viewer_core_api_examples.rst

These examples can be run in the :ref:`Viewer Console <ViewerConsole>`.

Working with meta data

# view meta data
>>> get_meta()

{'origin': 'AwesomeImager', 'version': '4107ff58a0c3d4d5d3c15c3d6a69f8798a20e3de', 'fps': 10.0, 'date': '20190426_152034', 'vmin': 323, 'vmax': 1529, 'orig_meta': {'source': 'AwesomeImager', 'version': '4107ff58a0c3d4d5d3c15c3d6a69f8798a20e3de', 'level_min': 323, 'stims': {}, 'time': '152034', 'date': '20190426', 'framerate': 10.0, 'level_max': 1529}}

# manually set meta data entries
>>> get_meta()['fps'] = 30.0

Open image

Use the :ref:`Viewer Core API <API_ViewerCore>` to open any arbitrary image

This example loads an image stored using numpy.save(), but this is applicable to images stored in any format that can eventually be represented as a numpy array in python. For example, you could also load image files stored in HDF5 format and load the numpy array that represents your image sequence.

import numpy as np

# clear the viewer work environment
clear_workEnv()

a = np.load('/path_to_image.npy')

# check what the axes order is
a.shape

# (1000, 512, 512) # for example
# looks like this is in [t, x, y]
# this can be transposed so we get [x, y, t]
# ImgData takes either [x, y, t] or [x, y, t, z] axes order

# Define a meta data dict
meta = \
    {
        "origin":      "Tutorial example",
        "fps":         10.0,
        "date":        "20200629_171823",
        "scanner_pos": [0, 1, 2, 3, 4, 5, 6]
    }

# Create ImgData instance
imgdata = ImgData(a.T, meta)  # use a.T to get [x, y, t]

# Create a work environment instance
work_env = ViewerWorkEnv(imgdata)

# Set the current Viewer Work Environment from this new instance
vi.viewer.workEnv = work_env

# Update the viewer with the new work environment
# this MUST be run whenever you replace the viewer work environment (the previous line)
update_workEnv()

This examples shows how you can import imaging data stored in an hdf5 file.

import h5py as h5py

#see h5py documentation, 'r' is for read
aa=h5py.File(datapath,'r')

# find the name of the dataset object
list(aa.keys())

# call the dataset object
dset=aa['data']

#convert from h5 dataset to numpy array
a=dset[()]

a.shape #in t,x,y

type(a) # make sure it's a numpy array

meta = \
    {
        "origin": "Tutorial example",
        "fps":    10.0,
        "date":    "20220602_cd036_000",
        "scanner_pos":  [0,1,2,3,4,5,6]
    }

# Create ImgData instance
imgdata = ImgData(a.T, meta)  # use a.T to get [x, y, t] (time last)

# Create a work environment instance
work_env = ViewerWorkEnv(imgdata)

# Set the current Viewer Work Environment from this new instance
vi.viewer.workEnv = work_env

# Update the viewer with the new work environment
# this MUST be run whenever you replace the viewer work environment (the previous line)
update_workEnv()

Image data

Image sequences are simply numpy arrays. For example extract the image sequence between frame 1000 and 2000.

.. seealso:: `Numpy array indexing <https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html>`_

# Get the current image sequence
seq = get_image()

# Trim the image sequence
trim = seq[:, :, 1000:2000]

# Set the viewer work environment image sequence to the trim one
vi.viewer.workEnv.imgdata.seq = trim

# Update the GUI with the new work environment
update_workEnv()

View analysis log

View the analysis log, such as batch manager processing history.

>>> get_workEnv().history_trace

[{'caiman_motion_correction': {'max_shifts_x': 32, 'max_shifts_y': 32, 'iters_rigid': 1, 'name_rigid': 'Does not matter', 'max_dev': 20, 'strides': 196, 'overlaps': 98, 'upsample': 4, 'name_elas': 'a1_t2', 'output_bit_depth': 'Do not convert', 'bord_px': 5}}, {'cnmfe': {'Input': 'Current Work Environment', 'frate': 10.0, 'gSig': 10, 'bord_px': 5, 'min_corr': 0.9600000000000001, 'min_pnr': 10, 'min_SNR': 1, 'r_values_min': 0.7, 'decay_time': 2, 'rf': 80, 'stride': 40, 'gnb': 8, 'nb_patch': 8, 'k': 8, 'name_corr_pnr': 'a8_t1', 'name_cnmfe': 'a1_t2', 'do_corr_pnr': False, 'do_cnmfe': True}}, {'cnmfe': {'Input': 'Current Work Environment', 'frate': 10.0, 'gSig': 10, 'bord_px': 5, 'min_corr': 0.9600000000000001, 'min_pnr': 14, 'min_SNR': 1, 'r_values_min': 0.7, 'decay_time': 4, 'rf': 80, 'stride': 40, 'gnb': 8, 'nb_patch': 8, 'k': 8, 'name_corr_pnr': '', 'name_cnmfe': 'a1_t2', 'do_corr_pnr': False, 'do_cnmfe': True}}]

Running scripts

You can use the :ref:`Script Editor <module_ScriptEditor>` to run scripts in the Viewer console for automating tasks such as batch creation. It basically allows you to use the :ref:`viewer console <ViewerConsole>` more conveniently with a text editor. The execution environment of the viewer console and script editor are identical.

Some example are provided for caiman modules and :ref:`stimulus mapping <module_StimulusMapping>`.