Metadata-Version: 2.1
Name: nd2
Version: 0.5.1
Summary: Yet another nd2 (Nikon NIS Elements) file reader
Author: Talley Lambert
Author-email: talley.lambert@gmail.com
License: BSD 3-Clause License
Project-URL: homepage, https://github.com/tlambert03/nd2
Project-URL: repository, https://github.com/tlambert03/nd2
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Python: <3.12,>=3.7
Description-Content-Type: text/markdown
Provides-Extra: legacy
Provides-Extra: test
Provides-Extra: dev
License-File: LICENSE

# nd2

[![License](https://img.shields.io/pypi/l/nd2.svg?color=green)](https://github.com/tlambert03/nd2/raw/main/LICENSE)
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[![codecov](https://codecov.io/gh/tlambert03/nd2/branch/main/graph/badge.svg)](https://codecov.io/gh/tlambert03/nd2)

Yet another `.nd2` (Nikon NIS Elements) file reader.

This reader provides a Cython wrapper for the official Nikon SDK.
(The actual reading of image frames, however, uses a direct memmap approach, instead of the SDK,
for performance reasons and to avoid occasional segfaults from the SDK.)

Features good metadata retrieval, and direct `to_dask` and `to_xarray` options for lazy and/or annotated arrays.

This library is tested against many nd2 files with the goal of maximizing compatibility and data extraction.
(If you find an nd2 file that fails in some way, please open an issue with the file!)

## install

```sh
pip install nd2
```

or from conda:

```sh
conda install -c conda-forge nd2
```

### extras

Legacy nd2 (JPEG2000) files are also supported, but require `imagecodecs`.  To install with support for these files use:

```sh
pip install nd2[legacy]
```

## usage and API

```python
import nd2
import numpy as np

my_array = nd2.imread('some_file.nd2')                          # read to numpy array
my_array = nd2.imread('some_file.nd2', dask=True)               # read to dask array
my_array = nd2.imread('some_file.nd2', xarray=True)             # read to xarray
my_array = nd2.imread('some_file.nd2', xarray=True, dask=True)  # read file to dask-xarray

# or open a file with nd2.ND2File
f = nd2.ND2File('some_file.nd2')

# (you can also use nd2.ND2File() as a context manager)
with nd2.ND2File('some_file.nd2') as ndfile:
    print(ndfile.metadata)
    ...


# ATTRIBUTES:   # example output
f.path          # 'some_file.nd2'
f.shape         # (10, 2, 256, 256)
f.ndim          # 4
f.dtype         # np.dtype('uint16')
f.size          # 1310720  (total voxel elements)
f.sizes         # {'T': 10, 'C': 2, 'Y': 256, 'X': 256}
f.is_rgb        # False (whether the file is rgb)
                # if the file is RGB, `f.sizes` will have
                # an additional {'S': 3} component

# ARRAY OUTPUTS
f.asarray()         # in-memory np.ndarray - or use np.asarray(f)
f.to_dask()         # delayed dask.array.Array
f.to_xarray()       # in-memory xarray.DataArray, with labeled axes/coords
f.to_xarray(delayed=True)   # delayed xarray.DataArray

                    # see below for examples of these structures
# METADATA          # returns instance of ...
f.attributes        # nd2.structures.Attributes
f.metadata          # nd2.structures.Metadata
f.frame_metadata(0) # nd2.structures.FrameMetadata (frame-specific meta)
f.experiment        # List[nd2.structures.ExpLoop]
f.rois              # Dict[int, nd2.structures.ROI]
f.voxel_size()      # VoxelSize(x=0.65, y=0.65, z=1.0)
f.text_info         # dict of misc info

f.binary_data       # any binary masks stored in the file.  See below.
f.custom_data       # bits of unstructured metadata that start with CustomData
f.recorded_data     # returns a dict of lists (passable to pandas.DataFrame) that
                    # the tabular "Recorded Data" view from in NIS Elements/Viewer
                    # with info for each frame in the experiment.

# allll the metadata we can find...
# no attempt made to standardize or parse it
# look in here if you're searching for metdata that isn't exposed in the above
f.unstructured_metadata()

f.close()           # don't forget to close when not using a contet manager!
f.closed            # boolean, whether the file is closed
```

## Metadata structures

These follow the structure of the nikon SDK outputs (where relevant).
Here are some example outputs

<details>

<summary><code>attributes</code></summary>

```python
Attributes(
    bitsPerComponentInMemory=16,
    bitsPerComponentSignificant=16,
    componentCount=2,
    heightPx=32,
    pixelDataType='unsigned',
    sequenceCount=60,
    widthBytes=128,
    widthPx=32,
    compressionLevel=None,
    compressionType=None,
    tileHeightPx=None,
    tileWidthPx=None,
    channelCount=2
)
```

</details>

<details>

<summary><code>metadata</code></summary>

*Note: the `metadata` for legacy (JPEG2000) files will be a plain unstructured dict.*

```python
Metadata(
    contents=Contents(channelCount=2, frameCount=60),
    channels=[
        Channel(
            channel=ChannelMeta(name='Widefield Green', index=0, colorRGB=65371, emissionLambdaNm=535.0, excitationLambdaNm=None),
            loops=LoopIndices(NETimeLoop=None, TimeLoop=0, XYPosLoop=1, ZStackLoop=2),
            microscope=Microscope(
                objectiveMagnification=10.0,
                objectiveName='Plan Fluor 10x Ph1 DLL',
                objectiveNumericalAperture=0.3,
                zoomMagnification=1.0,
                immersionRefractiveIndex=1.0,
                projectiveMagnification=None,
                pinholeDiameterUm=None,
                modalityFlags=['fluorescence']
            ),
            volume=Volume(
                axesCalibrated=[True, True, True],
                axesCalibration=[0.652452890023035, 0.652452890023035, 1.0],
                axesInterpretation=(
                    <AxisInterpretation.distance: 'distance'>,
                    <AxisInterpretation.distance: 'distance'>,
                    <AxisInterpretation.distance: 'distance'>
                ),
                bitsPerComponentInMemory=16,
                bitsPerComponentSignificant=16,
                cameraTransformationMatrix=[-0.9998932296054086, -0.014612644841559427, 0.014612644841559427, -0.9998932296054086],
                componentCount=1,
                componentDataType='unsigned',
                voxelCount=[32, 32, 5],
                componentMaxima=[0.0],
                componentMinima=[0.0],
                pixelToStageTransformationMatrix=None
            )
        ),
        Channel(
            channel=ChannelMeta(name='Widefield Red', index=1, colorRGB=22015, emissionLambdaNm=620.0, excitationLambdaNm=None),
            loops=LoopIndices(NETimeLoop=None, TimeLoop=0, XYPosLoop=1, ZStackLoop=2),
            microscope=Microscope(
                objectiveMagnification=10.0,
                objectiveName='Plan Fluor 10x Ph1 DLL',
                objectiveNumericalAperture=0.3,
                zoomMagnification=1.0,
                immersionRefractiveIndex=1.0,
                projectiveMagnification=None,
                pinholeDiameterUm=None,
                modalityFlags=['fluorescence']
            ),
            volume=Volume(
                axesCalibrated=[True, True, True],
                axesCalibration=[0.652452890023035, 0.652452890023035, 1.0],
                axesInterpretation=(
                    <AxisInterpretation.distance: 'distance'>,
                    <AxisInterpretation.distance: 'distance'>,
                    <AxisInterpretation.distance: 'distance'>
                ),
                bitsPerComponentInMemory=16,
                bitsPerComponentSignificant=16,
                cameraTransformationMatrix=[-0.9998932296054086, -0.014612644841559427, 0.014612644841559427, -0.9998932296054086],
                componentCount=1,
                componentDataType='unsigned',
                voxelCount=[32, 32, 5],
                componentMaxima=[0.0],
                componentMinima=[0.0],
                pixelToStageTransformationMatrix=None
            )
        )
    ]
)
```

</details>

<details>

<summary><code>experiment</code></summary>

```python
[
    TimeLoop(
        count=3,
        nestingLevel=0,
        parameters=TimeLoopParams(
            startMs=0.0,
            periodMs=1.0,
            durationMs=0.0,
            periodDiff=PeriodDiff(avg=16278.339965820312, max=16411.849853515625, min=16144.830078125)
        ),
        type='TimeLoop'
    ),
    XYPosLoop(
        count=4,
        nestingLevel=1,
        parameters=XYPosLoopParams(
            isSettingZ=True,
            points=[
                Position(stagePositionUm=[26950.2, -1801.6000000000001, 498.46000000000004], pfsOffset=None, name=None),
                Position(stagePositionUm=[31452.2, -1801.6000000000001, 670.7], pfsOffset=None, name=None),
                Position(stagePositionUm=[35234.3, 2116.4, 664.08], pfsOffset=None, name=None),
                Position(stagePositionUm=[40642.9, -3585.1000000000004, 555.12], pfsOffset=None, name=None)
            ]
        ),
        type='XYPosLoop'
    ),
    ZStackLoop(count=5, nestingLevel=2, parameters=ZStackLoopParams(homeIndex=2, stepUm=1.0, bottomToTop=True, deviceName='Ti2 ZDrive'), type='ZStackLoop')
]
```

</details>

<details>

<summary><code>rois</code></summary>

ROIs found in the metadata are available at `ND2File.rois`, which is a
`dict` of `nd2.structures.ROI` objects, keyed by the ROI ID:

```python
{
    1: ROI(
        id=1,
        info=RoiInfo(
            shapeType=<RoiShapeType.Rectangle: 3>,
            interpType=<InterpType.StimulationROI: 4>,
            cookie=1,
            color=255,
            label='',
            stimulationGroup=0,
            scope=1,
            appData=0,
            multiFrame=False,
            locked=False,
            compCount=2,
            bpc=16,
            autodetected=False,
            gradientStimulation=False,
            gradientStimulationBitDepth=0,
            gradientStimulationLo=0.0,
            gradientStimulationHi=0.0
        ),
        guid='{87190352-9B32-46E4-8297-C46621C1E1EF}',
        animParams=[
            AnimParam(
                timeMs=0.0,
                enabled=1,
                centerX=-0.4228425369685782,
                centerY=-0.5194951478743071,
                centerZ=0.0,
                rotationZ=0.0,
                boxShape=BoxShape(
                    sizeX=0.21256931608133062,
                    sizeY=0.21441774491682075,
                    sizeZ=0.0
                ),
                extrudedShape=ExtrudedShape(sizeZ=0, basePoints=[])
            )
        ]
    ),
    ...
}
```

</details>

<details>

<summary><code>text_info</code></summary>

```python
{
    'capturing': 'Flash4.0, SN:101412\r\nSample 1:\r\n  Exposure: 100 ms\r\n  Binning: 1x1\r\n  Scan Mode: Fast\r\nSample 2:\r\n  Exposure: 100 ms\r\n  Binning: 1x1\r\n  Scan Mode: Fast',
    'date': '9/28/2021  9:41:27 AM',
    'description': 'Metadata:\r\nDimensions: T(3) x XY(4) x λ(2) x Z(5)\r\nCamera Name: Flash4.0, SN:101412\r\nNumerical Aperture: 0.3\r\nRefractive Index: 1\r\nNumber of Picture Planes: 2\r\nPlane #1:\r\n Name: Widefield Green\r\n Component Count: 1\r\n Modality: Widefield Fluorescence\r\n Camera Settings:   Exposure: 100 ms\r\n  Binning: 1x1\r\n  Scan Mode: Fast\r\n Microscope Settings:   Nikon Ti2, FilterChanger(Turret-Lo): 3 (FITC)\r\n  Nikon Ti2, Shutter(FL-Lo): Open\r\n  Nikon Ti2, Shutter(DIA LED): Closed\r\n  Nikon Ti2, Illuminator(DIA): Off\r\n  Nikon Ti2, Illuminator(DIA) Iris intensity: 3.0\r\n  Analyzer Slider: Extracted\r\n  Analyzer Cube: Extracted\r\n  Condenser: 1 (Shutter)\r\n  PFS, state: On\r\n  PFS, offset: 7959\r\n  PFS, mirror: Inserted\r\n  PFS, Dish Type: Glass\r\n  Zoom: 1.00x\r\n  Sola, Shutter(Sola): Active\r\n  Sola, Illuminator(Sola) Voltage: 100.0\r\nPlane #2:\r\n Name: Widefield Red\r\n Component Count: 1\r\n Modality: Widefield Fluorescence\r\n Camera Settings:   Exposure: 100 ms\r\n  Binning: 1x1\r\n  Scan Mode: Fast\r\n Microscope Settings:   Nikon Ti2, FilterChanger(Turret-Lo): 4 (TRITC)\r\n  Nikon Ti2, Shutter(FL-Lo): Open\r\n  Nikon Ti2, Shutter(DIA LED): Closed\r\n  Nikon Ti2, Illuminator(DIA): Off\r\n  Nikon Ti2, Illuminator(DIA) Iris intensity: 1.5\r\n  Analyzer Slider: Extracted\r\n  Analyzer Cube: Extracted\r\n  Condenser: 1 (Shutter)\r\n  PFS, state: On\r\n  PFS, offset: 7959\r\n  PFS, mirror: Inserted\r\n  PFS, Dish Type: Glass\r\n  Zoom: 1.00x\r\n  Sola, Shutter(Sola): Active\r\n  Sola, Illuminator(Sola) Voltage: 100.0\r\nTime Loop: 3\r\n- Equidistant (Period 1 ms)\r\nZ Stack Loop: 5\r\n- Step: 1 µm\r\n- Device: Ti2 ZDrive',
    'optics': 'Plan Fluor 10x Ph1 DLL'
}
```

</details>

<details>

<summary><code>custom_data</code></summary>

No attempt is made to parse this data.  It will vary from file to file, but you may find something useful here:

```python
{
    'StreamDataV1_0': {
        'Vector_StreamAnalogIn': '',
        'Vector_StreamDigitalIn': '',
        'Vector_AnalogIn': '',
        'Vector_DigitalIn': '',
        'Vector_Other': '',
        'Vector_StreamAnalogOut': '',
        'Vector_StreamDigitalOut': '',
        'Vector_AnalogOut': '',
        'Vector_DigitalOut': ''
    },
    'NDControlV1_0': {
        'NDControl': {
            'LoopState': {'no_name': [529, 529, 529, 529, 529]},
            'PlayFPS': {'no_name': [20.0, 20.0, 0.0, 20.0, 0.0]},
            'LoopSize': {'no_name': [3, 4, 0, 5, 0]},
            'LoopPosition': {'no_name': [2, 3, 0, 4, 0]},
            'LoopSelection': {'no_name': [b'AAAA', b'AAAAAA==', b'', b'AAAAAAA=', b'']},
            'LoopRangeSelection': {'no_name': [b'AQEB', b'AQEBAQ==', b'', b'AQEBAQE=', b'']},
            'LoopEventSelection': {'no_name': [b'AAAA', b'AAAAAA==', b'', b'AAAAAAA=', b'']},
            'FramesInRange': '',
            'LoopStep': {'no_name': [0, 0, 0, 0, 0]},
            'UserEventType': 2,
            'SelectionStyle': 0,
            'FramesBefore': 2,
            'FramesAfter': 1,
            'TimeBefore': 1.0,
            'TimeAfter': 1.0
        }
    },
    'LUTDataV1_0': {
        'ViewLut': True,
        'LutParam': {
            'Gradient': 0,
            'GradientBrightField': 0,
            'MinSrc': 0,
            'MaxSrc': 65535,
            'GammaSrc': 1.0,
            'MinDst': 0,
            'MaxDst': 65535,
            'ColorSpace': 4,
            'Representation': 0,
            'LutComponentCount': 2,
            'GroupCount': 1,
            'CompLutParam': {
                '00': {'MinSrc': [82, 0.0], 'MaxSrc': [113, 1.0], 'GammaSrc': 1.0, 'MinDst': 0, 'MaxDst': 65535, 'Group': 0},
                '01': {'MinSrc': [82, 0.0], 'MaxSrc': [114, 1.0], 'GammaSrc': 1.0, 'MinDst': 0, 'MaxDst': 65535, 'Group': 0},
                '02': {'MinSrc': [0, 0.0], 'MaxSrc': [65535, 1.0], 'GammaSrc': 1.0, 'MinDst': 0, 'MaxDst': 65535, 'Group': 0}
            },
            'LutDataSpectral': {
                'GainTrueColor': 1.0,
                'OffsetTrueColor': 0.0,
                'GainGrayScale': 1.0,
                'OffsetGrayScal': 0.0,
                'SpectralColorMode': 0,
                'Group00': {
                    'ColorGroup': 16711680,
                    'ColorCustom': 16711680,
                    'GainCustom': 1.0,
                    'OffsetCustom': 0.0,
                    'GainGrouped': 1.0,
                    'OffsetGrouped': 0.0
                }
            }
        },
        'EnableAutoContrast': True,
        'EnableAutoWhite': True,
        'AutoWhiteColor': 16777215,
        'RatioDesc': {
            'Numer': 0,
            'Denom': 1,
            'NumOffset': 0,
            'DenOffset': 0,
            'Min': 0.0,
            'Max': 2.0,
            'BkgndSize': 0,
            'Calibrated': True,
            'Cal.dKd': 224.0,
            'Cal.dVisc': 1.0,
            'Cal.dFmin': 255.0,
            'Cal.dFmax': 1.0,
            'Cal.dRmin': 0.0,
            'Cal.dRmax': 2.0,
            'Cal.dTMeasCalMin': 0.0,
            'Cal.dTMeasCalMax': 0.0,
            'PickFromGraph': True,
            'RatioViewEnabled': True
        },
        'GraphSelected': -1,
        'GraphVerticalSplit': True,
        'GrayGraph': True,
        'ShowAllComp': True,
        'ShowSpectralGraph': True,
        'GraphScale': 0,
        'GraphZoom00': 1.0,
        'GraphOffset00': 0.0,
        'GraphZoom01': 1.0,
        'GraphOffset01': 0.0,
        'GraphZoom02': 1.0,
        'GraphOffset02': 0.0
    },
    'GrabberCameraSettingsV1_0': {
        'GrabberCameraSettings': {
            'CameraUniqueName': 'Hamamatsu C11440-22C SN:101412',
            'CameraUserName': 'Flash4.0, SN:101412',
            'CameraFamilyName': 'ecmC11440_22C',
            'OverloadedUniqueName': '',
            'ModifiedAtJDN': 2459486.07103009,
            'FormatFast': {
                'Desc': {
                    'UniqueName': 'FMT 1x1 16',
                    'Interpretation': 1,
                    'FQModeUsage': 15,
                    'CanExecAsyncSampleGet': True,
                    'Fps': 30.00300030003,
                    'Sensitivity': 1.0,
                    'SensorPixels': {'cx': 2048, 'cy': 2044},
                    'SensorMicrons': {'cx': 13312, 'cy': 13286},
                    'SensorMin': {'cx': 4, 'cy': 4},
                    'SensorStep': {'cx': 2, 'cy': 2},
                    'BinningX': 1.0,
                    'BinningY': 1.0,
                    'SensorSource': {'left': 0, 'top': 0, 'right': 2048, 'bottom': 2044},
                    'FormatText': '16-bit - No Binning',
                    'FormatDesc': '16-bit - No Binning (30.0 FPS)',
                    'CamCorrReq': True,
                    'Comp': 1,
                    'Bpc': 16,
                    'UsageFlags': 1
                },
                'SensorUser': {'left': 512, 'top': 512, 'right': 544, 'bottom': 544}
            },
            'FormatQuality': {
                'Desc': {
                    'UniqueName': 'FMT 1x1 16',
                    'Interpretation': 1,
                    'FQModeUsage': 15,
                    'CanExecAsyncSampleGet': True,
                    'Fps': 30.00300030003,
                    'Sensitivity': 1.0,
                    'SensorPixels': {'cx': 2048, 'cy': 2044},
                    'SensorMicrons': {'cx': 13312, 'cy': 13286},
                    'SensorMin': {'cx': 4, 'cy': 4},
                    'SensorStep': {'cx': 2, 'cy': 2},
                    'BinningX': 1.0,
                    'BinningY': 1.0,
                    'SensorSource': {'left': 0, 'top': 0, 'right': 2048, 'bottom': 2044},
                    'FormatText': '16-bit - No Binning',
                    'FormatDesc': '16-bit - No Binning (30.0 FPS)',
                    'CamCorrReq': True,
                    'Comp': 1,
                    'Bpc': 16,
                    'UsageFlags': 1
                },
                'SensorUser': {'left': 512, 'top': 512, 'right': 544, 'bottom': 544}
            },
            'PropertiesFast': {
                'Exposure': 100.0,
                'LiveSpeedUp': 1,
                'CaptureQuality': 75,
                'CaptureMaxExposure': 10000.0,
                'QuantilRelative': True,
                'QuantilPromile': 0.1,
                'QuantilPixels': 100,
                'EnableAutoExposure': True,
                'ScanMode': 2,
                'Average': 1,
                'Integrate': 1,
                'AverageToQuality': 0.0,
                'AverageCH': '',
                'IntegrateCH': '',
                'AverageToQualityCH': '',
                'IntegrateToQualityCH': '',
                'FlexibleHeight': -1,
                'Negate': 0,
                'MultiExcitation': ''
            },
            'PropertiesFast_Extra': {'PropGroupCount': 0, 'PropGroupUsageArray': {}, 'PropGroupNameArray': {}},
            'PropertiesQuality': {
                'Exposure': 100.0,
                'LiveSpeedUp': 1,
                'CaptureQuality': 75,
                'CaptureMaxExposure': 10000.0,
                'QuantilRelative': True,
                'QuantilPromile': 0.1,
                'QuantilPixels': 100,
                'EnableAutoExposure': True,
                'ScanMode': 2,
                'Average': 1,
                'Integrate': 1,
                'AverageToQuality': 0.0,
                'AverageCH': '',
                'IntegrateCH': '',
                'AverageToQualityCH': '',
                'IntegrateToQualityCH': '',
                'FlexibleHeight': -1,
                'Negate': 0,
                'MultiExcitation': ''
            },
            'PropertiesQuality_Extra': {
                'PropGroupCount': 1,
                'PropGroupUsageArray': {'0': 0},
                'PropGroupNameArray': {'0': 'Use Stored ROI'}
            },
            'Metadata': {
                'Key': 'MV=0,TA=0,CH=1',
                'ChannelCount': 1,
                'Channels': {
                    'Channel_0': {
                        'Color': 22015,
                        'Name': 'Widefield Red',
                        'EmWavelength': 620.0,
                        'ChannelIsActive': True,
                        'ExWavelength': 540.5,
                        'MaxSaturatedValue': 4294967295
                    }
                }
            },
            'LightPath': {
                'TypeID': 0,
                'ExcitationSourceKey': 'LIGHT-EPI',
                'ExcitationSourceName': '',
                'EPIAdditionalFilterKey': '',
                'EPIAdditionalFilterName': '',
                'DIAAdditionalFilterKey': '',
                'DIAAdditionalFilterName': '',
                'LastEmissionFilterKey1': 'Turret-Lo',
                'LastEmissionFilterName1': 'Nikon Ti2, FilterChanger(Turret-Lo)',
                'SetColorManually': True,
                'MultiViewEnabled': True,
                'UpdateLPAutomatically': True
            },
            'ROI': {'Left': 512, 'Top': 512, 'Right': 544, 'Bottom': 544}
        },
        'GrabberCameraSettingsFQMode': 1
    },
    'CustomDataV2_0': {
        'CustomTagDescription_v1.0': {
            'Tag0': {'ID': 'Camera_ExposureTime1', 'Type': 3, 'Group': 2, 'Size': 60, 'Desc': 'Exposure Time', 'Unit': 'ms'},
            'Tag1': {'ID': 'PFS_OFFSET', 'Type': 2, 'Group': 1, 'Size': 60, 'Desc': 'PFS Offset', 'Unit': ''},
            'Tag2': {'ID': 'PFS_STATUS', 'Type': 2, 'Group': 1, 'Size': 60, 'Desc': 'PFS Status', 'Unit': ''},
            'Tag3': {'ID': 'X', 'Type': 3, 'Group': 1, 'Size': 60, 'Desc': 'X Coord', 'Unit': 'µm'},
            'Tag4': {'ID': 'Y', 'Type': 3, 'Group': 1, 'Size': 60, 'Desc': 'Y Coord', 'Unit': 'µm'},
            'Tag5': {'ID': 'Z', 'Type': 3, 'Group': 1, 'Size': 60, 'Desc': 'Z Coord', 'Unit': 'µm'},
            'Tag6': {'ID': 'Z1', 'Type': 3, 'Group': 1, 'Size': 60, 'Desc': 'Ti2 ZDrive', 'Unit': 'µm'}
        }
    },
    'AppInfo_V1_0': {
        'SWNameString': 'NIS-Elements AR',
        'GrabberString': 'Hamamatsu',
        'VersionString': '5.20.02 (Build 1453)',
        'CopyrightString': 'Copyright © 1991-2019  Laboratory Imaging,  http://www.lim.cz',
        'CompanyString': 'NIKON Corporation',
        'NFRString': ''
    },
    'AcqTimeV1_0': 2459486.07044662
}
```

</details>

<details>

<summary><code>recorded_data</code></summary>

This property returns a `dict` of equal-length sequences.
It matches the tabular data reported in the `Image Properties > Recorded Data` tab of the NIS Viewer.

(There will be a column for each tag in the `CustomDataV2_0` section of `custom_data` above.)

```python
{
    'Time [s]': array([ 1.32686654,  1.69089657,  2.04194662,  2.38194662,  2.63795663,
        8.7022286 ,  9.03626864,  9.33031869,  9.63934872,  9.90636874,
       11.48143856, 11.7964786 , 12.0894786 , 12.37153866, 12.66546859]),
    'Z-Series': array([-2., -1.,  0.,  1.,  2., -2., -1.,  0.,  1.,  2., -2., -1.,  0.,
        1.,  2.]),
    'Exposure Time [ms]': array([100., 100., 100., 100., 100., 100., 100., 100., 100., 100., 100.,
       100., 100., 100., 100.]),
    'PFS Offset []': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32),
    'PFS Status []': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32),
    'X Coord [µm]': array([31452.2, 31452.2, 31452.2, 31452.2, 31452.2, 31452.2, 31452.2,
       31452.2, 31452.2, 31452.2, 31452.2, 31452.2, 31452.2, 31452.2,
       31452.2]),
    'Y Coord [µm]': array([-1801.6, -1801.6, -1801.6, -1801.6, -1801.6, -1801.6, -1801.6,
       -1801.6, -1801.6, -1801.6, -1801.6, -1801.6, -1801.6, -1801.6,
       -1801.6]),
    'Z Coord [µm]': array([552.74, 553.74, 554.74, 555.74, 556.74, 552.7 , 553.7 , 554.68,
       555.7 , 556.64, 552.68, 553.68, 554.68, 555.68, 556.68]),
    'Ti2 ZDrive [µm]': array([552.74, 553.74, 554.74, 555.74, 556.74, 552.7 , 553.7 , 554.68,
       555.7 , 556.64, 552.68, 553.68, 554.68, 555.68, 556.68])
}
```

You can pass the output of `recorded_data` to `pandas.DataFrame`:

```python
In [13]: pd.DataFrame(nd2file.recorded_data)
Out[13]:
     Time [s]  Z-Series  Exposure Time [ms]  PFS Offset []  PFS Status []  X Coord [µm]  Y Coord [µm]  Z Coord [µm]  Ti2 ZDrive [µm]
0    1.326867      -2.0               100.0              0              0       31452.2       -1801.6        552.74           552.74
1    1.690897      -1.0               100.0              0              0       31452.2       -1801.6        553.74           553.74
2    2.041947       0.0               100.0              0              0       31452.2       -1801.6        554.74           554.74
3    2.381947       1.0               100.0              0              0       31452.2       -1801.6        555.74           555.74
4    2.637957       2.0               100.0              0              0       31452.2       -1801.6        556.74           556.74
5    8.702229      -2.0               100.0              0              0       31452.2       -1801.6        552.70           552.70
6    9.036269      -1.0               100.0              0              0       31452.2       -1801.6        553.70           553.70
7    9.330319       0.0               100.0              0              0       31452.2       -1801.6        554.68           554.68
8    9.639349       1.0               100.0              0              0       31452.2       -1801.6        555.70           555.70
9    9.906369       2.0               100.0              0              0       31452.2       -1801.6        556.64           556.64
10  11.481439      -2.0               100.0              0              0       31452.2       -1801.6        552.68           552.68
11  11.796479      -1.0               100.0              0              0       31452.2       -1801.6        553.68           553.68
12  12.089479       0.0               100.0              0              0       31452.2       -1801.6        554.68           554.68
13  12.371539       1.0               100.0              0              0       31452.2       -1801.6        555.68           555.68
14  12.665469       2.0               100.0              0              0       31452.2       -1801.6        556.68           556.68

```

</details>

<details>

<summary><code>binary_data</code></summary>

This property returns an `nd2.BinaryLayers` object representing all of the
binary masks in the nd2 file.

A `nd2.BinaryLayers` object is a sequence of individual `nd2.BinaryLayer`
objects (one for each binary layer found in the file).  Each `BinaryLayer` in
the sequence is a named tuple that has, among other things, a `name` attribute,
and a `data` attribute that is list of numpy arrays (one for each frame in the
experiment) or `None` if the binary layer had no data in that frame.

The most common use case will be to cast either the entire `BinaryLayers` object
or an individual `BinaryLayer` to a `numpy.ndarray`:

```python
>>> import nd2
>>> nd2file = nd2.ND2File('path/to/file.nd2')
>>> binary_layers = nd2file.binary_data

# The output array will have shape
# (n_binary_layers, *coord_shape, *frame_shape).
>>> np.asarray(binary_layers)
```

For example, if the data in the nd2 file has shape `(nT, nZ, nC, nY, nX)`, and
there are 4 binary layers, then the output of `np.asarray(nd2file.binary_data)` will
have shape `(4, nT, nZ, nY, nX)`.  (Note that the `nC` dimension is not present
in the output array, and the binary layers are always in the first axis).

You can also cast an individual `BinaryLayer` to a numpy array:

```python
>>> binary_layer = binary_layers[0]
>>> np.asarray(binary_layer)
```

</details>


## Contributing / Development

To test locally and contribute.  Clone this repo, then:

```
pip install -e .[dev]
```

To download sample data:

```
pip install requests
python scripts/download_samples.py
```

then run tests:

```
pytest
```

(and feel free to open an issue if that doesn't work!)

## alternatives

- [pims_nd2](https://github.com/soft-matter/pims_nd2) - *pims-based reader. ctypes wrapper around the v9.00 (2015) SDK*
- [nd2reader](https://github.com/rbnvrw/nd2reader) - *pims-based reader, using reverse-engineered file headers. mostly tested on NIS Elements 4.30.02*
- [nd2file](https://github.com/csachs/nd2file) - *another pure-python, chunk map reader, unmaintained?*
- [pyND2SDK](https://github.com/aarpon/pyND2SDK) - *windows-only cython wrapper around the v9.00 (2015) SDK. not on PyPI*

The motivating factors for this library were:

- support for as many nd2 files as possible, with a large test suite
- pims-independent delayed reader based on dask
- axis-associated metadata via xarray
- combined approach of SDK and direct binary reads
