Awesome

<div align="center">

</div>  <div align="center"> <img src="https://github.com/rraadd88/htsimaging/assets/9945034/66107572-ef49-4852-a228-f6b6aa9b42c8" alt="logo" /> <h1 align="center">htsimaging</h1> High-Throughput Single-cell Imaging analysis. <a href="https://github.com/rraadd88/htsimaging#examples">Examples</a> · <a href="https://github.com/rraadd88/htsimaging#api">Explore the API</a> </div>

Examples

📈 Single-cell protein abundance and its normalization
📈 Single-cell protein abundance by marker localization
🖼️ Visualization of the images.
📈 Quantitative analysis of endocytosis.
📈 Single-particle tracking (SPT).
📈 Calculating the recovery rate from a bleach-chase data

Installation

pip install htsimaging              # with basic dependencies

With additional dependencies as required:

pip install htsimaging[spt]         # for the analysis of the Single-Particle Tracking e.g. endocytosis.

How to cite?

Using BibTeX:

@software{Dandage_htsimaging,
  title   = {htsimaging: High-Throughput Single-cell Imaging analysis in python},
  author  = {Dandage, Rohan},
  year    = {2023},
  url     = {https://zenodo.org/doi/10.5281/zenodo.3697134},
  version = {v1.0.5},
  note    = {The URL is a DOI link to the permanent archive of the software.},
}

DOI link: , or
Using citation information from CITATION.CFF file.

Future directions, for which contributions are welcome

Command-line usage.

Similar projects

API

<kbd>module</kbd> `htsimaging.bleach_chase`

<kbd>module</kbd> `htsimaging.bleach_chase.stat`

Statistical analysis of the bleach-chase

<kbd>function</kbd> `exp1`

exp1(x, lag, amplitude, rate)

One-term exponential.

Parameters:

x (list): input vector
lag (float): lag
amplitude (float): amplitude
rate (float): rate

<kbd>function</kbd> `get_scores`

get_scores(df: DataFrame) → DataFrame

Calculate the rates other parameters.

Parameters:

df (pd.DataFrame): input table.

Returns:

pd.DataFrame: table with the parameters.

<kbd>module</kbd> `htsimaging.endocytosis.io`

Processing of the paths of input images to create configurations and metadata.

<kbd>function</kbd> `make_project_cfg`

make_project_cfg(
    prjd: str,
    output_dir_path: str,
    bright_fn_marker: str = None,
    segmented_fn_marker: str = None,
    magnification: int = None,
    image_ext: str = 'tif',
    cores: int = 1,
    test: bool = False,
    force: bool = False
) → dict

Make the confguration for the analysis run.

Args:

prjd (str): path to the directory with the images.
output_dir_path (str): output directory path.
bright_fn_marker (type): marker in the path of the bright field images.
segmented_fn_marker (type): marker in the path of the segmented images.
cores (int, optional): number of cores. Defaults to 1.
test (bool, optional): test-mode. Defaults to False.
force (bool, optional): over-write theoutputs. Defaults to False.

Returns:

dict: metadata

Notes:

Infer the magnification from the filenames: if 'T1C1' in cfg['bright_fn_marker']: cfg['magnification']=150 elif cfg['bright_fn_marker']=='_t': cfg['magnification']=100

<kbd>function</kbd> `make_cell_cfg`

make_cell_cfg(
    cfg: dict,
    frames: list,
    cells: list,
    trial: str,
    celli: int,
    cellbox: list,
    params_get_signal_summary_by_roi: dict = {'xy_center': None, 'width': 20, 'fun_summary_frame': 'min', 'fun_summary_frames': 'median'},
    filterby_centroid: bool = False,
    scale_signal_cytoplasm: float = 1.5,
    test: bool = False,
    force: bool = False
) → dict

Make the configuration for an individual cell.

Args:

cfg (dict): metadata.
frames (list): list of frames.
cells (list): list of cells.
trial (str): trial name.
celli (int): index of the cell.
cellbox (list): bounding box of the cell
params_get_signal_summary_by_roi (dict, optional): parameters for the aggregation of the values at the ROI. Defaults to {'xy_center':None,'width':20, 'fun_summary_frame':'min', 'fun_summary_frames':'median' }.
test (bool, optional): test-mode. Defaults to False.
force (bool, optional): over-write the output. Defaults to False.

Returns:

dict: metadata

<kbd>module</kbd> `htsimaging.endocytosis`

<kbd>module</kbd> `htsimaging.endocytosis.vid`

To make the video of the timelapse images.

<kbd>function</kbd> `make_gif`

make_gif(
    cellcfg=None,
    frames: list = None,
    t_cor: DataFrame = None,
    img_bright=None,
    outd: str = None,
    particle2color: dict = None,
    test: bool = False,
    force: bool = False
)

Make a .gif file out of frames.

<kbd>module</kbd> `htsimaging.endocytosis.viz`

Visualizations.

<kbd>function</kbd> `plot_properties_cell`

plot_properties_cell(cellcfg, df2, cols_colorby, colx='x', coly='y')

Plot properties of the cell.

Args:

cellcfg (type): config of a cell.
df2 (type): input dataframe.
cols_colorby (type): columns to color by.
colx (str, optional): column with the x values. Defaults to 'x'.
coly (str, optional): column with the y values. Defaults to 'y'.

<kbd>function</kbd> `image_locate_particles`

image_locate_particles(
    df1: DataFrame,
    frame,
    img_region,
    annotate_particles: str = False,
    fig=None,
    ax: Axes = None
) → Axes

Plot image with particles.

Args:

df1 (pd.DataFrame): input dataframe.
frame (type): image frame.
img_region (type): regions in the image.
annotate_particles (str, optional): annotate the paticles or not. Defaults to False.
fig (type, optional): figure object. Defaults to None.
ax (plt.Axes, optional): subplot object. Defaults to None.

Returns:

plt.Axes: description

<kbd>function</kbd> `image_trajectories`

image_trajectories(
    dtraj: DataFrame,
    img_gfp=None,
    img_bright=None,
    label: bool = True,
    fig=None,
    ax: Axes = None
) → Axes

Plot trajectories.

Args:

dtraj (pd.DataFrame): input dataframe with the trajectories.
img_gfp (type, optional): channel image e.g. GFP. Defaults to None.
img_bright (type, optional): segmentation image e.g. bright field. Defaults to None.
label (bool, optional): label. Defaults to True.
fig (type, optional): figure object. Defaults to None.
ax (plt.Axes, optional): subplot object. Defaults to None.

Returns:

plt.Axes: subplot

<kbd>function</kbd> `plot_moving_particles`

plot_moving_particles(
    t_cor: DataFrame,
    img_bright=None,
    frame=None,
    framei: int = 0,
    particle2color=None,
    test: bool = False,
    outd: str = None
)

Plot moving particles.

Args:

t_cor (pd.DataFrame): input table
img_bright (type, optional): segmentation raw image (e.g. bright field). Defaults to None.
frame (type, optional): image frame. Defaults to None.
framei (int, optional): image frame index. Defaults to 0.
particle2color (type, optional): particle-wise colors. Defaults to None.
test (bool, optional): test-mode. Defaults to False.
outd (str, optional): path to the output directory. Defaults to None.

<kbd>module</kbd> `htsimaging.lib.io`

I/O

<kbd>function</kbd> `read_image`

read_image(imp: str)

Read image.

Args:

imp (str): path to the image file.

Returns: np.array

TODOs: For a tiff file: from skimage.external import tifffile

<kbd>function</kbd> `arr2vid`

arr2vid(
    arr_list: list,
    regions: list,
    kins_mean: float,
    vid_fh: str,
    xpixels: list,
    ypixels: list,
    dpi: int = 100
) → str

From array to video.

Args:

arr_list (list): list of frames.
regions (list): regions
kins_mean (float): kinetics
vid_fh (str): video file path
xpixels (list): pixels allong x-axis.
ypixels (list): pixels allong y-axis.
dpi (int, optional): DPI resolution. Defaults to 100.

Returns:

str: path of the video

<kbd>function</kbd> `makevid`

makevid(
    gfp_list_stb: list,
    brf_list_stb: list,
    cmap_gfp: str,
    cmap_brf: str,
    vid_fh: str,
    conditionn: int = None,
    interval=None,
    dpi: int = 300
) → str

Convert to a video.

Args:

gfp_list_stb (list): channel (e.g. GFP) images.
brf_list_stb (list): segmented (e.g. bright-field) images.
cmap_gfp (str): colormap for the channel images.
cmap_brf (str): colormap for the segmented images.
vid_fh (str): path to the video file.
conditionn (int, optional): title. Defaults to None.
interval (type, optional): inerval of the frames. Defaults to None.
dpi (int, optional): DPI resolution. Defaults to 300.

Returns:

str: path to the video file.

<kbd>function</kbd> `nd2arr_list`

nd2arr_list(nd_dh: str = None, nd_fns: list = [], nd_fh: str = None) → list

Raw image to list of arrays.

Args:

nd_dh (str, optional): directory containing raw files e.g. nd2. Defaults to None.
nd_fns (list, optional): file names. Defaults to [].
nd_fh (str, optional): path to the files. Defaults to None.

Returns:

list: list of arrays

<kbd>function</kbd> `to_csv`

to_csv(
    fh_xls='../test/test.xlsx',
    nd2_dh='/media/Transcend/20160219_000356_267',
    cores=16
)

Convert nd2 files to csv using parallel processing.

Args:

fh_xls (str, optional): metadata file. Defaults to '../test/test.xlsx'.
nd2_dh (str, optional): path of the directory containing raw images. Defaults to "/media/Transcend/20160219_000356_267".
cores (int, optional): number of cores. Defaults to 16.

<kbd>module</kbd> `htsimaging.lib`

<kbd>module</kbd> `htsimaging.lib.stat`

Statistics.

<kbd>function</kbd> `power`

power(x, A, B)

power law equation.

<kbd>function</kbd> `power_residuals`

power_residuals(p, y, x)

Deviations of data from fitted 4PL curve

<kbd>function</kbd> `power_peval`

power_peval(x, p)

Evaluated value at x with current parameters.

<kbd>function</kbd> `line`

line(x, m, C)

power law equation.

<kbd>function</kbd> `line_residuals`

line_residuals(p, y, x)

Deviations of data from fitted 4PL curve

<kbd>function</kbd> `line_peval`

line_peval(x, p)

Evaluated value at x with current parameters.

<kbd>function</kbd> `logistic4`

logistic4(x, A, B, C, D)

<kbd>function</kbd> `logistic4_residuals`

logistic4_residuals(p, y, x)

Deviations of data from fitted 4PL curve

<kbd>function</kbd> `logistic4_peval`

logistic4_peval(x, p)

Evaluated value at x with current parameters.

<kbd>function</kbd> `logistic5`

logistic5(x, A, B, C, D, E)

<kbd>function</kbd> `logistic5_residuals`

logistic5_residuals(p, y, x)

Deviations of data from fitted 4PL curve

<kbd>function</kbd> `logistic5_peval`

logistic5_peval(x, p)

Evaluated value at x with current parameters.

<kbd>function</kbd> `fit_power`

fit_power(x, y, p0: list = [0, 1], plot: bool = False)

Fit power law.

Args:

x (vector-like): x values.
y (vector-like): y values.
p0 (list, optional): starting parameters. Defaults to [0, 1].
plot (bool, optional): to plot or not. Defaults to False.

Returns:

tuple: outputs.

<kbd>function</kbd> `fit_line`

fit_line(x, y, p0: list = [0, 1], plot: bool = False) → tuple

Fit power law.

Args:

x (vector-like): x values.
y (vector-like): y values.
p0 (list, optional): description. Defaults to [0, 1].
plot (bool, optional): description. Defaults to False.

Returns:

tuple: outputs

<kbd>function</kbd> `get_slope`

get_slope(df: DataFrame, ds: Series) → float

Get slope for a section of the line.

Args:

df (pd.DataFrame): input dataframe.
ds (pd.Series): section of the line.

Returns:

float: slope.

<kbd>function</kbd> `get_inflection_point`

get_inflection_point(df: DataFrame, threshold_slope: float = 0.25) → DataFrame

Get inflation point.

Args:

df (pd.DataFrame): input dataframe.
threshold_slope (float, optional): threshold on the slope. Defaults to 0.25.

Returns:

pd.DataFrame: output dataframe.

<kbd>module</kbd> `htsimaging.lib.utils`

Utilities for the image processing.

<kbd>function</kbd> `filterframe`

filterframe(frame, cutoff: float = 0)

Filter a frame.

Args:

frame (array-like): input frame.
cutoff (float, optional): cutoff on the values. Defaults to 0.

Returns:

array-like: filtered frame.

<kbd>function</kbd> `filterframes`

filterframes(frames: list, cutoff: float = 0) → list

Filter the frames.

Args:

frames (list): list of frames.
cutoff (float, optional): threshold on the values. Defaults to 0.

Returns:

list: list of frames.

<kbd>function</kbd> `get_data_by_regions`

get_data_by_regions(
    regions: list,
    img=None,
    prop_type: str = 'area'
) → DataFrame

Get properties by regions.

Args:

regions (list): list of the regions.
img (array-like, optional): input image. Defaults to None.
prop_type (str, optional): property type. Defaults to 'area'.

Returns:

pd.DataFrame: output dataframe.

<kbd>function</kbd> `filter_regions`

filter_regions(
    regions: list,
    kind='labeled',
    img=None,
    prop_type: str = 'area',
    mn: float = 0,
    mx: float = 0,
    test: bool = False,
    plotp: str = None
) → list

Filter regions.

Args:

regions (np.array): segmented image, labeled with measure.label(regions).
img (array-like, optional): image. Defaults to None.
prop_type (str, optional): property type. Defaults to 'area'.
mn (float, optional): minimum value. Defaults to 0.
mx (float, optional): maximum value. Defaults to 0.
test (bool, optional): test the filtering. Defaults to False.
plotp (str, optional): path to the plot. Defaults to None.

Raises:

ValueError: img is required if filtering is t be done by the intensity.

Returns:

list: list of the filtered regions.

Notes:

Prefer regionprops_table which is new in sklean's version 0.16.

<kbd>function</kbd> `smoothen`

smoothen(img)

Smoothen the image.

Args:

img (array-like): input image.

Returns:

array-like: output image

<kbd>function</kbd> `smoothenframes`

smoothenframes(frames: list) → list

Smoothen the images.

Args:

frames (list): list of frames.

Returns:

list: list of frames.

<kbd>function</kbd> `get_regions`

get_regions(img)

Get regions.

Args:

img (array-like): input image

<kbd>function</kbd> `raw2phasecorr`

raw2phasecorr(arr_list: list, clip: int = 0) → list

Correct for the relative translative offset by phase correlation between images.

Args:

arr_list (list): list of frames.
clip (int, optional): threshold. Defaults to 0.

Returns:

list: output frames.

<kbd>function</kbd> `phasecorr_with`

phasecorr_with(imlist: list, imlist2: list = None, clip: int = 0)

Correct for the relative translative offset by phase correlation with a set of given images.

Args:

imlist (list): test images
imlist2 (list, optional): reference images. Defaults to None.
clip (int, optional): threshold. Defaults to 0.

Returns:

list: corrected images.

<kbd>function</kbd> `imlistcropper`

imlistcropper(imlist: list, loci: int) → list

Crop a list of images.

Args:

imlist (list): list of images.
loci (int): locations.

Returns:

list: output images.

<kbd>function</kbd> `imclipper`

imclipper(im_stb, clip: float)

Crop an image.

Args:

im_stb (array-like): input image
clip (float): threshold.

<kbd>function</kbd> `get_cellprops`

get_cellprops(
    regions,
    intensity_imgtype2img,
    properties=['area', 'bbox_area', 'convex_area', 'eccentricity', 'equivalent_diameter', 'euler_number', 'extent', 'filled_area', 'label', 'major_axis_length', 'max_intensity', 'mean_intensity', 'min_intensity', 'minor_axis_length', 'orientation', 'perimeter', 'solidity', 'centroid']
) → DataFrame

Get cell properties.

Args:

regions (list): regions in a frame.
intensity_imgtype2img (type): intensities.
properties (list, optional): description. Defaults to ['area', 'bbox_area', 'convex_area', 'eccentricity', 'equivalent_diameter', 'euler_number', 'extent', 'filled_area', 'label', 'major_axis_length', 'max_intensity', 'mean_intensity', 'min_intensity', 'minor_axis_length', 'orientation', 'perimeter', 'solidity', 'centroid', ].

Returns:

pd.DataFrame: output dataframe.

<kbd>function</kbd> `get_signal_summary_by_roi`

get_signal_summary_by_roi(
    cellframes: list,
    xy_center: tuple = None,
    width: int = 20,
    fun_summary_frame: str = 'min',
    fun_summary_frames: str = 'median'
)

Place of the roi in the image is defined by

Args:

cellframes (list): list of frames.
xy_center (tuple, optional): position of the center. Defaults to None.
width (int, optional): width of the ROI. Defaults to 20.
fun_summary_frame (str, optional): function to summarise a frame. Defaults to 'min'.
fun_summary_frames (str, optional): function to summarise a list of frames. Defaults to 'median'.

Returns: summary value

<kbd>module</kbd> `htsimaging.segment`

<kbd>module</kbd> `htsimaging.segment.region`

Processing of the segmented regions.

<kbd>function</kbd> `segmentation2cells`

segmentation2cells(
    imsegp: str,
    kind: str = 'yeast',
    fiterby_border_thickness: int = None,
    magnification: int = 100,
    test: bool = False,
    **kws: dict
) → list

Segment the image to the single cells.

Args:

imsegp (str): description
fiterby_border_thickness (int, optional): description. Defaults to 100.
magnification (int, optional): description. Defaults to 100.
plotp (str, optional): description. Defaults to None.

Returns:

list: description

Examples:

Parameters: prop_type='area',mn=100,mx=8000 at 1.5X prop_type='area',mn=1500,mx=12000

<kbd>function</kbd> `get_cellboxes`

get_cellboxes(
    regions: list,
    cellbox_width: int = 150,
    test: bool = False
) → list

Get the bounding boxes of the cells.

Args:

regions (list): regions.
cellbox_width (int, optional): width of the bounding box of the cell. Defaults to 150.
test (bool, optional): test-mode. Defaults to False.

Returns:

list: list of the bounding boxes for cells.

<kbd>function</kbd> `arr_list2regions`

arr_list2regions(arr_list: list, time_increment: int) → tuple

Parameterized cell-segmentation for the time lapse images.

Args:

arr_list (list): frames of images.
time_increment (int): time interval.

Returns:

tuple: regions and table with intensities.

<kbd>module</kbd> `htsimaging.spt.io`

I/O

<kbd>function</kbd> `to_frames`

to_frames(input_path: str, channeli=None)

Convert to frames.

Args:

input_path (str): path to the raw data.

Returns:

list: list of frames.

<kbd>function</kbd> `expt_dh2expt_info`

expt_dh2expt_info(expt_dh: str) → DataFrame

Make configuration using the directory structure for an experiment.

Args:

expt_dh (str): str

Returns:

pd.DataFrame: output dataframe.

<kbd>function</kbd> `createinfo`

createinfo(expt_dh: str) → str

Create information file.

Args:

expt_dh (str): path to the directory containing the raw data.

Returns: path to the file containing the metadata.

<kbd>module</kbd> `htsimaging.spt`

<kbd>module</kbd> `htsimaging.spt.stat`

Statistical analysis of the single particle tracking.

<kbd>function</kbd> `test_locate_particles`

test_locate_particles(
    cellcfg: dict,
    params_locate: dict,
    frame=None,
    force: bool = False,
    test: bool = False
) → bool

Test locating of the particles.

Args:

cellcfg (dict): the cell level configuration.
params_locate (dict): parameters provided for the location.
frame (np.array, optional): image frame. Defaults to None.
force (bool, optional): over-write the outputs. Defaults to False.
test (bool, optional): test mode. Defaults to False.

Returns: bool

<kbd>function</kbd> `to_msd`

to_msd(
    frames: list,
    coff_intesity_perc: float = 75,
    diameter=11,
    cores: int = 4,
    test: bool = False
) → tuple

MSD from the nd file.

Args:

frames (str): 2D frames.

Returns:

tuple: outputs.

<kbd>function</kbd> `trim_returns`

trim_returns(df1: DataFrame) → DataFrame

Trim images.

Args:

df1 (pd.DataFrame): input dataframe.

Returns:

pd.DataFrame: output dataframe.

<kbd>function</kbd> `fill_frame_jumps`

fill_frame_jumps(df1: DataFrame, jump_length) → DataFrame

Fill the frame jumps.

Args:

df1 (pd.DataFrame): input dataframe.
jump_length (type): length of the jump.

Returns:

pd.DataFrame: output dataframe.

<kbd>function</kbd> `cellcfg2distances`

cellcfg2distances(
    cellcfg: dict,
    params: dict,
    subtract_drift: bool = False,
    test: bool = False,
    force: bool = False
)

Calculate distances from cell configuration.

Args:

cellcfg (dict): configuration
params (type, optional): parameters. Defaults to { 'locate':{'diameter':11, # round to odd number 'noise_size':1, 'separation':15, 'threshold':4000, 'preprocess':True, 'invert':False, 'max_iterations':50, 'percentile':0, 'engine':'numba', }, 'link_df':{ 'search_range':5, 'memory':1, 'link_strategy':'drop',}, 'filter_stubs':{'threshold':4}, 'get_distance_from_centroid':{'center':[75,75]}, }.
force (bool, optional): over-write the outputs. Defaults to False.

<kbd>function</kbd> `apply_cellcfgp2distances`

apply_cellcfgp2distances(cellcfgp: str)

Wrapper around cellcfg2distances for multiprocessing.

Args:

cellcfgp (str): path to the configuration file.

<kbd>function</kbd> `get_distance_from_centroid`

get_distance_from_centroid(df1: DataFrame, center: list = [75, 75]) → DataFrame

Get distance from the centroid.

Args:

df1 (pd.DataFrame): input dataframe.
center (list, optional): center point. Defaults to [75,75].

Returns:

pd.DataFrame: output dataframe.

<kbd>function</kbd> `distance_effective`

distance_effective(particle, frame1, frame2, t_cor: DataFrame) → float

Effective distance between frames.

Args:

particle : particle
frame1 (np.array): a frame.
frame2 (np.array): another frame.
t_cor (pd.DataFrame): t_cor.

Returns:

float: distance

<kbd>function</kbd> `get_distance_travelled`

get_distance_travelled(t_cor: DataFrame) → DataFrame

Distance travelled.

Args:

t_cor (pd.DataFrame): input dataframe.

Returns:

pd.DataFrame: output dataframe.

<kbd>module</kbd> `htsimaging.viz.colors`

GLobally used variables.

Global Variables

r
g
b
cmap_gfp_list

<kbd>module</kbd> `htsimaging.viz.image`

Visualization of the images.

<kbd>function</kbd> `image_background`

image_background(
    img_region=None,
    img=None,
    cmap: str = 'binary_r',
    alpha=1,
    linewidths=1,
    colors='cyan',
    kws_region={},
    show_scalebar=None,
    scalebar_color: str = 'w',
    show_cbar: bool = True,
    test=False,
    ax=None,
    **kws_img
) → Axes

Plot the image, to be used as a background to the annotations.

Args:

img_region (type, optional): segmentation image. Defaults to None.
img (type, optional): image with intensity values. Defaults to None.
cmap (str, optional): colormap name. Defaults to 'binary_r'.
alpha (int, optional): transparency. Defaults to 1.
linewidths (int, optional): segmentation contour line width. Defaults to 1.
colors (str, optional): color of the segmentation line. Defaults to 'cyan'.
kws_region (dict, optional): parameters provided to the segmentation plot. Defaults to {}.
show_scalebar (type, optional): show scale bar. Defaults to None.
scalebar_color (str, optional): color of the scale bar. Defaults to 'w'.
show_cbar (bool, optional): show colorbar. Defaults to True.
test (bool, optional): test-mode. Defaults to False.
ax (type, optional): subplot object. Defaults to None.

Keyword Args: parameters provided to the plt.imshow.

Returns: plt.Axes

<kbd>function</kbd> `annot_cells`

annot_cells(label_image, show_boxes: bool = False, ax: Axes = None) → Axes

Annotate the cells on an image.

Args:

label_image (type): image with the labeled regions
show_boxes (bool, optional): show boxes around regions. Defaults to False.
ax (plt.Axes, optional): plt.Axes. Defaults to None.

Returns: plt.Axes

<kbd>function</kbd> `image_regions_annotated`

image_regions_annotated(
    img_region,
    img,
    show_boxes: bool = False,
    **kws_img
) → Axes

Image with the annotated regions. Usage: for QC of the segmentation.

Args:

img_region (type): image with segmentated regions.
img (type): image with intensity.
show_boxes (bool, optional): whether to show the boxes around the regions. Defaults to False.

Keyword Args: parameters provided to the image_background function.

Returns: plt.Axes

<kbd>module</kbd> `htsimaging.viz`

<kbd>module</kbd> `htsimaging.viz.stat`

Visualization of the statistics.

<kbd>function</kbd> `dist_signal`

dist_signal(
    img,
    threshold: float = None,
    label_threshold: float = None,
    params_axvline: dict = {'color': 'r', 'linestyle': 'dashed'},
    ax: Axes = None,
    **kws
) → Axes

Plot the distribution of intensity.

Args:

img (type): inpput image
threshold (float, optional): threshold applied. Defaults to None.
label_threshold (float, optional): label of the threshold. Defaults to None.
params_axvline (type, optional): parameters provided to the vertical line plot. Defaults to {'color':'r','linestyle':'dashed'}.
ax (plt.Axes, optional): subplot object. Defaults to None.

Keyword Args: parameters provided to the hist function.

Returns: plt.Axes

<kbd>function</kbd> `plot_summary_stats`

plot_summary_stats(input_paths: list, ax: Axes = None) → Axes

Plot summary stats for a set of images e.g. time-lapse images.

Args:

input_paths (list): list of paths of the images.
ax (plt.Axes, optional): subplot object. Defaults to None.

Returns: plt.Axes

Awesome

Examples

Installation

How to cite?

Future directions, for which contributions are welcome

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API

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<kbd>function</kbd> exp1

<kbd>function</kbd> get_scores

<kbd>module</kbd> htsimaging.endocytosis.io

<kbd>function</kbd> make_project_cfg

<kbd>function</kbd> make_cell_cfg

<kbd>module</kbd> htsimaging.endocytosis

<kbd>module</kbd> htsimaging.endocytosis.vid

<kbd>function</kbd> make_gif

<kbd>module</kbd> htsimaging.endocytosis.viz

<kbd>function</kbd> plot_properties_cell

<kbd>function</kbd> image_locate_particles

<kbd>function</kbd> image_trajectories

<kbd>function</kbd> plot_moving_particles

<kbd>module</kbd> htsimaging.lib.io

<kbd>function</kbd> read_image

<kbd>function</kbd> arr2vid

<kbd>function</kbd> makevid

<kbd>function</kbd> nd2arr_list

<kbd>function</kbd> to_csv

<kbd>module</kbd> htsimaging.lib

<kbd>module</kbd> htsimaging.lib.stat

<kbd>function</kbd> power

<kbd>function</kbd> power_residuals

<kbd>function</kbd> power_peval

<kbd>function</kbd> line

<kbd>function</kbd> line_residuals

<kbd>function</kbd> line_peval

<kbd>function</kbd> logistic4

<kbd>function</kbd> logistic4_residuals

<kbd>function</kbd> logistic4_peval

<kbd>function</kbd> logistic5

<kbd>function</kbd> logistic5_residuals

<kbd>function</kbd> logistic5_peval

<kbd>function</kbd> fit_power

<kbd>function</kbd> fit_line

<kbd>function</kbd> get_slope

<kbd>function</kbd> get_inflection_point

<kbd>module</kbd> htsimaging.lib.utils

<kbd>function</kbd> filterframe

<kbd>function</kbd> filterframes

<kbd>function</kbd> get_data_by_regions

<kbd>function</kbd> filter_regions

<kbd>function</kbd> smoothen

<kbd>function</kbd> smoothenframes

<kbd>function</kbd> get_regions

<kbd>function</kbd> raw2phasecorr

<kbd>function</kbd> phasecorr_with

<kbd>function</kbd> imlistcropper

<kbd>function</kbd> imclipper

<kbd>function</kbd> get_cellprops

<kbd>function</kbd> get_signal_summary_by_roi

<kbd>module</kbd> htsimaging.segment

<kbd>module</kbd> htsimaging.segment.region

<kbd>function</kbd> segmentation2cells

<kbd>function</kbd> get_cellboxes

<kbd>function</kbd> arr_list2regions

<kbd>module</kbd> htsimaging.spt.io

<kbd>function</kbd> to_frames

<kbd>function</kbd> expt_dh2expt_info

<kbd>function</kbd> createinfo

<kbd>module</kbd> htsimaging.spt

<kbd>module</kbd> htsimaging.spt.stat

<kbd>function</kbd> test_locate_particles

<kbd>function</kbd> to_msd

<kbd>function</kbd> trim_returns

<kbd>function</kbd> fill_frame_jumps

<kbd>function</kbd> cellcfg2distances

<kbd>function</kbd> apply_cellcfgp2distances

<kbd>function</kbd> get_distance_from_centroid

<kbd>function</kbd> distance_effective

<kbd>function</kbd> get_distance_travelled

<kbd>module</kbd> `htsimaging.bleach_chase`

<kbd>module</kbd> `htsimaging.bleach_chase.stat`

<kbd>function</kbd> `exp1`

<kbd>function</kbd> `get_scores`

<kbd>module</kbd> `htsimaging.endocytosis.io`

<kbd>function</kbd> `make_project_cfg`

<kbd>function</kbd> `make_cell_cfg`

<kbd>module</kbd> `htsimaging.endocytosis`

<kbd>module</kbd> `htsimaging.endocytosis.vid`

<kbd>function</kbd> `make_gif`

<kbd>module</kbd> `htsimaging.endocytosis.viz`

<kbd>function</kbd> `plot_properties_cell`

<kbd>function</kbd> `image_locate_particles`

<kbd>function</kbd> `image_trajectories`

<kbd>function</kbd> `plot_moving_particles`

<kbd>module</kbd> `htsimaging.lib.io`

<kbd>function</kbd> `read_image`

<kbd>function</kbd> `arr2vid`

<kbd>function</kbd> `makevid`

<kbd>function</kbd> `nd2arr_list`

<kbd>function</kbd> `to_csv`

<kbd>module</kbd> `htsimaging.lib`

<kbd>module</kbd> `htsimaging.lib.stat`

<kbd>function</kbd> `power`

<kbd>function</kbd> `power_residuals`

<kbd>function</kbd> `power_peval`

<kbd>function</kbd> `line`

<kbd>function</kbd> `line_residuals`

<kbd>function</kbd> `line_peval`

<kbd>function</kbd> `logistic4`

<kbd>function</kbd> `logistic4_residuals`

<kbd>function</kbd> `logistic4_peval`

<kbd>function</kbd> `logistic5`

<kbd>function</kbd> `logistic5_residuals`

<kbd>function</kbd> `logistic5_peval`

<kbd>function</kbd> `fit_power`

<kbd>function</kbd> `fit_line`

<kbd>function</kbd> `get_slope`

<kbd>function</kbd> `get_inflection_point`

<kbd>module</kbd> `htsimaging.lib.utils`

<kbd>function</kbd> `filterframe`

<kbd>function</kbd> `filterframes`

<kbd>function</kbd> `get_data_by_regions`

<kbd>function</kbd> `filter_regions`

<kbd>function</kbd> `smoothen`

<kbd>function</kbd> `smoothenframes`

<kbd>function</kbd> `get_regions`

<kbd>function</kbd> `raw2phasecorr`

<kbd>function</kbd> `phasecorr_with`

<kbd>function</kbd> `imlistcropper`

<kbd>function</kbd> `imclipper`

<kbd>function</kbd> `get_cellprops`

<kbd>function</kbd> `get_signal_summary_by_roi`

<kbd>module</kbd> `htsimaging.segment`

<kbd>module</kbd> `htsimaging.segment.region`

<kbd>function</kbd> `segmentation2cells`

<kbd>function</kbd> `get_cellboxes`

<kbd>function</kbd> `arr_list2regions`

<kbd>module</kbd> `htsimaging.spt.io`

<kbd>function</kbd> `to_frames`

<kbd>function</kbd> `expt_dh2expt_info`

<kbd>function</kbd> `createinfo`

<kbd>module</kbd> `htsimaging.spt`

<kbd>module</kbd> `htsimaging.spt.stat`

<kbd>function</kbd> `test_locate_particles`

<kbd>function</kbd> `to_msd`

<kbd>function</kbd> `trim_returns`

<kbd>function</kbd> `fill_frame_jumps`

<kbd>function</kbd> `cellcfg2distances`

<kbd>function</kbd> `apply_cellcfgp2distances`

<kbd>function</kbd> `get_distance_from_centroid`

<kbd>function</kbd> `distance_effective`

<kbd>function</kbd> `get_distance_travelled`

<kbd>module</kbd> `htsimaging.viz.colors`

<kbd>module</kbd> `htsimaging.viz.image`

<kbd>function</kbd> `image_background`

<kbd>function</kbd> `annot_cells`

<kbd>function</kbd> `image_regions_annotated`

<kbd>module</kbd> `htsimaging.viz`

<kbd>module</kbd> `htsimaging.viz.stat`