PhotosynQ | Python

Truly Collaborative Plant Research

PhotosynQ helps you to make your plant research more efficient. For advanced analysis, this package allows you to pull data right into Python.

New python users should consider installing Anaconda which includes both the python interpreter and the Jupyter python editor.

For advanced users, we recommend using Spyder to edit and run python code.

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Installation

Install using pip in the terminal.

pip install git+https://github.com/Photosynq/PhotosynQ-Python.git --upgrade --no-cache-dir

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Getting started

• A user account on PhotosynQ is required

Standard usage

Retrieve project data and meta-data in a convenient DataFrame

import photosynq_py as ps

# use your photosynq account to login (you will be prompted for your password)
email = "[email protected]"
ps.login(email)

# retrieve a dataframe with data from the given project ID
projectId = 1556
df = ps.get_project_dataframe(projectId)

# logout
ps.logout();

Other Functions

Advanced users can retrieve project data and meta-data/info separately, as json strings.

getProjectInfo, getProjectData and buildProjectDataFrame (below) are components of getProjectDataFrame (above)

ps.login( "[email protected]" )
projectId = 1556
info = ps.get_project_info(projectId)
data = ps.get_project_data(projectId, processed_data=False) # Use raw data
df = ps.build_project_dataframe(info, data)
ps.logout()

build_notebook_dataframe allows to build a DataFrame from the Desktop Application's notebook. Open the measurements you are interested by checking the corresponding checkboxes in the notebook and select "Open Selected" from the "Action" menu. Then click the ☰ symbol and select on "Save Data (json)" from the dropdown menu. The saved file can be imported.

df = ps.build_notebook_dataframe('exported_notebook_file.json')