feat: improve handling of optional dependencies

analyses/cms-open-data-ttbar/ttbar_analysis_pipeline.ipynb

@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "0dc37683",
    "metadata": {},
@@ -25,7 +24,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "404927fe",
    "metadata": {},
@@ -37,7 +35,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "bd72d323",
    "metadata": {},
@@ -65,8 +62,6 @@
     "from coffea.nanoevents import NanoAODSchema\n",
     "from coffea.analysis_tools import PackedSelection\n",
     "import copy\n",
-    "from func_adl import ObjectStream\n",
-    "from func_adl_servicex import ServiceXSourceUpROOT\n",
     "import hist\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -78,7 +73,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "cd573bb1",
    "metadata": {},
@@ -135,7 +129,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "141d6520",
    "metadata": {},
@@ -407,7 +400,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "90dd4c9e",
    "metadata": {},
@@ -454,7 +446,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "b910d3d5",
    "metadata": {},
@@ -473,7 +464,7 @@
    },
    "outputs": [],
    "source": [
-    "def get_query(source: ObjectStream) -> ObjectStream:\n",
+    "def get_query(source):\n",
     "    \"\"\"Query for event / column selection: >=4j >=1b, ==1 lep with pT>30 GeV + additional cuts,\n",
     "    return relevant columns\n",
     "    *NOTE* jet pT cut is set lower to account for systematic variations to jet pT\n",
@@ -554,7 +545,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "d8f08fc1",
    "metadata": {},
@@ -574,6 +564,12 @@
    "outputs": [],
    "source": [
     "if USE_SERVICEX:\n",
+    "    try:\n",
+    "        from func_adl_servicex import ServiceXSourceUpROOT\n",
+    "    except ImportError:\n",
+    "        print(\"cannot import func_adl_servicex, which is a required dependency when using ServiceX\")\n",
+    "        raise\n",
+    "\n",
     "    # dummy dataset on which to generate the query\n",
     "    dummy_ds = ServiceXSourceUpROOT(\"cernopendata://dummy\", \"Events\", backend_name=\"uproot\")\n",
     "\n",
@@ -597,7 +593,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "c28a9e49",
    "metadata": {},
@@ -649,10 +644,10 @@
     "    executor = processor.FuturesExecutor(workers=utils.config[\"benchmarking\"][\"NUM_CORES\"])\n",
     "\n",
     "run = processor.Runner(\n",
-    "    executor=executor, \n",
-    "    schema=NanoAODSchema, \n",
-    "    savemetrics=True, \n",
-    "    metadata_cache={}, \n",
+    "    executor=executor,\n",
+    "    schema=NanoAODSchema,\n",
+    "    savemetrics=True,\n",
+    "    metadata_cache={},\n",
     "    chunksize=utils.config[\"benchmarking\"][\"CHUNKSIZE\"])\n",
     "\n",
     "if USE_SERVICEX:\n",
@@ -670,8 +665,8 @@
     "t0 = time.monotonic()\n",
     "# processing\n",
     "all_histograms, metrics = run(\n",
-    "    fileset, \n",
-    "    treename, \n",
+    "    fileset,\n",
+    "    treename,\n",
     "    processor_instance=TtbarAnalysis(USE_INFERENCE, USE_TRITON)\n",
     ")\n",
     "exec_time = time.monotonic() - t0\n",
@@ -701,7 +696,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "d7bb4428",
    "metadata": {},
@@ -732,6 +726,8 @@
     }
    ],
    "source": [
+    "import utils.plotting  # noqa: E402\n",
+    "\n",
     "utils.plotting.set_style()\n",
     "\n",
     "all_histograms[\"hist_dict\"][\"4j1b\"][120j::hist.rebin(2), :, \"nominal\"].stack(\"process\")[::-1].plot(stack=True, histtype=\"fill\", linewidth=1, edgecolor=\"grey\")\n",
@@ -767,7 +763,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "bed3df8b",
    "metadata": {},
@@ -884,7 +879,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "7c334dd3",
    "metadata": {},
@@ -917,7 +911,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "e904cd3c",
    "metadata": {},
@@ -941,6 +934,8 @@
    },
    "outputs": [],
    "source": [
+    "import utils.rebinning  # noqa: E402\n",
+    "\n",
     "cabinetry_config = cabinetry.configuration.load(\"cabinetry_config.yml\")\n",
     "\n",
     "# rebinning: lower edge 110 GeV, merge bins 2->1\n",
@@ -952,7 +947,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "f36dc601",
    "metadata": {},
@@ -1000,7 +994,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "c74e4361",
    "metadata": {},
@@ -1038,7 +1031,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "bd480eec",
    "metadata": {},
@@ -1069,7 +1061,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "3a293479",
    "metadata": {},
@@ -1125,7 +1116,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "14dc4b23",
    "metadata": {},
@@ -1173,7 +1163,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "7f60c316",
    "metadata": {},
@@ -1317,7 +1306,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "a2ce2d14",
    "metadata": {},

analyses/cms-open-data-ttbar/ttbar_analysis_pipeline.py

@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.7
+#       jupytext_version: 1.15.2
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -52,8 +52,6 @@
 from coffea.nanoevents import NanoAODSchema
 from coffea.analysis_tools import PackedSelection
 import copy
-from func_adl import ObjectStream
-from func_adl_servicex import ServiceXSourceUpROOT
 import hist
 import matplotlib.pyplot as plt
 import numpy as np
@@ -388,7 +386,7 @@ def postprocess(self, accumulator):
 # Define the func_adl query to be used for the purpose of extracting columns and filtering.
 
 # %%
-def get_query(source: ObjectStream) -> ObjectStream:
+def get_query(source):
     """Query for event / column selection: >=4j >=1b, ==1 lep with pT>30 GeV + additional cuts,
     return relevant columns
     *NOTE* jet pT cut is set lower to account for systematic variations to jet pT
@@ -475,6 +473,12 @@ def get_query(source: ObjectStream) -> ObjectStream:
 
 # %%
 if USE_SERVICEX:
+    try:
+        from func_adl_servicex import ServiceXSourceUpROOT
+    except ImportError:
+        print("cannot import func_adl_servicex, which is a required dependency when using ServiceX")
+        raise
+
     # dummy dataset on which to generate the query
     dummy_ds = ServiceXSourceUpROOT("cernopendata://dummy", "Events", backend_name="uproot")
 
@@ -512,10 +516,10 @@ def get_query(source: ObjectStream) -> ObjectStream:
     executor = processor.FuturesExecutor(workers=utils.config["benchmarking"]["NUM_CORES"])
 
 run = processor.Runner(
-    executor=executor, 
-    schema=NanoAODSchema, 
-    savemetrics=True, 
-    metadata_cache={}, 
+    executor=executor,
+    schema=NanoAODSchema,
+    savemetrics=True,
+    metadata_cache={},
     chunksize=utils.config["benchmarking"]["CHUNKSIZE"])
 
 if USE_SERVICEX:
@@ -533,8 +537,8 @@ def get_query(source: ObjectStream) -> ObjectStream:
 t0 = time.monotonic()
 # processing
 all_histograms, metrics = run(
-    fileset, 
-    treename, 
+    fileset,
+    treename,
     processor_instance=TtbarAnalysis(USE_INFERENCE, USE_TRITON)
 )
 exec_time = time.monotonic() - t0
@@ -552,6 +556,8 @@ def get_query(source: ObjectStream) -> ObjectStream:
 # We built histograms in two phase space regions, for multiple physics processes and systematic variations.
 
 # %%
+import utils.plotting  # noqa: E402
+
 utils.plotting.set_style()
 
 all_histograms["hist_dict"]["4j1b"][120j::hist.rebin(2), :, "nominal"].stack("process")[::-1].plot(stack=True, histtype="fill", linewidth=1, edgecolor="grey")
@@ -644,6 +650,8 @@ def get_query(source: ObjectStream) -> ObjectStream:
 # We will use `cabinetry` to combine all histograms into a `pyhf` workspace and fit the resulting statistical model to the pseudodata we built.
 
 # %%
+import utils.rebinning  # noqa: E402
+
 cabinetry_config = cabinetry.configuration.load("cabinetry_config.yml")
 
 # rebinning: lower edge 110 GeV, merge bins 2->1

analyses/cms-open-data-ttbar/utils/__init__.py

@@ -6,5 +6,7 @@
 from . import metrics as metrics
 from . import ml as ml
 from . import plotting as plotting
-from . import rebinning as rebinning
 from . import systematics as systematics
+
+
+# to avoid issues: only import submodules if dependencies are present on worker nodes too

analyses/cms-open-data-ttbar/utils/file_input.py

@@ -2,10 +2,18 @@
 import numpy as np
 import os
 from pathlib import Path
-from servicex import ServiceXDataset
 import tqdm
 import urllib
 
+
+try:
+    from servicex import ServiceXDataset
+except ImportError:
+    # if servicex is not available, ServiceXDatasetGroup cannot be used
+    # this is fine for worker nodes: only needed where main notebook is executed
+    pass
+
+
 # If local_data_cache is a writable path, this function will download any missing file into it and
 # then return file paths corresponding to these local copies.
 def construct_fileset(n_files_max_per_sample, use_xcache=False, af_name="", local_data_cache=None, input_from_eos=False, xcache_atlas_prefix=None):
@@ -112,6 +120,7 @@ def download_file(url, out_file):
     with tqdm.tqdm(unit='B', unit_scale=True, unit_divisor=1024, miniters=1, desc=out_path.name) as t:
         urllib.request.urlretrieve(url, out_path.absolute(), reporthook=tqdm_urlretrieve_hook(t))
 
+
 class ServiceXDatasetGroup():
     def __init__(self, fileset, backend_name="uproot", ignore_cache=False):
         self.fileset = fileset