From e911b791aea09eed73a397a8795c550a3abcb00e Mon Sep 17 00:00:00 2001 From: Henry Pinkard <7969470+henrypinkard@users.noreply.github.com> Date: Sat, 27 Jul 2024 08:55:52 +0200 Subject: [PATCH 1/4] small improvement for headless --- pycromanager/headless.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pycromanager/headless.py b/pycromanager/headless.py index 63d7f9cb..e038ca32 100644 --- a/pycromanager/headless.py +++ b/pycromanager/headless.py @@ -52,10 +52,11 @@ def start_headless( port=port, debug=debug) if python_backend: Engine(Core()) + else: + # make sure any Java processes are cleaned up when Python exits + atexit.register(stop_headless) def stop_headless(debug=False): terminate_core_instances(debug=debug) Engine.get_instance().shutdown() -# make sure any Java processes are cleaned up when Python exits -atexit.register(stop_headless) \ No newline at end of file From 73eb26cc137881fdfc7bfda2fc97170b90acbc21 Mon Sep 17 00:00:00 2001 From: Henry Pinkard <7969470+henrypinkard@users.noreply.github.com> Date: Mon, 26 Aug 2024 11:09:47 -0400 Subject: [PATCH 2/4] add tests for notifications and futures --- docs/source/index.rst | 7 - pycromanager/test/test_notifications.py | 58 ------ .../test/test_notifications_futures.py | 167 ++++++++++++++++++ 3 files changed, 167 insertions(+), 65 deletions(-) delete mode 100644 pycromanager/test/test_notifications.py create mode 100644 pycromanager/test/test_notifications_futures.py diff --git a/docs/source/index.rst b/docs/source/index.rst index 66a90e8e..dcce3fb9 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -46,10 +46,3 @@ Information on the original motivations for Pycro-Manager can be found in the `p - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/pycromanager/test/test_notifications.py b/pycromanager/test/test_notifications.py deleted file mode 100644 index 0b725f19..00000000 --- a/pycromanager/test/test_notifications.py +++ /dev/null @@ -1,58 +0,0 @@ -""" -Tests for notification API and asynchronous acquisition API -""" -from pycromanager import multi_d_acquisition_events, Acquisition, AcqNotification -import time -import numpy as np - -# TODO: add tests for timing of blocking until different parts of the hardware sequence -# def test_async_images_read(launch_mm_headless, setup_data_folder): -# start = time.time() -# events = multi_d_acquisition_events(num_time_points=10, time_interval_s=0.5) -# with Acquisition(directory=setup_data_folder, show_display=False) as acq: -# future = acq.acquire(events) -# -# future.await_execution({'time': 5}, AcqNotification.Hardware.POST_HARDWARE) - - - -def test_async_image_read(launch_mm_headless, setup_data_folder): - events = multi_d_acquisition_events(num_time_points=10, time_interval_s=0.5) - with Acquisition(directory=setup_data_folder, name='test_async_image_read', show_display=False) as acq: - future = acq.acquire(events) - image = future.await_image_saved({'time': 5}, return_image=True) - assert np.all(image == acq.get_dataset().read_image(time=5)) - acq.get_dataset().close() - -def test_async_image_read_sequence(launch_mm_headless, setup_data_folder): - events = multi_d_acquisition_events(num_time_points=10, time_interval_s=0) - with Acquisition(directory=setup_data_folder, name='test_async_image_read_sequence', show_display=False) as acq: - future = acq.acquire(events) - image = future.await_image_saved({'time': 5}, return_image=True) - assert np.all(image == acq.get_dataset().read_image(time=5)) - acq.get_dataset().close() - -def test_async_images_read(launch_mm_headless, setup_data_folder): - events = multi_d_acquisition_events(num_time_points=10, time_interval_s=0.5) - with Acquisition(directory=setup_data_folder, name='test_async_images_read', show_display=False) as acq: - future = acq.acquire(events) - images = future.await_image_saved([{'time': 7}, {'time': 8}, {'time': 9}], return_image=True) - assert (len(images) == 3) - - # Make sure the returned images were the correct ones - on_disk = [acq.get_dataset().read_image(time=t) for t in [7, 8, 9]] - assert all([np.all(on_disk[i] == images[i]) for i in range(3)]) - acq.get_dataset().close() - -def test_async_images_read_sequence(launch_mm_headless, setup_data_folder): - events = multi_d_acquisition_events(num_time_points=10, time_interval_s=0) - with Acquisition(directory=setup_data_folder, name='test_async_images_read_sequence', show_display=False) as acq: - future = acq.acquire(events) - images = future.await_image_saved([{'time': 7}, {'time': 8}, {'time': 9}], return_image=True) - assert (len(images) == 3) - - # Make sure the returned images were the correct ones - on_disk = [acq.get_dataset().read_image(time=t) for t in [7, 8, 9]] - assert all([np.all(on_disk[i] == images[i]) for i in range(3)]) - acq.get_dataset().close() - diff --git a/pycromanager/test/test_notifications_futures.py b/pycromanager/test/test_notifications_futures.py new file mode 100644 index 00000000..a2fc51ee --- /dev/null +++ b/pycromanager/test/test_notifications_futures.py @@ -0,0 +1,167 @@ +""" +Tests for notification API and asynchronous acquisition API +""" +from pycromanager import multi_d_acquisition_events, Acquisition, AcqNotification +import time +import numpy as np + +# Existing tests + +def test_async_image_read(launch_mm_headless, setup_data_folder): + events = multi_d_acquisition_events(num_time_points=10, time_interval_s=0.5) + with Acquisition(directory=setup_data_folder, name='test_async_image_read', show_display=False) as acq: + future = acq.acquire(events) + image = future.await_image_saved({'time': 5}, return_image=True) + assert np.all(image == acq.get_dataset().read_image(time=5)) + acq.get_dataset().close() + +def test_async_image_read_sequence(launch_mm_headless, setup_data_folder): + events = multi_d_acquisition_events(num_time_points=10, time_interval_s=0) + with Acquisition(directory=setup_data_folder, name='test_async_image_read_sequence', show_display=False) as acq: + future = acq.acquire(events) + image = future.await_image_saved({'time': 5}, return_image=True) + assert np.all(image == acq.get_dataset().read_image(time=5)) + acq.get_dataset().close() + +def test_async_images_read(launch_mm_headless, setup_data_folder): + events = multi_d_acquisition_events(num_time_points=10, time_interval_s=0.5) + with Acquisition(directory=setup_data_folder, name='test_async_images_read', show_display=False) as acq: + future = acq.acquire(events) + images = future.await_image_saved([{'time': 7}, {'time': 8}, {'time': 9}], return_image=True) + assert (len(images) == 3) + + # Make sure the returned images were the correct ones + on_disk = [acq.get_dataset().read_image(time=t) for t in [7, 8, 9]] + assert all([np.all(on_disk[i] == images[i]) for i in range(3)]) + acq.get_dataset().close() + +def test_async_images_read_sequence(launch_mm_headless, setup_data_folder): + events = multi_d_acquisition_events(num_time_points=10, time_interval_s=0) + with Acquisition(directory=setup_data_folder, name='test_async_images_read_sequence', show_display=False) as acq: + future = acq.acquire(events) + images = future.await_image_saved([{'time': 7}, {'time': 8}, {'time': 9}], return_image=True) + assert (len(images) == 3) + + # Make sure the returned images were the correct ones + on_disk = [acq.get_dataset().read_image(time=t) for t in [7, 8, 9]] + assert all([np.all(on_disk[i] == images[i]) for i in range(3)]) + acq.get_dataset().close() + +# New tests + +def test_notification_callback_receives_notifications(launch_mm_headless, setup_data_folder): + """ + Test that the notification callback function receives notifications during acquisition. + """ + notifications = [] + def notification_callback(notification): + notifications.append(notification) + + events = multi_d_acquisition_events(num_time_points=2) + + with Acquisition(directory=setup_data_folder, name='test_notification_callback', + notification_callback_fn=notification_callback, show_display=False) as acq: + acq.acquire(events) + + assert len(notifications) > 0 + +def test_notification_callback_receives_start_notification(launch_mm_headless, setup_data_folder): + """ + Test that the notification callback receives an acquisition start notification. + """ + notifications = [] + def notification_callback(notification): + notifications.append(notification) + + events = multi_d_acquisition_events(num_time_points=1) + + with Acquisition(directory=setup_data_folder, name='test_start_notification', + notification_callback_fn=notification_callback, show_display=False) as acq: + acq.acquire(events) + + assert any(n.milestone == AcqNotification.Acquisition.ACQ_STARTED for n in notifications) + +def test_notification_callback_receives_image_saved_notification(launch_mm_headless, setup_data_folder): + """ + Test that the notification callback receives an image saved notification. + """ + notifications = [] + def notification_callback(notification): + notifications.append(notification) + + events = multi_d_acquisition_events(num_time_points=1) + + with Acquisition(directory=setup_data_folder, name='test_image_saved_notification', + notification_callback_fn=notification_callback, show_display=False) as acq: + acq.acquire(events) + + assert any(n.milestone == AcqNotification.Image.IMAGE_SAVED for n in notifications) + +def test_notification_callback_receives_finish_notification(launch_mm_headless, setup_data_folder): + """ + Test that the notification callback receives an acquisition finished notification. + """ + notifications = [] + def notification_callback(notification): + notifications.append(notification) + + events = multi_d_acquisition_events(num_time_points=1) + + with Acquisition(directory=setup_data_folder, name='test_finish_notification', + notification_callback_fn=notification_callback, show_display=False) as acq: + acq.acquire(events) + + assert any(n.milestone == AcqNotification.Acquisition.ACQ_EVENTS_FINISHED for n in notifications) + +def test_acquisition_future_await_execution(launch_mm_headless, setup_data_folder): + """ + Test that AcquisitionFuture.await_execution works correctly. + """ + events = multi_d_acquisition_events(num_time_points=3, time_interval_s=0.1) + + with Acquisition(directory=setup_data_folder, name='test_await_execution', + show_display=False) as acq: + future = acq.acquire(events) + future.await_execution(milestone=AcqNotification.Hardware.POST_HARDWARE, axes={'time': 1}) + # If we reach this point without timing out, the test passes + +def test_acquisition_future_await_image_saved_single(launch_mm_headless, setup_data_folder): + """ + Test that AcquisitionFuture.await_image_saved works for a single image. + """ + events = multi_d_acquisition_events(num_time_points=3, time_interval_s=0.1) + + with Acquisition(directory=setup_data_folder, name='test_await_image_single', + show_display=False) as acq: + future = acq.acquire(events) + image = future.await_image_saved({'time': 1}, return_image=True) + assert isinstance(image, np.ndarray) + assert image.shape == (512, 512) # Assuming default image size + +def test_acquisition_future_await_image_saved_multiple(launch_mm_headless, setup_data_folder): + """ + Test that AcquisitionFuture.await_image_saved works for multiple images. + """ + events = multi_d_acquisition_events(num_time_points=5, time_interval_s=0.1) + + with Acquisition(directory=setup_data_folder, name='test_await_image_multiple', + show_display=False) as acq: + future = acq.acquire(events) + images = future.await_image_saved([{'time': 2}, {'time': 3}, {'time': 4}], return_image=True) + assert len(images) == 3 + assert all(isinstance(img, np.ndarray) for img in images) + +def test_acquisition_future_image_consistency(launch_mm_headless, setup_data_folder): + """ + Test that images returned by AcquisitionFuture match those saved on disk. + """ + events = multi_d_acquisition_events(num_time_points=5, time_interval_s=0.1) + + with Acquisition(directory=setup_data_folder, name='test_image_consistency', + show_display=False) as acq: + future = acq.acquire(events) + images = future.await_image_saved([{'time': 2}, {'time': 3}, {'time': 4}], return_image=True) + + dataset = acq.get_dataset() + on_disk = [dataset.read_image(time=t) for t in [2, 3, 4]] + assert all(np.array_equal(on_disk[i], images[i]) for i in range(3)) \ No newline at end of file From b05b832e1d766fa43f1d31ddb1ba6e9f87475025 Mon Sep 17 00:00:00 2001 From: Henry Pinkard <7969470+henrypinkard@users.noreply.github.com> Date: Tue, 27 Aug 2024 22:46:46 -0400 Subject: [PATCH 3/4] major documentation overhaul --- docs/source/acq_events.rst | 92 +-- docs/source/acq_hooks.rst | 69 +- docs/source/acq_overview.rst | 236 ++++--- docs/source/adaptive_acq.rst | 79 +++ docs/source/advanced_usage.rst | 29 - docs/source/apis.rst | 46 +- .../application_notebooks/PSF_viewer.ipynb | 596 ------------------ .../application_notebooks/PSF_viewer.py | 355 ----------- docs/source/backends.rst | 96 +++ docs/source/calling_java.rst | 7 - docs/source/conf.py | 7 +- docs/source/core.rst | 23 +- ...re.png => event_hook_processor_figure.png} | Bin ...xternal_hardware_triggering_tutorial.ipynb | 2 +- docs/source/hardware_triggering.rst | 79 ++- docs/source/headless_mode.rst | 87 --- docs/source/image_saved_callbacks.rst | 79 ++- docs/source/img_processors.rst | 147 +++-- docs/source/index.rst | 3 +- .../intermittent_Z_T.ipynb | 5 +- .../multi-d-acq-tutorial.ipynb | 0 docs/source/notifications.rst | 28 + docs/source/overview_figure.png | Bin 144654 -> 0 bytes docs/source/performance_guide.rst | 9 - docs/source/pm_arch_full.png | Bin 0 -> 287625 bytes docs/source/pm_architecture.png | Bin 169319 -> 0 bytes docs/source/pycromanager_banner.png | Bin 84255 -> 85895 bytes docs/source/read_data.rst | 92 --- docs/source/setup.rst | 22 +- docs/source/special_acqs.rst | 65 +- docs/source/three_backends.png | Bin 0 -> 708195 bytes docs/source/tutorials.rst | 21 +- docs/source/user_guide.rst | 81 ++- pycromanager/__init__.py | 3 + pycromanager/acquisition/acq_future.py | 23 +- 35 files changed, 865 insertions(+), 1516 deletions(-) create mode 100644 docs/source/adaptive_acq.rst delete mode 100644 docs/source/advanced_usage.rst delete mode 100644 docs/source/application_notebooks/PSF_viewer.ipynb delete mode 100644 docs/source/application_notebooks/PSF_viewer.py create mode 100644 docs/source/backends.rst rename docs/source/{acquisition_figure.png => event_hook_processor_figure.png} (100%) rename docs/source/{application_notebooks => }/external_hardware_triggering_tutorial.ipynb (99%) delete mode 100644 docs/source/headless_mode.rst rename docs/source/{application_notebooks => }/intermittent_Z_T.ipynb (97%) rename docs/source/{application_notebooks => }/multi-d-acq-tutorial.ipynb (100%) create mode 100644 docs/source/notifications.rst delete mode 100644 docs/source/overview_figure.png delete mode 100644 docs/source/performance_guide.rst create mode 100644 docs/source/pm_arch_full.png delete mode 100644 docs/source/pm_architecture.png delete mode 100644 docs/source/read_data.rst create mode 100644 docs/source/three_backends.png diff --git a/docs/source/acq_events.rst b/docs/source/acq_events.rst index f8c3ac37..f9251051 100644 --- a/docs/source/acq_events.rst +++ b/docs/source/acq_events.rst @@ -4,40 +4,51 @@ Custom Acquisition Events **************************************************************** -An acquisition event is a Python ``dict`` object with a specific structure. The :meth:`multi_d_acquisition_events` function can be used to create these events, but since it only covers a limited number of use cases, it often may be more useful to create them manually. +Acquisition events in Pycro-Manager are Python dictionaries that define how hardware is controlled and images are acquired. While the ``multi_d_acquisition_events()`` function can generate events for common scenarios, custom events offer greater flexibility and control for more complex imaging protocols. +A full description of all possible fields in an acquisition event can be found in the :ref:`acq_event_spec`. -Every event must have an ``axes`` field that uniquely identifies the image that the event will produce. This field is a dictionary which contains the name and position of each axis used to identify the image. The position can either be an int or a string. -For example, in a timelapse of ten images would vary only over the ``time`` axis, and the first two events would be: +Basic Structure +--------------- + +Every event must have an ``axes`` field to uniquely identify the resulting image: + +.. code-block:: python + + event = { + 'axes': {'time': 0, 'z': 3, 'channel': 'DAPI'} + } + +The position in each axis can be either an int or a string. + +In most cases, each event will produce a single image. Thus, a series of images in a time-lapse acquisition might be defined as: .. code-block:: python - event_0 = { 'axes': {'time': 0} } - event_1 = { 'axes': {'time': 1} } + event_0 = { 'axes': {'time': 0} } + event_1 = { 'axes': {'time': 1} } + event_2 = { 'axes': {'time': 2} } -A full description of all possible fields in an acquisition event can be found in the :ref:`acq_event_spec`. -The following example shows the a the events used to acquire a z-stack. Note that this is primarily for demonstration purposes, as the same events can be generated more conveniently using the :meth:`multi_d_acquisition_events` function. +In addition to the ``axes`` field, events other fields that determine how the hardware is controlled (see :ref:`acq_event_spec` for a complete listing of these). +For example, the ``'z'`` field specifies the z position of the focus stage in microns. A Z-stack acquisition can be performed by creating events with different z positions: .. code-block:: python - with Acquisition('/path/to/data', 'acq_name') as acq: - # create one event for the image at each z-slice - events = [] - for index, z_um in enumerate(np.arange(start=0, stop=10, step=0.5)): - evt = { - # 'axes' is required. It is used by the image viewer and data storage to - # identify the acquired image - 'axes': {'z': index}, - - # the 'z' field provides the z position in µm - 'z': z_um - } - events.append(evt) + with Acquisition('/path/to/data', 'z_stack_acq') as acq: + events = [] + for index, z_um in enumerate(np.arange(start=0, stop=10, step=0.5)): + evt = { + 'axes': {'z': index}, # this indexes the resulting image + 'z': z_um # this specifies the z position in microns + } + events.append(evt) + acq.acquire(events) + + - acq.acquire(events) @@ -45,28 +56,37 @@ Creating custom acquisition events provides more flexibility in controlling hard .. code-block:: python - with Acquisition('/path/to/data', 'acq_name') as acq: - events = [] - for index in range(10): - evt = { - 'axes': {'arbitrary_axis_name': index}, - #'properties' for the manipulation of hardware by specifying an arbitrary - #list of properties - 'properties': - [['device_name', 'property_name', 'property_value'], - ['device_name_2', 'property_name_2', 'property_value_2']] - } - events.append(evt) + with Acquisition('/path/to/data', 'acq_name') as acq: + events = [] + for index in range(10): + evt = { + 'axes': {'arbitrary_axis_name': index}, + # 'properties' allows manipulation of hardware by specifying an arbitrary + # list of properties + 'properties': [ + ['device_name', 'property_name', 'property_value'], + ['device_name_2', 'property_name_2', 'property_value_2'] + ] + } + events.append(evt) + acq.acquire(events) + - acq.acquire(events) +For a practical example of custom events in action, see the `Intermittent Z-T Acquisition tutorial `_, which demonstrates how to acquire alternating time series and z-stacks. +.. toctree:: + :maxdepth: 1 + :hidden: + + intermittent_Z_T.ipynb The channel axis ========================== -The axis ``"channel"`` has a special significance because it not only determines how the corresponding image is stored, it also determines how it is displayed in the default viewer. Images with different ``"channel"`` values that match on all the other axes will by default be overlayed in a multi-channel image. +The ``channel`` axis has unique behavior: it not only determines the storage of images like other axes but also their display in the default viewer (:ref:`viewers`). Images with different ``channel`` values but matching other axes are overlaid in the default viewer. + +In Micro-Manager, hardware settings for different channels are typically controlled by providing the group and preset name of a `Config group `_. This is specified using the ``config_group`` field of acquisition events. These hardware control instructions can be specified independently of how the image is stored/displayed in the acquisition event. -In Micro-Manager, hardware settings for different channels are typically controlled by providing the group and preset name of a `Config group `_. This is specified using the ``config_group`` field of acquisition events. These hardware control instructions can be specified independently of how the image is stored/displayed in the acquisition event .. code-block:: python diff --git a/docs/source/acq_hooks.rst b/docs/source/acq_hooks.rst index 7ae3102f..7b23e7be 100644 --- a/docs/source/acq_hooks.rst +++ b/docs/source/acq_hooks.rst @@ -4,63 +4,52 @@ Acquisition hooks **************************************************************** -Acquisition hooks can be used for several purposes: 1) Executing arbitrary code at different points within the acquisition cycle. For example, this could be used to incorporate devices outside of micro-manager into the acquisition cycle. 2) Modifying/deleting acquisition events in progress, for example to skip certain channels, or applying focus corrections on-the-fly. 3) Communcation with external devices at specific points in the acquisition cycle to enable the use of hardware TTL triggering for fast acquisitions -Hooks can either be executed before the hardware updates for a particular acquisition event (a ``pre_hardware_hook``), just after the hardware updates, just before the image is captured (a ``post_hardware_hook``), or after the camera has been instructed to take images or wait for an external trigger (a ``post_camera_hook``). +Acquisition hooks allow you to inject custom code at specific points in the acquisition process. This could be used, for example, to: -The simplest type of acquisition hook is function that takes a single argument (the current acquisition event). Pass this function to the acquisition by adding it as an argument in the constructor. This form might be used, for example, to control other hardware and have it synchronized with acquisition. +1. Execute arbitrary code during the acquisition cycle +2. Modify or delete acquisition events on-the-fly +3. Communicate with external devices for :ref:`hardware_triggering` -.. code-block:: python - - def hook_fn(event): - ### Do some other stuff here ### - return event - - # pass in the function as a post_hardware_hook - with Acquisition(directory='/path/to/saving/dir', name='acquisition_name', - post_hardware_hook_fn=hook_fn) as acq: - ### acquire some stuff ### +Types of Hooks +-------------- +There are three types of hooks, each executed at a different point in the acquisition cycle: -Acquisition hooks can also be used to modify or delete acquisition events: - -.. code-block:: python +1. ``pre_hardware_hook``: Executed before hardware updates +2. ``post_hardware_hook``: Executed after hardware updates, just before image capture +3. ``post_camera_hook``: Executed after the camera has been instructed to take images or wait for an external trigger - def hook_fn(event): - if some_condition: - return event - # condition isn't met, so delete this event by not returning it -Depending on where in the acquisition cycle the hook is, modifying or deleting the event may not have any effect. For example, modifying an event in a ``post_camera_hook_fn`` won't have any effect since the hardware has already been moved and the camera started. In contrast, in a ``pre_hardware_hook_fn``, the event can be modified and the acquistion engine will use the modified event. For example, the z position could be changed in the hook function, which would cause the acquisition engine to move the microscope's focus drive to a different position than it otherwise woudl have prior to taking an image. +Basic Usage +----------- -A hook function that takes two arguments can also be used in cases where one wants to submit additional acquisition events. The second argument, ``event_queue``, can be used for submitting additional acquisition events: +The simplest hook is a function that takes a single argument (the current acquisition event): .. code-block:: python - - def hook_fn(event, event_queue): - - ### create a new acquisition event in response to something ### - #event = - event_queue.put(event) - - return event + def hook_fn(event): + # Custom code here + return event -If additional events will be submitted here, the typical syntax of ``with Acquisition...`` cannot be used because it will automatically close the acquisition too soon. Instead the acquisition should be created as: + with Acquisition(directory='/path/to/saving/dir', name='acquisition_name', + post_hardware_hook_fn=hook_fn) as acq: + # Acquisition code here -.. code-block:: python - acq = Acquisition(directory='/path/to/saving/dir', name='acquisition_name', - post_hardware_hook_fn=hook_fn) +Modifying or Deleting Events +---------------------------- -When it is finished, it can be closed and cleaned up by passing an ``None`` to the ``event_queue``. +Hooks can modify or delete events by returning a modified event or not returning an event: .. code-block:: python - def hook_fn(event, event_queue): + def hook_fn(event): + if some_condition: + return modified_event + # Delete event by not returning anything - if acq_end_condition: - event_queue.put(None) - else: - return event +The effect of modifying or deleting events depends on the hook's position in the acquisition cycle. For example: +- ``post_camera_hook_fn``: Modifications have no effect as hardware movement and camera activation have already occurred. +- ``pre_hardware_hook_fn``: Changes are fully applied. For example, modifying the z-position will cause the microscope to adjust its focus accordingly before image capture. diff --git a/docs/source/acq_overview.rst b/docs/source/acq_overview.rst index 44ac0694..068f1eac 100644 --- a/docs/source/acq_overview.rst +++ b/docs/source/acq_overview.rst @@ -1,138 +1,228 @@ +.. _acq_overview: + ****************************************************** Acquisitions ****************************************************** -The :class:`Acquisition` class is a flexible abstraction built to support a multitude of microscopy experiments. Its essential functions are to parse a set of instructions from the user known as "acquisition events", control the microscope hardware according to these, and efficiently retrieve, save, and provide access to image data from the camera(s). Each of these functionalities can be further modified and/or customized in a variety of ways. +Basic usage +=========== -The following shows a minimal example of using :class:`Acquisition` class to acquire a sequence of 5 images from the camera and save them to disk: -.. code-block:: python - from pycromanager import Acquisition, multi_d_acquisition_events +The :class:`Acquisition` class in Pycro-Manager supports a wide range of microscopy experiments. Its core functions are: - with Acquisition(directory='/path/to/saving/dir', name='acquisition_name') as acq: - events = multi_d_acquisition_events(num_time_points=5) - acq.acquire(events) +1. Parse user-defined "acquisition events" +2. Control microscope hardware based on these events +3. Efficiently retrieve, save, and provide access to camera image data -The data will be displayed in the default :ref:`image viewer ` as it is acquired, and it can be also be accessed through the :class:`Dataset` class: +Here's a minimal example that acquires a sequence of 5 images: .. code-block:: python - dataset = acq.get_dataset() - img = dataset.read_image(time=0) # a numpy array + from pycromanager import Acquisition, multi_d_acquisition_events + with Acquisition(directory='/path/to/saving/dir', name='acquisition_name') as acq: + events = multi_d_acquisition_events(num_time_points=5) + acq.acquire(events) +Alternatively, if you don't provide a ``directory`` and ``name``, the acquired data will be stored in RAM instead of being saved to disk. -Standard Multi-Dimensional Acquisitions -========================================= +Multi-Dimensional Acquisitions +``````````````````````````````` -In Micro-Manager/Pycro-Manager, "multi-dimensional acquisitions" refer to experiments where images are systematically collected across various combinations of time, z-stack, channel, and xy position axes (non-standard axes outside of these four require additional customization, as described further below). Pycro-Manager provides the :meth:`multi_d_acquisition_events` function for generating the instructions (acquisition events) to acquire this type of data. +Pycro-Manager supports "multi-dimensional acquisitions" across time, z-stack, channel, and xy position axes, using +the :ref:`multi_d_acq_events` function. More information about the usage of this function can be found in the `MDA Tutorial `_ -The following shows an example of acquiring a single z-stack: + +Here's an example that acquires a 4D dataset with 4 time points, 6 z-planes, and 2 channels: .. code-block:: python - from pycromanager import Acquisition, multi_d_acquisition_events + with Acquisition(directory='/path/to/saving/dir', name='acquisition_name') as acq: + events = multi_d_acquisition_events( + num_time_points=4, time_interval_s=0, + channel_group='Channel', channels=['DAPI', 'FITC'], + z_start=0, z_end=6, z_step=0.4, + order='tcz') + acq.acquire(events) + +.. toctree:: + :maxdepth: 1 + :hidden: + + multi-d-acq-tutorial.ipynb + - with Acquisition(directory='/path/to/saving/dir', name='acquisition_name') as acq: - # Generate the events for a single z-stack - events = multi_d_acquisition_events(z_start=0, z_end=10, z_step=0.5) - acq.acquire(events) +Reading Data +============ -In addition to z-stacks, this function can also be used to do timelapses, different channels, and multiple XY stage positions. This example shows how to run a multi-channel timelapse with z-stacks: +Acquired data can be accessed programmatically using a :class:`Dataset` object: .. code-block:: python - with Acquisition(directory='/path/to/saving/dir', name='acquisition_name') as acq: - events = multi_d_acquisition_events( - num_time_points=4, time_interval_s=0, - channel_group='Channel', channels=['DAPI', 'FITC'], - z_start=0, z_end=6, z_step=0.4, - order='tcz') - acq.acquire(events) + dataset = acq.get_dataset() + img = dataset.read_image(time=0) # returns a numpy array -More information on this function can be found in the `MDA Tutorial `_ +For finished acquisitions, open the dataset from disk: +.. code-block:: python + + from pycromanager import Dataset + dataset = Dataset('/path/to/data') +Individual images can be accessed using :meth:`read_image`: + +.. code-block:: python -Special types of Acquisitions -================================ + img = dataset.read_image(z=0) + img_metadata = dataset.read_metadata(z=0) -In addition to the regular :class:`Acquisition` class, Pycro-manager provides a few :ref:`special types of Acquisitions `, with features designed for specific applications. +Opening Data as Dask Array +````````````````````````````` + +For efficient handling of large datasets, open all data at once as a dask array. This can be used to open datasets that are too large to fit in memory, by only loading the data that is needed for a particular operation: + +.. code-block:: python - - :class:`XYTiledAcquisition` can be used to image samples that are larger than a single field of view by moving around an XY stage and capturing multiple images, then stitching them together into a single contiguous image. It uses a special, multi-resolution file format to facilitate efficient visualization of the data at multiple scales - - An :class:`ExploreAcquisition` is a special type of :class:`XYTiledAcquisition` that provides a user interface for moving around a XY and Z stage and telling the microscope where to Image - - The :class:`MagellanAcquisition` is a special type of :class:`XYTiledAcquisition` for controlling the `Micro-magellan `_ plugin. This plugin provides additional features for mapping samples, defining parts of the sample to image, and collecting specialized 3D datasets. + dask_array = dataset.as_array() + # Perform operations like numpy arrays + # For example, take max intenisty projection along axis 0 + max_intensity = np.max(dask_array[0, 0], axis=0) +You can also slice along particular axes: -Hardware sequencing -===================== -An important function of the acquisition engine underlying Pycro-Manager is to enable hardware sequencing. In hardware sequencing, multiple images are captured without the computer and hardware having to communicate between each one. In certain cases, this can dramatically increase the speed with which data is acquired. For high-performance applications, hardware sequencing is essential to speed and sufficiently precise synchronization between different hardware components. +.. code-block:: python -The :doc:`application_notebooks/external_hardware_triggering_tutorial` tutorial shows an example of this in action. + dask_array = dataset.as_array(z=0, time=2) -Pycro-manager acquisitions will automatically try to use hardware sequencing when the following conditions are met: - - 1. there are no delays requested between successive image - 2. Any hardware that changes positions between successive images also supports being sent a sequence of instructions that it can execute at once - 3. The events to be sequenced over were all submitted to ``acq.acquire()`` in a single call. -If an acquisition hook is being used and hardware sequencing is engaged, the ``event`` that gets passed to the hook will not being a single python ``dict``, but instead a ``list`` of ``dict`` objects representing a sequence of events. It will also only be called once, for the whole sequence, instead of once for each event. +Data Visualization +============================= -If desired, hardware sequencing can be turned off by submitting events for acquisition one at a time. For a list of acquisition events called ``events``: +Pycro-Manager offers flexible options for visualizing while it is being acquired, described in :doc:`viewers`. +Datasets that have already been acquired can be visualized using napari: .. code-block:: python - with Acquisition(directory='/path/to/saving/dir', name='acquisition_name') as acq: - # Create a list of events - events = multi_d_acquisition_events(num_time_points=10) - # but submit them one at a time so sequencing doesn't occur - for event in events: - acq.acquire(event) + dask_array = dataset.as_array() + + # Visualize data using napari + import napari + viewer = napari.Viewer() + viewer.add_image(dask_array) + napari.run() +.. toctree:: + :maxdepth: 1 + :hidden: + + viewers -Customizing Acquisitions -============================ -While executing the :class:`Acquisition` code, several operations are taking place concurrently: events are being interpreted and queued for execution, hardware is being controlled and monitored, and images are being retrieved and saved. To ensure efficient execution, these operations are performed in parallel wherever feasible. -To tailor an :class:`Acquisition` to your needs, you may need to interact with specific parts of this process. For this, Pycro-manager provides dedicated APIs, each designed to enable customization for a specific piece of the acquisition process: +Advanced Acquisition Features +============================= - - Creating :ref:`acq_events` tells the :class:`Acquisition` what to acquire and how to acquire it. - - :ref:`acq_hooks` allow alterations to the standard progression of hardware control through injecting custom user code. - - :ref:`img_processors` allow modification/processing of image data prior to saving/displaying, or for images to be rerouted to custom endpoints instead of being saved to disk. +Pycro-Manager offers a range of advanced features for customizing and extending the basic acquisition functionality, enabling complex experimental designs and specialized imaging techniques. -The figure below shows an overview of what happens behind the scenes during an acquisition and where the each API fits. Each color represents a distinct thread that is operating asynchronously from the others. +Customizing Acquisition Behavior +```````````````````````````````` +Pycro-Manager offers several features to modify the acquisition process: -.. figure:: acquisition_figure.png +- :ref:`acq_events`: Define custom acquisition instructions. +- :ref:`acq_hooks`: Inject custom code at specific points in the acquisition process. +- :ref:`img_processors`: Modify or process image data on-the-fly. +- :doc:`image_saved_callbacks`: Execute custom code when images are saved. + +The following figure illustrates how these features integrate into the acquisition process: + +.. figure:: event_hook_processor_figure.png :width: 800 - :alt: Overview figure of pycro-manager Acquisitions + :alt: Overview of Pycro-Manager's acquisition process + + **Pycro-Manager's acquisition process.** Blue: acquisition events from code or GUI. Green: hardware control thread for optimized instructions and image acquisition. Magenta: image saving and display. Acquisition events, hooks, and image processors enable customization throughout the process. Image saved callbacks (not shown) occur after saving and display. + +.. toctree:: + :maxdepth: 1 + :hidden: + + acq_events + acq_hooks + img_processors + image_saved_callbacks + +Monitoring Progress with Notifications +``````````````````````````````````````` + +Receive real-time updates about the acquisition process with :doc:`notifications`, enabling better experiment tracking and debugging: + +.. toctree:: + :maxdepth: 1 + :hidden: - **Pycro-Manager's high-level programming interface.** The data acquisition process in Pycro-Manager starts with (blue) a source of acquisition events, which can come from code or a graphical user interface. These events are passed to (green) the hardware control thread, which optimizes them to take advantage of hardware triggering where available, sends instructions to hardware, and acquires images. (Magenta) The resulting images are then saved and displayed in the GUI. The three main abstractions of the Pycro-Manager high-level programming interface (acquisition events, acquisition hooks, and image processors) enable fine-grained control and customization of this process. + notifications -############################### + +Adaptive Microscopy +``````````````````````````````` + +:doc:`adaptive_acq`: shows how to modify acquisition based on acquired data, allowing for dynamic and responsive experiments (also known as "smart microscopy"). + +.. toctree:: + :maxdepth: 1 + :hidden: + + adaptive_acq + + +Tiling Fields-of-View for large samples +```````````````````````````````````````` + +Pycro-Manager provides :ref:`special_acqs` + +- :class:`XYTiledAcquisition`: For imaging large samples across multiple fields of view. +- :class:`ExploreAcquisition`: Interactive XY and Z navigation for sample exploration. +- :class:`MagellanAcquisition`: Advanced features for sample mapping and 3D datasets. + + +.. toctree:: + :maxdepth: 1 + :hidden: + + special_acqs + + +Hardware triggering +``````````````````````````````` + +:doc:`hardware_triggering`: describes how to use hardware synchronization to maximize acquisition speed and precision. .. toctree:: - :maxdepth: 3 - :caption: Contents: - - acq_events - acq_hooks - img_processors - image_saved_callbacks - viewers - special_acqs - + :maxdepth: 1 + :hidden: + + hardware_triggering + + + + + + + + + + + diff --git a/docs/source/adaptive_acq.rst b/docs/source/adaptive_acq.rst new file mode 100644 index 00000000..2c3956af --- /dev/null +++ b/docs/source/adaptive_acq.rst @@ -0,0 +1,79 @@ +.. _adaptive_acq: + +====================== +Adaptive Acquisitions +====================== + + +Pycro-Manager's ``Acquisition`` class allows the acquisition process to be modified based on acquired data. This enables dynamic, responsive experiments, also known as "smart microscopy". + +Key to the design is the balance between performance and code readability. Parallelization is essential for efficient smart microscopy, which requires simultaneous hardware control, image acquisition, and data processing. However, the logic of an experiment is more easily understood as a sequence of decisions and actions. The API bridges this gap, enabling that looks like a single sequential script but takes advantage of parallelizing operations when possible. + +Unlike simple acquisitions where all events are sent in a single ``Acquisition.submit(...)`` call, adaptive acquisitions require multiple ``submit`` calls. Events are submitted, they produce images, those images are analyzed, and based on this analysis, new events are created. + +The key object in adaptive microscopy is the ``AcquisitionFuture``. Any time an event or events are submitted, an ``AcquisitionFuture`` is returned. The ``AcquisitionFuture`` can be used to access specific data as soon as it is ready. + +An example showing an experiment which alternates between fast and slow time-lapses based on image analysis is shown below: + +Example: Alternating Time-lapses +-------------------------------- + +.. code-block:: python + + from pycromanager import Acquisition, multi_d_acquisition_events + + def analyze_image(image): + # Placeholder for image analysis + return image.max() > threshold + + with Acquisition(directory='/path/to/save', name='adaptive_acq') as acq: + for loop_index in range(10): # Run 10 adaptive cycles + # Acquire a single image + event = {'axes': {'loop_index': loop_index}} + future = acq.submit(event) + + # Wait for the image and analyze it + image = future.await_image_saved(event['axes']) + + if analyze_image(image): + # Fast time-lapse: 100 images, 1 second apart + events = multi_d_acquisition_events(num_time_points=100, time_interval_s=1) + else: + # Slow time-lapse: 5 images, 20 seconds apart + events = multi_d_acquisition_events(num_time_points=5, time_interval_s=20) + + # Add the loop index to the events so that successive timelapses have unique axes + for event in events: + event['axes']['loop_index'] = loop_index + + acq.submit(events) + + + +Additional Features +------------------- + +1. **Awaiting Multiple Images** + + The ``AcquisitionFuture`` can wait for multiple images simultaneously, only returning when all images are saved: + + .. code-block:: python + + future = acq.submit(events) + images = future.await_image_saved([{'time': 2}, {'time': 3}, {'time': 4}]) + + This is useful when analysis requires multiple images, such as 3D image processing on a Z-stack. + + +2. **Awaiting Specific Execution Milestones** + + Specific execution milestones can be awaited: + + .. code-block:: python + + future = acq.submit(events) + future.await_execution(milestone=AcqNotification.Hardware.POST_HARDWARE, axes={'time': 1}) + + + +For further information, see the :ref:`adaptive_acq_api` API diff --git a/docs/source/advanced_usage.rst b/docs/source/advanced_usage.rst deleted file mode 100644 index 3dd08d9e..00000000 --- a/docs/source/advanced_usage.rst +++ /dev/null @@ -1,29 +0,0 @@ -******************* -Advanced Usage -******************* - -Advanced use cases may require a deeper understanding of how pycromanager works. - -.. figure:: pm_architecture.png - :width: 800 - :alt: Architecture of pycro-manager - - **Pycro-manager architecture overview** (Purple) The Micro-manager core and device layer abstract the operation of specific hardware devices into a generic API for hardware control. (Blue) The Micro-Manager Java layer contains high performance libraries for hardware control (i.e. the acquisition engine), data saving, and image display, as well as a graphical user interface and plugin interface. (Green) Pycro-Manager calls these Java libraries, and the c++ libraries that sit below them through a "Bridge" that dynamically translates between Java and Python - - - -.. figure:: headless_architecture.png - :width: 800 - :alt: Architecture of pycro-manager - - **Headless mode (using Java backend)** In headless mode, pycro-manager can be run without the micro-manager user interface, enabling the implementation of custom user interfaces or no user interfaces at all. - - -.. toctree:: - :maxdepth: 3 - :caption: Contents: - - performance_guide - hardware_triggering - - diff --git a/docs/source/apis.rst b/docs/source/apis.rst index f9290472..30dfa528 100644 --- a/docs/source/apis.rst +++ b/docs/source/apis.rst @@ -2,6 +2,16 @@ API Reference ============== +Installation +#################### + +.. _download_install_api: + +.. currentmodule:: mmpycorex +.. autofunction:: download_and_install_mm + +.. autofunction:: find_existing_mm_install + .. _acq_event_spec: Acquisition event specification @@ -83,31 +93,42 @@ The following shows all possible fields in an acquisition event (not all of whic Acquisition APIs ####################################################### -Acquisition -============== + .. currentmodule:: pycromanager.acquisition.acquisition_superclass .. autoclass:: Acquisition :members: .. currentmodule:: pycromanager +.. autoclass:: AcqNotification + :members: + + +.. _multi_d_acq_events: multi_d_acquisition_events -=========================== +===================================== .. autofunction:: multi_d_acquisition_events -XYTiledAcquisition + + + +.. _adaptive_acq_api: + +Adaptive acquisition ===================== +.. autoclass:: AcquisitionFuture + :members: + + +Specific acquisition types +============================ .. autoclass:: XYTiledAcquisition :members: -ExploreAcquisition -===================== .. autoclass:: ExploreAcquisition :members: -MagellanAcquisition -======================= .. autoclass:: MagellanAcquisition :members: @@ -115,23 +136,20 @@ MagellanAcquisition Headless mode ####################################################### -start_headless -=========================== + .. autofunction:: start_headless -stop_headless -=========================== .. autofunction:: stop_headless Dataset ############################################## -.. currentmodule:: pycromanager +.. currentmodule:: ndstorage .. autoclass:: Dataset :members: -.. currentmodule:: ndtiff +.. currentmodule:: ndstorage .. autoclass:: NDTiffDataset :members: .. autoclass:: NDTiffPyramidDataset diff --git a/docs/source/application_notebooks/PSF_viewer.ipynb b/docs/source/application_notebooks/PSF_viewer.ipynb deleted file mode 100644 index 5150ed0e..00000000 --- a/docs/source/application_notebooks/PSF_viewer.ipynb +++ /dev/null @@ -1,596 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Streaming data from micro-manager to napari: PSF Viewer\n", - "\n", - "developed by Wiebke Jahr, [Danzl lab], IST Austria, (c) 2020 \n", - "latest version [on github] \n", - "\n", - "\n", - "If you use this tool, please cite: \n", - "pycro-manager: Pinkard, H., Stuurman, N., Ivanov, I.E. et al. Pycro-Manager: open-source software for customized and reproducible microscope control. Nat Methods (2021). doi: [10.1038/s41592-021-01087-6] \n", - "napari: napari contributors (2019). napari: a multi-dimensional image viewer for python. doi: [10.5281/zenodo.3555620] \n", - "\n", - "This notebook shows how to acquire data using `micromanager`, then use `pycro-manager` to stream it to `napari`. \n", - "Buttons to start and stop data acquisition are added to the `napari` window using the `magic-gui` package. In this example, the data displayed in `napari` is resliced to get a live PSF viewer. However, reslicing is only a small example for the data analysis possible using `napari`. \n", - "\n", - "Here are two [videos] showing the PSF viewer in action: \n", - "- PSFViewer-ExternalStageControl_1080p.mp4: z-stage controlled via `micromanager` \n", - "- PSFViewer-InternalStageControl_1080p.mp4: z-stage controlled via external DAQ control \n", - "\n", - "Since the amount of data that can be transferred between `micromanager` and `pycro-manager` is currently limited to 100 MB/s, it's important that no more data is transferred to ensure smooth execution of the software. \n", - "For both movies, camera acquisition parameters in `micromanager` were set to: \n", - "- 11-bit depth, \n", - "- chip-size cropped to the central 512x512 px. \n", - "- external trigger start (trigger comming at 45 Hz) \n", - "- exposure time set to 0.01 ms \n", - "\n", - "Tested on: \n", - "- macOS Catalina using `micromanager 2.0.0-gamma1-20210221` \n", - "\n", - "[on github]: https://github.com/wiebkejahr/pycro-manager\n", - "[Danzl lab]: https://danzl-lab.pages.ist.ac.at/\n", - "[videos]: https://www.dropbox.com/sh/fpr2nitlhfb68od/AAArXxDLclfXWhsyF0x_fP7Ja?dl=0\n", - "[10.1038/s41592-021-01087-6]: https://doi.org/10.1038/s41592-021-01087-6\n", - "[10.5281/zenodo.3555620]: https://doi.org/10.5281/zenodo.3555620" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# only execute first time to install all required packages\n", - "# has been tested with the indicated package versions\n", - "#!pip install pycromanager==0.10.9 napari==0.4.5 pyqt5==5.15.1 magicgui==0.2.5 yappi==1.3.2\n", - "# newest: magicgui==0.2.6, but there's an error message when connecting the buttons\n", - "# when updating pycromanager, you may have to update micro-manager as well\n", - "# when updating magicgui, napari may have to be updated" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "import numpy as np\n", - "import queue\n", - "#import yappi # needed for benchmarking multithreaded code\n", - "\n", - "import napari\n", - "from napari.qt import thread_worker\n", - "from magicgui import magicgui\n", - "\n", - "from pycromanager import Acquisition, multi_d_acquisition_events\n", - "\n", - "# open napari in an extra window\n", - "%gui qt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## define constants\n", - "some constants for microscope parameters \n", - "and display options \n", - "global variables for multithreading" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "# data acquired on microscope or simulated?\n", - "simulate = False\n", - "# z-stage controlled through micromanager, or externally?\n", - "z_stack_external = False\n", - "# clip image to central part. Speeds up display as data size is reduced\n", - "# is used as size for simulating data\n", - "clip =[128, 128]\n", - "# um / px, for correct scaling in napari\n", - "size_um = [0.16, 0.16]\n", - "# start in um, end in um, number of slices, active slice\n", - "z_range = [0, 50, 200, 0]\n", - "#z_range = [1100, 1150, 200, 0]\n", - "# rescale z dimension independently for display\n", - "z_scale = 1\n", - "# sleep time to keep software responsive\n", - "sleep_time = 0.05\n", - "# contrast limits for display\n", - "clim = [100, 300]\n", - "# number of color channels, active channel\n", - "channels = [1, 0] \n", - "# color map for display\n", - "cmap = ['plasma', 'viridis']\n", - "# layer names for the channels\n", - "layer_names = ['GFP', 'RFP']\n", - "\n", - "# initialize global variables\n", - "# flag to break while loops\n", - "acq_running = False\n", - "# empty queue for image data and z positions\n", - "img_queue = queue.Queue()\n", - "# xyz data stack\n", - "data = np.random.rand(z_range[2], clip[0], clip[1]) * clim[1]\n", - "\n", - "# if z-stage is controlled through micromanager:\n", - "# need bridge to move stage at beginning of stack\n", - "# USE WITH CAUTION: only tested with micromanager demo config\n", - "if not(simulate) and not(z_stack_external):\n", - " from pycromanager import Core\n", - " #get object representing micro-manager core\n", - " core = Core()\n", - " print(core)\n", - " core.set_position(z_range[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "dev_names = core.get_loaded_devices()\n", - "for ii in range(dev_names.size()):\n", - " print(ii, dev_names.get(ii))\n", - "\n", - "print(core.get_property(\"Camera\", \"PixelType\"))\n", - "#print(core.get_property(\"Z\", \"Label\"))\n", - "\n", - "stage_xy = core.get_xy_stage_position()\n", - "pos = [stage_xy.get_x(), stage_xy.get_y()]\n", - "print(pos)\n", - "core.set_position(100)\n", - "print('z stage: ', core.get_position())\n", - "core.stop('Z') # this doesnt work, just continues moving\n", - "print('z stage: ', core.get_position()) \n", - "core.set_position(z_range[0]) # this also doesn't work\n", - "time.sleep(5)\n", - "print('z stage: ', core.get_position())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Function to write data into Queue\n", - "This function is shared by the image acquisition / simulation routine. \n", - "Shapes data as needed and keeps track of both z_position and active channel." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def place_data(image):\n", - " \"\"\" fnc to place image data into the queue.\n", - " Keeps track of z-position in stacks and of active color channels.\n", - " Inputs: np.array image: image data\n", - " Global variables: image_queue to write image and z position\n", - " z_range to keep track of z position\n", - " channels to keep track of channels\n", - " \"\"\"\n", - " global img_queue\n", - " global z_range\n", - " global channels\n", - " img_queue.put([channels[1], z_range[3], np.ravel(image)])\n", - " z_range[3] = (z_range[3]+1) % z_range[2]\n", - " if z_range[3] == 0:\n", - " channels[1] = (channels[1]+1) % channels[0]\n", - " \n", - " #print(z_range, channels)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## create dummy image and and put into stack\n", - "creates dummy image of constant brightness \n", - "use for testing purposes without microscope \n", - "stack of increasing brightness helps to identify glitches" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def simulate_image(b, size = [128,128]):\n", - " \"\"\" fnc to simulate an image of constant brightness\n", - " and call fnc to place it into the queue.\n", - " Inputs: int b: brightness\n", - " np.array size: # of px in image in xy.\n", - " \"\"\"\n", - " place_data(np.ones(size) * b)\n", - " \n", - " \n", - "def simulate_data(ii, z_range):\n", - " \"\"\" fnc to create images with constant, but increasing brightness.\n", - " Inputs: int ii: counter to increase brightness\n", - " int z_range: number of slices in stack\"\"\"\n", - " for zz in range(z_range[2]):\n", - " brightness = (ii+1) * (zz+1) / ((z_range[2]+1)) * clim[1]\n", - " simulate_image(brightness, clip)\n", - " time.sleep(sleep_time)\n", - " # need sleep time especially when simulated datasize is small or this will kill CPU" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## image process function and pycromanager acquisition\n", - "grabs and clips acquired image \n", - "built pycromanager acquisition events \n", - "acquire data and send to image_process_fn " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def grab_image(image, metadata):\n", - " \"\"\" image_process_fnc to grab image from uManager, clip it to central part\n", - " and call the fnc that will put it into the queue.\n", - " Inputs: array image: image from micromanager\n", - " metadata from micromanager\n", - " \"\"\"\n", - " \n", - " size = np.shape(image)\n", - " image_clipped = image[(size[0]-clip[0])//2:(size[0]+clip[0])//2,\n", - " (size[1]-clip[1])//2:(size[1]+clip[1])//2]\n", - " place_data(image_clipped)\n", - " \n", - " return image, metadata\n", - "\n", - "\n", - "def acquire_data(z_range):\n", - " \"\"\" micro-manager data acquisition. Creates acquisition events for z-stack.\n", - " This example: use custom events, not multi_d_acquisition because the \n", - " z-stage is not run from micro-manager but controlled via external DAQ.\"\"\"\n", - " with Acquisition(directory=None, name=None, \n", - " show_display=True, \n", - " image_process_fn = grab_image) as acq:\n", - " events = []\n", - " for index, z_um in enumerate(np.linspace(z_range[0], z_range[1], z_range[2])):\n", - " evt = {\"axes\": {\"z_ext\": index}, \"z_ext\": z_um}\n", - " events.append(evt)\n", - " acq.acquire(events)\n", - " \n", - " \n", - "def acquire_multid(z_range):\n", - " \"\"\" micro-manager data acquisition. Creates acquisition events for z-stack.\n", - " This example: use multi_d_acquisition because the z-stage is run \n", - " from micro-manager.\n", - " Unless hardware triggering is set up in micro-manager, this will be fairly slow:\n", - " micro-manager does not sweep the z-stage, but acquires plane by plane. \"\"\"\n", - " with Acquisition(directory=None, name=None, \n", - " show_display=False, \n", - " image_process_fn = grab_image) as acq:\n", - " events = multi_d_acquisition_events(z_start=z_range[0], z_end=z_range[1], \n", - " z_step=(z_range[1]-z_range[0])/(z_range[2]-1))\n", - " acq.acquire(events)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## napari update display\n", - "is called whenever the thread worker checking the queue yields an image \n", - "adds images into xyz stack and updates data" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def display_napari(pos_img):\n", - " \"\"\" Unpacks z position and reshapes image from pos_img. Writes image into correct \n", - " slice of data, and updates napari display.\n", - " Called by worker thread yielding elements from queue.\n", - " Needs to be in code before worker thread connecting to it.\n", - " Inputs: array pos_img: queue element containing z position and raveled image data.\n", - " Global variables: np.array data: contains image stack\n", - " img_queue: needed only to send task_done() signal.\n", - " \"\"\"\n", - " global data\n", - " global img_queue\n", - " if pos_img is None:\n", - " return\n", - " # read image and z position\n", - " image = np.reshape(pos_img[2:],(clip[0], clip[1]))\n", - " z_pos = pos_img[1]\n", - " color = pos_img[0]\n", - "\n", - " # write image into correct slice of data and update display\n", - " data[z_pos] = np.squeeze(image)\n", - " layer = viewer.layers[color]\n", - " layer.data = data\n", - " #print(\"updating \", z_pos, color)\n", - "\n", - " img_queue.task_done()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## worker threads appending data to queue and reading from queue" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "@thread_worker\n", - "def append_img(img_queue):\n", - " \"\"\" Worker thread that adds images to a list.\n", - " Calls either micro-manager data acquisition or functions for simulating data.\n", - " Inputs: img_queue \"\"\"\n", - " # start microscope data acquisition\n", - " if not simulate:\n", - " if z_stack_external:\n", - " while acq_running:\n", - " acquire_data(z_range)\n", - " time.sleep(sleep_time)\n", - " else:\n", - " while acq_running:\n", - " acquire_multid(z_range)\n", - " time.sleep(sleep_time)\n", - "\n", - " # run with simulated data\n", - " else:\n", - " ii = 0\n", - " while acq_running:\n", - " simulate_data(ii, z_range)\n", - " ii = ii + 1\n", - " #print(\"appending to queue\", ii)\n", - " time.sleep(sleep_time)\n", - " \n", - " \n", - "@thread_worker(connect={'yielded': display_napari})\n", - "def yield_img(img_queue):\n", - " \"\"\" Worker thread that checks whether there are elements in the \n", - " queue, reads them out.\n", - " Connected to display_napari function to update display \"\"\"\n", - " global acq_running\n", - " \n", - " while acq_running:\n", - " time.sleep(sleep_time)\n", - " # get elements from queue while there is more than one element\n", - " # playing it safe: I'm always leaving one element in the queue\n", - " while img_queue.qsize() > 1:\n", - " #print(\"reading from queue \", img_queue.qsize())\n", - " yield img_queue.get(block = False)\n", - "\n", - " # read out last remaining elements after end of acquisition\n", - " while img_queue.qsize() > 0:\n", - " yield img_queue.get(block = False)\n", - " print(\"acquisition done\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## define functions to start and stop acquisition\n", - "connect to gui buttons using magic_gui \n", - "`start_acq` restarts workers, resets `acq_running` flag and resets `z_range[3]`, ie z_pos \n", - "`stop_acq` sets `acq_running` flag to `False`, which will stop the worker threads" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "@magicgui(call_button = \"Start\")\n", - "def start_acq():\n", - " \"\"\" Called when Start button in pressed. Starts workers and resets global variables\"\"\"\n", - " print(\"starting threads...\")\n", - " global acq_running\n", - " global z_range\n", - " if not(acq_running):\n", - " z_range[3] = 0\n", - " acq_running = True\n", - " # comment in when benchmarking\n", - " #yappi.start()\n", - " worker1 = append_img(img_queue)\n", - " worker2 = yield_img(img_queue)\n", - " worker1.start()\n", - " #worker2.start() # doesn't need to be started bc yield is connected\n", - " else:\n", - " print(\"acquisition already running!\")\n", - " \n", - " \n", - "@magicgui(call_button = \"Stop\")\n", - "def stop_acq():\n", - " print(\"stopping threads\")\n", - " # set global acq_running to False to stop other workers\n", - " global acq_running\n", - " global core\n", - " acq_running = False\n", - " if not(simulate) and not(z_stack_external):\n", - " print('z stage stopping: ', core.get_position())\n", - " core.stop(\"Z\") # this doesnt work, just continues moving. eventually micromanager memory overflows\n", - " print('z stage stopped: ', core.get_position()) \n", - " core.set_position(z_range[0]) # this also doesn't work\n", - " core.wait_for_device(\"Z\")\n", - " #time.sleep(5)\n", - " print('z stage zeroed: ', core.get_position())\n", - " # comment in when benchmarking\n", - " # yappi.stop()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## \"Main\" function: start napari and worker threads\n", - "(re-)opens napary viewer \n", - "initializes view with random data \n", - "sets scale, contrast etc and rolls view. \n", - "add GUI buttons for start stop \n", - "there's a glitch when acquisition is stopped and restarted too quickly" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "viewer already closed or never opened\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "starting threads...\n", - "stopping threads\n", - "acquisition donez stage stopping: 37.688442211055275\n", - "\n", - "z stage stopped: 37.939698492462306\n", - "z stage zeroed: 38.19095477386934\n", - "stopping threads\n", - "z stage stopping: 34.42211055276382\n", - "z stage stopped: 34.67336683417085\n", - "z stage zeroed: 0\n", - "stopping threads\n", - "z stage stopping: 17.336683417085425\n", - "z stage stopped: 17.336683417085425\n", - "z stage zeroed: 0\n", - "stopping threads\n" - ] - } - ], - "source": [ - "# check if viewer is already open\n", - "# if yes: close and reopen\n", - "try:\n", - " if viewer:\n", - " viewer.close()\n", - "except:\n", - " print(\"viewer already closed or never opened\")\n", - "viewer = napari.Viewer(ndisplay=2)\n", - "\n", - "# initialize napari viewer with stack view and random data, reslice view\n", - "scale = [(z_range[1]-z_range[0])/z_range[2]*z_scale, size_um[1], size_um[0]]\n", - "layers = [viewer.add_image(data, \n", - " name = layer_names[c],\n", - " colormap = cmap[c],\n", - " interpolation = 'nearest',\n", - " blending = 'additive',\n", - " rendering = 'attenuated_mip',\n", - " scale = scale,\n", - " contrast_limits = clim) \n", - " for c in range(channels[0])]\n", - "viewer.dims._roll()\n", - "\n", - "# set sliders to the middle of the stack for all three dimensions.\n", - "# doesn't work anymore after fixing scaling\n", - "# would have to be done for both layers\n", - "#for dd, dim in enumerate(layers[0].data.shape):\n", - "# viewer.dims.set_point(dd, dim*scale[2-dd]//2)\n", - "\n", - "# add start stop buttons to napari gui\n", - "viewer.window.add_dock_widget(start_acq)\n", - "viewer.window.add_dock_widget(stop_acq)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get output from yappi\n", - "only needs to be run when benchmarking code" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print('z stage zeroed: ', core.get_position())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#only needs to be executed when yappi is used\n", - "threads = yappi.get_thread_stats()\n", - "for thread in threads:\n", - " print(\n", - " \"Function stats for (%s) (%d)\" % (thread.name, thread.id)\n", - " ) # it is the Thread.__class__.__name__\n", - " yappi.get_func_stats(ctx_id=thread.id).print_all()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/source/application_notebooks/PSF_viewer.py b/docs/source/application_notebooks/PSF_viewer.py deleted file mode 100644 index 84d4dac0..00000000 --- a/docs/source/application_notebooks/PSF_viewer.py +++ /dev/null @@ -1,355 +0,0 @@ -""" -Streaming data from micro-manager to napari: PSF Viewer - -developed by Wiebke Jahr, Danzl lab, IST Austria, (c) 2020 -latest version on github: https://github.com/wiebkejahr/pycro-manager - -If you use this tool, please cite: -pycro-manager: Pinkard, H., Stuurman, N., Ivanov, I.E. et al. Pycro-Manager: open-source software for customized and reproducible microscope control. Nat Methods (2021). https://doi.org/10.1038/s41592-021-01087-6 -napari: napari contributors (2019). napari: a multi-dimensional image viewer for python. https://doi.org/10.5281/zenodo.3555620 - -This notebook shows how to acquire data using micromanager, then use pycro-manager to stream it to napari. Buttons to start and stop data acquisition are added to the napari window using the magic-gui package. In this example, the data displayed in napari is resliced to get a live PSF viewer. However, reslicing is only a small example for the data analysis possible using napari. - -Here are two videos showing the PSF viewer in action: -https://www.dropbox.com/sh/fpr2nitlhfb68od/AAArXxDLclfXWhsyF0x_fP7Ja?dl=0 - - PSFViewer-ExternalStageControl_1080p.mp4: z-stage controlled via micromanager - PSFViewer-InternalStageControl_1080p.mp4: z-stage controlled via external DAQ control - -Since the amount of data that can be transferred between micromanager and pycro-manager is currently limited to 100 MB/s, it's important that no more data is transferred to ensure smooth execution of the software. -For both movies, camera acquisition parameters in micromanager were set to: - - 11-bit depth, - chip-size cropped to the central 512x512 px. - external trigger start (trigger comming at 45 Hz) - exposure time set to 0.01 ms - -Tested on: - - macOS Catalina using micromanager 2.0.0-gamma1-20201206 - Windows 10 using micromanager 2.0.0-gamma1-20201208 - - -# required packages: -# only execute first time to install all required packages -# has been tested with the indicated package versions -#!pip install pycromanager==0.10.9 napari==0.4.5 pyqt5==5.15.1 magicgui==0.2.5 yappi==1.3.2 -# newest: magicgui==0.2.6, but there's an error message when connecting the buttons -""" - -import time -import numpy as np -import queue -#import yappi # needed for benchmarking multithreaded code -import napari -from napari.qt import thread_worker -from magicgui import magicgui -from pycromanager import JavaBackendAcquisition, multi_d_acquisition_events -# open napari in an extra window, only needed for jupyter notebooks -#%gui qt - - - -######################################################################### -# Define constants # -######################################################################### -# data acquired on microscope or simulated? -simulate = True -# z-stage controlled through micromanager, or externally? -z_stack_external = True -# clip image to central part. Speeds up display as data size is reduced -# is used as size for simulating data -clip =[512, 512] -# um / px, for correct scaling in napari -size_um = [0.16, 0.16] -# start in um, end in um, number of slices, active slice -z_range = [0, 50, 200, 0] -#z_range = [1100, 1150, 200, 0] -# rescale z dimension independently for display -z_scale = 1 -# sleep time to keep software responsive -sleep_time = 0.01 -# contrast limits for display -clim = [100, 300] -# number of color channels, active channel -channels = [1, 0] -# color map for display -cmap = ['plasma', 'viridis'] -# layer names for the channels -layer_names = ['GFP', 'RFP'] - -# initialize global variables -# flag to break while loops -acq_running = False -# empty queue for image data and z positions -img_queue = queue.Queue() -# xyz data stack -data = np.random.rand(z_range[2], clip[0], clip[1]) * clim[1] - -# if z-stage is controlled through micromanager: -# need bridge to move stage at beginning of stack -# USE WITH CAUTION: only tested with micromanager demo config -if not(simulate) and not(z_stack_external): - from pycromanager import _Bridge - bridge = _Bridge() - #get object representing micro-manager core - core = bridge.get_core() - print(core) - core.set_position(z_range[0]) - - - -######################################################################### -# function to write data into queue # -######################################################################### -def place_data(image): - """ fnc to place image data into the queue. - Keeps track of z-position in stacks and of active color channels. - Inputs: np.array image: image data - Global variables: image_queue to write image and z position - z_range to keep track of z position - channels to keep track of channels - """ - global img_queue - global z_range - global channels - img_queue.put([channels[1], z_range[3], np.ravel(image)], block = False) - z_range[3] = (z_range[3]+1) % z_range[2] - if z_range[3] == 0: - channels[1] = (channels[1]+1) % channels[0] - #print(z_range, channels) - - - -######################################################################### -# functions creating dummy data # -######################################################################### -def simulate_image(b, size = [128,128]): - """ fnc to simulate an image of constant brightness - and call fnc to place it into the queue. - Inputs: int b: brightness - np.array size: # of px in image in xy. - """ - place_data(np.ones(size) * b) - - -def simulate_data(ii, z_range): - """ fnc to create images with constant, but increasing brightness. - Inputs: int ii: counter to increase brightness - int z_range: number of slices in stack""" - for zz in range(z_range[2]): - brightness = (ii+1) * (zz+1) / ((z_range[2]+1)) * clim[1] - simulate_image(brightness, clip) - time.sleep(2*sleep_time) - # need sleep time especially when simulated datasize is small or this will kill CPU - - - -######################################################################### -# Image processor functions grabbing data from micromanager # -######################################################################### -def grab_image(image, metadata): - """ image_process_fnc to grab image from uManager, clip it to central part - and call the fnc that will put it into the queue. - Inputs: array image: image from micromanager - metadata from micromanager - """ - size = np.shape(image) - image_clipped = image[(size[0]-clip[0])//2:(size[0]+clip[0])//2, - (size[1]-clip[1])//2:(size[1]+clip[1])//2] - place_data(image_clipped) - - return image, metadata - - -def acquire_data(z_range): - """ micro-manager data acquisition. Creates acquisition events for z-stack. - This example: use custom events, not multi_d_acquisition because the - z-stage is not run from micro-manager but controlled via external DAQ.""" - with JavaBackendAcquisition(directory=None, name=None, - show_display=True, - image_process_fn = grab_image) as acq: - events = [] - for index, z_um in enumerate(np.linspace(z_range[0], z_range[1], z_range[2])): - evt = {"axes": {"z_ext": index}, "z_ext": z_um} - events.append(evt) - acq.acquire(events) - - -def acquire_multid(z_range): - """ micro-manager data acquisition. Creates acquisition events for z-stack. - This example: use multi_d_acquisition because the z-stage is run - from micro-manager. - Unless hardware triggering is set up in micro-manager, this will be fairly slow: - micro-manager does not sweep the z-stage, but acquires plane by plane. """ - with JavaBackendAcquisition(directory=None, name=None, - show_display=False, - image_process_fn = grab_image) as acq: - events = multi_d_acquisition_events(z_start=z_range[0], z_end=z_range[1], - z_step=(z_range[1]-z_range[0])/(z_range[2]-1)) - acq.acquire(events) - - - -######################################################################### -# Function updating napari display # -######################################################################### -def display_napari(pos_img): - """ Unpacks z position and reshapes image from pos_img. Writes image into correct - slice of data, and updates napari display. - Called by worker thread yielding elements from queue. - Needs to be in code before worker thread connecting to it. - Inputs: array pos_img: queue element containing z position and raveled image data. - Global variables: np.array data: contains image stack - img_queue: needed only to send task_done() signal. - """ - global data - global img_queue - if pos_img is None: - return - # read image and z position - image = np.reshape(pos_img[2:],(clip[0], clip[1])) - z_pos = pos_img[1] - color = pos_img[0] - - # write image into correct slice of data and update display - data[z_pos] = np.squeeze(image) - layer = viewer.layers[color] - layer.data = data - #print("updating ", z_pos, color) - - img_queue.task_done() - - - -######################################################################### -# Thread workers appending and reading data to / from queue # -######################################################################### -@thread_worker -def append_img(img_queue): - """ Worker thread that adds images to a list. - Calls either micro-manager data acquisition or functions for simulating data. - Inputs: img_queue """ - # start microscope data acquisition - if not simulate: - if z_stack_external: - while acq_running: - acquire_data(z_range) - time.sleep(sleep_time) - else: - while acq_running: - acquire_multid(z_range) - time.sleep(sleep_time) - - # run with simulated data - else: - ii = 0 - while acq_running: - simulate_data(ii, z_range) - ii = ii + 1 - #print("appending to queue", ii) - time.sleep(sleep_time) - - -@thread_worker(connect={'yielded': display_napari}) -def yield_img(img_queue): - """ Worker thread that checks whether there are elements in the - queue, reads them out. - Connected to display_napari function to update display """ - global acq_running - - while acq_running: - time.sleep(sleep_time) - # get elements from queue while there is more than one element - # playing it safe: I'm always leaving one element in the queue - while img_queue.qsize() > 1: - #print("reading from queue ", img_queue.qsize()) - yield img_queue.get(block = False) - - # read out last remaining elements after end of acquisition - while img_queue.qsize() > 0: - yield img_queue.get(block = False) - print("acquisition done") - - -######################################################################### -# Functions to start / stop acquisition, buttons in napari # -######################################################################### -@magicgui(call_button="Start") -def start_acq(): - """ Called when Start button in pressed. Starts workers and resets global variables""" - print("starting threads...") - global acq_running - global z_range - if not(acq_running): - z_range[3] = 0 - acq_running = True - # comment in when benchmarking - #yappi.start() - worker1 = append_img(img_queue) - worker2 = yield_img(img_queue) - worker1.start() - #worker2.start() # doesn't need to be started bc yield is connected - else: - print("acquisition already running!") - - -@magicgui(call_button = "Stop") -def stop_acq(): - print("stopping threads") - # set global acq_running to False to stop other workers - global acq_running - global core - acq_running = False - if not(simulate) and not(z_stack_external): - print('z stage stopping: ', core.get_position()) - core.stop("Z") # this doesnt work, just continues moving. eventually micromanager memory overflows - print('z stage stopped: ', core.get_position()) - core.set_position(z_range[0]) # this also doesn't work - core.wait_for_device("Z") - #time.sleep(5) - print('z stage zeroed: ', core.get_position()) - - - -######################################################################### -# Main function starting napari window and initializing views # -######################################################################### - -if __name__ == "__main__": - with napari.gui_qt(): - # check if viewer is already open - # if yes: close and reopen - try: - if viewer: - viewer.close() - except: - print("viewer already closed or never opened") - viewer = napari.Viewer(ndisplay=2) - - # initialize napari viewer with stack view and random data, reslice view - scale = [(z_range[1]-z_range[0])/z_range[2]*z_scale, size_um[1], size_um[0]] - layers = [viewer.add_image(data, - name = layer_names[c], - colormap = cmap[c], - interpolation = 'nearest', - blending = 'additive', - rendering = 'attenuated_mip', - scale = scale, - contrast_limits = clim) - for c in range(channels[0])] - viewer.dims._roll() - - # set sliders to the middle of the stack for all three dimensions. - # doesn't work anymore after fixing scaling - # would have to be done for both layers - #for dd, dim in enumerate(layers[0].data.shape): - # viewer.dims.set_point(dd, dim*scale[2-dd]//2) - - # define start stop buttons and add to napari gui - # these will break when upgrading to magicgui v0.2.0 - gui_start = start_acq.Gui() - gui_stop = stop_acq.Gui() - viewer.window.add_dock_widget(gui_start) - viewer.window.add_dock_widget(gui_stop) - bridge.close() diff --git a/docs/source/backends.rst b/docs/source/backends.rst new file mode 100644 index 00000000..21226a16 --- /dev/null +++ b/docs/source/backends.rst @@ -0,0 +1,96 @@ +.. _backends: + +******************* +Backends +******************* + +Overview +========= + +Pycro-Manager's high-level API is built upon two mutually exclusive backends, each with unique advantages. Understanding this requires a brief overview of the Micro-Manager architecture. + +The Micro-Manager Core, responsible for low-level device control, is implemented in C++. The Core can be wrapped in either Java or Python. The Micro-Manager application, including its GUI, is built in Java using the Java wrapper. + +Pycro-Manager can communicate with the Micro-Manager Core in two ways: through the Java wrapper (dynamically translated to Python) or directly via the Python wrapper. The following figure illustrates the components of both Pycro-Manager and Micro-Manager: + +.. figure:: pm_arch_full.png + :alt: Full architecture of Pycro-Manager and Micro-Manager application + +Pycro-Manager offers three operational modes, each utilizing different components: + +1. **Full Micro-Manager application mode**: Uses the Java backend, providing access to the complete Micro-Manager GUI and all its features. +2. **Headless mode (Java)**: Employs the Java backend without the Micro-Manager GUI. +3. **Headless mode (Python)**: Utilizes the Python backend. + +.. figure:: three_backends.png + :alt: Three ways of running Pycro-Manager + +The choice of mode primarily impacts performance and GUI availability. Regardless of the backend, users interact with Pycro-Manager through its Python API. + + +Performance: Java vs Python Backend +==================================== + +The main difference between backends is performance. Java-Python communication can be slow (typically less than 100 MB/s). Pycro-Manager is designed to minimize data transfer across this boundary when using the Java backend. For instance, in ``Acquisition``, data is acquired and saved within Java code for maximum speed. The Python backend is advantageous when you need to rapidly process data without saving, such as running a custom Python-based live viewer. + +The APIs remain consistent across backends, allowing the same code to be used with either backend. For example, development can begin using the Micro-Manager GUI, and later switch to the Python backend if performance bottlenecks are encountered. + + + +.. _headless_mode: + +Headless Mode +============== + + +Headless mode allows you to use Pycro-manager without having to launch Micro-manager. The :meth:`start_headless` method should be run prior to any other calls. This function launches Micro-Manager core and the acquisition engine, which is used by the ``Acquisition`` class. To shut down the headless mode, call :meth:`stop_headless`. + +Headless mode supports both Java and Python backends. The Java backend uses the same components as the full Micro-Manager application, while the Python backend provides pure Python implementations of these components. + +This lightweight environment is ideal for custom applications, such as implementing your own user interface or running Pycro-Manager on a server. + +To use headless mode: + +.. code-block:: python + + from pycromanager import Core, start_headless + mm_app_path = '/path/to/micromanager' + config_file = mm_app_path + "/MMConfig_demo.cfg" + + # Start the headless process (Java backend) + start_headless(mm_app_path, config_file, python_backend=False) + + # Now use Pycro-Manager APIs as normal + core = Core() + + + + +Installing Java for Mac OS +-------------------------------- +Running headless mode with the Java backend is easy on Windows, because the correct version of Java comes bundled with the Micro-Manager installer. However, on Mac OS, this is not the case. As a result, it can be helpful to manually install a compatible version of Java. + +This can be done through Python as follows: First install the Python package ``install-jdk``. + +.. code-block:: shell + + pip install install-jdk + + +Then open a python environment and run the following code: + +.. code-block:: python + + import jdk + print(jdk.install('11')) + +The location where Java was installed will be printed, which should be something like: ``/Users/pm/.jdk/jdk-11.0.14.1+1``. Next, find the location of the java application on this path, which is likely found by appending ``/Contents/Home/bin/java``. + +Now, you're ready to run headless mode with this installed Java version. You just need to pass the location of Java to the ``start_headless`` function: + +.. code-block:: python + + java_loc = '/Users/pm/.jdk/jdk-11.0.14.1+1/Contents/Home/bin/java' + start_headless(mm_app_path, config_file, java_loc=java_loc, timeout=5000) + + diff --git a/docs/source/calling_java.rst b/docs/source/calling_java.rst index 892ec81a..bc6f571e 100644 --- a/docs/source/calling_java.rst +++ b/docs/source/calling_java.rst @@ -1,10 +1,3 @@ - -********************************************* -Calling Java code -********************************************* - - - .. _calling_custom_java: Calling custom Java code diff --git a/docs/source/conf.py b/docs/source/conf.py index a704c275..0c602984 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -58,8 +58,13 @@ # a list of builtin themes. # html_theme = "sphinx_rtd_theme" +html_theme_options = { + 'collapse_navigation': True, + 'navigation_depth': 10, +} + # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ["_static"] +# html_static_path = ["_static"] diff --git a/docs/source/core.rst b/docs/source/core.rst index ed077a41..7005a004 100644 --- a/docs/source/core.rst +++ b/docs/source/core.rst @@ -1,11 +1,26 @@ .. _control_core: -Low-level control with MMCore -********************************************** +Low-level Control with MMCore +============================= -The example below shows how to call the Java bindings for the micro-manager core from Python. Because the core API is discovered at runtime and translated into Python, the easiest way to discover which functions are available is to type ``core.`` and type tab to use IPython autocomplete. Alternatively, the documentation for the Java version of the core API can be found `here `_. Note that function names will be automatically translated from the camelCase Java convention to the Python convention of underscores between words (e.g. ``setExposure`` becomes ``set_exposure``) +Pycro-Manager provides access to the Micro-Manager core via the `mmpycorex `_ library. The behavior of the ``Core`` object depends on whether the Python or Java backend is in use (see :ref:`headless_mode`): + +- Java backend: Returns an automatically Python-converted version of the Java core object +- Python backend: Returns a direct reference to a `pymmcore `_ object + +While both wrap the same underlying object, there may be slight differences in their APIs. + +Discovering Available Functions +------------------------------- + +To explore available functions: + +1. In IPython, type ``core.`` and press Tab for autocomplete suggestions. +2. Refer to the `Java version of the core API documentation `_. + +.. note:: + Function names are automatically translated from Java's camelCase to Python's snake_case (e.g., ``setExposure`` becomes ``set_exposure``). -An alternative mechanism for controlling the Micro-Manager core is through [pymmcore](https://github.com/micro-manager/pymmcore/), which exposes an almost identical API (but lacks the many additional features of Pycro-Manager). Pymmcore requires a bit more work in to get a configuration loaded and set up and doesn't have it's own GUI. .. code-block:: python diff --git a/docs/source/acquisition_figure.png b/docs/source/event_hook_processor_figure.png similarity index 100% rename from docs/source/acquisition_figure.png rename to docs/source/event_hook_processor_figure.png diff --git a/docs/source/application_notebooks/external_hardware_triggering_tutorial.ipynb b/docs/source/external_hardware_triggering_tutorial.ipynb similarity index 99% rename from docs/source/application_notebooks/external_hardware_triggering_tutorial.ipynb rename to docs/source/external_hardware_triggering_tutorial.ipynb index 6b90f9aa..3c5aa2c4 100644 --- a/docs/source/application_notebooks/external_hardware_triggering_tutorial.ipynb +++ b/docs/source/external_hardware_triggering_tutorial.ipynb @@ -55,7 +55,7 @@ "metadata": {}, "outputs": [], "source": [ - "from pycromanager import Bridge, Acquisition, Core\n", + "from pycromanager import Acquisition, Core\n", "import numpy as np\n", "from pathlib import Path\n", "from time import sleep" diff --git a/docs/source/hardware_triggering.rst b/docs/source/hardware_triggering.rst index ffea72f4..5f56ea4e 100644 --- a/docs/source/hardware_triggering.rst +++ b/docs/source/hardware_triggering.rst @@ -1,33 +1,80 @@ .. _hardware_triggering: +Hardware Triggering and Sequencing +================================== -**************************************************** -Fast acquisition with hardware triggering -**************************************************** +Hardware triggering and sequencing are crucial for achieving fast, precisely synchronized image acquisition by minimizing computer-hardware communication. This approach can significantly reduce latency between image frames. -A standard acquisition is accomplished by sending commands from the computer to the devices each time a change (in, e.g., stage position or illumination) is required. This communication can add unnecessary latency (up to 100 ms) between image frames. Much faster and accurately timed operation is possible with most cameras (when acquiring a preset sequence of frames) as well as many other devices (when executing a pre-programmed sequence of commands). +How It Works +------------ -For the fastest data acquisition speeds, routing TTL (Transistor-Transistor Logic) pulses over signal cables between hardware devices is essential. In such setups, hardware components are loaded with sequences of instructions (e.g. physical positions on a stage or a sequence of exposures on a camera). The hardware sequence can then be executed independently of the computer, while images are read off of the camera as fast as possible. +In hardware-triggered setups: -The :class:`Acquisition` class has built in support for hardware sequencing, and it will automatically applied whenever it is supported by the hardware being used. There are two general synchronization strategies supported, which differ depending on what hardware device is the "leader" (i.e. the one sending out TTL pulses) and which one is the "follower" (i.e. the one receiving them). +1. Hardware components are pre-loaded with sequences of instructions (e.g., stage positions or digital outputs to control lasers). +2. The sequence is executed independently of the computer. +3. TTL (Transistor-Transistor Logic) pulses are routed between devices for synchronization. +4. Images are read from the camera as quickly as possible. -In the default strategy, the camera is assumed to be the leader device. This means that it will run as fast as possible, and other peripheral devices synchronized with it should update their positions based on the TTL pulses outputed by the camera. Whenever the camera supports this behavior, there is little to no delay between successive frames, and any hardware that needs to repositioned also supports sequenceing, this behavior will automatically occur. If there is a need to adjust additional hardware settings in between successive sequences, this can done using :ref:`acq_hooks`. +Automatic Hardware Sequencing +----------------------------- -In the second strategy, the camera is the "follower" device, and there is another external "leader" device which controls the synchronization between different hardware components. In this case, the camera should be placed into a mode where it will wait for external triggers before exposing. The specific properties that need to be set will differ from camera to camera, as this behavior is not currently a part of the micro-manager camera API. Once in this state, a pycro-manager :class:`Acquisition` will cause the camera to shift into a state where it waits for a TTL pulse to trigger each exposure. The only thing remaining is to signal to the external leader device that the camera is ready, so that the leader device can begin its synchronization routine. This signalling can be done with an `post_camera_hook_fn` that runs after the camera has set into a ready state. +The :class:`Acquisition` class automatically applies hardware sequencing when: +1. No delays are requested between successive images. +2. All hardware position changes support instruction sequencing. +3. All events are submitted to ``acq.acquire()`` in a single call. -.. code-block:: python +Synchronization Strategies +-------------------------- + +Pycro-Manager supports two general synchronization strategies: + +1. Camera as Leader (Default): + - The camera runs at maximum speed. + - Other devices update based on TTL pulses from the camera. + + +2. External Device as Leader: + - An external device timing device controls synchronization. + - The camera is set to wait for external triggers. + - Use a ``post_camera_hook_fn`` to signal the external leader device to start: + + .. code-block:: python - def hook_fn(event): - ### start external leader device here ### - return event + def hook_fn(event): + # Start external leader device here + return event - # pass in the function as a post_hardware_hook - with Acquisition(directory='/path/to/saving/dir', name='acquisition_name', - post_camera_hook_fn=hook_fn) as acq: - ### acquire some stuff ### + with Acquisition(directory='/path/to/saving/dir', name='acquisition_name', + post_camera_hook_fn=hook_fn) as acq: + # Acquisition code here + +Using Acquisition Hooks with Hardware Sequencing +------------------------------------------------ + +When hardware sequencing is engaged: +- The ``event`` passed to hooks will be a ``list`` of ``dict`` objects (a sequence of events). +- Hooks are called once for the whole sequence, not for each event. + +Disabling Hardware Sequencing +----------------------------- + +To disable hardware sequencing, submit events one at a time: + +.. code-block:: python + with Acquisition(directory='/path/to/saving/dir', name='acquisition_name') as acq: + events = multi_d_acquisition_events(num_time_points=10) + for event in events: + acq.acquire(event) +Practical Example: Light-Sheet Microscopy +-------------------------------------------- +This `notebook `_ shows an example of how to setup Pycro-Manager to run a microscope that utilizes an external controller as the leader device. Specifically, this tutorial controls a light-sheet microscope where a sample with fluorescent labels is scanned at a constant speed through an oblique light sheet. The stage controller provides the TTL signals that ensure the camera is synchronized to the scanning stage. This approach makes use of ``post hardware`` and ``post camera`` hook functions built into Pycro-Manager. Using these hook functions, it is possible to rapidly build and acquire a multiple terabyte acquisition consisting of millions of images. +.. toctree:: + :maxdepth: 1 + :hidden: + external_hardware_triggering_tutorial.ipynb diff --git a/docs/source/headless_mode.rst b/docs/source/headless_mode.rst deleted file mode 100644 index 3fabd305..00000000 --- a/docs/source/headless_mode.rst +++ /dev/null @@ -1,87 +0,0 @@ -.. _headless_mode: - -************************** -Headless Mode -************************** - -Headless mode allows you to use Pycro-manager without having to launch Micro-manager. The :meth:`start_headless` method should be run prior to any other calls. This function launches Micro-Manager core and the acqusition engine, which is used by the ``Acquisition`` class. - -Headless mode can be run with either a Java backend (i.e. the same components that run when you use pycromanager along with the Micro-Manager application) or a Python backend, which provides pure python versions of the same components. - -Headless mode provides a lightweight environment that can also be run without a GUI in order to use Pycro-manager as a hidden backend for custom applications. This could be useful, for example, if you want to implement your own user interface, or run Pycro-manager from a server environment. - - -.. code-block:: python - - from pycromanager import Core, start_headless - mm_app_path = '/path/to/micromanager' - config_file = mm_app_path + "/MMConfig_demo.cfg" - - # Start the headless process - start_headless(mm_app_path, config_file, timeout=5000) - - # now use pycro-manager APIs as normal - core = Core() - - -The example below shows headless mode in combination with an saved image callback, which calls a user-defined function whenever new data is stored (on disk or in RAM depending on the arguments to ``Acquisition``). This setup could be used to replace the pycro-manager viewer with a custom user interface (note the ``show_display=False`` in the acquisition). - - -.. code-block:: python - - from pycromanager import Acquisition, multi_d_acquisition_events, start_headless - - mm_app_path = '/path/to/micromanager' - config_file = mm_app_path + "/MMConfig_demo.cfg" - - # Start the Java process - start_headless(mm_app_path, config_file, timeout=5000) - - save_dir = r"C:\Users\henry\Desktop\data" - - def image_saved_fn(axes, dataset): - pixels = dataset.read_image(**axes) - # TODO: use the pixels for something, like post-processing or a custom image viewer - - with Acquisition(directory=save_dir, name="tcz_acq", show_display=False, - image_saved_fn=image_saved_fn, - ) as acq: - events = multi_d_acquisition_events( - num_time_points=5, - z_start=0, z_end=6, z_step=0.4, - ) - acq.acquire(events) - - # Another way to access to the saved data - d = acq.get_dataset() - - - -How to install Java for Mac OS -============================================= -Running headless mode with the Java backend is easy on Windows, because the correct version of Java comes bundled with the Micro-Manager installer. However, on Mac OS, this is not the case. As a result, it can be helpful to manually install a compatible version of Java. - -This can be done through Python as follows: First install the Python package ``install-jdk``. - -.. code-block:: shell - - pip install install-jdk - - -Then open a python environment and run the following code: - -.. code-block:: python - - import jdk - print(jdk.install('11')) - -The location where Java was installed will be printed, which should be something like: ``/Users/pm/.jdk/jdk-11.0.14.1+1``. Next, find the location of the java application on this path, which is likely found by appending ``/Contents/Home/bin/java``. - -Now, you're ready to run headless mode with this installed Java version. You just need to pass the location of Java to the ``start_headless`` function: - -.. code-block:: python - - java_loc = '/Users/pm/.jdk/jdk-11.0.14.1+1/Contents/Home/bin/java' - start_headless(mm_app_path, config_file, java_loc=java_loc, timeout=5000) - - diff --git a/docs/source/image_saved_callbacks.rst b/docs/source/image_saved_callbacks.rst index 56913399..562d1717 100644 --- a/docs/source/image_saved_callbacks.rst +++ b/docs/source/image_saved_callbacks.rst @@ -4,33 +4,42 @@ Saved image callbacks ************************** -Saved image callbacks, allow a user-supplied ``image_saved_fn`` to be automatically called by the :class:`Acquisition` as soon as a new image has been saved. The image can then be read directly from disk in Python. This avoids the speed limitations incurred by image processors. It is also a useful way to implement a custom user interface, because the function will be called each time there is new data and the UI should be updated. Alternatively, it can be used to start post-processing large datasets as soon as they are acquired. +Saved image callbacks provide a mechanism to execute custom code immediately after an image is saved, which: +1. Avoids speed limitations of image processors when using the Java backend (see :ref:`headless_mode`) +2. Enables custom user interface updates +3. Allows immediate post-processing of acquired data -The ``image_saved_fn`` takes two arguments, ``axes`` and ``dataset``. The first is the describe the unique identifier of the image (``z=0``, ``time=2``, etc.), and the second provides access to the :class:`Dataset` associated with the Acquisition. The pixels of the image that was just saved can be accessed by calling: +Basic Usage +----------- -.. code-block:: python +To use saved image callbacks, supply an ``image_saved_fn`` to the :class:`Acquisition`. This function takes two arguments: + +- ``axes``: A dictionary describing the unique identifier of the image (e.g., ``z=0``, ``time=2``) +- ``dataset``: A :class:`Dataset` object associated with the Acquisition - pixels = dataset.read_image(**axes) +Access the newly saved image as follows: +.. code-block:: python -Alternatively, a three argument version can be utilized in which the arguments are ``axes``, ``dataset``, and ``event_queue``. The event queue allows new acquisition events to be created in response to images being saved to disk. + def image_saved_fn(axes, dataset): + pixels = dataset.read_image(**axes) + # Process or display pixels here -A full example of using this feature is below: +Example: .. code-block:: python - from pycromanager import Acquisition, multi_d_acquisition_events, + from pycromanager import Acquisition, multi_d_acquisition_events def image_saved_fn(axes, dataset): pixels = dataset.read_image(**axes) - # TODO: use the pixels for something, like post-processing or a custom image viewer + # TODO: Use pixels for post-processing or custom visualization with Acquisition(directory=save_dir, name="tcz_acq", - image_saved_fn=image_saved_fn, - ) as acq: + image_saved_fn=image_saved_fn) as acq: events = multi_d_acquisition_events( num_time_points=5, z_start=0, z_end=6, z_step=0.4, @@ -39,3 +48,53 @@ A full example of using this feature is below: +Adapting acquisition from image saved callbacks +----------------------------------------------- + +.. note:: + + Adapting acquisition form image saved callbacks is an older feature. The newer :ref:`adaptive_acq` API is now the reccomended way to do this. However, the approach below still works. + +The ``event_queue`` allows you to create new acquisition events in response to saved images, enabling adaptive acquisition strategies. Use a three-argument version of ``image_saved_fn``: + +.. code-block:: python + + def image_saved_fn(axes, dataset, event_queue): + pixels = dataset.read_image(**axes) + # Process pixels + new_event = create_new_event(pixels) # Create a new acquisition event + event_queue.put(new_event) # Add the new event to the queue + + + +Custom user interface updates +----------------------------- + + +The example below shows :ref:`headless_mode` in combination with an saved image callback, which calls a user-defined function whenever new data is stored (on disk or in RAM depending on the arguments to ``Acquisition``). This setup could be used to replace the pycro-manager viewer with a custom user interface (note the ``show_display=False`` in the acquisition). + + +.. code-block:: python + + from pycromanager import Acquisition, multi_d_acquisition_events, start_headless + + mm_app_path = '/path/to/micromanager' + config_file = mm_app_path + "/MMConfig_demo.cfg" + + # Start the Java process + start_headless(mm_app_path, config_file, timeout=5000) + + save_dir = r"C:\Users\henry\Desktop\data" + + def image_saved_fn(axes, dataset): + pixels = dataset.read_image(**axes) + # TODO: use the pixels for something, like post-processing or a custom image viewer + + with Acquisition(directory=save_dir, name="tcz_acq", show_display=False, + image_saved_fn=image_saved_fn) as acq: + events = multi_d_acquisition_events(num_time_points=5, + z_start=0, z_end=6, z_step=0.4,) + acq.acquire(events) + + # Another way to access to the saved data + d = acq.get_dataset() diff --git a/docs/source/img_processors.rst b/docs/source/img_processors.rst index f29f82b4..dfa85ee8 100644 --- a/docs/source/img_processors.rst +++ b/docs/source/img_processors.rst @@ -4,30 +4,32 @@ Image processors ************************** -Image processors provide access to data as it is being acquired. This allows it to be modified, diverted to customized visualization and saving, or analyzed on-the-fly to control acquisition. +Image processors allow real-time access to acquired data for modification, custom visualization, saving, or on-the-fly analysis to control acquisition. -The simplest image processor function takes two arguments: the pixel data (a numpy array) and metadata (a python dictionary) of the current image. + +Basic Usage +----------- + +A simple image processor function takes two arguments: ``image`` (numpy array) and ``metadata`` (python dictionary). .. code-block:: python def img_process_fn(image, metadata): - - #add in some new metadata - metadata['a_new_metadata_key'] = 'a new value' + # Add new metadata + metadata['new_key'] = 'new value' - #modify the pixels by setting a 100 pixel square at the top left to 0 + # Modify image image[:100, :100] = 0 - #propogate the image and metadata to the default viewer and saving classes + # propogate the image and metadata to the default viewer and saving classes return image, metadata # run an acquisition using this image processor with Acquisition(directory='/path/to/saving/dir', name='acquisition_name', - image_process_fn=img_process_fn) as acq: - ### acquire some stuff ### + image_process_fn=img_process_fn) as acq: + # Acquisition code here - -One particularly useful metadata key is ``'Axes'`` which recovers the ``'axes'`` key that was in the **Acquisition event** in this image. +Tip: Access the original :ref:`acq_events` axes using ``metadata['Axes']``: .. code-block:: python @@ -36,111 +38,116 @@ One particularly useful metadata key is ``'Axes'`` which recovers the ``'axes'`` time_index = metadata['Axes']['time'] -Returning multiple or zero images -==================================== +Advanced Features +----------------- -Image processors are not required to take in one image and return one image. They can also return multiple images or no images. In the case of multiple images, they should be returned as a list of ``(image, metadata)`` tuples. The ``'Axes'`` or ``Channel`` metadata fields will need to be modified to uniquely identify the two images for the purposes of saving or the image viewer. +1. Returning multiple Images +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. code-block:: python +Image processors can return multiple images by returning a list of ``(image, metadata)`` tuples. Be sure to modify the ``'Axes'`` metadata field to uniquely identify each image for saving or viewing. For example, this code shows how to split into a single image into two images in different channels: - import copy +.. code-block:: python def img_process_fn(image, metadata): - - # copy pixels in this example, but in reality - # you might want to compute something different - image_2 = np.array(image, copy=True) - metadata_2 = copy.deepcopy(metadata) + metadata_2['Axes']['channel'] = 'New_channel' + metadata['Axes']['channel'] = 'Old_channel' + return [(image, metadata), (image_2, metadata_2)] - metadata_2['Channel'] = 'A_new_channel' - - #return as a list of tuples - return [(image, metadata), (image2, md_2)] +2. Custom Saving and Viewing +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Rather than returning one or more ``image, metadata`` tuples to propogate the image to the default viewer and saver, the image processing function can return nothing. This can be used if one wants to delete a specific image, or divert all images to customized saving/visualization code. If the latter behavior is desired, the :class:`Acquisition` should be created without the ``name`` and ``directory`` fields. - +To implement custom saving or viewing, return nothing from the image processor. Create the :class:`Acquisition` without ``name`` and ``directory`` fields: .. code-block:: python def img_process_fn(image, metadata): + # Custom saving or viewing logic here - ### send image and metadata somewhere ### - - # this acquisition won't show a viewer or save data + # This acquisition won't use the default viewer or save data with Acquisition(image_process_fn=img_process_fn) as acq: - ### acquire some stuff ### + # Acquisition code here -Adapting acquisition from image processors -============================================ +3. Processing multiple images +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -In certain cases one may want to create addition **acquisition events** in response to one of the images. A three argument processing function can be used for this purpose. The third argument is the ``event_queue`` to which additional acquisition events can be added +For operations on multiple images (e.g., Z-stacks), accumulate images until a complete set is available: .. code-block:: python - def img_process_fn_events(image, metadata, event_queue): + # The number of images per a full Z-stack + num_z_steps = 10 - ### create a new acquisition event in response to something in the image ### - # event = - event_queue.put(event) + def img_process_fn(image, metadata): + # accumulate individual Z images + if not hasattr(img_process_fn, "images"): + img_process_fn.images = [] + img_process_fn.images.append(image) - return image, metadata + if len(img_process_fn.images) == num_z_steps: + # if last image in z stack, combine into a ZYX array + zyx_array = np.stack(img_process_fn.images, axis=0) -In the case of using feedback from the image to control acquisition, the typical syntax of ``with Acquisition...`` cannot be used because it will automatically close the acquisition too soon. Instead the acquisition should be created as: + ### Do some processing on the 3D stack ### -.. code-block:: python + # This returns the original image and metadata, but in + # this scenario, a possible alternative is to return nothing + # until an entire Z-stack is processed + return image, metadata - acq = Acquisition(directory='/path/to/saving/dir', name='acquisition_name', - image_process_fn=img_process_fn) -``acq.acquire`` will then need to be called at least once, so that there is an feedback loop between processed images and new events will be started. + +Adapting acquisition from image processors +------------------------------------------- + +.. note:: + + Adapting acquisition form image processors is an older feature. The newer :ref:`adaptive_acq` API is now the reccomended way to do this. However, the approach below still works. -When it is finished, it can be closed and cleaned up by passing an ``None`` to the ``event_queue``. +To create additional :ref:`acq_events` based on acquired images, use a three-argument image processor: .. code-block:: python def img_process_fn_events(image, metadata, event_queue): - - if acq_end_condition: - event_queue.put(None) - else: - #continue adding more events + # Create a new acquisition event based on the image + new_event = create_new_event(image, metadata) + event_queue.put(new_event) + return image, metadata -Processing multiple images at once -==================================== -In many cases, it is useful to process multiple images at a time, rather than just a single image. For example, this could be useful when processing should only occur after collecting a 3D volume at the end of a Z-stack. To accomplish -this, the function can hold onto a list of images until it contains a full Z-stack before processing. +For adaptive acquisition, create the ``Acquisition`` object separately and call ``acquire`` manually: .. code-block:: python - # The number of images per a full Z-stack - num_z_steps = 10 + acq = Acquisition(directory='/path/to/saving/dir', name='acquisition_name', + image_process_fn=img_process_fn_events) + acq.acquire() # Start the feedback loop - def img_process_fn(image, metadata): - # accumulate individual Z images - if not hasattr(img_process_fn, "images"): - img_process_fn.images = [] - img_process_fn.images.append(image) +To end the acquisition, put ``None`` in the ``event_queue``: + +.. code-block:: python + + def img_process_fn_events(image, metadata, event_queue): + if acq_end_condition: + event_queue.put(None) + else: + # Continue adding more events - if len(img_process_fn.images) == num_z_steps: - # if last image in z stack, combine into a ZYX array - zyx_array = np.stack(img_process_fn.images, axis=0) - - ### Do some processing on the 3D stack ### - return image, metadata Performance -==================================== -In the current implementation, image processors pass data back and forth through the Java-Python transport layer, which requires serializing and deserializing data to pass it from one process to another. This introduces a speed limitation of ~100 MB/s for image processors. +------------ + +The performance of image processors is dependent on the backend used (see :ref:`headless_mode`). When running micro-manager with the Java backend (either by opening the Micro-Manager application or launching Java backend headless mode), images are acquired in a separate Java process and must be passed to the Python process for processing. This transfer is limited to ~100 MB/s. + +If speeds faster than this are required, consider using the :ref:`image_saved_callbacks` feature, which allows images to be saved to disk in Java code (which is can be much faster) and then read off the disk in Python. This can be significantly faster than using image processors. -However, there is a potential workaround for this through the use of :ref:`image_saved_callbacks`. Here, rather than intercepting images after they are acquired, but before they are written to disk, the images are written to disk in Java code (which is very fast) without passing over the Java-Python Bridge, and as soon as they are written, a signal is sent to the Acquisition that enables the data to be read off the disk. With fast enough hard drives, this can give access to acquired data significantly faster than image processors. +Alternatively, if the Micro-Manager application is not required, consider using the python backend, in which images are acquired and processed in the same Python process, avoiding the Java-Python transport layer entirely. diff --git a/docs/source/index.rst b/docs/source/index.rst index dcce3fb9..03ea4354 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -4,8 +4,9 @@ ``pycromanager`` is a python package for microscope hardware automation and image acquisition. It provides many components that can be used independently or in combination, with the aim of being flexible enough to enable a variety of applications. -The best place to get started is the :doc:`user_guide`. The :doc:`tutorials` section shows jupyter notebook tutorials of using pycro-manager for various applications, as well as some more in depth descriptions of certain features. +The best place to get started is the :doc:`user_guide`. +The :doc:`tutorials` section shows jupyter notebook tutorials of using pycro-manager for various applications. Information on the original motivations for Pycro-Manager can be found in the `publication `_, though its capabilities have since expanded. diff --git a/docs/source/application_notebooks/intermittent_Z_T.ipynb b/docs/source/intermittent_Z_T.ipynb similarity index 97% rename from docs/source/application_notebooks/intermittent_Z_T.ipynb rename to docs/source/intermittent_Z_T.ipynb index 166e9d5f..97eb4ec3 100644 --- a/docs/source/application_notebooks/intermittent_Z_T.ipynb +++ b/docs/source/intermittent_Z_T.ipynb @@ -53,7 +53,7 @@ "source": [ "import numpy as np\n", "\n", - "from pycromanager import Acquisition, Core, Studio, multi_d_acquisition_events" + "from pycromanager import Acquisition, Core, multi_d_acquisition_events" ] }, { @@ -73,8 +73,7 @@ }, "outputs": [], "source": [ - "mmc = Core()\n", - "mmStudio = Studio()" + "mmc = Core()" ] }, { diff --git a/docs/source/application_notebooks/multi-d-acq-tutorial.ipynb b/docs/source/multi-d-acq-tutorial.ipynb similarity index 100% rename from docs/source/application_notebooks/multi-d-acq-tutorial.ipynb rename to docs/source/multi-d-acq-tutorial.ipynb diff --git a/docs/source/notifications.rst b/docs/source/notifications.rst new file mode 100644 index 00000000..928e74e1 --- /dev/null +++ b/docs/source/notifications.rst @@ -0,0 +1,28 @@ +.. _notifications: + +============== +Notifications +============== + +Acquisitions support a notification system that provides real-time updates on the acquisition process. + +To receive notifications, pass a callback function to the ``Acquisition`` constructor. The callback function should accept a single argument, a ``AcqNotification`` object. + + .. code-block:: python + + def notification_callback(notification): + print(notification.milestone) + + with Acquisition(..., notification_callback_fn=notification_callback) as acq: + # acquisition code + +**Notification Types**: The system provides notifications for various stages of the acquisition process. 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over the Java-Python translation layer. The current implementation of :ref:`img_processors` are bound by this limit, so if extremely fast data rates are needed, they should be avoided. One alternative is to use :ref:`image_saved_callbacks`, which do not send data between Java and Python directly. Instead, after each image is written to disk, a small message is sent from Java to Python describing the location of the new data on disk. 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Data saved in this format can be read either during or after an acquisition. In either case, access to the underlying data is provided by a :class:`Dataset` object. - -Images can be loaded individually, or all data can be loaded simulataneously into a memory-mapped `dask `_ array. This is a "virtual" array, which means the whole dataset isn't loaded into RAM at first, but is instead "lazily" brought into RAM as each sub-part of it is used. This allows for processing of large datasets and viewing data in `napari `_. - -Creating a ``Dataset`` object -############################## - -There are two ways to do this, depending on whether the data is part of an in-progress acquisition or not. In the former case: - -.. code-block:: python - - from pycromanager import Acquisition - - with Acquisition('/path/to/saving/dir', 'saving_name') as acq: - - ### send some instructions so something is acquired ###### - - dataset = acq.get_dataset() - -Alternatively, to open a finished dataset from disk: - - -.. code-block:: python - - from pycromanager import Dataset - - #This path is to the top level of the dataset - data_path = '/path/to/data' - - dataset = Dataset(data_path) - - -Reading data -############################## - -Once opened, individual tiles can be accessed using :meth:`read_image`. This method accepts positions along different dimensions as argument. For example, to get the first image in a z stack, pass in `z=0` as an argument. - -.. code-block:: python - - img = dataset.read_image(z=0) - img_metadata dataset.read_metadata(z=0) - - #img is a numpy array, img_metadata is a dict - -To determine which axes are available, access the ``Dataset.axes`` attribute, which contains a dict with axis names as keys and a list of available indices as values. - -If the dataset was created by tiling multiple XY positions, tiles along the axis corresponding to XY positions can be indexed by their row and column positions: - -.. code-block:: python - - img = dataset.read_image(row=0, col=1) - - -Opening data as Dask array -############################## - -Rather than reading each image individually, all data can be opened at once in a single dask array. Using dask arrays enables all_data to be held in a single memory-mapped array. This means that the data are not loaded in RAM until they are used, enabing a convenient way to work with datasets larger than the computer's RAM. Dask arrays also enable `https://napari.org/tutorials/applications/dask `_ and allow for code to be prototyped on a small computers and scaled up to clusters without having to rewrite code. - -.. code-block:: python - - dask_array = dataset.as_array() - - #dask array can be used just like numpy array - #take max intenisty projection along axis 0 - max_intensity = np.max(all_data[0, 0], axis=0) - - #visualize data using napari - v = napari.Viewer() - v.add_image(dask_array) - napari.run() - - -If the data was acquired by an :class:`XYTiledAcquisition` or a :class:`MagellanAcquisition` the grid on XY images can be automatically stitched into one contiguous image: - -.. code-block:: python - - dask_array = dataset.as_array(stitched=True) - - -You can also slice along particular axes when creating the dask array: - -.. code-block:: python - - dask_array = dataset.as_array(z=0, time=2) - - diff --git a/docs/source/setup.rst b/docs/source/setup.rst index fc0098f0..b5d0f46a 100644 --- a/docs/source/setup.rst +++ b/docs/source/setup.rst @@ -9,7 +9,14 @@ Installation/setup - Download newest version of `micro-manager 2.0 `_ -- Open Micro-Manager, select tools-options, and check the box that says **Run server on port 4827** (you only need to do this once) +- Download and install Micro-Manager, either by downloding a nightly build from the `Micro-Manager website `_, or programatically using the :ref:`installation api ` function: + + .. code-block:: python + + from mmpycorex import download_and_install_mm + download_and_install_mm() + +- Open Micro-Manager, select **tools-options**, and check the box that says **Run server on port 4827** (you only need to do this once) Verify that installation worked ################################ @@ -30,16 +37,3 @@ which will give an output like: -################################ -Troubleshooting -################################ - -Upon creating the Bridge, you may see an error with something like: - -.. code-block:: none - - UserWarning: Version mistmatch between Java ZMQ server and Python client. - Java ZMQ server version: 2.4.0 - Python client expected version: 2.5.0 - -In this case case your Micro-manager version Pycro-manager versions are out of sync. The best fix is to down the latest versions of both. Uprgade to the latest Pycro-manager with: ``pip install pycromanager --upgrade`` \ No newline at end of file diff --git a/docs/source/special_acqs.rst b/docs/source/special_acqs.rst index cf57921b..86570003 100644 --- a/docs/source/special_acqs.rst +++ b/docs/source/special_acqs.rst @@ -1,47 +1,76 @@ .. _special_acqs: **************************************************************** -Special Acquisition Types +Acquisitions for XY Tiling Multiple Fields-of-View **************************************************************** + + +Pycro-Manager supports specialized Acquisition types for specific applications. The current special types are all for imaging large samples by capturing multiple fields of view and stitching them together. These include: + +1. :class:`XYTiledAcquisition` + - Images large samples by capturing multiple fields of view + - Stitches images into a single contiguous image + - Uses a multi-resolution file format for efficient visualization at multiple scales + +2. :class:`ExploreAcquisition` + - Extends XYTiledAcquisition + - Provides a user interface for XY and Z stage navigation + - Allows interactive selection of imaging locations + +3. :class:`MagellanAcquisition` + - Controls the `Micro-Magellan `_ plugin + - Offers sample mapping, region of interest definition, and specialized 3D dataset collection + +Stitching acquired images +-------------------------- + +For these acquisitions the images are acquired in a grid pattern and then stitched together. The stitching can be done automatically by setting the ``stitched`` argument to ``True`` in the ``as_array`` method. This will return a Dask array that is a single contiguous image, and doesn't pull individual tiles into memory until they are needed. This is useful for large images that don't fit in memory. + +.. code-block:: python + + dask_array = dataset.as_array(stitched=True) + + + + + + + + .. _xy_tiled_acq: XYTiled Acquisition -======================== -Pycro-manager has special support for acquisitions in which multiple images are tiled together to form large, high-resolution images. In this mode, data will automatically be saved in a multi-resolution pyramid, so that it can be efficiently viewed at multiple levels of zoom. These features are also available though `Micro-magellan `_, which provides an interactive GUI as well as other higher level features. +------------------- +XYTiled Acquisitions support imaging of large samples by tiling multiple images together. Data is saved in a multi-resolution pyramid for efficient viewing at different zoom levels. .. note:: - In order for this functionality to work, the current configuration must have a correctly calibrated affine transform matrix, which gives the corrspondence between the coordinate systems of the camera and the XY stage. This can be calibrated automatically in Micro-Manager by using the pixel size calibrator (under ``Devices``--``Pixel Size Calibration`` in the Micro-manager GUI). + This functionality requires a correctly calibrated affine transform matrix in the current configuration. Calibrate using the pixel size calibrator in Micro-Manager (``Devices``--``Pixel Size Calibration``). -To use these features, rather than creating an :class:`Acquisition`, a :class:`XYTiledAcquisition` will be used. These classes are almost identical, except that :class:`XYTiledAcquisition` has an additional required argument ``tile_overlap``, which gives the number of pixels by which adjacent tiles will overlap. Different XY fields of view can be acquired adding ``row`` and ``column`` indices in the ``axes`` of the acquisition event. +Different XY fields of view can be acquired adding ``row`` and ``column`` indices in the ``axes`` of the acquisition event. +Usage: .. code-block:: python from pycromanager import XYTiledAcquisition with XYTiledAcquisition(directory='/path/to/saving/dir', name='saving_name', tile_overlap=10) as acq: - #10 pixel overlap between adjacent tiles + # 10 pixel overlap between adjacent tiles - #acquire a 2 x 1 grid - acq.acquire({'axes': - {'row': 0, 'col': 0} - }) - acq.acquire({'axes': - {'row': 1, 'col': 0} - }) + # Acquire a 2 x 1 grid + acq.acquire({'axes': {'row': 0, 'col': 0}}) + acq.acquire({'axes': {'row': 1, 'col': 0}}) Explore Acquisitions -======================== - - -Explore acquisitions are XY tiled acquisitions, only with a special graphical user interface that enables a user to control image acquisition directly, as shown in the example below: +-------------------- +Explore Acquisitions extend XYTiled Acquisitions with a graphical interface for user-controlled image acquisition: .. image:: explore.gif :width: 800 @@ -52,7 +81,7 @@ Explore acquisitions are XY tiled acquisitions, only with a special graphical us Micro-Magellan Acquisition =============================== -Another alternative is to launch `Micro-magellan `_ acquisitions. These include both regular and `explore acquisitions `_, which launches an interactive GUI for navigating around a sample in XY and Z and clicking to collect images. +Another alternative is to launch `Micro-magellan `_ acquisitions. These include both regular and `explore acquisitions `_. Micro-Magellan acquisitions can be run using the :class:`MagellanAcquisition` class. The class requires as an argument either ``magellan_acq_index`` or ``magellan_explore``. The former corresponds to the position of the acquisition to be launched in the **Acquisition(s)** section of the Micro-Magellan GUI. Passing in 0 corresponds to the default acquisition. Greater numbers can be used to programatically control multiple acquisitions. 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