWVrXDk1BRDk0HuW1NHLl9maDJM/4CbQCSLTKTgzfMzrGgxs39tLc1ODSJZkb0b6tm/oRGHWc3tmSgnLs9TZzMwPh9l20orv/e5Xp59a4x9G5o4fWOBWDLLgU2NSMVS0rkCO1a7iKUyTC7ESKRzjM5F2L+pCZNORf+AG7NOyeq2GqYWInQ02DBqFVwd9gnyOdfnkUgkrGmzsGuNi1NX5rEbNZQBmRjmA2mmF2I8uCRK+FL/NBaDGplUikopmGwd2tLChVs+VrfbCYSzvHle+IzKZWExpFHKWN1h5uu/spFCscDUfJR4OsvudXXk83myBSiVy1y45aHeoWdyIY5GJeGhHU30tNpQiRSMuqOIJWLmvTEObGnCvGSZfeKqG4VMyoWbHsSyAp8+0I5eq+RbL90imSly/paHNy/Ok8kLva1V7TYk4goWjYwKYlwOHaeuLiAViYilCsx6Y1iMSvLZIiq5iPu31BFNZml0GpHKJEx7EpRKJUrlCg0ONV98YjmzvjitLhOTnigtTh2nry9wdTjAqRseKiLwhhMY9XLUSkVViFUiFtE3sEihXMGkE4AmdxUI5AoZ0wshntglqEcrZQJn6P5N9by49P47zEqKZXh8ZzuLwRQVirxzZRGdSs4r/ZOY9Aoaa3SY9Qqc7+nD/qJDVKksGZr/jPjKV77CuXPn8Pv92O326v+lUil79uzht3/7tz+0gf2iw+12s2fPHk6ePInL5fpQrvH9N4Y4ddXN7nWu9ykH/7z4yckRUpkS/QPCYx/d2caXvtlPOJ5l8woHDQ495XKF+hodNpOK5S02vMEEapWcXL7Ib/3FWcLxLDUWDZ+8v4NpT4Lckq5XKlugp9nMl5/ewInLcwzPBHlybzuJZJFvPHeNf/dwDyJJhTqrlkyuhMWgopwvcGrQQzpb4vT1BfZtaOBTD3QTT+U4dXUWuVRMJl8inhLkIj62u501S06JPzx2h9uTQUF8Miagbh7d0cKKNitvXpjFpJHx7uAin97fzeBUkP4B4Ufz8PYWro94qHcY+IPvXyUcz2LWK/lvv7KReoeRoYkAr56dxGnV0T/g5rMHO5jzpfGEUqjkYnatrefVczNYDUoWAwke3t7K0FQQnVZOPFVckg1PoFJIeXh7M1tX1/HugJtKpUQqV0IhExNLCTbMT+5tRS4V01Zn5oVTY3Q1GlnZZufUgBupRMTZQQ9719fR6DQwPBNh0h3hozvbaK030X/NTZNTx9GzUzitOq4O+/jyZ9Zy6Y4PrVJKPFWkVKmQzRboaDLi9qeEH7pRwdRcDK1KxuCkADLJ5wuY9Cr8kRTt9UbyxTKtTiPnbnmosylJpMusX2ZjZDaC06LhubfG+MKjPaiVCv7789d4al87pVKZV89M8+n7OxmaidDdYKJCGZ1GQSieJZ8vM+NNEIymeHhbK3qdgv5rbs7cWGRNh42h6RBP7WtDpZDzJ89dZ2OPg2gyy/JmK6FEjnqrglJFhEYtQ6cSDLLEYilTC1E2r6hheCZGd7NQCrpwa5EndrZzbSLImjYr4USaOptQ11/fbWfam+TY+Vk+ua8Dp1XNpWE/6WwRsajCfWvrODfo4f6NTfz+dy4hEYt49L5m2hvMDE8HiKZKzCzGWAymWdlmocGhY3mLla9+9xK/9tHluP1xEIlJZ0voVBISmRJUKqxps6JVKzh6fkpQJY7kWNduRSQVUSpXKOTLKJVSxGIRapmUbLHEn/zoOttXOTHqlPQ0mxBLoP+ah456Awv+JO/eWORLj6/k+piPeoeOGrOOYDxFOlNELhcLCgZl+N7RYVa1W0mkc6zpsLOi1UoimWNgPMjMYoyuRgO7V9dxdTxMg13DlZEAm3psLAZTrGgyc/zqAv5Iil1r67l428OBLY2cG/TS22nFH0kTSxWwmZQ01ujJZEt0LMnTf1jxc3cmX/va1zh16hSf//znOXXqVPV4++23/0Ulkv8v4r08lLu7i/fGz6tVfmxPFwc3uPjar2zksw/23OOd0uAw8KkHllGuwI/eGiMYzvDM0SF++6/O81LfRFVkcl23jd/42Cp29jbw8f2dPLy9maf2dVRhyRduLTI8E8RqFPzC37kyy85eF6+dFWTj+wYW+fr3B3jj3AwOh5EHt7ZUS2kXbnv44bE7XBvxcX00yAsnJhm442dTt5XV7Tb++uXbvHVhCm8oyacfWMYXnljJkVNTzCxE2dBTww+Pj3L5jhe9RsqaDjvNTgOvn5/EpJPxax9dwcZldtQqGT1tdk7fWLgHLl3vMBJP5ehps7F/fWNVUmZkLs6DW5v4pQMdGHUqkvk86zotNNjVtNebOHdzAZ1WTlutkb29tXzxiVU8saedTK5IqQKRRAZfJMnt6TCrWswsazFycGM93/jVTdiNGl4+PYNYVmRDjwNfJMuVYS+nry/w9qU5DmxupL3exII/Sf+Am8HxEH/2wiDFYpmHtrfS0WCm1ipY7m7qqSGeLjA6GyGcyJPOFfGF0iASU2vSUS6DVimjVABvOMvwTBSTTk5zjYb1PTXIZWL0aiVtdUbKZQEAYNIrsJt1DM+EiCXzvHZ6hom5KJ880I5WIyOcyPDU/nZEwKw3wfIWq8CIt6o43CcQWE2qMpl0EbVKTDAq8J+ee3uUi7cXqbWqlhrMBT5/sJtZb5JFf4In97Zi0itortNTEQmcjhaXMBm/2DdFPHWXaCjnwOZm7BYZOo0Uq1GxBD6wY9AKK2+lUoLDqmN8PsbaLruQNMQCukuhkhJJZZn3Jbg1GcIbyhCOpdm3vpFXz0ywa0mCyGXVMz4fplCG1lotnz7QxUNbmzDqBLVnubzCJ/a3YdTK2drjpKVWz8XbXsxGDRPzUWptGrLFMjcmvNgtaqxGFZPuCGq1lAl3FG8wCZQ5dmEaTyCBN5pkMRBn60onZwY9GDQy8oUS2ZywEJRLReSKZZ4+2MH5Wx7q7Fom3Am+9r3LBCIZPME0Loue6yN+EmkBDTnvT9BYY+DyHS/BaBp3MMGKFhP/7uFu5DIZ6WKFYqmIP5bh3esLfPfoCGKxiGxFENMMRLIks0U0agUz7ghdDSZC8Sy1Nh2pdJ7x+QS/99eXOH/rn07a/ofiA/VMfvmXf5kbN24AAiHwy1/+MouLix/muP7Fxc/iodyNo2en3qfj9b/HsctuvvLXl6qKxO/1TglEUpy76aGnxYxSIan2OAYnAsx5ozx9qIfGGgPfeO4azxwdQimXYjKo2bWuntG5CCtarbx2ZopHd7TRP+DmyKkJ1nULHIeHtreglMnuEbEMRFL3JLSP7W3nxOV5XuqfZGOPnZ29LkwGBQq5glNX3Ri0csKJAn/8w6s8c3SIU5eFPonDquXUFcGd8sipSfZtaEKplGDWKyiVQKuUMj4fpW9ggV/++klGZyNIpRIC0RRf/kwvTx/qqZb7nj02zLoeZ9U75dJtL29dnKMskXDupod3Ls7T0WgkEMszPh/h4fvqSaSK/M8XbzEwGsJm0lTLe3s3NOAJJtnUVYPTquP7x0e5ORHh1lSQ+WB6SSPJwbPHJnAY1XiCSRod+iUotAK7WUUqW6RUKVdLhA/vaKJU/qmLXSCa4uCWJrzhJJPuKB/b3UEkkefsoAeNSobFIGcxkkAsBn84RSSRQy4Ts2VVLaeuLtBUZ8CiV3H8whwjsxH6r7kRiwXGeFOtnmA0zeO72vGHkzyxu4XuZgs1Jh2RRIFvvTyEw6hg3p9EKZegVkqwGVSolHLWdtrwR3O8eNaDO5hg1hPnke2tROI5ZjwJLHqhES2RiLCbVcQyBd44O8sPj4/R2WAmEMli1ql5+9Ic929qpFjOoZRL2bHahUahIJUp8dalOcSiIqKSgtdOz/CtI0PUWDSEEwWujHoFO+hyCYW0hM2owhdO47BqGZ4Ok0gVOXVlnrcvzbOizcLOtbU01xn4yalp8sU8Rp2KaCLD7352HU6HhuMX58lmS9yeivKDYyPU1+i4PRlELpcgQkRrnYkfnxgnW4RwLMfe9Q1cveOlrd6ISaNEoxTT1WghlSlxeyrCR3e1ky1WeLl/mqHpKLFUQbDIjuU4enaGeruaWouSf/+xFWTzRcqIeO6tUeE3oVORyxWZWEig08hoXFLelohFtDgNJNJ5KuIcH9nWyrw/zeBEgIObmygWS/zSwWWMzkVptOtIZvK8c2WRszcXCUYz6NQyYskCD25t4Km9HbgDGabmBbWBploDh0+OYzXI8EbyROJpcoUyr5+dYttK1z0Q+1lP9EOb/+ADJpPf+Z3f4eTJk9y8eZMf/OAH1NbW8pWvfOVDHdi/xPj7YL3/kI7X/65IvLhEgrzb5zh3c5FD25q4Phrg2PmZqi7UqjYbX/n2JYYmA9XHLwYTvNQ3zi9//SQXbi6yYZmDW5NBVrfbaGsws6vXRSCaJZvL0+Q08PalOc4vyU7ctfbULV3XblKxY3UtnmCSHWvqiCXztNQZEVXKOC1aji8hVR7b2Ur/gJu1HTb6Btys765heCZEo13DfWvq7gEmOCw6PvXAMj7/0DKWt9mZ9ye5fMdHKlvg1NV5ntrfyacf6Kar2Uo8leOdy3P3oOTu39jE6esLGLRyXHYNEpGIA5vr2b66jlSmxM2JADvX1BKMlAlEUxzY2MD1MT+vn5kE4PQ1NxdvLfDDY2MoVULfZ8tyoanZUKdnyhNjy4raqpCjSi2iocbA6UE3Oo2Udd0O/u6VIXK5AvetcHJgg4v/8Rs7iKeEcuPdZN7dZMUfzfKRba0cOTWFUiGiq0HPoW1NaNUyUtkS337pDhq5mHS+zLw/yfELc7x0apxPP9COJ5ji9bOTPLm3lc9/pItda+tZ9CdJpIpMuWO88u40b12cYXVbDTazdokJnWEhIHBV4qkSxy/MMeGOUy5XiKYySMUiVrbbiMRzQrNWIWVdZy1XRnwMjPixGZXUWg1EEnkS6SIapRyFVMKONXV0NBhw++Pcmgyx4E+yd30DC74kbl8aKmUGRryIxBVS6RxtLhNH+qaRyYR+nEQiwmEUAAsvnJjAYZSjUcuRihS89O4EhVKZKXeER3a0LvW3cnQ3WrgzI0zmhUKRp/a1EU0VaXCouX9TE+5AnBqLlkd3tDC5EOPSkJfrYwHevDDNU/uExva5wUX8sRTru+2EEmleeneKQqlAd6MRrUpGNJlhIZCiRAVPMMGGTqF8nM8JXJv2Oj0LgRRWgwqDWvB9D8cLHO6b5sZ4GINWwaunJ2hzmZh0RxDLiuxZ18DxC3NLTqYFHtzWyG8+tYpCqcy+DY3cnogyOhfCpJOzvMVKNpdDKpXw/IkxwvEcCrUEk1bFlWEf21bWEk/miKVK3JoMkMqWGRgLcPLKPC+cnEQiFjE0FWJ9twO1Us74fJRLw35UcjF1Vi3FSu6e37VaKftQ578PlEzm5+f5zd/8Tfr6+nj00Uf50pe+9K9Ogv6Dxs+C9f5DOl7vVSTe1eu6h6wYT+V4uX+K0dkIW1fW0uA04Akm+MJjK6sJ5EjfOLvXC6WuZU1WXj09RSpbIJkp3FN6i6dyPH2oh//2KxvZu7GFzz7Yw79/cg3t9SYkInhidxtNNTqUcinZfJHn3holkshz7Pwcp64KEjKxZIGtK2vJ5Eoo5FIGJwLoVfKlJJWpor+6miz85NQkKqXkZ7pTNjqNXLwtJLuNPTXVctxbF2erhM737o4e3dlC39V5vvI3F3hsSabkubfGGJ2NkEgVCUQz/OitUcGZ0q5nYMRHZ4OFhWCKGU+CXL7AnC/KxEKMaDLPQzta+MmpsXskSsoFBDjnRQG9tmddHbkcTLoj7FjjwhcW4Ll1VjXzgTS/93dXuD4WYmoh+r6d3Y41dazrrmExEOfJva1EYnkaHAZmFuOkM8Uq4a3eaeT6aIBkSph4x91xGmwGnn97gnaXgWS2yK2pMKPzYbaurKNvwI1CJl7a/aqJJbP4w0kObWvk5mQYpVyyxFERXtuhLQ1cHfEjlciwmdT4Q0kGRvyks4IfjEYO5TJsX13L47vb8EdTmHRynBYVtyYDhOJp1EoJj+5s45UzMxza2lRFwxWLFVwONTO+BK0uE55Qgu2r6phwR3h0RzOnrgXoatSzotXK5eElhnyrmVaXEZFEwp0ZP11NFua8SQ6fmkKMUOZav8xBKlvg8Z0tbOyuYctyJ+F4HotexfffHONbL91CLJbg80VZ1WGjq8lEb5dAMG2rNzK1GOOhHUK5C6Cn1cqtiRCdDSbiiQK+SI6Ltz3M+dO8c8WNWS2hrd7IlbEgR8/PoVQJIIRYuoBcJiESz9FcaySdLTLrT/LIfc28e32BaU9MkMsJJnh4WwsvnpyhIhZ4PHVWHW+enSWdKYFYzPPvjOGLJlnR4uCFE5MoZSL2rXOxst1B34CbeEowlEsmi7xyZpIndrdw/OIc9TV63jw/w2M7haqCCEGZ4Dc+tpK3Ls4hl0q4MuwjnRWkeB7b1YZCLqen1cLRs/O01+v4L5/fwNoOy4cq8ggfMJkUCgUAzp49y6ZNmyiVSqTTHx7E7P+2+HmIrrtx1yv+vX4oIEyou9cJmkidTQYu3vailMv5q5duVhNQZ6MJuVTCR7a18MaSdldPs5lbk4Gf6WVQ7zACwq7JbFDxwJZmPravk70bG9m1rgFPMAHAY7vaiCYz7FlfL2iBWTUEYxkuDnkplMqcvr7AxHyMPz88yGO72vjk/Z002LU4zBq29Fj56i9v5OP7u3+mh8KsN8rL/dNMzkfpaDBUVQJe6pu4Z/d2t9wnQsRL/ZPMeROYDGrOL8m7JDKFqidMV5OF0zfcePxxdq51cWMiyMCInzqrmkSmxNe+cxWHWUm+WKJULFJn0+MJJvjsg8v42J4uZr0xAQKtknFl2MfBLS28cGKMJ3a1k0wXMOnk7FhTx+N72qvXn/El6Ls2X32fD2xu4K2Lc3zpm/0cOTnO7vXN9LRaUSikvHx6glUdFjQqCa1OIytbzDgtGrascCCTiSmXynz5s73U2LVsXemk1WWiXBZIia+enuHUVUFL7dIdHzVWNb5wiuH5KCqZiGJRxIQ7gi8ikFED0SzbV1rJ5is8urONN8/PEEtkODPoZVevi3/zUBfRZJ43ryyQzRXpajRhMcsplsrYDAra6g08vL0Vtz+NSi7CoZOzqs3G25fneGh7E/s31WMyyhGLZEQTBW5OBOhssHG4b5wndrWjkis4fX2RcllEKJbhzfNzZDJ57ltTy4Xbfv7yhRvIZXL0ajEf29XKozubmfTE6HAZkYhAJpPwxz+8hkErR6WS4QtlWPDH+PSBdnq77By7MMM71xZ5/u0xzg562LjMwX/4uOBi+trpGWwmFbU2Pd9+6Q5vnZ9FIpGgVcnYs66BvgE3y5os9A24yeRKxDJUEZVz3gTPHRvBoFXy7vVFbk+GGJkPoVNL2brSyRtnZ8nmCuxYU0uhUCGXL+K06nj17BT71zfSUmtnfacVjazCgc0CgvEnS1L0mXQBJDn+7SPdRFMlvvK3lwhE0uzqdRFJ5Nm9zsWMN8redS7EYgkrWi3IZBUObK6nXKrw5N5WNnTb6aw34Q6lqgTg3evqmXRH0amlJJN5SuUyHn8Cg07FyStuzt708KfPD76PUP2Ljg+UTNauXcvBgwfJZrOsXbuWp59+mi1btnyoA/u/LT4I8/S9O5K7vgPZfJHLd3ysarOw4EtyYHMjg+OBqn7XN399Gwe3tHD8wiw/emu0yibfuaaOh7e13uNl4A0lqs//s/o4SrmUZ4/d4fUzMzx3fITzNxdoc5m4NOTlUwc6eWhHK2qFFINWiVQsZke1hFVfLWHt2dDIo7vauTAU5D9/+9L7XCnvRmONkV29LrasrMETSlex8I/c1/K+96pYLHP03ExVybhUKPHglibC8TRNdk21IatRSfiVR1exqsPBgj9ehQE/vqe9SvgLRnMMT4fxR3JcvuNlfVcNy5qt3JkO8qO3J3D74nxsXzv7NjSQyRbobjZTLJXJ5QvIZWI0KgkznmhVXubc4CI71tQzMhPi43vbaHDo3leaM+lUuP1xuhpNJFJ50rkyf3F4kFtTYWwmDW0NJkZnw8jlEt6+NM93Xx+iWCxh1ckRi4WG9u51LlRKOcMzIT7/kW7CsRwuuw6lXITTaqgmvUl3DJNOxm9+cg1SsYIfnxwjHBPEO3/0zjhbVjiQSEQ01piwGQXtMIlEzLg7xv86PIQnlCIUz3NjNEilXKa7xUi+BDdnhN1XJlukxWnEblChUyoYmgpz+voCaoWMYxemaXOZODO4gFiW49C2JqYXolgMKu7fWM/92+qRSqRLjn8Vzt1aJJkpc/TCHA6zmpf7pxmdi1Fr01Zr/aNzQfL5EguBBCcHFrCbtJy6KpQm7wptznkT/K+XbmHUKauK1RqFjJf6xwWfkZVOLt72IhYLDow7lzghO5dKtOdvCjvTvRvq2dVbx45VTorinCBtk8yxa6WFwYkgqWyeJ/e2srrdwbQngUYlQyaVMumO0Ooy8ac/vsEzR4dwWLWE0gKBdWevixlvkg6XgSaXgVgCXBZ99fVdvO2lpU7L//PJtbS6dKzrdlJj0eL2JaixqigVRSxrMiGXS1jWZkCplGI1KvEE00tir2qsegUf2drK9dEg0WSOYrGIUilYYWuUCk4u9S3fS6j+MOIDJZOvfOUrfPWrX+X5559HLBbz+c9/nt/93d/90Ab1rzXuJpC7nvF36+8PbG5CLBHz1qV53jg7Xe1D7FnXgM2kqZaDAtEsDpOS//ZvNzDrS3Kkf5z17/EyuFs+8oaS1T7Oq6eneO3MJL/89ZOMzIQYn49SLAu7jrurtzlvgmePjxKOZdi1roH9mxpxWtUEIml+9bEVfOqBZfe8Drc3fk/ZZ94X/Zmvt8GhRqsWrHnvqgRsW1X7vvPMBhX7N7pordPxqx9dzq71jTyys42da2q5PBpkeCbEFz66go/t6eKtSzMMz0b4wfFxhqdCbFvhpN7x0zKi1ajg8Z3tP31db43i80UpFwTW+vI2C4FwBom4Qo1Vg0QsIpXLE4jkcPuSHDs/x/eOjjLuDvPwjhbUShlikYjebjueSJYfvT1WNRr76K429BoFxWKZbK5AZ4MZnVpRnUgGJwLM+6M0OIw8sr21qqVGuYxEKuGFUxOYtXJWd1pZ32UnVyjwiX2dzHtTnB1cRCwWoZDLmVoMVyfx+9bWEU0V+fbLt5HJcqxss3F12M+mHiu/9vhK2hpMnLg8z8VbCwSi6SWiq7Pa83rj7CzjcxFWdtioteuriLNXTk+xb4OLB7c1o1ZJKZThlTOTpHMFDmxuoKPBiFmvxGKQ09NsoVSSolVI6Gy0cGsyiEopp5ATVb1q8sUSHQ0mpBIRPU16dEpBFbnvmptEKsejO5v5zU+swWnRcn08QGudgYNbmjgzuEBvl53ztxfpaTVXfwtP7GknXygRSxZIpXPM+wXyZjpb5OjZKR7d0cL0YpwJd4LWOi1fenwlG3usdDcYkcskyMQitEop7fUmWpt0zC1muDrs41MH2skUFEwtxKmxCG6n33tjiBanDoNGjohyVeXh7ve9XC6jVIhxB5PoNVI+ub+DcXeMUhFiyRxnb7qrxNDtq+r4zmsj/P/+4gx/+8owMomYWW+MUgVsBjXeSIpYqkg0madSkCGqlMjkypy4PM+5m4t0NRoJxjNMLITZ2GPHZddSQUw0KZScpz2x95j6NfzzOy1KJBL8fj9/8Ad/wG/91m8RiUT+WWVM/m+Muwnk6pDnffX3Q9ta+NxHeti3oUHQxlJKq2Wzu4rFVdLk1la84QznbnqIJgTi1Vf/3cZq+eyZo0N85dsXq1+wT9zfwYunJgjHs7xwYpT2emN1RXVjIvA+KXwQdjBSiZh0tkgkce9KJ57KMeOJ3lNeu1tWe28EIim+/+YYf/SDgSqZraXO8DPruoFIilK5QjCW41sv3ubZY8N4Q0mcdmGFp5RJ0GikXB3yEEnkuTnmZ1evi4VgmkxREJ98+lAPf/Crm9i3oYlXz07eM76CWMzJa25qbVoiiQInrszRYNdTKJdpqzUiEUs4NeDmzKCnuoPqbrLy2K52PrK9mf95eJDOBnO1TLIYTHBoaxMDIz6OnZ/GbFDR2WDj1NV5osksj+9u4b9+fiOr2mz83rcu8f03hljX46SrycTmlU72bmjk6rCP9ctqmPWl+aNnruH2x6tkTsGJU4BuX7y5SEutmVlvjFXtNmxGgZzY1WjmLw6PsrHbypoOGxeHgnzjh9cYmxFspA16wRFwywonr54R3o9ANMOTe1upc+j5xg8GBK0olxGxmKp45/ELs0zMh8nkC6xqs3P8whyxhOC/47RpOXJqij/98Q2uDAXQqOUc6RvnUwc6efP8DMPTQVYtedV89mAX8VSBWW8cm0XNxGIMq0HOp/Z3YDIoiCYKHD41gV4tmEm9dnaa66NBDFoFNRYlH9neQjZX5sJtL5+6v5MtK5zklnbxW1fVceGWF6NOwfmbHryhDHajiq5GEwuBFL5whndveDh1ZYHhuTAGrYyuejM3xoMcvziLQiQnksjxiX0d1Jh0qGQ5HtzSTLkorULBR2bDdDUZaakzcuG2556ep8OiY3Q6QiCSYVWLkY56E9fHAky4IwRiGXQaJXPeGJ8+0M3hvvF7vosOi47d65vZ31tHi0uPWiFnwZ+kUCpybcyDQqbgcN+YoEa+pg6DTolSLuNHb09QZ9NxeyrMy+9OolvyW3lkezOLwQTf/PVt7+tb/qLjA2WE73znO3z729+ms7OTnp4ennnmGb71rW99qAP7lxj/WN2bQCRVTSCH+8Z/6neyu4VCSYCbKuXSasL4+P4ulHJpVbH4bilJr1Ew643yypJPRjiepbHGQKPTeM91vCEBavzHX9zCrt6GKkCgt6uGTz2wDJFYxNVhH4e2NvP0oZ+NUPvxO2NMLsSY9sTJ5otVXk06W+DMoJdgNMVvPLmKQDRVfV/eaxlazgsN50yuiF4j5WvvSXjvjZ+cHGHSHSWVLt6rKaaU4fHH+cT+NprqjNyaCHO4b5ymGg0HtzagVkn4wkeXMzYbr5bbXHYDeo2COpuOOW+M//jJNTy1vxOXTY/drEarlAnKtZ023MEUU+4IWrWc0dkoD25r5Mm97WxeUcPXfmUjh7a1kM0Xq4n48ElhR7Ks2cSKFhuz3gQzngTj81FmvVEuDy/S0WBgfZeFUCzP2FLTXqWQYjWqCMcy6NQKwU0vV2B9twP4qWxOjU1X/Y78+OQY2SXBxp3rXBzpH6ehxsDfvXYHsaTMgc0NmPVyOhrM3JgIUyhV6B9w0+w08MPj4ySSWZY1GUEk5hvPXsNm1BCMpnhwazMd9eZ7FZv9Mfaua2Tf+joe3dnGfWtqSWSL6FQK3P44e9bXE0vl2LGmjuHpcHXFbTcrCUTTdDVZUCmlbFnp5M0L87TXG/iNJ1ezoacWvUbG47vbyOcqvHFujllviu++MYLVIDDaV7RYOHp+qrrYMGjldDdaSGfL3JwIcfLKPKVSmUKpyIwnRiKd5bH7Wnixb5zHd7XzyrvT7FnnYk2nnT/58Q0Qwb6NjZSW+iMHNjfgtGiZXkySyBZwmAWV4FxBIORmCyXC8TSZgoJUNodIVuChHU38ymMrWNNpQwaMzIZRKGRMuCP8xpOrePpQz5LL6iRX7/hJ5UCvU/DUvg48wQyvnZ7h0m0PjTUGfnh8mDaXCU8wwW9/ei1PH+rBG0oCcGs6RCqbxxtKIpGAw6iiq8GKQpbDadUhFsPVYT+DYwE66jU8uK2ROV8cuVzM6g4b10b9dDSYefatMZprjVWE5ocZHyiZvPLKKzz33HM8/fTT/NIv/RLPPvssr7322oc9tn9R8UG4JH9f3CUeCiteC08f6uGbv76NUCzPl//q/D3y9ne3qbPeexFEbn8MEHoRq9rs1QnhyrCvOtG/9zpbVzqrZa/3AgTiKUHSe86b4AdvjhBP5VCr7oUUKuVSPnl/J7vX1TM+H+HHb4/yH/7sND9+Z5grd7z0tJqxGjX83et3WNthrya+3/qLs7zy7hjPHB0ili0xMhPikR3NXB7ygYj3RSCSIpUpcfTcNGqVtNqj+ezBTt69NkcyW6Sz3kL/exrwBq2SRKrEG2dnsRo197xHs55otQdl0Ch49/pi9TN7dGc7L/VP8Il97cRTRX5yYhyDRsn5W4vcmgqSy5eRimFkNsKRkxOcG3Tfi9Tb2Y5aJeHJve1cX2r8h+NZBkb8OMxa1q+wshDMEIwXq4CBh3Y0sXF5DT85Mc7p6wscPjmON5Tivz9/g6f2d9JcIxiptdUbkFGpfnar2mzcv7mF8zc96JUKGmoMzHlj/M7Ta7gzFWVZi4m3L81zbnARKhXkUlG15PHgtkZ0WiW//3dX0Guk/P6/EZL4Lz+6CoNWQVuDmQObG3hgSxOP7mzm7Stuvva9S8z7U5y/5eHCbS8bu4XdTJ1dz5kbi4RiWcQisJqUuKxqnj64jBqLjmujAawGOWqZBL1Gyqfu7xCUa8UwMhPk7KCHeCLH8++McXBLE4PjQbqbzLx+dooHtzVi0MnRaZTkcgX+8+c38NCOViqU6VuC/e5YU8cn97fji+T48xcGGZmJUKbC+m47OrWYrSudNNb+tD9x/MIc5XIZsRh+6VAncpmUM4MLDIz4OTu4gF4jI5bMUVnyWblwc5FbUxGksiITC3F8wTzxVJETV+ZotBvIVmAxmLmHuBpP5ZbKsvVYjGr+9PkbXBryIpeKaHEZ2LGmDleNnivDPoxagTNT79Dzo7dHefbYHf7u1dtcG/YyH8hw+MQEaqUUm1HNsQuzlMtlRudzbOyysmNVLVtWOqm1qpGKFShlUi4N+ehutCCqQE+zmdVtFr7w0R50avk/em76fxMfuFal1Wqrt3U63T/az+T/xvggXJJ/KO4mkLur80Kp/PfK2z9zdIjnjo/cs7V22Q3V52qu0fHQ9mYaanRsWObgP/zZaZ49Nkw4lmF4aQIfngndw9K/2/TOZAtVOO6BzY2cvr7wM7+Im1Y4OX19gdVttuqEHY7nOXJqktSSzEpvp50fHBvl6h1P9bU0OPRVhEtXk4VXTk/T3WShocb4vvdEIhbTP+DGqBXIe6euzrN/gwuLXoXNKDRsXzg5ujTh6tm20kF3k4U5X5wHtzXijSTuKSHc3aE9sLmJSCLH5Ts+wvEsZ264OXJyjAaHgVi6UJ18zt30IJWKq77nbQ0mCoUyJr2Kv3lliO8fHaomYodNcBr8m1fuVBv/74WDS8sKJuajvPKuUFJSKaX0NFur13rj/AyP7mzFrFfymQe6eOGdUb7815eotSjZsqKGWKZI4D27vXSmwAObG3nrygx6jZTNPU4SqRKD40GCkSw71tRR79By7paXSFxAW31ifzt71zVycyLA7l4XZwY9SJbK1S/1TfDb//McN8d8ZHNlLt32UmNWc2M8wMo2G8cvzHL45Dhz3gSvnplgWZOVN8/P0N1kJpLII5dKePfaIouhNM+8eQdvKMHju9o5cmqKb78yhMMs+KT/9cu3uTUT5aX+CX75kR7uzETparIwMOLlY0vaZnajBoVUyvELc1y548OgU3Br0s/4XLjKiZJKBJn9tkZT9T184cQkK5vMdDabUMoUXBn2Me2OVb8DD+1oIpbMk04X8IQyhCM5THolj+1sxmRQM+eL89FdbRh0AipvR289fQNufJEiJq2SQrHCzYkA21Y5uTUdou/KPFajovr8u9f9FDH50d0dVSRhY62Gm5NhUumcMOZaDeu7Bf7X8hYrJy7Ps6e3nnBcWDCqVTKuDvt4cEsLJr2Ks4OL6DRKVEo5mXyeP37uBgOjghTR9lUuTl6dQ6uS0NkoGIjVWdX4Ijn+8sgtYql/+tz0QeMDJZO6ujq+//3vUygUKBQKPPPMM9TWvr9R+q81PgiX5IPEe/sFd7kn/7u8/ZxP2JFcuOVjMZi4px8CQmL70xdu8Pw7Y+zpdVV5Ju9cnkMqFbOu206uWBYmu/dwYsIxQabl//mf55BI4C9/ayeP7my7p1H/XiTI3ab/e/sqZr2cfRsauDrq58HNAh8hHM9y+NR4lbn+Ur8ghzHujtPoUL9v/O+Nu6oCd5uIUomYzkYzh/vGCUTT7Ox14XJo6WoyUGvV8Wcv3OS1M1NcGvKxtsPGt1+6Q6VU5r98bgMPbW+p7h4lYhFffnpD9TP7xIEusrkiUqmIy0P+6utxWFS0ufRcuhOgrd6IN5hCrZL+VJRzQJDNUcqlVTvmTK6IWinhqX0d9+z2RLIi7fVGTDoVwWiKHWtq74Fu715XRyZXpLFGh0YlrX5umXyZZKbECyfHsRo1/NkLg6xtFxQNzgwuUGfVcX00QGOtjsOnxnlyTzuheBa1UkKtVctj97Vw+oYA4f6bV+4glQoy8acGBIVls0HF0HSwmuwjqRz914Tbi8EUH13iN8z7ktWxmvQqHtnZxgObGxmeCfO5Q928fXmOZqeBSDzDJ/Z1IpVLGHdHqmimuxL+zU4Db1yYoaPeiFopI5ktcnXYR41ZgJ1vXunkxkSAXLHIzl4Xa7vsyKUSmmqMVMpg0itRK8T82mPLaXbqq6jAuwsGlUaJL5Dh7E03+za4WNZiYetKO9/44ha2ragFyuzsrSedLfHquUna6gwY9Sphl6dR8sc/HODGqJ9am4aXl8ACb12cQSKFZDrH4zvbqiKnL/ZPIReLq4jJy3d81claKZfy5N5W2hvNzHpSHL8wx4XbfqYXY4jE0uoCrP+6m4d2NKFRSxkY8ZPJlXj97CSfOdjBuZsLvPLuJLU2LaVShVlPmM4GC71ddtJZQY7lzKAbh0WFxaAmEs+yGEr9/9s777Corvz/vxnaDAND71WaAjYEQSwgYkNQsSVmTaLJpm1M2WSzq6ZtNmX9mc1uYtlkk+9mo4mpxhZRjBUVRECsICBFygADAzMwwzADM8P9/XG516kwdDD39Tw+wnDLOefeOZ9zPhXe7jzadf2ns+VYGOM76LnJFHpN9EjR2NiI1157DdeuXQNBEJg+fTo++uijcSVQRiLRo6JLNWQPSyLrJDPmCiU4c7UOp/Nq6ISLe9OLcL6Aj8QoH72JeH/GHYgkZLbYedM9IZNrJ2s0lIxy3/Ei+HvY4av0YjrB4o4XZsPDmUxYePRiJRIivXE6rwbrkkK04mVqm9rg62YPUZucFk5U27/JuIMzebVYFOOHFfGBdPLKaSHOeGH1FOQWC8l66DrX1IW6NiXMDp0vRzlfjEeXhKCjsxuAGXb+eBNTglxwu6IZIokCwb72iJ7khpa2ThSUNGFTahj2avTv821JEEvkcOSR5Zfv8Vvxzpe5EEkUCJ/giOfXTIWdjTXqmqXIutEAa0tz2NtawtrCHJ1qNdhWlujsUmJ1onaROEGLlFYfUu2ubWyFSKyAtLML3i52UKq6ceYqH7mFAnCsLZA6h1R3UQk7qbbfrWnF6oRA/OuHm/Tnf94wA6rubuz4+hoWz/TDoQsV4Fhb4PnVk5FfIkSIrz0KK0W4XioEl2MBa0sWJge50M9h2ewAvPLJRXocPnppLnb+eB1BPo6Qd6pQVNmCqIluUCjVAAAWCLCtLdHRqaKvKZOr8M+X54HHtUZjSzvs7dj4NacKDc3tcHXiwJnHAcuchR9P38WCaC842XJgxiJQ1SDDzXIhpgW7wtOFi+J7zfBw4UImV8Pf3RZ3qsToVCqxMj6QzHJ8rRZ+7rb47y8l+H3qRBy+eA9xUzzQ3qGCSNKJ7m41ls8NRpCvA/hNbfBxs8f3p4oxyc8J1Y2k2lcsVZLODGHumDvVE9+eKsGGxaH4+MdbSJnti3aFGh5OHMi7uvHdybta78eBs3dRWNGM6DB3TA1ygUDcAVlHJ53kNLOAFAQyuRpn82uRNNMXjy8Lh1AsQ41AAjbbAp/+fBtJUd6QdKiQX9yIF9dNxe4DtzBjohsKK5sRN8UDVhYsdKm60dnZjWqBBPa21lg6yxdXiprQ3qFEk1iG9YtC0NzWBaVSidqmDsye7IX9v95BUrQfXBzY2H+yBGnxQfj8SBFiw93h4sDB4QsVdAJY6nkNJyYJEwq5XI7u7m5wucMbSTkcjIQwGSr2ZxTjdB6ZUHDV/GCwrSzoCZqC+vJoIhTL8NquLDJ6ODEYGTnVUKm7MXuKJx5bFgaVqltrIvn4j/G419CGXT/ehL2tFSInkrYWXSElkXXixY8y4erAxtoFwZg+0Z22gxgTaprnUu2m+rUifgIsWCwcyqzQ+vJSgtiYUKbOXzU/EHOmesGOa41tn2Zh0UwfNIo76Ukjv7gRaxcEI2qSOy3APJy5WD5vAg6eK8dTKyNQwW9Dq1SO1NmBCPZ3AkCqDykhxbOzgZeLHc7mVqKlvQtmMIO9rRWsLS1QWS/RGieqvZrt1hXapVXNuHC9AebmZrhW2oS50zz1sjJrPnc7GyscOFuGbU9EIedWI51deVmcH9yd7bA3vQj5xY1YEO1NZ2+OCXeHm4MNmlo76GtvXBaKcD9HdLNYdBwT1bbFsb5w4nEgFHeAZ2sJZx4HlfUSlNWK8fDCENQLZahvkZOZoZNCUMGXaC1q9mfcQZeyG2YsM1y+1YA3n4jCuYJ6XL7VgPULQ9BNEKRLuUSB+EhPTA1yho8bDx/uvwaOtQViJ3tA1tEJP3cepoa44LtTJQj2dUB7hxrOPGscyqzAa49MR36JEHOmeeDC9XrYWJujrLYNns42ZOr/Ww1ImR2AtPnBqGlsxVv/yYWrAxsbU8LQ0qbAV+nF9CJjWrAT3HqM7BIZWaL6n99exx8fnobW9k7crhChoKQJS2J94enMQatMCUc7NgorWyDvVGLlvCDs/Okmpga6wt+Di/AgJ9hyrHH88j10damREOWJrBuNYFubo0nUgTXzA3AqvwETPO1wJr8GE/2cIJN3wcmBDbWagKczFwJRBzycbHCnSoxJvrYI8nbGtbJGeDhy0ShuR2SIB8rrxXDi2cDKgoULN+rR1aXEmgV+OJMvRGYBH2sXBKKlrQvtHZ2wtbFG9q0GrE2cgLZ2FY5frupzsTZUmKTmEgqFePnllzF37lwkJiZi69ataGtrG+62/Sah8lHJFEoIxXLaXqG7qtAVJMB9AzuXbQmWObAohoxct7e1Bo9rrZeM0oZjif0nS+gyrk52ltjxwmyDUfhPrYxARJALPjtUiB9OlWrlE8svbjQaS6LZbsobLXGGL9KzqzBjImlXWD0/iJ6E07MqsePrPJRUtRgcF5FEgcOZlbDuOX6Cpz1O5/MxOdAZWx8nPWLmTvNEabUYf9p5ibb/rIwPxIp5Qfh8WxKCfOzRKpXD3o6D9/bm4+D5UuxNL4KdDQuxEe7IKWrGtn9fxv6MO7DlsiGVqZCeVQVphwrtcm2bypEL5bQ9i3pWuhmk+UIJiu+JQIDAxet1qBFIkXWzASvmTcDHf4ynXTap8dFUL+4/UQIuxxxLZ/mByzGHu7MdJLJOOk7GxZ50BS6vbUPenUZMDnCAuTlZZfO1R6ZD3N6FjPw6bPs0BwfPk0ZetZrA0ll+CPDk4YfTd2Fva43Dmffw4f5rKKsVIynKG5YWFgjw5NHX/s+hQiyK8aFT41wtakBZbSsm+jkgs4APtbob8s5uOjg061YdrCxYWBDtCz8PO6xbEIL6FgU+PXQbqXP9MT/KG97OHKxNCsXBzApcvlmHNYnB4FpboaCkEZbmpNPAybxqhE1wxO3yZnCsLcBvkiEiyAmLZ/khu0eVc/hCBQ6dL4OfuwMZ8+LvhCZRO1rbFVqxFsXVbbCyMkdLqwytsk40CEmbWsaVKlhamsORZ42wAEdMneAMsMwhknTh21/vImGaF6wtLWFtTbpIXy6sR4NYDusep5HaRglY5iyUVkto5wpHHhsNPcb5Q5kVZGXP/FpwrC1hz7XCyZwafH2iBCwWyHZO90B1kwIffncNbe0qyOQKtLar8fZ/c2FuZo5uNQFC3Q3HHqeEW+Xt9Hv487lKpMwJwPJ5gThfwIcth3QV/ulcmV6F1+HEJGGydetW+Pn54ciRI/jpp5/g6OjIJHocJihbRMQEJ9ojqD+Gs02pEfjwhdmwsbZCTqEAz62arOVfvjElAu//YRY2pkSAbWWBpGg/3CoX4qWHpmJlQiitotElyMdey2XUnCC9i/w87DAzzB1v/ifXYLoG3YjbdnkXnOw5SJrpDV93GzyRGgYqZEnRpUJBiQB+Hvb48UwpisqFeuOiGXzVpVSTFfs4VqSh1pZUhbW0KZBzWwBBiwwXrtdh92vz6ZUZ28oC14oFSJ0dSPeHKqDlwOXQUeEiiQJlta24UdZM26wahFJYssxo20HK7AAczqyALceSFnQHzpbBhmOpJbR9XHkI9XOEhxOH9kiLCXdHTqEAuw9c1xKcPK61lg0ucYYvFkb7IzHaBw8lTYKiSwUe15r28lJ2kRHdKbP98eKa6egkCBzPqsbe4yVgWZrT+n2RRAEHrjXKaltx4Xodvjt1F3uPl2DNgmBcvl1/P+4oKQRVjR3Yvu8qiqtFevETlPv58Zx7WBLni7qWdqTM9ce8SG/sP1mMxCgfxE1xh7+HPY5cqkREgBNmTfZAeW1bT14zLtrlamRcroaquxt3KoVInTsBnm62KChpwomcKqyYF4jDF+9BqeyGj6sdnO2scP5aPRxsLbBopi8m+jpAqe7u8QAT0jY9RZcKajUgEHXg64wynLtah6kai4wdm+egqKIFE7wdkHWzATKFCpMDnbFhySTMneYDO44FEiK9YGbBQiW/DRev10HQIsOhixUID7AHATN6LDML+LBkkwul1QmkXSmvkHQOUHcTsLZiIdDHCYtifNHW3gVrSzOkJQTiRrkQbbKungSYLDjYWCN+ui+4XGvafbucL0awjwu9WONwLPHTubsob2iDpzMH8dM9EezN1QohOJ5dhf87WoiU2QFol5NONIYqvA4nJqm5UlJScPz4ca3Pli1bhhMnTgxbw4aa8aTmAshJmHIX7WubqqlKSs+qRP4dAaoapAZVSIZUU6bYeiSyThw8X47LtxqwbHYAkmcHgG1lgdrGVrz5n1y6CNefNkTpqbUM2XpWJwbj4PlyWn1DRYuXVLXgxzOl8POwN6pyo65fL5TgwvV6lNe2YsYkN6TMIcfodlkjzl+rp0vEPrwwGO4adoxXPrmIhOleYJmz0CqVIy0+EJnXG1DOFyM2wh3NbV3ILOBjUYwv3BxtwG9qh0SmgJebLTq7uiFp78L8KG9EBLr2alPStCOdz78HgViBzi41ZHIVpoe6kMGRPf1cFOOLtUmhWs9B0aXCj6dLaZWUvS0bB86W4bnVk7H/ZCmeWh6GTw8V4XeLglDd2IHzBXw8vTICZXxy4n5saQjkXWo0t3XRXkgdCjXEEtKOtH5RKBKjfQGQQlYolqGjU4m3P8+l353tz8WCZWFOLzLSsyohaG5HgCcPQd4OeOfLXKTM9sfxy9WQKZR4dEkIfFx5+OpEMSJDXdGlIj3DzFlmmBfpDQ9nG/xwirRN7PzTbFy41oQaQRsmBznDwZaNokoR6oRShE9wRtbNBjy+bBKqGyRw5LHx05kyqNTdWL8oFBYsAh1dasgValTUteLhhRPh5WqLP+28hClBLgAIlNaIERPujnNX+fQ7mHH5HsIn2OPy7UZaFUipiVQqNRbN9MU9gQQNLR2QK9S4cL0Om9dOwZ17YpTzxQjxdcSdey1YtyAE0eGeAIDcQj6Kq9oQ4muPXy5VYlaEB64UCfDahii4OnK13lnqu9bY2IrsO0JcK21EsI8jgn24KOfLkF/ciOdWR6BVokB5XTsu32rAinn+aG7rglqlhpWVBc7m1+LVR6bju1OlmBXhAV930vliUoAz+f2MI1MaCcWyYU/uqIlJOxN3d3fU1NTQv+tWXmQYenhca5MSRGrW+6BclIvuiehIbU0PDkFLu1bcBZWrqy9Bkp5ViT/tvAQvZw6Son1w5EIFzuSR74OvuwOWxvnTWX8vXq8DoK2WOp1Xg9qmNq17t0rlWqu8zp6d16QAZ6ydr52egkrJT40LQArFbZ/mwNwcmBriip/OlNHuy1NC3OHUU6rU240sK0tBqfpyihoRM8kFDnYc/PW/+bDjWuDlhyOxfF4IlsX5YfvzcXg0ORyLZwXAhmOO1LmB4FhZoLRaBJ6tFT785hp+uVSB1LmBtOpH81lRSTQBUqh8d7oCjS0dCAtwwkMLQxAV5oG0BDJ1v0yhhEjSqeeC3dGT9dnVgY2wAKf7qq+TpdiwNAR8YTtmT/WEt5s9Pbb7TpQg2JuHVx+ZDntbG7S1q1BS1YJn0ybjl4tVyL/TiGkhzvjopblQdxN4dvtZ5BY2QNDSDldHrp53lJe7Ay1IqPfr6KV7aJPJoe5JcU7tYKaHuIBny8aVogakxQfi0o16ZN+sx4JoH8yd5gkuxxwqpYq+vjmL3DXNneKFwooWmLMIuDiwYcuxgrO9Nf64bgqc7dk4nVeLg+fKkRDpg5fWTcEvl+5B0qGEo60VnHhW8POwx/Z9V3E8uxLL505AQUkjLt6ow7xpXlrejBJZJ2aEukAiU0EiU9I7Tjsba7R3dMLcwhxv/zcPAMBvkmLmJFekxU+Au7MN8osbERHoBA8nMhZs94FbdLBwZb0UYQH2EIhktLv7lCAXOlCQemclMrL2y685Fbhc3IyjFyuRGOmD8wV8tLSSrt/PrYxAY4sc//2lBOYg8Mbj0fj5XCXEbR2Im+xF59k6cK4MYT33amhpx/pFobh8qwG2HEtUC9qwL70Ir3+Wg6MXynv9bg8lJgkTMzMzpKWl4aWXXsIrr7yCFStWoLW1Fc899xyee+65Ad14586d2L17N/27RCLBM888g+TkZGzYsAFCoVDvHIIgsGPHDixduhTLli1DQUHBgO49nuhtotedsLuUaqxLCgGXbYkQXwc9QWSuoaKZH+UDcxbLoPqMisIF7k8gghYZcouFyMip1lO9rZofrKeS01VL+brZa01SfjqTlqsjF9WCVgBARLCr0ZT8AMAX3s/9VSNox6Hz5XptejQ5HJvXTkF7hwqvfHIR+46TX3xRmxwbUyLw1Ipw2LCt6ev8crEKyi4lhGIZ3J3t6HuK2uS4dKMBDS3kqj1qkgfO5JFf6IPnyrV2ddT/GZfvYfvePGRcvgeAFGBzpnri+t1mFFeJ4erIBdvKAu1y0v3VmErTyZ6DdQsCaVsVNSbJcQHwdrUDx8oSJVUtqG+W0M91zlRPzIv0xcQAZ0wMcERFnRgbl0yEpQULi2N9EezjgP8dK0ZhZQuOXqxE3GQP3KuXYMuebHpy3JQaYdBlm1K/BfvaQ9rRjW9/rUCIrx2eSAmHDcccUZPccCzrHhJn+CK/WIDHl4bgjSdnwsHWClOCXMg4nKN3UFzVgvefi6UF1/nrfPxuaSjuNZCZGZJnBYBlBtytk+LA2bu06igmzAWVDVLMmuwBDxdbyLsIOPE49MIj62YDlGoVEiK9wWVb0u+en4cdnl87FYfOl6NF2ombd5vg62pLqxxFEjmS4ybQ1zl66R7cHLj46PsbkHQo4e/hgFXxgbC2tICHk04grKAVYmkXbLnsnuh2AdYvJL+DmosDatFXVCGEp6sdTvRk984tJrM43+WL4OrAxaXbAvx4phwx4e44W1CHwioR1iWFYMYkd3A1AncneDlg6Uxv7HhhNpbPC0GNQEIGMbpyMDPMA1m3GjAtxAWHMitGTKCYpExLTU1Famoq/Xt8fPyAbyiVSrF9+3YcP34cTz31FP35J598gujoaHzxxRc4cuQIPvjgA3zyySda5/7666+oqKjAiRMnUF1djWeeeQYZGRm/2QBKasKmVEnUbmZhjJ9BIeTqyIWjnSU2LZuEdkUXcosa9dRoumowagI5cLYMMye5YUaoK32OpgcTdYzmTujR5DCsiA+kV2abUiOwONYXXq48CMUyOoBSqVbp3VfzWF18XMmqhzfLhfBwsoHVJHMUlDTp+dGzWCx6ZXqjTAj0eDE9v2YK7ta2ooIvJiezHhfPU/l1eipAJ3sOZoa544fTZfj98jB8dqgI00JccbNMiFUajgMUii4VympbyaqF9hwk9giG8z07kHv1bfSYzZnqjahJ7hC3daCgtFlv/ABgRpgHtv37MkQSBYStMrz7LDkJk6oTOWZN9sJX6aWImuiCt56YCTdnUq3BtrIA28oC86Z5IrcnwG394hCcyq2FOcsMkvZOLI71QWhPtltqclwa5wcPZzs6wJOCUtmlzg1EbIQ77cbcoVDhhbVTcTyLVHMlRHrjdH41WQiqUoTr5SL4uNni554cVJkFfEwPdYW9LYd+J6obWmHGAj2Zt0gV8HC2xWeHChEW4IQaQRu2PDYDXBtLnMypgUrdDS6bBUtLcwhb1fR1V8UH4puTpZAplJgW7ILoMHc42XNAgADRTQYBO/MsETfZG1/8chvTQ12RHOeLKSFOqOnJJ5dZwEfCdB9cuMFH4gxvNIllOJFVCUtzAu5ONpAru+nj5kf5gG1pDkc7K0ja5aSRv6oFXi5cekwPnC3D7CmeOJ1Htru4ugV2HEvMnuqJW+VCxIa7w8qShZiIQOz4ugAqdTeSon2RUyjAS+umw8XJGv4eDqhvbEU31LBhmyMm3B02bHOYmZnB3dkOojY5rpU2Q9LRhU3JZL6vtUlBtKvzocwKLIkLGHbbiUlXX7VqFf3zjz/+iIcffnjANzx79iwCAgLwxBNPaH2emZmJb7/9FgApvN59910olUpYWt5P5XHhwgUsW7YMLBYLEyZMgJeXF65fv46ZM2dqXUsikUAikWh9JhAMfw3k0UB3wu6LtIRQCMUy2HGt8ez2s/QLvzDGD61Shdaqi5pYdAUU9XN6VqWWMJo9xVOvOJhuuyjh4OrIpbfp7/w+lo7xICdzb/h7OhgUJJT+eVNqBKoFrXj7c9JeEx7ghNlTPOnj0rMqcban/sf5Aj7WJgbjiyNFpCHazpq2G6yZPwHvPhMLG7YlPUGeL+AjZU4Are+mxuRY1j3MmeqJ7FukGictIdjgGBeUNIFjbYEgH9Ljjm1lgUUxZIRzQUkTzuTV0MK7S6mGpysPqa48vUVAfbOUDl6tb5bC1YGLtz/PRWKUD5bG+eP7M6WYO80Ti2N90dauxImcajKmJiUMk4OcoVR3w8eNh30neiaV85VYvzAEArEcdc0dcOJZ04kGKfuUh7Odng1N183Z1ZGLBdFkQGzkRFfY23Ho35141lizIBSCJtLbaHGMLzrkKkwKcEYFX4zXfjcdtU3tuFNJxgMtmxNECy7qWZmD3B3PnupJ2wDCA121jlF0EVi/eCIOni9FsDcPseEzEB7oClF7JxSdKlhamOOVTy7iudWTcSavFjfvCnuCXHm4U9WEsABn3K0RY3KQM9QqFvaeuIvwAAds2xgFRxtLSOVdOJNH2iaK7onw65UazJ3mBWd7K/C4Fli3IBg8Wwtk5NTAgkWgtFoCEN0IC3DGnoO36XZuWDIRFhYsrF0QhJa2Thy7VI2HFwZjWawvFkR743h2Na6XCtEqJXPVXS1uBIdtjvVJE3C7shmtN+VwsOOguKoFCZFeCPGxh0u4DZolHXDv8ei0sGAh2NcB3q5cHM+5hyAfR3g4chA1yY22G0p7VGzDSb9T//7www+DumFaWhqeeeYZmJuba33e1NQEV1fyhbGwsICtrS1EIpHeMZq2GldXV4NCYt++fUhKStL6t2HDhkG1eyyjOWGbkiOMUrPoRu1TUdyUeknTs8vQCpzS4WfkVGF/xh288slFHLlQppXQsTeoFDITfLRVXrqrYgpN+xBA5iFbEE26Qgf52NOCjGqbZs2XOdN8aA8rUWsHrVY6mHkP7s62WnnLKLUbNbaUui7I2xGbUiPw9z/EYVViiME2sq0ssCklDLGTPXDxOh+FZUKI2uSobJDqqbJ0+9MhV9LX2ZteRLsnr188ERuWTtIS9GZqNaYFuyLrZgMmT3BGo0hGC7EqgRSv7cpCQbEALJaKVoHNnuqJKSFkIserdxrBYgFBPSVnX10/HesXT9R7f3TdnKkUPBtTyGdnaWGOg+fLcaNMiKdWhGPDUrIcQXVjK9YuCESYvxOsLFngWJkhMtQNpbViWFmYwc7WGlYWZpDIOmmPv02pEXjv2VgsnBUALsca04NcsOXRKHqsJbJOrE4MplVwQrEMv1yswof7r2HHN9fo947LscLJHnXs/pOlWBTji0n+jlg80xteTjZokajQ3tGJ3y8PJ3Nt5ZGVNWuaZLhV0YysoiZalXngXBkiQ10QH+mNG2VCdCq7YWPFQliAMyZ4O5BR/T3/O9pxepJ3miPQm4cPX5gNlVqN8moxOFb3o95/PFMOjg2543DiWWFpnD8uXK/D9dImPJkahqybDZjgRZZ/WDTTH+cL+Fg40w+Xbwtwu1KMv36Zi8JKMf2u8LjWsGFbQNbRhZgwD1TwxbDnWsGxx27oyLOGsuv+uzVc9FtUmRrjmJGRge3bt2t9FhgYiL1795p8L90094bubSgV/saNG7V2UwC5M3mQBQqgnyPMmLqLwpBKbFNqBL0jMQS1G1m/KJRWbT26dCL+c6gQHGsLiKVdeG1XltFARk0PJwB0vMim1Ah6R2IIXfsQtRvbmBKBZbMDtLxWNNVuVM0XgJwAl88NhJM9By1SpZZaad/xIrouSsxkL6376u7++vKQmRzkjJNXqjApwBm7f76FlLn+mDzBAaF+9pC0KxEW4IgupZruT0FpI9Td3fTqf1GsH60Wk3Yo8ez2s3hp3XQtlVxGHh/lfDF+nxKGmxUtcHfiwtmegyAfe/x0poyOP/jopbnwd+/A1sej0NAshY8bKYAr69pwOpcPb1cbrIoPQkW9BIcuVKCqQQqOtQXMzO47EVC7Ds0yBBmX76GtRwhQTgRn8msREegCCwsWMq/Xw8/DHj+fu4nHloagXd6Nm+VNeGp5GE7l1+Obk2SKnaMXK1BaLcK6BSGYFupO52jrUKhws4xMmLlhyUS0yjoh7TGaL4rxg7+nA70AyC9uJG0aVhZoblXgeqkQcZM9kFMowOJYf6yMD8Le9CK8/p/cnnK+lpjo74AWSScyC/hYlxgMpVqFNQmBkMpVSM+uQtQkd7S0dWDOVC+Ym5vBxd4Kzy6PQItUDn5zB65k3MG8aR5YFOMLfpOULv+8av4EiKVKfPnLHfxhzRQ0ihWQdChx6UY95k33wqUb9VjYU0xO0aXCpRsNmBLojIUzfSGWduKns2QNonMF5K6aSm/v5sDBwwtD8dG31+kFRXKcHzhsK/C41liTGIpqQSv8PRzg6shBenYVfNxtkRjljTZZJ7wMlIEYavotTCZPnmzSccnJyUhOTjb5um5ubmhuboaHhwdUKhXa29vh4OCgdYy7u7uWYV4oFBr0KuPxeODx9FUkDzrGbBe6aE7oho4xJkg0hdUPp+/i821JtDCq4EvgyLOmJzJNVRGFrrpE13XYmCABDNuHAOip2iiM2Y6ofmv+XXP1LWi5jWBfRzjZc7A/oxitUjlWJUxAtxnLZFWiqyMX6xJDsPvnWxBJyJiX2AiyHoiiU4Vfr1SB3yRFfKQ36ZqqoYI7d5WP5XMDkRjlg7Z2shKfSKKAQCxDtaANT60IRzdB4JeLZGqYyUEuOJVbS9sJYsLdIRTLaduPqyMXC2ZOgKBFirAJLgBIoSpqk+Py7Qa0SjpQ2yTD0YuVkCmUWDF3AtQE8NOZMgjFcmxKjdASwtR7UFbbitIaMRJmeGFhjC+tNrxe2oTIiW5YmxiCD/dfg0iigK2NNfKKa+HnYQ+pvJu2jYilXagXkvXj//X9DXoBIhTLcKWwgXZvL68jA6Rze8aCWkwczixHtaANsREe+OZkKUTtnXBxYGPlPH+I25Xw97CDVNaplWGbbWWJn86UYV1iMMqkZFBjp1IFM5YZYEago1OJudM80alUo6pBiqVxlmiXqaHqJtAkkdM14Smb2LqkIGz9dw5cHdhYMz8I/t722Pbvy3B1YMPV3gafFdym3aLJYlsTsSg2AADowncHzpbhyeXhOJNfiylBLsju8cqaFuyExXF+uFHSBDcHa+SWNNEqyaVxfjidx8fpvBq8/NA03KpooYXqwtgA3K5oQXNrJw5n3sO6JMO76KHGJDVXfX09/e/5559HQ0MDxGJx3yf2g4SEBBw5cgQAcOLECURHR2vZSwDS8H/s2DGo1WpUV1ejqqoKU6ZMGdJ2jHf6cifed7xIy7upPxhSjWka22PC3Q2qigB9dYmgpV2vvG1fUBHiVBAmJdxU6m7UNEq1PNOoBIx99QeAXmYAKg9Yq1SOEF97nMqvx5ufXTFagtgQ0RGe94PK5gdDJOlCZV0bCkqakBjpQ2dBjgl3R7CPvd79N6VGYFViEO3i3dIqh39PzZLmNjl9bRYLtKrPz8MOro5cvQzUAGhbCIWTPQdt7QrMmeqD4z2eRVy2JaaFuGi5ZVOqI11bGFUu98K1eiyd5Y86oRQbFoWiqbUDP5y5iyZRO91GYWsHVvckjPy5xzvLiceGo50V1i247wZ+s1yIfcdJl9YQXwe678He9nBxYCM+0puuYKlSkTs5zbK0x7OqsHRWAKaGuuJMXi2u3xXidF4t2Cwzui1m6EZilA8q6ltx9iof4rYOWFqaw8WBA3cXO5zMqYFMrkT2zQaIJApU1IphbgGwrSxhZWkOuUKpVV7AkWeDdUkhELYq0CLtgpeLHdb2eOCdvVpDe6JxOeZ456lYWpBQUN/XhBm+WJcUgrpmKeb01CIyY7HgYMvBwcx7uFYuwuHMeyirFuGldVORGOWL2xVCrE4IgpMdG/nFjYgMdcU3J0tRVCFEWW0rLt2oH5FswRQm7UweeeQRNDU1gcvlgsViQSqVwtzcHI6Ojti5cydmzJgx6Ia8/PLL2Lp1K1JSUmBnZ4ePPvoIAGmwP3fuHD744AMsXboUt27dwooVKwAAH3zwAdhs9qDvPRLUNJLlWUeC3nYk1ISed6cRS2b5w8PZVu843TxgmsRHehtc8Wvmzfropbl6qiBddYmHsy0Wxfghp7ABq+cHmbzq1zyOqqtS29SOi9frwLOxMpjM0pSgTN3VN49rjeS4CZB1KvHdqXJ6ciUj9x36HCcAWDEvkN6d1Qql4LIt4GzPwfnrfFplxeVYwt3ZDhtTIjA/ypv03OkxvPu5O9DxMiyWGR5PDsfCmPvlBjR3frqqPt3x193Bidrk5I6mQ0kbutPiAzEt1J1um+6CQHPcqR3wynmkeikswBntnUp0KNTILRTA2tIcqxKCaI+8Y5fuG/pnTXbHe8/G0iot2klifjC+OFrUo/prwl82ROGZVVPoZyeRdcLFgYOD58vBZVtgQbQP8u400udTO1YqQwDVB3c3e2xKtUdilDfsbKyx45t8zJniCS7HCjMmuuLHM3fxzMoI3Ksn09VHBDhCpSZQUNKE2VN9UN0ogaJLCaVKBXtba8RN8UDObQHipnigS6nW2wVH9XjgyRRKrFsQhOfSIsAyN4c5i2XwnaFy76XODUR8pDedT+7cVT4eXjQRy+YEoLGFLLHcoVBj14FbeHhhMMICnHHoQgUmB9ohLT6QzoP28/kyhPg6wN2Ji0aRDLMme45IFLxJEfDbtm1DbGws0tLSAJAuutnZ2Vi/fj3++te/4sCBA8PdzkEzmhHwpiREHCn2HS9C3h0yISL1BdRMt6KretJEMwuw5t+oRJBU1PTu1+YbnWR1bSbGrmkquvfe8cIcbNmTDZW6GwkzfODuyOk1M3FfUcK/5lSgvkWhF43f2zgB+io96l7WVhawsjRHbmEDutXdYJmzkDDDF3vTi1AtaIO/BxmAmDLXHwuj/eFkz6HbeOBsCdTdgDkLWJc0ie5/X5kSFF0q2nNPMyPCtyfv4G5NK5bN9oOHsy38PR0gapPjva9yMTXIGbcqWvDWE7H0Tk33mSq6VPj5bBmt3qTsFJrvgZWlOVqlCmzZk02rggJ97OmMBFTfqGzLvb2fhvrRIVcabZ+hZKjA/e8jlXWg4I4Alwvr4e/JQ6iXA9yc2Dh3rZ4cmzl+qBP27M5s2fB0YeN6mQjF98QIm+CERxZPMvje6N7j2KVySDvUuHCNj5S5AVg6K0AvUwSV2PVMXo3W86QSuGqO73Orp9Aq5WBfezyVEoard5u13rnDmWU4cqFybCV6LCkpoQUJACxZsgSFhYUIDw+HUjn8XgLjGar+CLWyrekJzBstfFxtsW5BEN0mTRWTrpFbU/V0Nq+K9nDR/ZuhvFmmcCbX+DWNobtd1723h7Mtkmf7ISnaF3VNUjozsaGtPlX90Zj6Kj2rEvJOJbyc2Xj1kenwdGb3OU6AYQ+o9KxKvP5ZDgpKGgEA/zt2B//64Qb+d+wOnR1gYTRpeDdnmUEmV9PqSFdHLkRtcjqnVbtcDVGbHPszivHPbwv6LH4klXVizYJgvYwI9rZsVDVIIWztou1VTvYcTA9xRea1ekwPcaVtR58fvom71S161z2dV4Njl+5hfpQPble20OokzcJqpdVkPI+wVYHS2jZakGiOv0dPJuQbZUI8vSLc4PvZ3CqjVV9Rk9zQLG7X2knqYkiQCFracbNciPkzvFBR1wphazvmTPeBmyMXILpxo6IFf/3yKuxtLZE8yw9sS0s0CGWYEuQEaysWvNwc8MjiMLz2aJSeINH0YqQCPx9NDkeHXAm2lSUKShoRE+aBdpmS9uIzlNjVnGVGq6oPnS/FlcJ6vfFVqtRalTc93XhkvEpP3IpQLMORC5VjT82lUqlw9+5dhIaGAgDu3r2L7u5udHZ2QqUa/kaOZ/zcHbS23IYqCvaXgdZNUXSp8HVGCVTqbiyI9qU9Y6gvojEjt6hNjkMXKg16oxgLUDTUTs3V+pJZ/jh8Uf+avaGrquELJfBx5ende9lssnaKTKFE/HRvrYBGagUrFMu0hLyuswBlj3l1/XQ6AM2Jx0Z4oDN83R2wKMYPF67XYfncCXrt1lXp2XAscfRiJaaFuGBvejE6FCotRwkqO8CZnrgYtrU5Mi5XaxnkAWiln0mO86cnId0+akLVt7ld0YJNKWFIjPbT6h/l2q0ZI6Sp8pPIOtHQIoWLAxcf7L1K7872ZxQjp7ABq+ZPgIMtG93d3UidEwtXRy5WJwbDytKc3kX879gdfL4tSctLUHf8F8z0vl8w6hppYBZLu+BoZ0W/az5u9rTaz4lnDZ8BqI1zixowNdgVt3rqqrz5WS69kqfilpx4bPx8jpyI/TzsEDfZAx98dQ2LYvwwexqZy0z3mWfdrIGnEw+lNTWYO81PSxuxfvFEZN2sQ3SYO+SdKrpoHOVIkBjlg3v1behUKvHq+uk4lV+NxGjfnp2sJbhsC/xyqRpcjgVulgvxwR9m6ak665ulOJlTQ7+nCyK9kZYQSO9MRkLNZdIdXnvtNTz22GMICQlBd3c3qqur8dFHH2HXrl1YuHDhcLdx3LMpNQKJ0aQ+nGKgAsGY95IpaOq6fd1sDaqjDAkFJ3sOYsLdkXfnvjeKoXboxrto/j37Jl9rtb58biDiJnugTtiOx5dNRNLMgF7brjn53SpvQquUzOob4uuAR5PDte7d2aWi9fOOPGu60JeuampRjC99DU1BQgmcdUkh5BdbYzFA2UseTQ6DHdcKB8+Xg8Uy03sWujaYJ1PD8J/DhRBJFPjpTJmWJxxAviOUWqZRKEFnl1rPHdeQzel0Xg3CAxywKjEQvgbq21DR+CKJAnuPFyNuqhftOLEuKQQZOVV0aWdNlZLmin/lvCB8sPfq/aDSaDKppb2tFcRSJQ5n3kNilA8W9DxD6llQ79rjyZPQIVdqeQlSbr30Iktj0bVsdgCuFjcBAFhmoNU1b2yaiYaWdjycFITWdiUETW3wMLD7oN4XQNt+KGqT49D5StjbWuH3y8Pxr+9vaL2P7k62dIDo7KmeSM+6h+S4AKTODcSKeON2PXKcpfi/I8WYH+UDPw9tbURynB/SEoLwn0OFegs5AGRdmigvSDrU+NcPN7Aoxhdn8mpw9moN5k7zhFKlwuypnrSqVXPHRb23VHDr+QKyvklGbi0dXDsSKi7ARGGSkJCAX3/9FVevXoWFhQUiIyNhb2+PKVOmaNWGZzCM7iQ2UIHQ3zgSQxiLVNfE0JeGwzbH3KmeELcroOhS4VZ5E15cOxWn8qv12qHbztlTPPHz+XJ6gqcmSAc7MolfRKBLn+3WcnteEIqMnGraPbOuqQ3eGl8wO641XUr1SqEA65JCDcapONiRap6Z4R70ubrPiuqbpuGd6qNmTrDe3JABILjHO0kzIlnXVmPRE8jr7srTE0aAvoCiBP+h8+XIvF6HdYkhmDbRnT7e1ZGLEF8HONtzDO5cqHeBKpimGb+jSai/s9bE7+9B7sx83Lh0dU7NjAma118Y44cfT5fi64wSLftRtaAVm1IjtHaEE7x48HefhG4CkHeR2YZXxAfS6pqfz9/FRH9H5JU0o0bQBof5IfAwkG/22KUK3KuX0H2+n21ARbtjF1eLsCDaB1k3G7By3gRcvt2A/DsC+LrZwsvFDqdya7QmYh7Xmq4qqktHp1Jr17hkpre2A4CzHdyd7VDBb8PpvFrYcS3phZyiS4UZE90Q4ueEf/bEkGguAADg9Y1kho/kXuK/AHJBsjDGBywWC9v+fRkcawtYWZmjUSiBu4FsEkONSTaT7u5uHDhwAPv27cPnn3+Ob775BiqV6jcpSEzR6+serzmJidrkfeq4jTEUteb3ZxTjlU8uGqw9YoxqQSuOZ1Xju1N3cTyrGm3idni52GH3z7fg5WKn1w7ddjrZcxA10R23yoV4Ji2C9rDq7zhQbpS+Hjwt90xnB+1JmfLfL6wUIbknJ5GubcXK0hwHzpbB3tYKthwyx5ghWwjVN18dlUp/n4WrI1dLRaMrSPamF2klWwT03XEBQCrXfv8kHZ0o54vJWI0fbujZfx5NDscji0ONuos72XP6tHftTS+CBYvA335/P/njo8lhCPXh0Xr7VfMnACCrRWk+Syrzsab9aG96Ed7+PBd704vocVB0qWg7UvbNBjqj78mcaqxOJO09U4PdED/dBzWCNvh52OPDb6/p9fdIZjmuFjcaTJzp4WJH57ZytLXC0lg/pM4JQH4xmaNOJlcibppXT0ZtGY5crKQzVv90tgTZNxvw09kSvfHhN95PtEllWt6UGoEPnp+l5XDzaHI4dr82Hw8lTaTHmW1lAV83W9yrE9NjOSnAkbZxUUG3SnV3r4IEAH65VIE3PruCCn4bVsSTpaB/OlOGjNzaXs8bKkzy5vrHP/6BkpISPPLII+ju7saPP/6IoKAgvP766yPRxiFhKLy5+vLgMfW8waiqgIGryEz1ujJ0fU3vlIQob7z52RX6Opo63N6uo+txM5hxMOVcQ/3QbMOFa7V0Cd6lcX4wN2dpRVlrehEZG+/+PgtD3mOClnZs2ZOt4ZE22+DEYcwr8GZpo5Zdhzq/t7bpPgtqkaT7PgjFMnx68KZefRmqLavmT0DcZE9k5NQg+1YD5kz1xPkCvtZz0bSVzY/y1qqX8u4zsfD3dICiS0V7MW1YMhFNYrnWd0azL0XlQnzy003YcizRLlfib0/H0MlDX/8sB/OmecKcxcIpnfoy1YJW7E2/g9XzglBQ1ox79W2oapBCplBidUIgOlUEGnrcsjXHub6pDb/m8enkjktmetMR5VR9nNhwNyyODYCTPZt2WujPXEH1j9/YihtlIhy9WInfLQ5FYrSfSd6g5wtqsLdnl6jp1Uj9bshdf6gx6Vtw6dIlHDx4kA4inD9/PlasWDGuhMlgMZbOwxR07RC9ZfY1hYGep+t/b6j9xibpTakRWBDtjVvlInxzoljrOoYEiaF26t5vMONgyrlUdLvmCl+zDRP9HfG/Y3foaOzcQgFU6m7MnuKJFfFk3w2Nh6G08xR9xZ4Y+kJTOdGo8TQkSDSjuDWTYUpkncgvbsSK+ACwrSyh6CJtE70JW0MT3S8XKw1Ofq6OXKydH4IPv72mZTOh2nI48x4iQ91wvoAPJx6b/lxT9aerntNSmXk6aKXo0SzktjDm/lhojrObsw0ttDRLFLg6crE0zhcSmQqXrtdpqan2phch+1YDFkR7A+Ys2msuIdIbF67XYfpEN+z4ugAiiQLRYd3Y8VwsVD2pmtjWVj1VIm0wLdgZbLa2PZGyZXFtrLAxJWJAcwXVPxdHWxw4m0vbuEL8HbQyTjcIJfDUUVkpulT47te7dDbrTalhyL5Vp5XAcySKZJmk5iIIQisa3crKSi86/UFnoO6vmudrMlKlNDURislKbpMDnZBf3KiXkLEv1ZObky0OnC1Dzu1G1DdL8f5z+jUvesOQKmsw49DXuX1F+2smtnS0s8KiGD9YmLNgb2tN67N1x4MsFHYRObfr9K6nm7jRVBRdKpTzxXh0SSjK+WK9cRKKZWDr1KJh93xzeVxruDhyIJGp8NOZMkhlql6fo6hNTk90OYUNEIplfbo669aXoeqQBPva45mVEfD3JH9vlyvp43RVf5qFwixYBN56IgYWLAJCsUwrRY/ms9uy57LeszuTW4msmw1aglWzf0Fe9nQd+iMXKyEUy2hBLGiRoUOuwqFMMi/YnGleyCkUYE1iMMInuGBpnB+SZwdgzhQPnMyvwxuf5mBvehGc7DlYvzAIIf5O2PnjTfxySTuJ6saUCHz8x3jaHtTe0YmEnrojCZHeaJcp9J55fbPU4LugWTPmpYenwceVh0UxvnTxuRtlLXrvx89n72JZnC+WxPngz4/PQFiAE11bZd2CYCTHjaEa8JMmTcLf//531NTUoKamBn//+99pN+HfErrpPMYbro5czAxzR2GlCDPD3PVWK33ZATT/PjXYTc+O0BumZDMeSoxlu9VlU2oEdrwwG48sDtN7vlR/wyc44tUNkQCAjJwqRIa64j+HCrE/4w59nb4m5N5gW1lg1mQv7P/1LmZN9tIadyoW40ZFM8IC7PHXJ2MQFmAPd42xnzPVSysFilTWafA5pmdV4q0vcrAg2gd+HnaYGUbWJTl5pUpvoaTb/k2pEdj+fBy9eNiUGoHpwa744mgR9h0vojP+bkqN6DWdj5M9B2rCDO99lQdvVy7kXUq9thp7dkKxDF5uPLqwlBOPjZXxgXT/RG1yfH+aTNcSH+mJNzZG61WP9HTmwseNB3OWGV3j/eD5ckhknVCryfxfPu48LWHFb2rDlFA3eozL+WK6kBuFDef+4trLlQdOj22GwzaHl87OncoIrWnv0RQQqXMDMT3EFbt+vInC8iYIRGSWa5lCibLaVq3vkVAsQ0NLOxTKbly53YR/fH0N9xpIG468UwVhmwIctpXxl28IMclm0t7ejvfffx+XLl1Cd3c35s6dizfffBP29obVG2OR8VYDfjjpK+q7LztAf+0ExiKwhxtDUegDQdNeFORjj/8cKuxRh7jiyRURtIePrvqoL5WXLrrjSkU+a+q9FV1Kg0LckF5d83qaz8DPww5vPRmjpVPf/dp8AOROx5AaTLdtlK2AOv/jP8b36iEIaKsARW1y/HKpUitKvLcaKtT5eYV1qBXKtWqda7LveBGUSjUsLMz1xqO6oRX+ng44daUKtY1SWFiY49xVMvvCivhA2p4YN8Vdz25CjXE5X4xgH0etv+mqFA09N+r7Vt8spYudOfHY+H/Px2kVRqNS3VBj+9qjM1AjkEIs6URLm5z28tL8Ht2tasGZAj6dCNPPww5LZ/nClm2FdoUSy+cZrrsz1PT6jV6+fLnW705OTgDIiPhHH30Ux44dG76WMQwbfelPTU2QaCpkcaj7wZCDESSmTtD7M4pxo4z0HpszbeCLB83gutN5tVgyyx+LYnxRUdcKfw97vPnZFXpS0bSNGTPA9tZ+3XHp7ib6zJNFsToxWC/wUvN6mq7VyXEBWnEqmmpbQ/p+Q+lajKWmN4bueEjlnVrjOm+6l1bWaEOu0Z1dKnxzsgxRE13wl0dnwMFOfxw1gw+pncXCmd64USbS6oOiS4WL1/iICXeHmyNHK2DXz92+xy1c2x6oWZCNuvbiWb567vq6MTSaz0QzHiQxygeO9jZ652uObUtrB+xsLODnZgsV0Y2Zim69rOChAc4ouNtEuz3HTfbE8nkh/V7MDJZedyZ5eXm9nhwTEzPkDRoumJ3J6KHoUuHFjzLB5VhAJldh92vzByRQvj9VgpIqESYFGM+JBPQvV5gpGFr11za1aXm0bX8+jjYEG7t/fzx8qHuuiA9A/PTeBUl/rtuXh53u9dYmhfS6qxS1yWFh0Xt6fkPjIW+XI8NAmWRNdJ0n6htbkVUogEyuJuOVonyw0cB5v+ZUwNmeAxceWZFwcrCbXh8AGOwXtXvpDd33wZizQ28agHqhhH5fjJ2v2X/qWrrBmNTzzLrJhwPXEmrCDNNCDATfjAC9fqPHk7BgGLuwrSywMj6wzzorFMZceoViOR2o2Nuqy1hamIGwP6MYxVUteGHdVMwMv18W2IrF0lphapYYNnT//nj4CFra6VX7LxerMGeql8HjgP57GeqOq5Wlud4xut6HG5ZMRHldG4K97fXOP3G5qk9BpjkeT6ROwqHz5ThfwMfGZaG0a7AuhtRcXu4O8G/uwKc/3ybtKQV8LJ+nvXsRtLSjXd7Vk5yTLJ8bHe5lsM7PuqQQ5N8R0Fl1dYWEMXWubgE5Y56FvS0ANN8XzfM1BZBm/ZhOpUpP6KRnVeLSDT7iJnvh8IWKEVcj69Lvsr0MDAOhrzorFMYM9VaW5lqBioYmQeB+vERvzhKmlhWmJuo798TYc+AWfe296UX4y78vw83RGh/8YZbBVbXu/fvjDdhb+eS+El32dl3Nc4ViWa9OEZrXaRLLkVsoQJNY24mhP04H1HhM8LanBeW+E3cNHtub80RshBcWRGnXftFEqVIhKtRTyyGhuqFV7/37/lQJHWei7ia0ErJm32rAofNlemOjOX667tuDnbwpYaabeDTndj2+P1WK/ztShLNXa5Ac54eWNjkksk4yu8RkL7oeDRk8GjQqggRghAnDCGLKjsSYS6spEee6rrnGorl7yxSsiaGJur5ZSvv9l/OleOOzK0Y91HTvb24OxIS7w9ywHNRiU2oEPnxhNtYvnkh/ZmzyN8XLkDr3/NUa7E0vwp4DN03KQNCbwOBxrbWKeun2V9eDjse11vKsouJMdDFUrIxi3/Ei3Cgn683rOlUoulQ4eqES7fJOg/fQrIsik3fiavH9KHk3x/sCPGV2AI5e1M64qzv2pmRsMHXRQh2r6UFGuWxzORbILOCjUSRHTLg7ZAoyZ9vxbLKC4uXCesyZ6oniKhHS4gORljAyxnZDjI4IYxgUA42AH+v0VXa4t0BFY+oeXb1zb5mCDaGr8qEMqPfq27RSdvQVQKmb1TUh0ltL1WGI/GIhPRYLY/x6zcvW146EOrejU0ULw96yDWte15jKUNGlQt4dMm5JLJFr5a7qzZNuU2oEHXRpDEMGeM0dy39/uYOIQBetv7OtLBDg5UDmKUuYYPQePK412FaWdK40qv+bUiOwcKY3eLYcWFiw6LEHQI/fpRt8qNTdONxLjRyAzNasmYi0L9Q9KeUp77bcokYcvViJh5OCMD/KBxxrc4gknbTH1smcaux+bT79HqTMCYDdCBrbDcHsTMY4uqqDkY7XGGn6Uof1Nelp7iJ0gxYpLxtDZYV7Q3ei3pQagedXTdZaPfcl3CkhROdv6kOQ6O7SAPS5MzO2Wtbc1dlYWyAxiizz68SzpsfaUAArhbGdD5UDTalWI8jHHnWN7Th2qcykGJ++jNwAYGGhPT31tmOhSJ0biKfSpsLd3dHoPfhCCU7n1eLijTr4e9ghMtSZ/lvmtQa8+FEmpB1d9NhoBhLGTfbCYQM1cnRViKKeOvEiSWefO5S96UXY8mkO7LgW+OiluVibFIoDZ8sgaJHhx7MVWJMYjPipHnDiWSG+JxiSesep9yC3qHHU5wWT4kweBMajN5eul85oxWuMJyjDfG9xEL152Zi665PIOvGnnZdoD7V/vjzPJEO/Kd5CFH2lcunrWF00z9X0DvrhVKlJ3knGaGxqwwmN3FXLYnxwMp8/oBgfqmRxbx5qul5eA8GQh55ujjRdL0ChWIZ3/puLyFBXugbPY8nheuPV2/eU6h8FXyjBG5/m6OW5683DS9d7jrqfTKFExAQnbNsUwxjgGe5jLIPtYLMGP+hQX7LeVrHGBEl/dn08rjUSIr0hlpCpM0wRJHvTi/D2F7km2WsAw7s0YzsSU+wfmueq1N1Iz6rE2fwaLdWfoKW939mc1SyWVgp2tZmZXooRU6Aiw8/kVvVq2B+sIAHI3eXf/zCLtkntO16E9/6XR+8eDTkzuDpysTjGD7lFjXDkWePi9XraEK45Xsa+p4Yi331ceVo7ViquxdgO3cmeo9UuQUs72FYWWL8olE65cvEav9/ZzYcCZjYao/C41lga50/HVQxVkkhT6CtCfrTor63IkN69t2v3t1aMoUJixhiIvQYwzUuoL1uTLnvTi9DWrsC10mZwrC20EgJ6ONsavFZv74RuIB6lwjM07saeIeXYIJIocPhiJRbG+OJMXu2AXLtNDdbLvdOEoxcq8dLD02i1HACjWZsBYGWPgftQj82EKqKmO16631PN/p0v4GNxrC89TlQdEt2EqaY8R81qjj+cvguVuhv8pna8+FFmv7ObD5ZREyY7d+4Ei8XCiy++CACoqKjAW2+9BZlMBjabjXfeeQdhYdoDoVQqERsbC19fX/qzQ4cOwdwU95hxiJ2NlV7xJmB4k0Saku56NBhounpTV7H9nZApTJ3oeouKHiiaE7OpiwxqUluXGAwzMxYKSppgbga8/1wsnaZF91p9vRNCsQybUiO0JkhD9PYMNQXSzDB3PJYcjpVGqhv2tqig1GOPLQ3FxAlOBotZAWTdk6M9Rbf2nyyhC2Utjum9ABVACpQlPXVyAONjr/m7MYFLYUoJB000Y5GybzXAiWeN2AgP+Lpz8fO5igFlNx8sI67mkkqleP311/G///1P6/M333wTTz/9NI4ePYo//vGP2LJli965paWliIyMxNGjR+l/D6ogGUjxqMFiyD1xLDBSY2FqLIwmgpZ2k4/dlBqBj16aOyghTRmzDankTBGA1KR26VYdXayLxTIzWPwL6Pud0HS17k2QmPIMdZNJGiuRYEwVSamGAzxsUSvswJufXTGoUqxtatOKzZg3zRsbUyKQOicAhzIrelVzUuPfm+Awhm7/eqMvlatmLFLK7AD8mlsDMzOyHZSRPj7SG50jMG9QjLgwOXv2LAICAvDEE09ofb5u3TrEx8cDACZOnIiGhga9c2/fvg2RSISHHnoIDz30kNF0LxKJBHw+X+ufQCAY+s4MI6NhHxmot5MxhkpvO5Jj0Z9rG6qO2BeDGVPKO+3ohfIBCVdqItyUGoGXH56OR5PD8eJD03p1Xe3tnejP4sPUZ6gpkHTfn74EEuXRtyYpBJdvNcCJx0b2rQbUNrZqHefrZq8Vm7E6MYQsw5xZAY61BczMDHvG9VXSwBT68uIzpZ/UOFMZr9PmB2NJrD+uFApw8HwFXU3Shm0+ourqUfPm2r17NwDQai5N3nnnHXR2dmL79u1an//www9obm7G5s2bUVxcjKeffhrHjh2jE1BqXnvPnj0G7zuevLmA0YkpGQqbyUCrUvbGWIqvMbU64lCh6Z3m4cylV9Gmqv104z6GokIk0H+1qKn3Nfb+mKLulMg66ZQtvbWrtrFVa0d2/moNqgRSrYqSFKI2Obb8O5uu7rhj85w+VaiD+R4Z67+x7NB/2nmR9jJbNX8C5kz1HnG757B9MzMyMvSEQWBgIPbu3Wv0HIIg8OGHH+LmzZv4+uuv9f6+fv16+ufw8HBMnToV165dw8KFC7WO27hxI1atWqX1mUAgwIYNGwbQk9HF0BdvKFwje2ModiQDrUrZGyMtSHoz5JpSHXEo0cwkO3eap57evjc04z6ybjbA08kGovZOuNpzkBTjb9L9jb0Tm1IjTHYmAEx7hr29P6bYhs7l12rtmNYvnmjweF3V3uQgZ+w9XmzQScKGY6lV3dHQ90/T7XcwtkdFlwoXrtfBkWeNC9frsLZnJ2fMiYNtZYG1C4Lx3a934cizxvHsGiyN63858MEybN/O5ORkJCcnm3y8SqXCli1b0NjYiK+//hp2dvpfziNHjmDGjBnw8yMrh+lWgKTg8Xjg8freTo5HhqpGx1BgbJU5lIkWRwtTdlabUiOweJYvvFxG5l3T9U4zVbhqCqJ1iUGoa+mgY0KGYrc31Cvgwbw/gpZ22h5ys0yItITAXvunuTAz5iSRnlWJ/DsCupaIIQGlKTxS5gQYnPRN3akYS4zamxNHYpQfpB1KHDxXPmphA2NDZwBgx44daG9vx//+9z9YWRmuDFZaWoobN27gnXfeQWVlJYqLixEVFTXCLR09dCOLTXV7HQ76Ujf0x212rGHqzmqgHmaDYaDPe2NKBJbM8oOVhQW+7VGXZRbwsXJe75PtaGHs/elrzD2cbZEQ6YWqBgnWJAZhRbzxXFWGFma6Oy3KftFb+hldt9/kWF+9Sb+/OxXdHRi1S+5tJ7hiXhAWx/r/thM9ikQifPvtt7h37x7WrVuHlStXYuXKlQBIG8cbb7wBANi8eTNEIhFSU1Px8ssvY8eOHbC1tR3Npo8opqSTGAlM9a4aCUEyHJ5dpmTiHQ1vu8GwP6MYW/ZcxvlrtWPiHTIF3XHXHPOzV2uMGvwd7Ngoq20Di2V8eust5YtugbF1SSHgsi0R4utg0NtPN1WOuytPy3NvoF6SlFDQTWBK7XJ6O2c0YNKpjEOG22ZiCqOxKh/pNvQV/DbaY2BqcJ6h4lQqVfeov0MDIT2rEmev1mBasCvOF+ire/uTyqQ/KmNT1IH8pjaD8SLAwG0ohp4d5VywNM4PKXPGzu6fESYMA2Y0vavGSp6y0RqDIxfKYGlhDqVKjbSEkD6PH6x33VjypBOKZfj7vnxMD3bFjXIh3noiVkswGhLyxibzoVqYmbKwGKh3l+azWzLLD6/tyoJK3Y01iYFw4nEg7egasTrvvTE23g6GccloTi4DjVgfjnaMNBJZJ8TSLtqIbsoOZTA2rNHagRkTYK6OXEwLdsU5I55VpqYykcg6h0SQmJqKx9WRO6C67LrPLjHKBxw2C20yFY5eLDb5HRhuxoTNhIFhIAwkYv1BoLNLpZVY0dQo54FMNqNlG+otAlzUJtcSDoZS3BtKZaKZ/n9/RjG2fZqNs/nVg26rqQGZurYPQ/CFEvpn3UJkFJtSIxA3xVPrHehQdBm8Xn8yNAwWZmfCMK7pbWcwFlZrw8Fw5PkyxmjsAPta6Wu6OpvqRKCZO0wi60ROYQMiQ13x9YkS1DfL8Fhy+KDel77iX0zxEKRUcUvj/KBWo1e1pJ+7Q59xTiOdZ4+xmTA8kAxHBP5YYzizO+tee6RtJqao1gZj7zibX42vT5RAJFHAz8MOsyZ70FmKh+t96e2d1Kxrkjw7gK6oaKiuiiaCFqlBQTLSGRoAZmfC8AAyXBH4Y43hEiSGVrQjbRsyJdJ9MPaOpJn+4De149xVPlYnBOLrjNJhf196s1v5uPKQMtcf3d0A24plsPyEIYwJiJHO0AAwNhOGcYYpySNNiRP5rWPIzgAMLnP0UNtT+hJgg7mfokuFJrEMr66fjrLaVjr2ZmV8wIAz7Zr6bhpDrlAj43I1ZHIVXX7CzsZwALcpUIkgR6qUBCNMGMYNphgwKYzVLWfoPfvtQDNH96dK5VAw2PuxrSzg6sDFv364AQ9nW+TdacS8aV5ok6nolPr9oT/vpiE0gyjv1rTqOT0M1JA+EjsSCkaYMIwLDJUx7gtmR6JPb5HfFP2tuzLSHl9DcT9Nd+Hjl6uwcKYvPF25tIeUsV2ZofEy9m72p12a2S1C/Rxo77Anl4fjZE4lSqpEOHLhbr/7OZIwwoRhXMCoroYGU1Py9MceM9K1d4bifpruwnOmemLV/BAkzfTrdVdmbEdn6N0cyM5pY0oEPv5jPDYsDafd3if5O0IsVeKr9GKIpUo0tkj73deRgvHmYhhXPKjuviPNcKTkGWmPr6G4X71QolewypCXnGY9GSceGx//MV5v/Kh3cyizM4yGV9ZAYXYmDOMKRpAMDcORl2ukPb6G4n6GKh8a2pWZsqOj3s2h3KlplucdKa+sgcLsTBjGFbVNbfA1kkyPgWG46c+Obih3asbiScYSzM6EYdywN70Ib352pd+eNgwMhjDFiUOX/uzoBitINNs31gUJwAgThnFCbVObVvxDbWPraDeJYRyzP6MYf9p5Cb9crBjtphhksK7GowEjTBjGBb5u9lq6Y9363QwMpiKRdeLC9TqEBTjh4PlyHB1jAmUgbvBjAUaYMIwbNqVG4P3nYkcsopfhwYTHtcbqxCDcLBNCJFHg0PnyMVUpc7y6wTO5uRjGFcyOhGEoaJV2ImqSm8Ga7mPB/Xww9WdGC0aYMDAw/KaQyDpxMqcaMoUS4QFOiI/0pv82lrJNjydBAoyimmvnzp3YvXs3/Xt+fj5iY2OxcuVKrFy5Etu2bdM7p6urC3/+85+RnJyMVatWoaJibOk6GRhGkvGiSx9rUGokLtsSoX6O9KQ9Xm0VY4UR35lIpVJs374dx48fx1NPPUV/fvv2bTz55JN49tlnjZ77zTffgMPhICMjA/n5+di6dSsOHDgwEs1mYBhTjKUV9HjEkBqJEjLUuI63ncFoM+I7k7NnzyIgIABPPPGE1ue3b99GdnY20tLS8Nxzz6GhoUHv3MzMTKxYsQIAMHPmTIjFYtTX149IuxkYxgrMCnpoMCQsmGzTA2fEhUlaWhqeeeYZmJuba31uZ2eHxx9/HEeOHEFCQgJeeeUVvXObmprg6upK/+7q6gqBQKB3nEQiAZ/P1/pn6DgGhvHIePX2GS8w4zkwhk3NlZGRge3bt2t9FhgYiL179xo8/t1336V/fuSRR/DPf/4TUqkUdna9R36yWPrycN++fdizZ0//G83AME4Yj94+DA82wyZMkpOTkZycbNKx3d3d+Pzzz/V2LBYW2s1zc3ODUCiEv78/AEAoFMLNzU3vehs3bsSqVau0PhMIBNiwYUN/u8HAMGYZjCAZ6Qy/DP1nLLgo94cx8TaxWCycPn0a/v7+WLZsGY4cOYJp06aBw9HOg5OQkICjR48iOjoaV69ehbW1Nby8vPSux+PxwOPpZwNlYGAgqxQeOFuGdUkhSJ0bONrNYTDA96dK6BrwjyyeNNrNMYkxEwG/Y8cOfP3110hJScHBgwfx/vvvAwC+//577Ny5EwDw2GOPoaurCykpKfjggw/w4YcfjmaTGRjGHSNdFZGh/0hknRCK5ahqkEIolo8bBwsmBT0Dw28MZmcythnK4lojydhvIQMDw5CSOjcQC2P8xsUENRqMtj2JKq5FCfzx8pzGRysZGBiGlPEyQY00Y2XXNh4F/pixmTAwMDCMJmPNnjSeBAnACBMGBgYGAENbu/23CDNaDAwMDD2MR/XSWIHZmTAwMDBowAiSgcEIEwYGhiFB1CYf7SYwjCKMMGFgYBg0+44X4ZVPLmLf8aLRbgrDKMEIEwYGhkEhapPj3FU+RBIF+T+zQ/lNwggThnHDaLtqPkgM5Vg62XOwINoHTjw2+b89p++TGB44GEsTw7hgrASTPQgMx1huTInA8rmBjCD5DcPsTBjGPGMtmGw8M5xjyQiS3zaMMGEY8zDBZEMHM5YMwwWTNZhh3DDaCfgeJJixZBhqmJ0Jw7iBmfyGDmYsGYYaRpgwMDAwMAwaRpgwMDAwMAwaRpgwMIxT6pulo90EBgYaRpgwMIxD9qYXYdu/L2NvOpO+hGFswAgTBoZxRn2zFOcLyPQl5wv4qBdKRrtJDAyjFwG/c+dOsFgsvPjiiwCA1atXQ61WAwAUCgVqa2tx8eJFuLi40OcolUrExsbC19eX/uzQoUMwNzcf2cYzMIwiXi52SIzywfkCPhKjfODlyhvtJjEwjLwwkUql2L59O44fP46nnnqK/vzQoUP0z3/5y1+watUqLUECAKWlpYiMjMSXX345Yu1lYBiLbEqNwOJYX0aQMIwZRlzNdfbsWQQEBOCJJ54w+PecnByUlJTg6aef1vvb7du3IRKJ8NBDD+Ghhx5CXl7ecDeXgWHMwggShrHEiO9M0tLSAAC7d+82+Pddu3bhlVdeMai6MjMzQ1JSEjZv3ozi4mI8/fTTOHbsGJycnLSOk0gkkEi09cgCgWBoOsDAwMDAoMewCZOMjAxs375d67PAwEDs3bvX6DllZWUQi8VITEw0+Pf169fTP4eHh2Pq1Km4du0aFi5cqHXcvn37sGfPnoE3noGBgYGhXwybMElOTkZycnK/zjlz5gyWLVtm9O9HjhzBjBkz4OfnBwAgCAKWlpZ6x23cuBGrVq3S+kwgEGDDhg39ag8DAwMDg2mMqQQ9N27cwMaNG43+vbS0FDdu3MA777yDyspKFBcXIyoqSu84Ho8HHo/RJzMwMDCMFGMqzqS2thbu7u5an509exZvvPEGAGDz5s0QiURITU3Fyy+/jB07dsDW1nY0msrAwMDAoMFvJgV9dXU1Fi9ejG+//RYeHh6j3RwGBgaGcYOHhwcsLHpXZI0pNddwIhQKAYCxmzAwMDD0E1PqQP1mdiYKhQKFhYVwdXXtd8Q8Zbz/Le1qmD4/+H3+rfUXYPo80D4zOxMN2Gw2oqOjB3UNDw+P31yVRqbPDz6/tf4CTJ+HgzFlgGdgYGBgGJ8wwoSBgYGBYdAwwoSBgYGBYdAwwsQEeDweXnjhhd9UICTT5wef31p/AabPw8lvxpuLgYGBgWH4YHYmDAwMDAyDhhEmDAwMDAyDhhEmfXDs2DEsW7YMixYtwrfffjvazRkQe/bsQUpKClJSUvDhhx8CAC5fvozly5dj8eLF+Pjjj+lji4uLsWbNGixZsgRvvPEGVCoVAKC+vh4bNmzA0qVL8Yc//AEymQwAWTvmmWeeQXJyMjZs2EBnGhgL7NixA1u3bgUwdP3q6urCn//8ZyQnJ2PVqlWoqKgYnc4Z4Ny5c1i9ejWWLl2K999/H8CD/ZyPHj1Kv9c7duwA8OA+5/b2dqSmpoLP5wMY/uc6oP4TDEYRCAREYmIiIRaLCZlMRixfvpwoKysb7Wb1i+zsbOLhhx8mOjs7ia6uLuLxxx8njh07RiQkJBA1NTWEUqkknnzySSIzM5MgCIJISUkhrl+/ThAEQWzbto349ttvCYIgiGeeeYZIT08nCIIg9uzZQ3z44YcEQRDE3/72N+Lzzz8nCIIgDh8+TLz88ssj20EjXL58mYiNjSW2bNlCEMTQ9eu///0v8dZbbxEEQRB5eXnE2rVrR6pLvVJTU0PMnTuXaGhoILq6uohHHnmEyMzMfGCfc0dHBzFz5kyipaWFUCqVxNq1a4ns7OwH8jnfuHGDSE1NJSIiIoja2lpCLpcP+3MdSP8ZYdILhw4dIrZt20b/vmfPHmL37t2j2KL+c/fuXfrlIgjy5dm9ezfx+OOP058dPnyY2Lp1K8Hn84mkpCT68/z8fOKxxx4jurq6iMjISEKpVBIEQRD19fXEggULCIIgiMTERKK+vp4gCIJQKpVEZGQk0dXVNQI9M45YLCbWrVtHfPXVV8SWLVuGtF+PPvookZ+fT18rKSmJqKurG8HeGebLL78k/v73v9O/CwQCIjc394F9zlKplIiKiiL4fD4hl8uJtLQ0Ijc394F8zq+//jqRn59PJCYmErW1tSPyXAfSf0bN1QtNTU1wdXWlf3dzc0NjY+Motqj/hISEYPr06QCAqqoqnDhxAmZmZgb7pdtfV1dXNDY2QiwWw9bWls7NQ30OaI+RhYUFbG1tIRKJRqh3hnn77bfxyiuv0K6QQ9kvQ9caCyWhq6uroVar8fvf/x4rVqzAd999Z/T9fRCes62tLV5++WUkJycjPj4e3t7esLS0fCCf8wcffKCVCmoknutA+s8Ik14gDHhNm5mZjUJLBk9ZWRmefPJJbNmyha5UqYmZmZnR/vZ3HFis0XutDhw4AE9PT8TFxdGfDXe/RrO/FGq1Gjk5OfjHP/6Bn376Cbdv36b165o8KM+5pKQEBw8exPnz55GVlQUWi4Xs7Gy94x605wz0/30eqf6PjdEZo7i7u6O5uZn+vampCW5ubqPYooFRUFCATZs24U9/+hNWrVpltF+6nwuFQri5ucHJyQnt7e1Qq9VanwPkqog6R6VSob29HQ4ODiPXOR1OnDiB7OxsrFy5Ert27cK5c+dw4MCBIeuXm5ublvFZ85zRxMXFBXFxcXBycgKbzUZSUhKys7Mf2OeclZWFuLg4ODs7w8rKCqtXr0Zubu4D/5wB4/PSUD7XgfSfESa9MHv2bOTk5EAkEkEul+PUqVOIj48f7Wb1i4aGBmzevBkfffQRUlJSAADTpk3DvXv3aNVIeno6rSqwtrZGQUEBAODIkSOIj4+HpaUloqOjceLECa3PASAhIQFHjhwBQE7k0dHRsLS0HPmO9vDVV18hPT0dR48exUsvvYQFCxZg+/btQ9avhIQEHD16FABw9epVWFtbw8vLa+Q7qkNiYiKysrIgkUigVqtx6dIlLF269IF9zpMmTcLly5fR0dEBgiBw7tw5xMTEPPDPGRiZ7++A+j8oy9BvgF9++YVISUkhFi9eTHzxxRej3Zx+89577xHTp08nVqxYQf/77rvviMuXLxPLly8nFi9eTHzwwQdEd3c3QRAEUVxcTKxZs4ZYunQp8eqrrxKdnZ0EQRAEn88nHn30USI5OZl48sknidbWVoIgSGP3s88+Syxbtox4+OGHidra2lHrqy4HDx6kvbmGql8KhYL4y1/+QixbtoxIS0sjCgsLR6dzBjhw4AD9rv7tb38j1Gr1A/2cP//8c2LJkiVEamoqsW3bNkKhUDzQz5kywBMEMezPdSD9Z9KpMDAwMDAMGkbNxcDAwMAwaBhhwsDAwMAwaBhhwsDAwMAwaBhhwsDAwMAwaBhhwsDAwMAwaBhhwvCb4/bt23jppZcAALdu3cLbb789pNc/cOAAnWH6+++/xxdffDEk19Vst6mIRCJMnDhxSO7PwNAbFqPdAAaGkWbKlCnYtWsXAKC8vHzI860VFBQgJCQEAPDII48M2XU1283AMNZghAnDb47c3Fy89957+L//+z/s2rULUqkU27Ztw/bt23Hu3Dl89tlnUCqVYLPZ2LJlCyIjI7F7927cuHEDTU1NmDhxIrZu3Yq3334bLS0tEAqF8Pb2xieffIJr167h3LlzyM7OBpvNhkgkglgsxttvv42ysjK8++67aG1thZmZGZ588kmkpaUhNzcXH3/8MXx9fVFWVoauri68/fbbmDVrlsF2p6enY+vWrbC1tUVpaSkEAgECAwPxr3/9C1wuF6dOncLHH38MDoeDyZMna13jwIED+P7779Hd3Q0HBwe89dZbmDBhAp544glERETgL3/5Cy5fvoytW7fi0KFDcHFxGclHwzCeGfo4TQaGsc2VK1eIlJQUgiDIKPlnnnmGIAiCuHfvHpGamkqIRCKCIMj0/XPmzCFkMhmxa9cuYsmSJXQa771799J1ILq7u4mnnnqK+PLLLwmCIIgtW7YQ//3vfwmCIIhdu3YRf/vb3wilUkkkJSURv/76K0EQZIr4efPmEdeuXSOuXLlChIWFEXfu3CEIgkwnv2HDhl7bvWXLFq06NWlpacTPP/9MCIVCIioqiq6785///IcIDQ0lCIIgcnNzid/97ndER0cHQRAEcenSJSI5OZkgCIJobGwkZs+eTZw+fZqIj48n8vLyhmy8GX4bMDsTBoYesrOz0dTUhE2bNtGfmZmZoaamBgAwffp0Oo33xo0bcfXqVXz11VeoqqpCWVkZpk2bZvTaVVVV6OzsxOLFiwGQyfoWL16MS5cuITY2Fl5eXggLCwMAhIeH4/Dhw322d968ebCysgIAhIaGoq2tDQUFBQgNDUVwcDAA4OGHH8a//vUvAEBmZiaqq6uxfv16+hptbW1obW2Fm5sb3nvvPTz//PN48cUXMXPmTFOHjYEBAKPmYmCg6e7uRlxcHD755BP6s4aGBri5ueH06dOwsbGhP//HP/6BW7duYc2aNYiNjYVKpTKY6lvz2roQBEGXVWWz2fTnxtKG62LoHN1zKeFHtWHlypX485//TP/e1NQEe3t7AKT9yMXFBbdv3+7z3gwMujDeXAy/aczNzekJfdasWcjOzqbrXV+4cAErVqxAZ2en3nlZWVnYuHEj0tLS4OzsjMuXL9MpvjWvSTFhwgRYWlri1KlTAIDGxkb8+uuvmD179pD2Jzo6GuXl5SgpKQEAHDp0iP7bnDlzcPz4cTQ1NQEgPc02btwIgPRq+/rrr3Hw4EFIJBLs27dvSNvF8ODD7EwYftNERkbik08+webNm/Hvf/8b7777Ll599VUQBAELCwt89tlnWjsSis2bN+PDDz/Ep59+CnNzc8yYMYNWh8XHx+O9997TOt7S0hKffvop3n//fezevRtqtRqbN2/GrFmzkJubO2T9cXJywkcffYTXXnsNlpaWWuqqefPm4emnn8aTTz4JMzMz2NraYs+ePZDJZHj11Vfx5ptvwt3dHf/v//0/rFu3DjNnzkR4ePiQtY3hwYbJGszAwMDAMGgYNRcDAwMDw6BhhAkDAwMDw6BhhAkDAwMDw6BhhAkDAwMDw6BhhAkDAwMDw6BhhAkDAwMDw6BhhAkDAwMDw6BhhAkDAwMDw6D5//rbj/dPhhjPAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -345,31 +430,24 @@ "source": [ "# plot samples\n", "pypesto.visualize.sampling_fval_traces(result2)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "For the saving of optimization history, we refer to \n", - "[store.ipynb](sampling_diagnostics.ipynb)." - ], "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "For the saving of optimization history, we refer to \n", + "[store.ipynb](sampling_diagnostics.ipynb)." + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -383,9 +461,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/doc/example/petab_import.ipynb b/doc/example/petab_import.ipynb index b606772b3..077e14c33 100644 --- a/doc/example/petab_import.ipynb +++ b/doc/example/petab_import.ipynb @@ -14,6 +14,17 @@ "In this notebook, we illustrate how to use [pyPESTO](https://github.com/icb-dcm/pypesto.git) together with [PEtab](https://github.com/petab-dev/petab.git) and [AMICI](https://github.com/icb-dcm/amici.git). We employ models from the [benchmark collection](https://github.com/benchmarking-initiative/benchmark-models-petab), which we first download:" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# install if not done yet\n", + "# !apt install libatlas-base-dev swig\n", + "# %pip install pypesto[amici,petab] --quiet" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -23,8 +34,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "fatal: destination path 'tmp/benchmark-models' already exists and is not an empty directory.\r\n", - "Already up to date.\r\n" + "fatal: destination path 'tmp/benchmark-models' already exists and is not an empty directory.\n", + "Already up to date.\n" ] } ], @@ -480,7 +491,18 @@ "outputs": [ { "data": { - "text/plain": "[147.5440308019394,\n 149.5878368928219,\n 149.58822002126522,\n 154.7306294235784,\n 156.3410332994205,\n 159.05273185070513,\n 171.1340766484108,\n 249.74597383547857,\n 249.7459974423845,\n 249.74599767995497]" + "text/plain": [ + "[147.5440308019394,\n", + " 149.5878368928219,\n", + " 149.58822002126522,\n", + " 154.7306294235784,\n", + " 156.3410332994205,\n", + " 159.05273185070513,\n", + " 171.1340766484108,\n", + " 249.74597383547857,\n", + " 249.7459974423845,\n", + " 249.74599767995497]" + ] }, "execution_count": 12, "metadata": {}, @@ -505,7 +527,9 @@ "outputs": [ { "data": { - "text/plain": "" + "text/plain": [ + "" + ] }, "execution_count": 13, "metadata": {}, @@ -513,8 +537,10 @@ }, { "data": { - "text/plain": "
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\n" 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\n", 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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -542,23 +570,35 @@ }, { "cell_type": "markdown", - "source": [ - "We can also conveniently visualize the model fit. This plots the petab visualization using optimized parameters." - ], "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "We can also conveniently visualize the model fit. This plots the petab visualization using optimized parameters." + ] }, { "cell_type": "code", "execution_count": 14, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": "{'plot1': ,\n 'plot2': ,\n 'plot3': }" + "text/plain": [ + "{'plot1': ,\n", + " 'plot2': ,\n", + " 'plot3': }" + ] }, "execution_count": 14, "metadata": {}, @@ -566,8 +606,10 @@ }, { "data": { - "text/plain": "
", - "image/png": 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\n" 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -580,18 +622,12 @@ "from pypesto.visualize.model_fit import visualize_optimized_model_fit\n", "visualize_optimized_model_fit(petab_problem=petab_problem,\n", " result=result)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -605,7 +641,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" }, "toc": { "base_numbering": 1, diff --git a/doc/example/prior_definition.ipynb b/doc/example/prior_definition.ipynb index 73c6dd941..24f34c695 100644 --- a/doc/example/prior_definition.ipynb +++ b/doc/example/prior_definition.ipynb @@ -16,6 +16,16 @@ "**CAUTION**: The user needs to specify the **negative** _log-likelihood_, while the _log-prior_ is internally mulitplied by -1." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# install if not done yet\n", + "# %pip install pypesto --quiet" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -221,7 +231,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -235,9 +245,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.1" + "version": "3.9.7" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/doc/example/rosenbrock.ipynb b/doc/example/rosenbrock.ipynb index a89c4a248..2d867b33d 100644 --- a/doc/example/rosenbrock.ipynb +++ b/doc/example/rosenbrock.ipynb @@ -14,6 +14,16 @@ "Here, we perform optimization for the Rosenbrock banana function, which does not require an AMICI model. In particular, we try several ways of specifying derivative information." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# install if not done yet\n", + "# %pip install pypesto --quiet" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -946,7 +956,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -960,7 +970,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/doc/example/sampler_study.ipynb b/doc/example/sampler_study.ipynb index 3b31f6e2e..5c200699d 100644 --- a/doc/example/sampler_study.ipynb +++ b/doc/example/sampler_study.ipynb @@ -14,6 +14,17 @@ "In this notebook, we perform a short study of how various samplers implemented in pyPESTO perform." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# install if not done yet\n", + "# !apt install libatlas-base-dev swig\n", + "# %pip install pypesto[amici,petab,pymc3,emcee] --quiet" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1266,7 +1277,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1280,7 +1291,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/doc/example/sampling_diagnostics.ipynb b/doc/example/sampling_diagnostics.ipynb index 49896d427..3c1aa7fc4 100644 --- a/doc/example/sampling_diagnostics.ipynb +++ b/doc/example/sampling_diagnostics.ipynb @@ -14,6 +14,17 @@ "In this notebook, we illustrate how to assess the quality of your MCMC samples, e.g. convergence and auto-correlation, in pyPESTO." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# install if not done yet\n", + "# !apt install libatlas-base-dev swig\n", + "# %pip install pypesto[amici,petab] --quiet" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -345,9 +356,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -749,7 +758,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -763,7 +772,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/doc/example/store.ipynb b/doc/example/store.ipynb index 57f0758a2..27a63f017 100644 --- a/doc/example/store.ipynb +++ b/doc/example/store.ipynb @@ -9,6 +9,16 @@ "This notebook illustrates how simulations and results can be saved to file." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# install if not done yet\n", + "# %pip install pypesto --quiet" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -165,8 +175,10 @@ }, { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n", 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\n" 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -327,21 +341,12 @@ }, { "cell_type": "code", + "execution_count": 7, "metadata": { "pycharm": { "name": "#%%\n" } }, - "source": [ - "# record the history and store to CSV\n", - "history_options = pypesto.HistoryOptions(trace_record=True, storage_file='history.hdf5')\n", - "\n", - "# Run optimizaitons\n", - "result = optimize.minimize(\n", - " problem=problem, optimizer=optimizer,\n", - " n_starts=n_starts, history_options=history_options)" - ], - "execution_count": 7, "outputs": [ { "name": "stderr", @@ -390,25 +395,25 @@ "100%|██████████| 20/20 [00:08<00:00, 2.26it/s]\n" ] } + ], + "source": [ + "# record the history and store to CSV\n", + "history_options = pypesto.HistoryOptions(trace_record=True, storage_file='history.hdf5')\n", + "\n", + "# Run optimizaitons\n", + "result = optimize.minimize(\n", + " problem=problem, optimizer=optimizer,\n", + " n_starts=n_starts, history_options=history_options)" ] }, { "cell_type": "code", + "execution_count": 8, "metadata": { "pycharm": { "name": "#%%\n" } }, - "source": [ - "print(\"History type: \", type(result.optimize_result.list[0].history))\n", - "# print(\"Function value trace of best run: \", result.optimize_result.list[0].history.get_fval_trace())\n", - "\n", - "fig, ax = plt.subplots(1, 2)\n", - "visualize.waterfall(result, ax=ax[0])\n", - "visualize.optimizer_history(result, ax=ax[1])\n", - "fig.set_size_inches((15, 5))" - ], - "execution_count": 8, "outputs": [ { "name": "stdout", @@ -419,14 +424,25 @@ }, { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } + ], + "source": [ + "print(\"History type: \", type(result.optimize_result.list[0].history))\n", + "# print(\"Function value trace of best run: \", result.optimize_result.list[0].history.get_fval_trace())\n", + "\n", + "fig, ax = plt.subplots(1, 2)\n", + "visualize.waterfall(result, ax=ax[0])\n", + "visualize.optimizer_history(result, ax=ax[1])\n", + "fig.set_size_inches((15, 5))" ] }, { @@ -445,18 +461,12 @@ }, { "cell_type": "code", + "execution_count": 9, "metadata": { "pycharm": { "name": "#%%\n" } }, - "source": [ - "# Run optimizaitons\n", - "result = optimize.minimize(\n", - " problem=problem, optimizer=optimizer,\n", - " n_starts=n_starts)" - ], - "execution_count": 9, "outputs": [ { "name": "stderr", @@ -505,28 +515,79 @@ "100%|██████████| 20/20 [00:00<00:00, 53.45it/s]\n" ] } + ], + "source": [ + "# Run optimizaitons\n", + "result = optimize.minimize(\n", + " problem=problem, optimizer=optimizer,\n", + " n_starts=n_starts)" ] }, { "cell_type": "code", + "execution_count": 10, "metadata": { "pycharm": { "name": "#%%\n" } }, - "source": [ - "result.optimize_result.list[0:2]" - ], - "execution_count": 10, "outputs": [ { "data": { - "text/plain": "[{'id': '4',\n 'x': array([1.00000001, 1. , 1. , 0.99999999, 0.99999999,\n 1.00000001, 1.00000001, 1.00000004, 1.00000008, 1.00000014]),\n 'fval': 2.6045655152986313e-13,\n 'grad': array([ 7.38459033e-06, -4.78320167e-07, -6.50914679e-07, -1.47726642e-06,\n -1.39575141e-05, 9.14793168e-06, -7.58437136e-06, 4.50055738e-07,\n 1.01219510e-05, -4.24214104e-06]),\n 'hess': None,\n 'res': None,\n 'sres': None,\n 'n_fval': 76,\n 'n_grad': 76,\n 'n_hess': 0,\n 'n_res': 0,\n 'n_sres': 0,\n 'x0': array([ 0.33383114, 2.09297901, -1.77381628, -1.60663808, -2.85350433,\n 0.71050093, -1.190691 , 0.91974885, -2.34344618, 1.21791823]),\n 'fval0': 18119.670540771178,\n 'history': ,\n 'exitflag': 0,\n 'time': 0.017375946044921875,\n 'message': 'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'},\n {'id': '16',\n 'x': array([1. , 1. , 1.00000001, 1. , 1.00000001,\n 1.00000003, 1.00000007, 1.00000009, 1.00000017, 1.00000039]),\n 'fval': 7.312572536347769e-13,\n 'grad': array([ 3.34432881e-06, -6.16413761e-06, 1.25983886e-05, -5.34613024e-06,\n -7.50765648e-06, 1.11777438e-06, 1.94167105e-05, -5.91496342e-06,\n -2.50337361e-05, 1.19659990e-05]),\n 'hess': None,\n 'res': None,\n 'sres': None,\n 'n_fval': 70,\n 'n_grad': 70,\n 'n_hess': 0,\n 'n_res': 0,\n 'n_sres': 0,\n 'x0': array([ 2.58291438, 2.48719491, 2.93132676, -0.75290073, 0.34568409,\n 0.60255167, -0.68200823, -1.01952663, -2.47953741, 2.14959561]),\n 'fval0': 14770.270006296314,\n 'history': ,\n 'exitflag': 0,\n 'time': 0.016092777252197266,\n 'message': 'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'}]" + "text/plain": [ + "[{'id': '4',\n", + " 'x': array([1.00000001, 1. , 1. , 0.99999999, 0.99999999,\n", + " 1.00000001, 1.00000001, 1.00000004, 1.00000008, 1.00000014]),\n", + " 'fval': 2.6045655152986313e-13,\n", + " 'grad': array([ 7.38459033e-06, -4.78320167e-07, -6.50914679e-07, -1.47726642e-06,\n", + " -1.39575141e-05, 9.14793168e-06, -7.58437136e-06, 4.50055738e-07,\n", + " 1.01219510e-05, -4.24214104e-06]),\n", + " 'hess': None,\n", + " 'res': None,\n", + " 'sres': None,\n", + " 'n_fval': 76,\n", + " 'n_grad': 76,\n", + " 'n_hess': 0,\n", + " 'n_res': 0,\n", + " 'n_sres': 0,\n", + " 'x0': array([ 0.33383114, 2.09297901, -1.77381628, -1.60663808, -2.85350433,\n", + " 0.71050093, -1.190691 , 0.91974885, -2.34344618, 1.21791823]),\n", + " 'fval0': 18119.670540771178,\n", + " 'history': ,\n", + " 'exitflag': 0,\n", + " 'time': 0.017375946044921875,\n", + " 'message': 'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'},\n", + " {'id': '16',\n", + " 'x': array([1. , 1. , 1.00000001, 1. , 1.00000001,\n", + " 1.00000003, 1.00000007, 1.00000009, 1.00000017, 1.00000039]),\n", + " 'fval': 7.312572536347769e-13,\n", + " 'grad': array([ 3.34432881e-06, -6.16413761e-06, 1.25983886e-05, -5.34613024e-06,\n", + " -7.50765648e-06, 1.11777438e-06, 1.94167105e-05, -5.91496342e-06,\n", + " -2.50337361e-05, 1.19659990e-05]),\n", + " 'hess': None,\n", + " 'res': None,\n", + " 'sres': None,\n", + " 'n_fval': 70,\n", + " 'n_grad': 70,\n", + " 'n_hess': 0,\n", + " 'n_res': 0,\n", + " 'n_sres': 0,\n", + " 'x0': array([ 2.58291438, 2.48719491, 2.93132676, -0.75290073, 0.34568409,\n", + " 0.60255167, -0.68200823, -1.01952663, -2.47953741, 2.14959561]),\n", + " 'fval0': 14770.270006296314,\n", + " 'history': ,\n", + " 'exitflag': 0,\n", + " 'time': 0.016092777252197266,\n", + " 'message': 'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'}]" + ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } + ], + "source": [ + "result.optimize_result.list[0:2]" ] }, { @@ -542,20 +603,18 @@ }, { "cell_type": "code", + "execution_count": 11, "metadata": { "pycharm": { "name": "#%%\n" } }, - "source": [ - "# plot waterfalls\n", - "visualize.waterfall(result, size=(15,6))" - ], - "execution_count": 11, "outputs": [ { "data": { - "text/plain": "" + "text/plain": [ + "" + ] }, "execution_count": 11, "metadata": {}, @@ -563,18 +622,25 @@ }, { "data": { - "text/plain": "
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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } + ], + "source": [ + "# plot waterfalls\n", + "visualize.waterfall(result, size=(15,6))" ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Save optimization result as HDF5 file\n", "\n", @@ -582,25 +648,16 @@ "optimization result, and the profiling and sampling results if available, to HDF5.\n", "All of them can be disabled with boolean flags\n", "(see [the documentation](https://pypesto.readthedocs.io/en/latest/api_store.html#pypesto.store.write_result))" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", + "execution_count": 12, "metadata": { "pycharm": { "name": "#%%\n" } }, - "source": [ - "fn = tempfile.mktemp(\".hdf5\")\n", - "\n", - "# Write result\n", - "save_to_hdf5.write_result(result, fn)" - ], - "execution_count": 12, "outputs": [ { "name": "stderr", @@ -609,6 +666,12 @@ "Warning: There is no sampling_result, which you tried to save to hdf5.\n" ] } + ], + "source": [ + "fn = tempfile.mktemp(\".hdf5\")\n", + "\n", + "# Write result\n", + "save_to_hdf5.write_result(result, fn)" ] }, { @@ -627,6 +690,15 @@ { "cell_type": "code", "execution_count": 13, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stderr", @@ -647,13 +719,7 @@ "source": [ "# Read result and problem\n", "result = read_from_hdf5.read_result(fn)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "code", @@ -662,7 +728,52 @@ "outputs": [ { "data": { - "text/plain": "[{'id': '4',\n 'x': array([1.00000001, 1. , 1. , 0.99999999, 0.99999999,\n 1.00000001, 1.00000001, 1.00000004, 1.00000008, 1.00000014]),\n 'fval': 2.6045655152986313e-13,\n 'grad': array([ 7.38459033e-06, -4.78320167e-07, -6.50914679e-07, -1.47726642e-06,\n -1.39575141e-05, 9.14793168e-06, -7.58437136e-06, 4.50055738e-07,\n 1.01219510e-05, -4.24214104e-06]),\n 'hess': None,\n 'res': None,\n 'sres': None,\n 'n_fval': 76,\n 'n_grad': 76,\n 'n_hess': 0,\n 'n_res': 0,\n 'n_sres': 0,\n 'x0': array([ 0.33383114, 2.09297901, -1.77381628, -1.60663808, -2.85350433,\n 0.71050093, -1.190691 , 0.91974885, -2.34344618, 1.21791823]),\n 'fval0': 18119.670540771178,\n 'history': None,\n 'exitflag': 0,\n 'time': 0.017375946044921875,\n 'message': 'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'},\n {'id': '16',\n 'x': array([1. , 1. , 1.00000001, 1. , 1.00000001,\n 1.00000003, 1.00000007, 1.00000009, 1.00000017, 1.00000039]),\n 'fval': 7.312572536347769e-13,\n 'grad': array([ 3.34432881e-06, -6.16413761e-06, 1.25983886e-05, -5.34613024e-06,\n -7.50765648e-06, 1.11777438e-06, 1.94167105e-05, -5.91496342e-06,\n -2.50337361e-05, 1.19659990e-05]),\n 'hess': None,\n 'res': None,\n 'sres': None,\n 'n_fval': 70,\n 'n_grad': 70,\n 'n_hess': 0,\n 'n_res': 0,\n 'n_sres': 0,\n 'x0': array([ 2.58291438, 2.48719491, 2.93132676, -0.75290073, 0.34568409,\n 0.60255167, -0.68200823, -1.01952663, -2.47953741, 2.14959561]),\n 'fval0': 14770.270006296314,\n 'history': None,\n 'exitflag': 0,\n 'time': 0.016092777252197266,\n 'message': 'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'}]" + "text/plain": [ + "[{'id': '4',\n", + " 'x': array([1.00000001, 1. , 1. , 0.99999999, 0.99999999,\n", + " 1.00000001, 1.00000001, 1.00000004, 1.00000008, 1.00000014]),\n", + " 'fval': 2.6045655152986313e-13,\n", + " 'grad': array([ 7.38459033e-06, -4.78320167e-07, -6.50914679e-07, -1.47726642e-06,\n", + " -1.39575141e-05, 9.14793168e-06, -7.58437136e-06, 4.50055738e-07,\n", + " 1.01219510e-05, -4.24214104e-06]),\n", + " 'hess': None,\n", + " 'res': None,\n", + " 'sres': None,\n", + " 'n_fval': 76,\n", + " 'n_grad': 76,\n", + " 'n_hess': 0,\n", + " 'n_res': 0,\n", + " 'n_sres': 0,\n", + " 'x0': array([ 0.33383114, 2.09297901, -1.77381628, -1.60663808, -2.85350433,\n", + " 0.71050093, -1.190691 , 0.91974885, -2.34344618, 1.21791823]),\n", + " 'fval0': 18119.670540771178,\n", + " 'history': None,\n", + " 'exitflag': 0,\n", + " 'time': 0.017375946044921875,\n", + " 'message': 'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'},\n", + " {'id': '16',\n", + " 'x': array([1. , 1. , 1.00000001, 1. , 1.00000001,\n", + " 1.00000003, 1.00000007, 1.00000009, 1.00000017, 1.00000039]),\n", + " 'fval': 7.312572536347769e-13,\n", + " 'grad': array([ 3.34432881e-06, -6.16413761e-06, 1.25983886e-05, -5.34613024e-06,\n", + " -7.50765648e-06, 1.11777438e-06, 1.94167105e-05, -5.91496342e-06,\n", + " -2.50337361e-05, 1.19659990e-05]),\n", + " 'hess': None,\n", + " 'res': None,\n", + " 'sres': None,\n", + " 'n_fval': 70,\n", + " 'n_grad': 70,\n", + " 'n_hess': 0,\n", + " 'n_res': 0,\n", + " 'n_sres': 0,\n", + " 'x0': array([ 2.58291438, 2.48719491, 2.93132676, -0.75290073, 0.34568409,\n", + " 0.60255167, -0.68200823, -1.01952663, -2.47953741, 2.14959561]),\n", + " 'fval0': 14770.270006296314,\n", + " 'history': None,\n", + " 'exitflag': 0,\n", + " 'time': 0.016092777252197266,\n", + " 'message': 'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'}]" + ] }, "execution_count": 14, "metadata": {}, @@ -680,7 +791,9 @@ "outputs": [ { "data": { - "text/plain": "" + "text/plain": [ + "" + ] }, "execution_count": 15, "metadata": {}, @@ -688,8 +801,10 @@ }, { "data": { - "text/plain": "
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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -705,7 +820,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -719,7 +834,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/doc/example/synthetic_data.ipynb b/doc/example/synthetic_data.ipynb index a89855d47..23bea0da0 100644 --- a/doc/example/synthetic_data.ipynb +++ b/doc/example/synthetic_data.ipynb @@ -13,6 +13,17 @@ "Additional requirements for this notebook can be installed with `pip install amici petab`." ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# install if not done yet\n", + "# !apt install libatlas-base-dev swig\n", + "# %pip install pypesto[amici,petab] --quiet" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -306,7 +317,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -320,7 +331,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/doc/index.rst b/doc/index.rst index 37d398d82..1bd04f8f5 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -12,9 +12,6 @@ Welcome to pyPESTO's documentation! .. image:: https://codecov.io/gh/ICB-DCM/pyPESTO/branch/master/graph/badge.svg :target: https://codecov.io/gh/ICB-DCM/pyPESTO :alt: Code coverage -.. image:: https://api.codacy.com/project/badge/Grade/134432ddad0e464b8494587ff370f661 - :target: https://www.codacy.com/app/dweindl/pyPESTO?utm_source=github.com&utm_medium=referral&utm_content=ICB-DCM/pyPESTO&utm_campaign=Badge_Grade - :alt: Code quality .. image:: https://readthedocs.org/projects/pypesto/badge/?version=latest :target: https://pypesto.readthedocs.io/en/latest/?badge=latest :alt: Documentation status diff --git a/doc/install.rst b/doc/install.rst index bf6040d75..f0e3673ca 100644 --- a/doc/install.rst +++ b/doc/install.rst @@ -5,7 +5,7 @@ Install and upgrade Requirements ------------ -This package requires Python 3.6 or later. +This package requires Python 3.7 or later. It is tested on Linux using Travis continuous integration. diff --git a/pypesto/__init__.py b/pypesto/__init__.py index 16f17c04f..7c36e7be5 100644 --- a/pypesto/__init__.py +++ b/pypesto/__init__.py @@ -1,3 +1,4 @@ +# noqa: D400,D205 """ pyPESTO ======= diff --git a/pypesto/engine/base.py b/pypesto/engine/base.py index 25f508b3b..71d95a5d4 100644 --- a/pypesto/engine/base.py +++ b/pypesto/engine/base.py @@ -1,3 +1,4 @@ +"""Abstract engine base class.""" from typing import List import abc @@ -5,9 +6,7 @@ class Engine(abc.ABC): - """ - Abstract engine base class. - """ + """Abstract engine base class.""" def __init__(self): pass diff --git a/pypesto/engine/mpi_pool.py b/pypesto/engine/mpi_pool.py index 4b99b93f5..efd918c9a 100644 --- a/pypesto/engine/mpi_pool.py +++ b/pypesto/engine/mpi_pool.py @@ -1,3 +1,4 @@ +"""Engines with multi-node parallelization.""" from mpi4py.futures import MPIPoolExecutor from mpi4py import MPI import cloudpickle as pickle @@ -21,9 +22,9 @@ def work(pickled_task): class MPIPoolEngine(Engine): """ - Parallelize the task execution using - `mpi4py `_. + Parallelize the task execution. + Uses `mpi4py `_. To be called with: ``mpiexec -np #Workers+1 python -m mpi4py.futures YOURFILE.py`` """ @@ -32,7 +33,8 @@ def __init__(self): super().__init__() def execute(self, tasks: List[Task], progress_bar: bool = True): - """Pickle tasks and distribute work to workers. + """ + Pickle tasks and distribute work to workers. Parameters ---------- @@ -41,7 +43,6 @@ def execute(self, tasks: List[Task], progress_bar: bool = True): progress_bar: Whether to display a progress bar. """ - pickled_tasks = [pickle.dumps(task) for task in tasks] n_procs = MPI.COMM_WORLD.Get_size() # Size of communicator diff --git a/pypesto/engine/multi_process.py b/pypesto/engine/multi_process.py index e476efd85..351f8d818 100644 --- a/pypesto/engine/multi_process.py +++ b/pypesto/engine/multi_process.py @@ -1,3 +1,4 @@ +"""Engines with multi-process parallelization.""" from multiprocessing import Pool import cloudpickle as pickle import os diff --git a/pypesto/engine/multi_thread.py b/pypesto/engine/multi_thread.py index d80dd6ced..b8f9d9c4d 100644 --- a/pypesto/engine/multi_thread.py +++ b/pypesto/engine/multi_thread.py @@ -1,3 +1,4 @@ +"""Engines with multi-threading parallelization.""" from concurrent.futures import ThreadPoolExecutor import copy import os diff --git a/pypesto/engine/single_core.py b/pypesto/engine/single_core.py index 108af01f3..7d9e2bdc9 100644 --- a/pypesto/engine/single_core.py +++ b/pypesto/engine/single_core.py @@ -1,3 +1,4 @@ +"""Engines without parallelization.""" from typing import List from tqdm import tqdm @@ -7,8 +8,9 @@ class SingleCoreEngine(Engine): """ - Dummy engine for sequential execution on one core. Note that the - objective itself may be multithreaded. + Dummy engine for sequential execution on one core. + + Note that the objective itself may be multithreaded. """ def __init__(self): diff --git a/pypesto/engine/task.py b/pypesto/engine/task.py index 3edf30bc6..38aea7d31 100644 --- a/pypesto/engine/task.py +++ b/pypesto/engine/task.py @@ -1,11 +1,14 @@ +"""Abstract Task class.""" import abc class Task(abc.ABC): """ - A task is one of a list of independent - execution tasks that are submitted to the execution engine - to be executed using the execute() method, commonly in parallel. + Abstract Task class. + + A task is one of a list of independent execution tasks that are + submitted to the execution engine to be executed using the execute() + method, commonly in parallel. """ def __init__(self): @@ -13,6 +16,4 @@ def __init__(self): @abc.abstractmethod def execute(self): - """ - Execute the task and return its results. - """ + """Execute the task and return its results.""" diff --git a/pypesto/ensemble/constants.py b/pypesto/ensemble/constants.py index cf5f9fe4f..8d58368b6 100644 --- a/pypesto/ensemble/constants.py +++ b/pypesto/ensemble/constants.py @@ -1,6 +1,4 @@ -""" -This is for (string) constants used in the ensemble module. -""" +"""Constants used in the ensemble module.""" from enum import Enum @@ -57,6 +55,8 @@ class EnsembleType(Enum): + """Class to specify the type of ensemble.""" + ensemble = 1 sample = 2 unprocessed_chain = 3 diff --git a/pypesto/ensemble/covariance_analysis.py b/pypesto/ensemble/covariance_analysis.py index 3bce6f33f..e8531f500 100644 --- a/pypesto/ensemble/covariance_analysis.py +++ b/pypesto/ensemble/covariance_analysis.py @@ -10,16 +10,15 @@ def get_covariance_matrix_parameters(ens: Ensemble) -> np.ndarray: Compute the covariance of ensemble parameters. Parameters - ========== + ---------- ens: Ensemble object containing a set of parameter vectors Returns - ======= + ------- covariance_matrix: covariance matrix of ensemble parameters """ - # call lowlevel routine using the parameter ensemble return np.cov(ens.x_vectors.transpose()) @@ -31,20 +30,18 @@ def get_covariance_matrix_predictions( Compute the covariance of ensemble predictions. Parameters - ========== + ---------- ens: Ensemble object containing a set of parameter vectors and a set of predictions or EnsemblePrediction object containing only predictions - prediction_index: index telling which prediction from the list should be analyzed Returns - ======= + ------- covariance_matrix: covariance matrix of ensemble predictions """ - # extract the an array of predictions from either an Ensemble object or an # EnsemblePrediction object dataset = get_prediction_dataset(ens, prediction_index) @@ -67,46 +64,38 @@ def get_spectral_decomposition_parameters( Compute the spectral decomposition of ensemble parameters. Parameters - ========== + ---------- ens: Ensemble object containing a set of parameter vectors - normalize: flag indicating whether the returned Eigenvalues should be normalized with respect to the largest Eigenvalue - only_separable_directions: return only separable directions according to cutoff_[absolute/relative]_separable - cutoff_absolute_separable: Consider only eigenvalues of the covariance matrix above this cutoff (only applied when only_separable_directions is True) - cutoff_relative_separable: Consider only eigenvalues of the covariance matrix above this cutoff, when rescaled with the largest eigenvalue (only applied when only_separable_directions is True) - only_identifiable_directions: return only identifiable directions according to cutoff_[absolute/relative]_identifiable - cutoff_absolute_identifiable: Consider only low eigenvalues of the covariance matrix with inverses above of this cutoff (only applied when only_identifiable_directions is True) - cutoff_relative_identifiable: Consider only low eigenvalues of the covariance matrix when rescaled with the largest eigenvalue with inverses above of this cutoff (only applied when only_identifiable_directions is True) Returns - ======= + ------- eigenvalues: Eigenvalues of the covariance matrix - eigenvectors: Eigenvectors of the covariance matrix """ @@ -140,47 +129,39 @@ def get_spectral_decomposition_predictions( Compute the spectral decomposition of ensemble predictions. Parameters - ========== + ---------- ens: Ensemble object containing a set of parameter vectors and a set of predictions or EnsemblePrediction object containing only predictions - normalize: flag indicating whether the returned Eigenvalues should be normalized with respect to the largest Eigenvalue - only_separable_directions: return only separable directions according to cutoff_[absolute/relative]_separable - cutoff_absolute_separable: Consider only eigenvalues of the covariance matrix above this cutoff (only applied when only_separable_directions is True) - cutoff_relative_separable: Consider only eigenvalues of the covariance matrix above this cutoff, when rescaled with the largest eigenvalue (only applied when only_separable_directions is True) - only_identifiable_directions: return only identifiable directions according to cutoff_[absolute/relative]_identifiable - cutoff_absolute_identifiable: Consider only low eigenvalues of the covariance matrix with inverses above of this cutoff (only applied when only_identifiable_directions is True) - cutoff_relative_identifiable: Consider only low eigenvalues of the covariance matrix when rescaled with the largest eigenvalue with inverses above of this cutoff (only applied when only_identifiable_directions is True) Returns - ======= + ------- eigenvalues: Eigenvalues of the covariance matrix - eigenvectors: Eigenvectors of the covariance matrix """ @@ -209,51 +190,42 @@ def get_spectral_decomposition_lowlevel( Compute the spectral decomposition of ensemble parameters or predictions. Parameters - ========== + ---------- matrix: symmetric matrix (typically a covariance matrix) of parameters or predictions - normalize: flag indicating whether the returned Eigenvalues should be normalized with respect to the largest Eigenvalue - only_separable_directions: return only separable directions according to cutoff_[absolute/relative]_separable - cutoff_absolute_separable: Consider only eigenvalues of the covariance matrix above this cutoff (only applied when only_separable_directions is True) - cutoff_relative_separable: Consider only eigenvalues of the covariance matrix above this cutoff, when rescaled with the largest eigenvalue (only applied when only_separable_directions is True) - only_identifiable_directions: return only identifiable directions according to cutoff_[absolute/relative]_identifiable - cutoff_absolute_identifiable: Consider only low eigenvalues of the covariance matrix with inverses above of this cutoff (only applied when only_identifiable_directions is True) - cutoff_relative_identifiable: Consider only low eigenvalues of the covariance matrix when rescaled with the largest eigenvalue with inverses above of this cutoff (only applied when only_identifiable_directions is True) Returns - ======= + ------- eigenvalues: Eigenvalues of the covariance matrix - eigenvectors: Eigenvectors of the covariance matrix """ - # get the eigenvalue decomposition eigenvalues, eigenvectors = np.linalg.eigh(matrix) diff --git a/pypesto/ensemble/dimension_reduction.py b/pypesto/ensemble/dimension_reduction.py index 42c0af641..3883cd0d2 100644 --- a/pypesto/ensemble/dimension_reduction.py +++ b/pypesto/ensemble/dimension_reduction.py @@ -19,31 +19,29 @@ def get_umap_representation_parameters( normalize_data: bool = False, **kwargs) -> Tuple: """ + UMAP of parameter ensemble. + Compute the representation with reduced dimensionality via umap (with a given number of umap components) of the parameter ensemble. Allows to pass on additional keyword arguments to the umap routine. Parameters - ========== + ---------- ens: Ensemble objects containing a set of parameter vectors - n_components: number of components for the dimension reduction - normalize_data: flag indicating whether the parameter ensemble should be rescaled with mean and standard deviation Returns - ======= + ------- umap_components: first components of the umap embedding - umap_object: returned fitted umap object from umap.UMAP() """ - # call lowlevel routine using the parameter vector ensemble return _get_umap_representation_lowlevel( dataset=ens.x_vectors.transpose(), @@ -60,35 +58,32 @@ def get_umap_representation_predictions( normalize_data: bool = False, **kwargs) -> Tuple: """ + UMAP of ensemble prediction. + Compute the representation with reduced dimensionality via umap (with a given number of umap components) of the ensemble predictions. Allows to pass on additional keyword arguments to the umap routine. Parameters - ========== + ---------- ens: Ensemble objects containing a set of parameter vectors and a set of predictions or EnsemblePrediction object containing only predictions - prediction_index: index telling which prediction from the list should be analyzed - n_components: number of components for the dimension reduction - normalize_data: flag indicating whether the parameter ensemble should be rescaled with mean and standard deviation Returns - ======= + ------- umap_components: first components of the umap embedding - umap_object: returned fitted umap object from umap.UMAP() """ - # extract the an array of predictions from either an Ensemble object or an # EnsemblePrediction object dataset = get_prediction_dataset(ens, prediction_index) @@ -109,35 +104,32 @@ def get_pca_representation_parameters( rescaler: Union[Callable, None] = None ) -> Tuple: """ + PCA of parameter ensemble. + Compute the representation with reduced dimensionality via principal component analysis (with a given number of principal components) of the parameter ensemble. Parameters - ========== + ---------- ens: Ensemble objects containing a set of parameter vectors - n_components: number of components for the dimension reduction - rescale_data: flag indicating whether the principal components should be rescaled using a rescaler function (e.g., an arcsinh function) - rescaler: callable function to rescale the output of the PCA (defaults to numpy.arcsinh) Returns - ======= + ------- principal_components: principal components of the parameter vector ensemble - pca_object: returned fitted pca object from sklearn.decomposition.PCA() """ - return _get_pca_representation_lowlevel( dataset=ens.x_vectors.transpose(), n_components=n_components, @@ -154,39 +146,35 @@ def get_pca_representation_predictions( rescaler: Union[Callable, None] = None ) -> Tuple: """ + PCA of ensemble prediction. + Compute the representation with reduced dimensionality via principal component analysis (with a given number of principal components) of the ensemble prediction. Parameters - ========== + ---------- ens: Ensemble objects containing a set of parameter vectors and a set of predictions or EnsemblePrediction object containing only predictions - prediction_index: index telling which prediction from the list should be analyzed - n_components: number of components for the dimension reduction - rescale_data: flag indicating whether the principal components should be rescaled using a rescaler function (e.g., an arcsinh function) - rescaler: callable function to rescale the output of the PCA (defaults to numpy.arcsinh) Returns - ======= + ------- principal_components: principal components of the parameter vector ensemble - pca_object: returned fitted pca object from sklearn.decomposition.PCA() """ - # extract the an array of predictions from either an Ensemble object or an # EnsemblePrediction object dataset = get_prediction_dataset(ens, prediction_index) @@ -206,36 +194,33 @@ def _get_umap_representation_lowlevel( normalize_data: bool = False, **kwargs) -> Tuple: """ - Compute the representation with reduced dimensionality via principal - component analysis (with a given number of principal components) of the - parameter ensemble. + Low level UMAP of parameter ensemble. + + Compute the representation with reduced dimensionality via uniform + manifold approximation and projection (with a given number of principal + components) of the parameter ensemble. Parameters - ========== + ---------- dataset: numpy array containing either the ensemble predictions or the parameter ensemble itself - n_components: number of components for the dimension reduction - rescale_data: flag indicating whether the principal components should be rescaled using a rescaler function (e.g., an arcsinh function) - rescaler: callable function to rescale the output of the PCA (defaults to numpy.arcsinh) Returns - ======= + ------- umap_components: first components of the umap embedding - umap_object: returned fitted umap object from umap.UMAP() """ - # create a umap object umap_object = umap.UMAP(n_components=n_components, **kwargs) @@ -256,36 +241,33 @@ def _get_pca_representation_lowlevel( rescaler: Union[Callable, None] = None ) -> Tuple: """ + Low level PCA of parameter ensemble. + Compute the representation with reduced dimensionality via principal component analysis (with a given number of principal components) of the parameter ensemble. Parameters - ========== + ---------- dataset: numpy array containing either the ensemble predictions or the parameter ensemble itself - n_components: number of components for the dimension reduction - rescale_data: flag indicating whether the principal components should be rescaled using a rescaler function (e.g., an arcsinh function) - rescaler: callable function to rescale the output of the PCA (defaults to numpy.arcsinh) Returns - ======= + ------- principal_components: principal components of the parameter vector ensemble - pca_object: returned fitted pca object from sklearn.decomposition.PCA() """ - # create a PCA object and decompose the dataset pca_object = sklearn.decomposition.PCA(n_components=n_components) pca_object.fit(dataset) diff --git a/pypesto/ensemble/ensemble.py b/pypesto/ensemble/ensemble.py index 406e89925..f8529ea5c 100644 --- a/pypesto/ensemble/ensemble.py +++ b/pypesto/ensemble/ensemble.py @@ -32,7 +32,9 @@ class EnsemblePrediction: """ - A ensemble prediction consists of an ensemble, i.e., a set of parameter + Class of ensemble prediction. + + An ensemble prediction consists of an ensemble, i.e., a set of parameter vectors and their identifiers such as a sample, and a prediction function. It can be attached to a ensemble-type object """ @@ -45,7 +47,7 @@ def __init__( lower_bound: Sequence[np.ndarray] = None, upper_bound: Sequence[np.ndarray] = None): """ - Constructor. + Initialize. Parameters ---------- @@ -88,8 +90,9 @@ def __init__( def __iter__(self): """ - __iter__ makes the instances of the class iterable objects, allowing to - apply functions such as __dict__ to them. + Make the instances of the class iterable objects. + + Allows to apply functions such as __dict__ to them. """ yield PREDICTOR, self.predictor yield PREDICTION_ID, self.prediction_id @@ -102,10 +105,12 @@ def __iter__(self): def condense_to_arrays(self): """ - This functions reshapes the predictions results to an array and adds - them as a member to the EnsemblePrediction objects. It's meant to be - used only if all conditions of a prediction have the same observables, - as this is often the case for large-scale data sets taken from online + Add prediction result to EnsemblePrediction object. + + Reshape the prediction results to an array and add them as a + member to the EnsemblePrediction objects. It's meant to be used only + if all conditions of a prediction have the same observables, as this + is often the case for large-scale data sets taken from online databases or similar. """ # prepare for storing results over all predictions @@ -150,9 +155,11 @@ def compute_summary(self, compute_weighted_sigma: bool = False ) -> Dict: """ - Compute the mean, the median, the standard deviation and possibly - percentiles from the ensemble prediction results. Those summary results - are added as a data member to the EnsemblePrediction object. + Compute summary from the ensemble prediction results. + + Summary includes the mean, the median, the standard deviation and + possibly percentiles. Those summary results are added as a data + member to the EnsemblePrediction object. Parameters ---------- @@ -184,9 +191,11 @@ def compute_summary(self, def _stack_outputs(ic: int): """ + Stack outputs. + Group outputs for different parameter vectors of one ensemble together, if they belong to the same simulation condition, and - stacks them in one array + stack them in one array. """ # Were outputs computed if self.prediction_results[0].conditions[ic].output is None: @@ -199,9 +208,11 @@ def _stack_outputs(ic: int): def _stack_outputs_sensi(ic: int): """ + Stack output sensitivities. + Group output sensitivities for different parameter vectors of one - ensemble together, if the belong to the same simulation condition, - and stacks them in one array + ensemble together, if they belong to the same simulation condition, + and stack them in one array. """ # Were output sensitivitiess computed if self.prediction_results[0].conditions[ic].output_sensi is None: @@ -214,9 +225,11 @@ def _stack_outputs_sensi(ic: int): def _stack_weights(ic: int) -> np.ndarray: """ + Stack weights. + Group weights for different parameter vectors of one ensemble together, if they belong to the same simulation condition, and - stacks them in one array + stack them in one array Parameters ---------- @@ -237,9 +250,11 @@ def _stack_weights(ic: int) -> np.ndarray: def _stack_sigmas(ic: int): """ + Stack sigmas. + Group sigmas for different parameter vectors of one ensemble together, if they belong to the same simulation condition, and - stacks them in one array + stack them in one array. """ # Were outputs computed if self.prediction_results[0].conditions[ic].output_sigmay is None: @@ -253,8 +268,10 @@ def _stack_sigmas(ic: int): def _compute_summary(tmp_array, percentiles_list, weights, tmp_sigmas=None): """ - Computes means, standard deviation, median, and requested - percentiles for a set of stacked simulations + Compute summary for a set of stacked simulations. + + Summary includes means, standard deviation, median, and requested + percentiles. """ summary = {} summary[MEAN] = np.average(tmp_array, axis=-1, weights=weights) @@ -381,12 +398,14 @@ def compute_chi2(self, amici_objective: AmiciObjective): class Ensemble: """ - A ensemble is a wrapper around an numpy array. It comes with some - convenience functionality: It allows to map parameter values via - identifiers to the correct parameters, it allows to compute summaries of - the parameter vectors (mean, standard deviation, median, percentiles) more - easily, and it can store predictions made by pyPESTO, such that the - parameter ensemble and the predictions are linked to each other. + An ensemble is a wrapper around a numpy array. + + It comes with some convenience functionality: It allows to map parameter + values via identifiers to the correct parameters, it allows to compute + summaries of the parameter vectors (mean, standard deviation, median, + percentiles) more easily, and it can store predictions made by pyPESTO, + such that the parameter ensemble and the predictions are linked to each + other. """ def __init__(self, @@ -398,7 +417,7 @@ def __init__(self, lower_bound: np.ndarray = None, upper_bound: np.ndarray = None): """ - Constructor. + Initialize. Parameters ---------- @@ -471,7 +490,8 @@ def from_sample( upper_bound: np.ndarray = None, **kwargs, ): - """Construct an ensemble from a sample. + """ + Construct an ensemble from a sample. Parameters ---------- @@ -522,7 +542,8 @@ def from_optimization_endpoints( max_size: int = np.inf, **kwargs, ): - """Construct an ensemble from an optimization result. + """ + Construct an ensemble from an optimization result. Parameters ---------- @@ -584,7 +605,8 @@ def from_optimization_history( distribute: bool = True, **kwargs, ): - """Construct an ensemble from the history of an optimization. + """ + Construct an ensemble from the history of an optimization. Parameters ---------- @@ -672,8 +694,9 @@ def from_optimization_history( def __iter__(self): """ - __iter__ makes the instances of the class iterable objects, allowing to - apply functions such as __dict__ to them. + Make the instances of the class iterable objects. + + Allows to apply functions such as __dict__ to them. """ yield X_VECTOR, self.x_vectors yield NX, self.n_x @@ -692,8 +715,10 @@ def _map_parameters_by_objective( default_value: float = None, ): """ + Create mapping for parameters from ensebmle to predictor. + The parameters of the ensemble don't need to have the same ordering as - in the predictor. This functions maps them onto each other + in the predictor. """ # create short hands parameter_ids_objective = predictor.amici_objective.x_names @@ -723,7 +748,8 @@ def predict( progress_bar: bool = True ) -> EnsemblePrediction: """ - Convenience function to run predictions for a full ensemble: + Run predictions for a full ensemble. + User needs to hand over a predictor function and settings, then all results are grouped as EnsemblePrediction for the whole ensemble @@ -815,10 +841,11 @@ def predict( def compute_summary(self, percentiles_list: Sequence[int] = (5, 20, 80, 95)): """ - This function computes the mean, the median, the standard deviation - and possibly percentiles for the parameters of the ensemble. - Those summary results are added as a data member to the - EnsemblePrediction object. + Compute summary for the parameters of the ensemble. + + Summary includes the mean, the median, the standard deviation and + possibly percentiles. Those summary results are added as a data + member to the EnsemblePrediction object. Parameters ---------- @@ -843,6 +870,8 @@ def compute_summary(self, def check_identifiability(self) -> pd.DataFrame: """ + Check identifiability of ensemble. + Use ensemble mean and standard deviation to assess (in a rudimentary way) whether or not parameters are identifiable. Returns a dataframe with tuples, which specify whether or not the lower and the upper @@ -909,8 +938,7 @@ def entries_per_start(fval_traces: List['np.ndarray'], max_size: int, max_per_start: int, ): """ - Creates the indices of each start that will be included - in the ensemble. + Create the indices of each start that will be included in the ensemble. Parameters ---------- @@ -929,7 +957,6 @@ def entries_per_start(fval_traces: List['np.ndarray'], ------- A list of number of candidates per start that are to be included in the ensemble. - """ # choose possible candidates ens_ind = [np.flatnonzero(fval <= cutoff) for fval in fval_traces] @@ -968,7 +995,7 @@ def get_vector_indices(trace_start: np.ndarray, n_vectors: int, distribute: bool, ): """ - Returns the indices to be taken into an ensemble. + Return the indices to be taken into an ensemble. Parameters ---------- @@ -988,7 +1015,6 @@ def get_vector_indices(trace_start: np.ndarray, ------- The indices to include in the ensemble. """ - candidates = np.flatnonzero(trace_start <= cutoff) if distribute: diff --git a/pypesto/ensemble/task.py b/pypesto/ensemble/task.py index 30f3f70c8..dfbee37e6 100644 --- a/pypesto/ensemble/task.py +++ b/pypesto/ensemble/task.py @@ -32,6 +32,7 @@ def __init__( self.id = id def execute(self) -> List[Any]: + """Execute the task.""" logger.info(f"Executing task {self.id}.") results = [] for index in range(self.vectors.shape[1]): diff --git a/pypesto/ensemble/utils.py b/pypesto/ensemble/utils.py index 7c9adc980..ae05d7dad 100644 --- a/pypesto/ensemble/utils.py +++ b/pypesto/ensemble/utils.py @@ -21,7 +21,7 @@ def read_from_csv(path: str, lower_bound: np.ndarray = None, upper_bound: np.ndarray = None): """ - function for creating an ensemble from a csv file + Create an ensemble from a csv file. Parameters ---------- @@ -78,7 +78,7 @@ def read_ensemble_from_hdf5(filename: str, Which type of ensemble to create. From History, from Optimization or from Sample. - Returns: + Returns ------- ensemble: Ensemble object of parameter vectors @@ -110,7 +110,7 @@ def read_from_df(dataframe: pd.DataFrame, lower_bound: np.ndarray = None, upper_bound: np.ndarray = None): """ - function for creating an ensemble from a csv file + Create an ensemble from a csv file. Parameters ---------- @@ -150,6 +150,18 @@ def read_from_df(dataframe: pd.DataFrame, def write_ensemble_prediction_to_h5(ensemble_prediction: EnsemblePrediction, output_file: str, base_path: str = None): + """ + Write an `EnsemblePrediction` to hdf5. + + Parameters + ---------- + ensemble_prediction: + The prediciton to be saved. + output_file: + The filename of the hdf5 file. + base_path: + An optional filepath where the file should be saved to. + """ # parse base path base = Path('') if base_path is not None: @@ -212,11 +224,13 @@ def write_ensemble_prediction_to_h5(ensemble_prediction: EnsemblePrediction, def get_prediction_dataset(ens: Union[Ensemble, EnsemblePrediction], prediction_index: int = 0) -> np.ndarray: """ - Extract an array of prediction from either an Ensemble object which - contains a list of predictions of from an EnsemblePrediction object. + Extract an array of prediction. + + Can be done from either an Ensemble object which contains a list of + predictions of from an EnsemblePrediction object. Parameters - ========== + ---------- ens: Ensemble objects containing a set of parameter vectors and a set of predictions or EnsemblePrediction object containing only predictions @@ -225,11 +239,10 @@ def get_prediction_dataset(ens: Union[Ensemble, EnsemblePrediction], index telling which prediction from the list should be analyzed Returns - ======= + ------- dataset: numpy array containing the ensemble predictions """ - if isinstance(ens, Ensemble): dataset = ens.predictions[prediction_index] elif isinstance(ens, EnsemblePrediction): @@ -245,7 +258,7 @@ def get_prediction_dataset(ens: Union[Ensemble, EnsemblePrediction], def read_ensemble_prediction_from_h5( predictor: Union[Callable[[Sequence], PredictionResult], None], input_file: str): - + """Read an ensemble prediction from an HDF5 File.""" # open file with h5py.File(input_file, 'r') as f: pred_res_list = [] @@ -288,7 +301,7 @@ def read_ensemble_prediction_from_h5( def decode_array(array: np.ndarray) -> np.ndarray: - """Decodes array of bytes to string""" + """Decode array of bytes to string.""" for i in range(len(array)): array[i] = array[i].decode() return array diff --git a/pypesto/logging.py b/pypesto/logging.py index 1672b503d..51e50e160 100644 --- a/pypesto/logging.py +++ b/pypesto/logging.py @@ -1,3 +1,4 @@ +# noqa: D400,D205 """ Logging ======= @@ -13,20 +14,16 @@ def log(name: str = 'pypesto', console: bool = True, filename: str = ''): """ - Log messages from a specified name with a specified level to any - combination of console and file. + Log messages from `name` with `level` to any combination of console/file. Parameters ---------- name: The name of the logger. - level: The output level to use. - console: If True, messages are logged to console. - filename: If specified, messages are logged to a file with this name. """ diff --git a/pypesto/objective/aesara.py b/pypesto/objective/aesara.py index 841716b68..fa2c2ca98 100644 --- a/pypesto/objective/aesara.py +++ b/pypesto/objective/aesara.py @@ -1,7 +1,9 @@ """ -This adds an interface for the construction of loss functions +Aesara models interface. + +Adds an interface for the construction of loss functions incorporating aesara models. This permits computation of derivatives using a -combination of objective based methods and aeara based backpropagation +combination of objective based methods and aeara based backpropagation. """ import numpy as np @@ -23,11 +25,12 @@ class AesaraObjective(ObjectiveBase): """ - Wrapper around an ObjectiveBase which computes the gradient at each - evaluation, caching it for later calls. + Wrapper around an ObjectiveBase. + + Computes the gradient at each evaluation, caching it for later calls. Caching is only enabled after the first time the gradient is asked for - and disabled whenever the cached gradient is not used, - in order not to increase computation time for derivative-free samplers. + and disabled whenever the cached gradient is not used, in order not to + increase computation time for derivative-free samplers. Parameters ---------- @@ -88,9 +91,11 @@ def __init__(self, self.inner_ret: ResultDict = {} def check_mode(self, mode) -> bool: + """See `ObjectiveBase` documentation.""" return mode == MODE_FUN def check_sensi_orders(self, sensi_orders, mode) -> bool: + """See `ObjectiveBase` documentation.""" if not self.check_mode(mode): return False else: @@ -103,7 +108,12 @@ def call_unprocessed( mode: str, **kwargs ) -> ResultDict: + """ + See `ObjectiveBase` for more documentation. + Main method to overwrite from the base class. It handles and + delegates the actual objective evaluation. + """ # hess computation in aesara requires grad if 2 in sensi_orders and 1 not in sensi_orders: sensi_orders = (1, *sensi_orders) @@ -166,7 +176,7 @@ def __init__(self, else: self._log_prob_grad = None - def perform(self, node, inputs, outputs, params=None): + def perform(self, node, inputs, outputs, params=None): # noqa # note that we use precomputed values from the outer # AesaraObjective.call_unprocessed here, which which means we can # ignore inputs here @@ -174,8 +184,12 @@ def perform(self, node, inputs, outputs, params=None): outputs[0][0] = np.array(log_prob) def grad(self, inputs, g): - # the method that calculates the gradients - it actually returns the - # vector-Jacobian product - g[0] is a vector of parameter values + """ + Calculate the hessian. + + Actually returns the vector-hessian product - g[0] is a vector of + parameter values. + """ theta, = inputs log_prob_grad = self._log_prob_grad(theta) return [g[0] * log_prob_grad] @@ -184,6 +198,7 @@ def grad(self, inputs, g): class AesaraObjectiveGradOp(Op): """ Aesara wrapper around a (non-normalized) log-probability gradient function. + This Op will be called with a vector of values and also return a vector of values - the gradients in each dimension. @@ -209,7 +224,7 @@ def __init__(self, else: self._log_prob_hess = None - def perform(self, node, inputs, outputs, params=None): + def perform(self, node, inputs, outputs, params=None): # noqa # note that we use precomputed values from the outer # AesaraObjective.call_unprocessed here, which which means we can # ignore inputs here @@ -217,8 +232,12 @@ def perform(self, node, inputs, outputs, params=None): outputs[0][0] = log_prob_grad def grad(self, inputs, g): - # the method that calculates the hessian - it actually returns the - # vector-hessian product - g[0] is a vector of parameter values + """ + Calculate the hessian. + + Actually returns the vector-hessian product - g[0] is a vector of + parameter values. + """ theta, = inputs log_prob_hess = self._log_prob_hess(theta) return [g[0].dot(log_prob_hess)] @@ -227,6 +246,7 @@ def grad(self, inputs, g): class AesaraObjectiveHessOp(Op): """ Aesara wrapper around a (non-normalized) log-probability Hessian function. + This Op will be called with a vector of values and also return a matrix of values - the Hessian in each dimension. @@ -247,7 +267,7 @@ def __init__(self, obj: self._objective: AesaraObjective = obj self._coeff: float = coeff - def perform(self, node, inputs, outputs, params=None): + def perform(self, node, inputs, outputs, params=None): # noqa # note that we use precomputed values from the outer # AesaraObjective.call_unprocessed here, which which means we can # ignore inputs here diff --git a/pypesto/objective/aggregated.py b/pypesto/objective/aggregated.py index d39d35654..ed8aa1d86 100644 --- a/pypesto/objective/aggregated.py +++ b/pypesto/objective/aggregated.py @@ -8,9 +8,7 @@ class AggregatedObjective(ObjectiveBase): - """ - This class aggregates multiple objectives into one objective. - """ + """Aggregates multiple objectives into one objective.""" def __init__( self, @@ -18,8 +16,7 @@ def __init__( x_names: Sequence[str] = None, ): """ - Constructor. - + Initialize objective. Parameters ---------- @@ -30,7 +27,6 @@ def __init__( (Details see documentation of x_names in :class:`pypesto.ObjectiveBase`) """ - # input typechecks if not isinstance(objectives, Sequence): raise TypeError(f'Objectives must be a Sequence, ' @@ -51,6 +47,7 @@ def __init__( super().__init__(x_names=x_names) def __deepcopy__(self, memodict=None): + """Create copy of objective.""" other = AggregatedObjective( objectives=[deepcopy(objective) for objective in self._objectives], x_names=deepcopy(self.x_names), @@ -61,6 +58,7 @@ def __deepcopy__(self, memodict=None): return other def check_mode(self, mode: str) -> bool: + """See `ObjectiveBase` documentation.""" return all( objective.check_mode(mode) for objective in self._objectives @@ -71,6 +69,7 @@ def check_sensi_orders( sensi_orders: Tuple[int, ...], mode: str, ) -> bool: + """See `ObjectiveBase` documentation.""" return all( objective.check_sensi_orders(sensi_orders, mode) for objective in self._objectives @@ -83,16 +82,24 @@ def call_unprocessed( mode: str, **kwargs, ) -> ResultDict: + """ + See `ObjectiveBase` for more documentation. + + Main method to overwrite from the base class. It handles and + delegates the actual objective evaluation. + """ return aggregate_results([ objective.call_unprocessed(x, sensi_orders, mode, **kwargs) for objective in self._objectives ]) def initialize(self): + """See `ObjectiveBase` documentation.""" for objective in self._objectives: objective.initialize() def get_config(self) -> dict: + """Return basic information of the objective configuration.""" info = super().get_config() for n_obj, obj in enumerate(self._objectives): info[f'objective_{n_obj}'] = obj.get_config() @@ -101,15 +108,13 @@ def get_config(self) -> dict: def aggregate_results(rvals: Sequence[ResultDict]) -> ResultDict: """ - Aggregrate the results from the provided sequence of ResultDicts into a - single ResultDict. + Aggregrate the results from the provided ResultDicts into a single one. Parameters ---------- rvals: results to aggregate """ - # rvals are guaranteed to be consistent as _check_sensi_orders checks # whether each objective can be called with the respective # sensi_orders/mode diff --git a/pypesto/objective/amici.py b/pypesto/objective/amici.py index 256f122bd..e9e6c01b5 100644 --- a/pypesto/objective/amici.py +++ b/pypesto/objective/amici.py @@ -46,9 +46,7 @@ def create_edatas(self, model: AmiciModel) -> Sequence['amici.ExpData']: class AmiciObjective(ObjectiveBase): - """ - This class allows to create an objective directly from an amici model. - """ + """Allows to create an objective directly from an amici model.""" def __init__( self, @@ -66,7 +64,7 @@ def __init__( calculator: Optional[AmiciCalculator] = None, ): """ - Constructor. + Initialize objective. Parameters ---------- @@ -198,6 +196,7 @@ def __init__( self.custom_timepoints = None def get_config(self) -> dict: + """Return basic information of the objective configuration.""" info = super().get_config() info['x_names'] = self.x_names info['model_name'] = self.amici_model.getName() @@ -207,6 +206,7 @@ def get_config(self) -> dict: return info def initialize(self): + """See `ObjectiveBase` documentation.""" super().initialize() self.reset_steadystate_guesses() self.calculator.initialize() @@ -298,6 +298,7 @@ def check_sensi_orders( sensi_orders: Tuple[int, ...], mode: str, ) -> bool: + """See `ObjectiveBase` documentation.""" sensi_order = max(sensi_orders) # dynamically obtain maximum allowed sensitivity order @@ -316,6 +317,7 @@ def check_sensi_orders( return sensi_order <= max_sensi_order def check_mode(self, mode: str) -> bool: + """See `ObjectiveBase` documentation.""" return mode in [MODE_FUN, MODE_RES] def call_unprocessed( @@ -326,6 +328,14 @@ def call_unprocessed( edatas: Sequence['amici.ExpData'] = None, parameter_mapping: 'ParameterMapping' = None, ): + """ + Call objective function without pre- or post-processing and formatting. + + Returns + ------- + result: + A dict containing the results. + """ sensi_order = max(sensi_orders) x_dct = self.par_arr_to_dct(x) @@ -367,7 +377,9 @@ def par_arr_to_dct(self, x: Sequence[float]) -> Dict[str, float]: def apply_steadystate_guess(self, condition_ix: int, x_dct: Dict) -> None: """ - Use the stored steadystate as well as the respective sensitivity ( + Apply steady state guess to `edatas[condition_ix].x0`. + + Use the stored steadystate as well as the respective sensitivity ( if available) and parameter value to approximate the steadystate at the current parameters using a zeroth or first order taylor approximation: @@ -399,9 +411,10 @@ def store_steadystate_guess( rdata: 'amici.ReturnData', ) -> None: """ - Store condition parameter, steadystate and steadystate sensitivity in - steadystate_guesses if steadystate guesses are enabled for this - condition + Store condition parameter, steadystate and steadystate sensitivity. + + Stored in steadystate_guesses if steadystate guesses are enabled for + this condition. """ if condition_ix not in self.steadystate_guesses['data']: return @@ -444,8 +457,7 @@ def set_custom_timepoints( timepoints_global: Sequence[Union[float, int]] = None, ) -> 'AmiciObjective': """ - Create a copy of this objective that will be evaluated at custom - timepoints. + Create a copy of this objective that is evaluated at custom timepoints. The intended use is to aid in predictions at unmeasured timepoints. @@ -493,7 +505,7 @@ def check_gradients_match_finite_differences( *args, **kwargs ) -> bool: - """Check if gradients match finite differences (FDs) + """Check if gradients match finite differences (FDs). Parameters ---------- diff --git a/pypesto/objective/amici_calculator.py b/pypesto/objective/amici_calculator.py index c6d3caeb4..6ad638c32 100644 --- a/pypesto/objective/amici_calculator.py +++ b/pypesto/objective/amici_calculator.py @@ -23,10 +23,7 @@ class AmiciCalculator: - """ - Class to perform the actual call to AMICI and obtain requested objective - function values. - """ + """Class to perform the AMICI call and obtain objective function values.""" def __init__(self): self._known_least_squares_safe = False @@ -126,6 +123,7 @@ def calculate_function_values(rdatas, x_ids: Sequence[str], parameter_mapping: 'ParameterMapping', fim_for_hess: bool): + """Calculate the function values from rdatas and return as dict.""" # full optimization problem dimension (including fixed parameters) dim = len(x_ids) diff --git a/pypesto/objective/amici_util.py b/pypesto/objective/amici_util.py index 37fa15fd4..f40d31e42 100644 --- a/pypesto/objective/amici_util.py +++ b/pypesto/objective/amici_util.py @@ -29,8 +29,7 @@ def map_par_opt_to_par_sim( amici_model: AmiciModel ) -> np.ndarray: """ - From the optimization vector, create the simulation vector according - to the mapping. + Create simulation vector from optimization vector using the mapping. Parameters ---------- @@ -61,15 +60,14 @@ def map_par_opt_to_par_sim( def create_plist_from_par_opt_to_par_sim(mapping_par_opt_to_par_sim): - warnings.warn("This function will be removed in future releases. ", - DeprecationWarning) """ + Create list of parameter indices for which sensitivity is to be computed. + From the parameter mapping `mapping_par_opt_to_par_sim`, create the simulation plist according to the mapping `mapping`. Parameters ---------- - mapping_par_opt_to_par_sim: array-like of str len == n_par_sim, the entries are either numeric, or optimization parameter ids. @@ -80,6 +78,8 @@ def create_plist_from_par_opt_to_par_sim(mapping_par_opt_to_par_sim): List of parameter indices for which the sensitivity needs to be computed """ + warnings.warn("This function will be removed in future releases. ", + DeprecationWarning) plist = [] # iterate over simulation parameter indices @@ -134,10 +134,9 @@ def par_index_slices( The simulation to optimization parameter mapping. Returns - ---------- + ------- par_sim_slice: array of simulation parameter indices - par_opt_slice: array of optimization parameter indices """ @@ -161,8 +160,9 @@ def add_sim_grad_to_opt_grad( opt_grad: np.ndarray, coefficient: float = 1.0): """ - Sum simulation gradients to objective gradient according to the provided - mapping `mapping_par_opt_to_par_sim`. + Sum simulation gradients to objective gradient. + + Uses the provided mapping `mapping_par_opt_to_par_sim` for summing up. Parameters ---------- @@ -180,7 +180,6 @@ def add_sim_grad_to_opt_grad( coefficient: Coefficient for sim_grad when adding to opt_grad. """ - par_sim_slice, par_opt_slice = par_index_slices(par_opt_ids, par_sim_ids, condition_map_sim_var) @@ -204,14 +203,12 @@ def add_sim_hess_to_opt_hess( opt_hess: np.ndarray, coefficient: float = 1.0): """ - Sum simulation hessians to objective hessian according to the provided - mapping `mapping_par_opt_to_par_sim`. + Sum simulation hessians to objective hessian. Parameters ---------- Same as for add_sim_grad_to_opt_grad, replacing the gradients by hessians. """ - par_sim_slice, par_opt_slice = par_index_slices(par_opt_ids, par_sim_ids, condition_map_sim_var) @@ -245,8 +242,8 @@ def sim_sres_to_opt_sres(par_opt_ids: Sequence[str], sim_sres: np.ndarray, coefficient: float = 1.0): """ - Sum simulation residual sensitivities to objective residual sensitivities - according to the provided mapping. + + Sum simulation residual sensitivities to objective residual sensitivities. Parameters ---------- @@ -292,7 +289,7 @@ def get_error_output( sensi_order: int, mode: str, dim: int): - """Default output upon error. + """Get default output upon error. Returns values indicative of an error, that is with nan entries in all vectors, and a function value, i.e. nllh, of `np.inf`. @@ -324,6 +321,7 @@ def get_error_output( def init_return_values(sensi_order, mode, dim, error=False): + """Initialize return values.""" if error: fval = np.inf sval = np.nan @@ -351,7 +349,7 @@ def init_return_values(sensi_order, mode, dim, error=False): def filter_return_dict(ret): - """Filters return dict for non-None values""" + """Filter return dict for non-None values.""" return { key: val for key, val in ret.items() diff --git a/pypesto/objective/base.py b/pypesto/objective/base.py index 9de13ca7d..15bf6e56e 100644 --- a/pypesto/objective/base.py +++ b/pypesto/objective/base.py @@ -16,6 +16,8 @@ class ObjectiveBase(abc.ABC): """ + Abstract objective class. + The objective class is a simple wrapper around the objective function, giving a standardized way of calling. Apart from that, it manages several things including fixing of parameters and history. @@ -54,6 +56,7 @@ def __init__( self.history = HistoryBase() def __deepcopy__(self, memodict=None) -> 'ObjectiveBase': + """Create deepcopy of objective object.""" other = type(self)() # maintain type for derived classes for attr, val in self.__dict__.items(): other.__dict__[attr] = copy.deepcopy(val) @@ -63,31 +66,37 @@ def __deepcopy__(self, memodict=None) -> 'ObjectiveBase': # the objective supports. @property def has_fun(self) -> bool: + """Check whether function is defined.""" return self.check_sensi_orders((0,), MODE_FUN) @property def has_grad(self) -> bool: + """Check whether gradient is defined.""" return self.check_sensi_orders((1,), MODE_FUN) @property def has_hess(self) -> bool: + """Check whether Hessian is defined.""" return self.check_sensi_orders((2,), MODE_FUN) @property - def has_hessp(self) -> bool: + def has_hessp(self) -> bool: # noqa # Not supported yet return False @property def has_res(self) -> bool: + """Check whether residuals are defined.""" return self.check_sensi_orders((0,), MODE_RES) @property def has_sres(self) -> bool: + """Check whether residual sensitivities are defined.""" return self.check_sensi_orders((1,), MODE_RES) @property def x_names(self) -> Union[List[str], None]: + """Parameter names.""" if self._x_names is None: return self._x_names @@ -99,7 +108,9 @@ def x_names(self) -> Union[List[str], None]: ] def initialize(self): - """Initialize the objective function. + """ + Initialize the objective function. + This function is used at the beginning of an analysis, e.g. optimization, and can e.g. reset the objective memory. By default does nothing. @@ -114,8 +125,10 @@ def __call__( **kwargs, ) -> Union[float, np.ndarray, Tuple, ResultDict]: """ - Method to obtain arbitrary sensitivities. This is the central method - which is always called, also by the get_* methods. + Obtain arbitrary sensitivities. + + This is the central method which is always called, also by the + get_* methods. There are different ways in which an optimizer calls the objective function, and in how the objective function provides information @@ -186,8 +199,7 @@ def call_unprocessed( **kwargs, ) -> ResultDict: """ - Call objective function without pre- or post-processing and - formatting. + Call objective function without pre- or post-processing and formatting. Parameters ---------- @@ -216,6 +228,7 @@ def check_mode(self, mode: str) -> bool: ---------- mode: Whether to compute function values or residuals. + Returns ------- flag: @@ -230,8 +243,9 @@ def check_mode(self, mode: str) -> bool: def get_config(self) -> dict: """ - Get the configuration information of the objective - function and return it as a dictonary. + Get the configuration information of the objective function. + + Return it as a dictonary. """ info = {'type': self.__class__.__name__} return info @@ -242,8 +256,7 @@ def check_sensi_orders( mode: str, ) -> bool: """ - Check if the objective is able to compute the requested - sensitivities. + Check if the objective is able to compute the requested sensitivities. Either `check_sensi_orders` or the `fun_...` functions must be overwritten in derived classes. @@ -288,9 +301,11 @@ def output_to_tuple( **kwargs: Union[float, np.ndarray], ) -> Tuple: """ - Return values as requested by the caller, since usually only a subset - is demanded. One output is returned as-is, more than one output are - returned as a tuple in order (fval, grad, hess). + Return values as requested by the caller. + + Usually only a subset of outputs is demanded. One output is returned + as-is, more than one output are returned as a tuple in order (fval, + grad, hess). """ output = () if mode == MODE_FUN: @@ -312,7 +327,6 @@ def output_to_tuple( # The following are convenience functions for getting specific outputs. def get_fval(self, x: np.ndarray) -> float: """Get the function value at x.""" - fval = self(x, (0,), MODE_FUN) return fval @@ -344,11 +358,13 @@ def update_from_problem( x_fixed_vals: Sequence[float], ): """ - Handle fixed parameters. Later, the objective will be given parameter - vectors x of dimension dim, which have to be filled up with fixed - parameter values to form a vector of dimension dim_full >= dim. - This vector is then used to compute function value and derivatives. - The derivatives must later be reduced again to dimension dim. + Handle fixed parameters. + + Later, the objective will be given parameter vectors x of dimension + dim, which have to be filled up with fixed parameter values to form + a vector of dimension dim_full >= dim. This vector is then used to + compute function value and derivatives. The derivatives must later + be reduced again to dimension dim. This is so as to make the fixing of parameters transparent to the caller. @@ -370,7 +386,6 @@ def update_from_problem( Vector of the same length as x_fixed_indices, containing the values of the fixed parameters. """ - pre_post_processor = FixedParametersProcessor( dim_full=dim_full, x_free_indices=x_free_indices, @@ -387,6 +402,8 @@ def check_grad_multi_eps( **kwargs, ): """ + Compare gradient evaluation. + Equivalent to the `ObjectiveBase.check_grad` method, except multiple finite difference step sizes are tested. The result contains the lowest finite difference for each parameter, and the corresponding @@ -442,8 +459,10 @@ def check_grad( detailed: bool = False, ) -> pd.DataFrame: """ - Compare gradient evaluation: Firstly approximate via finite - differences, and secondly use the objective gradient. + Compare gradient evaluation. + + Firstly approximate via finite differences, and secondly use the + objective gradient. Parameters ---------- @@ -470,11 +489,10 @@ def check_grad( mean). Returns - ---------- + ------- result: gradient, finite difference approximations and error estimates. """ - if x_indices is None: x_indices = list(range(len(x))) @@ -613,7 +631,7 @@ def check_gradients_match_finite_differences( multi_eps=None, **kwargs, ) -> bool: - """Check if gradients match finite differences (FDs) + """Check if gradients match finite differences (FDs). Parameters ---------- diff --git a/pypesto/objective/constants.py b/pypesto/objective/constants.py index 159fcf433..29091c1de 100644 --- a/pypesto/objective/constants.py +++ b/pypesto/objective/constants.py @@ -1,7 +1,4 @@ -""" -This is for (string) constants used in the objective module. -""" - +"""Constants used in the objective module.""" MODE_FUN = 'mode_fun' # mode for function values MODE_RES = 'mode_res' # mode for residuals diff --git a/pypesto/objective/finite_difference.py b/pypesto/objective/finite_difference.py index db0ec3826..756d51c3e 100644 --- a/pypesto/objective/finite_difference.py +++ b/pypesto/objective/finite_difference.py @@ -144,7 +144,8 @@ def _update( fun: Callable, fd_method: str, ) -> None: - """Actually update. Wants to be called in `update` explicitly. + """ + Actually update. Wants to be called in `update` explicitly. Run FDs with various deltas and pick the ones, separately for each parameter, with the best stability properties. @@ -152,7 +153,6 @@ def _update( The parameters are the same as for :func:`pypesto.objective.finite_difference.FDDelta.update`. """ - # calculate gradients for all deltas for all parameters nablas = [] # iterate over deltas @@ -322,6 +322,7 @@ def __deepcopy__( self, memodict: Dict = None, ) -> 'FD': + """Create deepcopy of Objective.""" other = self.__class__.__new__(self.__class__) for attr, val in self.__dict__.items(): other.__dict__[attr] = copy.deepcopy(val) @@ -329,22 +330,27 @@ def __deepcopy__( @property def has_fun(self) -> bool: + """Check whether function is defined.""" return self.obj.has_fun @property def has_grad(self) -> bool: + """Check whether gradient is defined.""" return self.grad is not False and self.obj.has_fun @property def has_hess(self) -> bool: + """Check whether Hessian is defined.""" return self.hess is not False and self.obj.has_fun @property def has_res(self) -> bool: + """Check whether residuals are defined.""" return self.obj.has_res @property def has_sres(self) -> bool: + """Check whether residual sensitivities are defined.""" return self.sres is not False and self.obj.has_res def call_unprocessed( @@ -354,9 +360,12 @@ def call_unprocessed( mode: str, **kwargs, ) -> ResultDict: - # This is the main method to overwrite from the base class, it handles - # and delegates the actual objective evaluation. + """ + See `ObjectiveBase` for more documentation. + Main method to overwrite from the base class. It handles and + delegates the actual objective evaluation. + """ if mode == MODE_FUN: result = self._call_mode_fun( x=x, sensi_orders=sensi_orders, **kwargs) @@ -483,8 +492,10 @@ def _call_from_obj_fun( **kwargs, ) -> Tuple[Tuple[int, ...], ResultDict]: """ - Helper function that calculates from the objective the sensitivities - that are supposed to be calculated from the objective and not via FDs. + Call objective function for sensitivities. + + Calculate from the objective the sensitivities that are supposed to + be calculated from the objective and not via FDs. """ # define objective sensis sensi_orders_obj = [] @@ -509,8 +520,10 @@ def _call_from_obj_res( **kwargs, ) -> Tuple[Tuple[int, ...], ResultDict]: """ - Helper function that calculates from the objective the sensitivities - that are supposed to be calculated from the objective and not via FDs. + Call objective function for sensitivities in residual mode. + + Calculate from the objective the sensitivities that are supposed to + be calculated from the objective and not via FDs. """ # define objective sensis sensi_orders_obj = [] @@ -528,7 +541,8 @@ def _call_from_obj_res( def unit_vec(dim: int, ix: int) -> np.ndarray: - """Unit vector of dimension `dim` at coordinate `ix`. + """ + Return unit vector of dimension `dim` at coordinate `ix`. Parameters ---------- diff --git a/pypesto/objective/function.py b/pypesto/objective/function.py index f7cd3fea0..eb41fe0d1 100644 --- a/pypesto/objective/function.py +++ b/pypesto/objective/function.py @@ -8,6 +8,8 @@ class Objective(ObjectiveBase): """ + Objective class. + The objective class allows the user explicitly specify functions that compute the function value and/or residuals as well as respective derivatives. @@ -16,7 +18,6 @@ class Objective(ObjectiveBase): Parameters ---------- - fun: The objective function to be minimized. If it only computes the objective function value, it should be of the form @@ -25,7 +26,6 @@ class Objective(ObjectiveBase): where x is an 1-D array with shape (n,), and n is the parameter space dimension. - grad: Method for computing the gradient vector. If it is a callable, it should be of the form @@ -34,7 +34,6 @@ class Objective(ObjectiveBase): If its value is True, then fun should return the gradient as a second output. - hess: Method for computing the Hessian matrix. If it is a callable, it should be of the form @@ -44,19 +43,16 @@ class Objective(ObjectiveBase): If its value is True, then fun should return the gradient as a second, and the Hessian as a third output, and grad should be True as well. - hessp: Method for computing the Hessian vector product, i.e. ``hessp(x, v) -> array_like, shape (n,)`` computes the product H*v of the Hessian of fun at x with v. - res: Method for computing residuals, i.e. ``res(x) -> array_like, shape(m,).`` - sres: Method for computing residual sensitivities. If it is a callable, it should be of the form @@ -65,12 +61,12 @@ class Objective(ObjectiveBase): If its value is True, then res should return the residual sensitivities as a second output. - x_names: Parameter names. None if no names provided, otherwise a list of str, length dim_full (as in the Problem class). Can be read by the problem. """ + def __init__( self, fun: Callable = None, @@ -91,30 +87,36 @@ def __init__( @property def has_fun(self) -> bool: + """Check whether function is defined.""" return callable(self.fun) @property def has_grad(self) -> bool: + """Check whether gradient is defined.""" return callable(self.grad) or self.grad is True @property def has_hess(self) -> bool: + """Check whether Hessian is defined.""" return callable(self.hess) or self.hess is True @property - def has_hessp(self) -> bool: + def has_hessp(self) -> bool: # noqa # Not supported yet return False @property def has_res(self) -> bool: + """Check whether residuals are defined.""" return callable(self.res) @property def has_sres(self) -> bool: + """Check whether residual sensitivities are defined.""" return callable(self.sres) or self.sres is True def get_config(self) -> dict: + """Return basic information of the objective configuration.""" info = super().get_config() info['x_names'] = self.x_names sensi_order = 0 @@ -132,8 +134,7 @@ def call_unprocessed( **kwargs, ) -> ResultDict: """ - Call objective function without pre- or post-processing and - formatting. + Call objective function without pre- or post-processing and formatting. Returns ------- diff --git a/pypesto/objective/history.py b/pypesto/objective/history.py index 3ee406cb2..3b831b14d 100644 --- a/pypesto/objective/history.py +++ b/pypesto/objective/history.py @@ -20,8 +20,10 @@ def trace_wrap(f): """ - Wrapper around trace getters that transforms input `ix` vectors to a valid - index list, and reduces for integer `ix` the output to a single value. + Wrap around trace getters. + + Transform input `ix` vectors to a valid index list, and reduces for + integer `ix` the output to a single value. """ def wrapped_f( self, ix: Union[Sequence[int], int, None] = None, @@ -49,8 +51,7 @@ def wrapped_f( class HistoryOptions(dict): """ - Options for the objective that are used in optimization, profiling - and sampling. + Options for the objective that are used in optimization. In addition implements a factory pattern to generate history objects. @@ -107,6 +108,7 @@ def __init__(self, self.storage_file = storage_file def __getattr__(self, key): + """Allow to use keys as attributes.""" try: return self[key] except KeyError: @@ -120,7 +122,7 @@ def assert_instance( maybe_options: Union['HistoryOptions', Dict] ) -> 'HistoryOptions': """ - Returns a valid options object. + Return a valid options object. Parameters ---------- @@ -134,7 +136,7 @@ def assert_instance( def create_history( self, id: str, x_names: Sequence[str] ) -> 'History': - """Factory method creating a :class:`History` object. + """Create a :class:`History` object; Factory method. Parameters ---------- @@ -144,7 +146,6 @@ def create_history( Parameter names. """ # create different history types based on the inputs - if self.storage_file is None: if self.trace_record: return MemoryHistory(options=self) @@ -175,7 +176,7 @@ class HistoryBase(abc.ABC): """ def __len__(self): - """Number of stored entries in the history""" + """Define length by number of stored entries in the history.""" raise NotImplementedError() def update( @@ -205,39 +206,40 @@ def finalize(self): @property def n_fval(self) -> int: - """Number of function evaluations.""" + """Return number of function evaluations.""" raise NotImplementedError() @property def n_grad(self) -> int: - """Number of gradient evaluations.""" + """Return number of gradient evaluations.""" raise NotImplementedError() @property def n_hess(self) -> int: - """Number of Hessian evaluations.""" + """Return number of Hessian evaluations.""" raise NotImplementedError() @property def n_res(self) -> int: - """Number of residual evaluations.""" + """Return number of residual evaluations.""" raise NotImplementedError() @property def n_sres(self) -> int: - """Number or residual sensitivity evaluations.""" + """Return number or residual sensitivity evaluations.""" raise NotImplementedError() @property def start_time(self) -> float: - """Start time.""" + """Return start time.""" raise NotImplementedError() def get_x_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[np.ndarray], np.ndarray]: - """Parameters. + """ + Return parameters. Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened. @@ -248,7 +250,8 @@ def get_fval_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[float], float]: - """Function values. + """ + Return function values. Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened. @@ -259,7 +262,8 @@ def get_grad_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: - """Gradients. + """ + Return gradients. Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened. @@ -270,7 +274,8 @@ def get_hess_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: - """Hessians. + """ + Return hessians. Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened. @@ -281,7 +286,8 @@ def get_res_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: - """Residuals. + """ + Residuals. Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened. @@ -292,7 +298,8 @@ def get_sres_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: - """Residual sensitivities. + """ + Residual sensitivities. Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened. @@ -303,7 +310,8 @@ def get_chi2_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[float], float]: - """Chi2 values. + """ + Chi2 values. Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened. @@ -314,7 +322,8 @@ def get_schi2_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: - """Chi2 sensitivities. + """ + Chi2 sensitivities. Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened. @@ -325,7 +334,8 @@ def get_time_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[float], float]: - """Cumulative execution times. + """ + Cumulative execution times. Takes as parameter an index or indices and returns corresponding trace values. If only a single value is requested, the list is flattened. @@ -333,16 +343,14 @@ def get_time_trace( raise NotImplementedError() def get_trimmed_indices(self): - """ - Returns indices of the history to get a monotonically - decreasing history. - """ + """Get indices for a monotonically decreasing history.""" fval_trace = self.get_fval_trace() return np.where(fval_trace <= np.fmin.accumulate(fval_trace))[0] class History(HistoryBase): - """Tracks numbers of function evaluations only, no trace. + """ + Track number of function evaluations only, no trace. Parameters ---------- @@ -391,14 +399,13 @@ def update( self._update_counts(sensi_orders, mode) def finalize(self): + """See `HistoryBase` docstring.""" pass def _update_counts(self, sensi_orders: Tuple[int, ...], mode: str): - """ - Update the counters. - """ + """Update the counters.""" if mode == MODE_FUN: if 0 in sensi_orders: self._n_fval += 1 @@ -414,31 +421,40 @@ def _update_counts(self, @property def n_fval(self) -> int: + """See `HistoryBase` docstring.""" return self._n_fval @property def n_grad(self) -> int: + """See `HistoryBase` docstring.""" return self._n_grad @property def n_hess(self) -> int: + """See `HistoryBase` docstring.""" return self._n_hess @property def n_res(self) -> int: + """See `HistoryBase` docstring.""" return self._n_res @property def n_sres(self) -> int: + """See `HistoryBase` docstring.""" return self._n_sres @property def start_time(self) -> float: + """See `HistoryBase` docstring.""" return self._start_time class MemoryHistory(History): - """Tracks numbers of function evaluations and keeps an in-memory + """ + Class for optimization history stored in memory. + + Track number of function evaluations and keeps an in-memory trace of function evaluations. Parameters @@ -453,6 +469,7 @@ def __init__(self, options: Union[HistoryOptions, Dict] = None): self._trace: Dict[str, Any] = {key: [] for key in self._trace_keys} def __len__(self): + """Define length of history object.""" return len(self._trace[TIME]) def update( @@ -462,6 +479,7 @@ def update( mode: str, result: ResultDict ) -> None: + """See `History` docstring.""" super().update(x, sensi_orders, mode, result) self._update_trace(x, mode, result) @@ -479,6 +497,7 @@ def get_x_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[np.ndarray], np.ndarray]: + """See `HistoryBase` docstring.""" return [self._trace[X][i] for i in ix] @trace_wrap @@ -486,6 +505,7 @@ def get_fval_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[float], float]: + """See `HistoryBase` docstring.""" return [self._trace[FVAL][i] for i in ix] @trace_wrap @@ -493,6 +513,7 @@ def get_grad_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: + """See `HistoryBase` docstring.""" return [self._trace[GRAD][i] for i in ix] @trace_wrap @@ -500,6 +521,7 @@ def get_hess_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: + """See `HistoryBase` docstring.""" return [self._trace[HESS][i] for i in ix] @trace_wrap @@ -507,6 +529,7 @@ def get_res_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: + """See `HistoryBase` docstring.""" return [self._trace[RES][i] for i in ix] @trace_wrap @@ -514,6 +537,7 @@ def get_sres_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: + """See `HistoryBase` docstring.""" return [self._trace[SRES][i] for i in ix] @trace_wrap @@ -521,6 +545,7 @@ def get_chi2_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[float], float]: + """See `HistoryBase` docstring.""" return [self._trace[CHI2][i] for i in ix] @trace_wrap @@ -528,6 +553,7 @@ def get_schi2_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: + """See `HistoryBase` docstring.""" return [self._trace[SCHI2][i] for i in ix] @trace_wrap @@ -535,6 +561,7 @@ def get_time_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[float], float]: + """See `HistoryBase` docstring.""" return [self._trace[TIME][i] for i in ix] @@ -585,6 +612,7 @@ def __init__(self, self._update_counts_from_trace() def __len__(self): + """Define length of history object.""" return len(self._trace) def _update_counts_from_trace(self): @@ -601,6 +629,7 @@ def update( mode: str, result: ResultDict ) -> None: + """See `History` docstring.""" super().update(x, sensi_orders, mode, result) self._update_trace(x, mode, result) @@ -613,9 +642,7 @@ def _update_trace(self, x: np.ndarray, mode: str, result: ResultDict): - """ - Update and possibly store the trace. - """ + """Update and possibly store the trace.""" if not self.options.trace_record: return @@ -662,9 +689,7 @@ def _update_trace(self, self._save_trace() def _init_trace(self, x: np.ndarray): - """ - Initialize the trace. - """ + """Initialize the trace.""" if self.x_names is None: self.x_names = [f'x{i}' for i, _ in enumerate(x)] @@ -706,8 +731,10 @@ def _init_trace(self, x: np.ndarray): def _save_trace(self, finalize: bool = False): """ - Save to file via pd.DataFrame.to_csv() if `self.storage_file` is - not None and other conditions apply. + Save to file via pd.DataFrame.to_csv(). + + Only done, if `self.storage_file` is not None and other conditions. + apply. """ if self.file is None: return @@ -728,6 +755,7 @@ def get_x_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[np.ndarray], np.ndarray]: + """See `HistoryBase` docstring.""" return list(self._trace[X].values[ix]) @trace_wrap @@ -735,6 +763,7 @@ def get_fval_trace( self, ix: Union[int, Sequence[int], None], trim: bool = False ) -> Union[Sequence[float], float]: + """See `HistoryBase` docstring.""" return list(self._trace[(FVAL, np.nan)].values[ix]) @trace_wrap @@ -742,6 +771,7 @@ def get_grad_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: + """See `HistoryBase` docstring.""" return list(self._trace[GRAD].values[ix]) @trace_wrap @@ -749,6 +779,7 @@ def get_hess_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: + """See `HistoryBase` docstring.""" return list(self._trace[(HESS, np.nan)].values[ix]) @trace_wrap @@ -756,6 +787,7 @@ def get_res_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: + """See `HistoryBase` docstring.""" return list(self._trace[(RES, np.nan)].values[ix]) @trace_wrap @@ -763,6 +795,7 @@ def get_sres_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: + """See `HistoryBase` docstring.""" return list(self._trace[(SRES, np.nan)].values[ix]) @trace_wrap @@ -770,6 +803,7 @@ def get_chi2_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[float], float]: + """See `HistoryBase` docstring.""" return list(self._trace[(CHI2, np.nan)].values[ix]) @trace_wrap @@ -777,6 +811,7 @@ def get_schi2_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: + """See `HistoryBase` docstring.""" return list(self._trace[SCHI2].values[ix]) @trace_wrap @@ -784,11 +819,13 @@ def get_time_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[float], float]: + """See `HistoryBase` docstring.""" return list(self._trace[(TIME, np.nan)].values[ix]) class Hdf5History(History): - """Stores a representation of the history in an HDF5 file. + """ + Stores a representation of the history in an HDF5 file. Parameters ---------- @@ -810,6 +847,7 @@ def __init__(self, self._generate_hdf5_group() def __len__(self): + """Define length of history object.""" with h5py.File(self.file, 'r') as f: return f[f'history/{self.id}/trace/'].attrs[ 'n_iterations'] @@ -821,27 +859,31 @@ def update( mode: str, result: ResultDict ) -> None: + """See `History` docstring.""" super().update(x, sensi_orders, mode, result) self._update_trace(x, sensi_orders, mode, result) def get_history_directory(self): + """Return filepath.""" return self.file def finalize(self): + """See `History` docstring.""" super().finalize() @staticmethod def load(id: str, file: str): - """Loads the History object from memory.""" + """Load the History object from memory.""" loaded_h5history = Hdf5History(id, file) loaded_h5history._recover_options(file) return loaded_h5history def _recover_options(self, file: str): """ - Recovers options when loading the hdf5 history from memory - by testing which entries were recorded. + Recover options when loading the hdf5 history from memory. + + Done by testing which entries were recorded. """ trace_record = self._check_for_not_nan_entries(X) trace_record_grad = self._check_for_not_nan_entries(GRAD) @@ -866,7 +908,7 @@ def _recover_options(self, file: str): self.options = restored_history_options def _check_for_not_nan_entries(self, hdf5_group: str) -> bool: - """Checks if there exist not-nan entries stored for a given group""" + """Check if there exist not-nan entries stored for a given group.""" group = self._get_hdf5_entries(hdf5_group, ix=None) for entry in group: @@ -879,9 +921,7 @@ def _check_for_not_nan_entries(self, hdf5_group: str) -> bool: def _update_counts(self, sensi_orders: Tuple[int, ...], mode: str): - """ - Update the counters in the hdf5 - """ + """Update the counters in the hdf5.""" with h5py.File(self.file, 'a') as f: if mode == MODE_FUN: @@ -904,31 +944,37 @@ def _update_counts(self, @property def n_fval(self) -> int: + """See `HistoryBase` docstring.""" with h5py.File(self.file, 'r') as f: return f[f'history/{self.id}/trace/'].attrs['n_fval'] @property def n_grad(self) -> int: + """See `HistoryBase` docstring.""" with h5py.File(self.file, 'r') as f: return f[f'history/{self.id}/trace/'].attrs['n_grad'] @property def n_hess(self) -> int: + """See `HistoryBase` docstring.""" with h5py.File(self.file, 'r') as f: return f[f'history/{self.id}/trace/'].attrs['n_hess'] @property def n_res(self) -> int: + """See `HistoryBase` docstring.""" with h5py.File(self.file, 'r') as f: return f[f'history/{self.id}/trace/'].attrs['n_res'] @property def n_sres(self) -> int: + """See `HistoryBase` docstring.""" with h5py.File(self.file, 'r') as f: return f[f'history/{self.id}/trace/'].attrs['n_sres'] @property def trace_save_iter(self): + """After how many iterations to store the trace.""" with h5py.File(self.file, 'r') as f: return f[f'history/{self.id}/trace/']\ .attrs['trace_save_iter'] @@ -938,10 +984,7 @@ def _update_trace(self, sensi_orders: Tuple[int], mode: str, result: ResultDict): - """ - Update and possibly store the trace. - """ - + """Update and possibly store the trace.""" if not self.options.trace_record: return @@ -976,9 +1019,7 @@ def _update_trace(self, 'n_iterations'] += 1 def _generate_hdf5_group(self, f: h5py.File = None): - """ - Generates the group in the hdf5 file, if it does not exist yet. - """ + """Generate the group in the hdf5 file, if it does not exist yet.""" try: with h5py.File(self.file, 'a') as f: if f'history/{self.id}/trace/' not in f: @@ -998,17 +1039,19 @@ def _get_hdf5_entries( entry_id: str, ix: Union[int, Sequence[int], None] = None, ) -> Sequence: - """Get entries for field `entry_id` from HDF5 file, for indices `ix`. + """ + Get entries for field `entry_id` from HDF5 file, for indices `ix`. Parameters - ------------ + ---------- entry_id: The key whose trace is returned. ix: Index or list of indices of the iterations that will produce the trace. + Returns - -------- + ------- The entries ix for the key entry_id. """ if ix is None: @@ -1032,6 +1075,7 @@ def get_x_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[np.ndarray], np.ndarray]: + """See `HistoryBase` docstring.""" return self._get_hdf5_entries(X, ix) @trace_wrap @@ -1039,6 +1083,7 @@ def get_fval_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[float], float]: + """See `HistoryBase` docstring.""" return self._get_hdf5_entries(FVAL, ix) @trace_wrap @@ -1046,6 +1091,7 @@ def get_grad_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: + """See `HistoryBase` docstring.""" return self._get_hdf5_entries(GRAD, ix) @trace_wrap @@ -1053,6 +1099,7 @@ def get_hess_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: + """See `HistoryBase` docstring.""" return self._get_hdf5_entries(HESS, ix) @trace_wrap @@ -1060,6 +1107,7 @@ def get_res_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: + """See `HistoryBase` docstring.""" return self._get_hdf5_entries(RES, ix) @trace_wrap @@ -1067,6 +1115,7 @@ def get_sres_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: + """See `HistoryBase` docstring.""" return self._get_hdf5_entries(SRES, ix) @trace_wrap @@ -1074,6 +1123,7 @@ def get_chi2_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[float], float]: + """See `HistoryBase` docstring.""" return self._get_hdf5_entries(CHI2, ix) @trace_wrap @@ -1081,6 +1131,7 @@ def get_schi2_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[MaybeArray], MaybeArray]: + """See `HistoryBase` docstring.""" return self._get_hdf5_entries(SCHI2, ix) @trace_wrap @@ -1088,13 +1139,15 @@ def get_time_trace( self, ix: Union[int, Sequence[int], None] = None, trim: bool = False ) -> Union[Sequence[float], float]: + """See `HistoryBase` docstring.""" return self._get_hdf5_entries(TIME, ix) class OptimizerHistory: """ - Objective call history. Container around a History object, which keeps - track of optimal values. + Objective call history. + + Container around a History object, which keeps track of optimal values. Attributes ---------- @@ -1156,14 +1209,13 @@ def update(self, self._update_vals(x, result) def finalize(self): + """Finalize history.""" self.history.finalize() def _update_vals(self, x: np.ndarray, result: ResultDict): - """ - Update initial and best function values. - """ + """Update initial and best function values.""" # update initial point if np.allclose(x, self.x0): if self.fval0 is None: @@ -1238,6 +1290,7 @@ def _compute_vals_from_trace(self): self.extract_from_history(var, ix_try) def extract_from_history(self, var, ix): + """Get value of `var` at iteration `ix`.""" val = getattr(self.history, f'get_{var}_trace')(ix) if not np.all(np.isnan(val)): setattr(self, f'{var}_min', val) @@ -1245,8 +1298,9 @@ def extract_from_history(self, var, ix): def ndarray2string_full(x: Union[np.ndarray, None]) -> Union[str, None]: """ - Helper function that converts numpy arrays to string with 16 digit - numerical precision and no truncation for large arrays + Convert numpy arrays to string. + + Use 16 digit numerical precision and no truncation for large arrays Parameters ---------- @@ -1266,7 +1320,7 @@ def ndarray2string_full(x: Union[np.ndarray, None]) -> Union[str, None]: def string2ndarray(x: Union[str, float]) -> Union[np.ndarray, float]: """ - Helper function that converts string to numpy arrays + Convert string to numpy arrays. Parameters ---------- diff --git a/pypesto/objective/pre_post_process.py b/pypesto/objective/pre_post_process.py index b8ac772e4..793004245 100644 --- a/pypesto/objective/pre_post_process.py +++ b/pypesto/objective/pre_post_process.py @@ -6,9 +6,11 @@ class PrePostProcessor: """ - Implements the methods preprocess and postprocess that are called at the - beginning and at the end of the objective call, in order to handle the - mapping of optimization parameters to simulation parameters. + Implements the methods preprocess and postprocess. + + They are called at the beginning and at the end of the objective call, + in order to handle the mapping of optimization parameters to simulation + parameters. This class acts as a dummy base implementation, not performing any changes on the passed objects. @@ -39,8 +41,7 @@ def postprocess( self, result: Dict ) -> Dict: # pylint: disable=R0201 """ - Convert all arrays into np.ndarrays if necessary, and return them - without further modifications. + Convert all arrays into np.ndarrays if necessary, and return them. Parameters ---------- @@ -54,7 +55,7 @@ def reduce( self, x: np.ndarray ) -> np.ndarray: # pylint: disable=R0201 """ - Just return x without modifications. + Return x without modifications. Parameters ---------- @@ -73,9 +74,10 @@ def as_ndarrays( result: Dict ) -> Dict: """ - Convert all array_like objects to np.ndarrays. This has the advantage - of a uniform output datatype which offers various methods to assess - the data. + Convert all array_like objects to np.ndarrays. + + This has the advantage of a uniform output datatype which offers + various methods to assess the data. """ keys = [GRAD, HESS, RES, SRES] for key in keys: @@ -87,9 +89,7 @@ def as_ndarrays( class FixedParametersProcessor(PrePostProcessor): - """ - Extends the processor to handle the fixing of parameters. - """ + """Extends the processor to handle the fixing of parameters.""" def __init__(self, dim_full: int, @@ -103,9 +103,7 @@ def __init__(self, self.x_fixed_vals: np.ndarray = np.array(x_fixed_vals, dtype=float) def preprocess(self, x: np.ndarray) -> np.ndarray: - """Embed optimization vector to full vector with all simulation - parameters. - """ + """Embed optimization vector to full vector with all parameters.""" x = super().preprocess(x) x_full = np.zeros(self.dim_full) @@ -115,8 +113,18 @@ def preprocess(self, x: np.ndarray) -> np.ndarray: return x_full def reduce(self, x: np.ndarray) -> np.ndarray: - """Embed simulation vector to subsetted vector with optimization - parameters. + """ + Return x reduced to free indices. + + Parameters + ---------- + x: + Parameter vector for simulation. + + Returns + ------- + x: + Parameter vector for optimization. """ x = super().reduce(x) diff --git a/pypesto/objective/priors.py b/pypesto/objective/priors.py index 9c3e8df1c..eee63a2a7 100644 --- a/pypesto/objective/priors.py +++ b/pypesto/objective/priors.py @@ -24,7 +24,7 @@ class NegLogPriors(AggregatedObjective): class NegLogParameterPriors(ObjectiveBase): """ - This class implements Negative Log Priors on Parameters. + Implements Negative Log Priors on Parameters. Contains a list of prior dictionaries for the individual parameters of the format @@ -39,7 +39,6 @@ class NegLogParameterPriors(ObjectiveBase): Notes ----- - All callables should correspond to log-densities. That is, they return log-densities and their corresponding derivatives. Internally, values are multiplied by -1, since pyPESTO expects the @@ -50,23 +49,21 @@ def __init__(self, prior_list: List[Dict], x_names: Sequence[str] = None): """ - Constructor + Initialize. Parameters ---------- - prior_list: List of dicts containing the individual parameter priors. Format see above. - x_names: Sequence of parameter names (optional). """ - self.prior_list = prior_list super().__init__(x_names) def __deepcopy__(self, memodict=None): + """Create deepcopy of object.""" other = NegLogParameterPriors(deepcopy(self.prior_list)) return other @@ -76,7 +73,14 @@ def call_unprocessed( sensi_orders: Tuple[int, ...], mode: str ) -> ResultDict: + """ + Call objective function without pre- or post-processing and formatting. + Returns + ------- + result: + A dict containing the results. + """ res = {} res[FVAL] = self.neg_log_density(x) @@ -107,6 +111,7 @@ def call_unprocessed( def check_sensi_orders(self, sensi_orders: Tuple[int, ...], mode: str) -> bool: + """See `ObjectiveBase` documentation.""" if mode == MODE_FUN: for order in sensi_orders: if not (0 <= order <= 2): @@ -128,6 +133,7 @@ def check_sensi_orders(self, return True def check_mode(self, mode) -> bool: + """See `ObjectiveBase` documentation.""" if mode == MODE_FUN: return True elif mode == MODE_RES: @@ -138,10 +144,7 @@ def check_mode(self, mode) -> bool: f' {MODE_RES}, received {mode} instead.') def neg_log_density(self, x): - """ - Computes the negative log-density for a parameter - vector x. - """ + """Evaluate the negative log-density at x.""" density_val = 0 for prior in self.prior_list: density_val -= prior['density_fun'](x[prior['index']]) @@ -149,10 +152,7 @@ def neg_log_density(self, x): return density_val def gradient_neg_log_density(self, x): - """ - Computes the gradient of the negative log-density for a parameter - vector x. - """ + """Evaluate the gradient of the negative log-density at x.""" grad = np.zeros_like(x) for prior in self.prior_list: @@ -161,10 +161,7 @@ def gradient_neg_log_density(self, x): return grad def hessian_neg_log_density(self, x): - """ - Computes the hessian of the negative log-density for a parameter - vector x. - """ + """Evaluate the hessian of the negative log-density at x.""" hessian = np.zeros((len(x), len(x))) for prior in self.prior_list: @@ -174,10 +171,7 @@ def hessian_neg_log_density(self, x): return hessian def hessian_vp_neg_log_density(self, x, p): - """ - Computes the hessian vector product of the hessian of the - negative log-density for a parameter vector x with a vector p. - """ + """Compute vector product of the hessian at x with a vector p.""" h_dot_p = np.zeros_like(p) for prior in self.prior_list: @@ -187,16 +181,15 @@ def hessian_vp_neg_log_density(self, x, p): return h_dot_p def residual(self, x): - """ - Computes the residual representation of the prior for a parameter - vector x, if available. - """ + """Evaluate the residual representation of the prior at x.""" return np.asarray([prior['residual'](x[prior['index']]) for prior in self.prior_list]) def residual_jacobian(self, x): """ - Computes the Jacobian of the residual representation of the prior + Evaluate residual Jacobian. + + Evaluate the Jacobian of the residual representation of the prior for a parameter vector x w.r.t. x, if available. """ sres = np.zeros((len(self.prior_list), len(x))) @@ -211,29 +204,24 @@ def get_parameter_prior_dict(index: int, prior_type: str, prior_parameters: list, parameter_scale: str = 'lin'): - """ - Returns the prior dict used to define priors for some default priors. + Return the prior dict used to define priors for some default priors. index: index of the parameter in x_full - prior_type: Prior is defined in LINEAR=untransformed parameter space, unless it starts with "parameterScale". prior_type can be any of {"uniform", "normal", "laplace", "logNormal", "parameterScaleUniform", "parameterScaleNormal", "parameterScaleLaplace"} - prior_parameters: Parameters of the priors. Parameters are defined in linear scale. - parameter_scale: scale in which the parameter is defined (since a parameter can be log-transformed, while the prior is always defined in the linear space, unless it starts with "parameterScale") """ - log_f, d_log_f_dx, dd_log_f_ddx, res, d_res_dx = \ _prior_densities(prior_type, prior_parameters) @@ -249,27 +237,27 @@ def get_parameter_prior_dict(index: int, elif parameter_scale == 'log': def log_f_log(x_log): - """log-prior for log-parameters""" + """Log-prior for log-parameters.""" return log_f(np.exp(x_log)) def d_log_f_log(x_log): - """derivative of log-prior w.r.t. log-parameters""" + """First derivative of log-prior w.r.t. log-parameters.""" return d_log_f_dx(np.exp(x_log)) * np.exp(x_log) def dd_log_f_log(x_log): - """second derivative of log-prior w.r.t. log-parameters""" + """Second derivative of log-prior w.r.t. log-parameters.""" return np.exp(x_log) * \ (d_log_f_dx(np.exp(x_log)) + np.exp(x_log) * dd_log_f_ddx(np.exp(x_log))) if res is not None: def res_log(x_log): - """residual-prior for log-parameters""" + """Residual-prior for log-parameters.""" return res(np.exp(x_log)) if d_res_dx is not None: def d_res_log(x_log): - """residual-prior for log-parameters""" + """Residual-prior for log-parameters.""" return d_res_dx(np.exp(x_log)) * np.exp(x_log) return {'index': index, @@ -284,27 +272,27 @@ def d_res_log(x_log): log10 = np.log(10) def log_f_log10(x_log10): - """log-prior for log10-parameters""" + """Log-prior for log10-parameters.""" return log_f(10**x_log10) def d_log_f_log10(x_log10): - """derivative of log-prior w.r.t. log10-parameters""" + """Rerivative of log-prior w.r.t. log10-parameters.""" return d_log_f_dx(10**x_log10) * log10 * 10**x_log10 def dd_log_f_log10(x_log10): - """second derivative of log-prior w.r.t. log10-parameters""" + """Second derivative of log-prior w.r.t. log10-parameters.""" return log10**2 * 10**x_log10 * \ (dd_log_f_ddx(10**x_log10) * 10**x_log10 + d_log_f_dx(10**x_log10)) if res is not None: def res_log(x_log10): - """residual-prior for log10-parameters""" + """Residual-prior for log10-parameters.""" return res(10**x_log10) if d_res_dx is not None: def d_res_log(x_log10): - """residual-prior for log10-parameters""" + """Residual-prior for log10-parameters.""" return d_res_dx(10**x_log10) * log10 * 10**x_log10 return {'index': index, @@ -324,7 +312,9 @@ def _prior_densities(prior_type: str, Callable, Callable]: """ - Returns a tuple of Callables of the (log-)density (in untransformed = + Create prior density functions. + + Return a tuple of Callables of the (log-)density (in untransformed = linear scale), unless prior_types starts with "parameterScale", together with their first + second derivative (= sensis) w.r.t. the parameters. If possible, a residual representation and its first @@ -333,19 +323,6 @@ def _prior_densities(prior_type: str, entries will be `None`. Currently the following distributions are supported: - - Parameters - ---------- - - prior_type: - string identifier indicating the distribution to be used. Here - "transformed" parameter scale refers to the scale in which - optimization is performed. For example, for parameters with scale - "log", "parameterScaleNormal" will apply a normally distributed prior - to logarithmic parameters, while "normal" will apply a normally - distributed prior to linear parameters. For parameters with scale - "lin", "parameterScaleNormal" and "normal" are equivalent. - * uniform: Uniform distribution on transformed parameter scale. * parameterScaleUniform: @@ -367,6 +344,16 @@ def _prior_densities(prior_type: str, * logUniform * logLaplace + Parameters + ---------- + prior_type: + string identifier indicating the distribution to be used. Here + "transformed" parameter scale refers to the scale in which + optimization is performed. For example, for parameters with scale + "log", "parameterScaleNormal" will apply a normally distributed prior + to logarithmic parameters, while "normal" will apply a normally + distributed prior to linear parameters. For parameters with scale + "lin", "parameterScaleNormal" and "normal" are equivalent. prior_parameters: parameters for the distribution @@ -385,10 +372,7 @@ def _prior_densities(prior_type: str, * logNormal: - prior_parameters[0]: mean of log-parameters - prior_parameters[1]: standard deviation of log-parameters - - """ - if prior_type in ['uniform', 'parameterScaleUniform']: def log_f(x): @@ -490,18 +474,14 @@ def dd_log_f_ddx(x): def _get_linear_function(slope: float, intercept: float = 0): - """ - Returns a linear function - """ + """Return a linear function.""" def function(x): return slope * x + intercept return function def _get_constant_function(constant: float): - """ - Defines a callable, that returns the constant, regardless of the input. - """ + """Define a callable returning the constant, regardless of the input.""" def function(x): return constant return function diff --git a/pypesto/objective/util.py b/pypesto/objective/util.py index 7edc89d03..cdc357e2e 100644 --- a/pypesto/objective/util.py +++ b/pypesto/objective/util.py @@ -3,7 +3,7 @@ def _check_none(fun): - """Wrapper: Return None if any input argument is None.""" + """Return None if any input argument is None; Wrapper function.""" def checked_fun(*args, **kwargs): if any(x is None for x in [*args, *(kwargs.values())]): return None @@ -51,11 +51,12 @@ def schi2_to_grad(schi2: np.ndarray) -> np.ndarray: @_check_none def sres_to_grad(res: np.ndarray, sres: np.ndarray): - """Translate residual sensitivities to function value gradien, assuming - `fval = 0.5*sum(res**2)`. + """Translate residual sensitivities to function value gradient. + + Assumes `fval = 0.5*sum(res**2)`. See also :func:`chi2_to_fval`. - """ + """ return schi2_to_grad(sres_to_schi2(res, sres)) diff --git a/pypesto/optimize/__init__.py b/pypesto/optimize/__init__.py index 6066c6eb9..b30bb6374 100644 --- a/pypesto/optimize/__init__.py +++ b/pypesto/optimize/__init__.py @@ -1,3 +1,4 @@ +# noqa: D400,D205 """ Optimize ======== diff --git a/pypesto/optimize/optimize.py b/pypesto/optimize/optimize.py index 7ca4709a1..a922502a0 100644 --- a/pypesto/optimize/optimize.py +++ b/pypesto/optimize/optimize.py @@ -11,7 +11,7 @@ from .optimizer import Optimizer, ScipyOptimizer from .options import OptimizeOptions from .task import OptimizerTask -from .util import check_hdf5_mp, fill_hdf5_file +from .util import check_hdf5_mp, fill_hdf5_file, autosave logger = logging.getLogger(__name__) @@ -27,9 +27,10 @@ def minimize( progress_bar: bool = True, options: OptimizeOptions = None, history_options: HistoryOptions = None, + filename: str = "Auto" ) -> Result: """ - This is the main function to call to do multistart optimization. + Do multistart optimization. Parameters ---------- @@ -57,6 +58,11 @@ def minimize( Various options applied to the multistart optimization. history_options: Optimizer history options. + filename: + Name of the hdf5 file, where the result will be saved. Default is + "Auto", in which case it will automatically generate a file named + `year_month_day_optimization_result.hdf5`. Deactivate saving by + setting filename to `None`. Returns ------- @@ -64,7 +70,6 @@ def minimize( Result object containing the results of all multistarts in `result.optimize_result`. """ - # optimizer if optimizer is None: optimizer = ScipyOptimizer() @@ -116,10 +121,10 @@ def minimize( # define tasks tasks = [] - filename = None + filename_hist = None if history_options.storage_file is not None and \ history_options.storage_file.endswith(('.h5', '.hdf5')): - filename = check_hdf5_mp(history_options, engine) + filename_hist = check_hdf5_mp(history_options, engine) for startpoint, id in zip(startpoints, ids): task = OptimizerTask( @@ -129,14 +134,16 @@ def minimize( id=id, options=options, history_options=history_options, + report_hess=options.report_hess, + report_sres=options.report_sres, ) tasks.append(task) # do multistart optimization ret = engine.execute(tasks, progress_bar=progress_bar) - if filename is not None: - fill_hdf5_file(ret, filename) + if filename_hist is not None: + fill_hdf5_file(ret, filename_hist) # aggregate results for optimizer_result in ret: @@ -145,4 +152,10 @@ def minimize( # sort by best fval result.optimize_result.sort() + if filename == "Auto" and filename_hist is not None: + filename = filename_hist + autosave(filename=filename, + result=result, + type="optimization") + return result diff --git a/pypesto/optimize/optimizer.py b/pypesto/optimize/optimizer.py index 3b7802619..4a9feea37 100644 --- a/pypesto/optimize/optimizer.py +++ b/pypesto/optimize/optimizer.py @@ -58,8 +58,9 @@ def history_decorator(minimize): """ - Default decorator for the minimize() method to initialize and extract - information stored in the history. + Initialize and extract information stored in the history. + + Default decorator for the minimize() method. """ def wrapped_minimize(self, problem, x0, id, allow_failed_starts, @@ -105,6 +106,8 @@ def wrapped_minimize(self, problem, x0, id, allow_failed_starts, def time_decorator(minimize): """ + Measure time of optimization. + Default decorator for the minimize() method to take time. Currently, the method time.time() is used, which measures the wall-clock time. @@ -124,8 +127,9 @@ def wrapped_minimize(self, problem, x0, id, allow_failed_starts, def fix_decorator(minimize): """ - Default decorator for the minimize() method to include also fixed - parameters in the result arrays (nans will be inserted in the + Include also fixed parameters in the result arrays of minimize(). + + Default decorator for the minimize() method (nans will be inserted in the derivatives). """ @@ -150,10 +154,7 @@ def wrapped_minimize(self, problem, x0, id, allow_failed_starts, def fill_result_from_objective_history( result: OptimizerResult, optimizer_history: OptimizerHistory): - """ - Overwrite function values in the result object with the values recorded in - the history. - """ + """Overwrite values in the result object with values in the history.""" update_vals = True # check history for better values # value could be different e.g. if constraints violated @@ -243,15 +244,14 @@ def read_result_from_file(problem: Problem, history_options: HistoryOptions, class Optimizer(abc.ABC): """ - This is the optimizer base class, not functional on its own. + Optimizer base class, not functional on its own. + An optimizer takes a problem, and possibly a start point, and then performs an optimization. It returns an OptimizerResult. """ def __init__(self): - """ - Default constructor. - """ + """Initialize base class.""" @abc.abstractmethod @fix_decorator @@ -264,8 +264,9 @@ def minimize( id: str, history_options: HistoryOptions = None, ) -> OptimizerResult: - """" + """ Perform optimization. + Parameters ---------- problem: @@ -280,12 +281,11 @@ def minimize( @abc.abstractmethod def is_least_squares(self): + """Check whether optimizer is a least squares optimizer.""" return False def get_default_options(self): - """ - Create default options specific for the optimizer. - """ + """Create default options specific for the optimizer.""" return None @@ -299,6 +299,7 @@ def check_finite_bounds(lb, ub): class ScipyOptimizer(Optimizer): """ Use the SciPy optimizers. + Find details on the optimizer and configuration options at: https://docs.scipy.org/doc/scipy/reference/generated/scipy.\ optimize.minimize.html#scipy.optimize.minimize @@ -329,6 +330,7 @@ def minimize( id: str, history_options: HistoryOptions = None, ) -> OptimizerResult: + """Perform optimization. Parameters: see `Optimizer` documentation.""" lb = problem.lb ub = problem.ub objective = problem.objective @@ -438,9 +440,11 @@ def minimize( return optimizer_result def is_least_squares(self): + """Check whether optimizer is a least squares optimizer.""" return re.match(r'(?i)^(ls_)', self.method) def get_default_options(self): + """Create default options specific for the optimizer.""" if self.is_least_squares(): options = {'max_nfev': 1000, 'disp': False} else: @@ -454,6 +458,8 @@ class IpoptOptimizer(Optimizer): def __init__( self, options: Dict = None): """ + Initialize. + Parameters ---------- options: @@ -472,7 +478,7 @@ def minimize( id: str, history_options: HistoryOptions = None, ) -> OptimizerResult: - + """Perform optimization. Parameters: see `Optimizer` documentation.""" if cyipopt is None: raise ImportError( "This optimizer requires an installation of ipopt. You can " @@ -503,13 +509,12 @@ def minimize( ) def is_least_squares(self): + """Check whether optimizer is a least squares optimizer.""" return False class DlibOptimizer(Optimizer): - """ - Use the Dlib toolbox for optimization. - """ + """Use the Dlib toolbox for optimization.""" def __init__(self, options: Dict = None): @@ -532,7 +537,7 @@ def minimize( id: str, history_options: HistoryOptions = None, ) -> OptimizerResult: - + """Perform optimization. Parameters: see `Optimizer` documentation.""" lb = problem.lb ub = problem.ub check_finite_bounds(lb, ub) @@ -565,16 +570,16 @@ def get_fval_vararg(*x): return optimizer_result def is_least_squares(self): + """Check whether optimizer is a least squares optimizer.""" return False def get_default_options(self): + """Create default options specific for the optimizer.""" return {'maxiter': 10000} class PyswarmOptimizer(Optimizer): - """ - Global optimization using pyswarm. - """ + """Global optimization using pyswarm.""" def __init__(self, options: Dict = None): super().__init__() @@ -593,6 +598,7 @@ def minimize( id: str, history_options: HistoryOptions = None, ) -> OptimizerResult: + """Perform optimization. Parameters: see `Optimizer` documentation.""" lb = problem.lb ub = problem.ub if pyswarm is None: @@ -614,17 +620,21 @@ def minimize( return optimizer_result def is_least_squares(self): + """Check whether optimizer is a least squares optimizer.""" return False class CmaesOptimizer(Optimizer): """ Global optimization using cma-es. + Package homepage: https://pypi.org/project/cma-es/ """ def __init__(self, par_sigma0: float = 0.25, options: Dict = None): """ + Initialize. + Parameters ---------- par_sigma0: @@ -634,7 +644,6 @@ def __init__(self, par_sigma0: float = 0.25, options: Dict = None): options: Optimizer options that are directly passed on to cma. """ - super().__init__() if options is None: @@ -652,7 +661,7 @@ def minimize( id: str, history_options: HistoryOptions = None, ) -> OptimizerResult: - + """Perform optimization. Parameters: see `Optimizer` documentation.""" lb = problem.lb ub = problem.ub @@ -677,12 +686,14 @@ def minimize( return optimizer_result def is_least_squares(self): + """Check whether optimizer is a least squares optimizer.""" return False class ScipyDifferentialEvolutionOptimizer(Optimizer): """ Global optimization using scipy's differential evolution optimizer. + Package homepage: https://docs.scipy.org/doc/scipy/reference/generated\ /scipy.optimize.differential_evolution.html @@ -721,6 +732,7 @@ def minimize( id: str, history_options: HistoryOptions = None, ) -> OptimizerResult: + """Perform optimization. Parameters: see `Optimizer` documentation.""" bounds = list(zip(problem.lb, problem.ub)) result = scipy.optimize.differential_evolution( @@ -733,12 +745,14 @@ def minimize( return optimizer_result def is_least_squares(self): + """Check whether optimizer is a least squares optimizer.""" return False class PyswarmsOptimizer(Optimizer): """ Global optimization using pyswarms. + Package homepage: https://pyswarms.readthedocs.io/en/latest/index.html Parameters @@ -782,6 +796,7 @@ def minimize( id: str, history_options: HistoryOptions = None, ) -> OptimizerResult: + """Perform optimization. Parameters: see `Optimizer` documentation.""" lb = problem.lb ub = problem.ub @@ -804,15 +819,14 @@ def minimize( def successively_working_fval(swarm: np.ndarray) -> np.ndarray: """Evaluate the function for all parameters in the swarm object. - Parameters: - ----------- + Parameters + ---------- swarm: np.ndarray, shape (n_particales_in_swarm, n_parameters) - Returns: - -------- + Returns + ------- result: np.ndarray, shape (n_particles_in_swarm) """ - n_particles = swarm.shape[0] result = np.zeros(n_particles) # iterate over the particles in the swarm @@ -832,18 +846,22 @@ def successively_working_fval(swarm: np.ndarray) -> np.ndarray: return optimizer_result def is_least_squares(self): + """Check whether optimizer is a least squares optimizer.""" return False class NLoptOptimizer(Optimizer): """ Global/Local optimization using NLopt. + Package homepage: https://nlopt.readthedocs.io/en/latest/ """ def __init__(self, method=None, local_method=None, options: Dict = None, local_options: Dict = None): """ + Initialize. + Parameters ---------- method: @@ -858,7 +876,6 @@ def __init__(self, method=None, local_method=None, options: Dict = None, local_options: Optimizer options for the local method """ - super().__init__() if options is None: @@ -934,7 +951,7 @@ def minimize( id: str, history_options: HistoryOptions = None, ) -> OptimizerResult: - + """Perform optimization. Parameters: see `Optimizer` documentation.""" opt = nlopt.opt(self.method, problem.dim) valid_options = ['ftol_abs', 'ftol_rel', 'xtol_abs', 'xtol_rel', @@ -988,12 +1005,14 @@ def nlopt_objective(x, grad): return optimizer_result def is_least_squares(self): + """Check whether optimizer is a least squares optimizer.""" return False class FidesOptimizer(Optimizer): """ Global/Local optimization using the trust region optimizer fides. + Package Homepage: https://fides-optimizer.readthedocs.io/en/latest """ @@ -1004,6 +1023,8 @@ def __init__( verbose: Optional[int] = logging.INFO ): """ + Initialize. + Parameters ---------- options: @@ -1013,7 +1034,6 @@ def __init__( that switches from the problem.objective provided Hessian ( approximation) to a BFGS approximation will be used. """ - super().__init__() if hessian_update == 'Hybrid': @@ -1042,7 +1062,7 @@ def minimize( id: str, history_options: HistoryOptions = None, ) -> OptimizerResult: - + """Perform optimization. Parameters: see `Optimizer` documentation.""" if fides is None: raise ImportError( "This optimizer requires an installation of fides. You can " @@ -1094,4 +1114,5 @@ def minimize( return optimizer_result def is_least_squares(self): + """Check whether optimizer is a least squares optimizer.""" return False diff --git a/pypesto/optimize/options.py b/pypesto/optimize/options.py index 333e8f549..9b714c95c 100644 --- a/pypesto/optimize/options.py +++ b/pypesto/optimize/options.py @@ -13,15 +13,27 @@ class OptimizeOptions(dict): allow_failed_starts: Flag indicating whether we tolerate that exceptions are thrown during the minimization process. + report_sres: + Flag indicating whether sres will be stored in the results object. + Deactivating this option will improve memory consumption for large + scale problems. + report_hess: + Flag indicating whether hess will be stored in the results object. + Deactivating this option will improve memory consumption for large + scale problems. """ def __init__(self, startpoint_resample: bool = False, - allow_failed_starts: bool = True): + allow_failed_starts: bool = True, + report_sres: bool = True, + report_hess: bool = True): super().__init__() self.startpoint_resample: bool = startpoint_resample self.allow_failed_starts: bool = allow_failed_starts + self.report_sres: bool = report_sres + self.report_hess: bool = report_hess def __getattr__(self, key): try: @@ -37,11 +49,10 @@ def assert_instance( maybe_options: Union['OptimizeOptions', Dict] ) -> 'OptimizeOptions': """ - Returns a valid options object. + Return a valid options object. Parameters ---------- - maybe_options: OptimizeOptions or dict """ if isinstance(maybe_options, OptimizeOptions): diff --git a/pypesto/optimize/result.py b/pypesto/optimize/result.py index 18aabf21f..a7ca5387b 100644 --- a/pypesto/optimize/result.py +++ b/pypesto/optimize/result.py @@ -6,9 +6,11 @@ class OptimizerResult(dict): """ - The result of an optimizer run. Used as a standardized return value to - map from the individual result objects returned by the employed - optimizers to the format understood by pypesto. + The result of an optimizer run. + + Used as a standardized return value to map from the individual result + objects returned by the employed optimizers to the format understood by + pypesto. Can be used like a dict. @@ -53,7 +55,6 @@ class OptimizerResult(dict): Notes ----- - Any field not supported by the optimizer is filled with None. """ @@ -107,7 +108,7 @@ def __getattr__(self, key): def update_to_full(self, problem: Problem) -> None: """ - Updates values to full vectors/matrices + Update values to full vectors/matrices. Parameters ---------- diff --git a/pypesto/optimize/task.py b/pypesto/optimize/task.py index 6a5465581..a9f7b5390 100644 --- a/pypesto/optimize/task.py +++ b/pypesto/optimize/task.py @@ -10,9 +10,7 @@ class OptimizerTask(Task): - """ - A multistart optimization task, performed in `pypesto.minimize`. - """ + """A multistart optimization task, performed in `pypesto.minimize`.""" def __init__( self, @@ -21,7 +19,10 @@ def __init__( x0: np.ndarray, id: str, options: 'pypesto.optimize.OptimizeOptions', - history_options: HistoryOptions): + history_options: HistoryOptions, + report_hess: bool, + report_sres: bool, + ): """ Create the task object. @@ -39,6 +40,12 @@ def __init__( Options object applying to optimization. history_options: Optimizer history options. + report_hess: + Flag indicating whether the Hessian is to be stored in + results + report_sres: + Flag indicating whether residual sensitivity is to be stored in + results """ super().__init__() @@ -48,12 +55,19 @@ def __init__( self.id = id self.options = options self.history_options = history_options + self.report_hess = report_hess + self.report_sres = report_sres def execute(self) -> 'pypesto.optimize.OptimizerResult': + """Execute the task.""" logger.info(f"Executing task {self.id}.") optimizer_result = self.optimizer.minimize( problem=self.problem, x0=self.x0, id=self.id, allow_failed_starts=self.options.allow_failed_starts, history_options=self.history_options) + if not self.report_hess: + optimizer_result.hess = None + if not self.report_sres: + optimizer_result.sres = None return optimizer_result diff --git a/pypesto/optimize/util.py b/pypesto/optimize/util.py index 35d674af9..2ae350162 100644 --- a/pypesto/optimize/util.py +++ b/pypesto/optimize/util.py @@ -1,6 +1,11 @@ +"""Utility functions for :py:func:`pypesto.optimize.minimize`.""" +import datetime + from ..engine import Engine, SingleCoreEngine from ..objective import HistoryOptions from ..store.save_to_hdf5 import get_or_create_group +from ..store import write_result +from ..result import Result from pathlib import Path from typing import Union @@ -13,8 +18,9 @@ def check_hdf5_mp( engine: Engine, ) -> Union[str, None]: """ - Create a folder for partial HDF5 files, - if a parallelization engine will be used. + Create a folder for partial HDF5 files. + + If no parallelization engine is used, do nothing. Parameters ---------- @@ -52,6 +58,8 @@ def fill_hdf5_file( filename: str ) -> None: """ + Create single history file pointing to files of multiple starts. + Create links in `filename` to the history of each start contained in ret, the results of the optimization. @@ -71,3 +79,29 @@ def fill_hdf5_file( result['history'].file, f'history/{id}' ) + + +def autosave(filename: str, result: Result, + type: str): + """ + Save the result of optimization, profiling or sampling automatically. + + Parameters + ---------- + filename: + Either the filename to save to or "Auto", in which case it will + automatically generate a file named + `year_month_day_{type}_result.hdf5`. + result: + The result to be saved. + type: + Either `optimization`, `sampling` or `profiling`. Depending on the + method the function is called in. + """ + if filename is None: + return None + if filename == "Auto": + time = datetime.datetime.now().strftime("%Y_%d_%m_%H_%M_%S") + filename = time+f"_{type}_result.hdf5" + write_result(result=result, overwrite=True, + optimize=True, filename=filename) diff --git a/pypesto/petab/importer.py b/pypesto/petab/importer.py index 558fa9fa1..50ef135e0 100644 --- a/pypesto/petab/importer.py +++ b/pypesto/petab/importer.py @@ -1,3 +1,4 @@ +"""Contains the PetabImporter class.""" import pandas as pd import numpy as np import os @@ -6,7 +7,7 @@ import shutil import logging import tempfile -from typing import Iterable, List, Literal, Optional, Sequence, Union, Callable +from typing import Iterable, List, Optional, Sequence, Union, Callable from ..problem import Problem from ..objective import AmiciObjective, AmiciObjectBuilder, AggregatedObjective @@ -31,6 +32,13 @@ class PetabImporter(AmiciObjectBuilder): + """ + Importer for Petab files. + + Create an `amici.Model`, an `objective.AmiciObjective` or a + `pypesto.Problem` from Petab files. + """ + MODEL_BASE_DIR = "amici_models" def __init__(self, @@ -39,6 +47,10 @@ def __init__(self, model_name: str = None, validate_petab: bool = True): """ + Initialize importer. + + Parameters + ---------- petab_problem: Managing access to the model and data. output_folder: @@ -71,9 +83,7 @@ def __init__(self, def from_yaml(yaml_config: Union[dict, str], output_folder: str = None, model_name: str = None) -> 'PetabImporter': - """ - Simplified constructor using a petab yaml file. - """ + """Simplified constructor using a petab yaml file.""" petab_problem = petab.Problem.from_yaml(yaml_config) return PetabImporter( @@ -86,11 +96,12 @@ def check_gradients( *args, rtol: float = 1e-2, atol: float = 1e-3, - mode: Literal = None, + mode: Union[str, List[str]] = None, multi_eps=None, **kwargs, ) -> bool: - """Check if gradients match finite differences (FDs) + """ + Check if gradients match finite differences (FDs). Parameters ---------- @@ -143,8 +154,7 @@ def create_model(self, force_compile: bool = False, **kwargs) -> 'amici.Model': """ - Import amici model. If necessary or force_compile is True, compile - first. + Import amici model. Parameters ---------- @@ -182,10 +192,7 @@ def create_model(self, return self._create_model() def _create_model(self) -> 'amici.Model': - """ - No checks, no compilation, just load the model module and return - the model. - """ + """Load model module and return the model, no checks/compilation.""" # load moduĺe module = amici.import_model_module(module_name=self.model_name, module_path=self.output_folder) @@ -194,9 +201,7 @@ def _create_model(self) -> 'amici.Model': return model def _must_compile(self, force_compile: bool): - """ - Check whether the model needs to be compiled first. - """ + """Check whether the model needs to be compiled first.""" # asked by user if force_compile: return True @@ -218,15 +223,15 @@ def _must_compile(self, force_compile: bool): def compile_model(self, **kwargs): """ - Compile the model. If the output folder exists already, it is first - deleted. + Compile the model. + + If the output folder exists already, it is first deleted. Parameters ---------- kwargs: Extra arguments passed to `amici.SbmlImporter.sbml2amici`. """ - # delete output directory if os.path.exists(self.output_folder): shutil.rmtree(self.output_folder) @@ -240,9 +245,7 @@ def compile_model(self, **kwargs): **kwargs) def create_solver(self, model: 'amici.Model' = None) -> 'amici.Solver': - """ - Return model solver. - """ + """Return model solver.""" # create model if model is None: model = self.create_model() @@ -255,9 +258,7 @@ def create_edatas( model: 'amici.Model' = None, simulation_conditions=None ) -> List['amici.ExpData']: - """ - Create list of amici.ExpData objects. - """ + """Create list of amici.ExpData objects.""" # create model if model is None: model = self.create_model() @@ -436,10 +437,10 @@ def create_predictor( def create_prior(self) -> NegLogParameterPriors: """ - Creates a prior from the parameter table. Returns None, if no priors - are defined. - """ + Create a prior from the parameter table. + Returns None, if no priors are defined. + """ prior_list = [] if petab.OBJECTIVE_PRIOR_TYPE in self.petab_problem.parameter_df: @@ -473,9 +474,10 @@ def create_prior(self) -> NegLogParameterPriors: def create_startpoint_method(self): """ - Creates a startpoint method, if the PEtab problem specifies an - initializationPrior. Returns None, if no initializationPrior - is specified. + Create a startpoint method. + + If the PEtab problem specifies an initializationPrior. Returns None, + if no initializationPrior is specified. """ if petab.INITIALIZATION_PRIOR_TYPE \ not in self.petab_problem.parameter_df: @@ -542,8 +544,7 @@ def rdatas_to_measurement_df( model: 'amici.Model' = None ) -> pd.DataFrame: """ - Create a measurement dataframe in the petab format from - the passed `rdatas` and own information. + Create a measurement dataframe in the petab format. Parameters ---------- @@ -572,8 +573,11 @@ def rdatas_to_simulation_df( self, rdatas: Sequence['amici.ReturnData'], model: 'amici.Model' = None ) -> pd.DataFrame: - """Same as `rdatas_to_measurement_df`, execpt a petab simulation - dataframe is created, i.e. the measurement column label is adjusted. + """ + See `rdatas_to_measurement_df`. + + Execpt a petab simulation dataframe is created, i.e. the measurement + column label is adjusted. """ return self.rdatas_to_measurement_df(rdatas, model).rename( columns={petab.MEASUREMENT: petab.SIMULATION}) @@ -584,6 +588,8 @@ def prediction_to_petab_measurement_df( predictor: AmiciPredictor = None ) -> pd.DataFrame: """ + Cast prediction into a dataframe. + If a PEtab problem is simulated without post-processing, then the result can be cast into a PEtab measurement or simulation dataframe @@ -618,9 +624,11 @@ def prediction_to_petab_simulation_df( prediction: PredictionResult, predictor: AmiciPredictor = None ) -> pd.DataFrame: - """Same as `prediction_to_petab_measurement_df`, except a PEtab - simulation dataframe is created, i.e. the measurement column label is - adjusted. + """ + See `prediction_to_petab_measurement_df`. + + Except a PEtab simulation dataframe is created, i.e. the measurement + column label is adjusted. """ return self.prediction_to_petab_measurement_df( prediction, predictor).rename( @@ -632,11 +640,12 @@ def _find_output_folder_name( model_name: str, ) -> str: """ - Find a name for storing the compiled amici model in. If available, - use the sbml model name from the `petab_problem` or the provided - `model_name` (latter is given priority), otherwise create a unique name. - The folder will be located in the `PetabImporter.MODEL_BASE_DIR` - subdirectory of the current directory. + Find a name for storing the compiled amici model in. + + If available, use the sbml model name from the `petab_problem` or the + provided `model_name` (latter is given priority), otherwise create a + unique name. The folder will be located in the + `PetabImporter.MODEL_BASE_DIR` subdirectory of the current directory. """ # check whether location for amici model is a file if os.path.exists(PetabImporter.MODEL_BASE_DIR) and \ @@ -665,7 +674,5 @@ def _find_output_folder_name( def _find_model_name(output_folder: str) -> str: - """ - Just re-use the last part of the output folder. - """ + """Just re-use the last part of the output folder.""" return os.path.split(os.path.normpath(output_folder))[-1] diff --git a/pypesto/petab/pysb_importer.py b/pypesto/petab/pysb_importer.py index 8c5393b8e..c3afe8c66 100644 --- a/pypesto/petab/pysb_importer.py +++ b/pypesto/petab/pysb_importer.py @@ -10,18 +10,21 @@ class PetabImporterPysb(PetabImporter): - """Import for experimental PySB-based PEtab problems""" + """Import for experimental PySB-based PEtab problems.""" def __init__(self, petab_problem: 'amici.petab_import_pysb.PysbPetabProblem', output_folder: str = None): """ + Initialize importer. + + Parameters + ---------- petab_problem: Managing access to the model and data. output_folder: Folder to contain the amici model. """ - super().__init__(petab_problem, model_name=petab_problem.pysb_model.name, output_folder=output_folder, @@ -29,15 +32,15 @@ def __init__(self, def compile_model(self, **kwargs): """ - Compile the model. If the output folder exists already, it is first - deleted. + Compile the model. + + If the output folder exists already, it is first deleted. Parameters ---------- kwargs: Extra arguments passed to `amici.SbmlImporter.sbml2amici`. """ - # delete output directory if os.path.exists(self.output_folder): shutil.rmtree(self.output_folder) diff --git a/pypesto/predict/amici_predictor.py b/pypesto/predict/amici_predictor.py index c0be1cea9..76659dd4e 100644 --- a/pypesto/predict/amici_predictor.py +++ b/pypesto/predict/amici_predictor.py @@ -13,8 +13,9 @@ class AmiciPredictor: """ - Do forward simulations (predictions) with parameter vectors, - for an AMICI model. The user may supply post-processing methods. + Do forward simulations/predictions for an AMICI model. + + The user may supply post-processing methods. If post-processing methods are supplied, and a gradient of the prediction is requested, then the sensitivities of the AMICI model must also be post-processed. There are no checks here to ensure that the sensitivities @@ -26,6 +27,7 @@ class AmiciPredictor: output, these checks are also left to the user. An example for such a check is provided in the default output (see _default_output()). """ + def __init__(self, amici_objective: AmiciObjective, amici_output_fields: Union[Sequence[str], None] = None, @@ -37,7 +39,7 @@ def __init__(self, condition_ids: Union[Sequence[str], None] = None, ): """ - Constructor. + Initialize predictor. Parameters ---------- @@ -126,6 +128,8 @@ def __call__( include_sigmay: bool = False ) -> PredictionResult: """ + Call the predictor. + Simulate a model for a certain prediction function. This method relies on the AmiciObjective, which is underlying, but allows the user to apply any post-processing of the results, the @@ -222,9 +226,10 @@ def _get_outputs(self, include_sigmay: bool = False ) -> Tuple[List, List, List]: """ - This function splits the calls to amici into smaller chunks, as too - large ReturnData objects from amici including many simulations can be - problematic in terms of memory + Split the calls to amici into smaller chunks. + + Too large ReturnData objects from amici including many simulations + can be problematic in terms of memory. Parameters ---------- @@ -235,8 +240,8 @@ def _get_outputs(self, mode: Whether to compute function values or residuals. - Returns: - -------- + Returns + ------- timepoints: List of np.ndarrays, every entry includes the output timepoints of the respective condition @@ -254,7 +259,6 @@ def _get_outputs(self, prediction output. Necessary for evaluation of weighted means of Ensembles. """ - # Do we have a maximum number of simulations allowed? n_edatas = len(self.amici_objective.edatas) if self.max_chunk_size is None: @@ -283,9 +287,10 @@ def _get_outputs(self, def _default_output(amici_outputs): """ - Default output of prediction, equals to observables of AMICI model. - We need to check that call to AMICI was successful (status == 0), - before writing the output + Create default output of prediction. + + Equals to observables of AMICI model. We need to check that call + to AMICI was successful (status == 0), before writing the output. """ amici_nt = [len(edata.getTimepoints()) for edata in self.amici_objective.edatas] @@ -355,7 +360,8 @@ def _default_output(amici_outputs): def _wrap_call_to_amici(self, amici_outputs, x, sensi_orders, mode, parameter_mapping, edatas): """ - The only purpose of this function is to encapsulate the call to amici: + Encapsulate the call to amici. + This allows to use variable scoping as a mean to clean up the memory after calling amici, which is beneficial if large models with large datasets are used. diff --git a/pypesto/predict/result.py b/pypesto/predict/result.py index 303d3a2b8..57eddf937 100644 --- a/pypesto/predict/result.py +++ b/pypesto/predict/result.py @@ -1,3 +1,4 @@ +"""PredictionResult and PredictionConditionResult.""" import numpy as np import pandas as pd import h5py @@ -22,9 +23,10 @@ class PredictionConditionResult: """ - This class is a light-weight wrapper for the prediction of one simulation - condition of an amici model. It should provide a common api how amici - predictions should look like in pyPESTO. + Light-weight wrapper for the prediction of one simulation condition. + + It should provide a common api how amici predictions should look like in + pyPESTO. """ def __init__(self, @@ -36,7 +38,7 @@ def __init__(self, output_sigmay: np.ndarray = None, x_names: Sequence[str] = None): """ - Constructor. + Initialize PredictionConditionResult. Parameters ---------- @@ -67,6 +69,7 @@ def __init__(self, range(output_sensi.shape[1])] def __iter__(self): + """Allow usage like a dict.""" yield 'timepoints', self.timepoints yield 'output_ids', self.output_ids yield 'x_names', self.x_names @@ -76,6 +79,7 @@ def __iter__(self): yield 'output_sigmay', self.output_sigmay def __eq__(self, other): + """Check equality of two PredictionConditionResults.""" def to_bool(expr): if isinstance(expr, bool): return expr @@ -100,11 +104,12 @@ def to_bool(expr): class PredictionResult: """ - This class is a light-weight wrapper around predictions from pyPESTO made - via an amici model. It's only purpose is to have fixed format/api, how - prediction results should be stored, read, and handled: as predictions are - a very flexible format anyway, they should at least have a common - definition, which allows to work with them in a reasonable way. + Light-weight wrapper around prediction from pyPESTO made by an AMICI model. + + Its only purpose is to have fixed format/api, how prediction results + should be stored, read, and handled: as predictions are a very flexible + format anyway, they should at least have a common definition, + which allows to work with them in a reasonable way. """ def __init__(self, @@ -112,7 +117,7 @@ def __init__(self, condition_ids: Sequence[str] = None, comment: str = None): """ - Constructor. + Initialize PredictionResult. Parameters ---------- @@ -139,6 +144,7 @@ def __init__(self, self.comment = comment def __iter__(self): + """Allow usage like an iterator.""" parameter_ids = None if self.conditions: parameter_ids = self.conditions[0].x_names @@ -149,6 +155,7 @@ def __iter__(self): yield 'parameter_ids', parameter_ids def __eq__(self, other): + """Check equality of two PredictionResults.""" if not isinstance(other, PredictionResult): return False if self.comment != other.comment: @@ -162,7 +169,7 @@ def __eq__(self, other): def write_to_csv(self, output_file: str): """ - This method saves predictions to a csv file. + Save predictions to a csv file. Parameters ---------- @@ -172,6 +179,8 @@ def write_to_csv(self, output_file: str): def _prepare_csv_output(output_file): """ + Prepare a folder for output. + If a csv is requested, this routine will create a folder for it, with a suiting name: csv's are by default 2-dimensional, but the output will have the format n_conditions x n_timepoints x n_outputs @@ -234,14 +243,14 @@ def write_to_h5(self, output_file: str, base_path: str = None): """ - This method saves predictions to an h5 file. It appends to the file if - the file already exists. + Save predictions to an h5 file. + + It appends to the file if the file already exists. Parameters ---------- output_file: path to file/folder to which results will be written - base_path: base path in the h5 file """ @@ -284,8 +293,9 @@ def write_to_h5(self, @staticmethod def _check_existence(output_path): """ - Checks whether a file or a folder already exists and appends a - timestamp if this is the case + Check whether a file or a folder already exists. + + Append a timestamp if this is the case. """ output_path_out = output_path while output_path_out.exists(): diff --git a/pypesto/predict/task.py b/pypesto/predict/task.py index a2358756f..d8fbde6a7 100644 --- a/pypesto/predict/task.py +++ b/pypesto/predict/task.py @@ -41,6 +41,7 @@ def __init__( self.id = id def execute(self) -> 'pypesto.predict.PredictionResult': # noqa: F821 + """Execute and return the prediction.""" logger.info(f"Executing task {self.id}.") prediction = self.predictor(self.x, self.sensi_orders, self.mode) return prediction diff --git a/pypesto/problem.py b/pypesto/problem.py index 3c1abba45..c2ac88ffb 100644 --- a/pypesto/problem.py +++ b/pypesto/problem.py @@ -1,3 +1,4 @@ +# noqa: D400,D205 """ Problem ======= @@ -23,9 +24,10 @@ class Problem: """ - The problem formulation. A problem specifies the objective function, - boundaries and constraints, parameter guesses as well as the parameters - which are to be optimized. + The problem formulation. + + A problem specifies the objective function, boundaries and constraints, + parameter guesses as well as the parameters which are to be optimized. Parameters ---------- @@ -72,7 +74,6 @@ class Problem: Notes ----- - On the fixing of parameter values: The number of parameters dim_full the objective takes as input must @@ -160,38 +161,46 @@ def __init__( @property def lb(self) -> np.ndarray: + """Return lower bounds of free parameters.""" return self.lb_full[self.x_free_indices] @property def ub(self) -> np.ndarray: + """Return upper bounds of free parameters.""" return self.ub_full[self.x_free_indices] @property def lb_init(self) -> np.ndarray: + """Return initial lower bounds of free parameters.""" return self.lb_init_full[self.x_free_indices] @property def ub_init(self) -> np.ndarray: + """Return initial upper bounds of free parameters.""" return self.ub_init_full[self.x_free_indices] @property def x_guesses(self) -> np.ndarray: + """Return guesses of the free parameter values.""" return self.x_guesses_full[:, self.x_free_indices] @property def dim(self) -> int: + """Return dimension only considering non fixed parameters.""" return self.dim_full - len(self.x_fixed_indices) @property def x_free_indices(self) -> List[int]: + """Return non fixed parameters.""" return sorted(set(range(0, self.dim_full)) - set(self.x_fixed_indices)) def normalize(self) -> None: """ + Process vectors. + Reduce all vectors to dimension dim and have the objective accept vectors of dimension dim. """ - for attr in ['lb_full', 'lb_init_full', 'ub_full', 'ub_init_full']: value = self.__getattribute__(attr) if value.size == 1: @@ -238,7 +247,7 @@ def normalize(self) -> None: def set_x_guesses(self, x_guesses: Iterable[float]): """ - Sets the x_guesses of a problem. + Set the x_guesses of a problem. Parameters ---------- @@ -253,9 +262,7 @@ def set_x_guesses(self, def fix_parameters(self, parameter_indices: SupportsIntIterableOrValue, parameter_vals: SupportsFloatIterableOrValue) -> None: - """ - Fix specified parameters to specified values - """ + """Fix specified parameters to specified values.""" parameter_indices = _make_iterable_if_value(parameter_indices, 'int') parameter_vals = _make_iterable_if_value(parameter_vals, 'float') @@ -280,10 +287,7 @@ def fix_parameters(self, def unfix_parameters(self, parameter_indices: SupportsIntIterableOrValue ) -> None: - """ - Free specified parameters - """ - + """Free specified parameters.""" # check and adapt input parameter_indices = _make_iterable_if_value(parameter_indices, 'int') @@ -364,7 +368,8 @@ def get_reduced_vector( x_indices: Optional[List[int]] = None ) -> Union[np.ndarray, None]: """ - Keep only those elements, which indices are specified in x_indices + Keep only those elements, which indices are specified in x_indices. + If x_indices is not provided, delete fixed indices. Parameters @@ -408,7 +413,8 @@ def get_reduced_matrix( return x def full_index_to_free_index(self, full_index: int): - """Calculate index in reduced vector from index in full vector. + """ + Calculate index in reduced vector from index in full vector. Parameters ---------- @@ -426,8 +432,9 @@ def full_index_to_free_index(self, full_index: int): def print_parameter_summary(self) -> None: """ - Prints a summary of what parameters are being optimized and - parameter boundaries. + Print a summary of parameters. + + Include what parameters are being optimized and parameter boundaries. """ print( # noqa: T001 (print) pd.DataFrame( @@ -459,10 +466,10 @@ def _type_conversion_with_check(index: int, valuename: str, convtype: str) -> Union[float, int]: """ - Converts values to the requested type, raises and appropriate error if - not possible. - """ + Convert values to the requested type. + Raises and appropriate error if not possible. + """ if convtype not in _convtypes: raise ValueError(f'Unsupported type {convtype}') @@ -487,10 +494,7 @@ def _make_iterable_if_value(value: Union[SupportsFloatIterableOrValue, SupportsIntIterableOrValue], convtype: str) -> Union[Iterable[SupportsFloat], Iterable[SupportsInt]]: - """ - Converts scalar values to iterables if input is scalar, may update type - """ - + """Convert scalar values to iterables for scalar input, may update type.""" if convtype not in _convtypes: raise ValueError(f'Unsupported type {convtype}') diff --git a/pypesto/profile/approximate.py b/pypesto/profile/approximate.py index 0d6f7e34d..ee068b992 100644 --- a/pypesto/profile/approximate.py +++ b/pypesto/profile/approximate.py @@ -20,9 +20,11 @@ def approximate_parameter_profile( n_steps: int = 100, ) -> Result: """ - Calculate profiles based on an approximation via a normal likelihood - centered at the chosen optimal parameter value, with the covariance matrix - being the Hessian or FIM. + Calculate profile approximation. + + Based on an approximation via a normal likelihood centered at the chosen + optimal parameter value, with the covariance matrix being the Hessian or + FIM. Parameters ---------- diff --git a/pypesto/profile/options.py b/pypesto/profile/options.py index 0a2d608f2..a68efe306 100644 --- a/pypesto/profile/options.py +++ b/pypesto/profile/options.py @@ -60,6 +60,7 @@ def __init__(self, self.magic_factor_obj_value = magic_factor_obj_value def __getattr__(self, key): + """Allow usage of keys like attributes.""" try: return self[key] except KeyError: @@ -73,7 +74,7 @@ def create_instance( maybe_options: Union['ProfileOptions', Dict] ) -> 'ProfileOptions': """ - Returns a valid options object. + Return a valid options object. Parameters ---------- diff --git a/pypesto/profile/profile.py b/pypesto/profile/profile.py index a06654c6a..b2eab4a76 100644 --- a/pypesto/profile/profile.py +++ b/pypesto/profile/profile.py @@ -5,6 +5,7 @@ from ..optimize import Optimizer from ..problem import Problem from ..result import Result +from ..optimize.util import autosave from .profile_next_guess import next_guess from .options import ProfileOptions from .util import initialize_profile @@ -23,10 +24,11 @@ def parameter_profile( result_index: int = 0, next_guess_method: Union[Callable, str] = 'adaptive_step_regression', profile_options: ProfileOptions = None, - progress_bar: bool = True + progress_bar: bool = True, + filename: str = "Auto" ) -> Result: """ - This is the main function to call to do parameter profiling. + Call to do parameter profiling. Parameters ---------- @@ -58,6 +60,11 @@ def parameter_profile( Various options applied to the profile optimization. progress_bar: Whether to display a progress bar. + filename: + Name of the hdf5 file, where the result will be saved. Default is + "Auto", in which case it will automatically generate a file named + `year_month_day_profiling_result.hdf5`. Deactivate saving by + setting filename to `None`. Returns ------- @@ -125,4 +132,8 @@ def create_next_guess(x, par_index, par_direction_, profile_options_, result.profile_result.list[-1][indexed_profile['index']] = \ indexed_profile['profile'] + autosave(filename=filename, + result=result, + type="profiling") + return result diff --git a/pypesto/profile/profile_next_guess.py b/pypesto/profile/profile_next_guess.py index b3e152c5e..440b01d4e 100644 --- a/pypesto/profile/profile_next_guess.py +++ b/pypesto/profile/profile_next_guess.py @@ -18,8 +18,9 @@ def next_guess( global_opt: float ) -> np.ndarray: """ - This function creates the next initial guess for the optimizer in - order to compute the next profile point. Different proposal methods + Create the next initial guess for the optimizer. + + Used in order to compute the next profile point. Different proposal methods are available. Parameters @@ -46,7 +47,6 @@ def next_guess( next_guess: The next initial guess as base for the next profile point. """ - if update_type == 'fixed_step': return fixed_step(x, par_index, par_direction, profile_options, problem) @@ -117,8 +117,10 @@ def adaptive_step( order: int = 1 ) -> np.ndarray: """ - group of more complex methods for point proposal, step size is - automatically computed by a line search algorithm (hence: adaptive) + Group of more complex methods for point proposal. + + Step size is automatically computed by a line search algorithm (hence: + adaptive). Parameters ---------- @@ -247,11 +249,11 @@ def handle_profile_history( options: ProfileOptions ) -> Tuple: """ - Computes the very first step direction update guesses, check whether - enough steps have been taken for applying regression, computes - regression or simple extrapolation. - """ + Compute the very first step direction update guesses. + Check whether enough steps have been taken for applying regression, + computes regression or simple extrapolation. + """ # set the update direction delta_x_dir = np.zeros(len(x)) delta_x_dir[par_index] = par_direction @@ -294,10 +296,10 @@ def get_reg_polynomial( options: ProfileOptions ) -> List[float]: """ - Computes the regression polynomial which is used to step proposal - extrapolation from the last profile points - """ + Compute the regression polynomial. + Used to step proposal extrapolation from the last profile points + """ # determine interpolation order reg_max_order = np.floor(n_profile_points / 2) reg_order = np.min([reg_max_order, options.reg_order]) @@ -346,9 +348,10 @@ def do_line_seach( options: ProfileOptions ) -> np.ndarray: """ - Performs the line search based on the objective function we want to - reach, based on the current position in parameter space and on the - first guess for the proposal. + Perform the line search. + + Based on the objective function we want to reach, based on the current + position in parameter space and on the first guess for the proposal. """ # Was the initial step too big or too small? if direction == 'increase': @@ -394,9 +397,7 @@ def next_x_interpolate( last_x: np.ndarray, next_obj_target: float ) -> np.ndarray: - """ - Interpolate between the last two steps- - """ + """Interpolate between the last two steps.""" delta_obj = np.abs(next_obj - last_obj) add_x = np.abs(last_obj - next_obj_target) * ( next_x - last_x) / delta_obj @@ -410,9 +411,7 @@ def clip( lower: Union[float, np.ndarray], upper: Union[float, np.ndarray] ) -> Union[float, np.ndarray]: - """ - Restrict a scalar or a vector to given bounds. - """ + """Restrict a scalar or a vector to given bounds.""" if isinstance(vector_guess, float): vector_guess = np.max([np.min([vector_guess, upper]), lower]) else: diff --git a/pypesto/profile/result.py b/pypesto/profile/result.py index cda964ed2..bce56116c 100644 --- a/pypesto/profile/result.py +++ b/pypesto/profile/result.py @@ -3,9 +3,11 @@ class ProfilerResult(dict): """ - The result of a profiler run. The standardized return return value from - pypesto.profile, which can either be initialized from an OptimizerResult - or from an existing ProfilerResult (in order to extend the computation). + The result of a profiler run. + + The standardized return value from pypesto.profile, which can + either be initialized from an OptimizerResult or from an existing + ProfilerResult (in order to extend the computation). Can be used like a dict. @@ -37,7 +39,6 @@ class ProfilerResult(dict): Notes ----- - Any field not supported by the profiler or the profiling optimizer is filled with None. Some fields are filled by pypesto itself. """ @@ -78,6 +79,7 @@ def __init__(self, self.message = message def __getattr__(self, key): + """Allow usage of keys like attributes.""" try: return self[key] except KeyError: @@ -97,7 +99,7 @@ def append_profile_point(self, n_grad: int = 0, n_hess: int = 0) -> None: """ - This function appends a new point to the profile path. + Append a new point to the profile path. Parameters ---------- @@ -122,7 +124,6 @@ def append_profile_point(self, n_hess: Number of Hessian evaluations performed to find `x`. """ - # short function to append to numpy vectors def append_to_vector(field_name, val): field_new = np.zeros(self[field_name].size + 1) @@ -151,13 +152,12 @@ def append_to_vector(field_name, val): def flip_profile(self) -> None: """ - This function flips the profiling direction (left-right) - Profiling direction needs to be changed once (if the profile is new), - or twice if we append to an existing profile. + Flip the profiling direction (left-right). - All profiling paths are flipped in-place. + Profiling direction needs to be changed once (if the profile is new), + or twice if we append to an existing profile. All profiling paths + are flipped in-place. """ - self.x_path = np.fliplr(self.x_path) self.fval_path = np.flip(self.fval_path) self.ratio_path = np.flip(self.ratio_path) diff --git a/pypesto/profile/task.py b/pypesto/profile/task.py index a40ad3553..271d9fcf1 100644 --- a/pypesto/profile/task.py +++ b/pypesto/profile/task.py @@ -55,8 +55,8 @@ def __init__( self.options = options def execute(self) -> 'pypesto.profile.ProfilerResult': + """Compute profile in descending and ascending direction.""" logger.info(f"Executing task {self.i_par}.") - # compute profile in descending and ascending direction for par_direction in [-1, 1]: # flip profile self.current_profile.flip_profile() diff --git a/pypesto/profile/util.py b/pypesto/profile/util.py index b5ef44d34..6c2d39497 100644 --- a/pypesto/profile/util.py +++ b/pypesto/profile/util.py @@ -1,3 +1,4 @@ +"""Utility function for profile module.""" import numpy as np import scipy.stats from typing import Any, Dict, Tuple, Iterable @@ -10,6 +11,8 @@ def chi2_quantile_to_ratio(alpha: float = 0.95, df: int = 1): """ + Compute profile likelihood threshold. + Transform lower tail probability `alpha` for a chi2 distribution with `df` degrees of freedom to a profile likelihood ratio threshold. @@ -34,8 +37,9 @@ def calculate_approximate_ci( xs: np.ndarray, ratios: np.ndarray, confidence_ratio: float ) -> Tuple[float, float]: """ - Calculate approximate confidence interval based on profile. Interval - bounds are linerly interpolated. + Calculate approximate confidence interval based on profile. + + Interval bounds are linerly interpolated. Parameters ---------- @@ -84,7 +88,7 @@ def initialize_profile( profile_list: int ) -> float: """ - This function initializes profiling based on a previous optimization. + Initialize profiling based on a previous optimization. Parameters ---------- @@ -148,7 +152,9 @@ def fill_profile_list( global_opt: float ) -> None: """ - This is a helper function for initialize_profile + Fill a ProfileResult. + + Helper function for `initialize_profile`. Parameters ---------- @@ -169,7 +175,6 @@ def fill_profile_list( global_opt: log-posterior at global optimum. """ - if optimizer_result[GRAD] is not None: gradnorm = np.linalg.norm(optimizer_result[GRAD]) else: diff --git a/pypesto/profile/validation_intervals.py b/pypesto/profile/validation_intervals.py index 985dc3e4f..c5e54da12 100644 --- a/pypesto/profile/validation_intervals.py +++ b/pypesto/profile/validation_intervals.py @@ -24,10 +24,12 @@ def validation_profile_significance( lsq_objective: bool = False, return_significance: bool = True, ) -> float: - """ - A Validation Interval for significance alpha is a confidence region/ - interval for a new validation experiment. [#Kreutz]_ et al. - (This method per default returns the significance = 1-alpha!) + r""" + Compute significance of Validation Interval. + + It is a confidence region/interval for a new validation experiment. + [#Kreutz]_ et al. (This method per default returns the significance = + 1-alpha!) The reasoning behind their approach is, that a validation data set is outside the validation interval, if fitting the full data set @@ -78,14 +80,11 @@ def validation_profile_significance( within the Confidence Interval (False). I.e. alpha = 1-significance. - .. [#Kreutz] Kreutz, Clemens, Raue, Andreas and Timmer, Jens. - “Likelihood based observability analysis and - confidence intervals for predictions of dynamic models”. - BMC Systems Biology 2012/12. - doi:10.1186/1752-0509-6-120 - - """ - + .. [#Kreutz] Kreutz, Clemens, Raue, Andreas and Timmer, Jens. + “Likelihood based observability analysis and + confidence intervals for predictions of dynamic models”. + BMC Systems Biology 2012/12. doi:10.1186/1752-0509-6-120 + """ if (result_full_data is not None) and (optimizer is not None): raise UserWarning("optimizer will not be used, as a result object " "for the full data set is provided.") diff --git a/pypesto/profile/walk_along_profile.py b/pypesto/profile/walk_along_profile.py index 7f8ebd558..dbb42289f 100644 --- a/pypesto/profile/walk_along_profile.py +++ b/pypesto/profile/walk_along_profile.py @@ -22,9 +22,11 @@ def walk_along_profile( i_par: int ) -> ProfilerResult: """ - This function computes half a profile, by walking ahead in positive - direction until some stopping criterion is fulfilled. A two-sided profile - is obtained by flipping the profile direction. + Compute half a profile. + + Walk ahead in positive direction until some stopping criterion is + fulfilled. A two-sided profile is obtained by flipping the profile + direction. Parameters ---------- @@ -50,7 +52,6 @@ def walk_along_profile( current_profile: The current profile, modified in-place. """ - # create variables which are needed during iteration stop_profile = False diff --git a/pypesto/result.py b/pypesto/result.py index 11e5b1acb..6fa0c0bf1 100644 --- a/pypesto/result.py +++ b/pypesto/result.py @@ -1,3 +1,4 @@ +# noqa: D400,D205 """ Result ====== @@ -18,9 +19,7 @@ class OptimizeResult: - """ - Result of the minimize() function. - """ + """Result of the :py:func:`pypesto.optimize.minimize` function.""" def __init__(self): self.list = [] @@ -37,14 +36,11 @@ def append( optimizer_result: The result of one (local) optimizer run. """ - self.list.append(optimizer_result) self.sort() def sort(self): - """ - Sort the optimizer results by function value fval (ascending). - """ + """Sort the optimizer results by function value fval (ascending).""" def get_fval(res): return res.fval if not np.isnan(res.fval) else np.inf @@ -52,10 +48,10 @@ def get_fval(res): def as_dataframe(self, keys=None) -> pd.DataFrame: """ - Get as pandas DataFrame. If keys is a list, - return only the specified values. - """ + Get as pandas DataFrame. + If keys is a list, return only the specified values, otherwise all. + """ lst = self.as_list(keys) df = pd.DataFrame(lst) @@ -64,15 +60,15 @@ def as_dataframe(self, keys=None) -> pd.DataFrame: def as_list(self, keys=None) -> Sequence: """ - Get as list. If keys is a list, - return only the specified values. + Get as list. + + If keys is a list, return only the specified values. Parameters ---------- keys: list(str), optional Labels of the field to extract. """ - lst = self.list if keys is not None: @@ -81,10 +77,7 @@ def as_list(self, keys=None) -> Sequence: return lst def get_for_key(self, key) -> list: - """ - Extract the list of values for the specified key as a list. - """ - + """Extract the list of values for the specified key as a list.""" return [res[key] for res in self.list] @@ -135,15 +128,16 @@ def set_profiler_result( profiler_result: 'profile.ProfilerResult', i_par: int, profile_list: int = None) -> None: - """Write a profiler result to the result object at `i_par` of profile - list `profile_list`. + """ + Write a profiler result to the result object. Parameters ---------- profiler_result: The result of one (local) profiler run. i_par: - Integer specifying the parameter index. + Integer specifying the parameter index where to put + profiler_result. profile_list: Index specifying the profile list. Defaults to the last list. """ @@ -155,8 +149,7 @@ def get_profiler_result( self, i_par: int, profile_list: int = None ): """ - Get theprofiler result at parameter index `i_par` of profile list - `profile_list`. + Get the profiler result at parameter index `i_par` of `profile_list`. Parameters ---------- @@ -171,9 +164,7 @@ def get_profiler_result( class SampleResult: - """ - Result of the sample() function. - """ + """Result of the sample() function.""" def __init__(self): pass @@ -182,24 +173,20 @@ def __init__(self): class Result: """ Universal result object for pypesto. + The algorithms like optimize, profile, sample fill different parts of it. Attributes ---------- - problem: pypesto.Problem The problem underlying the results. - optimize_result: The results of the optimizer runs. - profile_result: The results of the profiler run. - sample_result: The results of the sampler run. - """ def __init__(self, problem=None): diff --git a/pypesto/sample/__init__.py b/pypesto/sample/__init__.py index db6f0f966..0588202ca 100644 --- a/pypesto/sample/__init__.py +++ b/pypesto/sample/__init__.py @@ -1,3 +1,4 @@ +# noqa: D400,D205 """ Sample ====== diff --git a/pypesto/sample/adaptive_metropolis.py b/pypesto/sample/adaptive_metropolis.py index 3cd154a92..c454b5210 100644 --- a/pypesto/sample/adaptive_metropolis.py +++ b/pypesto/sample/adaptive_metropolis.py @@ -7,9 +7,7 @@ class AdaptiveMetropolisSampler(MetropolisSampler): - """ - Metropolis-Hastings sampler with adaptive proposal covariance. - """ + """Metropolis-Hastings sampler with adaptive proposal covariance.""" def __init__(self, options: Dict = None): super().__init__(options) @@ -20,6 +18,7 @@ def __init__(self, options: Dict = None): @classmethod def default_options(cls): + """Return the default options for the sampler.""" return { # controls adaptation degeneration velocity of the proposals # in [0, 1], with 0 -> no adaptation, i.e. classical @@ -43,6 +42,7 @@ def default_options(cls): } def initialize(self, problem: Problem, x0: np.ndarray): + """Initialize the sampler.""" super().initialize(problem, x0) if self.options['cov0'] is not None: @@ -133,8 +133,9 @@ def update_history_statistics( def regularize_covariance(cov: np.ndarray, reg_factor: float) -> np.ndarray: """ - Regularize the estimated covariance matrix of the sample. Useful if the - estimated covariance matrix is ill-conditioned. + Regularize the estimated covariance matrix of the sample. + + Useful if the estimated covariance matrix is ill-conditioned. Increments the diagonal a little to ensure positivity. Parameters diff --git a/pypesto/sample/adaptive_parallel_tempering.py b/pypesto/sample/adaptive_parallel_tempering.py index 72f79911e..3321ef274 100644 --- a/pypesto/sample/adaptive_parallel_tempering.py +++ b/pypesto/sample/adaptive_parallel_tempering.py @@ -1,3 +1,4 @@ +"""AdaptiveParallelTemperingSampler class.""" from typing import Dict, Sequence import numpy as np @@ -9,6 +10,7 @@ class AdaptiveParallelTemperingSampler(ParallelTemperingSampler): @classmethod def default_options(cls) -> Dict: + """Get default options for sampler.""" options = super().default_options() # scaling factor for temperature adaptation options['eta'] = 100 diff --git a/pypesto/sample/auto_correlation.py b/pypesto/sample/auto_correlation.py index 2cac3b47e..10f8d6d13 100644 --- a/pypesto/sample/auto_correlation.py +++ b/pypesto/sample/auto_correlation.py @@ -3,8 +3,9 @@ def autocorrelation_sokal(chain: np.ndarray) -> np.ndarray: """ - Estimate the integrated autocorrelation time of a MCMC chain - using Sokal's adaptive truncated periodogram estimator. + Estimate the integrated autocorrelation time of a MCMC chain. + + Uses Sokal's adaptive truncated periodogram estimator. - Haario, H., Laine, M., Mira, A. et al. DRAM: Efficient adaptive MCMC. Stat Comput 16, 339–354 (2006). @@ -18,12 +19,12 @@ def autocorrelation_sokal(chain: np.ndarray) -> np.ndarray: Parameters ---------- chain: The MCMC chain. + Returns ------- tau_est: An estimate of the integrated autocorrelation time of the MCMC chain. """ - nsamples, npar = chain.shape tau_est = np.zeros((npar)) diff --git a/pypesto/sample/diagnostics.py b/pypesto/sample/diagnostics.py index 2c4113e68..85787e6a5 100644 --- a/pypesto/sample/diagnostics.py +++ b/pypesto/sample/diagnostics.py @@ -1,3 +1,4 @@ +"""Calculate different diagnostics of the sampling result.""" import numpy as np import logging @@ -10,7 +11,7 @@ def geweke_test(result: Result, zscore: float = 2.) -> int: """ - Calculates the burn-in of MCMC chains. + Calculate the burn-in of MCMC chains. Parameters ---------- @@ -44,7 +45,7 @@ def geweke_test(result: Result, zscore: float = 2.) -> int: def auto_correlation(result: Result) -> float: """ - Calculates the autocorrelation of the MCMC chains. + Calculate the autocorrelation of the MCMC chains. Parameters ---------- @@ -108,7 +109,6 @@ def effective_sample_size(result: Result) -> float: Estimate of the effective sample size of the MCMC chains. """ - # Check if autocorrelation is available if result.sample_result.auto_correlation is None: # Calculate autocorrelation diff --git a/pypesto/sample/emcee.py b/pypesto/sample/emcee.py index 9a81735aa..45a40e173 100644 --- a/pypesto/sample/emcee.py +++ b/pypesto/sample/emcee.py @@ -26,6 +26,8 @@ def __init__( run_args: dict = None, ): """ + Initialize sampler. + Parameters ---------- nwalkers: The number of walkers in the ensemble. @@ -63,6 +65,7 @@ def initialize( problem: Problem, x0: Union[np.ndarray, List[np.ndarray]], ) -> None: + """Initialize the sampler.""" self.problem = problem # extract for pickling efficiency @@ -108,11 +111,12 @@ def log_prob(x): problem.x_guesses_full = problem.x_guesses_full[x0.shape[0]:] def sample(self, n_samples: int, beta: float = 1.) -> None: - # the method returns the most recent sample state + """Return the most recent sample state.""" self.state = self.sampler.run_mcmc( self.state, n_samples, **self.run_args) def get_samples(self) -> McmcPtResult: + """Get the samples into the fitting pypesto format.""" # all walkers are concatenated, yielding a flat array trace_x = np.array([self.sampler.get_chain(flat=True)]) trace_neglogpost = np.array([- self.sampler.get_log_prob(flat=True)]) diff --git a/pypesto/sample/geweke_test.py b/pypesto/sample/geweke_test.py index d3a4e9f39..db4471ec1 100644 --- a/pypesto/sample/geweke_test.py +++ b/pypesto/sample/geweke_test.py @@ -1,3 +1,4 @@ +"""Helper function for `geweke_test`.""" import logging import warnings from typing import Tuple @@ -28,7 +29,6 @@ def spectrum(x: np.ndarray, spectral_density: The spectral density. """ - if nfft is None: nfft = np.min(len(x), 256) @@ -71,7 +71,7 @@ def spectrum(x: np.ndarray, def spectrum0(x: np.ndarray) -> np.ndarray: """ - Calculates the spectral density at frequency zero. + Calculate the spectral density at frequency zero. Parameters ---------- @@ -83,7 +83,6 @@ def spectrum0(x: np.ndarray) -> np.ndarray: spectral_density_zero: Spectral density at zero. """ - n_samples, n_par = x.shape spectral_density_zero = np.zeros((1, n_par)) @@ -98,10 +97,11 @@ def calculate_zscore(chain: np.ndarray, a: float = 0.1, b: float = 0.5) -> Tuple[float, float]: """ - Performs a Geweke test on a chain using the first - "a" fraction and the last "b" fraction of it for - comparison. Test for equality of the means of the - first a% and last b% of a Markov chain. + Perform a Geweke test on a chain. + + Use the first "a" fraction and the last "b" fraction of it for + comparison. Test for equality of the means of the first a% and last b% + of a Markov chain. See: Stephen P. Brooks and Gareth O. Roberts. @@ -123,7 +123,6 @@ def calculate_zscore(chain: np.ndarray, p: Significance level of the Geweke test. """ - nsamples, _ = chain.shape # Define First fraction @@ -163,7 +162,7 @@ def calculate_zscore(chain: np.ndarray, def burn_in_by_sequential_geweke(chain: np.ndarray, zscore: float = 2.) -> int: """ - Calculates the burn-in of MCMC chains. + Calculate the burn-in of MCMC chains. Parameters ---------- @@ -179,7 +178,6 @@ def burn_in_by_sequential_geweke(chain: np.ndarray, of the chain do not differ significantly regarding Geweke test. """ - nsamples, npar = chain.shape # number of fragments n = 20 diff --git a/pypesto/sample/metropolis.py b/pypesto/sample/metropolis.py index 4ca5ac8e9..d2572c9f6 100644 --- a/pypesto/sample/metropolis.py +++ b/pypesto/sample/metropolis.py @@ -9,9 +9,7 @@ class MetropolisSampler(InternalSampler): - """ - Simple Metropolis-Hastings sampler with fixed proposal variance. - """ + """Simple Metropolis-Hastings sampler with fixed proposal variance.""" def __init__(self, options: Dict = None): super().__init__(options) @@ -25,12 +23,14 @@ def __init__(self, options: Dict = None): @classmethod def default_options(cls): + """Return the default options for the sampler.""" return { 'std': 1., # the proposal standard deviation 'show_progress': True, # whether to show the progress } def initialize(self, problem: Problem, x0: np.ndarray): + """Initialize the sampler.""" self.problem = problem self.neglogpost = problem.objective self.neglogpost.history = History() @@ -43,7 +43,7 @@ def initialize(self, problem: Problem, x0: np.ndarray): self.trace_neglogprior = [self.neglogprior(x0)] def sample(self, n_samples: int, beta: float = 1.): - # load last recorded particle + """Load last recorded particle.""" x = self.trace_x[-1] lpost = - self.trace_neglogpost[-1] lprior = - self.trace_neglogprior[-1] @@ -61,16 +61,27 @@ def sample(self, n_samples: int, beta: float = 1.): self.trace_neglogprior.append(-lprior) def make_internal(self, temper_lpost: bool): + """ + Allow the inner samplers to be used as inner samplers. + + Can be called by parallel tempering samplers during initialization. + Default: Do nothing. + + Parameters + ---------- + temper_lpost: + Whether to temperate the posterior or only the likelihood. + """ self.options['show_progress'] = False self.temper_lpost = temper_lpost def _perform_step(self, x: np.ndarray, lpost: np.ndarray, lprior: np.ndarray, beta: float): """ - Perform a step: Propose new parameter, evaluate and check whether to - accept. - """ + Perform a step. + Propose new parameter, evaluate and check whether to accept. + """ # propose step x_new: np.ndarray = self._propose_parameter(x) @@ -127,6 +138,13 @@ def _update_proposal(self, x: np.ndarray, lpost: float, """Update the proposal density. Default: Do nothing.""" def get_last_sample(self) -> InternalSample: + """Get the last sample in the chain. + + Returns + ------- + internal_sample: + The last sample in the chain in the exchange format. + """ return InternalSample( x=self.trace_x[-1], lpost=- self.trace_neglogpost[-1], @@ -134,11 +152,20 @@ def get_last_sample(self) -> InternalSample: ) def set_last_sample(self, sample: InternalSample): + """ + Set the last sample in the chain to the passed value. + + Parameters + ---------- + sample: + The sample that will replace the last sample in the chain. + """ self.trace_x[-1] = sample.x self.trace_neglogpost[-1] = - sample.lpost self.trace_neglogprior[-1] = - sample.lprior def get_samples(self) -> McmcPtResult: + """Get the samples into the fitting pypesto format.""" result = McmcPtResult( trace_x=np.array([self.trace_x]), trace_neglogpost=np.array([self.trace_neglogpost]), diff --git a/pypesto/sample/parallel_tempering.py b/pypesto/sample/parallel_tempering.py index 2c02ae7b3..f81d7cf8b 100644 --- a/pypesto/sample/parallel_tempering.py +++ b/pypesto/sample/parallel_tempering.py @@ -41,6 +41,7 @@ def __init__( @classmethod def default_options(cls) -> Dict: + """Return the default options for the sampler.""" return { 'max_temp': 5e4, 'exponent': 4, @@ -50,7 +51,7 @@ def default_options(cls) -> Dict: def initialize(self, problem: Problem, x0: Union[np.ndarray, List[np.ndarray]]): - # initialize all samplers + """Initialize all samplers.""" n_chains = len(self.samplers) if isinstance(x0, list): x0s = x0 @@ -63,6 +64,7 @@ def initialize(self, def sample( self, n_samples: int, beta: float = 1.): + """Sample and swap in between samplers.""" # loop over iterations for i_sample in tqdm(range(int(n_samples))): # TODO test # sample diff --git a/pypesto/sample/pymc3.py b/pypesto/sample/pymc3.py index e6e035745..434f4817a 100644 --- a/pypesto/sample/pymc3.py +++ b/pypesto/sample/pymc3.py @@ -1,3 +1,4 @@ +"""Pymc3Sampler.""" import numpy as np from typing import Union import logging @@ -44,11 +45,29 @@ def __init__(self, step_function=None, **kwargs): @classmethod def translate_options(cls, options): + """ + Translate options and fill in defaults. + + Parameters + ---------- + options: + Options configuring the sampler. + """ if not options: options = {'chains': 1} return options def initialize(self, problem: Problem, x0: np.ndarray): + """ + Initialize the sampler. + + Parameters + ---------- + problem: + The problem for which to sample. + x0: + Should, but is not required to, be used as initial parameter. + """ self.problem = problem if x0 is not None: if len(x0) != problem.dim: @@ -61,6 +80,16 @@ def initialize(self, problem: Problem, x0: np.ndarray): def sample( self, n_samples: int, beta: float = 1.): + """ + Sample the problem. + + Parameters + ---------- + n_samples: + Number of samples to be computed. + beta: + Inverse temperature for the log probability function. + """ problem = self.problem log_post_fun = TheanoLogProbability(problem, beta) trace = self.trace @@ -102,6 +131,7 @@ def sample( self.data = data def get_samples(self) -> McmcPtResult: + """Convert result from Pymc3 to McmcPtResult.""" # parameter values trace_x = np.asarray( self.data.posterior.to_array()).transpose((1, 2, 0)) diff --git a/pypesto/sample/result.py b/pypesto/sample/result.py index 125bf18b2..1465e9c3d 100644 --- a/pypesto/sample/result.py +++ b/pypesto/sample/result.py @@ -3,10 +3,10 @@ class McmcPtResult(dict): - """The result of a sampler run using Markov-chain Monte Carlo, and - optionally parallel tempering. + """ + The result of a sampler run using Markov-chain Monte Carlo. - Can be used like a dict. + Currently result object of all supported samplers. Can be used like a dict. Parameters ---------- @@ -80,6 +80,7 @@ def __init__(self, f"trace_neglogprior.shape={trace_neglogprior.shape}") # noqa def __getattr__(self, key): + """Allow usage of keys like attributes.""" try: return self[key] except KeyError: diff --git a/pypesto/sample/sample.py b/pypesto/sample/sample.py index f81be4446..177430515 100644 --- a/pypesto/sample/sample.py +++ b/pypesto/sample/sample.py @@ -5,6 +5,7 @@ from ..problem import Problem from ..result import Result +from ..optimize.util import autosave from .sampler import Sampler from .adaptive_metropolis import AdaptiveMetropolisSampler @@ -16,10 +17,11 @@ def sample( n_samples: int, sampler: Sampler = None, x0: Union[np.ndarray, List[np.ndarray]] = None, - result: Result = None + result: Result = None, + filename: str = "Auto" ) -> Result: """ - This is the main function to call to do parameter sampling. + Call to do parameter sampling. Parameters ---------- @@ -38,6 +40,11 @@ def sample( result: A result to write to. If None provided, one is created from the problem. + filename: + Name of the hdf5 file, where the result will be saved. Default is + "Auto", in which case it will automatically generate a file named + `year_month_day_sampling_result.hdf5`. Deactivate saving by + setting filename to `None`. Returns ------- @@ -78,4 +85,8 @@ def sample( # record results result.sample_result = sampler_result + autosave(filename=filename, + result=result, + type="sampling") + return result diff --git a/pypesto/sample/sampler.py b/pypesto/sample/sampler.py index b0b888e4e..5c080f3aa 100644 --- a/pypesto/sample/sampler.py +++ b/pypesto/sample/sampler.py @@ -1,3 +1,4 @@ +"""Various Sampler classes.""" import abc import numpy as np from typing import Dict, List, Union @@ -50,7 +51,8 @@ def get_samples(self) -> McmcPtResult: @classmethod def default_options(cls) -> Dict: - """Convenience method to set/get default options. + """ + Set/Get default options. Returns ------- @@ -61,7 +63,8 @@ def default_options(cls) -> Dict: @classmethod def translate_options(cls, options): - """Convenience method to translate options and fill in defaults. + """ + Translate options and fill in defaults. Parameters ---------- @@ -80,7 +83,9 @@ def translate_options(cls, options): class InternalSample: """ - This is the exchange object provided and accepted by + Internal sample class. + + Exchange object provided and accepted by `InternalSampler.get_last_sample()`, `InternalSampler.set_last_sample()`. It carries all information needed to check whether to swap between chains, and to continue the chain from the updated sample. @@ -131,9 +136,10 @@ def set_last_sample(self, sample: InternalSample): def make_internal(self, temper_lpost: bool): """ - This function can be called by parallel tempering samplers during - initialization to allow the inner samplers to adjust to them - being used as inner samplers. Default: Do nothing. + Allow the inner samplers to be used as inner samplers. + + Can be called by parallel tempering samplers during initialization. + Default: Do nothing. Parameters ---------- diff --git a/pypesto/sample/theano.py b/pypesto/sample/theano.py index 8f84d14e1..8d5bc42c8 100644 --- a/pypesto/sample/theano.py +++ b/pypesto/sample/theano.py @@ -43,13 +43,18 @@ def __init__(self, problem: Problem, beta: float = 1.): self._log_prob_grad = None def perform(self, node, inputs, outputs, params=None): + """Calculate the gradients of the objective function at the inputs.""" theta, = inputs log_prob = self._log_prob(theta) outputs[0][0] = np.array(log_prob) def grad(self, inputs, g): - # the method that calculates the gradients - it actually returns the - # vector-Jacobian product - g[0] is a vector of parameter values + """ + Calculate the gradients. + + Actually returns the vector-Jacobian product - g[0] is a vector of + parameter values. + """ if self._log_prob_grad is None: # indicates gradient not available return [NullType] @@ -61,6 +66,7 @@ def grad(self, inputs, g): class TheanoLogProbabilityGradient(tt.Op): """ Theano wrapper around a (non-normalized) log-probability gradient function. + This Op will be called with a vector of values and also return a vector of values - the gradients in each dimension. @@ -81,6 +87,7 @@ def __init__(self, problem: Problem, beta: float = 1.): lambda x: - beta * self._objective(x, sensi_orders=(1,)) def perform(self, node, inputs, outputs, params=None): + """Calculate the gradients of the objective function at the inputs.""" theta, = inputs # calculate gradients log_prob_grad = self._log_prob_grad(theta) diff --git a/pypesto/sample/util.py b/pypesto/sample/util.py index 4a1916012..3dda9fe89 100644 --- a/pypesto/sample/util.py +++ b/pypesto/sample/util.py @@ -1,4 +1,4 @@ -"""A set of helper functions""" +"""A set of helper functions.""" import numpy as np import logging from typing import Tuple diff --git a/pypesto/startpoint/assign.py b/pypesto/startpoint/assign.py index c8cfb13d2..8468489c3 100644 --- a/pypesto/startpoint/assign.py +++ b/pypesto/startpoint/assign.py @@ -88,7 +88,8 @@ def resample_startpoints( x_guesses: np.ndarray, startpoint_method: StartpointMethod, ): - """Resample startpoints having non-finite value. + """ + Resample startpoints having non-finite value. Check all proposed startpoints and resample ones with non-finite value via the startpoint_method. @@ -116,7 +117,6 @@ def resample_startpoints( startpoints: Startpoints with all finite function values, shape (n_starts, n_par). """ - n_starts = startpoints.shape[0] resampled_startpoints = np.zeros_like(startpoints) diff --git a/pypesto/startpoint/base.py b/pypesto/startpoint/base.py index a67dc73ab..fa4f75abe 100644 --- a/pypesto/startpoint/base.py +++ b/pypesto/startpoint/base.py @@ -53,6 +53,8 @@ def __init__( function: Callable, ): """ + Initialize. + Parameters ---------- function: The callable sampling startpoints. @@ -66,6 +68,7 @@ def __call__( ub: np.ndarray, objective: ObjectiveBase, ) -> np.ndarray: + """Call function.""" return self.function( n_starts=n_starts, lb=lb, ub=ub, objective=objective, ) diff --git a/pypesto/startpoint/latin_hypercube.py b/pypesto/startpoint/latin_hypercube.py index 1e02faf2a..b14efddfd 100644 --- a/pypesto/startpoint/latin_hypercube.py +++ b/pypesto/startpoint/latin_hypercube.py @@ -84,13 +84,16 @@ def _latin_hypercube_unit( class LatinHypercubeStartpoints(StartpointMethod): """Generate latin hypercube-sampled startpoints. - See e.g. https://en.wikipedia.org/wiki/Latin_hypercube_sampling.""" + See e.g. https://en.wikipedia.org/wiki/Latin_hypercube_sampling. + """ def __init__( self, smooth: bool = True, ): """ + Initialize. + Parameters ---------- smooth: @@ -107,6 +110,7 @@ def __call__( ub: np.ndarray, objective: ObjectiveBase = None, ) -> np.ndarray: + """Call function.""" return _latin_hypercube( n_starts=n_starts, lb=lb, diff --git a/pypesto/startpoint/uniform.py b/pypesto/startpoint/uniform.py index f68e25690..765f10084 100644 --- a/pypesto/startpoint/uniform.py +++ b/pypesto/startpoint/uniform.py @@ -53,6 +53,7 @@ def __call__( ub: np.ndarray, objective: ObjectiveBase = None, ) -> np.ndarray: + """Call function.""" return _uniform(n_starts=n_starts, lb=lb, ub=ub) diff --git a/pypesto/store/hdf5.py b/pypesto/store/hdf5.py index 866cd21fd..35eb812a3 100644 --- a/pypesto/store/hdf5.py +++ b/pypesto/store/hdf5.py @@ -1,4 +1,4 @@ -"""Convenience functions for working with HDF5 files""" +"""Convenience functions for working with HDF5 files.""" import h5py import numpy as np @@ -10,10 +10,10 @@ def write_array(f: h5py.Group, path: str, values: Collection) -> None: """ - Write array to hdf5 + Write array to hdf5. Parameters - ------------- + ---------- f: h5py.Group where dataset should be created path: @@ -21,7 +21,6 @@ def write_array(f: h5py.Group, values: array to write """ - if all(isinstance(x, Integral) for x in values): write_int_array(f, path, values) elif all(isinstance(x, Real) for x in values): @@ -37,10 +36,10 @@ def write_string_array(f: h5py.Group, path: str, strings: Collection) -> None: """ - Write string array to hdf5 + Write string array to hdf5. Parameters - ------------- + ---------- f: h5py.Group where dataset should be created path: @@ -58,10 +57,10 @@ def write_float_array(f: h5py.Group, values: Collection[Number], dtype='f8') -> None: """ - Write float array to hdf5 + Write float array to hdf5. Parameters - ------------- + ---------- f: h5py.Group where dataset should be created path: @@ -83,10 +82,10 @@ def write_int_array(f: h5py.Group, values: Collection[int], dtype=' Problem: ---------- objective: Objective function which is currently not saved to storage. + Returns ------- problem: A problem instance with all attributes read in. """ - # create empty problem if objective is None: objective = Objective() @@ -143,19 +143,20 @@ def read(self, objective: ObjectiveBase = None) -> Problem: class OptimizationResultHDF5Reader: """ - Reader of the HDF5 result files written - by class OptimizationResultHDF5Writer. + Reader of the HDF5 result files written by OptimizationResultHDF5Writer. Attributes - ------------- + ---------- storage_filename: HDF5 result file name """ + def __init__(self, storage_filename: str): """ + Initialize reader. + Parameters ---------- - storage_filename: str HDF5 result file name """ @@ -163,9 +164,7 @@ def __init__(self, storage_filename: str): self.results = Result() def read(self) -> Result: - """ - Read HDF5 result file and return pyPESTO result object. - """ + """Read HDF5 result file and return pyPESTO result object.""" with h5py.File(self.storage_filename, "r") as f: if '/problem' in f['/']: problem_reader = ProblemHDF5Reader(self.storage_filename) @@ -182,19 +181,20 @@ def read(self) -> Result: class SamplingResultHDF5Reader: """ - Reader of the HDF5 result files written - by class SamplingResultHDF5Writer. + Reader of the HDF5 result files written by SamplingResultHDF5Writer. Attributes - ------------- + ---------- storage_filename: HDF5 result file name """ + def __init__(self, storage_filename: str): """ + Initialize reader. + Parameters ---------- - storage_filename: str HDF5 result file name """ @@ -202,9 +202,7 @@ def __init__(self, storage_filename: str): self.results = Result() def read(self) -> Result: - """ - Read HDF5 result file and return pyPESTO result object. - """ + """Read HDF5 result file and return pyPESTO result object.""" sample_result = {} with h5py.File(self.storage_filename, "r") as f: if '/problem' in f['/']: @@ -227,29 +225,28 @@ def read(self) -> Result: class ProfileResultHDF5Reader: """ - Reader of the HDF5 result files written - by class OptimizationResultHDF5Writer. + Reader of the HDF5 result files written by OptimizationResultHDF5Writer. Attributes - ------------- + ---------- storage_filename: HDF5 result file name """ + def __init__(self, storage_filename: str): """ + Initialize reader. + Parameters ---------- - - storage_filename: str + storage_filename: HDF5 result file name """ self.storage_filename = storage_filename self.results = Result() def read(self) -> Result: - """ - Read HDF5 result file and return pyPESTO result object. - """ + """Read HDF5 result file and return pyPESTO result object.""" profiling_list = [] with h5py.File(self.storage_filename, "r") as f: if '/problem' in f['/']: @@ -278,8 +275,7 @@ def read_result(filename: str, sample: bool = False, ) -> Result: """ - This is a function that saves the whole pypesto.Result object in an - HDF5 file. With booleans one can choose more detailed what to save. + Save the whole pypesto.Result object in an HDF5 file. Parameters ---------- @@ -344,27 +340,28 @@ def read_result(filename: str, def load_objective_config(filename: str): """ - Load the objective information stored in f + Load the objective information stored in f. Parameters ---------- filename: The name of the file in which the information are stored. - Returns: + Returns + ------- A dictionary of the information, stored instead of the actual objective in problem.objective. """ - with h5py.File(filename, 'r') as f: info_str = f['problem/config'][()].decode() info = ast.literal_eval(info_str) return info -def optimization_result_from_history(filename: str): +def optimization_result_from_history(filename: str) -> Result: """ - Converts a saved hdf5 History to an optimization result. + Convert a saved hdf5 History to an optimization result. + Used for interrupted optimization runs. Parameters @@ -372,11 +369,11 @@ def optimization_result_from_history(filename: str): filename: The name of the file in which the information are stored. - Returns: + Returns + ------- A result object in which the optimization result is constructed from history. But missing "Time, Message and Exitflag" keys. """ - result = Result() with h5py.File(filename, 'r') as f: for id_name in f['history'].keys(): diff --git a/pypesto/store/save_to_hdf5.py b/pypesto/store/save_to_hdf5.py index d9e54eb78..c3bbcee8a 100644 --- a/pypesto/store/save_to_hdf5.py +++ b/pypesto/store/save_to_hdf5.py @@ -1,3 +1,4 @@ +"""Include functions for saving various results to hdf5.""" import os import logging from typing import Union @@ -16,11 +17,12 @@ def check_overwrite(f: Union[h5py.File, h5py.Group], overwrite: bool, target: str): """ - Checks whether target already exists. Sends a warning if - overwrite=False, deletes the target if overwrite=True. + Check whether target already exists. + + Sends a warning if overwrite=False, deletes the target if overwrite=True. Attributes - ------------- + ---------- f: file or group where existence of a group with the path group_path should be checked target: name of the group, whose existence is checked @@ -41,26 +43,24 @@ class ProblemHDF5Writer: Writer of the HDF5 problem files. Attributes - ------------- + ---------- storage_filename: HDF5 result file name """ def __init__(self, storage_filename: str): """ + Initialize writer. + Parameters ---------- - storage_filename: str HDF5 problem file name """ self.storage_filename = storage_filename def write(self, problem, overwrite: bool = False): - """ - Write HDF5 problem file from pyPESTO problem object. - """ - + """Write HDF5 problem file from pyPESTO problem object.""" # Create destination directory if isinstance(self.storage_filename, str): basedir = os.path.dirname(self.storage_filename) @@ -90,11 +90,10 @@ def write(self, problem, overwrite: bool = False): def get_or_create_group(f: Union[h5py.File, h5py.Group], group_path: str) -> h5py.Group: """ - Helper function that returns a group object for the group with group_path - relative to f. Creates it if it doesn't exist. + Return/create a group object for the group with group_path relative to f. Attributes - ------------- + ---------- f: file or group where existence of a group with the path group_path should be checked group_path: the path or simply the name of the group that should exist in f @@ -102,7 +101,7 @@ def get_or_create_group(f: Union[h5py.File, h5py.Group], Returns ------- grp: - hdf5 group object with specified path. + hdf5 group object with specified path relative to f. """ if group_path in f: grp = f[group_path] @@ -116,26 +115,24 @@ class OptimizationResultHDF5Writer: Writer of the HDF5 result files. Attributes - ------------- + ---------- storage_filename: HDF5 result file name """ def __init__(self, storage_filename: str): """ + Initialize Writer. + Parameters ---------- - storage_filename: str HDF5 result file name """ self.storage_filename = storage_filename def write(self, result: Result, overwrite=False): - """ - Write HDF5 result file from pyPESTO result object. - """ - + """Write HDF5 result file from pyPESTO result object.""" # Create destination directory if isinstance(self.storage_filename, str): basedir = os.path.dirname(self.storage_filename) @@ -167,25 +164,24 @@ class SamplingResultHDF5Writer: Writer of the HDF5 sampling files. Attributes - ------------- + ---------- storage_filename: HDF5 result file name """ def __init__(self, storage_filename: str): """ + Initialize Writer. + Parameters ---------- - storage_filename: str HDF5 result file name """ self.storage_filename = storage_filename def write(self, result: Result, overwrite: bool = False): - """ - Write HDF5 sampling file from pyPESTO result object. - """ + """Write HDF5 sampling file from pyPESTO result object.""" # if there is no sample available, log a warning and return # SampleResult is only a dummy class created by the Result class # and always indicates the lack of a sampling result. @@ -220,26 +216,24 @@ class ProfileResultHDF5Writer: Writer of the HDF5 result files. Attributes - ------------- + ---------- storage_filename: HDF5 result file name """ def __init__(self, storage_filename: str): """ + Initialize Writer. + Parameters ---------- - storage_filename: str HDF5 result file name """ self.storage_filename = storage_filename def write(self, result: Result, overwrite: bool = False): - """ - Write HDF5 result file from pyPESTO result object. - """ - + """Write HDF5 result file from pyPESTO result object.""" # Create destination directory if isinstance(self.storage_filename, str): basedir = os.path.dirname(self.storage_filename) @@ -275,11 +269,12 @@ def write_result(result: Result, filename: str, overwrite: bool = False, problem: bool = True, - optimize: bool = True, - profile: bool = True, - sample: bool = True, + optimize: bool = False, + profile: bool = False, + sample: bool = False, ): - """Save whole pypesto.Result to hdf5 file. + """ + Save whole pypesto.Result to hdf5 file. Boolean indicators allow specifying what to save. @@ -300,6 +295,10 @@ def write_result(result: Result, sample: Read the sample result. """ + if not any([optimize, profile, sample]): + optimize = True + profile = True + sample = True if problem: pypesto_problem_writer = ProblemHDF5Writer(filename) diff --git a/pypesto/version.py b/pypesto/version.py index c49a95c35..75cf7831c 100644 --- a/pypesto/version.py +++ b/pypesto/version.py @@ -1 +1 @@ -__version__ = "0.2.8" +__version__ = "0.2.9" diff --git a/pypesto/visualize/__init__.py b/pypesto/visualize/__init__.py index d55ba2e3d..1907b4a54 100644 --- a/pypesto/visualize/__init__.py +++ b/pypesto/visualize/__init__.py @@ -1,3 +1,4 @@ +# noqa: D400,D205 """ Visualize ========= diff --git a/pypesto/visualize/clust_color.py b/pypesto/visualize/clust_color.py index b3c9f9865..583b223a4 100644 --- a/pypesto/visualize/clust_color.py +++ b/pypesto/visualize/clust_color.py @@ -13,21 +13,17 @@ def assign_clusters(vals): Parameters ---------- - vals: numeric list or array List to be clustered. Returns ------- - clust: numeric list - Indicating the corresponding cluster of each element from - 'vals'. - + Indicating the corresponding cluster of each element from + 'vals'. clustsize: numeric list Size of clusters, length equals number of clusters. """ - # sanity checks if vals is None or len(vals) == 0: return [], [] @@ -65,24 +61,19 @@ def assign_clustered_colors(vals, balance_alpha=True, highlight_global=True): Parameters ---------- - vals: numeric list or array List to be clustered and assigned colors. - balance_alpha: bool (optional) Flag indicating whether alpha for large clusters should be reduced to avoid overplotting (default: True) - highlight_global: bool (optional) flag indicating whether global optimum should be highlighted Returns ------- - colors: list of RGBA One for each element in 'vals'. """ - # sanity checks if vals is None or len(vals) == 0: return [] @@ -150,7 +141,6 @@ def assign_colors(vals, colors=None, balance_alpha=True, Parameters ---------- - vals: numeric list or array List to be clustered and assigned colors. @@ -166,11 +156,9 @@ def assign_colors(vals, colors=None, balance_alpha=True, Returns ------- - colors: list of RGBA One for each element in 'vals'. """ - # sanity checks if vals is None or len(vals) == 0: return np.array([]) @@ -217,25 +205,23 @@ def assign_colors_for_list( colors: Optional[Union[RGBA, List[RGBA], np.ndarray]] = None ) -> Union[List[List[float]], np.ndarray]: """ - Creates a list of colors for a list of items or checks - a user-provided list of colors and uses this if everything is ok + Create a list of colors for a list of items. + + Can also check a user-provided list of colors and use this if + everything is ok. Parameters ---------- - num_entries: number of results in list - colors: list of colors, or single color Returns ------- - colors: List of RGBA, one for each element in 'vals'. """ - # if the user did not specify any colors: if colors is None: # default colors will be used, on for each entry in the result list. @@ -264,27 +250,23 @@ def delete_nan_inf(fvals: np.ndarray, x: Optional[np.ndarray] = None, xdim: Optional[int] = 1) -> Tuple[np.ndarray, np.ndarray]: """ - Delete nan and inf values in fvals. If parameters 'x' are passed, also - the corresponding entries are deleted. + Delete nan and inf values in fvals. + + If parameters 'x' are passed, also the corresponding entries are deleted. Parameters ---------- - x: array of parameters - fvals: array of fval - xdim: dimension of x, in case x dimension cannot be inferred Returns ------- - x: array of parameters without nan or inf - fvals: array of fval without nan or inf """ diff --git a/pypesto/visualize/dimension_reduction.py b/pypesto/visualize/dimension_reduction.py index c33c08dde..2ca90d7ed 100644 --- a/pypesto/visualize/dimension_reduction.py +++ b/pypesto/visualize/dimension_reduction.py @@ -16,8 +16,10 @@ def projection_scatter_umap(umap_coordinates: np.ndarray, components: Sequence[int] = (0, 1), **kwargs): """ - Plot a scatter plots for UMAP coordinates. Creates either one or multiple - scatter plots, depending on the number of coordinates passed to it. + Plot a scatter plots for UMAP coordinates. + + Creates either one or multiple scatter plots, depending on the number of + coordinates passed to it. Parameters ---------- @@ -64,7 +66,9 @@ def projection_scatter_umap_original(umap_object: UmapTypeObject, components: Sequence[int] = (0, 1), **kwargs): """ - Wrapper around umap.plot.points. Similar to projection_scatter_umap, but + See `projection_scatter_umap` for more documentation. + + Wrapper around umap.plot.points. Similar to `projection_scatter_umap`, but uses the original plotting routine from umap.plot. Parameters @@ -72,10 +76,8 @@ def projection_scatter_umap_original(umap_object: UmapTypeObject, umap_object: umap object (returned as second output by get_umap_representation) to be shown as scatter plot - color_by: A sequence/list of floats, which specify the color in the colormap - components: Components to be plotted (corresponds to columns of umap_coordinates) @@ -84,7 +86,6 @@ def projection_scatter_umap_original(umap_object: UmapTypeObject, ax: matplotlib.Axes The plot axes. """ - # reduce, if necessary umap_object.embedding_ = umap_object.embedding_[:, components] @@ -96,15 +97,16 @@ def projection_scatter_pca(pca_coordinates: np.ndarray, components: Sequence[int] = (0, 1), **kwargs): """ - Plot a scatter plots for PCA coordinates. Creates either one or multiple - scatter plots, depending on the number of coordinates passed to it. + Plot a scatter plot for PCA coordinates. + + Creates either one or multiple scatter plots, depending on the number of + coordinates passed to it. Parameters ---------- pca_coordinates: array of pca coordinates (returned as first output by the routine get_pca_representation) to be shown as scatter plot - components: Components to be plotted (corresponds to columns of pca_coordinates) @@ -145,14 +147,14 @@ def ensemble_crosstab_scatter_lowlevel(dataset: np.ndarray, **kwargs): """ Plot cross-classification table of scatter plots for different coordinates. + Lowlevel routine for multiple UMAP and PCA plots, but can also be used to - visualize, e.g., parameter traces across optimizer runs + visualize, e.g., parameter traces across optimizer runs. Parameters ---------- dataset: array of data points to be shown as scatter plot - component_labels: labels for the x-axes and the y-axes @@ -161,7 +163,6 @@ def ensemble_crosstab_scatter_lowlevel(dataset: np.ndarray, axs: A dictionary of plot axes. """ - # We got more than two components. Create a cross-classification table n_components = dataset.shape[1] axs = _create_crosstab_axes(n_components) @@ -206,41 +207,31 @@ def ensemble_scatter_lowlevel(dataset, scatter_size: float = 0.5, invert_scatter_order: bool = False): """ - Create a scatter plot + Create a scatter plot. Parameters ---------- dataset: array of data points in reduced dimension - ax: Axes object to use. - size: Figure size (width, height) in inches. Is only applied when no ax object is specified - x_label: The x-axis label - y_label: The y-axis label - color_by: A sequence/list of floats, which specify the color in the colormap - color_map: A colormap name known to pyplot - background_color: Background color of the axes object (defaults to black) - marker_type: Type of plotted markers - scatter_size: Size of plotted markers - invert_scatter_order: Specifies the order of plotting the scatter points, can be important in case of overplotting @@ -250,7 +241,6 @@ def ensemble_scatter_lowlevel(dataset, ax: matplotlib.Axes The plot axes. """ - # first get the data to check identifiability # axes if ax is None: @@ -282,7 +272,7 @@ def ensemble_scatter_lowlevel(dataset, def _create_crosstab_axes(n_comp: int): """ - Create a figure with cross-classification table of axes + Create a figure with cross-classification table of axes. Parameters ---------- diff --git a/pypesto/visualize/ensemble.py b/pypesto/visualize/ensemble.py index 9384f06a2..f1a7a424e 100644 --- a/pypesto/visualize/ensemble.py +++ b/pypesto/visualize/ensemble.py @@ -15,31 +15,28 @@ def ensemble_identifiability(ensemble: Ensemble, ax: Optional[plt.Axes] = None, size: Optional[Tuple[float]] = (12, 6)): """ - Plots an overview about how many parameters hit the parameter bounds based + Visualize identifiablity of parameter ensemble. + + Plot an overview about how many parameters hit the parameter bounds based on a ensemble of parameters. confidence intervals/credible ranges are computed via the ensemble mean plus/minus 1 standard deviation. This highlevel routine expects a ensemble object as input. Parameters ---------- - ensemble: ensemble of parameter vectors (from pypesto.ensemble) - ax: Axes object to use. - size: Figure size (width, height) in inches. Is only applied when no ax object is specified Returns ------- - ax: matplotlib.Axes The plot axes. """ - # first get the data to check identifiability id_df = ensemble.check_identifiability() @@ -60,7 +57,9 @@ def ensemble_identifiability_lowlevel(none_hit: np.ndarray, ax: Optional[plt.Axes] = None, size: Optional[Tuple[float]] = (16, 10)): """ - Plots an overview about how many parameters hit the parameter bounds based + Low-level identifiablity routine. + + Plot an overview about how many parameters hit the parameter bounds based on a ensemble of parameters. Confidence intervals/credible ranges are computed via the ensemble mean plus/minus 1 standard deviation. This lowlevel routine works with numpy arrays which define the confidence @@ -68,37 +67,29 @@ def ensemble_identifiability_lowlevel(none_hit: np.ndarray, Parameters ---------- - none_hit: 2-dimensional array of confidence interval/credible ranges for identifiable parameters - lb_hit: 2-dimensional array of confidence interval/credible ranges for parameters which hit the lower parameter bound - ub_hit: 2-dimensional array of confidence interval/credible ranges for parameters which hit the upper parameter bound - both_hit: 2-dimensional array of confidence interval/credible ranges for parameters which hit both parameter bounds - ax: Axes object to use. - size: Figure size (width, height) in inches. Is only applied when no ax object is specified Returns ------- - ax: matplotlib.Axes The plot axes. """ - # define some short hands for later plotting n_par = sum([none_hit.shape[0], lb_hit.shape[0], ub_hit.shape[0], both_hit.shape[0]]) @@ -175,12 +166,13 @@ def ensemble_identifiability_lowlevel(none_hit: np.ndarray, def _prepare_identifiability_plot(id_df: pd.DataFrame): """ - This routine groups model parameters based on a ensemble object into - four categories, based on the mean of the parameter ensemble plus/minus - 1 standard deviation: Parameters that hit both bounds, parameters that hit - only the lower [or upper] bound, and parameters that hit no bounds. - It returns them as four numpy arrays, together with their confidence - intervals/credible ranges. + Group model parameters based on an ensemble object . + + Can group into four categories, based on the mean of the parameter + ensemble plus/minus 1 standard deviation: Parameters that hit both + bounds, parameters that hit only the lower [or upper] bound, + and parameters that hit no bounds. It returns them as four numpy arrays, + together with their confidence intervals/credible ranges. Parameters ---------- @@ -206,7 +198,6 @@ def _prepare_identifiability_plot(id_df: pd.DataFrame): 2-dimensional array of confidence interval/credible ranges for parameters which hit both parameter bounds """ - # prepare both_hit = [] lb_hit = [] @@ -252,7 +243,9 @@ def _create_patches(none_hit: np.ndarray, ub_hit: np.ndarray, both_hit: np.ndarray): """ - Creates matplotlib.patches.PatchCollection objects from numpy arrays with + Create patches for identifiability analysis. + + Create matplotlib.patches.PatchCollection objects from numpy arrays with confidence intervals/credible ranges, which visualize identifiability properties of the model parameters. diff --git a/pypesto/visualize/misc.py b/pypesto/visualize/misc.py index c1a737fcd..815a64811 100644 --- a/pypesto/visualize/misc.py +++ b/pypesto/visualize/misc.py @@ -20,33 +20,26 @@ def process_result_list(results, colors=None, legends=None): """ - assigns colors and legends to a list of results, check user provided lists + Assign colors and legends to a list of results, check user provided lists. Parameters ---------- - results: list or pypesto.Result list of pypesto.Result objects or a single pypesto.Result - colors: list, optional list of RGBA colors - legends: str or list labels for line plots Returns ------- - results: list of pypesto.Result list of pypesto.Result objects - colors: list of RGBA One for each element in 'results'. - legends: list of str labels for line plots """ - # check how many results were passed single_result = False legend_error = False @@ -97,30 +90,25 @@ def process_offset_y(offset_y: Optional[float], scale_y: str, min_val: float) -> float: """ - compute offset for y-axis, depend on user settings + Compute offset for y-axis, depend on user settings. Parameters ---------- - offset_y: value for offsetting the later plotted values, in order to ensure positivity if a semilog-plot is used - scale_y: Can be 'lin' or 'log10', specifying whether values should be plotted on linear or on log10-scale - min_val: Smallest value to be plotted Returns ------- - offset_y: float value for offsetting the later plotted values, in order to ensure positivity if a semilog-plot is used """ - # check whether the offset specified by the user is sufficient if offset_y is not None: if (scale_y == 'log10') and (min_val + offset_y <= 0.): @@ -140,24 +128,20 @@ def process_offset_y(offset_y: Optional[float], def process_y_limits(ax, y_limits): """ - apply user specified limits of y-axis + Apply user specified limits of y-axis. Parameters ---------- - ax: matplotlib.Axes, optional Axes object to use. - y_limits: ndarray y_limits, minimum and maximum, for current axes object Returns ------- - ax: matplotlib.Axes, optional Axes object to use. """ - # apply y-limits, if they were specified by the user if y_limits is not None: y_limits = np.array(y_limits) @@ -214,9 +198,10 @@ def process_y_limits(ax, y_limits): def process_start_indices(start_indices: Union[int, Iterable[int]], max_length: int) -> List[int]: """ - helper function that processes the start_indices and - creates an array of indices if a number was provided and checks that the - indices do not exceed the max_index + Process the start_indices. + + Create an array of indices if a number was provided and checks that the + indices do not exceed the max_index. Parameters ---------- @@ -226,7 +211,6 @@ def process_start_indices(start_indices: Union[int, Iterable[int]], max_length: maximum possible index for the start_indices """ - if isinstance(start_indices, Number): start_indices = range(int(start_indices)) @@ -281,8 +265,7 @@ def apparent_composite_color_component( bg_alpha: float = bg[RGBA_ALPHA], ) -> float: """ - Composite a foreground color component over a background color - component. + Composite a foreground over a background color component. Porter and Duff equations are used for alpha compositing. diff --git a/pypesto/visualize/model_fit.py b/pypesto/visualize/model_fit.py index 0567b16cd..ddd330b0a 100644 --- a/pypesto/visualize/model_fit.py +++ b/pypesto/visualize/model_fit.py @@ -3,6 +3,7 @@ Currently only for PEtab problems. """ +import matplotlib.axes import numpy as np import matplotlib.pyplot as plt import amici.petab_import as petab_import @@ -20,10 +21,12 @@ def visualize_optimized_model_fit(petab_problem: petab.Problem, result: Union[Result, Sequence[Result]], + start_index: int = 0, **kwargs - ): + ) -> Union[matplotlib.axes.Axes, None]: """ Visualize the optimized model fit of a PEtab problem. + Function calls the PEtab visualization file of the petab_problem and visualizes the fit of the optimized parameter. Common additional argument is `subplot_dir` to specify the directory each subplot is @@ -36,10 +39,12 @@ def visualize_optimized_model_fit(petab_problem: petab.Problem, The :py:class:`petab.Problem` that was optimized. result: The result object from optimization. + start_index: + The index of the optimization run in `result.optimize_result.list`. Returns ------- - ax: Axis object of the created plot. + axes: `matplotlib.axes.Axes` object of the created plot. None: In case subplots are saved to file """ if petab_problem is not None: @@ -48,7 +53,7 @@ def visualize_optimized_model_fit(petab_problem: petab.Problem, problem_parameters = \ dict(zip(petab_problem.parameter_df.index, - result.optimize_result.list[0]['x'])) + result.optimize_result.list[start_index]['x'])) amici_model = petab_import.import_petab_problem( petab_problem, @@ -65,11 +70,11 @@ def visualize_optimized_model_fit(petab_problem: petab.Problem, petab_problem.measurement_df) # function to call, to plot data and simulations - ax = plot_problem(petab_problem=petab_problem, - simulations_df=sim_df, - **kwargs - ) - return ax + axes = plot_problem(petab_problem=petab_problem, + simulations_df=sim_df, + **kwargs + ) + return axes def time_trajectory_model( @@ -81,9 +86,11 @@ def time_trajectory_model( start_index: int = 0, state_ids: Union[str, Sequence[str]] = None, state_names: Union[str, Sequence[str]] = None, - observable_ids: Union[str, Sequence[str]] = None,): + observable_ids: Union[str, Sequence[str]] = None, +) -> Union[matplotlib.axes.Axes, None]: """ Visualize the time trajectory of the model with given timepoints. + It does this by calling the amici plotting routines. Parameters @@ -109,9 +116,9 @@ def time_trajectory_model( Returns ------- - axes: `matplotlib.axes.Axes` object of the plot. + axes: + `matplotlib.axes.Axes` object of the plot. """ - if problem is None: problem = result.problem # add timepoints as needed @@ -155,8 +162,9 @@ def _time_trajectory_model_with_states( state_names: Sequence[str], observable_ids: Union[str, Sequence[str]]): """ - Helper function for time_trajectory_model. Visualizes both states and - observables. + Visualizes both, states and observables. + + Helper function for time_trajectory_model. Parameters ---------- @@ -174,7 +182,8 @@ def _time_trajectory_model_with_states( Returns ------- - axes: `matplotlib.axes.Axes` object of the plot. + axes: + `matplotlib.axes.Axes` object of the plot. """ # if state_name, state_id or observable_id is not None, get indices # for these the AMICI plotting functions default to all indices if @@ -222,8 +231,9 @@ def _time_trajectory_model_without_states( rdatas: Union['amici.ReturnData', Sequence['amici.ReturnData']], observable_ids: Union[str, Sequence[str]]): """ - Helper function for time_trajectory_model. Visualizes both states and - observables. + Visualize both, states and observables. + + Helper function for time_trajectory_model. Parameters ---------- @@ -237,7 +247,8 @@ def _time_trajectory_model_without_states( Returns ------- - axes: `matplotlib.axes.Axes` object of the plot. + axes: + `matplotlib.axes.Axes` object of the plot. """ # if observable_id's is not None, get indices for these # the AMICI plotting functions default to all indices if `None` is diff --git a/pypesto/visualize/optimization_stats.py b/pypesto/visualize/optimization_stats.py index 0b77a1fda..d3239ecb3 100644 --- a/pypesto/visualize/optimization_stats.py +++ b/pypesto/visualize/optimization_stats.py @@ -20,8 +20,7 @@ def optimization_run_properties_one_plot( plot_type: str = 'line' ) -> matplotlib.axes.Axes: """ - Plot stats for all optimization properties specified in properties_to_plot - on one plot. + Plot stats for allproperties specified in properties_to_plot on one plot. Parameters ---------- @@ -46,9 +45,11 @@ def optimization_run_properties_one_plot( Returns ------- + ax: + The plot axes. Examples - ------- + -------- optimization_properties_per_multistart( result1, properties_to_plot=['time'], @@ -133,9 +134,8 @@ def optimization_run_properties_per_multistart( ax: The plot axes. - Examples - ------- + -------- optimization_properties_per_multistart( result1, properties_to_plot=['time'], @@ -155,7 +155,6 @@ def optimization_run_properties_per_multistart( [result1, result2], properties_to_plot=['time', 'n_fval'], colors=[[.5, .9, .9, .3], [.2, .1, .9, .5]]) """ - if properties_to_plot is None: properties_to_plot = ['time', 'n_fval', 'n_grad', 'n_hess', 'n_res', 'n_sres'] @@ -189,6 +188,7 @@ def optimization_run_property_per_multistart( plot_type: str = 'line') -> matplotlib.axes.Axes: """ Plot stats for an optimization run property specified by opt_run_property. + It is possible to plot a histogram or a line plot. In a line plot, on the x axis are the numbers of the multistarts, where the multistarts are ordered with respect to a function value. On the y axis of the line plot @@ -223,7 +223,6 @@ def optimization_run_property_per_multistart( ax: The plot axes. """ - supported_properties = { 'time': 'Wall-clock time (seconds)', 'n_fval': 'Number of function evaluations', @@ -289,12 +288,10 @@ def stats_lowlevel(result: Result, legend: Optional[str] = None, plot_type: str = 'line'): """ - Plot values of the optimization run property specified by property name - across different multistarts + Plot values of the optimization run property across different multistarts. Parameters ---------- - result: Optimization result obtained by 'optimize.py' property_name: @@ -321,7 +318,6 @@ def stats_lowlevel(result: Result, ax: The plot axes. """ - fvals = result.optimize_result.get_for_key('fval') values = result.optimize_result.get_for_key(property_name) values, fvals = delete_nan_inf(fvals, values) diff --git a/pypesto/visualize/optimizer_convergence.py b/pypesto/visualize/optimizer_convergence.py index 777ca7b2d..bfd93e432 100644 --- a/pypesto/visualize/optimizer_convergence.py +++ b/pypesto/visualize/optimizer_convergence.py @@ -13,6 +13,8 @@ def optimizer_convergence(result: pypesto.Result, yscale: str = 'log', size: Tuple[float] = (18.5, 10.5)) -> plt.Axes: """ + Visualize to help spotting convergence issues. + Scatter plot of function values and gradient values at the end of optimization. Optimizer exit-message is encoded by color. Can help identifying convergence issues in optimization and guide tolerance @@ -20,7 +22,6 @@ def optimizer_convergence(result: pypesto.Result, Parameters ---------- - result: Optimization result obtained by 'optimize.py' @@ -39,7 +40,6 @@ def optimizer_convergence(result: pypesto.Result, Returns ------- - ax: matplotlib.Axes The plot axes. """ diff --git a/pypesto/visualize/optimizer_history.py b/pypesto/visualize/optimizer_history.py index aa23508fd..ee7f8a595 100644 --- a/pypesto/visualize/optimizer_history.py +++ b/pypesto/visualize/optimizer_history.py @@ -26,62 +26,50 @@ def optimizer_history(results, reference=None, legends=None): """ - Plot history of optimizer. Can plot either the history of the cost - function or of the gradient norm, over either the optimizer steps or - the computation time. + Plot history of optimizer. + + Can plot either the history of the cost function or of the gradient + norm, over either the optimizer steps or the computation time. Parameters ---------- - results: pypesto.Result or list Optimization result obtained by 'optimize.py' or list of those - ax: matplotlib.Axes, optional Axes object to use. - size: tuple, optional Figure size (width, height) in inches. Is only applied when no ax object is specified - trace_x: str, optional What should be plotted on the x-axis? Possibilities: 'time', 'steps' Default: 'steps' - trace_y: str, optional What should be plotted on the y-axis? Possibilities: 'fval', 'gradnorm', 'stepsize' Default: 'fval' - scale_y: str, optional May be logarithmic or linear ('log10' or 'lin') - offset_y: float, optional Offset for the y-axis-values, as these are plotted on a log10-scale Will be computed automatically if necessary - colors: list, or RGBA, optional list of colors, or single color color or list of colors for plotting. If not set, clustering is done and colors are assigned automatically - y_limits: float or ndarray, optional maximum value to be plotted on the y-axis, or y-limits - start_indices: list or int list of integers specifying the multistart to be plotted or int specifying up to which start index should be plotted - reference: list, optional List of reference points for optimization results, containing et least a function value fval - legends: list or str Labels for line plots, one label per result object Returns ------- - ax: matplotlib.Axes The plot axes. """ @@ -123,40 +111,30 @@ def optimizer_history_lowlevel(vals, scale_y='log10', colors=None, ax=None, Parameters ---------- - vals: list of numpy arrays list of 2xn-arrays (x_values and y_values of the trace) - scale_y: str, optional May be logarithmic or linear ('log10' or 'lin') - colors: list, or RGBA, optional list of colors, or single color color or list of colors for plotting. If not set, clustering is done and colors are assigned automatically - ax: matplotlib.Axes, optional Axes object to use. - size: tuple, optional see waterfall - x_label: str label for x-axis - y_label: str label for y-axis - legend_text: str Label for line plots Returns ------- - ax: matplotlib.Axes The plot axes. """ - # axes if ax is None: ax = plt.subplots()[1] @@ -220,19 +198,16 @@ def get_trace(result: Result, trace_x: Optional[str], trace_y: Optional[str]) -> Tuple[str, str, List[np.ndarray]]: """ - Get the values of the optimizer trace from the pypesto.Result object + Get the values of the optimizer trace from the pypesto.Result object. Parameters ---------- - result: pypesto.Result Optimization result obtained by 'optimize.py'. - trace_x: str, optional What should be plotted on the x-axis? Possibilities: 'time', 'steps' Default: 'steps' - trace_y: str, optional What should be plotted on the y-axis? Possibilities: 'fval'(later also: 'gradnorm', 'stepsize') @@ -240,17 +215,13 @@ def get_trace(result: Result, Returns ------- - vals: - list of - + list of (x,y)-values. x_label: - label for x-axis to be plotted later - + label for x-axis to be plotted later. y_label: - label for y-axis to be plotted later + label for y-axis to be plotted later. """ - # get data frames histories: List[History] = result.optimize_result.get_for_key('history') @@ -309,40 +280,33 @@ def get_vals( start_indices: Iterable[int] ) -> Tuple[List[np.ndarray], float, str]: """ - Postprocesses the values of the optimization history, depending on the - options set by the user (e.g. scale_y, offset_y, start_indices) + Postprocess the values of the optimization history. + + Depending on the options set by the user (e.g. scale_y, offset_y, + start_indices). Parameters ---------- - vals: list list of numpy arrays of dimension 2 x len(start_indices) - scale_y: str, optional May be logarithmic or linear ('log10' or 'lin') - offset_y: float offset for the y-axis, as this is supposed to be in log10-scale - y_label: str Label for y axis - start_indices: list of integers specifying the multi start indices to be plotted Returns ------- - vals: list list of numpy arrays - offset_y: offset for the y-axis, if this is supposed to be in log10-scale - y_label: Label for y axis """ - # get list of indices if start_indices is None: start_indices = np.array(range(len(vals))) @@ -383,36 +347,30 @@ def handle_options(ax: plt.Axes, y_limits: Union[float, np.ndarray], offset_y: float): """ + Apply post-plotting transformations to the axis object. + Get the limits for the y-axis, plots the reference points, will do - more at a later time point. This function is there to apply whatever - kind of post-plotting transformations to the axis object. + more at a later time point. Parameters ---------- - ref: List of reference points for optimization results, containing et least a function value fval - vals: list of numpy arrays of size 2 x number of values - ax: Axes object to use. - y_limits: maximum value to be plotted on the y-axis, or y-limits - offset_y: offset for the y-axis, if this is supposed to be in log10-scale Returns ------- - ax: matplotlib.Axes The plot axes. """ - # handle y-limits ax = process_y_limits(ax, y_limits) diff --git a/pypesto/visualize/parameters.py b/pypesto/visualize/parameters.py index 792d1ea41..d3dd30209 100644 --- a/pypesto/visualize/parameters.py +++ b/pypesto/visualize/parameters.py @@ -68,7 +68,6 @@ def parameters( ax: The plot axes. """ - # parse input (results, colors, legends) = process_result_list(results, colors, legends) @@ -82,8 +81,7 @@ def parameters( "'all', 'free_only' or a list of indices") def scale_parameters(x): - """Scale ``x`` from [lb, ub] to interval given by ``scale_to_interval`` - """ + """Scale `x` from [lb, ub] to interval given by `scale_to_interval`.""" if scale_to_interval is None or scale_to_interval is False: return x @@ -159,14 +157,11 @@ def parameter_hist( List of integers specifying the multistarts to be plotted or int specifying up to which start index should be plotted - Returns ------- ax: - The plot axes. - + The plot axes. """ - if ax is None: ax = plt.subplots()[1] fig = plt.gcf() @@ -204,52 +199,39 @@ def parameters_lowlevel( legend_text: Optional[str] = None, balance_alpha: bool = True ) -> matplotlib.axes.Axes: - """ Plot parameters plot using list of parameters. Parameters ---------- - xs: Including optimized parameters for each startpoint. Shape: (n_starts, dim). - fvals: Function values. Needed to assign cluster colors. - lb, ub: The lower and upper bounds. - x_labels: Labels to be used for the parameters. - ax: Axes object to use. - size: see parameters - colors: One for each element in 'fvals'. - linestyle: linestyle argument for parameter plot - legend_text: Label for line plots - balance_alpha: Flag indicating whether alpha for large clusters should be reduced to avoid overplotting (default: True) Returns ------- - ax: The plot axes. """ - # parse input xs = np.array(xs) fvals = np.array(fvals) @@ -314,42 +296,34 @@ def handle_inputs( start_indices: Optional[Union[int, Iterable[int]]] = None ) -> Tuple[np.ndarray, np.ndarray, List[str], np.ndarray, List[np.ndarray]]: """ - Computes the correct bounds for the parameter indices to be plotted and - outputs the corresponding parameters and their labels + Compute the correct bounds for the parameter indices to be plotted. + + Outputs the corresponding parameters and their labels. Parameters ---------- - result: Optimization result obtained by 'optimize.py'. - parameter_indices: Specifies which parameters should be plotted. - lb, ub: If not None, override result.problem.lb, problem.problem.ub. Dimension either result.problem.dim or result.problem.dim_full. - start_indices: list of integers specifying the multistarts to be plotted or int specifying up to which start index should be plotted Returns ------- - lb, ub: Dimension either result.problem.dim or result.problem.dim_full. - x_labels: ytick labels to be applied later on - fvals: objective function values which are needed for plotting later - xs: parameter values which will be plotted later """ - # retrieve results fvals = result.optimize_result.get_for_key('fval') xs = result.optimize_result.get_for_key('x') diff --git a/pypesto/visualize/profiles.py b/pypesto/visualize/profiles.py index ebf434f52..d3a5a0e0d 100644 --- a/pypesto/visualize/profiles.py +++ b/pypesto/visualize/profiles.py @@ -22,8 +22,9 @@ def profiles(results: Union[Result, Sequence[Result]], ratio_min: float = 0., show_bounds: bool = False): """ - Plot classical 1D profile plot (using the posterior, e.g. Gaussian like - profile) + Plot classical 1D profile plot. + + Using the posterior, e.g. Gaussian like profile. Parameters ---------- @@ -57,7 +58,6 @@ def profiles(results: Union[Result, Sequence[Result]], ax: matplotlib.Axes The plot axes. """ - # parse input results, profile_list_ids, colors, legends = process_result_list_profiles( results, profile_list_ids, colors, legends) @@ -110,8 +110,9 @@ def profiles_lowlevel( color=None, legend_text: str = None, x_labels=None, show_bounds: bool = False, lb_full=None, ub_full=None): """ - Lowlevel routine for profile plotting, working with a list of arrays - only, opening different axes objects in case + Lowlevel routine for profile plotting. + + Working with a list of arrays only, opening different axes objects in case. Parameters ---------- @@ -143,7 +144,6 @@ def profiles_lowlevel( ax: matplotlib.Axes The plot axes. """ - # axes if ax is None: ax = [] @@ -238,7 +238,7 @@ def profile_lowlevel( color=None, legend_text: str = None, show_bounds: bool = False, lb: float = None, ub: float = None): """ - Lowlevel routine for plotting one profile, working with a numpy array only + Lowlevel routine for plotting one profile, working with a numpy array only. Parameters ---------- @@ -265,7 +265,6 @@ def profile_lowlevel( ax: matplotlib.Axes The plot axes. """ - # parse input fvals = np.asarray(fvals) @@ -308,7 +307,6 @@ def handle_reference_points(ref, ax, profile_indices): profile_indices: list of integer values List of integer values specifying which profiles should be plotted. """ - if len(ref) > 0: # loop over axes objects for i_par, i_ax in enumerate(ax): @@ -330,8 +328,7 @@ def handle_inputs( profile_list: int, ratio_min: float): """ - Retrieves the values of the profiles to be plotted later from a - pypesto.ProfileResult object + Retrieve the values of the profiles to be plotted. Parameters ---------- @@ -341,13 +338,15 @@ def handle_inputs( List of integer values specifying which profiles should be plotted. profile_list: int, optional Index of the profile list to be used for profiling. + ratio_min: int, optional + Exclude values where profile likelihood ratio is smaller than + ratio_min. Returns ------- fvals: numeric list Including values that need to be plotted. """ - # extract ratio values values from result fvals = [] for i_par in range(0, len(result.profile_result.list[profile_list])): @@ -376,8 +375,9 @@ def process_result_list_profiles(results: Result, colors: Sequence[np.array], legends: Union[str, list]) -> Sequence[int]: """ - assigns colors and legends to a list of results while taking care of the - special cases for profile plotting + Assign colors and legends to a list of results. + + Takes also care of the special cases for profile plotting. Parameters ---------- @@ -396,7 +396,6 @@ def process_result_list_profiles(results: Result, corrected list of integer values specifying which profiles should be plotted. """ - # ensure list of ids if isinstance(profile_list_ids, int): profile_list_ids = [profile_list_ids] @@ -425,10 +424,11 @@ def process_profile_indices( profile_indices: Sequence[int], profile_list_ids: Union[int, Sequence[int]]): """ - Retrieves the indices of the parameter for which profiles should be - plotted later from a list of pypesto.ProfileResult objects - """ + Clean up profile_indices to be plotted. + Retrieve the indices of the parameter for which profiles should be + plotted later from a list of pypesto.ProfileResult objects. + """ # get all parameter indices, for which profiles were computed plottable_indices = set() for result in results: diff --git a/pypesto/visualize/reference_points.py b/pypesto/visualize/reference_points.py index 6940f4efe..b551b2817 100644 --- a/pypesto/visualize/reference_points.py +++ b/pypesto/visualize/reference_points.py @@ -5,27 +5,24 @@ class ReferencePoint(dict): """ - Reference point for plotting. Should contain a parameter value and an - objective function value, may also contain a color and a legend. + Reference point for plotting. + + Should contain a parameter value and an objective function value, + may also contain a color and a legend. Can be used like a dict. Attributes ---------- - x: ndarray Reference parameters. - fval: float Function value, fun(x), for reference parameters. - color: RGBA, optional Color which should be used for reference point. - auto_color: boolean flag indicating whether color for this reference point should be assigned automatically or whether it was assigned by user - legend: str legend text for reference point """ @@ -93,6 +90,7 @@ def __init__(self, self.auto_color = True def __getattr__(self, key): + """Allow usage of keys as attributes.""" try: return self[key] except KeyError: @@ -104,21 +102,18 @@ def __getattr__(self, key): def assign_colors(ref): """ - Assigns colors to reference points, depending on user settings + Assign colors to reference points, depending on user settings. Parameters ---------- - ref: list of ReferencePoint Reference points, which need to get their color property filled Returns ------- - ref: list of ReferencePoint Reference points, which got their color property filled """ - # loop over reference points auto_color_count = 0 for i_ref in ref: @@ -141,32 +136,26 @@ def assign_colors(ref): def create_references(references=None, x=None, fval=None, color=None, legend=None) -> List[ReferencePoint]: """ - This function creates a list of reference point objects from user inputs + Create a list of reference point objects from user inputs. Parameters ---------- - references: ReferencePoint or dict or list, optional Will be converted into a list of RefPoints - x: ndarray, optional Parameter vector which should be used for reference point - fval: float, optional Objective function value which should be used for reference point - color: RGBA, optional Color which should be used for reference point. - legend: str legend text for reference point + Returns ------- - colors: list of RGBA One for each element in 'vals'. """ - # parse input (reference) ref = [] if references is not None: diff --git a/pypesto/visualize/sampling.py b/pypesto/visualize/sampling.py index bbc0b2d83..c21d9fce5 100644 --- a/pypesto/visualize/sampling.py +++ b/pypesto/visualize/sampling.py @@ -30,15 +30,6 @@ logger = logging.getLogger(__name__) -def sampling_fval_trace(*args, **kwargs): - warnings.warn( - '`sampling_fval_trace` is deprecated in favor of ' - '`sampling_fval_traces` and will be removed in a future version of ' - 'pyPESTO.' - ) - return sampling_fval_traces(*args, **kwargs) - - def sampling_fval_traces( result: Result, i_chain: int = 0, @@ -47,7 +38,8 @@ def sampling_fval_traces( title: str = None, size: Tuple[float, float] = None, ax: matplotlib.axes.Axes = None): - """Plot log-posterior (=function value) over iterations. + """ + Plot log-posterior (=function value) over iterations. Parameters ---------- @@ -71,7 +63,6 @@ def sampling_fval_traces( ax: The plot axes. """ - # get data which should be plotted _, params_fval, _, _, _ = get_data_to_plot(result=result, i_chain=i_chain, @@ -579,7 +570,10 @@ def sampling_prediction_trajectories( output_ids: Sequence[str] = None, weighting: bool = False ) -> matplotlib.axes.Axes: - """Plot MCMC-based prediction credibility intervals for the + """ + Visualize prediction trajectory of an EnsemblePrediction. + + Plot MCMC-based prediction credibility intervals for the model states or outputs. One or various credibility levels can be depicted. Plots are grouped by condition. @@ -632,7 +626,8 @@ def sampling_prediction_trajectories( for percentile in _get_level_percentiles(level) ] - summary = ensemble_prediction.compute_summary(percentiles_list=percentiles) + summary = ensemble_prediction.compute_summary( + percentiles_list=percentiles, weighting=weighting) all_condition_ids, all_output_ids = _get_condition_and_output_ids(summary) if condition_ids is None: @@ -752,8 +747,8 @@ def sampling_parameter_cis( size: Tuple[float, float] = None, ax: matplotlib.axes.Axes = None ) -> matplotlib.axes.Axes: - """Plot MCMC-based parameter credibility intervals for one or more - credibility levels. + """ + Plot MCMC-based parameter credibility intervals. Parameters ---------- @@ -842,15 +837,6 @@ def sampling_parameter_cis( return ax -def sampling_parameters_trace(*args, **kwargs): - warnings.warn( - '`sampling_parameters_trace` is deprecated in favor of ' - '`sampling_parameter_traces` and will be removed in a future version ' - 'of pyPESTO.' - ) - return sampling_parameter_traces(*args, **kwargs) - - def sampling_parameter_traces( result: Result, i_chain: int = 0, @@ -861,7 +847,8 @@ def sampling_parameter_traces( suptitle: str = None, size: Tuple[float, float] = None, ax: matplotlib.axes.Axes = None): - """Plot parameter values over iterations. + """ + Plot parameter values over iterations. Parameters ---------- @@ -891,7 +878,6 @@ def sampling_parameter_traces( ax: The plot axes. """ - # get data which should be plotted nr_params, params_fval, theta_lb, theta_ub, param_names = get_data_to_plot( result=result, i_chain=i_chain, stepsize=stepsize, @@ -962,7 +948,8 @@ def sampling_scatter( suptitle: str = None, diag_kind: str = "kde", size: Tuple[float, float] = None): - """Parameter scatter plot. + """ + Parameter scatter plot. Parameters ---------- @@ -984,7 +971,6 @@ def sampling_scatter( ax: The plot axes. """ - # get data which should be plotted nr_params, params_fval, theta_lb, theta_ub, _ = get_data_to_plot( result=result, i_chain=i_chain, stepsize=stepsize) @@ -1039,9 +1025,9 @@ def sampling_1d_marginals( Return -------- - ax: matplotlib-axes + ax: + matplotlib-axes """ - # get data which should be plotted nr_params, params_fval, theta_lb, theta_ub, param_names = get_data_to_plot( result=result, i_chain=i_chain, diff --git a/pypesto/visualize/waterfall.py b/pypesto/visualize/waterfall.py index 351984c5b..62082729f 100644 --- a/pypesto/visualize/waterfall.py +++ b/pypesto/visualize/waterfall.py @@ -27,48 +27,36 @@ def waterfall(results: Union[Result, Sequence[Result]], Parameters ---------- - results: Optimization result obtained by 'optimize.py' or list of those - ax: matplotlib.Axes, optional Axes object to use. - size: Figure size (width, height) in inches. Is only applied when no ax object is specified - y_limits: float or ndarray, optional maximum value to be plotted on the y-axis, or y-limits - scale_y: May be logarithmic or linear ('log10' or 'lin') - offset_y: offset for the y-axis, if it is supposed to be in log10-scale - start_indices: Integers specifying the multistart to be plotted or int specifying up to which start index should be plotted - reference: Reference points for optimization results, containing at least a function value fval - colors: Colors or single color for plotting. If not set, clustering is done and colors are assigned automatically - legends: Labels for line plots, one label per result object Returns ------- - ax: matplotlib.Axes The plot axes. """ - # parse input (results, colors, legends) = process_result_list(results, colors, legends) @@ -103,37 +91,28 @@ def waterfall_lowlevel(fvals, scale_y='log10', offset_y=0., ax=None, Parameters ---------- - fvals: numeric list or array Including values need to be plotted. - scale_y: str, optional May be logarithmic or linear ('log10' or 'lin') - offset_y: offset for the y-axis, if it is supposed to be in log10-scale - ax: matplotlib.Axes, optional Axes object to use. - size: tuple, optional see waterfall - colors: list, or RGBA, optional list of colors, or single color color or list of colors for plotting. If not set, clustering is done and colors are assigned automatically - legend_text: str Label for line plots Returns ------- - ax: matplotlib.Axes The plot axes. """ - # axes if ax is None: ax = plt.subplots()[1] @@ -212,35 +191,26 @@ def process_offset_for_list( references: Optional[Sequence[ReferencePoint]] = None, ) -> Tuple[List[np.ndarray], float]: """ - If we have a list of results, all should use the same offset_y, - which is computed by this function and added to the fvals + Compute common offset_y and add it to `fvals` of results. Parameters ---------- - offset_y: User provided offset_y - results: Optimization results obtained by 'optimize.py' - scale_y: May be logarithmic or linear ('log10' or 'lin') - start_indices: Integers specifying the multistart to be plotted or int specifying up to which start index should be plotted - - references: Reference points that will be plotted along with the results Returns ------- - fvals: List of arrays of function values for each result - offset_y: offset for the y-axis """ @@ -274,36 +244,30 @@ def process_offset_for_list( def handle_options(ax, max_len_fvals, ref, y_limits, offset_y): """ + Apply post-plotting transformations to the axis object. + Get the limits for the y-axis, plots the reference points, will do - more at a later time point. This function is there to apply whatever - kind of post-plotting transformations to the axis object. + more at a later time point. Parameters ---------- - ax: matplotlib.Axes, optional Axes object to use. - max_len_fvals: int maximum number of points - ref: list, optional List of reference points for optimization results, containing at least a function value fval - y_limits: float or ndarray, optional maximum value to be plotted on the y-axis, or y-limits - offset_y: offset for the y-axis, if it is supposed to be in log10-scale Returns ------- - ax: matplotlib.Axes The plot axes. """ - # handle reference points for i_ref in ref: # plot reference point as line diff --git a/setup.cfg b/setup.cfg index 84c03a2f1..21d824e14 100644 --- a/setup.cfg +++ b/setup.cfg @@ -33,6 +33,7 @@ classifiers = License :: OSI Approved :: BSD License Operating System :: OS Independent Programming Language :: Python + Programming Language :: Python :: 3.10 Programming Language :: Python :: 3.9 Programming Language :: Python :: 3.8 Programming Language :: Python :: 3.7 @@ -56,7 +57,7 @@ install_requires = h5py >= 3.1.0 tqdm >= 4.46.0 -python_requires = >=3.6 +python_requires = >=3.7 include_package_data = True # Where is my code diff --git a/test/base/test_engine.py b/test/base/test_engine.py index 3b6176b95..185b9fe2d 100644 --- a/test/base/test_engine.py +++ b/test/base/test_engine.py @@ -28,7 +28,8 @@ def _test_basic(engine): problem = pypesto.Problem(objective, lb, ub) optimizer = pypesto.optimize.ScipyOptimizer(options={'maxiter': 10}) result = pypesto.optimize.minimize( - problem=problem, n_starts=5, engine=engine, optimizer=optimizer) + problem=problem, n_starts=5, engine=engine, optimizer=optimizer, + filename=None) assert len(result.optimize_result.as_list()) == 5 @@ -46,7 +47,8 @@ def _test_petab(engine): problem = petab_importer.create_problem(objective) optimizer = pypesto.optimize.ScipyOptimizer(options={'maxiter': 10}) result = pypesto.optimize.minimize( - problem=problem, n_starts=3, engine=engine, optimizer=optimizer) + problem=problem, n_starts=3, engine=engine, optimizer=optimizer, + filename=None) assert len(result.optimize_result.as_list()) == 3 diff --git a/test/base/test_history.py b/test/base/test_history.py index 0fcdeadb3..ac0cef7a5 100644 --- a/test/base/test_history.py +++ b/test/base/test_history.py @@ -62,7 +62,8 @@ def check_history(self): n_starts=1, startpoint_method=pypesto.startpoint.uniform, options=optimize_options, - history_options=self.history_options + history_options=self.history_options, + filename=None ) for istart, start in enumerate(result.optimize_result.list): @@ -591,14 +592,16 @@ def test_hdf5_history_mp(): result_hdf5_mem = pypesto.optimize.minimize( problem=problem1, optimizer=optimizer1, n_starts=n_starts, history_options=history_options_mem, - engine=MultiProcessEngine() + engine=MultiProcessEngine(), + filename=None ) # optimizing with history saved in hdf5 and MultiProcessEngine result_memory_mp = pypesto.optimize.minimize( problem=problem2, optimizer=optimizer2, n_starts=n_starts, history_options=history_options_mp, - engine=MultiProcessEngine() + engine=MultiProcessEngine(), + filename=None ) history_entries = [X, FVAL, GRAD, HESS, RES, SRES, CHI2, SCHI2] @@ -635,7 +638,8 @@ def test_trim_history(): result = pypesto.optimize.minimize( problem=pypesto_problem, optimizer=optimizer, n_starts=1, history_options=history_options, - engine=MultiProcessEngine() + engine=MultiProcessEngine(), + filename=None ) fval_trace = result.optimize_result.list[0].history.get_fval_trace() fval_trace_trimmed = \ diff --git a/test/base/test_logging.py b/test/base/test_logging.py index 308d2eb84..04cdbd380 100644 --- a/test/base/test_logging.py +++ b/test/base/test_logging.py @@ -30,7 +30,10 @@ def fun(_): options = {'allow_failed_starts': True} # optimization - pypesto.optimize.minimize(problem, optimizer, 5, options=options) + pypesto.optimize.minimize(problem, + optimizer, 5, + options=options, + filename=None) # assert logging worked assert os.path.exists(filename) diff --git a/test/base/test_prior.py b/test/base/test_prior.py index 289dc8191..9323c9f79 100644 --- a/test/base/test_prior.py +++ b/test/base/test_prior.py @@ -92,7 +92,8 @@ def test_mode(scale, prior_type_list): ) result = pypesto.optimize.minimize( problem=test_problem, optimizer=optimizer, n_starts=10, - options=ooptions + options=ooptions, + filename=None ) # flat functions don't have local minima, so dont check this diff --git a/test/base/test_startpoint.py b/test/base/test_startpoint.py index 82b861093..f9f2c9aad 100644 --- a/test/base/test_startpoint.py +++ b/test/base/test_startpoint.py @@ -46,7 +46,7 @@ def test_latin_hypercube(): def test_ubounded_startpoints(spmethod): - """ Test Exceptions for non-finite lb/ub """ + """Test Exceptions for non-finite lb/ub""" for lb_, ub_ in [ (-np.inf * np.ones(lb.shape), ub), (lb, np.inf * np.ones(ub.shape)), diff --git a/test/base/test_store.py b/test/base/test_store.py index 68bd5cc68..6859045bf 100644 --- a/test/base/test_store.py +++ b/test/base/test_store.py @@ -173,7 +173,8 @@ def test_storage_profiling(): n_starts=n_starts) profile_original = profile.parameter_profile( problem=problem, result=result_optimization, - profile_index=[0], optimizer=optimizer) + profile_index=[0], optimizer=optimizer, + filename=None) fn = 'test_file.hdf5' try: @@ -236,7 +237,8 @@ def test_storage_sampling(): sample_original = sample.sample(problem=problem, sampler=sampler, n_samples=100, - x0=[x_0]) + x0=[x_0], + filename=None) fn = 'test_file.hdf5' try: @@ -288,14 +290,16 @@ def test_storage_all(): # Profiling result = profile.parameter_profile( problem=problem, result=result, - profile_index=[0], optimizer=optimizer) + profile_index=[0], optimizer=optimizer, + filename=None) # Sampling sampler = sample.AdaptiveMetropolisSampler() result = sample.sample(problem=problem, sampler=sampler, n_samples=100, - result=result) + result=result, + filename=None) # Read and write filename = 'test_file.hdf5' try: diff --git a/test/base/test_x_fixed.py b/test/base/test_x_fixed.py index be44ac62d..c6628177b 100644 --- a/test/base/test_x_fixed.py +++ b/test/base/test_x_fixed.py @@ -19,7 +19,8 @@ def test_optimize(): problem = create_problem() optimizer = pypesto.optimize.ScipyOptimizer() n_starts = 5 - result = pypesto.optimize.minimize(problem, optimizer, n_starts) + result = pypesto.optimize.minimize(problem, optimizer, n_starts, + filename=None) optimizer_result = result.optimize_result.list[0] assert len(optimizer_result.x) == 5 diff --git a/test/optimize/test_optimize.py b/test/optimize/test_optimize.py index c6c2519ea..4e3e0ef27 100644 --- a/test/optimize/test_optimize.py +++ b/test/optimize/test_optimize.py @@ -123,7 +123,8 @@ def test_unbounded_minimize(optimizer): optimizer=opt, n_starts=1, startpoint_method=pypesto.startpoint.uniform, - options=options + options=options, + filename=None ) return else: @@ -132,7 +133,8 @@ def test_unbounded_minimize(optimizer): optimizer=opt, n_starts=1, startpoint_method=pypesto.startpoint.uniform, - options=options + options=options, + filename=None ) # check that ub/lb were reverted @@ -194,7 +196,8 @@ def check_minimize(problem, library, solver, allow_failed_starts=False): optimizer=optimizer, n_starts=1, startpoint_method=pypesto.startpoint.uniform, - options=optimize_options + options=optimize_options, + filename=None ) assert isinstance(result.optimize_result.list[0]['fval'], float) @@ -205,6 +208,42 @@ def check_minimize(problem, library, solver, allow_failed_starts=False): assert result.optimize_result.list[0]['x'] is not None +def test_trim_results(problem): + """ + Test trimming of hess/sres from results + """ + + optimize_options = optimize.OptimizeOptions( + report_hess=False, report_sres=False + ) + prob = pypesto.Problem( + objective=rosen_for_sensi(max_sensi_order=2)['obj'], + lb=0 * np.ones((1, 2)), ub=1 * np.ones((1, 2)) + ) + + # hess + optimizer = optimize.FidesOptimizer(verbose=0) + result = optimize.minimize( + problem=prob, + optimizer=optimizer, + n_starts=1, + startpoint_method=pypesto.startpoint.uniform, + options=optimize_options, + ) + assert result.optimize_result.list[0].hess is None + + # sres + optimizer = optimize.ScipyOptimizer(method='ls_trf') + result = optimize.minimize( + problem=prob, + optimizer=optimizer, + n_starts=1, + startpoint_method=pypesto.startpoint.uniform, + options=optimize_options, + ) + assert result.optimize_result.list[0].sres is None + + def test_mpipoolengine(): """ Test the MPIPoolEngine by calling an example script with mpiexec. @@ -235,7 +274,8 @@ def test_mpipoolengine(): result2 = optimize.minimize(problem=problem, optimizer=optimizer, n_starts=2, - engine=pypesto.engine.MultiProcessEngine()) + engine=pypesto.engine.MultiProcessEngine(), + filename=None) for ix in range(2): assert_almost_equal(result1.optimize_result.list[ix]['x'], diff --git a/test/petab/test_amici_objective.py b/test/petab/test_amici_objective.py index 4f82a480c..fb9ff5bf5 100644 --- a/test/petab/test_amici_objective.py +++ b/test/petab/test_amici_objective.py @@ -61,7 +61,7 @@ def test_error_leastsquares_with_ssigma(): 'ls_trf', options={'max_nfev': 50}) with pytest.raises(RuntimeError): pypesto.optimize.minimize( - problem=problem, optimizer=optimizer, n_starts=1, + problem=problem, optimizer=optimizer, n_starts=1, filename=None, options=pypesto.optimize.OptimizeOptions(allow_failed_starts=False) ) @@ -94,7 +94,7 @@ def test_preeq_guesses(): ) result = pypesto.optimize.minimize( problem=problem, optimizer=optimizer, n_starts=1, - options=options + options=options, filename=None ) assert obj.steadystate_guesses['fval'] < np.inf diff --git a/test/petab/test_petab_import.py b/test/petab/test_petab_import.py index b243ce4d9..c3e435a88 100644 --- a/test/petab/test_petab_import.py +++ b/test/petab/test_petab_import.py @@ -71,7 +71,8 @@ def test_3_optimize(self): options={'maxiter': 10}) problem = importer.create_problem(obj) result = pypesto.optimize.minimize( - problem=problem, optimizer=optimizer, n_starts=2) + problem=problem, optimizer=optimizer, n_starts=2, + filename=None) self.assertTrue(np.isfinite( result.optimize_result.get_for_key('fval')[0])) diff --git a/test/petab/test_petab_import_pysb.py b/test/petab/test_petab_import_pysb.py index 6e9a34200..98b9fce21 100644 --- a/test/petab/test_petab_import_pysb.py +++ b/test/petab/test_petab_import_pysb.py @@ -42,7 +42,7 @@ def test_petab_pysb_optimization(): optimizer = optimize.ScipyOptimizer() result = optimize.minimize(problem=problem, optimizer=optimizer, - n_starts=10) + n_starts=10, filename=None) fvals = np.array(result.optimize_result.get_for_key('fval')) # ensure objective after optimization is not worse than for true parameters diff --git a/test/petab/test_sbml_conversion.py b/test/petab/test_sbml_conversion.py index 3d39b82b7..682d40f2b 100644 --- a/test/petab/test_sbml_conversion.py +++ b/test/petab/test_sbml_conversion.py @@ -98,7 +98,7 @@ def parameter_estimation( ) pypesto.optimize.minimize( - problem, optimizer, n_starts, options=optimize_options) + problem, optimizer, n_starts, options=optimize_options, filename=None) if __name__ == '__main__': diff --git a/test/profile/test_profile.py b/test/profile/test_profile.py index cb0b18392..deade3c75 100644 --- a/test/profile/test_profile.py +++ b/test/profile/test_profile.py @@ -42,7 +42,8 @@ def test_default_profiling(self): problem=self.problem, result=self.result, optimizer=self.optimizer, - next_guess_method=method) + next_guess_method=method, + filename=None) # check result self.assertTrue( @@ -89,7 +90,8 @@ def test_engine_profiling(self): result=self.result, optimizer=self.optimizer, next_guess_method='fixed_step', - engine=engine,) + engine=engine, + filename=None) # check results for count, _engine in enumerate(engines[1:]): @@ -120,7 +122,8 @@ def test_selected_profiling(self): profile_index=np.array([1]), next_guess_method='fixed_step', result_index=1, - profile_options=options) + profile_options=options, + filename=None) self.assertIsInstance(result.profile_result.list[0][1], profile.ProfilerResult) @@ -135,7 +138,8 @@ def test_selected_profiling(self): profile_index=np.array([0]), result_index=2, profile_list=0, - profile_options=options) + profile_options=options, + filename=None) self.assertIsInstance(result.profile_result.list[0][0], profile.ProfilerResult) @@ -147,7 +151,8 @@ def test_selected_profiling(self): optimizer=self.optimizer, next_guess_method='fixed_step', profile_index=np.array([0]), - profile_options=options) + profile_options=options, + filename=None) # check result self.assertIsInstance(result.profile_result.list[1][0], profile.ProfilerResult) @@ -159,7 +164,8 @@ def test_extending_profiles(self): problem=self.problem, result=self.result, optimizer=self.optimizer, - next_guess_method='fixed_step') + next_guess_method='fixed_step', + filename=None) # set new bounds (knowing that one parameter stopped at the bounds self.problem.lb_full = -4 * np.ones(2) @@ -171,7 +177,8 @@ def test_extending_profiles(self): optimizer=self.optimizer, next_guess_method='fixed_step', profile_index=np.array([1]), - profile_list=0) + profile_list=0, + filename=None) # check result self.assertTrue( isinstance(result.profile_result.list[0][0], @@ -226,7 +233,8 @@ def test_profile_with_history(): optimizer=optimizer, profile_index=np.array([0, 2, 4]), result_index=0, - profile_options=profile_options + profile_options=profile_options, + filename=None ) @@ -243,7 +251,7 @@ def test_profile_with_fixed_parameters(): optimizer = optimize.ScipyOptimizer(options={'maxiter': 50}) result = optimize.minimize( - problem=problem, optimizer=optimizer, n_starts=2) + problem=problem, optimizer=optimizer, n_starts=2, filename=None) for i_method, next_guess_method in enumerate([ 'fixed_step', 'adaptive_step_order_0', @@ -251,7 +259,8 @@ def test_profile_with_fixed_parameters(): print(next_guess_method) profile.parameter_profile( problem=problem, result=result, optimizer=optimizer, - next_guess_method=next_guess_method) + next_guess_method=next_guess_method, + filename=None) # standard plotting axes = visualize.profiles(result, profile_list_ids=i_method) @@ -263,7 +272,8 @@ def test_profile_with_fixed_parameters(): result.optimize_result.list[0]['x'][2:5]) profile.parameter_profile( problem=problem, result=result, optimizer=optimizer, - next_guess_method='adaptive_step_regression') + next_guess_method='adaptive_step_regression', + filename=None) def create_optimization_results(objective, dim_full=2): @@ -285,7 +295,8 @@ def create_optimization_results(objective, dim_full=2): optimizer=optimizer, n_starts=5, startpoint_method=pypesto.startpoint.uniform, - options=optimize_options + options=optimize_options, + filename=None ) return problem, result, optimizer diff --git a/test/profile/test_validation_intervals.py b/test/profile/test_validation_intervals.py index a129b19e5..755b66d77 100644 --- a/test/profile/test_validation_intervals.py +++ b/test/profile/test_validation_intervals.py @@ -31,10 +31,10 @@ def setUp(cls): # optimum f(0)=0 cls.result_training_data = optimize.minimize(cls.problem_training_data, - n_starts=5) + n_starts=5, filename=None) # Optimum f(1)=2 cls.result_all_data = optimize.minimize(cls.problem_all_data, - n_starts=5) + n_starts=5, filename=None) def test_validation_intervals(self): """Test validation profiles.""" diff --git a/test/sample/test_sample.py b/test/sample/test_sample.py index 7ceb573d6..9b9455d2c 100644 --- a/test/sample/test_sample.py +++ b/test/sample/test_sample.py @@ -105,7 +105,8 @@ def sample_petab_problem(): sampler = sample.AdaptiveMetropolisSampler() result = sample.sample(problem, n_samples=1000, sampler=sampler, - x0=np.array([3, -4])) + x0=np.array([3, -4]), + filename=None) return result @@ -167,11 +168,12 @@ def test_pipeline(sampler, problem): # optimization optimizer = optimize.ScipyOptimizer(options={'maxiter': 10}) result = optimize.minimize( - problem, n_starts=3, optimizer=optimizer) + problem, n_starts=3, optimizer=optimizer, filename=None) # sample result = sample.sample( - problem, sampler=sampler, n_samples=100, result=result) + problem, sampler=sampler, n_samples=100, result=result, + filename=None) # some plot visualize.sampling_1d_marginals(result) @@ -187,10 +189,11 @@ def test_ground_truth(): problem = gaussian_problem() - result = optimize.minimize(problem) + result = optimize.minimize(problem, filename=None) result = sample.sample(problem, n_samples=5000, - result=result, sampler=sampler) + result=result, sampler=sampler, + filename=None) # get samples of first chain samples = result.sample_result.trace_x[0].flatten() @@ -219,7 +222,8 @@ def test_ground_truth_separated_modes(): result = sample.sample(problem, n_samples=1e4, sampler=sampler, - x0=np.array([0.])) + x0=np.array([0.]), + filename=None) # get samples of first chain samples = result.sample_result.trace_x[0, :, 0] @@ -243,7 +247,8 @@ def test_ground_truth_separated_modes(): sampler = sample.AdaptiveMetropolisSampler() result = sample.sample(problem, n_samples=1e4, sampler=sampler, - x0=np.array([-2.])) + x0=np.array([-2.]), + filename=None) # get samples of first chain samples = result.sample_result.trace_x[0, :, 0] @@ -266,7 +271,8 @@ def test_ground_truth_separated_modes(): # initiated around the "second" mode of the distribution sampler = sample.AdaptiveMetropolisSampler() result = sample.sample(problem, n_samples=1e4, sampler=sampler, - x0=np.array([120.])) + x0=np.array([120.]), + filename=None) # get samples of first chain samples = result.sample_result.trace_x[0, :, 0] @@ -293,7 +299,8 @@ def test_multiple_startpoints(): internal_sampler=sample.MetropolisSampler(), n_chains=2 ) - result = sample.sample(problem, n_samples=10, x0=x0s, sampler=sampler) + result = sample.sample(problem, n_samples=10, x0=x0s, sampler=sampler, + filename=None) assert result.sample_result.trace_neglogpost.shape[0] == 2 assert [result.sample_result.trace_x[0][0], @@ -340,11 +347,12 @@ def test_geweke_test_unconverged(): sampler = sample.MetropolisSampler() # optimization - result = optimize.minimize(problem, n_starts=3) + result = optimize.minimize(problem, n_starts=3, filename=None) # sample result = sample.sample( - problem, sampler=sampler, n_samples=100, result=result) + problem, sampler=sampler, n_samples=100, result=result, + filename=None) # run geweke test (should not fail!) sample.geweke_test(result) @@ -357,11 +365,12 @@ def test_autocorrelation_pipeline(): sampler = sample.MetropolisSampler() # optimization - result = optimize.minimize(problem, n_starts=3) + result = optimize.minimize(problem, n_starts=3, filename=None) # sample result = sample.sample( - problem, sampler=sampler, n_samples=1000, result=result) + problem, sampler=sampler, n_samples=1000, result=result, + filename=None) # run auto-correlation with previous geweke sample.geweke_test(result) @@ -395,11 +404,12 @@ def test_autocorrelation_short_chain(): sampler = sample.MetropolisSampler() # optimization - result = optimize.minimize(problem, n_starts=3) + result = optimize.minimize(problem, n_starts=3, filename=None) # sample result = sample.sample( - problem, sampler=sampler, n_samples=10, result=result) + problem, sampler=sampler, n_samples=10, result=result, + filename=None) # manually set burn in to chain length (only for testing!!) chain_length = result.sample_result.trace_x.shape[1] @@ -468,7 +478,8 @@ def test_empty_prior(): sampler = sample.AdaptiveMetropolisSampler() result = sample.sample(test_problem, n_samples=50, sampler=sampler, - x0=np.array([0.])) + x0=np.array([0.]), + filename=None) # get log prior values of first chain logprior_trace = -result.sample_result.trace_neglogprior[0, :] @@ -498,7 +509,8 @@ def test_prior(): sampler = sample.AdaptiveMetropolisSampler() result = sample.sample(test_problem, n_samples=1e4, sampler=sampler, - x0=np.array([0.])) + x0=np.array([0.]), + filename=None) # get log prior values of first chain logprior_trace = -result.sample_result.trace_neglogprior[0, :] @@ -531,11 +543,11 @@ def test_samples_cis(): sampler = sample.MetropolisSampler() # optimization - result = optimize.minimize(problem, n_starts=3) + result = optimize.minimize(problem, n_starts=3, filename=None) # sample result = sample.sample( - problem, sampler=sampler, n_samples=2000, result=result) + problem, sampler=sampler, n_samples=2000, result=result, filename=None) # run geweke test sample.geweke_test(result) diff --git a/test/util.py b/test/util.py index 73fbf478e..0ac4633ea 100644 --- a/test/util.py +++ b/test/util.py @@ -24,7 +24,6 @@ def obj_for_sensi(fun, grad, hess, max_sensi_order, integrated, x): Parameters ---------- - fun, grad, hess: callable Functions computing the fval, grad, hess. max_sensi_order: int @@ -37,7 +36,6 @@ def obj_for_sensi(fun, grad, hess, max_sensi_order, integrated, x): Returns ------- - ret: dict With fields obj, max_sensi_order, x, fval, grad, hess. """ @@ -89,9 +87,7 @@ def arg_fun(x): def rosen_for_sensi(max_sensi_order, integrated=False, x=None): - """ - Rosenbrock function from scipy.optimize. - """ + """Rosenbrock function from scipy.optimize.""" if x is None: x = [0, 1] @@ -102,9 +98,7 @@ def rosen_for_sensi(max_sensi_order, integrated=False, x=None): def poly_for_sensi(max_sensi_order, integrated=False, x=0.): - """ - 1-dim polynomial for testing in 1d. - """ + """1-dim polynomial for testing in 1d.""" def fun(x): return (x - 2)**2 + 1 @@ -192,7 +186,7 @@ def fres(p): return fres def get_fsres(self): - """Residual sensitivities""" + """Residual sensitivities.""" return jacobian(self.get_fres()) def get_ffim(self): @@ -255,6 +249,7 @@ def get_problem(self): def load_amici_objective(example_name): + """Load an `AmiciObjective for test purposes.""" # name of the model that will also be the name of the python module model_name = 'model_' + example_name diff --git a/test/visualize/test_visualize.py b/test/visualize/test_visualize.py index 49c859d46..e40aefbe5 100644 --- a/test/visualize/test_visualize.py +++ b/test/visualize/test_visualize.py @@ -82,7 +82,8 @@ def sample_petab_problem(): sampler = sample.AdaptiveMetropolisSampler() result = sample.sample(problem, n_samples=1000, sampler=sampler, - x0=np.array([3, -4])) + x0=np.array([3, -4]), + filename=None) return result @@ -145,7 +146,8 @@ def create_optimization_history(): n_starts=5, startpoint_method=pypesto.startpoint.uniform, options=optimize_options, - history_options=history_options + history_options=history_options, + filename=None ) return result_with_trace @@ -386,6 +388,7 @@ def test_parameters_hist(): optimizer=optimizer, n_starts=10, startpoint_method=pypesto.startpoint.uniform, + filename=None ) visualize.parameter_hist(result_1, 'x1') @@ -632,6 +635,7 @@ def test_optimization_stats(): optimizer=optimizer, n_starts=10, startpoint_method=pypesto.startpoint.uniform, + filename=None ) result_2 = optimize.minimize( @@ -639,6 +643,7 @@ def test_optimization_stats(): optimizer=optimizer, n_starts=10, startpoint_method=pypesto.startpoint.uniform, + filename=None ) visualize.optimization_run_property_per_multistart(result_1, 'n_fval', @@ -927,7 +932,8 @@ def test_visualize_optimized_model_fit(): problem = importer.create_problem() result = optimize.minimize(problem=problem, - n_starts=1) + n_starts=1, + filename=None) # test call of visualize_optimized_model_fit visualize_optimized_model_fit(petab_problem=petab_problem, @@ -952,7 +958,8 @@ def test_time_trajectory_model(): problem = importer.create_problem() result = optimize.minimize(problem=problem, - n_starts=1) + n_starts=1, + filename=None) # test call of time_trajectory_model time_trajectory_model(result=result) diff --git a/tox.ini b/tox.ini index 2b338cf77..c64f1b502 100644 --- a/tox.ini +++ b/tox.ini @@ -114,11 +114,11 @@ deps = # flake8-commas >= 2.0.0 flake8-comprehensions >= 3.2.3 flake8-print >= 3.1.4 - # flake8-docstrings >= 1.5.0 + flake8-docstrings >= 1.6.0 commands = flake8 pypesto test setup.py description = - Run flake8 with various plugins + Run flake8 with various plugins. [testenv:doc] extras =