scopyon module¶
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class
scopyon.
Configuration
(filename=None, yaml=None)¶ Bases:
collections.abc.Mapping
Note
Requires yaml and pint.
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get
(k[, d]) → D[k] if k in D, else d. d defaults to None.¶
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save
(file)¶ Save configurations as YAML file.
- Parameters
file (str) – An output file name.
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update
(conf)¶
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property
yaml
¶
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class
scopyon.
DefaultConfiguration
¶ Bases:
scopyon.config.Configuration
Default configuration. All the settings are available in package data, scopyon.yaml. See https://github.com/ecell/scopyon/blob/master/scopyon/scopyon.yaml
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class
scopyon.
EPIFMSimulator
(config=None, method=None, rng=None)¶ Bases:
object
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base
()¶
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form_image
(inputs, start_time=0.0, exposure_time=None, full_output=False)¶ Form image.
- Parameters
inputs (array_like) – A list of points. The shape must be ‘(n, 3)’, where ‘n’ means the number of points.
start_time (float, optional) – A time to start detecting. Defaults to 0.0.
exposure_time (float, optional) – An exposure time. Defaults to detector.exposure_time in the configuration.
full_output (bool, optional) – True to return a dictionary containing optional outputs.
- Returns
An image. dict: only returned if full_output == True
A dictionary containing additional information. ‘expectation’ is 2-dimensional ndarray. ‘true_data’ is a dictionary containing a pair of a molecule ID and ndarray. The array has 7 elements, which are exposure time, photon state, X and Y in pixels, X and Y in meter-scale, and normalization.
- Return type
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generate_images
(inputs, num_frames, start_time=0.0, exposure_time=None, full_output=False)¶ Form image.
- Parameters
inputs (array_like) – A list of points. The shape must be ‘(n, 3)’, where ‘n’ means the number of points.
num_frames (int) – The number of frames taken.
start_time (float, optional) – A time to start detecting. Defaults to 0.0 in the configuration.
exposure_time (float, optional) – An exposure time. Defaults to detector_exposure_time in the configuration.
full_output (bool, optional) – True to return a dictionary containing optional outputs.
- Yields
Image – An image. dict: only returned if full_output == True
A dictionary containing additional information. ‘expectation’ is 2-dimensional ndarray. ‘true_data’ is a dictionary containing pairs of a molecule ID and ndarray. The array has 7 elements, which are exposure time, photon state, X and Y in pixels, X and Y in meter-scale, and normalization. If ‘photo_bleaching’ is active, ‘fluorescence_states’ contains pairs of a molecule ID and remaining photon budget.
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class
scopyon.
Image
(data)¶ Bases:
object
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PLOTTING
= <module 'scopyon._plotly' from '/home/docs/checkouts/readthedocs.org/user_builds/scopyon/checkouts/latest/scopyon/_plotly.py'>¶
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static
RGB
(red=None, green=None, blue=None)¶ Make an image from RGB arrays.
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as_8bit
(cmin=None, cmax=None, low=None, high=None)¶ Same as scipy.misc.bytescale
- Parameters
cmin (float, optional) – Defaults to the minimum in data.
cmax (float, optional) – Defaults to the maximum in data.
low (float, optional) – Defaults to 0
high (float, optional) – Defaults to 255.
- Returns
an Image object with 8bit data.
- Return type
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as_array
()¶
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property
dtype
¶
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static
load
(file)¶ Load an image.
- Parameters
file (str) – A file path.
- Returns
an image object
- Return type
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property
ndim
¶
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save
(filename, **kwargs)¶ Save the 8-bit image.
Note
Requires pillow to save in the image format. See also scopyon.Image.savefig.
- Parameters
filename (str) – An output file name. .npy, .csv or image formats are accepted.
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static
savefig
(filename, img, shapes=None)¶ Save figure.
- Parameters
filename (str) – An output file path.
img (ndarray) – An image data to be shown.
shapes (list, optional) – A list of shapes. shape is a dictionary consisting of x (row), y (column), sigma and color. sigma is a half size of the box (square).
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property
shape
¶
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show
(**kwargs)¶ Plot the 8-bit image.
Note
Requires plotly optionally. See also scopyon._plotly.show, scopyon._matplotlib.show.
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property
size
¶
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class
scopyon.
Video
¶ Bases:
object
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static
save
(filename, imgs, interval=100, dpi=None, cmin=None, cmax=None, low=None, high=None)¶ Make a video from images.
Note
Requires matplotlib.
- Parameters
filename (str) – An output file name.
imgs (list) – A list of Images.
interval (int, optional) – An interval between frames given in millisecond.
dpi (int, optional) – dpi.
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static
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scopyon.
create_simulator
(config=None, method=None, rng=None)¶ Return a simulator.
- Parameters
config (Configuration, optional) – Configurations.
method (str, optional) – A name of method used. The default is None (config.default).
rng (numpy.RandomState, optional) – A random number generator. The default is None.
- Returns
A simulator
- Return type
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scopyon.
form_image
(inputs, start_time=0.0, exposure_time=None, *, method=None, config=None, rng=None, full_output=False)¶ Form image.
- Parameters
inputs (array_like) – A list of points. The shape must be ‘(n, 3)’, where ‘n’ means the number of points.
start_time (float, optional) – A time to start detecting. Defaults to 0.0.
exposure_time (float, optional) – An exposure time. Defaults to detector.exposure_time in the configuration.
method (str, optional) – A name of method used. The default is None (‘default’).
config (Configuration, optional) – Configurations. The default is None.
rng (numpy.RandomState, optional) – A random number generator. The default is None.
full_output (bool, optional) – True to return a dictionary containing optional outputs.
- Returns
An image dict: only returned if full_output == True
A dictionary containing additional information. ‘expectation’ is 2-dimensional ndarray. ‘true_data’ is a dictionary containing a pair of a molecule ID and ndarray. The array has 7 elements, which are exposure time, photon state, X and Y in pixels, X and Y in meter-scale, and normalization.
- Return type
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scopyon.
generate_images
(inputs, num_frames, start_time=0.0, exposure_time=None, *, method=None, config=None, rng=None, full_output=False)¶ Form images.
- Parameters
inputs (array_like) – A list of points. The shape must be ‘(n, 3)’, where ‘n’ means the number of points.
num_frames (int) – The number of frames taken.
start_time (float, optional) – A time to start detecting. Defaults to 0.0.
exposure_time (float, optional) – An exposure time. Defaults to detector_exposure_time in the configuration.
method (str, optional) – A name of method used. The default is None (‘default’).
config (Configuration, optional) – Configurations. The default is None.
rng (numpy.RandomState, optional) – A random number generator. The default is None.
full_output (bool, optional) – True to return a dictionary containing optional outputs.
- Yields
Image – An image. dict: only returned if full_output == True
A dictionary containing additional information. ‘expectation’ is 2-dimensional ndarray. ‘true_data’ is a dictionary containing pairs of a molecule ID and ndarray. The array has 7 elements, which are exposure time, photon state, X and Y in pixels, X and Y in meter-scale, and normalization. If ‘photo_bleaching’ is active, ‘fluorescence_states’ contains pairs of a molecule ID and remaining photon budget.
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scopyon.
sample
(t, N, *, lower=None, upper=None, D=None, transmat=None, ndim=3, periodic=False, rng=None)¶ Generate the points.
- Parameters
t (arraylike) – The time points.
N (int or list) – The initial number of points for each state.
upper (Number or array, optional) – An upper limit of the position. Defaults to 1.
lower (Number or array, optional) – A lower limit of the position. Defaults to 0.
D (float or array-like, optional) – Diffusion constants. It is a constant or an array for each state. Defaults to 0.0.
transmat (array-like, optional) – A state transition rate matrix. It must be a square matrix of size n, where n is the number of states.
ndim (int, optional) –
periodic (bool, optional) – Defaults to False.
rng (numpy.RandomState, optional) – A random number generator.
- Returns
A list of points at each time point. An array of points. Each point consists of a coordinate, state, and its index.
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scopyon.
sample_inputs
(t, *, N=None, conc=None, lower=None, upper=None, D=None, start=0, ndim=3, rng=None)¶ Generate the input data.
- Parameters
t (arraylike) – The time points.
N (int or list, optional) – The number of points to be generated.
conc (float or list, optional) – The concentration of points. Either one of N or conc must be given.
upper (Number or array, optional) – An upper limit of the position. Defaults to 1.
lower (Number or array, optional) – A lower limit of the position. Defaults to 0.
D (float or array-like, optional) – Diffusion constants. It is a constant or an array contains constants along with each axis. Defaults to 0.0.
ndim (int, optional) –
start (int, optional) – The first index. Defaults to 0.
rng (numpy.RandomState, optional) – A random number generator.
- Returns
A pair of an array and the last ID.
An array of points. Each point consists of a coordinate, an index, p_state (defaults to 1) and cyc_id (defaults to inf).
The last ID. The sum of start and the number of points generated.
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scopyon._plotly.
show
(img, shapes=None)¶ Show an image.
Note
Requires plotly.express.
- Parameters
img (ndarray) – An image data to be shown.
shapes (list, optional) – A list of shapes. shape is a dictionary consisting of x (row), y (column), sigma and color. sigma is a half size of the box (square).
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scopyon._matplotlib.
show
(img, shapes=None)¶ Show an image.
- Parameters
img (ndarray) – An image data to be shown.
shapes (list, optional) – A list of shapes. shape is a dictionary consisting of x (row), y (column), sigma and color. sigma is a half size of the box (square).
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class
scopyon.analysis.
FullPTHMM
(n_components=1, min_var=1e-05, startprob_prior=1.0, transmat_prior=1.0, algorithm='viterbi', random_state=None, n_iter=10, tol=0.01, verbose=False, params='stdmv', init_params='stdmv')¶ Bases:
hmmlearn.base._BaseHMM
Hidden Markov Model for Particle Tracking.
- Parameters
n_components (int) – Number of states.
min_var (float, optional) – Floor on the variance to prevent overfitting. Defaults to 1e-5.
startprob_prior (array, optional) – shape (n_components, ). Parameters of the Dirichlet prior distribution for
startprob_
.transmat_prior (array, optional) – shape (n_components, n_components). Parameters of the Dirichlet prior distribution for each row of the transition probabilities
transmat_
.algorithm (string, optional) – Decoder algorithm. Must be one of “viterbi” or`”map”. Defaults to “viterbi”.
random_state (RandomState or an int seed, optional) – A random number generator instance.
n_iter (int, optional) – Maximum number of iterations to perform.
tol (float, optional) – Convergence threshold. EM will stop if the gain in log-likelihood is below this value.
verbose (bool, optional) – When
True
per-iteration convergence reports are printed tosys.stderr
. You can diagnose convergence via themonitor_
attribute.params (string, optional) – Controls which parameters are updated in the training process. Can contain any combination of ‘s’ for startprob, ‘t’ for transmat, ‘d’ for diffusivities, ‘m’ for intensity means and ‘v’ for intensity variances. Defaults to all parameters.
init_params (string, optional) – Controls which parameters are initialized prior to training. Can contain any combination of ‘s’ for startprob, ‘t’ for transmat, ‘d’ for diffusivities, ‘m’ for intensity means and ‘v’ for intensity variances. Defaults to all parameters.
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monitor\_
Monitor object used to check the convergence of EM.
- Type
ConvergenceMonitor
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startprob\_
shape (n_components, ). Initial state occupation distribution.
- Type
array
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transmat\_
shape (n_components, n_components). Matrix of transition probabilities between states.
- Type
array
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diffusivities\_
shape (n_components, 1). Diffusion constants for each state.
- Type
array
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intensity_means\_
shape (n_components, 1). Mean parameters of intensity distribution for each state.
- Type
array
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intensity_vars\_
shape (n_components, 1). Variance parameters of intensity distribution for each state.
- Type
array
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class
scopyon.analysis.
PTHMM
(n_diffusivities=3, n_oligomers=4, min_var=1e-05, startprob_prior=1.0, transmat_prior=1.0, algorithm='viterbi', random_state=None, n_iter=10, tol=0.01, verbose=False, params='stdmv', init_params='stdmv')¶ Bases:
hmmlearn.base._BaseHMM
Hidden Markov Model for Particle Tracking.
- Parameters
n_diffusivities (int) – Number of diffusivity states.
n_oligomers (int) – Number of oligomeric states. n_components is equal to (n_diffusivities * n_oliogmers).
min_var (float, optional) – Floor on the variance to prevent overfitting. Defaults to 1e-5.
startprob_prior (array, optional) – shape (n_components, ). Parameters of the Dirichlet prior distribution for
startprob_
.transmat_prior (array, optional) – shape (n_components, n_components). Parameters of the Dirichlet prior distribution for each row of the transition probabilities
transmat_
.algorithm (string, optional) – Decoder algorithm. Must be one of “viterbi” or`”map”. Defaults to “viterbi”.
random_state (RandomState or an int seed, optional) – A random number generator instance.
n_iter (int, optional) – Maximum number of iterations to perform.
tol (float, optional) – Convergence threshold. EM will stop if the gain in log-likelihood is below this value.
verbose (bool, optional) – When
True
per-iteration convergence reports are printed tosys.stderr
. You can diagnose convergence via themonitor_
attribute.params (string, optional) – Controls which parameters are updated in the training process. Can contain any combination of ‘s’ for startprob, ‘t’ for transmat, ‘d’ for diffusivities, ‘m’ for intensity means and ‘v’ for intensity variances. Defaults to all parameters.
init_params (string, optional) – Controls which parameters are initialized prior to training. Can contain any combination of ‘s’ for startprob, ‘t’ for transmat, ‘d’ for diffusivities, ‘m’ for intensity means and ‘v’ for intensity variances. Defaults to all parameters.
-
monitor\_
Monitor object used to check the convergence of EM.
- Type
ConvergenceMonitor
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startprob\_
shape (n_components, ). Initial state occupation distribution.
- Type
array
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transmat\_
shape (n_components, n_components). Matrix of transition probabilities between states.
- Type
array
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diffusivities\_
shape (n_diffusivities, 1). Diffusion constants for each state.
- Type
array
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intensity_means\_
shape (1, 1). Base mean parameter of intensity distributions.
- Type
array
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intensity_vars\_
shape (1, 1). Base Variance parameter of intensity distributions.
- Type
array
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scopyon.analysis.
blob_detection
(data, min_sigma=1, max_sigma=50, num_sigma=10, threshold=0.2, overlap=0.5)¶ Finds blobs in the given image. See also http://scikit-image.org/docs/dev/api/skimage.feature.html#skimage.feature.blob_log
Note
Requires scikit-image.
- Parameters
data (ndarray) – An image data.
min_sigma (float, optional) – The minimum standard deviation. Keep this low to detect smaller blobs. Defaults to 1.
max_sigma (float, optional) – The maximum standard deviation. Keep this high to detect larger blobs. Defaults to 50.
num_sigma (int, optional) – The number of intermediate values between min_sigma and max_sigma. Defaults to 10.
threshold (float, optional) – The absolute lower bound for scale space maxima. Reduce this to detect blobs with less intensities.
overlap (float, optional) – A value between 0 and 1.
- Returns
- Blobs detected.
Each row represents coordinates and the standard deviation, (x, y, r).
- Return type
ndarray
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scopyon.analysis.
spot_detection
(data, roi_size=6, blobs=None, processes=None, **kwargs)¶ Finds spots in the given image.
Note
Requires scipy.
- Parameters
data (ndarray) – An image data.
roi_size (float, optional) – A default value of a half of the ROI size. Defaults to 6.
blobs (ndarray, optional) – Blobs. Defaults to None. See also blob_detection.
- Returns
- Spots detected.
Each row represents center position x, y, intensity, background, height, and sigma, (center_x, center_y, intensity, bg, height, sigma).
- Return type
ndarray
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scopyon.constants.
Q_
¶ alias of
pint.quantity.build_quantity_class.<locals>.Quantity
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class
scopyon.constants.
Quantity
(value, units=None)¶ Bases:
pint.quantity.Quantity