[2]:

#   RUNME.setup
#  This helps set up your kernal's environment in order to avoid errors
import os

import numpy as np
from matplotlib import pyplot as plt

from canopyhydro.Cylinder import Cylinder
from canopyhydro.CylinderCollection import CylinderCollection
from canopyhydro.Forester import Forester

# Determines where configuration file is located
# file contains directory info and model input settings
config_file = os.environ[
    "CANOPYHYDRO_CONFIG"
] = f"{os.getcwd()}/canopyhydro_config.toml"
log_config = os.environ["CANOPYHYDRO_LOG_CONFIG"] = f"{os.getcwd()}/logging_config.yml"

Cylinder

The Cylinder class is used to represent the 3-D cylinders that make up a QSM

[4]:

# A trivial example of a Cylinder object


myCyl = Cylinder(
    cyl_id=1.0,
    x=[3, 6],
    y=[2, 4],
    z=[6, 12],
    radius=2.0,
    length=0.064433,
    branch_order=0.0,
    branch_id=0.0,
    volume=0.010021,
    parent_id=0.0,
    reverse_branch_order=32.0,
    segment_id=0.0,
)
print(myCyl)

Cylinder( cyl_id=1.0, x=[3, 6], y=[2, 4], z=[6, 12], radius=2.0, length=0.064433, branch_order=0.0, branch_id=0.0, volume=0.010021, parent_id=0.0, reverse_branch_order=32.0, segment_id=0.0

The most important function of Cylinder objects is their ability to return data regarding the projections onto planes. Cylinder objects utilize our custom ‘geometry’ module to calculate their projections onto the XY, XZ and YZ planes.

For more on this, see Projecting Cylinders

[10]:

# Projecting a cylinder onto the XY plane
import numpy as np

from canopyhydro.Cylinder import Cylinder
from canopyhydro.CylinderCollection import CylinderCollection

cyl = Cylinder(
    1, np.array([0, 1]), np.array([0, 1]), np.array([0, 1]), 1, 1, 0, 0, 1, 0, 0, 0
)
cyl.get_projection("XY")
print(cyl.projected_data["XY"]["polygon"])
print(cyl.projected_data["XY"]["base_vector"])
print(cyl.projected_data["XY"]["anti_vector"])
print(cyl.projected_data["XY"]["angle"])
print(cyl.projected_data["XY"]["area"])

POLYGON ((0.7055547485197222 -0.7086486608663443, 0.6927794439882502 -0.7206122246452861, 0.6793868045462438 -0.7318989317552199, 0.6654090505126061 -0.7424887326410364, 0.6508814456329607 -0.7523657839197849, 0.6358418765269868 -0.761518538618793, 0.6203303971002122 -0.7699397615971714, 0.6043887500439113 -0.777626472292711, 0.5880598778050475 -0.7845798192275877, 0.5713874351273125 -0.7908048926864933, 0.5544153144933113 -0.7963104835647774, 0.5371871946141021 -0.8011087975191887, 0.5197461206122052 -0.8052151342206525, 0.5021341228348492 -0.8086475417192888, 0.4843918794234269 -0.8114264557256392, 0.4665584259537658 -0.8135743330482637, 0.4486709137372394 -0.8151152875793871, 0.4307644168049181 -0.8160747361660723, 0.4128717862361091 -0.81647906052316, 0.3950235493693495 -0.8163552901089949, 0.3772478505606316 -0.8157308096593167, 0.3595704295270235 -0.8146330939104094, 0.3420146329184335 -0.813089470978599, 0.3246014545714764 -0.811126914925819, 0.3073495998873245 -0.8087718672450097, 0.290275569907372 -0.8060500863494255, 0.2733937609038954 -0.8029865236433437, 0.2567165756266195 -0.7996052243790617, 0.2402545427223602 -0.7959292512531837, 0.2240164412495102 -0.7919806285483623, 0.2080094276222298 -0.7877803045682026, 0.1922391627249603 -0.7833481301263573, 0.1767099373244321 -0.7787028509202096, 0.1614247942654083 -0.7738621117306963, 0.1463856462625561 -0.7688424705301826, 0.1315933883913461 -0.7636594207391983, 0.1170480046348049 -0.7583274200414502, 0.1027486680610392 -0.7528599243378058, 0.0886938343903684 -0.7472694255884285, 0.0748813288632188 -0.7415674924539188, 0.061308426443543 -0.7357648127983645, 0.0479719254905637 -0.7298712372577524, 0.0348682151073508 -0.7238958232052886, 0.0219933364311366 -0.7178468785603557, 0.0093430381703601 -0.7117320049902478, -0.0030871732801796 -0.7055581401438671, -0.0153019887994878 -0.6993315986349682, -0.0273062562232358 -0.6930581115600766, -0.0391049443978219 -0.6867428643938861, 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[0.57735027 0.57735027 0.57735027]
[-0.57735027 -0.57735027 -0.57735027]
0.6154797086703873
4.642087600836025

The main use of this functionality is shown below. Namely, projecting cylinders allows us to more readily provide visualizations of the tree canopy they represent

[5]:

# Here we show the 3D view and the 3 possible 2D projections of a Cylinder object
myCyl = Cylinder(
    cyl_id=1.0,
    x=[0, 3],
    y=[0, 2],
    z=[0, 6],
    radius=2.0,
    length=0.064433,
    branch_order=0.0,
    branch_id=0.0,
    volume=0.010021,
    parent_id=0.0,
    reverse_branch_order=32.0,
    segment_id=0.0,
)


fig = myCyl.draw_3D(show=False, draw_projections=True)

2024.10.14 22:21:30.286 |MainThread   | INFO    | geometry.py:617 -    draw_cylinders_3D() | model - Plotting cylinder in 3D

../_images/examples_intro_to_common_objects_7_1.png

In the above 3D representations, the entire surface of cylinder must be calculated, s this may be computationally intensive.

As such, the ‘get_projection’ function calculates statistics regarding 2D projections directly instead.

[6]:

# The get_projection function allows for the retrieval of
# projection data without the need to define the entire surface of the cylinder


myCyl = Cylinder(
    cyl_id=1.0,
    x=[3, 6],
    y=[2, 4],
    z=[6, 12],
    radius=2.0,
    length=0.064433,
    branch_order=0.0,
    branch_id=0.0,
    volume=0.010021,
    parent_id=0.0,
    reverse_branch_order=32.0,
    segment_id=0.0,
)

print("'myCyl' as seen from above")
myCyl.get_projection("XY")
myCyl.draw(plane="XY")
plt.show()
print("'myCyl' as seen from above")
print(
    f"The 'XY' projection of myCyl as an area of {round(myCyl.projected_data['XY']['area'],2)} cm^3"
)
print(
    f"       and the cylinder makes an angle of {round(myCyl.projected_data['XY']['angle'],2)} radians with the XY plane"
)


print("'myCyl' as seen from the 'fromt' of the tree")
myCyl.get_projection("XZ")
myCyl.draw(plane="XZ")
plt.show()
print(
    f"The 'XZ' projection of myCyl as an area of {round(myCyl.projected_data['XZ']['area'],2)} cm^3"
)
print(
    f"       and the cylinder makes an angle of {round(myCyl.projected_data['XZ']['angle'],2)} radians with the XZ plane"
)

print("'myCyl' as seen from one 'side' of the tree")
myCyl.get_projection("YZ")
myCyl.draw(plane="YZ")
plt.show()
print(
    f"The 'YZ' projection of myCyl as an area of {round(myCyl.projected_data['YZ']['area'],2)} cm^3"
)
print(
    f"       and the cylinder makes an angle of {round(myCyl.projected_data['YZ']['angle'],2)} radians with the YZ plane"
)

'myCyl' as seen from above

2024.10.14 22:21:35.784 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection

../_images/examples_intro_to_common_objects_9_2.png

'myCyl' as seen from above
The 'XY' projection of myCyl as an area of 25.19 cm^3
       and the cylinder makes an angle of 1.03 radians with the XY plane
'myCyl' as seen from the 'fromt' of the tree

2024.10.14 22:21:36.093 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection

../_images/examples_intro_to_common_objects_9_5.png

The 'XZ' projection of myCyl as an area of 30.42 cm^3
       and the cylinder makes an angle of 0.29 radians with the XZ plane
'myCyl' as seen from one 'side' of the tree

2024.10.14 22:21:36.325 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection

../_images/examples_intro_to_common_objects_9_8.png

The 'YZ' projection of myCyl as an area of 30.68 cm^3
       and the cylinder makes an angle of 0.44 radians with the YZ plane

For more information regarding these 2D projections, see Projecting Cylinders

Cylinder Collection

Cylinder Collections are just as they sound and, at the most basic level, a Cylinder Collection is defined as a list of 1 or more Cylinder objects.
Cylinder Collections almost always represent QSM’s (or parts of a QSM), and are meant to help users explore their QSMs.
Below, we demonstrate how one might initialize a cylinder collection using cylinder data (e.g. QSM data) stored in a CSV file.

[13]:

# Example showing the most basic possible cylinder Collection4
myCollection = CylinderCollection()
# The below file is one of our several testing files, featuring only
# the trunk of a tree and one of its branches
myCollection.from_csv("5_SmallTree.csv")

2024.10.14 18:34:11.877 |MainThread   | INFO    | CylinderCollection.py:295 -             from_csv() | model - Processing <_io.TextIOWrapper name='./data/input/5_SmallTree.csv' mode='r' encoding='UTF-8'>

{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
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{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}
{'cyl_id': 1, 'parent_id': 2, 'x': [3, 6], 'y': [4, 7], 'z': [5, 8], 'radius': 9, 'volume': 10, 'length': 12, 'segment_id': 15, 'branch_order': 20, 'reverse_branch_order': 21, 'branch_id': 24}

2024.10.14 18:34:11.958 |MainThread   | INFO    | CylinderCollection.py:323 -             from_csv() | model - ./data/input/5_SmallTree.csv initialized with 517 cylinders

2024.10.14 18:34:11.966 |MainThread   | INFO    | CylinderCollection.py:338 -    project_cylinders() | model - Projection into XY axis begun for file 5_SmallTree.csv

2024.10.14 18:34:14.668 |MainThread   | INFO    | CylinderCollection.py:347 -    project_cylinders() | model - Projection into XY axis complete for file 5_SmallTree.csv

2024.10.14 18:34:14.671 |MainThread   | WARNING | CylinderCollection.py:497 -        get_end_nodes() | model - Graph not initialized, running initialize_graph_from with default values

2024.10.14 18:34:15.260 |MainThread   | INFO    | CylinderCollection.py:774 - find_flow_components() | model - 5_SmallTree.csv found to have 70 drip components

reached_End of find flows

2024.10.14 18:34:15.269 |MainThread   | INFO    | CylinderCollection.py:845 -      calculate_flows() | model - np flow chars [Flow(num_cylinders=216.0, projected_area=16.51706021532694, surface_area=19.495074988550606, angle_sum=180.03288665020426, volume=3.9062960000000007, sa_to_vol=83646.49652248439, drip_node_id=0.0, drip_node_loc=(-0.299115, 2.537844, -0.598273)), None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]

2024.10.14 18:34:15.274 |MainThread   | INFO    | CylinderCollection.py:846 -      calculate_flows() | model -  flow chars []

2024.10.14 18:34:15.277 |MainThread   | INFO    | CylinderCollection.py:848 -      calculate_flows() | model -  togetha [Flow(num_cylinders=216.0, projected_area=16.51706021532694, surface_area=19.495074988550606, angle_sum=180.03288665020426, volume=3.9062960000000007, sa_to_vol=83646.49652248439, drip_node_id=0.0, drip_node_loc=(-0.299115, 2.537844, -0.598273))]

2024.10.14 18:34:15.454 |MainThread   | INFO    | CylinderCollection.py:1003 -           statistics() | model - Found hull alpha shape stats

2024.10.14 18:34:15.914 |MainThread   | INFO    | CylinderCollection.py:1023 -           statistics() | model - found projected areas

2024.10.14 18:34:16.689 |MainThread   | INFO    | utils.py:150 -            save_file() | model - [['total_psa', 'psa_w_overlap', 'stem_psa', 'stem_psa_w_overlap', 'tot_surface_area', 'stem_surface_area', 'tot_hull_area', 'tot_hull_boundary', 'stem_hull_area', 'stem_hull_boundary', 'num_drip_points', 'max_bo', 'topQuarterTotPsa', 'topHalfTotPsa', 'topThreeQuarterTotPsa', 'TotalShade', 'top_quarter_shade', 'top_half_shade', 'top_three_quarter_shade', 'DBH', 'volume', 'X_max', 'Y_max', 'Z_max', 'X_min', 'Y_min', 'Z_min', 'Order_zero_angle_avg', 'Order_zero_angle_std', 'Order_one_angle_avg', 'Order_one_angle_std', 'Order_two_angle_avg', 'Order_two_angle_std', 'Order_three_angle_avg', 'Order_three_angle_std', 'order_gr_four_angle_avg', 'order_gr_four_angle_std', 'file_name']]

2024.10.14 18:34:16.696 |MainThread   | INFO    | utils.py:177 -            save_file() | model - attempting to write to ./data/output//statistics/5_SmallTree__statistics.csv

2024.10.14 18:34:16.703 |MainThread   | INFO    | CylinderCollection.py:1003 -           statistics() | model - Found hull alpha shape stats

2024.10.14 18:34:17.101 |MainThread   | INFO    | CylinderCollection.py:1023 -           statistics() | model - found projected areas

2024.10.14 18:34:17.737 |MainThread   | INFO    | utils.py:150 -            save_file() | model - [['total_psa', 'psa_w_overlap', 'stem_psa', 'stem_psa_w_overlap', 'tot_surface_area', 'stem_surface_area', 'tot_hull_area', 'tot_hull_boundary', 'stem_hull_area', 'stem_hull_boundary', 'num_drip_points', 'max_bo', 'topQuarterTotPsa', 'topHalfTotPsa', 'topThreeQuarterTotPsa', 'TotalShade', 'top_quarter_shade', 'top_half_shade', 'top_three_quarter_shade', 'DBH', 'volume', 'X_max', 'Y_max', 'Z_max', 'X_min', 'Y_min', 'Z_min', 'Order_zero_angle_avg', 'Order_zero_angle_std', 'Order_one_angle_avg', 'Order_one_angle_std', 'Order_two_angle_avg', 'Order_two_angle_std', 'Order_three_angle_avg', 'Order_three_angle_std', 'order_gr_four_angle_avg', 'order_gr_four_angle_std', 'file_name']]

2024.10.14 18:34:17.740 |MainThread   | INFO    | utils.py:177 -            save_file() | model - attempting to write to ./data/output//statistics/5_SmallTree__statistics.csv

[13]:

'./data/output//statistics/5_SmallTree__statistics.csv'

The below demonstrates how these projection and drawing functionality of the cylinder class can be extended to allow for drawing, highlighting and filtering entire tree canopies.

We recommend sticking to 2D drawings when exploring your data as this is a lot less computationally intensive than the alternative. However the same ‘.draw’ function can generate 3D plots of cylinder collections!

[9]:

myCollection = CylinderCollection()
# The below file is one of our several testing files, featuring only
# the trunk of a tree and one of its branches
myCollection.from_csv("charlie_brown.csv")

# by filtering for cyl_id>100, we are only plotting the
# cylinders that are part of the branch
myCollection.draw("XZ", show=True, save=False)
myCollection.draw("XY", show=True, save=False)
myCollection.draw("YZ", show=True, save=False)
# myCollection.draw("3D", show=False, save=False, file_name_ext="2d_3d_comparison")


print("XZ Projection of a collection of cylinders")

2024.10.14 22:24:00.333 |MainThread   | INFO    | CylinderCollection.py:400 -                 draw() | model - 463 cylinders matched criteria

2024.10.14 22:24:00.339 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection

../_images/examples_intro_to_common_objects_15_2.png

2024.10.14 22:24:00.678 |MainThread   | INFO    | CylinderCollection.py:400 -                 draw() | model - 463 cylinders matched criteria

2024.10.14 22:24:00.682 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection

../_images/examples_intro_to_common_objects_15_5.png

2024.10.14 22:24:01.040 |MainThread   | INFO    | CylinderCollection.py:400 -                 draw() | model - 463 cylinders matched criteria

2024.10.14 22:24:01.043 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection

../_images/examples_intro_to_common_objects_15_8.png

XZ Projection of a collection of cylinders

This same ‘draw’ function can also be used to highlight features (that is, groups of cylinders) of interest in a collection, and to filter a collection to only display smaller sections.

[10]:

myCollection = CylinderCollection()
# The below file is one of our several testing files, featuring only
# the trunk of a tree and one of its branches
# myCollection.from_csv("10_MediumCollection.csv")

myCollection.draw(
    "XZ",
    filter_lambda=lambda: cyl_id > 50,
    highlight_lambda=lambda: branch_order > 0
)  # noqa


myCollection.draw(
    "XZ",
    filter_lambda=lambda: cyl_id > 50,
    highlight_lambda=lambda: cyl_id > 100
)

2024.10.14 22:24:21.455 |MainThread   | INFO    | CylinderCollection.py:400 -                 draw() | model - 462 cylinders matched criteria

2024.10.14 22:24:21.460 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection

2024.10.14 22:24:21.749 |MainThread   | INFO    | CylinderCollection.py:400 -                 draw() | model - 462 cylinders matched criteria

2024.10.14 22:24:21.751 |MainThread   | INFO    | geometry.py:563 -            draw_cyls() | model - Plotting cylinder collection

[10]:

<Axes: >

../_images/examples_intro_to_common_objects_17_5.png

../_images/examples_intro_to_common_objects_17_6.png

To understand how these features support canoPyHydro’s titular ‘flow identification’ functionality, check out Flow Identification and Drawing

Forester

Forester objects allow users to conveniently create and manage Cylinder Collections. In particular, Foresters are useful for reading in and processing QSM files.

When a Forester object is created, available file names are read from the default directory, ‘./data/input/’.

This list of available files can be accessed through the Forester.file_names attribute, as shown below

[4]:

# Creating a new Forester object
myForester = Forester()
print(
    f"Files available in {myForester.directory}: {list(map(str,myForester.file_names))}"
)

Files available in ./data/input/: ['data/input/Secrest10-08_000000.csv', 'data/input/10_MediumCollection.csv', 'data/input/1_TenCyls.csv', 'data/input/6_LargeTree.csv', 'data/input/charlie_brown.csv', 'data/input/example_tree.csv', 'data/input/minimal_tree.ods', 'data/input/minimal_tree.csv', 'data/input/5_SmallTree.csv']

Optionally, a custom path may be passed to the Forester object, In which case, the Forester will look for files in the passed directory instead

[5]:

# Passing a custom directory to the Forester object will change the directory attribute
directory = "/data/test/"
myForester = Forester("data/test/")
print(
    f"Files available in {myForester.directory}: {list(map(str,myForester.file_names))}"
)

Files available in data/test/: ['data/test/10_MediumCollection.csv', 'data/test/1_TenCyls.csv', 'data/test/3_HappyPathWTrunk.csv', 'data/test/2_EZ_projection.csv', 'data/test/4_LargeCollection.csv', 'data/test/5_SmallTree.csv', 'data/test/6_LargeTree.csv', 'data/test/8_DripPathMidBranch.csv', 'data/test/7_DripPathAdjToTrunk.csv', 'data/test/9_DripOnTrunk.csv']

The ‘qsm_to_collection’ function can be used create CylinderCollections from a specified file.

[13]:

from src.canopyhydro.Forester import Forester
# Importing a QSM file as a CylinderCollection
myForester = Forester("./data/input/")
myForester.qsm_to_collection("example_tree.csv")

cylCollections = myForester.cylinder_collections
firstCollection = cylCollections[0]

print(
    f"Forester has {len(cylCollections)} CylinderCollection, imported from {cylCollections[0].file_name}"
)

2024.10.14 21:09:45.801 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/input/example_tree.csv
2024.10.14 21:09:46.438 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - example_tree.csv initialized with 9998 cylinders

Forester has 1 CylinderCollection, imported from example_tree.csv

If ‘All’ is provided as the file name, all of the files in the given directory will be read in as CylinderCollections.

(Note that this may require a significant amount of memory.)

[23]:

# Reading in all files in the directory as collections
myForester = Forester("./data/test/")
myForester.qsm_to_collection("All")
cylCollections = myForester.cylinder_collections
firstCollection = cylCollections[0]
print(
    f"""Forester created {len(cylCollections)} CylinderCollections, imported from the following files
      {list(map(lambda x: x.file_name,cylCollections))}"""
)

2024.10.14 21:13:20.160 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/10_MediumCollection.csv
2024.10.14 21:13:21.528 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 10_MediumCollection.csv initialized with 10501 cylinders
2024.10.14 21:13:21.530 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/1_TenCyls.csv
2024.10.14 21:13:21.538 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 1_TenCyls.csv initialized with 10 cylinders
2024.10.14 21:13:21.540 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/3_HappyPathWTrunk.csv
2024.10.14 21:13:21.556 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 3_HappyPathWTrunk.csv initialized with 120 cylinders
2024.10.14 21:13:21.558 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/2_EZ_projection.csv
2024.10.14 21:13:21.562 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 2_EZ_projection.csv initialized with 5 cylinders
2024.10.14 21:13:21.564 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/4_LargeCollection.csv
2024.10.14 21:13:24.783 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 4_LargeCollection.csv initialized with 51524 cylinders
2024.10.14 21:13:24.787 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/5_SmallTree.csv
2024.10.14 21:13:24.833 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 5_SmallTree.csv initialized with 517 cylinders
2024.10.14 21:13:24.837 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/6_LargeTree.csv
2024.10.14 21:13:30.074 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 6_LargeTree.csv initialized with 83474 cylinders
2024.10.14 21:13:30.076 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/8_DripPathMidBranch.csv
2024.10.14 21:13:30.095 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 8_DripPathMidBranch.csv initialized with 131 cylinders
2024.10.14 21:13:30.097 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/7_DripPathAdjToTrunk.csv
2024.10.14 21:13:30.110 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 7_DripPathAdjToTrunk.csv initialized with 117 cylinders
2024.10.14 21:13:30.112 |MainThread   | INFO    | CylinderCollection.py:291 -             from_csv() | model - Processing data/test/9_DripOnTrunk.csv
2024.10.14 21:13:30.127 |MainThread   | INFO    | CylinderCollection.py:320 -             from_csv() | model - 9_DripOnTrunk.csv initialized with 198 cylinders

Forester created 10 CylinderCollections, imported from the following files
      ['10_MediumCollection.csv', '1_TenCyls.csv', '3_HappyPathWTrunk.csv', '2_EZ_projection.csv', '4_LargeCollection.csv', '5_SmallTree.csv', '6_LargeTree.csv', '8_DripPathMidBranch.csv', '7_DripPathAdjToTrunk.csv', '9_DripOnTrunk.csv']

Putting all that we have learned together, you can see that all of the statistics available through canoPyHydro can be generated with the below 10 lines of code

[ ]:

forest = Forester("data/input/")
forest.get_file_names()
forest.qsm_to_collection(file_name="5_SmallTree.csv")
collection = forest.cylinder_collections[0]
collection.project_cylinders("XY")
collection.initialize_digraph_from(in_flow_grade_lim=-0.16)
collection.find_flow_components()
print("finished_find_flow_components")
collection.calculate_flows()
pickle_collection(collection)