Tutorial about filtering LocData objects

%matplotlib inline

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import locan as lc

/tmp/ipykernel_1407/3049569888.py:4: DeprecationWarning: 
Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),
(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)
but was not found to be installed on your system.
If this would cause problems for you,
please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466
        
  import pandas as pd

lc.show_versions(system=False, dependencies=False, verbose=False)

Locan:
   version: 0.20.0.dev41+g755b969

Python:
   version: 3.11.6

Synthetic data

A random dataset is created.

rng = np.random.default_rng(seed=1)

locdata = lc.simulate_Thomas(parent_intensity=1e-5, region=((0, 1000), (0, 1000)), cluster_mu=100, cluster_std=10, seed=rng)

locdata.print_summary()

Jupyter environment detected. Enabling Open3D WebVisualizer.
[Open3D INFO] WebRTC GUI backend enabled.
[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.
identifier: "1"
comment: ""
source: SIMULATION
state: RAW
element_count: 705
frame_count: 0
creation_time {
  2024-03-14T11:08:45.309935Z
}

fig, ax = plt.subplots(nrows=1, ncols=1)
locdata.data.plot.scatter(x='position_x', y='position_y', ax=ax, color='Blue', label='locdata')
plt.show()

Select localizations according to property conditions

A LocData object carries localization data with certain properties for each localization.

We can select localisations according to property conditions.

locdata_select = lc.select_by_condition(locdata, condition='position_x<200')

fig, ax = plt.subplots(nrows=1, ncols=2)
locdata.data.plot.scatter(x='position_x', y='position_y', ax=ax[0], color='Blue', label='locdata')
locdata_select.data.plot.scatter(x='position_x', y='position_y', ax=ax[1], color='Blue', label='locdata')
plt.tight_layout
plt.show()

Select localizations in regions

Regions can be defined using the classes defined in the locan.data.region module. Please see the tutorial on Regions.

We can select localizations that within a given region.

region = region=lc.Rectangle((1, 1), 400, 400, 0)
locdata_roi = lc.select_by_region(locdata, region=region)

fig, ax = plt.subplots(nrows=1, ncols=2)
locdata.data.plot.scatter(x='position_x', y='position_y', ax=ax[0], color='Blue', label='locdata')
locdata_roi.data.plot.scatter(x='position_x', y='position_y', ax=ax[1], color='Blue', label='locdata')
plt.tight_layout
plt.show()

Select localizations from a region of interest (ROI)

Typically a region of interest is defined for a single dataset. The Roi class combines a region definition with a specific data reference and provides convenience methods like input/output.

Define a region of interest (roi):

roi = lc.Roi(region=lc.Rectangle((1, 1), 400, 400, 0), reference=locdata)

Create new LocData instance by selecting localizations within a roi.

locdata_roi = roi.locdata()

fig, ax = plt.subplots(nrows=1, ncols=2)
locdata.data.plot.scatter(x='position_x', y='position_y', ax=ax[0], color='Blue', label='locdata')
locdata_roi.data.plot.scatter(x='position_x', y='position_y', ax=ax[1], color='Blue', label='locdata')
plt.tight_layout
plt.show()

Save roi definitions (including region and reference) as yaml file:

roi

Roi(reference=<locan.data.locdata.LocData object at 0x7f0671269510>, region=Rectangle((1, 1), 400, 400, 0),  loc_properties=())

import tempfile
from pathlib import Path

with tempfile.TemporaryDirectory() as tmp_directory:
    file_path = Path(tmp_directory) / 'roi.yaml'

    roi.to_yaml(path=file_path)

    roi_new = lc.Roi.from_yaml(path = file_path)
    roi_new.reference = roi.reference

locdata_new = roi_new.locdata()
locdata_new.meta

/home/docs/checkouts/readthedocs.org/user_builds/locan/envs/latest/lib/python3.11/site-packages/locan/rois/roi.py:300: UserWarning: The localization data has to be saved and the file path provided, or the reference is lost.
  warnings.warn(

identifier: "5"
source: SIMULATION
state: MODIFIED
history {
  name: "make_Thomas"
  parameter: "{\'parent_intensity\': 1e-05, \'region\': ((0, 1000), (0, 1000)), \'expansion_factor\': 6, \'cluster_mu\': 100, \'cluster_std\': 10, \'clip\': True, \'shuffle\': True, \'seed\': Generator(PCG64) at 0x7F068CA67680}"
}
history {
  name: "locdata"
  parameter: "{\'self\': Roi(reference=<locan.data.locdata.LocData object at 0x7f0671269510>, region=Rectangle((1, 1), 400, 400, 0),  loc_properties=[]), \'reduce\': True}"
}
ancestor_identifiers: "1"
element_count: 206
frame_count: 0
creation_time {
  seconds: 1710414525
  nanos: 309935000
}
modification_time {
  seconds: 1710414525
  nanos: 309935000
}

Select a random subset of localizations

locdata_random = lc.random_subset(locdata, n_points=10, seed=rng)

fig, ax = plt.subplots(nrows=1, ncols=2)
locdata.data.plot.scatter(x='position_x', y='position_y', ax=ax[0], color='Blue', label='locdata')
locdata_random.data.plot.scatter(x='position_x', y='position_y', ax=ax[1], color='Blue', label='locdata')
plt.tight_layout
plt.show()