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feat: Get GCP distributed fitting example working #4

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feat: Get GCP distributed fitting example working #4

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c2a58eb
Add ipympl to binder requirements
matthewfeickert Jul 6, 2021
cc7f306
Add interactive matplotlib jupyterlab support
matthewfeickert Jul 6, 2021
3400607
Guard against 'No handles with labels found to put in legend' warning
matthewfeickert Jul 6, 2021
54afedb
Make super explicit that 200 is an HTTP status code
matthewfeickert Jul 6, 2021
841163b
Rename 'plot' -> 'plot_results'
matthewfeickert Jul 6, 2021
569b8cb
temp commit
matthewfeickert Jul 9, 2021
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2 changes: 2 additions & 0 deletions binder/requirements.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -5,3 +5,5 @@ funcx-endpoint==0.2.2
funcx==0.2.2
# gcp
aiohttp==3.7.4.post0
# interactive jupyterlab
ipympl==0.7.0
131 changes: 124 additions & 7 deletions google-cloud-platform/fittting-faas-gcp.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -31,9 +31,91 @@
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"# Interactive matplotlib\n",
"# %matplotlib widget\n",
"%matplotlib notebook"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a5d2ccc8-048d-42ff-a623-c8cb908b814a",
"metadata": {},
"outputs": [],
"source": [
"from descartes import PolygonPatch\n",
"\n",
"# ! pip install pandas\n",
"from shapely.geometry.polygon import Polygon"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1957ccfa-9637-4236-a9a6-374189fdfd25",
"metadata": {},
"outputs": [],
"source": [
"sys.path.insert(0, Path.cwd())\n",
"\n",
"from collections import namedtuple\n",
"\n",
"from interpolate import main as interpolate_main\n",
"from multiplex import main as multiplex_main\n",
"\n",
"# import ROOT\n",
"\n",
"\n",
"def make_results():\n",
" dataList = harvest_results(regions=[\"A\", \"C\"])\n",
" if not (dataList[0][1] or dataList[1][1]):\n",
" return None, None\n",
"\n",
" d = {\n",
" \"figureOfMerit\": \"CLsexp\",\n",
" \"modelDef\": \"msb,mn2,mn1\",\n",
" \"ignoreTheory\": True,\n",
" \"ignoreUL\": True,\n",
" \"debug\": False,\n",
" }\n",
" args = namedtuple(\"Args\", d.keys())(**d)\n",
" mux_data = multiplex_main(\n",
" args,\n",
" inputDataList=dataList,\n",
" ).to_dict(orient=\"records\")\n",
"\n",
" d = {\n",
" \"nominalLabel\": \"Nominal\",\n",
" \"xMin\": None,\n",
" \"xMax\": None,\n",
" \"yMin\": None,\n",
" \"yMax\": None,\n",
" \"smoothing\": \"0.1\",\n",
" \"areaThreshold\": 0,\n",
" \"xResolution\": 100,\n",
" \"yResolution\": 100,\n",
" \"xVariable\": \"msb\",\n",
" \"yVariable\": \"mn2\",\n",
" \"closedBands\": False,\n",
" \"forbiddenFunction\": \"x\",\n",
" \"debug\": False,\n",
" \"logX\": False,\n",
" \"logY\": False,\n",
" \"noSig\": False,\n",
" \"interpolation\": \"multiquadric\",\n",
" \"interpolationEpsilon\": 0,\n",
" \"level\": 1.64485362695,\n",
" \"useROOT\": False,\n",
" \"sigmax\": 5,\n",
" \"useUpperLimit\": False,\n",
" \"ignoreUncertainty\": False,\n",
" \"fixedParamsFile\": \"\",\n",
" }\n",
" args = namedtuple(\"Args\", d.keys())(**d)\n",
" r = interpolate_main(args, mux_data)\n",
" return r, dataList"
]
},
{
"cell_type": "markdown",
"id": "4f3d0ea7-d495-4899-8b94-c7ccbbeb8189",
Expand All @@ -49,7 +131,10 @@
"metadata": {},
"outputs": [],
"source": [
"def make_harvest_from_result(result, masses):\n",
"# def make_harvest_from_result(result, masses):\n",
"def make_harvest_from_result(masses, result):\n",
" # print(f\"{result=}\")\n",
" # print(f\"{masses=}\")\n",
" return {\n",
" \"CLs\": result[\"CLs_obs\"],\n",
" \"CLsexp\": result[\"CLs_exp\"][2],\n",
Expand Down Expand Up @@ -109,6 +194,8 @@
" # only use 60 GeV\n",
" if masses[2] != 60:\n",
" continue\n",
" # print(result) # DEBUG\n",
" # print(masses)\n",
" harvest.append(make_harvest_from_result(result, masses))\n",
" dataList.append((f\"region{region}\", harvest))\n",
" return dataList"
Expand Down Expand Up @@ -208,6 +295,9 @@
" r\"$m(\\tilde{\\chi}_2^0)$ [GeV]\", fontdict=dict(ha=\"right\", va=\"center\", size=20)\n",
" )\n",
"\n",
" # _handles, _labels = ax.get_legend_handles_labels()\n",
" # if _handles:\n",
" # ax.legend(loc=(0.05, 0.6))\n",
" ax.legend(loc=(0.05, 0.6))\n",
" ax.xaxis.set_label_coords(1.0, -0.1)\n",
" ax.yaxis.set_label_coords(-0.15, 1.0)\n",
Expand Down Expand Up @@ -307,8 +397,9 @@
" )\n",
" print(f\"Number of Patches: {len(patches)}\")\n",
" results = await make_requests(f\"{fitting_service_url}/region{region}\", patches[:])\n",
" all_ok = np.all([x[1][0] == 200 for x in results])\n",
" print(f\"->Number of Results: {len(results)} {time.time()-start} | {all_ok}\")\n",
" http_status_code_OK = 200\n",
" all_ok = np.all([x[1][0] == http_status_code_OK for x in results])\n",
" print(f\"-> Number of Results: {len(results)} {time.time()-start} | {all_ok}\")\n",
" done_live[region] = True\n",
" return results"
]
Expand All @@ -320,9 +411,13 @@
"metadata": {},
"outputs": [],
"source": [
"def plot(ax, data, color=\"steelblue\", label=\"(in progress)\", showInterPolated=True):\n",
"def plot_results(\n",
" ax, data, color=\"steelblue\", label=\"(in progress)\", showInterPolated=True\n",
"):\n",
" ax.cla()\n",
"\n",
" # print(data_live[\"A\"].items()) # DEBUG\n",
" # print((k, r) for k, r in data_live[\"A\"].items()) # DEBUG\n",
" hdata = [\n",
" (\n",
" \"regionA\",\n",
Expand Down Expand Up @@ -365,9 +460,9 @@
" global data_live\n",
" global done_live\n",
" while not (done_live[\"A\"] and done_live[\"C\"]):\n",
" plot(ax, data_live, showInterPolated=True)\n",
" plot_results(ax, data_live, showInterPolated=True)\n",
" await asyncio.sleep(1.0)\n",
" plot(ax, data_live, color=\"gold\", label=\"\")\n",
" plot_results(ax, data_live, color=\"gold\", label=\"\")\n",
" print(\"done plotting\")"
]
},
Expand Down Expand Up @@ -408,6 +503,7 @@
"outputs": [],
"source": [
"# cloud resources to re-perform stat. analysis\n",
"# fitting_service_url is a SECRET that should be of the form: https://country-region-project_name-123456.cloudfunctions.net\n",
"with open(\"fitting_service_url.txt\") as gcp_url_file:\n",
" fitting_service_url = str(gcp_url_file.read().rstrip())"
]
Expand Down Expand Up @@ -436,12 +532,33 @@
"outputs": [],
"source": [
"reset_data_live()\n",
"plot(ax, data_live)\n",
"plot_results(ax, data_live)\n",
"start = time.time()\n",
"results = await asyncio.gather(run_region(\"A\"), run_region(\"C\"), plotting_loop())\n",
"time.time() - start"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2df0f8fc",
"metadata": {},
"outputs": [],
"source": [
"plot_results(ax, data_live)\n",
"ax.figure.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0e0afd40-a4a1-4658-a198-d8254687d984",
"metadata": {},
"outputs": [],
"source": [
"# results"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down