Plotting and visual quality control =================================== Use field views to understand the input and cell views to check the geometry behind measurements. The walkthrough figures are linked from :doc:`three-channel` and :doc:`five-channel`. .. code-block:: python import bactoscoop.plot as bplt field_ids = bplt.list_field_ids(ic.image_folder_path, phase_channel="C1") fig = bplt.plot_field_channels( ic.image_folder_path, field_ids[0], {"C1": "Phase contrast", "C2": "DAPI", "C3": "GFP-DnaN"}, ) fig = bplt.plot_segmentation_review(ic.image_objects) image = ic.image_objects[0] cell_id = image.cells[0].cell_id fig = bplt.plot_cell_gallery(image, cell_id, channel=None) Select a cell after the relevant stage -------------------------------------- Curation and mesh filtering can remove a selected cell. Some field/cell choices in saved notebooks are example-specific. Use ``select_example_cell`` or ``pick_representative_cell`` to inspect available cells and verify the return value against its :doc:`../api/plot` entry. ``pick_detected_cell`` chooses a cell with requested object detections when possible. Signals and detections ---------------------- .. code-block:: python cell_id = bplt.pick_detected_cell(image, preferred_channels=("C3",)) fig = bplt.plot_signal_gallery( image, cell_id, channel_labels={"C3": "GFP-DnaN"}, show_objects=True, ) fig = bplt.plot_feature_overview(ic.merged_features, signal_column="C3_NC_ratio") Additional functions include histograms, feature scatter plots, morphology and signal summaries, normalized axial profiles, demographs, and pairwise metric heatmaps. See :doc:`../api/plot` for signatures and return shapes. Some functions display directly, while others return a figure or a collection of figures. In notebooks, explicitly display returned figures and close them after use when generating many plots. Distinguish display contrast from quantitative preprocessing: making a channel look brighter in a figure does not change its recorded feature values.