diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml new file mode 100644 index 00000000..fff00022 --- /dev/null +++ b/.github/workflows/e2e.yml @@ -0,0 +1,69 @@ + +name: E2E + +on: + schedule: + # Runs at 02:57am every day. + - cron: '57 2 * * *' +jobs: + # We only run the end to end tests if there have been new commits in the last + # 24 hours. + # https://stackoverflow.com/questions/63014786/how-to-schedule-a-github-actions-nightly-build-but-run-it-only-when-there-where + check_date: + runs-on: ubuntu-latest + name: Check latest commit + outputs: + should_run: ${{ steps.should_run.outputs.should_run }} + steps: + - uses: actions/checkout@v3 + + - name: print latest_commit + run: echo ${{ github.sha }} + + - id: should_run + continue-on-error: true + name: Check latest commit is less than a day old. + if: ${{ github.event_name == 'schedule' }} + run: | + sha=$(git rev-list --after="24 hours" ${{ github.sha }}) + if test -z $sha + then + echo "should_run=false" >> $GITHUB_OUTPUT + else + echo "should_run=true" >> $GITHUB_OUTPUT + fi + + test: + needs: check_date + if: ${{ needs.check_date.outputs.should_run != 'false' }} + timeout-minutes: 60 + strategy: + fail-fast: false + matrix: + os: ["ubuntu-latest"] + python-version: ["3.10"] + name: End to end tests + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + + - name: Install poetry + run: pipx install poetry + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + cache: 'poetry' + + - name: Update environment + run: | + poetry env use ${{ matrix.python-version }} && poetry install --with=test,cloud,cloud_dev + + - name: Run tests + run: | + poetry run modal token set \ + --token-id ${{ secrets.MODAL_TOKEN_ID }} \ + --token-secret ${{ secrets.MODAL_TOKEN_SECRET }} + # --env=confirm_solutions + poetry run python confirm/cloud/e2e_runner.py \ No newline at end of file diff --git a/confirm/cloud/clickhouse.py b/confirm/cloud/clickhouse.py index 3ca4e10b..86d14195 100644 --- a/confirm/cloud/clickhouse.py +++ b/confirm/cloud/clickhouse.py @@ -390,9 +390,7 @@ def connect( """ if job_id is None: config = get_ch_config(host, port, username, password) - test_host = keyring.get_password( - "clickhouse-confirm-test-host", os.environ["USER"] - ) + test_host = get_ch_test_host() if not ( (test_host is not None and test_host in config["host"]) or "localhost" in config["host"] @@ -440,9 +438,7 @@ def get_ch_config(host=None, port=None, username=None, password=None, database=N if "CLICKHOUSE_HOST" in os.environ: host = os.environ["CLICKHOUSE_HOST"] else: - host = keyring.get_password( - "clickhouse-confirm-test-host", os.environ["USER"] - ) + host = get_ch_test_host() if port is None: if "CLICKHOUSE_PORT" in os.environ: port = os.environ["CLICKHOUSE_PORT"] @@ -466,6 +462,13 @@ def get_ch_config(host=None, port=None, username=None, password=None, database=N ) +def get_ch_test_host(): + if "CLICKHOUSE_TEST_HOST" in os.environ: + return os.environ["CLICKHOUSE_TEST_HOST"] + else: + return keyring.get_password("clickhouse-confirm-test-host", os.environ["USER"]) + + def find_unique_job_id(client, attempts=3): for i in range(attempts): job_id = uuid.uuid4().hex @@ -499,7 +502,7 @@ def clear_dbs(client): Args: client: _description_ """ - test_host = keyring.get_password("clickhouse-confirm-test-host", os.environ["USER"]) + test_host = get_ch_test_host() if not ( (test_host is not None and test_host in client.url) or "localhost" in client.url ): diff --git a/confirm/cloud/e2e_runner.py b/confirm/cloud/e2e_runner.py new file mode 100644 index 00000000..27e927b9 --- /dev/null +++ b/confirm/cloud/e2e_runner.py @@ -0,0 +1,49 @@ +import sys + +import modal +import pytest + +import confirm.cloud.modal_util as modal_util + +stub = modal.Stub("e2e_runner") + +img = modal_util.get_image(dependency_groups=["test", "cloud"]) + + +def run_tests(argv=None): + if argv is None: + argv = ["--runslow", "tests", "imprint/tests"] + print(argv) + exitcode = pytest.main(argv) + print(exitcode) + return exitcode.value + + +@stub.function( + image=img, + gpu=modal.gpu.A100(), + retries=0, + mounts=( + modal.create_package_mounts(["confirm", "imprint"]) + + [ + modal.Mount(local_dir="./tests", remote_dir="/root/tests"), + modal.Mount(local_dir="./imprint/tests", remote_dir="/root/imprint/tests"), + modal.Mount( + local_dir="./imprint/tutorials", remote_dir="/root/imprint/tutorials" + ), + modal.Mount(local_dir="./", remote_dir="/root", recursive=False), + ] + ), + timeout=60 * 60 * 1, + secrets=[modal.Secret.from_name("confirm-secrets")], +) +def run_cloud_tests(argv=None): + return run_tests(argv=argv) + + +if __name__ == "__main__": + # run_tests() + with stub.run(): + argv = None if len(sys.argv) == 1 else sys.argv[1:] + exitcode = run_cloud_tests.call(argv) + sys.exit(exitcode) diff --git a/confirm/cloud/modal_secrets.py b/confirm/cloud/modal_secrets.py new file mode 100644 index 00000000..8756feb5 --- /dev/null +++ b/confirm/cloud/modal_secrets.py @@ -0,0 +1,18 @@ +import os + +import keyring +import modal + + +secrets = dict() +for (keyring_name, env_name) in [ + ("clickhouse-confirm-test-host", "CLICKHOUSE_HOST"), + ("clickhouse-confirm-test-host", "CLICKHOUSE_TEST_HOST"), + ("clickhouse-confirm-test-password", "CLICKHOUSE_PASSWORD"), + ("upstash-confirm-coordinator-host", "REDIS_HOST"), + ("upstash-confirm-coordinator-password", "REDIS_PASSWORD"), +]: + secrets[env_name] = keyring.get_password(keyring_name, os.environ["USER"]) + +stub = modal.Stub("confirm-secrets") +stub["secret"] = modal.Secret(secrets) diff --git a/confirm/cloud/modal_util.py b/confirm/cloud/modal_util.py index 3e4064cf..1c5a27e0 100644 --- a/confirm/cloud/modal_util.py +++ b/confirm/cloud/modal_util.py @@ -3,7 +3,7 @@ import modal -def get_image(): +def get_image(dependency_groups=["cloud"]): poetry_dir = Path(__file__).resolve().parent.parent.parent context_files = { @@ -17,7 +17,7 @@ def get_image(): "COPY /.pyproject.toml /tmp/poetry/pyproject.toml", "RUN cd /tmp/poetry && \\ ", " poetry config virtualenvs.create false && \\ ", - " poetry install --with=cloud --no-root", + f" poetry install --with={','.join(dependency_groups)} --no-root", ] return modal.Image.from_dockerhub( diff --git a/docs/ci.md b/docs/ci.md index 233ef6ac..2cb0c2af 100644 --- a/docs/ci.md +++ b/docs/ci.md @@ -1,6 +1,10 @@ # Continuous Integration -Related: [docs/packaging.md](./packaging.mdci.md) +Related: [docs/packaging.md](./packaging.md) + +## Nightly end to end tests + +Our end to end tests are run nightly via the [.github/workflows/e2e.yml](../.github/workflows/e2e.yml) actions workflow which launches [confirm/cloud/e2e_runner.py](../confirm/cloud/e2e_runner.py). That script runs all tests including slow tests on a Modal server with a single GPU. ## Tools - renovatebot is used to maintain dependency version: @@ -11,7 +15,7 @@ Related: [docs/packaging.md](./packaging.mdci.md) ## Clearing the Github Actions cache -We cache lots of stuff. If you need to clear the cache, run this command. +We cache lots of stuff including our Python environments. If you need to clear the cache, run this command. ``` gh actions-cache list --limit 100 | tail -n +5 | awk '{print $1}' | tr '\n' '\0' | xargs -0 -n1 gh actions-cache delete --confirm diff --git a/docs/packaging.md b/docs/packaging.md index 8227540e..17e7a2ee 100644 --- a/docs/packaging.md +++ b/docs/packaging.md @@ -48,6 +48,12 @@ However, without conda, we would: 2. Lose access to the large repository of easy-to-install packages that are often not-at-all-easy-to-install with pip/poetry. (e.g. sage!) This is not important for production jobs but it's nice to have this access for development. 3. Need to spend time updating various parts of our cloud setup, mainly the Dockerfiles. Updating Dockerfiles is painful because the iteration time is so slow. +## How to update pyproject.toml package versions? + +- [The poetry plugin "poetry up" helps here.](https://github.com/MousaZeidBaker/poetry-plugin-up) +- `poetry up --with=test,cloud,dev,cloud_dev` +- [Lots of discussion in this issue](https://github.com/python-poetry/poetry/issues/461) + ## Notes - [JAX doesn't have a "normal" package registry and doesn't use PyPI](https://github.com/google/jax/issues/5410) diff --git a/imprint/tutorials/ztest.ipynb b/imprint/tutorials/ztest.ipynb index e8173369..92e0aadb 100644 --- a/imprint/tutorials/ztest.ipynb +++ b/imprint/tutorials/ztest.ipynb @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -105,25 +105,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ + "true_drug_effect = 0\n", + "K = 10000\n", "samples = scipy.stats.norm.rvs(loc=true_drug_effect, size=K)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.0235" + "0.0242" ] }, - "execution_count": 13, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -134,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -143,7 +145,7 @@ "0.02275013194817921" ] }, - "execution_count": 12, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -154,16 +156,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "2.57" + "2.59" ] }, - "execution_count": 17, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -174,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -187,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -197,12 +199,12 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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enlW/hoaGWvsvf71cLkdExJEjR1rbH7UyVK1Wi4iIQqGQwE8DAAAAbEfCmmUymUw0Go11v6anp1v7L3+9OWwqk8m0VpBqhjGrWT5fTT6fT+6HAgAAALYVYU0CJiYmIiJWBDvfdunSpYiIGBkZ6UhNAAAAwPYgrNmkRw1tiogoFotRLBZjampqzd41pVIpMplMlEqldpcIAAAAbGPCmk2am5trfT8zM7PmfhcuXIh8Ph+FQmFFYFOv11tz1CxvCwAAACAiYle3C9gOxsfHY2pq6qHVnZqhSyaTibm5uRWrOmUymahUKlEul2N0dHTFkt6FQiHGxsY6VT4AAACwjfQ0Go1Gt4ugvebn5+PAgQMREXHnzp3o7+/vckUAAACw8yT1/G0YFAAAAECKCGsAAAAAUkRYAwAAAJAiwhoAAACAFBHWAAAAAKSIsAYAAAAgRYQ1AAAAACkirAEAAABIEWENAAAAQIoIawAAAABSRFgDAAAAkCLCGgAAAIAUEdYAAAAApIiwBgAAACBFhDUAAAAAKSKsAQAAAEgRYQ0AAABAighrAAAAAFJEWAMAAACQIsIaAAAAgBQR1gAAAACkiLAGAAAAIEWENQAAAAApIqwBAAAASBFhDQAAAECKCGsAAAAAUkRYAwAAAJAiwhoAAACAFBHWAAAAAKSIsAYAAAAgRYQ1AAAAACkirAEAAABIEWENAAAAQIoIawAAAABSRFgDAAAAkCLCGgAAAIAUEdYAAAAApIiwBgAAACBFhDUAAAAAKSKsAQAAAEgRYQ0AAABAighrAAAAAFJEWAMAAACQIqkMa5aWlmJpaanbZQAAAAB03K5unXhpaSlmZmaiUqlErVZrfa0mk8nEkSNHIpvNxtDQULz66qvR29vb4YoBAAAAktfRsGZpaSmmpqZicnKyFcw0Go0V+2Qymejr64uIiIWFhajX67G4uBiVSiUiInp6emJ0dDRyuVz8+Mc/jtOnTwtuAAAAgB2jI2HN0tJSnD59OsrlciucyefzUSgUIpfLRTabjb6+vti7d++abdy+fTtqtVpUq9V49913Y25uLs6cORNjY2MxOjoab731ltAGAAAA2PZ6Gt/u2tJmv/rVr2JsbCwiIkZGRmJ0dDQOHz7clrZrtVqUSqW4cOFC9PT0xLlz5+Jf//Vf29L2djY/Px8HDhyIiIg7d+5Ef39/lysCAACAnSep5+/EwpqlpaU4fvx41Gq1mJiYiJGRkXV7zjyOe/fuxeTkZLz11lvxz//8z3Ht2rXYs2dPIufaDoQ1AAAAkLyknr8TWQ3q5s2bcfDgwTh69GgsLCzEmTNnEgtqIiL27t0bY2NjsbCwEIcPH46DBw/Gf/7nfyZ2PgAAAICktH3Omtu3b8fx48fjwoUL8corr7S7+UeanJyMQqEQx48fj2q1Gt/97nc7XgMAAADAVrW9Z834+HjMzc11JahpKhaLcePGjdZcOQAAAADbRdt71ly6dKndTW5JNpuNixcvdrsMAAAAgE1JZM4aAAAAALZGWAMAAACQIm0fBtVuS0tLUavVIuKboU29vb1drggAAAAgOakMa27evBm//OUv4/bt25HNZmNgYCDq9XrUarVYXFyMfD4fo6OjcfDgwW6XCgAAANBWqQtrXn311di/f3+USqUYGBhYdZ9r167FyMhIvPrqq/HTn/60wxUCAAAAJKftc9bcvHlzy8eeOnUqTp06Fb/73e/WDGoiIk6cOBHvvvtuzM7OxpUrV7Z8PgAAAIC0aXtYUywW43/9r/8V169f39Rxt2/fjsXFxXjllVc2fMz58+fj7Nmzmy0RAAAAILXaHta8++67sXfv3sjn8/HGG29s+Lh6vR4vv/zyps/X19e36WMAAAAA0qrtYU02m425ubk4ePBgnD9/Pvbv37+hXjbZbDYmJyfjq6++2vC5Pvroo7h79+7jlAsAAACQKm0PayIiMplMlMvlyOfzMTw8HCdOnIgf//jHsbS0tOYxe/fujdOnT0cul9tQuPP2229HPp+Pc+fOtbN0AAAAgK5KbDWow4cPRyaTiXPnzsXIyEgMDw/HwMBAvP3223Hy5MlVjxkbG4tbt27FiRMnoqenJ3K5XGSz2db25vLdtVotIiIuXboUx48fT+pHAAAAAOi4nkaj0Uiq8cuXL8fi4mJree3x8fH4t3/7t3j11Vdjamoqent7Vz1uZmYmxsfH46OPPlp1e7FYXHdp7yfd/Px8HDhwICIi7ty5E/39/V2uCAAAAHaepJ6/Ew1r7t27F2+++Wb87ne/a71WrVZjeHg46vX6ur1smsfPzs5GrVaLvr6+yGazkc1mY+/evUmVvCMIawAAACB5ST1/JzYMKuKbeWiaQ5aacrlc3Lp1K8bHx+OVV16Jl19+Oaanp2PPnj2rHn/ixIk4ceJEkmUCAAAApEYiEwwvt1YvmFKpFLOzs/HXv/41MplM/J//83+SLgUAAAAg9RINaz766KMVEwR/W7OXzdmzZ+P06dPxv//3/47PP/88yZIAAAAAUi3RsObatWtRKBQeud/Y2Fh8+umn8be//S2y2Wz8+te/TrIsAAAAgNRKNKz593//9w3PN5PNZmNubi7Onj0bZ86ciRdeeEEvGwAAAOCJk9hqUG+//XbU6/XI5XJRqVSiXq/HwsJCZLPZGBwcjCNHjsTzzz+/6rG1Wi2Gh4fj5s2bce7cufjXf/3XJErcsawGBQAAAMnbVqtBffTRRzEyMhL79u2LkZGROHLkSGSz2chkMlGv16NWq8X58+djbm4uJiYm4kc/+tGK45u9bM6dOxdnzpyJixcvxvT0dHz3u99NolwAAACA1EikZ80///M/x/j4eJw+ffqR+/7sZz+Lf/7nf46f//znq25f3stmfHw8fvnLX7a73B1HzxoAAABIXlLP322fs+b111+PycnJDQU1ERHnz5+PTz/9NK5cubLq9mYvm9/97nfx1ltvxf/3//1/8Z//+Z/tLBkAAAAgNdoe1tRqtQ1PKtw0Pj4eFy9eXHefkZGRWFhYiO9+97uRy+XiF7/4xeOUCQAAAJBKbQ9rFhYWtnTcRkZjZTKZqFQqetkAAAAAO1bbw5qhoaFN9XpZWlqKV199NX784x9v+JjlvWyKxeJWygQAAABIpbaHNefPn4933303vve978Wvf/3reO+992Jpaam1fWlpKT777LN477334vXXX4+BgYE4cuTIQytCPUqzl81f//rXdv8IAAAAAF2TyNLd165di2KxGGfOnImenp4192s0GjE2NhZvvfVWEmUAAAAAbDtt71kTEbF3796oVCoxOzsbP/rRj+Lw4cPRaDSi0WjE3r174/Dhw3HmzJlYXFwU1AAAAAAsk0jPmqZcLhfT09NJngIAAABgR0mkZw0AAAAAWyOsAQAAAEgRYQ0AAABAirQ9rHn77bfb3eSWpakWAAAAgI1oe1izuLgYP/zhD+Orr75qd9MbtrS0FD/84Q+jXq93rQYAAACArWh7WHPmzJl4/vnn4+DBg/Gf//mf7W7+kW7evBlDQ0ORy+Xi5z//ecfPDwAAAPA4EpmzplQqxdmzZ+Pw4cPxi1/8IpaWlpI4zQpLS0vx5ptvxtDQUIyPj8fZs2cTPycAAABAuyU2wfDIyEjMzs7GpUuXYt++ffHjH/84kZ42n332Wbz++uuxb9++uHz5cszOzsZPf/rTtp8HAAAAoBN2Jdl4LpeLTz/9NM6dOxdvvvlmTE9PRy6Xi3w+H0ePHo18Ph+9vb2banNpaSlmZmaiUqnE7OxsVKvVaDQaUSqV4syZMwn9JAAAAACd0dNoNBqdOtnU1FScO3cuarVa9PT0REREJpOJI0eORCaTib6+vshkMrF///6IiLh7927U6/VYWFiIer0etVotarVaREQ0Go3IZrMxOjoaIyMjsXfv3k79GKk3Pz8fBw4ciIiIO3fuRH9/f5crAgAAgJ0nqefvjoY1TR999FFUKpW4ePFifPTRR5s6ttkz59SpU3H48OGEKtzehDUAAACQvKSevxMdBrWWw4cPx+HDh2NsbCwiIm7fvr2i18zyJbczmUxks9nIZrMxMDDQjXIBAAAAOqYrYc23DQwMxMDAQJw4caLbpQAAAAB0VWKrQQEAAACwecIaAAAAgBQR1gAAAACkiLAGAAAAIEWENQAAAAApIqwBAAAASBFhDQAAAECKCGsAAAAAUkRYAwAAAJAiwhoAAACAFBHWAAAAAKSIsAYAAAAgRYQ1AAAAACmyq9sFpFW9Xo+zZ89GtVqNiIiFhYWIiMjn8zExMRGZTOaRbZTL5ZicnFzxWqFQiLGxsbbXCwAAAOwMqQprbt68GbOzs3Hr1q04evRo/OhHP2ptu3z5cuzbty+OHz+eeB0zMzMxPj4eExMTUSqVWq9Xq9U4ceJEnDt3LiYnJ2NkZGTNNgqFQiwsLMT09HRks9mI+CYAOn36dAwODkalUmm9DgAA8KRbWlqK+fn5+Prrr+Ppp5+O/v7+6O3t7XZZ0BU9jUaj0e0i3nvvvRgfH2/1YomIKBaLcfHixdb/3759O0ZHR2NwcDB+97vfJVZLrVaL4eHhmJubW3X7zMxMFAqFiIioVCqRz+cf2md4eDhmZmbi9u3bq/bAGRoainq9Hrdu3Wpr7U3z8/Nx4MCBiIi4c+dO9Pf3J3IeAACAx9FoNOL999+P3/zmN/Ef//Ef8eDBg9a2p556Kk6ePBlvvPFGHDt2LHp6erpYKawuqefvrs9Zc+XKlSgUCjE3NxeNRiMOHz4cr7zyykP7DQwMxLvvvhu5XC5ef/31xOqZnJyMarXaCmS+bXk4s7zXTdPMzEyUy+UYGRlZc6jUxMRE1Gq1GB8fb0vNAAAA2021Wo3nnnsujh8/HpcvX14R1EREPHjwIMrlchw/fjyee+65Fb/ch52uq2HN7du3o1gsxsDAQFQqlfif//mfmJ2djUuXLq15zOnTp2Pv3r1x5cqVRGpq/gEwMzMTtVpt1X2aw5dmZ2cf2taco2atsCfim15DERFTU1OPVSsAAMB2VKlU4sUXX4xPPvlkQ/t/8skn8eKLL0alUkm4MkiHroY1pVIpRkZG4tNPP40TJ05s+LiJiYmHJu5tl9HR0chkMpHP59ecU6Y52fBq28vl8prblstkMlGv12NmZuYxKwYAANg+qtVqnDx5Mu7fv7+p4+7fvx8nT57Uw4YnQlfDmmvXrsX58+c3fdzevXtbgUm7FYvFWFxcXDOxrdfrUa/XIyIemq9mefDyqLCmud0fNAAAwJOi0WjEa6+9tumgpun+/fvxk5/8JFIw9SokqqthzcDAwJaPbQYmndYcopXJZGJiYmLFtmbwspFlvfv6+iIi4saNG+0tEAAAIKXef//9DQ99Wsuf//zn+OCDD9pUEaRTV5fuXlxc3NJx9+7d60qSWq/XW5MCT09PPxTK3L17d0ttbtb8/Py627/88stNtwkAAJC03/72t21r59ixY21pC9Koq2HNvn374urVq3Hy5MlNHffmm2+uO4Fvu9Xr9ZidnY3R0dHIZrNx4cKFyOVyq+63Uc2gZyvDuZrLggEAAGwXS0tLcfXq1ba0deXKlVhaWore3t62tAdp09VhUCMjI/HTn/40Pv/884e2rdVz5vLlyzE1NRWjo6NJlxflcjkKhUKcOHEiCoVCZDKZKJVKqwY1EVsLXro1nAsAAKCT5ufnH1qee6sePHgQX3zxRVvagjTqalhTLBbj4MGDkc1m44033oibN2+2tvX09KzY97333osf/vCH8eqrr8aZM2fi+eef70h9lUol5ubmotFoxIULF2J0dDT27du36ipOzXloknbnzp11v/74xz92pA4AAICN+vrrr9va3ldffdXW9iBNujoMKuKbECaXy8X58+dby3E3hwh973vfi4WFhVbvk0ajEblcLt56662u1JrL5WJubi727dsXhUIhSqVSjI2NtbZvZGLhdujv7+/IeQAAANrl6aefbmt7e/bsaWt7kCZd7VkT8c0y3Ldu3Yq33norGo1GNBqNWFxcjHq9HrVaLRYXF1uvl0qlmJ2d7Wq9mUwmRkZGIiJifHx8xdLb+/fv33A7zQCqUwEPAABAN/X398dTTz3VlrZ27doVzzzzTFvagjTqeljTNDY2FouLi3HmzJnI5XKxd+/e2Lt3b+RyuRgbG4tbt27FmTNnul1mRMSKyY2bvYEi/h68bGQemub8Ntlstq21AQAApFFvb++mF5dZy8mTJ00uzI7W9WFQy+3duzdKpVLXzl+tVmN4eDgymUxMT0+vGaQs7w2zvKfP8v3r9fq6vWaagY6wBgAAeFK88cYbUS6X29IO7GSp6VmTBpOTk1Gr1aJara7oMbNRR44caX3/qJWharVaRERHlyAHAADopmPHjsWhQ4ceq41nn302XnrppTZVBOkkrFlmeU+Yo0ePrrlfM2iJiMjn8yuOby7rvXyfb1s+X83y4wEAAHaynp6e+P3vfx+7d+/e0vG7d++Od95556HVg2Gn6WpYs7S09Mh9Xn311Th16lScOnUqJiYm4r333kusnlOnTkUmk4lbt25FsVhcc7/p6enW96Ojoyu2TUxMPLTPt126dCkiojVRMQAAwJMil8vF1atXNx3Y7N69O65evdr6BTnsZD2NRqPRrZM/9dRT8eDBg3X3uXfvXkR8M6yoVqtFpVKJjz76KP7whz8kUtP4+HjU6/U1h0HNzMy0hi5NT0+vGuoMDw9HuVyOW7durTonzeDgYCwsLMTi4mJ7i/9/5ufn48CBAxERcefOHUt9AwAAqVOtVuO1116LTz755JH7Pvvss/HOO+8IakidpJ6/u9qzZiM5UXNVqIGBgThx4kS89dZbcfbs2Th16lQiNZVKpchkMjE0NBTlcnnFyk7lcrk1AfHk5OSavW8uXLgQ+Xw+CoXCiuFQ9Xq9FfTMzc0lUj8AAMB2kMvl4uOPP47r169HsVh8aFnvXbt2xfDwcFy/fj3+9Kc/CWp4oqS+Z81ajh49Gjdu3GhzRX9Xq9WiVCrF7Oxs1Ov1WFhYiL6+vigWizExMbHuSk9N5XI5JicnVyzpXSgUYmxsLLG6I/SsAQAAtp+lpaX44osv4quvvoo9e/bEM888Y3luUi+p5+9tGdbcu3cvjhw5En/9618TqGr7E9YAAABA8nbkMKjN+uyzz+Ltt9+ObDa76lwwAAAAANvdrqRP8LOf/SwuXbrUmih4uUaj8dC4xI1oNBpRKpXaUR4AAABAqiTes+b8+fOxsLAQDx48iD/84Q/xox/9KBqNRmty4eb3G/0aGBiIubm5eP7555MuHQAAAKDjEu9Zs1w+n498Ph8zMzPx8ssvR09PT1y6dGlDk/VGRGSz2RgYGEi2SAAAAIAu6mhY05TP52N2djaOHj0ar7zySjdKAAAAAEilrk0wnMvl9JIBAAAA+JaurgY1OTnZzdMDAAAApE5XhkE1nThxYkP73bx5M2q1WkR80yPn4MGDCVYFAAAA0D1dDWs26u7du3H37t2o1+vx7rvvxuLiYly4cCF6e3u7XRoAAABAW22LsObEiRMreuFcvnw5xsfH43e/+10XqwIAAABov20R1nxbpVKJubm5bpcBAAAA0HapCGuuXLkSFy9ejFqtFvV6fd19m3PXjI2NdaAyAAAAgM7qeljzq1/9KsbHxyMiotFobOiYfD4fZ8+eTbIsAAAAgK7oalhz7969GBsbi0wmE0eOHIlcLhf79++PixcvRj6fj/3790fENxMM12q1qFar8bOf/Sx+/vOfd7NsAAAAgMR0NayZmpqKXC4X165di71797Zev3XrVvziF79YdbWnf/u3f4vXX3/d5MIAAADAjvQP3Tz5zMxMlMvlFUFNRMTg4GDMzs6uesyZM2cim83GlStXOlEiAAAAQEd1NaxpNBpx8ODBh17PZrNRqVTWPO7MmTNx8eLFBCsDAAAA6I6uhjU9PT2rvp7L5aJcLne4GgAAAIDu63rPmtUMDAzE3bt34+rVq2se21zCGwAAAGAn6WpYk81mY2lpKW7evBkTExPx2WeftbadPn06fvrTn8bnn3/+0HHXrl2Ler3euUIBAAAAOqSrYc34+Hj88pe/jNOnT8e5c+fi5Zdfbm37xS9+EYuLizE4OBi//vWv4+bNm/HZZ5/Fr371q3j55ZejWCx2sXIAAACAZHR16e6BgYHIZrNx7ty5iIgVS3Xv3bs3zp8/Hz/72c9ibGzsoWNHR0c7VicAAABAp3S1Z01ExMjISCwuLkalUnloue6RkZE4f/58NBqNFV/T09OrriIFAAAAsN31NNaa5TdlLl++HAsLC5HP52NgYKDb5aTa/Px8HDhwICIi7ty5E/39/V2uCAAAAHaepJ6/uzoMajNeeeWVbpcAAAAAkLiuD4PaqNu3b8err74av/rVr2Jpaanb5QAAAAAkYtuENQMDA1EqlWJgYCBOnDixYplvAAAAgJ1i24Q1Ed8ENq+88kpUKpUYHx/vdjkAAAAAbbetwpqmTCYT9Xq922UAAAAAtF1HJxh+7733olarxZEjR+L555/fcju3b9+OhYWF9hUGAAAAkBIdCWuuXLkSp0+fXtEbZt++fXHt2rX4/ve//8jjl5aWolarRa1Wi0qlElNTU1EsFhOsGAAAAKA7Eg9rLly4ED/72c8een1hYSFyuVzMzMzEv/zLv6zYdvPmzbh48WLMzMxErVZbdcjTqVOnkioZAAAAoGsSDWtu374do6Oja25vNBoxPDwctVotent7IyLi9ddfj6mpqRX7fFuhUIgf/ehH7S8YAAAAoMsSnWC4uWJTsViMubm5+J//+Z/W1+zsbJw4cSIWFhbizTffjIiIf/u3f4vJycloNBqtr9Xa/MMf/pBk2QAAAABd09NYLRFpg9u3b8fg4GCcO3cufv7zn6+53/j4eLz99ttx+/btyGQyERExNjYWhUIh+vr6IiJarw8MDCRR6o4zPz8fBw4ciIiIO3fuRH9/f5crAgAAgJ0nqefvxIZBTU5OxtDQ0LpBTUREqVSKy5cvx/DwcGQymZibmxPKAAAAAE+sxIZBzczMRD6f39C+Y2NjMTMzE+fOnRPUAAAAAE+0xMKaWq0WhUJhQ/s29/vpT3+aVDkAAAAA20JiYc29e/fiyJEjG9p3YGBAjxoAAACASDCs2ey8xdlsdsP7Li0tbbYcAAAAgG0hsbCmp6cnent7N7X/Rp04cSJu3ry5haoAAAAA0i2xsGazNtMTJ6HVxgEAAAC6LtFhUJ9//vmG999Mz5rbt29vpSQAAACA1NuVZOO5XG7DkwzPzs7GD3/4w0fuV6vVol6vP2ZlAAAAAOmUaFhTr9djZmZmw/tvZN9Go7GpXjgAAAAA20miYY25ZQAAAAA2J9GwZnx8PN588822tnnjxo14+eWX29omAAAAQFokGtaMjo7G3r1729pmPp+PbDbb1jYBAAAA0iLRpbv7+voSaTeTySTSLgAAAEC3JRbWlEql6O3tTaTtiYkJvWsAAACAHSmxYVBnzpxJqul45ZVXEmsbAAAAoJsSHQYFAAAAwOYIawAAAABSRFgDAAAAkCLCGgAAAIAUEdYAAAAApIiwBgAAACBFhDUAAAAAKSKsAQAAAEgRYQ0AAABAighrAAAAAFJEWAMAAACQIsIaAAAAgBTZ1e0Clrt582bMzs7GrVu34ujRo/GjH/2ote3y5cuxb9++OH78eBcrBAAAAEhWKsKa9957L8bHx6NarbZeKxaLK8KaXC4Xo6OjMT09Hb/73e+6USYAAABA4ro+DOrKlStRKBRibm4uGo1GHD58OF555ZWH9hsYGIh33303crlcvP76612oFAAAACB5XQ1rbt++HcViMQYGBqJSqcT//M//xOzsbFy6dGnNY06fPh179+6NK1eudLBSAAAAgM7oalhTKpViZGQkPv300zhx4sSGj5uYmIjJyckEKwMAAADojq7OWXPt2rX461//uunj9u7dGwsLCwlUBAAAANBdXe1ZMzAwsOVj6/V6+woBAAAASImuhjWLi4tbOu7evXvRaDTaXA0AAABA93U1rNm3b19cvXp108e9+eabUSgUEqgIAAAAoLu6OmfNyMhI/PSnP41cLhff/e53V2xbq+fM5cuXY2pqKubm5jpRIgAAAEBHdbVnTbFYjIMHD0Y2m4033ngjbt682drW09OzYt/33nsvfvjDH8arr74aZ86cieeff76zxQIAAAB0QE+jy5O/3Lt3L3K5XNy+fbsV0GQymYiI6Ovri4WFhdZkwo1GI3K5XMzOznap2u1hfn4+Dhw4EBERd+7cif7+/i5XBAAAADtPUs/fXe1ZE/HNMty3bt2Kt956KxqNRjQajVhcXIx6vR61Wi0WFxdbr5dKJUENAAAAsKN1PaxpGhsbi8XFxThz5kzkcrnYu3dv7N27N3K5XIyNjcWtW7fizJkz3S4TAAAAIFFdnWD4e9/7XszNzUVvb29EfNPLplQqdbMkAAAAgK7qas+au3fvxrVr17pZAgAAAECqdH0Y1OnTp+Pzzz/vdhkAAAAAqdD1sGZhYSGy2Wy8/vrrK5buBgAAAHgSdTWsqdfrMTo6Gp9++mkUi8X45S9/GUePHo233347lpaWulkaAAAAQFd0NazJ5/NRKpViYGAgTpw4EZcuXYqZmZlYXFyMXC4Xp06d0tsGAAAAeKL0NBqNRreLWMtHH30UZ8+ejdu3b8epU6diZGSktXIUa5ufn48DBw5ERMSdO3eiv7+/yxUBAADAzpPU83fX56xZz+HDh+PSpUtx48aN2Lt3b5w4cSJOnToV169f73ZpAAAAAIlIdVjTtLS0FPV6PRYXF6NcLkc+n4+jR492uywAAACAttvV7QLW895778Xk5GSUy+WIiGiO2MrlcvHmm292szQAAACARHQ1rDl69Ghcu3ZtxTw0n332WUxOTsbU1FTU6/WI+CakyWQyMTIyEqOjozEwMNCligEAAACS1dWwZm5uLm7fvh3f//7348qVKzE5ORkzMzMR8fdeNPl8PkZHR+OVV17pZqkAAAAAHdHVsCaTyUQul4tMJhP1er0V0OhFAwAAADypuj5nTaPRiMXFxYjQiwYAAACg62FNsxfNxMRE7N27t9vlAAAAAHRV18Oacrkcx48f73YZAAAAAKnwD908eb1ejyNHjnSzBAAAAIBU6WpYMz09vWLZbgAAAIAnXVfDGhMJAwAAAKzU1bDmcZw6darbJQAAAAC03bYMa+7duxflcrnbZQAAAAC0XeKrQX322Wdx8ODBNbdtxfT09NYLAgAAAEixRMOa119/PaampmLfvn0xOzv7UGiTzWajp6cnyRIAAAAAtpVEh0FNTk5Go9GIxcXFmJmZeWh7NpuNRqOxpS8AAACAnSjRsOb06dOt71999dWHtmcymRgcHIy5ublYXFzc8Ndbb72VZNkAAAAAXZN4z5q5ublYWFiI3t7eh7b39fVFsViMw4cPx969ezf8NTIykmTZAAAAAF2T+ATDhw8fXnNboVCIwcHBTbeZyWRiYGDgccoCAAAASKXEw5r1nDlzZsvHfvrpp22sBAAAACAdEh0GBQAAAMDmdLVnzdLS0qpz2Sz36quvtpb3zmazUSgU4vjx450oDwAAAKDjuhrW7Nu3Lx48eLDuPhcuXIiIiIWFhajVavHuu+9GqVSKP/zhD50oEQAAAKCjuhrWNBqNR+6zd+/e1n8HBgbixIkTUa1W49SpU3Hx4sVE6xsfH49yuRy1Wi2y2Wzkcrk4evRojI2Nbej4crkck5OTK14rFAobPh4AAAB48nR1zprm8KbNyuVyUavV2lzN39VqtRgaGorBwcGYm5uLRqMR09PTEfFNgDM4OBjVanXdNgqFQpw9ezYmJyejUqlEpVKJ6enpuHHjRgwODiZaPwAAALB9bcsJhu/duxf1ej2Rtuv1ehQKhZieno6RkZHIZDIR8U1AND09HWNjY60wZ63AZXh4OGZnZ+PatWuRzWZbr2cymZieno5MJhOFQiGR+gEAAIDtbVuFNZ999lm8/fbbkc1mV4Qg7TQ+Ph6lUmnN9kulUivAGR4efmj7zMxMlMvlFUHPt01MTEStVovx8fF2lQ0AAADsEImHNT/72c+ir68vnnrqqYe+Go3Gqq+v9TU4OBijo6OxuLgYpVIpkXovXboUxWJx3X0mJiYiIqJarcbMzMyKbc05atbrOdNsf2pq6nFKBQAAAHagxMOa8+fPx8LCQjx48CD+8Ic/xI9+9KNoNBqtyYWb32/0a2BgIObm5uL5559ve63VajXq9Xr09PQ8FMIsl8vlWt9XKpUV28rlckTEI3v+ZDKZqNfr654HAAAAePJ0dDWofD4f+Xw+ZmZm4uWXX46enp64dOnSmsOFvi2bzcbAwEBi9S2fg2ZycjLy+fyq+/X19bW+Xz53zvLg5VFhTTabjWq1GtVqdc3zAAAAAE+erizdnc/nY3Z2No4ePRqvvPJKN0pYVT6fb600NTo6uuZ+CwsLre8HBwdb3zdXiNpI+NQMfG7cuLHFagEAAICdqCthTcQ3Q4mS7CWzFZlMJubm5h653/Jlu5cPibp79+6mz7mVVa3m5+fX3f7ll19uuk0AAAAgHboW1kT8fTLe7aZZdy6XWzGEaTPBS7P3zfJeOht14MCBTR8DAAAAbA9dXbr7xIkT3Tz9lszMzLTmtrlw4cKKbVsJXrbSswYAAADYubras+ZxnDp1Ki5evNjx8zbnsimVSiuGQEWsnHg4SXfu3Fl3+5dffhkvvPBCR2oBAAAA2mtbhjX37t1rLZHdSaOjo1Gr1aJUKsXY2NhD2ze6qtXj6u/v78h5AAAAgM5LPKz57LPP4uDBg2tu24rp6emtF7RFU1NTMTU1tWZQExGxf//+DbfXHP7UqYAHAAAA2B4SDWtef/31mJqain379sXs7OxDoU02m42enp4kS2iLmZmZGB0djcnJyRgZGVlzv2bwspF5aJrz22Sz2XaUCAAAAOwQiU4wPDk5GY1GIxYXF2NmZuah7dlsNhqNxpa+OqVarcbw8HBMT0+vG9RErAxeHhXYNLcLawAAAIDlEg1rTp8+3fr+1VdffWh7JpOJwcHBmJubi8XFxQ1/vfXWW0mW3VKr1eLEiRMxPT0dxWLxoe31en1FCHXkyJHW949aGaq5olShUGhTtQAAAMBOkHjPmrm5uVhYWIje3t6Htvf19UWxWIzDhw/H3r17N/z1qB4u7VCv16NQKMS1a9cin8+vus/s7GxUq9XW/2cymdYKUc0wZq22m/uv1TYAAADwZEo0rImIVhCzmkKhEEePHt10m5lMJgYGBh63tDXV6/VWj5pvL8+9XKVSeWj7xMRERKw/CfKlS5ciIjoSOgEAAADbS0+jkxPAbBNDQ0ORz+fXDZJqtVqcPXs25ubmHpp3Znh4OMrlcty6dWvVOWkGBwdjYWEhFhcX2157RMT8/HwcOHAgIiLu3LljqW8AAABIQFLP34kv3b3dFAqFqFarK4Y3rWe1MObChQutYVSVSqW1T71ej+Hh4YiImJuba1/RAAAAwI6R+rDm5s2bcfHixYiIeOGFF+LEiROrzn/TDuVyedVVq9ay1hCpTCYTlUolyuVyjI6OrljSu1AoxNjYWDvKBQAAAHagrg6DevXVV+PevXsrXpucnIyDBw9GRMSpU6eiXC5HRLSW6963b1+Uy+X4l3/5l47Wup0YBgUAAADJS+r5O/EJhtczMjISlUolGo1GjI2NxaVLl1pBzeuvvx7T09PRaDTi9OnTcevWrVhcXIzJyckYGRmJpaWlbpYOAAAAkIiuDoPav39/jIyMxPnz51e8fu/evZicnIyenp4YGRmJ3/3ud61txWIxIiLGx8dXvA4AAACwE3S1Z81bb731UFAT8felrSMiSqXSQ9uLxWLUarVEawMAAADohq6GNWtNl1OpVCLim5WWkppMGAAAACCNuhrW9PT0rPp6tVqNnp6eNVdbiohYWFhIqiwAAACArulqWFOv1x967d69e60hToVCYc1ju7iIFQAAAEBiuhrW7N2796FVnaamplrf5/P5VY/71a9+FadOnUq0NgAAAIBu6GpYMzo6GsPDw63/v3nzZoyPj0dPT08Ui8XWMt7Lvf3223Hjxo04c+ZMBysFAAAA6IyuhjUnTpyIgwcPxlNPPRX79++PoaGhiIjIZDJx4cKF1n5LS0vx9ttvx9GjR2N0dDSq1Wp8/vnn3SobAAAAIDFdDWsiIiYnJ+PixYuRy+Xi8OHDMTIyErVarbUK1EcffRS5XC7eeuutWFxcjIGBgWg0GjEyMtLlygEAAADar6dhpt4dZ35+Pg4cOBAREXfu3In+/v4uVwQAAAA7T1LP313vWQMAAADA3+3qdgGree+991rLd2ez2Thy5EhrWBQAAADATpaqsGZiYiLOnTu36rbh4eGYmpoS2gAAAAA7WiqGQd28eTO+973vxblz56LRaKz6denSpchms3H16tVulwsAAACQmK73rPnoo49aS3YXi8UoFArR19cXmUwmIiLq9XrUarW4ceNGlMvlKBaLUa1W4/vf/34XqwYAAABIRtfDmhMnTsTIyEiUSqXYu3fvuvvW6/UYHh6OYrEYf/3rXztUIQAAAEDndHUY1IULFyKfz8f58+cfGdRERGQymahUKjEwMBC//vWvO1AhAAAAQGd1Nawpl8vx9ttvb/q48+fPx7//+78nUBEAAABAd3U1rGk0Glta3SmbzUaj0UigIgAAAIDu6mpY09PT05VjAQAAANKqq2HNwsLClo9dXFxsYyUAAAAA6dDVsGZoaCiuXr266ePefvvtyOfzCVQEAAAA0F1dXbp7bGwsjh49GtlsNr7//e9v6Jhr167F6OhozM3NJVwdAAAAQOd1tWdNNpuN8fHxyOVy8Ytf/CI+++yzNfe9efNmvP766/Hyyy/HmTNn4vnnn+9YnQAAAACd0tNIwbJKw8PDcfny5dakwdlstrVtYWEh6vV6RHyzelQul4vZ2dlulLltzM/Px4EDByIi4s6dO9Hf39/ligAAAGDnSer5u6s9a5qmp6fj/Pnz0dvbG41GI27duhW1Wi1qtVosLi5Go9GIRqMRpVJJUAMAAADsaF2ds2a5kZGROHXqVExOTsalS5eiVqtFxDe9bPL5fIyOjsbAwECXqwQAAABIViqGQdFehkEBAABA8nb0MCgAAAAAvtGxsObtt9+Oo0ePxve+9704evRo/PrXv+7UqQEAAAC2jcTnrPnss8+iUCi05qBpqlarMTk5GXNzc7Fnz56kywAAAADYFhLtWXPv3r3I5XJRq9VaKzot//r0009jYGAgPvvssyTLAAAAANg2Eu1ZMz4+HvV6PSIi8vl85HK52L9/f0RE3LhxI8rlciwsLMSbb74Z//7v/55kKQAAAADbQmJhze3bt2NqaiqGhoZiZmYm9u7du+p+w8PDMT09HRMTE/H9738/qXIAAAAAtoXEhkFNTk7Gvn374tq1a2sGNRER09PTcfjw4Th//nxSpQAAAABsG4mFNTMzMzExMRG9vb2P3PfNN998aAJiAAAAgCdRYmFNrVaLfD6/oX2LxWLMzs4mVQoAAADAtpFYWHPv3r3IZrMb3r/RaCRVCgAAAMC2kejS3RsZAtXU19eXYCUAAAAA20OiYc1m9PT0bGr/mzdvJlMIAAAAQBelJqzZrKGhoW6XAAAAANB22zKsuXfvnjluAAAAgB0psbCm0WjE559/nkjbs7Ozmx42BQAAALAd7Eqy8VwuF0eOHNnQvrVaLY4ePfrI/er1etRqtcctDQAAACCVEg1r6vV6zMzMbHj/arW6of0ajYaeNQAAAMCOlGhYY14ZAAAAgM1JNKwZHx+PN998s+3t/vKXv4xf/epXbW8XAAAAoNt6Ggl1f/mHf/iHqNVqcfDgwba3Xa/XY//+/fHgwYO2t70TzM/Px4EDByIi4s6dO9Hf39/ligAAAGDnSer5O9Glu/v6+hJpN5PJGGIFAAAA7EiJhTWlUil6e3uTaj5KpVJibQMAAAB0S2LDoOgew6AAAAAgedtyGBQAAAAAmyOsAQAAAEgRYQ0AAABAighrAAAAAFJEWAMAAACQIsIaAAAAgBQR1gAAAACkiLAGAAAAIEWENQAAAAApIqwBAAAASBFhDQAAAECKCGsAAAAAUkRYAwAAAJAiwhoAAACAFBHWAAAAAKTIrm4XAAAAsLS0FPPz8/H111/H008/Hf39/dHb29vtsgC6Qs8aAACgKxqNRly/fj2KxWL09fXFoUOH4gc/+EEcOnQo+vr6Ynh4OK5fvx6NRqPbpQJ0lLAGAADouGq1Gs8991wcP348Ll++HA8ePFix/cGDB1Eul+P48ePx3HPPRbVa7VKlAJ0nrAEAADqqUqnEiy++GJ988smG9v/kk0/ixRdfjEqlknBlAOkgrAEAADqmWq3GyZMn4/79+5s67v79+3Hy5Ek9bIAngrAGAADoiEajEa+99tqmg5qm+/fvx09+8hNz2AA7nrAGAADoiPfff3/DQ5/W8uc//zk++OCDNlUEkE7CGgAAoCN++9vfpqodgLQS1gAAAIlbWlqKq1evtqWtK1euxNLSUlvaAkgjYQ0AAJC4+fn5h5bn3qoHDx7EF1980Za2ANJIWAMAACTu66+/bmt7X331VVvbA0gTYQ0AAJC4p59+uq3t7dmzp63tAaSJsAYAAEhcf39/PPXUU21pa9euXfHMM8+0pS2ANBLWAAAAievt7Y2TJ0+2pa2TJ09Gb29vW9oCSCNhDQAA0BFvvPFGqtoBSCthDQAA0BHHjh2LQ4cOPVYbzz77bLz00kttqgggnYQ1AABAR/T09MTvf//72L1795aO3717d7zzzjvR09PT5soA0kVYAwAAdEwul4urV69uOrDZvXt3XL16NXK5XEKVAaSHsAYAAOioQqEQH3744YaHRD377LPx4YcfRqFQSLgygHQQ1gAAAB2Xy+Xi448/juvXr0exWHxoWe9du3bF8PBwXL9+Pf70pz/pUQM8UXZ1uwAAAODJ1NPTE8eOHYtjx47F0tJSfPHFF/HVV1/Fnj174plnnrE8N/DEEtYAAABd19vbK5wB+H8MgwIAAABIEWENAAAAQIoIawAAAABSRFgDAAAAkCLCGgAAAIAUEdYAAAAApIiwBgAAACBFhDUAAAAAKSKsAQAAAEgRYQ0AAABAighrAAAAAFJEWAMAAACQIsIaAAAAgBQR1gAAAACkiLAGAAAAIEWENQAAAAApIqwBAAAASBFhDQAAAECKCGsAAAAAUmRXtwtIu+Hh4Th69GiMjY1t+JhyuRyTk5MrXisUCptqAwAAAHgyCWu+pV6vR61Wi4sXL8bU1FTU6/XIZrMbPr5QKMTCwkJMT0+3jqvX63H69OkYHByMSqWyqfYAAACAJ4thUMv09PTEwMBAjI+Px+Dg4KZDleHh4ZidnY1r166tODaTycT09HRkMpkoFArtLhsAAADYQYQ1yzQajVhcXIxKpRIjIyPR19e34WNnZmaiXC7HyMhIZDKZVfeZmJiIWq0W4+PjbaoYAAAA2GmENW3SnKNmvZ4zxWIxIiKmpqY6UhMAAACw/Qhr2qRcLkdEPHLoVCaTiXq9HjMzM50oCwAAANhmhDVtsDx4eVRY09xerVYTrQkAAADYnoQ1bdAMXtaaq2a55jw4N27cSLIkAAAAYJuydHcb3L17d9PH1Ov1LZ9vfn5+3e1ffvnlltsGAAAAuktY0wabCV6avW8WFha2fL4DBw5s+VgAAAAg3QyDaoOtBC+P07MGAAAA2Ln0rGmD5jw0nXLnzp11t3/55ZfxwgsvdKgaAAAAoJ2ENW2wkYmF26m/v7+j5wMAAAA6xzCoNti/f/+G920Of+p0wAMAAABsD8KaNmgGLxuZh6Y5v002m02wIgAAAGC7Eta0wfLg5VGBTXO7sAYAAABYjbCmDY4cOdL6/lErQ9VqtYiIKBQKidYEAAAAbE/CmjbIZDKRy+Ui4u9hzGqWz1eTz+c7URoAAACwzQhr2mRiYiIiIqanp9fc59KlSxERMTIy0pGaAAAAgO1HWLMBG5k4uFgsRrFYjKmpqTV715RKpchkMlEqldpcIQAAALBTCGvWUK1WY3Z2NiIiZmZm1h3e1HThwoXI5/NRKBRW7F+v11tz1MzNzSVTMAAAALAj7Op2AWlSKBRidnb2oZ40tVotBgcHI+Kb+WZeffXVmJycfOj4TCYTlUolyuVyjI6OrljSu1AoxNjYWNI/AgAAALDN9TQajUa3i6C95ufn48CBAxERcefOnejv7+9yRQAAALDzJPX8bRgUAAAAQIoIawAAAABSRFgDAAAAkCLCGgAAAIAUEdYAAAAApIiwBgAAACBFhDUAAAAAKSKsAQAAAEgRYQ0AAABAighrAAAAAFJEWAMAAACQIsIaAAAAgBQR1gAAAACkiLAGAAAAIEWENQAAAAApIqwBAAAASBFhDQAAAECKCGsAAAAAUkRYAwAAAJAiwhoAAACAFBHWAAAAAKSIsAYAAAAgRYQ1AAAAACkirAEAAABIEWENAAAAQIoIawAAAABSRFgDAAAAkCLCGgAAAIAUEdYAAAAApIiwBgAAACBFhDUAAAAAKSKsAQAAAEgRYQ0AAABAighrAAAAAFJEWAMAAACQIsIaAAAAgBQR1gAAAACkiLAGAAAAIEWENQAAAAApIqwBAAAASBFhDQAAAECKCGsAAAAAUkRYAwAAAJAiwhoAAACAFNnV7QIAAGBpaSnm5+fj66+/jqeffjr6+/ujt7e322UBQFfoWQMAQFc0Go24fv16FIvF6Ovri0OHDsUPfvCDOHToUPT19cXw8HBcv349Go1Gt0sFgI4S1gAA0HHVajWee+65OH78eFy+fDkePHiwYvuDBw+iXC7H8ePH47nnnotqtdqlSgGg84Q1AAB0VKVSiRdffDE++eSTDe3/ySefxIsvvhiVSiXhygAgHYQ1AAB0TLVajZMnT8b9+/c3ddz9+/fj5MmTetgA8EQQ1gAA0BGNRiNee+21TQc1Tffv34+f/OQn5rABYMcT1gAA0BHvv//+hoc+reXPf/5zfPDBB22qCADSSVgDAEBH/Pa3v01VOwCQVsIaAAASt7S0FFevXm1LW1euXImlpaW2tAUAaSSsAQAgcfPz8w8tz71VDx48iC+++KItbQFAGglrAABI3Ndff93W9r766qu2tgcAaSKsAQAgcU8//XRb29uzZ09b2wOANBHWAACQuP7+/njqqafa0tauXbvimWeeaUtbAJBGwhoAABLX29sbJ0+ebEtbJ0+ejN7e3ra0BQBpJKwBAKAj3njjjVS1AwBpJawBAKAjjh07FocOHXqsNp599tl46aWX2lQRAKSTsAYAgI7o6emJ3//+97F79+4tHb979+545513oqenp82VAUC6CGsAAOiYXC4XV69e3XRgs3v37rh69WrkcrmEKgOA9BDWAADQUYVCIT788MMND4l69tln48MPP4xCoZBwZQCQDsIaAAA6LpfLxccffxzXr1+PYrH40LLeu3btiuHh4bh+/Xr86U9/0qMGgCfKrm4XAADAk6mnpyeOHTsWx44di6Wlpfjiiy/iq6++ij179sQzzzxjeW4AnljCGgAAuq63t1c4AwD/j2FQAAAAACkirAEAAABIEWENAAAAQIoIawAAAABSRFgDAAAAkCLCGgAAAIAUEdYAAAAApIiwBgAAACBFhDUAAAAAKSKsAQAAAEgRYQ0AAABAighrAAAAAFJEWAMAAACQIsIaAAAAgBQR1gAAAACkiLAGAAAAIEWENQAAAAApIqwBAAAASBFhDQAAAECKCGsAAAAAUkRYAwAAAJAiwhoAAACAFBHWAAAAAKTIrm4XAAAQEbG0tBTz8/Px9ddfx9NPPx39/f3R29vb7bIAADpOzxoAoGsajUZcv349isVi9PX1xaFDh+IHP/hBHDp0KPr6+mJ4eDiuX78ejUaj26UCAHSMsAYA6IpqtRrPPfdcHD9+PC5fvhwPHjxYsf3BgwdRLpfj+PHj8dxzz0W1Wu1SpQAAnSWsAQA6rlKpxIsvvhiffPLJhvb/5JNP4sUXX4xKpZJwZQAA3SesAQA6qlqtxsmTJ+P+/fubOu7+/ftx8uRJPWwAgB1PWAMAdEyj0YjXXntt00FN0/379+MnP/mJOWwAgB1NWAMAdMz777+/4aFPa/nzn/8cH3zwQZsqAgBIH2ENANAxv/3tb1PVDgBAGglrAICOWFpaiqtXr7alrStXrsTS0lJb2gIASBthDQDQEfPz8w8tz71VDx48iC+++KItbQEApI2wBgDoiK+//rqt7X311VdtbQ8AIC2ENQBARzz99NNtbW/Pnj1tbQ8AIC2ENQBAR/T398dTTz3VlrZ27doVzzzzTFvaAgBIG2ENANARvb29cfLkyba0dfLkyejt7W1LWwAAaSOsAQA65o033khVOwAAaSSsAQA65tixY3Ho0KHHauPZZ5+Nl156qU0VAQCkj7AGAOiYnp6e+P3vfx+7d+/e0vG7d++Od955J3p6etpcGQBAeghrAEiFpaWl+Mtf/hJ//OMf4y9/+UssLS11uyQSksvl4urVq5sObHbv3h1Xr16NXC6XUGUAAOkgrAGgaxqNRly/fj2KxWL09fXFoUOH4gc/+EEcOnQo+vr6Ynh4OK5fvx6NRqPbpdJmhUIhPvzwww0PiXr22Wfjww8/jEKhkHBlAADd19PwL+DElMvlmJycXPFaoVCIsbGxRM87Pz8fBw4ciIiIO3fuRH9/f6Lna4elpaWYn5+Pr7/+Op5++uno7++3yscTxmfgyVOtVuO1116LTz755JH7Hjp0KH7/+9/rUbEDNRqN+OCDD+I3v/lNXL16NR48eNDatmvXrjh58mS88cYb8dJLLxn6BACkTlLP37va0goPKRQKsbCwENPT05HNZiMiol6vx+nTp2NwcDAqlUrr9SdVo9GI999/P37zm9/Ef/zHf6z4B/pTTz3V+gf6sWPH/AN9h/IZeHJVKpU4efJk3L9/f0P7f/LJJ/Hiiy/G1atX9azYYXp6euLYsWNx7NixWFpaii+++CK++uqr2LNnTzzzzDNCWwDgiaRnTQKGh4djZmYmbt++HZlM5qHtQ0NDUa/X49atW4mcfzv0rPEbdXwGnlzVajVefPHFDQc1y+3evTs+/PBDnwUAAFIhqedvc9a02czMTJTL5RgZGVk1qImImJiYiFqtFuPj450tLiUqlUq8+OKLG3pIj/j7b9QrlUrCldEpPgNPrkajEa+99tqWgpqIiPv378dPfvITc9gAALCjCWvarDlHzXrd9IvFYkRETE1NdaSmNKlWq5sa+tB0//79OHnyZFSr1YQqo1N8Bp5s77///oZDurX8+c9/jg8++KBNFQEAQPoIa9qsXC5HRDxyPppMJhP1ej1mZmY6UVYq+I06PgP89re/TVU7AACQRsKaNloevDwqrGluf5J6CfiNOj4DT7alpaW4evVqW9q6cuVKLC0ttaUtAABIG2FNGzWDl7Xmqlmur68vIiJu3LiRZEmp4jfq+Aw82ebn51es+PU4Hjx4EF988UVb2gIAgLSxdHcb3b17d9PH1Ov1TR8zPz+/7vYvv/xy020mLYnfqFvOdXvxGeDrr79ua3tfffVVW9sDAIC0ENa00WaCl2bvm4WFhU2fp7ks2HaSxG/UPahvLz4DPP30021tb8+ePW1tDwAA0sIwqDbaSvCylZ4125HfqOMzQH9/fzz11FNtaWvXrl3xzDPPtKUtAABIGz1r2qg5D03S7ty5s+72L7/8Ml544YWO1LJRfqOOzwC9vb1x8uTJ1qp5j+PkyZN6VgEAsGMJa9poIxMLt0N/f39HztNOzd+ot2MYjN+ob08+A0REvPHGG20Ja9544402VAMAAOlkGFQb7d+/f8P7Noc/dSrg6bbmb9TbwW/UtyefASIijh07FocOHXqsNp599tl46aWX2lQRAACkj7CmjZrBy0bmoWnOb5PNZhOsKF3a9Ztwv1HfvnwG6Onpid///vexe/fuLR2/e/fueOedd6Knp6fNlQEAQHoIa9poefDyqMCmuf1JCmv8Rh2fASIicrlcXL16ddOBze7du+Pq1auRy+USqgwAANJBWNNGR44caX3/qJWharVaREQUCoVEa0oTv1HHZ4CmQqEQH3744YbDu2effTY+/PDDJ+rPTAAAnlzCmjbKZDKt3/g2w5jVLJ+vJp/Pd6K01PAbdXwGaMrlcvHxxx/H9evXo1gsPrSs965du2J4eDiuX78ef/rTn1x7AACeGMKaNpuYmIiIiOnp6TX3uXTpUkREjIyMdKSmtPEbdXwGaOrp6Yljx47F9PR0LCwsxF/+8pf4v//3/8Zf/vKXuHv3bly6dCmOHTumNxUAAE+Unkaj0eh2ETvN8PBwlMvluHXr1qpz0gwODsbCwkIsLi4mcv75+fk4cOBARETcuXMntUt9NxqN+OCDD+I3v/lNXL16dcWSzrt27YqTJ0/GG2+8ES+99JIHtR3KZwAAANjOknr+3tWWVljhwoULUa/Xo1AoRKVSaQU29Xo9hoeHIyJibm6umyWmQvM36seOHYulpaX44osv4quvvoo9e/bEM888Y2nmJ4DPAAAAwMOENQnIZDJRqVSiXC7H6OjoiiW9C4VCjI2NdbfAFOrt7fVg/oTzGQAAAPiGsCZBxWIxisVit8sAAAAAthETDAMAAACkiLAGAAAAIEWENQAAAAApIqwBAAAASBFhDQAAAECKCGsAAAAAUkRYAwAAAJAiwhoAAACAFBHWAAAAAKSIsAYAAAAgRYQ1AAAAACkirAEAAABIEWENAAAAQIoIawAAAABSRFgDAAAAkCLCGgAAAIAUEdYAAAAApIiwBgAAACBFhDUAAAAAKbKr2wXQfv/93//d+v7LL7/sYiUAAACwcy1/5l7+LP64hDU70N/+9rfW9y+88EIXKwEAAIAnw9/+9rc4ePBgW9oyDAoAAAAgRXoajUaj20XQXv/1X/8VH3/8cURE/NM//VPs2pX+DlRffvllqxfQH//4x/jOd77T5YrYLNdw+3MNtzfXb/tzDbc/13D7cw23N9dv+9uO1/C///u/W6NbnnvuufjHf/zHtrSb/qd4Nu0f//Ef4+jRo90uY8u+853vRH9/f7fL4DG4htufa7i9uX7bn2u4/bmG259ruL25ftvfdrqG7Rr6tJxhUAAAAAApIqwBAAAASBFhDQAAAECKCGsAAAAAUkRYAwAAAJAiwhoAAACAFBHWAAAAAKRIT6PRaHS7CAAAAAC+oWcNAAAAQIoIawAAAABSRFgDAAAAkCLCGgAAAIAUEdYAAAAApIiwBgAAACBFhDUAAAAAKSKsAQAAAEgRYQ0AAABAighrAAAAAFJkV7cL4Mk1Pj4e5XI5arVaZLPZyOVycfTo0RgbG2vbOcrlckxOTq54rVAotPUcfGN4eLit1290dDQWFhbi1KlTkcvlIpvNRkREvV6PWq0WFy9ejHK5HJVKpbWNx9Pua9jkPkxWku+v+3DzOvF5d08lyz21M/g7bXvz78rtzXNemzSgw27dutXI5XKNycnJxuLiYqPRaDTm5uYaxWKxERGNbDbbmJube+zz5PP5Ri6Xa9y6dav12uLiYqNYLDay2eyK19m8xcXFxtzcXGNsbKyRyWQaEdEYGxtrW/vNz8N6X9PT020735Mo6WvYaLgPk5b0++s+3JxOfN7dU8lyT21f/k7b3vy7cmfwnNdewho6anFxcd0baGxsrPUH5uPcZMVisZHJZFp/SHxbLpdrZLPZLbf/pIuIRiaTaeTz+cbk5GQjl8t19C/VYrG4Y/4Q7pZOXUP3YXI68f66DzeuU9fDPZUc99T25e+07c2/K3cGz3ntJ6yho0ZGRh6ZWjfT9Fwut6VzVCqVR/4BPz09nchvXJ5U+Xw+kb9Up6enG5VKpVEqlRqlUqlRqVTW/IOZx9Pua+g+TFan3l/34cZ04nq4p5LlntpZ/J22vfl35fbkOa/9hDV0VCaTeeQ+pVKplbpWKpVNn6OZnD/q2GaKz+NL6i/VdnSTZGPafQ3dh8nq1PvrPtyYTlwP91Sy3FM7i7/Ttjf/rtyePOe1n9Wg6JhqtRr1ej16enpiZmZmzf1yuVzr+0qlsunzlMvliIhHTg6WyWSiXq+vWwuwNe7DZHl/06UT18M1T5b3l/X4fMD6POclQ1hDx9Rqtdb33565e7m+vr7W9/V6fVPnWH5DPuombm6vVqubOgewPvdhsry/6dKJ6+GaJ8v7y3p8PuDRPOclQ1hDx+Tz+cjlcpHJZGJ0dHTN/RYWFlrfDw4ObuoczRsyk8k8ct/mHxY3btzY1DmA9bkPk+X9TZdOXA/XPFneX9bj8wGP5jkvGbu6XQBPjkwmE3Nzc4/cb3kCuryr3EbcvXt303VtNtWls2q1WoyPj0fEN9dqYWEh8vl8TExMbOgPazrPfZisbry/7sO1deJ6uKeS5Z5iPe6/ncW9lwzPeckQ1pA6za5zuVwu8vn8po7dzA3Z/AN5ecJLupw9ezbq9XpMT0+v+At0eHg4hoaGolKpPLIbJJ3nPkxWp99f9+H6OnE93FPJck+xHvffzuHe6z7PeZtjGBSpMjMz0xrzeOHChU0fv5UbcrsnrjtdpVJ56Dcd09PTEfFN90nXL33ch8nqxvvrPlxbJ66HeypZ7inW4/7bWdx73eM5b/OENaRKc4xjqVTadNe4iJWTVrG9lUql1l+eqykWixERra6spIf7MFmdfH/dh4/WievhnkqWe4r1uP92Bvde93nO2zxhDakxOjoatVotSqVSjI2NbakNY013jkd1Qy0UChERMTU1te1T853GfZisTr6/7sNH68T1cE8lyz3Fetx/O4N7r7s8522NsIaI+KaLWE9PT9u+hoaGNnX+qampmJqaeqwbOCJi//79G963+QfxTrnxu30NO235X7qzs7NdrKR9dso1dB8mew3T9P7uxPtwszpxPdJ0zXeiNL2/7qn0SdPng+S495LjOW/rTDBMRHzzQa5UKm1LkjczOdfMzEyMjo7G5ORkjIyMPNZ5mzfkRn6O5rjHnTKRWDevYTcs7wpZrVY3PUlZGu2Ua+g+TPYapun93Yn34WZ14nqk6ZrvRGl6f91T6ZOmzwfJce8lw3Pe4xHW0NKNP5Sq1WoMDw/H9PR0a6zo41h+Q9br9XXT1OaNvt1v4uV2wl8s9Xo9Tpw4EfV6fcOz8t+6dasDlXXGTriG7sNkr2En3t8n/T7cjE5cjyf9nkqae4r1uP+2N/de93jOe3yGQdE1tVotTpw4seYNXK/XY2ZmZlNtHjlypPX9o2YMb85G3hyjSjrMzMxEtVqNWq0W5XJ5zf2WX9/BwcFOlMYGuQ+T1Yn31324cZ24Hu6pZLmnWI/7b3tz73WH57z2ENbQFfV6PQqFQly7dm3N30LPzs5GtVrdVLuZTKY1u3jzJl3r/M39d0JPhp2kef1yudy6Kfzy6+sapov7MFmdeH/dhxvXievhnkqWe4r1uP+2N/de53nOax9hDR3X7I44PT297rJtlUplze3rjVWcmJiIiFh3eb5Lly5FRDz22Em2bq1rmM1mI5fLxdzc3LpdFyuVSkR8s9TiVpb/4/G5D7unXe+v+7A9kr4e7TwHq3NP4f7b3tx76eA5r80a0GG5XK4xNjbWmJ6eXvOrVCo1MplM49atWw8dXywWGxHRKBaLa56juc9qxzcajUY2m21kMpm2/UxPunw+34iIxsjIyIb2f9Q1rFQq67Y1NzfXiIhGJpNpLC4ubqVkvqXd13D5Pu7DZDzu++s+bK+kr0c7zsH63FM7h7/Ttjf/rty+POe1l7CGjmr+4bvRr9U8anuj0WgsLi428vl8I5vNrriR13qdrZubm2tkMplGRGz4fd3INSyVSo18Pt+oVCqt1xYXFxuTk5ONiGjk83nXsE2Suobuw2Q97vvrPmyvTlwP91Sy3FM7g7/Ttjf/rty+POe1n7CGjpment7UDZzL5VZtp5nGlkqlDZ0zn883isVio1gsNvL5/IaOY335fL71F+laX5lMZs3fYmz0Gt66dasxMjLSyOVyjUwm08hms41isdiYnp5O4sd6onTqGjYa7sOkbfX9dR8mI+nr8TjnYGPcU9uPv9O2N/+u3P485yWjp9FoNAIAAACAVDDBMAAAAECKCGsAAAAAUkRYAwAAAJAiwhoAAACAFBHWAAAAAKSIsAYAAAAgRYQ1AAAAACkirAEAAABIEWENAAAAQIoIawAAAABSRFgDAAAAkCLCGgAAAIAUEdYAAAAApIiwBgAAACBFhDUAAAAAKSKsAQAAAEiRXd0uAADYnur1epw4caLt7U5MTESxWGx7u2l27ty5OHv2bNvaW1xc3NB+1Wo1Jicno1artV5bWFiII0eOxPj4eGSz2Ziamorp6enW9mw2G5OTk22rFQB4mLAGANiSWq0W1Wo1IiJyuVycOnUqstlsZDKZ6Ovri4iI2dnZGB0dbR0zNjYWhUIh+vr6YmFhodXO9PR0zMzMRETEjRs3nriwZmRkJIrFYtTr9Th79myUy+WIiMhkMnHt2rUV72nTwsJC1Ov1qNVqUalUYmpqqrWtXq9HJpNZ83y1Wi0KhUIsLCxEqVR6KHwZHR2NoaGhyGazUavVYm5uLrLZbIyOjrbOI7ABgOT0NBqNRreLAAC2n5mZmSgUCjE2NhalUmnN/YaGhlqhzq1btyKbza7b3sjIyBMdBNRqtRgcHIyIb0Kwubm5DR3X7OlUrVZjbm4ucrncqvtVq9VWj6hmCLPW+SMiKpVK5PP5iIgYHByMWq0W+Xw+KpXKpn+2JI2Ojj7RnxsAdhZz1gAAW1Kv1yObza4b1Hzbt3uHLJfP52NkZKTV4+ZJtd57tJ5MJtMarrR8WNO3DQ8PR71ej+np6VWDs2w223o9k8m0gpqIiOnp6SiVSiuGRaXFpUuXul0CALSNsAYA2JJardb24UrNIIGtyWazUSwW1wxrarVaa9vyEObbmkPXjhw5suL1XC4XY2Nj6w6x6hafGwB2EmENALAld+/ejaNHj7a1zXw+v26vEB7t1KlTcevWrVW3Nd/bR4Utd+/e3dB+adGc7wgAdgphDQCwJc1hUKRLLpdr21CyrQ7J6jRz1QCw0whrAIAtWVhYSCSs2S69ObqtXq+3Vo1arrmC05NiZmZm1fcBALYzYQ0AsCWjo6OJBCubmbD4SbawsBAXL15cddvy5dJ3smq1GoVCodtlAEDbCWsAgC1Zb4LaxzE5ORmFQiGGhoZi3759MTQ01No2Ojra2jY8PLziuPHx8da2wcHB6OnpaS0Z3lSv12N4eHhF+/v27dtQXeVyuXVcoVCIwcHBGB0d7VovlvXOOzIy0vq+Xq9HT09P9PT0tIKN5a+t9nXu3LmIiJiamlrx+vJr8W2P+/7UarUYHx+PwcHBGBwcbLWzWq+ZmZmZ1j7LrfazmHgYgO1IWAMApMrRo0dbQ3mWP2gXCoUYHh6OSqUS1Wo1yuVyK1SIiBgcHIxcLrdixaNvy2Qyrfar1WrU6/VHPszX6/UYGhqK06dPx+joaMzNzUWlUmlN4js4ONiVYTgbXT47k8lEo9GIRqOx4pjma6t9NcOefD6/4vW5ubmH2m/H+zM1NRWDg4NRq9Vibm4ubt26FXNzczE9PR2VSuWh3jP5fD5u3bq1oZ/JsDoAtiNhDQCQKmNjYzE5ObliONS5c+didHR03d48IyMjUSqVHhlirNb+eoaGhqJarcbc3NxDS5VPTk5GNpuN4eHhjq1I1OyBMjU1teljNxpcbCbgeNz3p3lti8ViTE9Przh3JpOJycnJGB4eXnNolzAGgJ1IWAMApFJz8uJ6vR6VSmVFEFCpVKJUKsXY2Niaxz3KRh7yz507F7VaLUZGRtZst7kS0fj4+IbOuxnVavWhYT2Dg4MrehR10+O+P83gKZPJrBmylcvlLYdTALBdCWsAgFSr1WoPzU+Tz+dXDWo241HLUtfr9VbAsN6Evc3ePtVq9aE5ch5XLpd7aFjP4uJiKiZhbsf70zxu+Rw73zY5Odkaqmb+GQCeFMIaACD1jhw50vFznj17tvV9Lpdbd9/m9tnZ2URrivimR9DY2Nhjh1WP63Hfn2q12hoatd6KTuPj45HNZmNkZMSQJwCeGMIaACD1Njq0qZ2aQcJGAoJmfc1JdTvh1KlTHTvXah73/Vm+7Ph6YVxzMuHmcCoAeBLs6nYBAACP0o0eFctXlPr2MKzVFIvFOHr0aJIlrfCo92RoaGjV1Zva5XHfn+XH6zEDACsJawAA1tHX17fhZbI7KZvNxoULF1bdVq1WOzZ0bKvvj/lnAGBthkEBAE+khYWFdbc3h+48ar9uWqtHyuzsbAwODiZ67sd9fzrRm6Zer2+o1w8ApI2wBgB4Ij2qZ0dzFaPt2AOkUqk8ctLfx/W478/ySYXbvYoWAGx3whoAYEd51JLcTY+aDHj5ctTNyXTXU6vVolwub+jcSSuXy4lPyvy478+rr776WMdvRJp7RQHAeoQ1AMCOstHhNZcuXVp3ezabjVKpFBHR+u96RkdHUzFR7vj4eEQkv4LW474/mUymtcLTRlZ6ai7hvVodTcsnLW7+fzdWEgOAxyWsAQA6plM9HZpDgGZnZ1fdfu7cuRU9O9YyNjYWuVwuZmZm1u3VMTU1FdlstjU06HE8zns0NTUV586de+yAYqNDmx73/RkZGYl8Ph+1Wm1FT51va/a8WW1oVzabbf283+6hU6lUVgy3AoDtQlgDACSiXq/H1NTUivlISqXSQ70f1jp2+QpDU1NTm5ob5cKFC5HJZFY937lz5yJi5XLThUIhpqamVh2OMzc3F2NjYzE8PByjo6Mr6qjX6zE+Ph5zc3Mb6h3yKPV6fUU7tVpt3ferXq9HtVqNqampGBoaagUeq4Ua335P1wpXarVa632YnZ2NarW67nv/uO9PpVKJUqkUU1NTUSgUHvp5p6amolQqrbviVHPb+Ph46/zVajWq1WpbAjQA6LSeRqPR6HYRAMDOMDo6GlNTUxveP5PJxOLi4orXenp61j1mcnIyRkZGHtl2vV6Ps2fPtoKHvr6+WFhYiImJiSgWizEzMxOFQiEymUxks9no6+uLQqEQY2Njq7ZXrVZjcnKy1V4mk4m+vr4YHR2NYrG4kR93TePj460QqR3GxsZWDE1a7z3N5/NRqVRa78d6FhcX1xzq9bjvz1rHDw8Pb+h6V6vVGB8fj1qt1rqmaVxyHQA2QlgDAAAAkCKGQQEAAACkiLAGAAAAIEWENQAAAAApIqwBAAAASBFhDQAAAECKCGsAAAAAUkRYAwAAAJAiwhoAAACAFBHWAAAAAKSIsAYAAAAgRYQ1AAAAACkirAEAAABIEWENAAAAQIoIawAAAABSRFgDAAAAkCLCGgAAAIAUEdYAAAAApIiwBgAAACBFhDUAAAAAKSKsAQAAAEgRYQ0AAABAighrAAAAAFJEWAMAAACQIsIaAAAAgBQR1gAAAACkiLAGAAAAIEX+fyc9vHVyy4o9AAAAAElFTkSuQmCC", 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" ] @@ -225,12 +227,12 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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uSJLpdBp7e3sR8et6I3/+85+j1+v97vpFUcRoNIrhcBjtdvujjxRuNptxdHR010MGAAAAWLjzWDIaje7kduan5AAAAAA8pEot8AoAAACwamIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJI9WPQAA2Cbf/vDXVQ/hxv75lz+tegg3to7/nwGA6nBkCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABA8mjVA7hKv9+PwWAQ5+fni+81Go149epVFEWxuoEBAAAAG6myR5aUZRnNZjPKsozBYBAnJyeLf19//XU8ffo0hsPhqocJAAAAbJjKHllycHAQnU4nGo3G7y47OjqKer0ezWYzTk5Ool6vr2CEAAAAwCaq5JElw+Ewzs/PPxtK5hqNRjQajfjxxx8fcGQAAADApqtkLBmNRlGr1a7crtlsxng8foARAQAAANuikrFkOp3GeDyOsiyXbvfhw4drRRUAAACA66pkLKnX61GWZTx//nxpMBkOh0tP1QEAAAC4qUrGkna7HRERk8kkdnd3o9/v/26bTqcTERHdbvdBxwYAAABstkrGklqt9lEEabfbsb+/H9PpNCJ+DSXj8ThOTk5WNUQAAABgQ1X2o4OPjo6iVqvFwcFBRPx6lMne3l7UarXodDq3PqLk/fv3Sy8/Ozu71e0DAAAA66mysSQiotVqxcnJSezv7y++N51Oo9vtxnfffRf1ev2Lb/vJkyd3MUQAAABgw1TyNJy54+PjePnyZYxGoxgMBlEURUT8Gkz29/fj+Ph4tQMEAAAANk5ljyw5ODiIsiw/Wpek0WhEp9NZLPja6XTiw4cPX3RKzrt375ZefnZ2Fs+ePbvx7QIAAADrrZKxpN1ux2QyidPT04++XxRF9Hq9aLfbi48VPj4+jhcvXtz4lJzHjx/f5ZABAACADVG503Cm02n0+/2lR4vU6/X45ZdfFoGk1+s91PAAAACADVe5WDIcDiPi18VdlymKIt68eRMREW/fvr33cQEAAADboXKx5PT0dLGQ61WKorjVJ+IAAAAAfKpysaQoiijLMsqyvPZ1arXa/Q0IAAAA2CqViyXtdjsiYvGJN8uUZRmTySRevXp138MCAAAAtkTlYkmtVoterxedTifG4/HSbZ8/fx7dbtepOAAAAMCdqVwsiYg4PDyMbrcbzWYz2u12TKfTxWVlWUa/34+9vb148eJFHB0drXCkAAAAwKapZCyJiDg6OoqLi4soiiIODg5id3c3dnd34/nz53F6ehonJydCCQAAAHDnHq16AMsURRHdbnfVwwAAAAC2SGWPLAEAAABYBbEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIHm06gEAANX27Q9/XfUQAAAelCNLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAACStYglZVnG8fFx7O/vx97eXuzv78f+/n4Mh8NVDw0AAADYMJWPJf1+P54+fRofPnyIN2/exOnpaZycnMSbN2/ip59+in6/v+ohAgAAABvk0aoHsMzx8XF0Op0YjUbRaDQ+umw8Hi/+HR4ermiEAAAAwKapbCwZDofR6XTi6Ojod6EkImI0GkVZlg8/MAAAAGCjVTKWlGUZL1++jIiIV69efXabTqcTERHNZvPBxgUAAABsvkrGkh9//DHKsoxWqxVFUXx2m1qtFr1e72EHBgAAAGy8Si7wOl+01VEjAAAAwEOr3JEl0+l0sRbJd999t/jecDiM09PTKIoiXrx4EfV6fYWjBAAAADZV5WLJeDxefF0URfT7/Tg9PY12ux21Wi0mk0m8fPkyarVavH79+tLTdK7y/v37pZefnZ190e0CAAAA661yseT09HTx9WQyidPT0+h2u4vv1ev1ePPmTTx9+jSePn0av/zyyxcFkydPntzFcAEAAIANU7lYkj8OuNfrxWg0+t02RVFEt9uNdrsdnU7HQq8AAGysb3/466qHcGP//MufVj0EgFupXCzJlq1L0mg0IuLXxWDb7faN1zB59+7d0svPzs7i2bNnN7pNAAAAYP1VLpbkU2qWfRpOrVZbfN3r9W58dMnjx49vPDYAAABg81Xuo4O//vrrxdc5iHzOPKy8ffv2PocEAAAAbJHKxZKrAkn21VdfRcTH65wAAAAA3EblYklee+SqCHJ+fh4R8cUfHwwAAADwqcrFklqttogf0+n02tcBAAAAuAuViyUREX/+858jYnksKctyceTJixcvHmJYAAAAwBaoZCzpdDoREfHTTz9dus14PI6IX0/babVaDzIuAAAAYPNVMpbUarU4OjqKyWQSw+Hws9vMg8pgMHjIoQEAAAAbrpKxJCKi2+3G4eFhHBwcRL/fX3x/Op1Gs9mMiIiTkxPrlQAAAAB3qrKxJCKi1+vFaDSKwWAQu7u7sbu7G81mM+r1epyenn70yTkAAAAAd+HRqgdwlUajEY1GY9XDAAAAALZEpY8sAQAAAHhoYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABA8mjVAwAAYD19+8NfVz0EALgXjiwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjWNpZMp9PY3d1d9TAAAACADbO2seTg4CDKslz1MAAAAIANs5ax5Pj4OCaTyaqHAQAAAGygtYsl0+k0er3eqocBAAAAbKi1iyWdTie63e6qhwEAAABsqLWKJf1+P5rNZtRqtVUPBQAAANhQaxNLyrKMwWAQh4eHqx4KAAAAsMHWJpa8fPnSWiUAAADAvXu06gFcx3g8jlqtdqen37x//37p5WdnZ3e2LwAAAGB9rEUs6Xa7MRqN7vQ2nzx5cqe3BwAAAGyGyseSdrvt02/gjnz7w19XPYQb++df/rTqIQAAVJbnd3A/Kh1LJpNJRETU6/U7v+13794tvfzs7CyePXt25/sFAAAAqq3SsaTT6dz56Tdzjx8/vpfbBQAAANZbZT8Np9PpRLvdXvUwAAAAgC1TyVgynU5jOp1Gq9Va9VAAAACALVPJ03Da7XYMBoNVDwMAAADYQpU7sqQsyxiPx7G7uxs7Ozuf/be/v7/YPn9/OByucOQAAADAJqjckSVFUcRsNlu6zXA4jIODg4iIK7cFAAAAuInKHVkCAAAAsEprGUvOz89XPQQAAABgQ61lLDk5OVl8PR6PVzgSAAAAYNOsTSzpdDqLRV/7/f7i+81mM3Z2dmJ3dzem0+kKRwgAAABsgrWJJd1uNy4uLmI2m33238XFRdRqtVUPEwAAAFhzaxNLAAAAAB6CWAIAAACQiCUAAAAAiVgCAAAAkIglAAAAAIlYAgAAAJCIJQAAAACJWAIAAACQiCUAAAAAiVgCAAAAkIglAAAAAIlYAgAAAJCIJQAAAACJWAIAAACQiCUAAAAAiVgCAAAAkIglAAAAAIlYAgAAAJCIJQAAAACJWAIAAACQiCUAAAAAiVgCAAAAkIglAAAAAIlYAgAAAJCIJQAAAACJWAIAAACQiCUAAAAAiVgCAAAAkIglAAAAAIlYAgAAAJCIJQAAAACJWAIAAACQiCUAAAAAiVgCAAAAkIglAAAAAIlYAgAAAJCIJQAAAADJo1UPAAAA2Czf/vDXVQ/hi/zzL39a9RCoqHW8T7s/344jSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAErEEAAAAIBFLAAAAABKxBAAAACARSwAAAAASsQQAAAAgEUsAAAAAksrHkk6nE3t7e7GzsxN7e3txcHAQx8fHqx4WAAAAsKEqG0um02ns7+/H3t5enJycxGw2i8FgEBG/BZTJZLLiUQIAAACbppKxpCzLaDabMRgM4vDwMIqiiIiIer0eg8Egjo6OFjFlOp2udrAAAADARqlkLOl0OtHtdqNWq3328m63uwgoBwcHDzgyAAAAYNNVMpb8/PPP0Wq1lm7z6tWriIiYTCYxHo8fYlgAAADAFqhcLJlMJlGWZezs7CyNIPV6ffH1aDR6iKEBAAAAW6BysSSvQdLr9S7d7quvvlp8XZblfQ4JAAAA2CKViyWNRiPq9XoURRHtdvvS7c7Pzxdf7+3tPcTQAAAAgC3waNUD+FRRFHFycnLldvljg/MpOdf1/v37pZefnZ3d+DYBAACA9Ve5WHJd81N06vV6NBqNG1//yZMndz0kAAAAYAOsZSwZj8eLtU1ev3694tFwF7794a+rHsKN/fMvf1r1EKiwdbxPAwAAv1rLWDJfy6Tb7X7RKTgREe/evVt6+dnZWTx79uyLbhsAAABYX2sXS9rtdkyn0+h2u3F0dPTFt/P48eM7HBUAAACwKSr3aTjL9Pv96Pf7tw4lAAAAAJdZmyNLxuNxtNvt6PV6cXh4uOrhAAAAABtqLWLJZDKJg4ODGAwG0Wq1Vj0cAAAAYINV/jSc6XQaz58/vzSUlGUZ4/F4BSMDAAAANlGlY0lZltFsNuPNmzfRaDQ+u83bt29jMpk88MgAAACATVXZWFKW5eKIkmUfDzwajb7444MBAAAAPlXZNUueP38ejUYjptNpTKfTz24znU6j3+9Hu91+4NEBAAAAm6qSsaTZbMZkMrn26TW1Wu2eRwQAAABsi8qdhjMcDm+0YKtTcAAAAIC7VLkjS1qtVsxms1UPAwAAANhSlTuyBAAAAGCVxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACA5NGqB8Dd+/aHv656CHBn3J8BgIfiecfD8P+ZdeDIEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACARCwBAAAASMQSAAAAgEQsAQAAAEjEEgAAAIBELAEAAABIxBIAAACA5NGqB3CV4XAYvV7vo+81m804Ojpa0YgAAACATVbpWNJsNuP8/DwGg0HUarWIiCjLMl6+fBl7e3sxGo0W3wcAAAC4C5U9Defg4CDevn0bb968+SiIFEURg8EgiqKIZrO5whECAAAAm6iSsWQ8HsdwOIzDw8MoiuKz27x69Sqm02l0Op2HHRwAAACw0SoZS+ZrlCw7cqTVakVERL/ff5AxAQAAANuhkrFkOBxGRFy5HklRFFGWZYzH44cYFgAAALAFKhdLcvi4KpbML59MJvc6JgAAAGB7VC6WzMPHZWuVZF999VVERPz973+/zyEBAAAAW6RyHx384cOHG1+nLMsbX+f9+/dLL3/37t3i67Ozsxvf/ir95//7f1c9hK1w1X2oitw3AABgO6zT65X8mvs///nPCkfym8rFkpuEj/nRJ+fn5zfez5MnT6697bNnz258+2y+J//PqkcAAADweev6euVf//pXfPvtt6seRvVOw/mS8PElR5YAAAAAfE7ljiyZr0Ny3/JpNp/z73//O/7v//2/8d///d/xhz/8IR49qtz/qt85OztbHAXzt7/9Lb755psVj4jbMJ+bw1xuFvO5Wczn5jCXm8V8bg5zuVnuaz7/85//xL/+9a+IiPjjH/94J7d5W5UrANdZ2PUuPH78+Mpt/ud//ucBRnI/vvnmm2v9jKwH87k5zOVmMZ+bxXxuDnO5Wczn5jCXm+Wu57MKp95klTsN5+uvv772tvPTbx4qsAAAAACbr3KxZB4+rrMOyXx9k1qtdo8jAgAAALZJ5WJJDh9XBZP55WIJAAAAcFcqF0u+++67xddXfTLOdDqNiIhms3mvYwIAAAC2R+ViSVEUUa/XI+K3GPI5eb2SRqPxEEMDAAAAtkDlYklExKtXryIiYjAYXLrNzz//HBERh4eHDzImAAAAYDtUMpa0Wq1otVrR7/cvPbqk2+1GURTR7XYfeHQAAADAJtuZzWazVQ/ic8qyjIODg5hOpzEajRaLuF72fQAAAIC7UNlYMjccDqPX6330kcLNZjOOjo5WOzAAAABgI1U+lgAAAAA8pEquWQIAAACwKmIJAAAAQCKWAAAAACRiCQAAAEAilgAAAAAkYgkAAABAIpYAAAAAJGIJAAAAQPJo1QPYNMPhMHq93kffazabcXR0VJl9dDqdGA6HMZ1Oo1arRb1ej++///6LxnhwcPDF110H2zCfd3l/qLJNn8uyLOPHH3+MyWQSERHn5+cREdFoNOLVq1dRFMXNfpiK2/T5vMx0Oo39/f24uLj44tuomk2ey3a7Hefn5/HixYuo1+tRq9Ui4tfH63Q6jZ9++imGw2GMRqPFZetuk+czK8sy+v1+/PTTT1GW5eJ37KtXr6LVal37dqpuU+dzPB5Hp9OJ169fR61W27i/kZ+zqXOZ9fv9GAwGi+dAEZ4HrXIft53P+f6n0+nie+12+2Ffo8y4M41GY1av12enp6eL711cXMxardasVqt99P1V7OP09HRWr9dnvV5vdnFxMZvNZrOTk5NZq9WaRcSsVqvNTk5Olu7/4uJidnJyMjs6OpoVRTGLiNnR0dGtf64q2vT5vIv7w7rY9LkcjUazer0+GwwGH33/5ORk8Tjt9Xq3+wErZNPnc5l6vT7bpD/dmz6X8+2W/fv0cbvONn0+53q93qwoitnR0dHidvI4NuX37SbPZ6/Xu/Kx+em/RqNx6593VTZ5Luf7aTQas263+9Fjcjabzbrd7qwoCr9rH3AfdzWfn243v927+hmvY3Oeca1Yq9WaFUXxuwfo3HxiV7WPi4uLpXeso6OjxR+Dy7aJiFlRFLNGozHr9XqLJ+2bGEs2fT7v4v6wLjZ9Lud/OC4zGo0W1x+NRlf/MBW36fO5TLfbXVx3E2zDXC6LJa1Wa+1/v2bbMJ+z2W+Pw8/9Ph0MBrOiKGZFUVz9w1Tcps/n4eHhjWPJur6BtOlzOZv9+sJ+2XOc+XOhdZ3DbBvmc/579LL9F0Vx65/xujbjGdeKzR+Ay6LBYDC4VVi47T4ODw+vLKrzd6CXvfDKGo3GRsaSbZjP+7g/VNE2zOX8j86yd7w24V2x2Ww75vMyp6ens1qttjGxZFvmstVqzQaDwWw0Gs263e6s2+3ORqPRpU8A19W2zOdVP0N+Ab7OtmE+6/X67PDwcNbr9WaDwWDpv3q9Put2uzf/IStgG+ZyPkdXaTQas1ardeV2VbYN8zn/PbrsMTeP1g/xuFzv3+YVMX/n6Kp3bedHZqxiH9fZb37X8jrvQG9qLNmG+byP+0MVbcNczh+Hywr9/EX2ur/buQ3zuWxc8ycn6/5CbDbbnrlstVob8U7mVbZhPi8uLhZP8C+LXaenp9d6oVB12zCf131X+uTkZK3faNiGuTw8PLxWBJmfjrPONn0+T09Pr3Uk1zzoPMR8+jScOzAcDiMirlycrSiKKMsyxuPxg+5jMplEWZaxs7OzdN/1en3x9Wg0uvEYN8Wmz+c23R82fS4jfl3oqiiKaDQal45hvtDZui8guQ3z+Tn9fj+azebaz1+2rXO5qbZhPn/88ccoyzJardalC0XWarXo9Xprv8Drps9nWZYfXXaZsizj4OAgBoPBldtW1abPZcSvC5+Px+Moy3Lp/j98+LD2f0c3fT7n+57f/mW+++67iIgv/hlvQiy5pTxBV92p5pfPP63iofaRVxD+dEXj7Kuvvlp8fdUvnE21DfO5LfeHbZjLiIhWqxUXFxeXvlAry3JxnUajsXSMVbYt8/mpsixjMBjE4eHh0u3WybbO5abalvns9/sR8esnQWyybZnPV69eXTnOg4OD6Ha7a/spKtsyl/V6PcqyjOfPny/9PTwcDj0Puud93HY+T09PP7vNp/Jj8qY/402JJbc0n6Dr/CKdT/rf//73B91Ho9GIer0eRVFEu92+9Lr5Y7b29vZuNMZNsQ3zuS33h22Yy+v4+eefF2O8zpPDqtrW+Xz58uXSJxzraFvnclNtw3xOp9PFE/r5O5rT6TSOj4+j3W5Hp9O59yfsD2Ub5rMoiiuPLDk+Po5arbbWRwltw1xGxOJ6k8kkdnd3F2Ez63Q6ERHR7XavHGdVbct8Xtd8jDf9GW/q0b3e+hb48OHDja9z03efbruPoiji5OTkyuvkP/TXOTxxE23DfG7L/WEb5vI6+5o/QRgMBmv77ljEds7neDyOWq229ocNf2ob53I6nS4ei2VZxvn5eTQajXj16tVaPy4jtmM+87utRVFEv9+P09PTaLfbUavVYjKZxMuXL6NWq8Xr16/Xek63YT6vc71er/fRu9zraFvmslarRbfbXfyObbfb0ev1YjAYRK1Wi06nE+Px+Fr7qbJtmM+bhJP5fvPRLPfBkSW3dJM74fyPZ65pVdlHxG+HS9Xr9bU+TO02zOfdXX/Vtnku5+dw7u/vR61Wi5OTk7Wcw2wb57Pb7a71u2CX2ba5/PHHH6Pdbsfr169jMBjEaDSKk5OTmE6nsb+/f+9P9O7bNsxnftE8mUzi9PQ0ut3uImTW6/V48+ZNjMfjePr06VqfkrUN83mVly9fbsTv3m2ay6Ojo4/WlplMJrG3t7f4d3JystYRM2I75jN/vexv40P+3RRLbulL7iA3/SP6EPsYj8eLO97r169vvL9NYT7v5vpVsI1zORwOo9lsxvPnz6PZbEZRFNHtdtfyyKBPbdt8ttvtjXiy/jnbNpcRvy5g9+kT9fkT+729vbV+cb0N85lvq9frffaxOf99m4/oW0fbMJ/L9Pv9KP93Id91t21z2Wq1fndUw3Q6jW63uxGnyW3DfOZ4smzh1rwQ7H3//RRLbmnZ4jPrtI/5eWWb8sLqS5nPu7l+FWzjXLZarcW71rPZLF6/fh3tdjt2d3fvfbXw+7ZN8zl/Ureuj72rbNNcdrvdpZ+kMX9Bts4vrrdpPiOWPy7nT/L7/f7avjjbtvn8VKfT2YhQErF9c3l8fBwvX76M0Wj00anH86P4jo+P732s92lb5nP+N/Onn3767PXLsozT09PFkX33fcSQWHJLD3FI133vo91uL8rr0dHRve6r6szn5twfzOWvT+pPTk6iLMtoNptr/URhm+az0+ls3KKu2TbN5VXrzcw/WWX+bvY62ob5zPtf9mk4eb7X9TG8DfN5meFwGGVZxosXL+5xdA9nm+by4OBg8WZRo9GIVqsVv/zyy0efJNfpdNY6TG/LfM7XPSn/96O7s/n3ut3u4iiY+w48Ysktff3119fedv5E6KZ3xPvcR7/fj36/v/YvjO/Kts/nJt0ftn0u54qiWDxZWOdPbNiW+ex0OktXkN8E2zKX15FfXL99+/ZWt7Uq2zCfef9XBbD5fs3navZxm8dnXkdhE2zLXLbb7ZhMJjEajT76flEU0ev1Plqv5Pj42POgFe3jJo/Ner2+OHpkf38/ms3m4pPH5kcNzfd/34vg+zScW5rfQa7zjtC8gN10Uu9rH+PxeLFidC6v22yb53PT7g/bPJefajabi4/S6/V6a/mO5zbM53Q6Xbzjssm2YS6vK78jNplM1nIh5m2Yz5uM96uvvoqyLNf+SKFNns/PKf93YfSHePf+oWzDXE6n0+j3+0tPd6zX6/HLL7/E8+fPF5905HnQw+7jSx+b13k+tOxov7vgyJJbyneQq+5YX1rA7mMfk8kkDg4OYjAYbMQL47uyrfO5ifeHbZjL+WrvV32iRn7yt67vdm7DfM4/MWXTbcNclmUZ+/v7sbe3d+1V+9f1Y0q3YT7zkQZX7X/+AmJdX3Rvw3x+zs8//3yt/ayTbZjL+UKfV60zUxRFvHnzJiI8D3rofdzHa4x8dNC9v8kw41YuLi5mETGLiNnp6enSbefbjUajle7j9PR0VhTFpdtcXFxca4yNRmMWEbOjo6Mrt10X2zifd3V/qJptmMvDw8PF7S57HI5Go8V29Xr9ip+qmjZ9PvO+b/pvMBjc6OdctU2fy9lsNhsMBovb7Xa7S2/3OttV2TbM52w2mxVFca3H23y7Vqu1dLuq2pb5/FSr1ZpFxKzRaFz9A6yJbZjLw8PDWVEU1x5vvV73POgB93FfrzG63e6DPV4dWXJLRVEs3nFY9u5RPq/rpgXsLvdR/u9Cj2/evLl0m7dv367t+Xy3tW3zucn3h22Yy/zO5ffff3/p/vPY1vEw/4jNn8+iKGI2my39lw8zzt9ft09u2PS5jPjtSIR6vb50fjw2H34fX/p3789//vO19j8fw7ouErot8/mp+SfGresRQZ+zDXNZ/O/aFVcdBZGt69FD2zCfEb8eNTJfh+Yy80/KeYgFe8WSO/Dq1auIiKXny80P71t2+NGyB/pd7KMsy3j+/HkMBoOli1eNRqONWdzqS2zLfG7D/WHT5/LFixdRFEWcnp4ufUGWx7bOi4du+nxuk02fy1qttvg0qmVPzOcLErZarbW+L2z6fEb89qT8so+zjPjtBfdVkazqtmE+LxvrQ3x06kPa9LmcP6eZr8u2TFmWMZlMFuNdR5s+nxG//q7t9/vx8uXLz15nPB7HZDKJVqv1IG8y7Mxms9m972ULHBwcxHA4/Ohzn7O9vb04Pz+Pi4uLpddvtVqX3jlvu4/9/f1oNBpXvgP9448/XvkEL+LXBXXG43EcHh6u5UJJy2zDfN71/aGqNn0uO51OlGV56WNwPB4vFr8aDAZr/QQ+YvPnc5l+v794YrgJf7o3fS7H43EMBoNLH5uTyST29/ejKIr45Zdf1v4d7U2fz4hff98eHx9f+rt0vkbNZeNbJ9swn3NlWcbu7m5EhOe0S65f1bmc/20cjUZLXzzv7+/Hixcv1v7THjd9Puf7Pjo6+t0Cr2VZxtOnT6NWq8XJycmlt32n7v1Eny1xcXExazQas1qt9tE5Xpd9/1ORzj+/j33M1xe57r+rnJycLM7LvepnW0ebPp93fX+osk2fy9lsNjs6OprV6/XZYDCYXVxcLL4/GAxmRVHMiqKY9Xq9S8e/TrZhPi+T16hZx3WEPrUNc9ntdmeNRuOj+bq4uJj1er3F+dab8vdzG+ZzNvvtcZh/p56eni72f3Jycul118m2zOd8f/PtNmkdvrltmMv5GhaHh4e/23+v15vVarW1XRfqU5s+nycnJ7N6vf7R79KLi4vFc9pGo/HRc937tt6vgipoMBjMGo3GrNVqzVqt1qzRaFzrwdntdmdFUVxr25vuIy80d51/ly181Gg0FoHksn9FUcwODw+v/BnWxSbO513dH9bNJs5ldnp6Ojs8PJzV6/VZrVabFUUxq9Vqs6Ojowf9o/JQNn0+546Ojpb+3i2KYu1fbG/6XObH5vxx2Wq11m5h3uva9PmczX5dNHv+nCj/rt1E2zCfs9lvz3E3JXZ9zqbP5cXFxeLNo/ljs16vex70iXWYz9PT01mr1Vo8n51HklW8UeQ0HAAAAIDEAq8AAAAAiVgCAAAAkIglAAAAAIlYAgAAAJCIJQAAAACJWAIAAACQiCUAAAAAiVgCAAAAkIglAAAAAIlYAgAAAJCIJQAAAACJWAIAAACQiCUAAAAAiVgCAAAAkIglAAAAAIlYAgAAAJCIJQAAAACJWAIAAACQiCUAAAAAiVgCAAAAkIglAAAAAIlYAgAAAJCIJQAAAACJWAIAAACQiCUAAAAAyf8PsAB4quczNPIAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -238,7 +240,7 @@ "metadata": { "image/png": { "height": 414, - "width": 541 + "width": 549 } }, "output_type": "display_data" @@ -252,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -268,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -303,7 +305,7 @@ " \n", " \n", " 0\n", - " 4189501453469220864\n", + " 4284698819469508608\n", " True\n", " 0\n", " -0.99\n", @@ -312,7 +314,7 @@ " \n", " \n", " 1\n", - " 4189501453469220865\n", + " 4284698819469508609\n", " True\n", " 0\n", " -0.97\n", @@ -321,7 +323,7 @@ " \n", " \n", " 2\n", - " 4189501453469220866\n", + " 4284698819469508610\n", " True\n", " 0\n", " -0.95\n", @@ -330,7 +332,7 @@ " \n", " \n", " 3\n", - " 4189501453469220867\n", + " 4284698819469508611\n", " True\n", " 0\n", " -0.93\n", @@ -339,7 +341,7 @@ " \n", " \n", " 4\n", - " 4189501453469220868\n", + " 4284698819469508612\n", " True\n", " 0\n", " -0.91\n", @@ -352,14 +354,14 @@ ], "text/plain": [ " id active parent_id theta0 radii0 null_truth0\n", - "0 4189501453469220864 True 0 -0.99 0.01 True\n", - "1 4189501453469220865 True 0 -0.97 0.01 True\n", - "2 4189501453469220866 True 0 -0.95 0.01 True\n", - "3 4189501453469220867 True 0 -0.93 0.01 True\n", - "4 4189501453469220868 True 0 -0.91 0.01 True" + "0 4284698819469508608 True 0 -0.99 0.01 True\n", + "1 4284698819469508609 True 0 -0.97 0.01 True\n", + "2 4284698819469508610 True 0 -0.95 0.01 True\n", + "3 4284698819469508611 True 0 -0.93 0.01 True\n", + "4 4284698819469508612 True 0 -0.91 0.01 True" ] }, - "execution_count": 45, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -370,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -449,7 +451,7 @@ "4 15 0.001831 0.003262 0.003374" ] }, - "execution_count": 44, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -460,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -542,7 +544,7 @@ ], "metadata": { "kernelspec": { - "display_name": "base", + "display_name": "confirm", "language": "python", "name": "python3" }, @@ -556,12 +558,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8 | packaged by conda-forge | (main, Nov 22 2022, 08:26:04) [GCC 10.4.0]" + "version": "3.10.8" }, "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "d4d1e4263499bec80672ea0156c357c1ee493ec2b1c70f0acce89fc37c4a6abe" + "hash": "b4c6ec5b2d6c7b38df115d547b82cd53ca25eea58d87299956d35a9dc79f19f1" } } }, diff --git a/imprint/tutorials/ztest.md b/imprint/tutorials/ztest.md index de764ad5..4190b695 100644 --- a/imprint/tutorials/ztest.md +++ b/imprint/tutorials/ztest.md @@ -34,6 +34,8 @@ plt.show() ``` ```python +true_drug_effect = 0 +K = 10000 samples = scipy.stats.norm.rvs(loc=true_drug_effect, size=K) ``` diff --git a/poetry.lock b/poetry.lock index 16487596..1fe46806 100644 --- a/poetry.lock +++ b/poetry.lock @@ -114,7 +114,7 @@ python-dateutil = ">=2.7.0" [[package]] name = "asgiref" -version = "3.5.2" +version = "3.6.0" description = "ASGI specs, helper code, and adapters" category = "dev" optional = false @@ -147,21 +147,22 @@ python-versions = ">=3.6" [[package]] name = "attrs" -version = "22.1.0" +version = "22.2.0" description = "Classes Without Boilerplate" category = "dev" optional = false -python-versions = ">=3.5" +python-versions = ">=3.6" [package.extras] -dev = ["cloudpickle", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "mypy (>=0.900,!=0.940)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "sphinx", "sphinx-notfound-page", "zope.interface"] -docs = ["furo", "sphinx", "sphinx-notfound-page", "zope.interface"] -tests = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "zope.interface"] -tests-no-zope = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins"] +cov = ["attrs[tests]", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] +dev = ["attrs[docs,tests]"] +docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope.interface"] +tests = ["attrs[tests-no-zope]", "zope.interface"] +tests-no-zope = ["cloudpickle", "cloudpickle", "hypothesis", "hypothesis", "mypy (>=0.971,<0.990)", "mypy (>=0.971,<0.990)", "pympler", "pympler", "pytest (>=4.3.0)", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-mypy-plugins", "pytest-xdist[psutil]", "pytest-xdist[psutil]"] [[package]] name = "aws-cdk-asset-awscli-v1" -version = "2.2.34" +version = "2.2.49" description = "A library that contains the AWS CLI for use in Lambda Layers" category = "dev" optional = false @@ -200,14 +201,14 @@ typeguard = ">=2.13.3,<2.14.0" [[package]] name = "aws-cdk-aws-batch-alpha" -version = "2.55.1a0" +version = "2.59.0a0" description = "The CDK Construct Library for AWS::Batch" category = "dev" optional = false python-versions = "~=3.7" [package.dependencies] -aws-cdk-lib = ">=2.55.1,<3.0.0" +aws-cdk-lib = ">=2.59.0,<3.0.0" constructs = ">=10.0.0,<11.0.0" jsii = ">=1.72.0,<2.0.0" publication = ">=0.0.3" @@ -215,7 +216,7 @@ typeguard = ">=2.13.3,<2.14.0" [[package]] name = "aws-cdk-lib" -version = "2.55.1" +version = "2.59.0" description = "Version 2 of the AWS Cloud Development Kit library" category = "dev" optional = false @@ -294,14 +295,14 @@ dev = ["Sphinx (==4.3.2)", "black (==22.3.0)", "build (==0.8.0)", "flake8 (==4.0 [[package]] name = "boto3" -version = "1.26.32" +version = "1.26.45" description = "The AWS SDK for Python" category = "dev" optional = false python-versions = ">= 3.7" [package.dependencies] -botocore = ">=1.29.32,<1.30.0" +botocore = ">=1.29.45,<1.30.0" jmespath = ">=0.7.1,<2.0.0" s3transfer = ">=0.6.0,<0.7.0" @@ -310,7 +311,7 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] [[package]] name = "botocore" -version = "1.29.32" +version = "1.29.45" description = "Low-level, data-driven core of boto 3." category = "dev" optional = false @@ -395,7 +396,7 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [[package]] name = "clickhouse-connect" -version = "0.4.7" +version = "0.4.8" description = "ClickHouse core driver, SqlAlchemy, and Superset libraries" category = "dev" optional = false @@ -456,14 +457,14 @@ test = ["flake8 (==3.7.8)", "hypothesis (==3.55.3)"] [[package]] name = "constructs" -version = "10.1.194" +version = "10.1.212" description = "A programming model for software-defined state" category = "dev" optional = false python-versions = "~=3.7" [package.dependencies] -jsii = ">=1.72.0,<2.0.0" +jsii = ">=1.73.0,<2.0.0" publication = ">=0.0.3" typeguard = ">=2.13.3,<2.14.0" @@ -487,7 +488,7 @@ test-no-codebase = ["Pillow", "matplotlib", "pytest"] [[package]] name = "cryptography" -version = "38.0.4" +version = "39.0.0" description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers." category = "dev" optional = false @@ -497,9 +498,9 @@ python-versions = ">=3.6" cffi = ">=1.12" [package.extras] -docs = ["sphinx (>=1.6.5,!=1.8.0,!=3.1.0,!=3.1.1)", "sphinx-rtd-theme"] +docs = ["sphinx (>=1.6.5,!=1.8.0,!=3.1.0,!=3.1.1,!=5.2.0,!=5.2.0.post0)", "sphinx-rtd-theme"] docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"] -pep8test = ["black", "flake8", "flake8-import-order", "pep8-naming"] +pep8test = ["black", "ruff"] sdist = ["setuptools-rust (>=0.11.4)"] ssh = ["bcrypt (>=3.1.5)"] test = ["hypothesis (>=1.11.4,!=3.79.2)", "iso8601", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-subtests", "pytest-xdist", "pytz"] @@ -514,7 +515,7 @@ python-versions = ">=3.6" [[package]] name = "debugpy" -version = "1.6.4" +version = "1.6.5" description = "An implementation of the Debug Adapter Protocol for Python" category = "dev" optional = false @@ -565,7 +566,7 @@ python-versions = ">=3.6" [[package]] name = "exceptiongroup" -version = "1.0.4" +version = "1.1.0" description = "Backport of PEP 654 (exception groups)" category = "dev" optional = false @@ -627,15 +628,15 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc [[package]] name = "filelock" -version = "3.8.2" +version = "3.9.0" description = "A platform independent file lock." category = "dev" optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo (>=2022.9.29)", "sphinx (>=5.3)", "sphinx-autodoc-typehints (>=1.19.5)"] -testing = ["covdefaults (>=2.2.2)", "coverage (>=6.5)", "pytest (>=7.2)", "pytest-cov (>=4)", "pytest-timeout (>=2.1)"] +docs = ["furo (>=2022.12.7)", "sphinx (>=5.3)", "sphinx-autodoc-typehints (>=1.19.5)"] +testing = ["covdefaults (>=2.2.2)", "coverage (>=7.0.1)", "pytest (>=7.2)", "pytest-cov (>=4)", "pytest-timeout (>=2.1)"] [[package]] name = "flake8" @@ -733,7 +734,7 @@ python-versions = ">=3.6.1" [[package]] name = "identify" -version = "2.5.10" +version = "2.5.12" description = "File identification library for Python" category = "dev" optional = false @@ -752,7 +753,7 @@ python-versions = ">=3.5" [[package]] name = "importlib-metadata" -version = "5.2.0" +version = "6.0.0" description = "Read metadata from Python packages" category = "dev" optional = false @@ -789,7 +790,7 @@ tomli = {version = "*", markers = "python_version > \"3.6\" and python_version < [[package]] name = "ipykernel" -version = "6.19.2" +version = "6.19.4" description = "IPython Kernel for Jupyter" category = "dev" optional = false @@ -812,13 +813,11 @@ traitlets = ">=5.4.0" [package.extras] cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"] docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinxcontrib-github-alt"] -lint = ["black (>=22.6.0)", "mdformat (>0.7)", "ruff (>=0.0.156)"] test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov", "pytest-timeout"] -typing = ["mypy (>=0.990)"] [[package]] name = "ipython" -version = "8.7.0" +version = "8.8.0" description = "IPython: Productive Interactive Computing" category = "dev" optional = false @@ -861,7 +860,7 @@ python-versions = "*" [[package]] name = "ipywidgets" -version = "8.0.3" +version = "8.0.4" description = "Jupyter interactive widgets" category = "dev" optional = false @@ -1009,7 +1008,7 @@ python-versions = ">=3.7" [[package]] name = "jsii" -version = "1.72.0" +version = "1.73.0" description = "Python client for jsii runtime" category = "dev" optional = false @@ -1112,7 +1111,7 @@ test = ["pexpect"] [[package]] name = "jupyter-core" -version = "5.1.0" +version = "5.1.2" description = "Jupyter core package. A base package on which Jupyter projects rely." category = "dev" optional = false @@ -1124,7 +1123,7 @@ pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\" and platform_ traitlets = ">=5.3" [package.extras] -docs = ["myst-parser", "sphinxcontrib-github-alt", "traitlets"] +docs = ["myst-parser", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"] test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"] [[package]] @@ -1147,7 +1146,7 @@ test = ["click", "coverage", "pre-commit", "pytest (>=6.1.0)", "pytest-asyncio ( [[package]] name = "jupyter-server" -version = "2.0.1" +version = "2.0.6" description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications." category = "dev" optional = false @@ -1174,14 +1173,12 @@ traitlets = ">=5.6.0" websocket-client = "*" [package.extras] -docs = ["docutils (<0.20)", "ipykernel", "jinja2", "jupyter-client", "jupyter-server", "mistune (<1.0.0)", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxemoji", "tornado"] -lint = ["black (>=22.6.0)", "mdformat (>0.7)", "ruff (>=0.0.156)"] +docs = ["docutils (<0.20)", "ipykernel", "jinja2", "jupyter-client", "jupyter-server", "mistune (<1.0.0)", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxcontrib-spelling", "sphinxemoji", "tornado"] test = ["ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.4)", "pytest-timeout", "requests"] -typing = ["mypy (>=0.990)"] [[package]] name = "jupyter-server-terminals" -version = "0.4.2" +version = "0.4.3" description = "A Jupyter Server Extension Providing Terminals." category = "dev" optional = false @@ -1192,8 +1189,8 @@ pywinpty = {version = ">=2.0.3", markers = "os_name == \"nt\""} terminado = ">=0.8.3" [package.extras] -docs = ["jinja2", "jupyter-server", "mistune (<2.0)", "myst-parser", "nbformat", "packaging", "pydata-sphinx-theme", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxemoji", "tornado"] -test = ["coverage", "jupyter-server (>=2.0.0rc8)", "pytest (>=7.0)", "pytest-cov", "pytest-jupyter[server] (>=0.5.3)", "pytest-timeout"] +docs = ["jinja2", "jupyter-server", "mistune (<3.0)", "myst-parser", "nbformat", "packaging", "pydata-sphinx-theme", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxemoji", "tornado"] +test = ["coverage", "jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-cov", "pytest-jupyter[server] (>=0.5.3)", "pytest-timeout"] [[package]] name = "jupyterlab-pygments" @@ -1205,7 +1202,7 @@ python-versions = ">=3.7" [[package]] name = "jupyterlab-widgets" -version = "3.0.4" +version = "3.0.5" description = "Jupyter interactive widgets for JupyterLab" category = "dev" optional = false @@ -1382,7 +1379,7 @@ python-versions = "*" [[package]] name = "modal-client" -version = "0.41.482" +version = "0.43.586" description = "Python client library for Modal" category = "dev" optional = false @@ -1430,7 +1427,7 @@ tests = ["pytest (>=4.6)"] [[package]] name = "multidict" -version = "6.0.3" +version = "6.0.4" description = "multidict implementation" category = "dev" optional = false @@ -1624,7 +1621,7 @@ test = ["pytest", "pytest-console-scripts", "pytest-tornasync"] [[package]] name = "numpy" -version = "1.24.0" +version = "1.24.1" description = "Fundamental package for array computing in Python" category = "main" optional = false @@ -1742,27 +1739,27 @@ python-versions = "*" [[package]] name = "pillow" -version = "9.3.0" +version = "9.4.0" description = "Python Imaging Library (Fork)" category = "main" optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"] +docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"] tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"] [[package]] name = "platformdirs" -version = "2.6.0" +version = "2.6.2" description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." category = "dev" optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo (>=2022.9.29)", "proselint (>=0.13)", "sphinx (>=5.3)", "sphinx-autodoc-typehints (>=1.19.4)"] -test = ["appdirs (==1.4.4)", "pytest (>=7.2)", "pytest-cov (>=4)", "pytest-mock (>=3.10)"] +docs = ["furo (>=2022.12.7)", "proselint (>=0.13)", "sphinx (>=5.3)", "sphinx-autodoc-typehints (>=1.19.5)"] +test = ["appdirs (==1.4.4)", "covdefaults (>=2.2.2)", "pytest (>=7.2)", "pytest-cov (>=4)", "pytest-mock (>=3.10)"] [[package]] name = "pluggy" @@ -1791,7 +1788,7 @@ typing-extensions = "*" [[package]] name = "pre-commit" -version = "2.20.0" +version = "2.21.0" description = "A framework for managing and maintaining multi-language pre-commit hooks." category = "dev" optional = false @@ -1802,8 +1799,7 @@ cfgv = ">=2.0.0" identify = ">=1.0.0" nodeenv = ">=0.11.1" pyyaml = ">=5.1" -toml = "*" -virtualenv = ">=20.0.8" +virtualenv = ">=20.10.0" [[package]] name = "prometheus-client" @@ -1910,14 +1906,14 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [[package]] name = "pydantic" -version = "1.10.2" +version = "1.10.4" description = "Data validation and settings management using python type hints" category = "dev" optional = false python-versions = ">=3.7" [package.dependencies] -typing-extensions = ">=4.1.0" +typing-extensions = ">=4.2.0" [package.extras] dotenv = ["python-dotenv (>=0.10.4)"] @@ -1933,7 +1929,7 @@ python-versions = ">=3.6" [[package]] name = "pygments" -version = "2.13.0" +version = "2.14.0" description = "Pygments is a syntax highlighting package written in Python." category = "dev" optional = false @@ -1955,7 +1951,7 @@ diagrams = ["jinja2", "railroad-diagrams"] [[package]] name = "pyrsistent" -version = "0.19.2" +version = "0.19.3" description = "Persistent/Functional/Immutable data structures" category = "dev" optional = false @@ -2044,7 +2040,7 @@ python-versions = "*" [[package]] name = "pywinpty" -version = "2.0.9" +version = "2.0.10" description = "Pseudo terminal support for Windows from Python." category = "dev" optional = false @@ -2200,19 +2196,19 @@ crt = ["botocore[crt] (>=1.20.29,<2.0a.0)"] [[package]] name = "scipy" -version = "1.9.3" +version = "1.10.0" description = "Fundamental algorithms for scientific computing in Python" category = "main" optional = false -python-versions = ">=3.8" +python-versions = "<3.12,>=3.8" [package.dependencies] -numpy = ">=1.18.5,<1.26.0" +numpy = ">=1.19.5,<1.27.0" [package.extras] -dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"] -doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"] -test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] +dev = ["click", "doit (>=0.36.0)", "flake8", "mypy", "pycodestyle", "pydevtool", "rich-click", "typing_extensions"] +doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"] +test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] [[package]] name = "secretstorage" @@ -2241,7 +2237,7 @@ win32 = ["pywin32"] [[package]] name = "sentry-sdk" -version = "1.12.0" +version = "1.12.1" description = "Python client for Sentry (https://sentry.io)" category = "dev" optional = false @@ -2468,7 +2464,7 @@ telegram = ["requests"] [[package]] name = "traitlets" -version = "5.7.1" +version = "5.8.0" description = "Traitlets Python configuration system" category = "dev" optional = false @@ -2476,9 +2472,7 @@ python-versions = ">=3.7" [package.extras] docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"] -lint = ["black (>=22.6.0)", "mdformat (>0.7)", "ruff (>=0.0.156)"] -test = ["pre-commit", "pytest"] -typing = ["mypy (>=0.990)"] +test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"] [[package]] name = "typeguard" @@ -2624,7 +2618,7 @@ test = ["websockets"] [[package]] name = "widgetsnbextension" -version = "4.0.4" +version = "4.0.5" description = "Jupyter interactive widgets for Jupyter Notebook" category = "dev" optional = false @@ -2657,7 +2651,7 @@ testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools" [metadata] lock-version = "1.1" python-versions = "~3.10" -content-hash = "f0902b981d5dc29e26e9c44c71f320b4dee8c60016923886bfad30c3354db845" +content-hash = 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