Common python functions and patterns

Here you can find an ever growing list of useful python functions and scripting patterns. We will constantly update the page. All classes and individual methods can be found on our Java docs.

Useful python packages

# Accessing ImageJ/Fiji
import imagej
ij = imagej.init('/Applications/Fiji.app')

# Python packages
# Full access to Java in Python
import jpype
import jpype.imports

# Transformation of java class objects into python objects
import scyjava as sc

# Scientific computing
import numpy as np

# Manipulation and analysis of datasets/dataframes
import pandas as pd

# Data visualization
import matplotlib.pyplot as plt
import seaborn as sns

Access the different types of archives

# Single Molecule Archive
yamaFile = File('/Users/your_archive.yama')

# Single Molecule Archive
SingleMoleculeArchive = SingleMoleculeArchive(yamaFile)

# DNA Molecule Archive
DnaMoleculeArchive = DnaMoleculeArchive(yamaFile)

# Object Archive
ObjectArchive = ObjectArchive(yamaFile)

Basic commands from Mars

# the different types of archives share commands

# Number of molecules in Archive
archive.getNumberOfMolecules()

# Access parameter based on UID
archive.get(UID).getParameter('ParameterName')

# Access Metadata tags
archive.metadataHasTag(molecule.getMetadataUID(),'Meta Tag')

for UID in archive.getMoleculeUIDs():
    molecule = archive.get(UID)

    # Check if molecule was tagged with 'Tag'
    molecule.hasTag('Tag')

    #Check if molecule has no tag
    molecule.hasNoTag()

    # Check if molecule has parameter named 'ParameterName'
    molecule.hasParameter('ParameterName')

    # Check if molecule has region named 'RegionName'
    molecule.hasRegion('RegionName')

    # Check if molecule has position named 'PositionName'
    molecule.hasPosition('PositionName')

Creation of a dataframe from Archive entries (scijava)

# Table for the molecule as a pandas dataframe using the index
tableByIndex = sc._table_to_pandas(archive.get(0).getTable())
tableByIndex

# Table for the molecule as a pandas dataframe using UIDs
tableByUID = sc._table_to_pandas(archive.get('2AEygnwajcvHUBYGGUHcNa').getTable())
tableByUID

Looping

# For-loop to print out all UIDs in Archive
for UID in archive.getMoleculeUIDs():
    molecule = archive.get(UID)
    print(molecule.getUID())

# For-loop to calculate value with respect to category
dist_y_active = []
dist_y_unactive = []

for UID in archive.getMoleculeUIDs():
    table_y = _pandas.table_to_pandas(archive.get(UID).getDataTable())["y"]
    if archive.get(UID).hasTag("Active"):
        dist_y_active.append(max(table_y)-min(table_y))
    else:
        dist_y_unactive.append(max(table_y)-min(table_y))

Lambda and mapping function

# Map of each molecule in the Archive
molecules = map(lambda UID: archive.get(UID), archive.getMoleculeUIDs())

# Get UIDs from each Molecule in Archive
for molecule in molecules:
    print(molecule.getUID())      

# Get UIDs from Molecules tagged 'Active'
for molecule in molecules:
    if molecule.hasTag('Active'):
        print(molecule.getUID())

# Store value of specific parameter ('ParameterName')
listname = list(map(lambda UID: archive.get(UID).getParameter('ParameterName'), archive.getMoleculeUIDs()))

Access values of a SegmentsTable

# Access values of SegmentsTable for further analysis
# e. g. from kinetic change point analysis
A_values=[]
A_sigma=[]
B_values=[]
B_sigma=[]

for UID in archive.getMoleculeUIDs():
    if archive.get(UID).hasSegmentsTable("T","y"):
        A_values.append(archive.get(UID).getSegmentsTable("T",'y').getValue("A",0))
        A_sigma.append(archive.get(UID).getSegmentsTable("T",'y').getValue("sigma_A",0))
        B_values.append(archive.get(UID).getSegmentsTable("T",'y').getValue("B",0))
        B_sigma.append(archive.get(UID).getSegmentsTable("T",'y').getValue("sigma_B",0))