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In Bluesky, data streaming is realized via StreamResource and StreamDatum documents, which extend their discrete counterparts (Resource and Datum) used in step scanning. The eventual number of data points in the stream (e.g. image frames acquired during a flyscanning experiment) is typically unknown at the begininng of aquisition; furtehrmore, the frames may arrive .... become the part of the same resource and are often appended to the same file (in case of hdf5) by the detector. +we introduce the notion of Consolidators to facilitate random access by data access services, like Tiled. Consolidator are analogous to Handlers that have existed in Bluesky for a long time, but they enable working with streamed data. A Consolidator is defined for a particular stream of data and is tied to the underlying resource; it knows how to read the data from disk in the most efficient manner +In this notebook, we will explore the use of Consolidatorsan option for live plotting of streamed data + +```{code-cell} ipython3 +import ophyd_async +``` + +```{code-cell} ipython3 +ophyd_async.__version__ +``` + +```{code-cell} ipython3 +import os + +import matplotlib +import matplotlib.pyplot as plt +from ophyd_async.sim.demo import PatternDetector + +import bluesky.plans as bp +from bluesky.callbacks.core import CollectLiveStream +from bluesky.callbacks.mpl_plotting import LiveStreamPlot + +# if not os.getenv("GITHUB_ACTIONS"): +# matplotlib.use("QtAgg") +# plt.ion() +``` + +```{code-cell} ipython3 + + cl = CollectLiveStream() + pl = LiveStreamPlot(cl, data_key="PATTERN1-sum") + RE.subscribe(pl) + det = PatternDetector(name="PATTERN1-sum", path=tmp_path) + RE(bp.count([det], num=15), cl) + + +def test_hdf5_plotting_2d(RE, tmp_path): + cl = CollectLiveStream() + pl = LiveStreamPlot(cl, data_key="PATTERN1") + RE.subscribe(pl) + det = PatternDetector(name="PATTERN1", path=tmp_path) + RE(bp.count([det], num=15), cl) +``` diff --git a/pixi.lock b/pixi.lock index 84bd206..e1a261c 100644 --- a/pixi.lock +++ b/pixi.lock @@ -296,7 +296,7 @@ environments: - 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subdir: linux-64 - url: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-h4ab18f5_1.conda - sha256: cee16ab07a11303de721915f0a269e8c7a54a5c834aa52f74b1cc3a59000ade8 - md5: 9653f1bf3766164d0e65fa723cabbc54 - depends: - - libgcc-ng >=12 - - libzlib 1.3.1 h4ab18f5_1 - license: Zlib - license_family: Other - purls: [] - size: 93004 - timestamp: 1716874213487 - kind: conda name: zlib-ng version: 2.2.2 diff --git a/pixi.toml b/pixi.toml index 3876cad..cb8fbd0 100644 --- a/pixi.toml +++ b/pixi.toml @@ -26,17 +26,19 @@ jupyterlab = ">=4.2.5,<5" jupyterlab-myst = ">=2.4.2,<3" pydata-sphinx-theme = ">=0.15.4,<0.16" sphinxcontrib-serializinghtml = ">=1.1.10,<2" -bluesky = ">=1.13.0a3,<2" tiled-server = ">=0.1.0b8,<0.2" tiled-client = ">=0.1.0b8,<0.2" numpy = "<2.0.0" +aioca = ">=1.7,<2" [pypi-dependencies] sphinx = ">=8.0.2, <9" myst-nb = ">=1.1.1, <2" jupytext = ">=1.16.4, <2" sphinx-copybutton = ">=0.5.2, <0.6" -ophyd-async = ">=0.6.0, <0.7" +bluesky = { git = "https://github.com/bluesky/bluesky.git", rev = "51bfc0339a75d8ae1f6e697dba51f524322d34ef"} +ophyd-async = { version = ">=0.6.0", extras=["sim"]} +ophyd = ">=1.9.0, <2" [feature.jupyterlite.dependencies] jupyterlab = "~=4.2.4" From 3df34630f4ec1977783f603b5e5d1db0f6cafbdb Mon Sep 17 00:00:00 2001 From: Eugene M Date: Mon, 30 Sep 2024 15:32:55 -0400 Subject: [PATCH 05/17] ENH: draft of the stream plotting tutorial --- docs/tutorials/flyscanning/basics.md | 43 +--- docs/tutorials/flyscanning/json_writer.py | 29 +++ docs/tutorials/flyscanning/plotting.md | 212 ++++++++++++++++++ .../tutorials/flyscanning/sample_simulator.py | 45 ++++ 4 files changed, 290 insertions(+), 39 deletions(-) create mode 100644 docs/tutorials/flyscanning/json_writer.py create mode 100644 docs/tutorials/flyscanning/plotting.md create mode 100644 docs/tutorials/flyscanning/sample_simulator.py diff --git a/docs/tutorials/flyscanning/basics.md b/docs/tutorials/flyscanning/basics.md index bd593b9..005b390 100644 --- a/docs/tutorials/flyscanning/basics.md +++ b/docs/tutorials/flyscanning/basics.md @@ -11,49 +11,14 @@ kernelspec: name: python3 --- -# Live Plotting of Streamed Data +# Flyscanning Basics -A distictive characteristic of fly scanning plans is their reliance on continuous data streams rather than acquisition of data points invidually. In Bluesky, data streaming is realized via StreamResource and StreamDatum documents, which extend their discrete counterparts (Resource and Datum) used in step scanning. The eventual number of data points in the stream (e.g. image frames acquired during a flyscanning experiment) is typically unknown at the begininng of aquisition; furtehrmore, the frames may arrive .... become the part of the same resource and are often appended to the same file (in case of hdf5) by the detector. -we introduce the notion of Consolidators to facilitate random access by data access services, like Tiled. Consolidator are analogous to Handlers that have existed in Bluesky for a long time, but they enable working with streamed data. A Consolidator is defined for a particular stream of data and is tied to the underlying resource; it knows how to read the data from disk in the most efficient manner -In this notebook, we will explore the use of Consolidatorsan option for live plotting of streamed data +TODO -```{code-cell} ipython3 +```{code-cell} import ophyd_async ``` -```{code-cell} ipython3 -ophyd_async.__version__ -``` - -```{code-cell} ipython3 -import os - -import matplotlib -import matplotlib.pyplot as plt -from ophyd_async.sim.demo import PatternDetector - -import bluesky.plans as bp -from bluesky.callbacks.core import CollectLiveStream -from bluesky.callbacks.mpl_plotting import LiveStreamPlot - -# if not os.getenv("GITHUB_ACTIONS"): -# matplotlib.use("QtAgg") -# plt.ion() -``` - -```{code-cell} ipython3 - - cl = CollectLiveStream() - pl = LiveStreamPlot(cl, data_key="PATTERN1-sum") - RE.subscribe(pl) - det = PatternDetector(name="PATTERN1-sum", path=tmp_path) - RE(bp.count([det], num=15), cl) - +```{code-cell} -def test_hdf5_plotting_2d(RE, tmp_path): - cl = CollectLiveStream() - pl = LiveStreamPlot(cl, data_key="PATTERN1") - RE.subscribe(pl) - det = PatternDetector(name="PATTERN1", path=tmp_path) - RE(bp.count([det], num=15), cl) ``` diff --git a/docs/tutorials/flyscanning/json_writer.py b/docs/tutorials/flyscanning/json_writer.py new file mode 100644 index 0000000..61bb7ce --- /dev/null +++ b/docs/tutorials/flyscanning/json_writer.py @@ -0,0 +1,29 @@ +class JSONWriter: + """Callback to write a Bluesky stream into a JSON file. Useful for debugging. + + Parameters + ---------- + filepath : str + A desired path to a .json file used to save the data. + + """ + + def __init__(self, filepath: str): + if not filepath.endswith(".json"): + filepath = filepath + ".json" + self.filepath = filepath + + def __call__(self, name, doc): + import json + + if name == "start": + self.file = open(self.filepath, "w") + self.file.write("[\n") + + json.dump({"name": name, "doc": doc}, self.file) + + if name == "stop": + self.file.write("\n]") + self.file.close() + else: + self.file.write(",\n") diff --git a/docs/tutorials/flyscanning/plotting.md b/docs/tutorials/flyscanning/plotting.md new file mode 100644 index 0000000..eefabc9 --- /dev/null +++ b/docs/tutorials/flyscanning/plotting.md @@ -0,0 +1,212 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.16.4 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +```{code-cell} ipython3 + +``` + +# Live Plotting of Streamed Data + +A distictive characteristic of fly scanning plans is their reliance on continuous data streams rather than acquisition of data points invidually. In Bluesky, data streaming is realized via StreamResource and StreamDatum documents (as aooposed to Resource and Datum in step scanning). The eventual number of data points in the stream (e.g. image frames acquired during a flyscanning experiment) is typically unknown at the begininng of the aquisition; furtehrmore, the frames may arrive at non-uniformly spaced time intervals. Eventually, the frames become parts of the same resource and are often appended to the same file (in case of hdf5) by the detector. + + +We introduce the notion of Consolidators to facilitate random access by data access services, e.g. Tiled. Consolidators are analogous to Handlers that have existed in Bluesky for a long time, but they enable working with streamed data. A Consolidator is defined for a particular stream of data and is tied to the underlying resource; it knows how to read the data from disk in the most efficient manner. + + +In this notebook, we will explore the use of Consolidators as an option for live plotting of streamed data. + +```{code-cell} ipython3 +%load_ext autoreload +%autoreload 2 + +import os +import asyncio +import ophyd_async +import bluesky +from ophyd_async.sim.demo import PatternDetector, SimMotor +import bluesky.plans as bp +import bluesky.plan_stubs as bps +import bluesky.preprocessors as bpp +from bluesky.callbacks.core import CollectLiveStream +from bluesky.callbacks.mpl_plotting import LiveStreamPlot +from bluesky.run_engine import RunEngine +from pathlib import Path +from sample_simulator import SampleSimulator +from json_writer import JSONWriter + + +print(f"{ophyd_async.__version__=}\n{bluesky.__version__ =}") +``` + +```{code-cell} ipython3 +import matplotlib +import matplotlib.pyplot as plt + +%matplotlib widget +plt.ion() +``` + +```{code-cell} ipython3 +fig, ax = plt.subplots(1, 2, figsize=(13, 4)) +``` + +```{code-cell} ipython3 +det = PatternDetector(name="PATTERN1", path=Path('.')) +motx = SimMotor(name="Motor_X", instant=False) +moty = SimMotor(name="Motor_Y", instant=False) +await motx.velocity.set(2.0) +await moty.velocity.set(1.0) + +RE = RunEngine({}, during_task = SampleSimulator(det, motx, moty)) + +cl = CollectLiveStream() +pl_1d = LiveStreamPlot(cl, data_key="PATTERN1-sum", ax=ax[0]) +pl_2d = LiveStreamPlot(cl, data_key="PATTERN1", ax=ax[1], clim=(0, 16)) +wr = JSONWriter('./documents_test.json') + +RE.subscribe(cl) +RE.subscribe(pl_1d) +RE.subscribe(pl_2d) +RE.subscribe(wr) +``` + +```{code-cell} ipython3 +### Simple count plan without moving motors +RE(bp.count([det], num=5, delay=0.2)) +``` + +```{code-cell} ipython3 +### One-dimensional scan +plan = bp.scan([det], motx, 0, 2.5, 50) +RE(plan) +``` + +```{code-cell} ipython3 +### Spiral trajectory +spiral_plan = bp.spiral([det], motx, moty, x_start=0.0, y_start=0.0, + x_range=3.0, y_range=3.0, dr=0.3, nth=10) +RE(spiral_plan) + +# from bluesky.simulators import plot_raster_path +# plot_raster_path(spiral_plan, 'motor1', 'motor2', probe_size=.01) +``` + +```{code-cell} ipython3 + +``` + +```{code-cell} ipython3 + +``` + +```{code-cell} ipython3 + +``` + +```{code-cell} ipython3 +### A custom plan +def plan(): + yield from scan([det], motx, 0, 2.5, 50) + + yield from bps.unstage_all(det, motx) + +RE(plan()) +``` + +```{code-cell} ipython3 +RE.stop() +``` + +```{code-cell} ipython3 +await det.prepare(0) +await det.unstage() +``` + +```{code-cell} ipython3 + +``` + +```{code-cell} ipython3 +--- +jupyter: + outputs_hidden: true +--- +gen = bp.fly([det]) +for msg in gen: + print(msg) +``` + +```{code-cell} ipython3 +--- +jupyter: + outputs_hidden: true +--- +gen = bp.count([det], num=5, delay=0.5) +for msg in gen: + print(msg) +``` + +```{code-cell} ipython3 + +``` + +```{code-cell} ipython3 + +``` + +```{code-cell} ipython3 + +``` + +```{code-cell} ipython3 + +``` + +```{code-cell} ipython3 + +``` + +```{code-cell} ipython3 + +``` + +```{code-cell} ipython3 +from ophyd_async.sim.demo._pattern_detector._pattern_generator import generate_gaussian_blob, generate_interesting_pattern +import matplotlib.colors as mcolors +from mpl_toolkits.axes_grid1 import make_axes_locatable +import numpy as np + +fig, ax = plt.subplots(1, 1, figsize=(7, 7)) + +x_arr = np.linspace(0, 6, 100) +y_arr = np.linspace(-3.14, 3.14, 100) +xx, yy = np.meshgrid(x_arr, y_arr) + +im1 = generate_gaussian_blob(width=350, height=200) +im2 = generate_interesting_pattern(xx, yy) +``` + +```{code-cell} ipython3 +im = ax.imshow(im2) +divider = make_axes_locatable(ax) +cax = divider.append_axes("right", size="5%", pad=0.05) +plt.colorbar(im, cax=cax) +``` + +```{code-cell} ipython3 + +``` + +```{code-cell} ipython3 + +``` diff --git a/docs/tutorials/flyscanning/sample_simulator.py b/docs/tutorials/flyscanning/sample_simulator.py new file mode 100644 index 0000000..c136d23 --- /dev/null +++ b/docs/tutorials/flyscanning/sample_simulator.py @@ -0,0 +1,45 @@ +import asyncio +import threading +from bluesky.utils import DefaultDuringTask +from ophyd_async.sim.demo import PatternDetector, SimMotor +from typing import Optional + +class SampleSimulator(DefaultDuringTask): + + """A helper class that connects simulated PatternDetector to SimMotors to mimick an actual physical sample. + + Changes the signal produced by the pattern detector depending on the motors' positions. + + """ + + def __init__(self, det : PatternDetector, motx : Optional[SimMotor] = None, moty : Optional[SimMotor] = None): + self._loop = asyncio.new_event_loop() + self.det = det + self.motx = motx + self.moty = moty + super().__init__() + + @property + def motx_pos(self): + if self.motx is not None: + return self._loop.run_until_complete(self.motx.user_readback.get_value()) + else: return 0.0 + + @property + def moty_pos(self): + if self.moty is not None: + return self._loop.run_until_complete(self.moty.user_readback.get_value()) + else: return 0.0 + + def block(self, blocking_event): + def target(event): + while not event.wait(0.1): + self.det.writer.pattern_generator.x = self.motx_pos + self.det.writer.pattern_generator.y = self.moty_pos + + ev = threading.Event() + th = threading.Thread(target = target, args=(ev, ), daemon=True) + th.start() + super().block(blocking_event) + + ev.set() \ No newline at end of file From 3df35f3a93c9d9de0c6d0ded9e5ac2c298a740af Mon Sep 17 00:00:00 2001 From: Eugene M Date: Mon, 30 Sep 2024 15:34:33 -0400 Subject: [PATCH 06/17] MNT: remove changes --- docs/tutorials/flyscanning/basics.md | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/docs/tutorials/flyscanning/basics.md b/docs/tutorials/flyscanning/basics.md index 005b390..77ab23d 100644 --- a/docs/tutorials/flyscanning/basics.md +++ b/docs/tutorials/flyscanning/basics.md @@ -13,12 +13,4 @@ kernelspec: # Flyscanning Basics -TODO - -```{code-cell} -import ophyd_async -``` - -```{code-cell} - -``` +TODO \ No newline at end of file From 44c51c3e94fc087b7007ede10fa80ecd989ca22b Mon Sep 17 00:00:00 2001 From: Eugene M Date: Mon, 30 Sep 2024 15:41:03 -0400 Subject: [PATCH 07/17] MNT: fix typo --- docs/tutorials/flyscanning/sample_simulator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/flyscanning/sample_simulator.py b/docs/tutorials/flyscanning/sample_simulator.py index c136d23..112a631 100644 --- a/docs/tutorials/flyscanning/sample_simulator.py +++ b/docs/tutorials/flyscanning/sample_simulator.py @@ -6,7 +6,7 @@ class SampleSimulator(DefaultDuringTask): - """A helper class that connects simulated PatternDetector to SimMotors to mimick an actual physical sample. + """A helper class that connects simulated PatternDetector to SimMotors to mimic an actual physical sample. Changes the signal produced by the pattern detector depending on the motors' positions. From 749b1a2c2305ae6e101aa79b60c27d809925eeec Mon Sep 17 00:00:00 2001 From: Eugene M Date: Wed, 11 Dec 2024 13:35:21 -0500 Subject: [PATCH 08/17] ENH: Tiled integration tutorial --- docs/tutorials/flyscanning/json_writer.py | 29 --- docs/tutorials/flyscanning/plotting.md | 212 ------------------ .../tutorials/flyscanning/sample_simulator.py | 45 ---- docs/tutorials/tiled_integration/main.md | 106 +++++++++ 4 files changed, 106 insertions(+), 286 deletions(-) delete mode 100644 docs/tutorials/flyscanning/json_writer.py delete mode 100644 docs/tutorials/flyscanning/plotting.md delete mode 100644 docs/tutorials/flyscanning/sample_simulator.py create mode 100644 docs/tutorials/tiled_integration/main.md diff --git a/docs/tutorials/flyscanning/json_writer.py b/docs/tutorials/flyscanning/json_writer.py deleted file mode 100644 index 61bb7ce..0000000 --- a/docs/tutorials/flyscanning/json_writer.py +++ /dev/null @@ -1,29 +0,0 @@ -class JSONWriter: - """Callback to write a Bluesky stream into a JSON file. Useful for debugging. - - Parameters - ---------- - filepath : str - A desired path to a .json file used to save the data. - - """ - - def __init__(self, filepath: str): - if not filepath.endswith(".json"): - filepath = filepath + ".json" - self.filepath = filepath - - def __call__(self, name, doc): - import json - - if name == "start": - self.file = open(self.filepath, "w") - self.file.write("[\n") - - json.dump({"name": name, "doc": doc}, self.file) - - if name == "stop": - self.file.write("\n]") - self.file.close() - else: - self.file.write(",\n") diff --git a/docs/tutorials/flyscanning/plotting.md b/docs/tutorials/flyscanning/plotting.md deleted file mode 100644 index eefabc9..0000000 --- a/docs/tutorials/flyscanning/plotting.md +++ /dev/null @@ -1,212 +0,0 @@ ---- -jupytext: - text_representation: - extension: .md - format_name: myst - format_version: 0.13 - jupytext_version: 1.16.4 -kernelspec: - display_name: Python 3 (ipykernel) - language: python - name: python3 ---- - -```{code-cell} ipython3 - -``` - -# Live Plotting of Streamed Data - -A distictive characteristic of fly scanning plans is their reliance on continuous data streams rather than acquisition of data points invidually. In Bluesky, data streaming is realized via StreamResource and StreamDatum documents (as aooposed to Resource and Datum in step scanning). The eventual number of data points in the stream (e.g. image frames acquired during a flyscanning experiment) is typically unknown at the begininng of the aquisition; furtehrmore, the frames may arrive at non-uniformly spaced time intervals. Eventually, the frames become parts of the same resource and are often appended to the same file (in case of hdf5) by the detector. - - -We introduce the notion of Consolidators to facilitate random access by data access services, e.g. Tiled. Consolidators are analogous to Handlers that have existed in Bluesky for a long time, but they enable working with streamed data. A Consolidator is defined for a particular stream of data and is tied to the underlying resource; it knows how to read the data from disk in the most efficient manner. - - -In this notebook, we will explore the use of Consolidators as an option for live plotting of streamed data. - -```{code-cell} ipython3 -%load_ext autoreload -%autoreload 2 - -import os -import asyncio -import ophyd_async -import bluesky -from ophyd_async.sim.demo import PatternDetector, SimMotor -import bluesky.plans as bp -import bluesky.plan_stubs as bps -import bluesky.preprocessors as bpp -from bluesky.callbacks.core import CollectLiveStream -from bluesky.callbacks.mpl_plotting import LiveStreamPlot -from bluesky.run_engine import RunEngine -from pathlib import Path -from sample_simulator import SampleSimulator -from json_writer import JSONWriter - - -print(f"{ophyd_async.__version__=}\n{bluesky.__version__ =}") -``` - -```{code-cell} ipython3 -import matplotlib -import matplotlib.pyplot as plt - -%matplotlib widget -plt.ion() -``` - -```{code-cell} ipython3 -fig, ax = plt.subplots(1, 2, figsize=(13, 4)) -``` - -```{code-cell} ipython3 -det = PatternDetector(name="PATTERN1", path=Path('.')) -motx = SimMotor(name="Motor_X", instant=False) -moty = SimMotor(name="Motor_Y", instant=False) -await motx.velocity.set(2.0) -await moty.velocity.set(1.0) - -RE = RunEngine({}, during_task = SampleSimulator(det, motx, moty)) - -cl = CollectLiveStream() -pl_1d = LiveStreamPlot(cl, data_key="PATTERN1-sum", ax=ax[0]) -pl_2d = LiveStreamPlot(cl, data_key="PATTERN1", ax=ax[1], clim=(0, 16)) -wr = JSONWriter('./documents_test.json') - -RE.subscribe(cl) -RE.subscribe(pl_1d) -RE.subscribe(pl_2d) -RE.subscribe(wr) -``` - -```{code-cell} ipython3 -### Simple count plan without moving motors -RE(bp.count([det], num=5, delay=0.2)) -``` - -```{code-cell} ipython3 -### One-dimensional scan -plan = bp.scan([det], motx, 0, 2.5, 50) -RE(plan) -``` - -```{code-cell} ipython3 -### Spiral trajectory -spiral_plan = bp.spiral([det], motx, moty, x_start=0.0, y_start=0.0, - x_range=3.0, y_range=3.0, dr=0.3, nth=10) -RE(spiral_plan) - -# from bluesky.simulators import plot_raster_path -# plot_raster_path(spiral_plan, 'motor1', 'motor2', probe_size=.01) -``` - -```{code-cell} ipython3 - -``` - -```{code-cell} ipython3 - -``` - -```{code-cell} ipython3 - -``` - -```{code-cell} ipython3 -### A custom plan -def plan(): - yield from scan([det], motx, 0, 2.5, 50) - - yield from bps.unstage_all(det, motx) - -RE(plan()) -``` - -```{code-cell} ipython3 -RE.stop() -``` - -```{code-cell} ipython3 -await det.prepare(0) -await det.unstage() -``` - -```{code-cell} ipython3 - -``` - -```{code-cell} ipython3 ---- -jupyter: - outputs_hidden: true ---- -gen = bp.fly([det]) -for msg in gen: - print(msg) -``` - -```{code-cell} ipython3 ---- -jupyter: - outputs_hidden: true ---- -gen = bp.count([det], num=5, delay=0.5) -for msg in gen: - print(msg) -``` - -```{code-cell} ipython3 - -``` - -```{code-cell} ipython3 - -``` - -```{code-cell} ipython3 - -``` - -```{code-cell} ipython3 - -``` - -```{code-cell} ipython3 - -``` - -```{code-cell} ipython3 - -``` - -```{code-cell} ipython3 -from ophyd_async.sim.demo._pattern_detector._pattern_generator import generate_gaussian_blob, generate_interesting_pattern -import matplotlib.colors as mcolors -from mpl_toolkits.axes_grid1 import make_axes_locatable -import numpy as np - -fig, ax = plt.subplots(1, 1, figsize=(7, 7)) - -x_arr = np.linspace(0, 6, 100) -y_arr = np.linspace(-3.14, 3.14, 100) -xx, yy = np.meshgrid(x_arr, y_arr) - -im1 = generate_gaussian_blob(width=350, height=200) -im2 = generate_interesting_pattern(xx, yy) -``` - -```{code-cell} ipython3 -im = ax.imshow(im2) -divider = make_axes_locatable(ax) -cax = divider.append_axes("right", size="5%", pad=0.05) -plt.colorbar(im, cax=cax) -``` - -```{code-cell} ipython3 - -``` - -```{code-cell} ipython3 - -``` diff --git a/docs/tutorials/flyscanning/sample_simulator.py b/docs/tutorials/flyscanning/sample_simulator.py deleted file mode 100644 index 112a631..0000000 --- a/docs/tutorials/flyscanning/sample_simulator.py +++ /dev/null @@ -1,45 +0,0 @@ -import asyncio -import threading -from bluesky.utils import DefaultDuringTask -from ophyd_async.sim.demo import PatternDetector, SimMotor -from typing import Optional - -class SampleSimulator(DefaultDuringTask): - - """A helper class that connects simulated PatternDetector to SimMotors to mimic an actual physical sample. - - Changes the signal produced by the pattern detector depending on the motors' positions. - - """ - - def __init__(self, det : PatternDetector, motx : Optional[SimMotor] = None, moty : Optional[SimMotor] = None): - self._loop = asyncio.new_event_loop() - self.det = det - self.motx = motx - self.moty = moty - super().__init__() - - @property - def motx_pos(self): - if self.motx is not None: - return self._loop.run_until_complete(self.motx.user_readback.get_value()) - else: return 0.0 - - @property - def moty_pos(self): - if self.moty is not None: - return self._loop.run_until_complete(self.moty.user_readback.get_value()) - else: return 0.0 - - def block(self, blocking_event): - def target(event): - while not event.wait(0.1): - self.det.writer.pattern_generator.x = self.motx_pos - self.det.writer.pattern_generator.y = self.moty_pos - - ev = threading.Event() - th = threading.Thread(target = target, args=(ev, ), daemon=True) - th.start() - super().block(blocking_event) - - ev.set() \ No newline at end of file diff --git a/docs/tutorials/tiled_integration/main.md b/docs/tutorials/tiled_integration/main.md new file mode 100644 index 0000000..2274236 --- /dev/null +++ b/docs/tutorials/tiled_integration/main.md @@ -0,0 +1,106 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.16.4 +kernelspec: + display_name: Python 3 (ipykernel) + language: python + name: python3 +--- + +# Registering Bluesky Data in Tiled + ++++ + +In the standard Bluesky data model, it is assumed that each data point produced during an experiment is emitted in a separate Event document. During the acquisition, such high granularity enables fast feedback and supports low-latency downstream agents (e.g. live plotting), however the naive storage of individual documents as records in a noSQL database is not optimized for the retrieval of blocks of data spanning multiple rows. Instead, the recently developed solution presented here parses the Event data into a tabular format during ingestion. + +This tutorial introduces TiledWriter -- a specialized callback in Bluesky designed to aggregate and store the incoming data in a way that would facilitate future random access. While initially conceived as a mechanism for registering array data asynchronously written by area detectors (see Flyscanning tutorial), TiledWriter supports the ingestion of all existing synchronous data modalities (e.g. the usual Event/Datum documents) and transforms their contents into a tabular form, suitable for the efficient retrieval of columns. + ++++ + +### Initial Set-Up + +```{code-cell} ipython3 +import bluesky.plans as bp +from ophyd.sim import det, motor +from bluesky.run_engine import RunEngine +from bluesky.callbacks.tiled_writer import TiledWriter +from tiled.client import from_uri +from pprint import pprint +``` + +```{code-cell} ipython3 +!pip install canonicaljson +``` + +Start a local Tiled server, for example: +```bash +tiled serve catalog --temp --api-key=secret +``` + +```{code-cell} ipython3 +# Initialize Tiled client +client = from_uri("http://localhost:8000", api_key="secret") +client +``` + +```{code-cell} ipython3 +# Initialize RunEngine and subscribe it to TiledWriter +RE = RunEngine({}) +tw = TiledWriter(client) +RE.subscribe(tw) + +# Keep the documents for monitoring/debug +docs = [] +RE.subscribe(lambda name, doc : docs.append( (name, doc) )) +``` + +### Running the Acquisition + +```{code-cell} ipython3 +# Run the acquisition +docs.clear() +scan_id, = RE(bp.scan([det], motor, -5, 5, 10)) +print(f"Finished aquisition: {scan_id=}") +``` + +Executing the above cell would have produced a stream of Bluesky documents, which have been saved and can be inspected in the `docs` list. Specifically, the Descriptor (`docs[1]`) contains specification of all available `data_keys` produced by the experinment (values of detector, `det`, `motor` and `motor_setpoint`). + +```{code-cell} ipython3 +# Data specification from the Descriptor document +stream_name = docs[1][1]['name'] +print(f"Stream name: {stream_name}\n") +print("Specifications of recorded data:") +pprint(docs[1][1]['data_keys']) +``` + +The actual data values, along with their corresponding timestamps, are emmitted via Event documents (`docs[2:-1]`) + +```{code-cell} ipython3 +# Example of Event documents +pprint(docs[2:4]) +``` + +### Accessing Data in Tiled + ++++ + +The data produced during the experiment has been ingested into Tiled and stored in a container under the correpoding scan uuid. + +NOTE: The internal structure of the Bluesky container is not finzalized yet and will change in the future releases. + +```{code-cell} ipython3 +bs_run = client[scan_id] +bs_stream = bs_run[stream_name] +event_data = bs_stream['internal/events'] +event_data +``` + +It can now be accessed as a usual `pandas.DataFrame`: + +```{code-cell} ipython3 +event_data.read() +``` From 99a99b64c8c8edc4ae8581477a43e27f7eef4c0d Mon Sep 17 00:00:00 2001 From: Dan Allan Date: Wed, 11 Dec 2024 13:50:27 -0500 Subject: [PATCH 09/17] Use PyPI because conda is behind and missing a dep. --- docs/tutorials/tiled_integration/main.md | 4 ---- pixi.toml | 3 +-- 2 files changed, 1 insertion(+), 6 deletions(-) diff --git a/docs/tutorials/tiled_integration/main.md b/docs/tutorials/tiled_integration/main.md index 2274236..554fba8 100644 --- a/docs/tutorials/tiled_integration/main.md +++ b/docs/tutorials/tiled_integration/main.md @@ -32,10 +32,6 @@ from tiled.client import from_uri from pprint import pprint ``` -```{code-cell} ipython3 -!pip install canonicaljson -``` - Start a local Tiled server, for example: ```bash tiled serve catalog --temp --api-key=secret diff --git a/pixi.toml b/pixi.toml index cb8fbd0..96bbdb5 100644 --- a/pixi.toml +++ b/pixi.toml @@ -26,8 +26,6 @@ jupyterlab = ">=4.2.5,<5" jupyterlab-myst = ">=2.4.2,<3" pydata-sphinx-theme = ">=0.15.4,<0.16" sphinxcontrib-serializinghtml = ">=1.1.10,<2" -tiled-server = ">=0.1.0b8,<0.2" -tiled-client = ">=0.1.0b8,<0.2" numpy = "<2.0.0" aioca = ">=1.7,<2" @@ -39,6 +37,7 @@ sphinx-copybutton = ">=0.5.2, <0.6" bluesky = { git = "https://github.com/bluesky/bluesky.git", rev = "51bfc0339a75d8ae1f6e697dba51f524322d34ef"} ophyd-async = { version = ">=0.6.0", extras=["sim"]} ophyd = ">=1.9.0, <2" +tiled = { version =">=0.1.0b12,<0.2", extras=["all"]} [feature.jupyterlite.dependencies] jupyterlab = "~=4.2.4" From 7505563875bb51401fe2792274a0de6d16579e17 Mon Sep 17 00:00:00 2001 From: Dan Allan Date: Wed, 11 Dec 2024 14:28:58 -0500 Subject: [PATCH 10/17] Less context --- docs/tutorials/tiled_integration/main.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/tiled_integration/main.md b/docs/tutorials/tiled_integration/main.md index 554fba8..65acc34 100644 --- a/docs/tutorials/tiled_integration/main.md +++ b/docs/tutorials/tiled_integration/main.md @@ -15,9 +15,9 @@ kernelspec: +++ -In the standard Bluesky data model, it is assumed that each data point produced during an experiment is emitted in a separate Event document. During the acquisition, such high granularity enables fast feedback and supports low-latency downstream agents (e.g. live plotting), however the naive storage of individual documents as records in a noSQL database is not optimized for the retrieval of blocks of data spanning multiple rows. Instead, the recently developed solution presented here parses the Event data into a tabular format during ingestion. +In the standard Bluesky data model, it is assumed that each data point produced during an experiment is emitted in a separate Event document. During the acquisition, such high granularity enables fast feedback and supports low-latency downstream agents (e.g. live plotting), however the naive storage of individual documents as records in a database or lines in a file is not optimized for the retrieval of blocks of data spanning multiple rows. Instead, the recently developed solution presented here parses the Event data into a tabular format during ingestion. -This tutorial introduces TiledWriter -- a specialized callback in Bluesky designed to aggregate and store the incoming data in a way that would facilitate future random access. While initially conceived as a mechanism for registering array data asynchronously written by area detectors (see Flyscanning tutorial), TiledWriter supports the ingestion of all existing synchronous data modalities (e.g. the usual Event/Datum documents) and transforms their contents into a tabular form, suitable for the efficient retrieval of columns. +This tutorial introduces TiledWriter -- a specialized callback in Bluesky designed to aggregate and store the incoming data in a way that would facilitate future random access. TiledWriter consumes Bluesky documents (all types of them) stores their contents at rest via API calls into a Tiled server. It transforms the data streams into a tabular form, suitable for the efficient retrieval of columns. +++ From fa862d5b5853b486a9ad542395f92b83daf4aa71 Mon Sep 17 00:00:00 2001 From: Dan Allan Date: Wed, 11 Dec 2024 15:02:27 -0500 Subject: [PATCH 11/17] Tweak title --- docs/tutorials/tiled_integration/main.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorials/tiled_integration/main.md b/docs/tutorials/tiled_integration/main.md index 65acc34..fbd854b 100644 --- a/docs/tutorials/tiled_integration/main.md +++ b/docs/tutorials/tiled_integration/main.md @@ -11,7 +11,7 @@ kernelspec: name: python3 --- -# Registering Bluesky Data in Tiled +# Saving Bluesky Data in Tiled +++ From f07b832708da857188c60afb2c526662427b2515 Mon Sep 17 00:00:00 2001 From: Dan Allan Date: Wed, 11 Dec 2024 15:02:41 -0500 Subject: [PATCH 12/17] Run tiled server on thread --- docs/tutorials/tiled_integration/main.md | 27 + pixi.lock | 7130 +++++++--------------- pixi.toml | 1 + 3 files changed, 2133 insertions(+), 5025 deletions(-) diff --git a/docs/tutorials/tiled_integration/main.md b/docs/tutorials/tiled_integration/main.md index fbd854b..0a8f36a 100644 --- a/docs/tutorials/tiled_integration/main.md +++ b/docs/tutorials/tiled_integration/main.md @@ -37,6 +37,33 @@ Start a local Tiled server, for example: tiled serve catalog --temp --api-key=secret ``` +```{code-cell} ipython3 +:tags: [hide-cell] + +# This is equiavalent to 'tiled server catalog --temp --api-key=secret' on a thread. +# TODO Provide in tiled a more succinct way to do this. + +import tempfile +import uvicorn +from pathlib import Path +from tiled.catalog import from_uri as catalog_from_uri +from tiled.server.app import build_app +# TODO Expose this publicly in Tiled. +from tiled._tests.test_server import Server + +temp_directory = Path(tempfile.TemporaryDirectory().name) +temp_directory.mkdir() +catalog = catalog_from_uri( + temp_directory / "catalog.db", + writable_storage=temp_directory / "data", + init_if_not_exists=True, +) +app = build_app(catalog, authentication={"single_user_api_key": "secret"}) +server = Server(uvicorn.Config(app, port=8000)) +cm = server.run_in_thread() +cm.__enter__() +``` + ```{code-cell} ipython3 # Initialize Tiled client client = from_uri("http://localhost:8000", api_key="secret") diff --git a/pixi.lock b/pixi.lock index 920f9ed..df11f04 100644 --- a/pixi.lock +++ b/pixi.lock @@ -9,112 +9,56 @@ environments: linux-64: - 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size: 21409 - timestamp: 1726248679175 + size: 21809 + timestamp: 1732827613585 - kind: conda name: zstandard version: 0.23.0 diff --git a/pixi.toml b/pixi.toml index 385576c..96bbdb5 100644 --- a/pixi.toml +++ b/pixi.toml @@ -28,7 +28,6 @@ pydata-sphinx-theme = ">=0.15.4,<0.16" sphinxcontrib-serializinghtml = ">=1.1.10,<2" numpy = "<2.0.0" aioca = ">=1.7,<2" -pytest = ">=8.3.4,<9" [pypi-dependencies] sphinx = ">=8.0.2, <9" From 754bb983256669361805ab81324fda81f6a4938c Mon Sep 17 00:00:00 2001 From: Eugene M Date: Wed, 11 Dec 2024 18:16:10 -0500 Subject: [PATCH 14/17] ENH: add threaded Tiled server --- .../tiled_integration/tiled_server.py | 54 +++++++++++++++++++ 1 file changed, 54 insertions(+) create mode 100644 docs/tutorials/tiled_integration/tiled_server.py diff --git a/docs/tutorials/tiled_integration/tiled_server.py b/docs/tutorials/tiled_integration/tiled_server.py new file mode 100644 index 0000000..09502eb --- /dev/null +++ b/docs/tutorials/tiled_integration/tiled_server.py @@ -0,0 +1,54 @@ +import contextlib +import threading +import time +import uvicorn +import tempfile +import pathlib + +from tiled.server.app import build_app +from tiled.catalog import from_uri as catalog_from_uri + + +class ThreadedServer(uvicorn.Server): + + def install_signal_handlers(self): + pass + + @contextlib.contextmanager + def run_in_thread(self): + thread = threading.Thread(target=self.run) + thread.start() + try: + # Wait for server to start up, or raise TimeoutError. + for _ in range(100): + time.sleep(0.1) + if self.started: + break + else: + raise TimeoutError("Server did not start in 10 seconds.") + host, port = self.servers[0].sockets[0].getsockname() + yield f"http://{host}:{port}" + finally: + self.should_exit = True + thread.join() + + +class TiledServer: + + def __init__(self, port=8000, dir_path=None, api_key="secret"): + if dir_path is None: + dir_path = pathlib.Path(tempfile.TemporaryDirectory().name) + else: + dir_path = pathlib.Path(dir_path) + dir_path.mkdir(parents=True, exist_ok=True) + + self.catalog = catalog_from_uri( + dir_path / "catalog.db", + writable_storage=dir_path / "data", + init_if_not_exists=True, + ) + self.app = build_app(self.catalog, authentication={"single_user_api_key": api_key}) + self._cm = ThreadedServer(uvicorn.Config(self.app, port=port, loop="asyncio")).run_in_thread() + + def run(self): + return self._cm.__enter__() From 7983bf8335fc0a4b6078946b8dec9d659502fda0 Mon Sep 17 00:00:00 2001 From: Eugene M Date: Wed, 11 Dec 2024 18:16:30 -0500 Subject: [PATCH 15/17] ENH: use threaded Tiled server in the notebook --- docs/tutorials/tiled_integration/main.md | 79 +++++++++++++++--------- 1 file changed, 49 insertions(+), 30 deletions(-) diff --git a/docs/tutorials/tiled_integration/main.md b/docs/tutorials/tiled_integration/main.md index 0a8f36a..0b56e07 100644 --- a/docs/tutorials/tiled_integration/main.md +++ b/docs/tutorials/tiled_integration/main.md @@ -32,46 +32,26 @@ from tiled.client import from_uri from pprint import pprint ``` -Start a local Tiled server, for example: -```bash -tiled serve catalog --temp --api-key=secret -``` - ```{code-cell} ipython3 -:tags: [hide-cell] - -# This is equiavalent to 'tiled server catalog --temp --api-key=secret' on a thread. -# TODO Provide in tiled a more succinct way to do this. - -import tempfile -import uvicorn -from pathlib import Path -from tiled.catalog import from_uri as catalog_from_uri -from tiled.server.app import build_app -# TODO Expose this publicly in Tiled. -from tiled._tests.test_server import Server - -temp_directory = Path(tempfile.TemporaryDirectory().name) -temp_directory.mkdir() -catalog = catalog_from_uri( - temp_directory / "catalog.db", - writable_storage=temp_directory / "data", - init_if_not_exists=True, -) -app = build_app(catalog, authentication={"single_user_api_key": "secret"}) -server = Server(uvicorn.Config(app, port=8000)) -cm = server.run_in_thread() -cm.__enter__() +# Start a local Tiled server +# The following is equivalent to 'tiled server catalog --temp --api-key=secret' + +from tiled_server import TiledServer + +server = TiledServer(port=8000, api_key='secret', dir_path='tiled_data') +server.run() ``` ```{code-cell} ipython3 -# Initialize Tiled client +# Initialize a Tiled client + client = from_uri("http://localhost:8000", api_key="secret") client ``` ```{code-cell} ipython3 # Initialize RunEngine and subscribe it to TiledWriter + RE = RunEngine({}) tw = TiledWriter(client) RE.subscribe(tw) @@ -127,3 +107,42 @@ It can now be accessed as a usual `pandas.DataFrame`: ```{code-cell} ipython3 event_data.read() ``` + +### Reading Data Directly from File + +```{code-cell} ipython3 +import pandas as pd + +data_fpath = f'./tiled_data/data/{scan_id}/{stream_name}/internal/events/partition-0.csv' +df = pd.read_csv(data_fpath) +df +``` + +### Exploring the Tiled Catalog + +```{code-cell} ipython3 +import sqlite3 + +db_fpath = f'./tiled_data/catalog.db' +con = sqlite3.connect(db_fpath) +cur = con.cursor() +``` + +```{code-cell} ipython3 +res = cur.execute("SELECT name FROM sqlite_master WHERE type='table';") +res.fetchall() +``` + +```{code-cell} ipython3 +res = cur.execute("PRAGMA table_info('assets');") +res.fetchall() +``` + +```{code-cell} ipython3 +res = cur.execute("SELECT * FROM assets;") +res.fetchone() +``` + +```{code-cell} ipython3 + +``` From 7376d552c9593db6ade175d94bb9c726ba61bfa3 Mon Sep 17 00:00:00 2001 From: Eugene M Date: Wed, 11 Dec 2024 18:28:01 -0500 Subject: [PATCH 16/17] ENH: set default port to 0 --- docs/tutorials/tiled_integration/main.md | 8 ++++---- docs/tutorials/tiled_integration/tiled_server.py | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/tutorials/tiled_integration/main.md b/docs/tutorials/tiled_integration/main.md index 0b56e07..52ac766 100644 --- a/docs/tutorials/tiled_integration/main.md +++ b/docs/tutorials/tiled_integration/main.md @@ -38,14 +38,14 @@ from pprint import pprint from tiled_server import TiledServer -server = TiledServer(port=8000, api_key='secret', dir_path='tiled_data') -server.run() +server = TiledServer(api_key='secret', dir_path='tiled_data') +tiled_uri = server.run() ``` ```{code-cell} ipython3 # Initialize a Tiled client -client = from_uri("http://localhost:8000", api_key="secret") +client = from_uri(tiled_uri, api_key="secret") client ``` @@ -140,7 +140,7 @@ res.fetchall() ```{code-cell} ipython3 res = cur.execute("SELECT * FROM assets;") -res.fetchone() +res.fetchall() ``` ```{code-cell} ipython3 diff --git a/docs/tutorials/tiled_integration/tiled_server.py b/docs/tutorials/tiled_integration/tiled_server.py index 09502eb..bfe2890 100644 --- a/docs/tutorials/tiled_integration/tiled_server.py +++ b/docs/tutorials/tiled_integration/tiled_server.py @@ -35,7 +35,7 @@ def run_in_thread(self): class TiledServer: - def __init__(self, port=8000, dir_path=None, api_key="secret"): + def __init__(self, port=0, dir_path=None, api_key="secret"): if dir_path is None: dir_path = pathlib.Path(tempfile.TemporaryDirectory().name) else: From 8d63a68c7f41f09b57f35cceff23e206bfaa51a8 Mon Sep 17 00:00:00 2001 From: Eugene M Date: Thu, 12 Dec 2024 10:38:15 -0500 Subject: [PATCH 17/17] FIX: rename TiledServer to emphasize it's temporary --- docs/tutorials/tiled_integration/main.md | 8 ++++++-- docs/tutorials/tiled_integration/tiled_server.py | 2 +- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/docs/tutorials/tiled_integration/main.md b/docs/tutorials/tiled_integration/main.md index 52ac766..589f4e2 100644 --- a/docs/tutorials/tiled_integration/main.md +++ b/docs/tutorials/tiled_integration/main.md @@ -36,9 +36,9 @@ from pprint import pprint # Start a local Tiled server # The following is equivalent to 'tiled server catalog --temp --api-key=secret' -from tiled_server import TiledServer +from tiled_server import TempTiledServer -server = TiledServer(api_key='secret', dir_path='tiled_data') +server = TempTiledServer(api_key='secret', dir_path='tiled_data') tiled_uri = server.run() ``` @@ -146,3 +146,7 @@ res.fetchall() ```{code-cell} ipython3 ``` + +```{code-cell} ipython3 + +``` diff --git a/docs/tutorials/tiled_integration/tiled_server.py b/docs/tutorials/tiled_integration/tiled_server.py index bfe2890..9510168 100644 --- a/docs/tutorials/tiled_integration/tiled_server.py +++ b/docs/tutorials/tiled_integration/tiled_server.py @@ -33,7 +33,7 @@ def run_in_thread(self): thread.join() -class TiledServer: +class TempTiledServer: def __init__(self, port=0, dir_path=None, api_key="secret"): if dir_path is None: