Mapping

!pip install vfb-connect --upgrade

# Import libs and initialise API objects
from vfb_connect.cross_server_tools import VfbConnect
import pandas as pd

vc = VfbConnect()

import pymaid
import navis

navis.set_pbars(jupyter=False)
pymaid.set_pbars(jupyter=False)

# Connect to the VFB CATMAID server hosting the FAFB data
rm = pymaid.connect_catmaid(server="https://fafb.catmaid.virtualflybrain.org/", api_token=None, max_threads=10)

# Test call to see if connection works 
print(f'Server is running CATMAID version {rm.catmaid_version}')

# Many functions return JSON-compatible nested data structures. This function coverts them to DataFrame.
def summary_2_df(summary, sort=None):
    """Convert summary to DataFrame.  Optionally specify a set of columns to sort as a list of strings"""
    if sort:
        return pd.DataFrame.from_records(summary).sort_values(sort)
    else:
        return pd.DataFrame.from_records(summary)

# lets take some examples from a discovery query on the previous spreadhseet
sayin_tab = pd.DataFrame.from_records(vc.get_instances_by_dataset('Sayin2019', summary=True))
sayin_tab

oct_VPM3 = summary_2_df(vc.get_instances('octopaminergic VPM3 neuron', summary=True))
oct_VPM3

oct_VPM3_images = vc.neo_query_wrapper.get_images(oct_VPM3['id'], stomp=True, template='JRC2018Unisex', image_folder = 'oct_VPM3b')
oct_VPM3_images

oct_VPM3_images = vc.get_images_by_type('octopaminergic VPM3 neuron', stomp=True, template='JRC2018Unisex', image_folder = 'oct_VPM3')
nl = navis.read_swc('./oct_VPM3')
navis.plot3d(nl)