On-the-fly statistics¶
[This note-book is in oceantracker/tutorials_how_to/]
Scaling up particle numbers to millions will create large volumes of particle track data. Storing and analyzing these tracks is slow and rapidly becomes overwhelming. For example, building a heat map from a terabyte of particle tracks after a run has completed. Ocean tracker can build some particle statistics on the fly, without recording any particle tracks. This results in more manageable data volumes and analysis.
On-the-fly statistics record particle counts separately for each release group. It is also possible to subset the counts, ie only count particles which are stranded by the tide by designating a range of particle status values to count. Or, only count particles in a given vertical “z” range. Users can add multiple statistics, all calculated in from the same particles during the run. Eg. could add a particle statistic for each status type, for different depth ranges.
Statistics can be read, plotted or animated with OceanTrackers post-processing code, see below
The available “particle_statistics” classes with their individual settings are at …. add link
Currently there are two main classes of 2D particle statistics “gridded” which counts particles inside cells of a regular grid, and “polygon” which counts particles in a given list of polygons.
The user can add many particle statistics classes, all based on the same particles. For both types it is possible to only count a subset of these particles, by setting a min. and/or max status to count, or setting a min. and/or max. “z”, the vertical location. So could add several statistics classes, each counting particles in different layers, or classes to separately count those moving and those on the bottom hoping to be re-suspended.
Gridded statistics¶
These are heat maps of counts binned into cells of a regular grid. Along with heat maps of particle counts, users can optionally build a heat maps of named particle properties, eg. the value decaying particle property. To ensure the heat map grids are not too large or too coarse, by default grids are centred on each release group, thus there are different grid locations for each release group.
Polygon statistics¶
These particle counts can be used to calculate the connectivity between each release group and a user given list of “statistics” polygons. Also, used to estimate the influence of each release group on a particle property with each given statistics polygon. Polygon statistics count the particles from each point or polygon release within each statistics polygons. The statistics polygons are are completely independent of the polygons that might be used in any polygon release (they can be the same if the user gives both the same point coordinates). A special case of a polygon statistic, is the “residence_time” class, which can be used to calculate the fraction of particles from each release group remaining within each statistics polygon at each ‘update_interval’ as one way to estimate particle residence time for each release group.
Particle property statistics¶
Both types of statistics can also record sums of user designated particle properties within each given grid cell or statistics polygon, which originate from each release group. These sums enabling mean values of designated particle properties within each grid cell or polygon to be calculated. They can also be used to estimate the relative influence of each release group on the value of a particle property within each given grid cell or polygon.
A future version with allow estimating the variance of the designated property values and particle counts in each grid cell or polygon, for each release group.
Gridded/Heat map example¶
The below uses the helper class method to extends the minimal_example to add
Decaying particle property, eg. breakdown of a pollutant
Gridded time series of particle statistics as heat maps, which also builds a heat map of the pollutant
Plot the particle counts and pollutant as animated heatmap.
# Gridded Statistics example.py using class helper method
#------------------------------------------------
from oceantracker.main import OceanTracker
# make instance of oceantracker to use to set parameters using code, then run
ot = OceanTracker()
# ot.settings method use to set basic settings
ot.settings(output_file_base='heat_map_example', # name used as base for output files
root_output_dir='output', # output is put in dir 'root_output_dir'\\'output_file_base'
time_step= 600., # 10 min time step as seconds
write_tracks = False # particle tracks not needed for on fly
)
# ot.set_class, sets parameters for a named class
ot.add_class('reader',input_dir= '../demos/demo_hindcast/schsim3D', # folder to search for hindcast files, sub-dirs will, by default, also be searched
file_mask= 'demo_hindcast_schisim3D*.nc') # hindcast file mask
# add one release locations
ot.add_class('release_groups',
name='my_release_point', # optional name used to refere to group in plotting
points= [ [1599000, 5486200]], # ust be 1 by N list pairs of release locations
release_interval= 900, # seconds between releasing particles
pulse_size= 1000, # number of particles released each release_interval
)
# add a decaying particle property
# add and Age decay particle property, with exponential decay based on age, with time scale 1 hour
ot.add_class('particle_properties', # add a new property to particle_properties role
name ='a_pollutant', # must have a user given name to
class_name='oceantracker.particle_properties.age_decay.AgeDecay', # class_role is resuspension
initial_value= 1000,
decay_time_scale = 3600.) # time scale of age decay ie decays initial_value* exp(-age/decay_time_scale)
# add a gridded particle statistic
ot.add_class('particle_statistics',
name = 'my_heatmap',
class_name= 'oceantracker.particle_statistics.gridded_statistics2D.GriddedStats2D_timeBased',
# the below settings are optional
update_interval = 900, # time interval in sec, between doing particle statists counts
particle_property_list = ['a_pollutant'], # request a heat map for the decaying part. prop. added above
status_list =['moving'], # only count the particles which are moving
z_min =-2., # only count particles at locations above z=-2m
grid_size= [220, 221], # number of east and north cells in the heat map
grid_span = [20000, 10000],
release_group_centered_grids= True
)
# run oceantracker
case_info_file_name = ot.run()
helper: ---------------------------------------------------------------------- helper: Starting OceanTrackerhelper class, version 0.50.0030-2025-01-28 helper: Python version: 3.10.9 | packaged by conda-forge | (main, Jan 11 2023, 15:15:40) [MSC v.1916 64 bit (AMD64)] helper: ---------------------------------------------------------------------- helper: OceanTracker version 0.50.0030-2025-01-28 starting setup helper "main.py": helper: >>> Warning: Deleted contents of existing output dir helper: Output is in dir "f:H_Local_driveParticleTrackingoceantrackertutorials_how_tooutputheat_map_example" helper: hint: see for copies of screen output and user supplied parameters, plus all other output helper: >>> Note: to help with debugging, parameters as given by user are in "user_given_params.json" helper: >>> Warning: Numba has already been imported, some numba options may not be used (ignore SVML warning) helper: hint: Ensure any code using Numba is imported after Oceantracker is run, eg Oceantrackers "load_output_files.py" and "read_ncdf_output_files.py" helper: ---------------------------------------------------------------------- helper: Numba setup: applied settings, max threads = 32, physical cores = 32 helper: hint: cache code = False, fastmath= False helper: ---------------------------------------------------------------------- helper: - Built OceanTracker package tree, 0.016 sec helper: - Built OceanTracker sort name map, 0.000 sec helper: - Done package set up to setup ClassImporter, 0.016 sec setup: ---------------------------------------------------------------------- setup: OceanTracker version 0.50.0030-2025-01-28 setup: Starting user param. runner: "heat_map_example" at 2025-02-20T13:09:26.611670 setup: ---------------------------------------------------------------------- setup: - Start field group manager and readers setup setup: - Found input dir "../demos/demo_hindcast/schsim3D" setup: - Detected reader class_name = "oceantracker.reader.SCHISM_reader.SCHISMreader" setup: Hydro-model is "3D", type "SCHISMreader" setup: hint: Files found in dir and sub-dirs of "../demos/demo_hindcast/schsim3D" setup: Geographic coords = "False" setup: Hindcast start: 2017-01-01T00:30:00 end: 2017-01-01T23:30:00 setup: time step = 0 days 1 hrs 0 min 0 sec, number of time steps= 24 setup: grid bounding box = [1589789.000 5479437.000] to [1603398.000 5501640.000] setup: - Starting grid setup setup: - built node to triangles map, 0.000 sec setup: - built triangle adjacency matrix, 0.000 sec setup: - found boundary triangles, 0.000 sec setup: - built domain and island outlines, 0.652 sec setup: - calculated triangle areas, 0.000 sec setup: - Finished grid setup setup: - built barycentric-transform matrix, 0.000 sec setup: - Finished field group manager and readers setup, 0.764 sec setup: - Added release groups and found run start and end times, 0.002 sec setup: - Done initial setup of all classes, 0.015 sec setup: ---------------------------------------------------------------------- setup: - Starting heat_map_example, duration: 0 days 23 hrs 0 min 0 sec setup: From 2017-01-01T00:30:00 to 2017-01-01T23:30:00 setup: - Reading 24 time steps, for hindcast time steps 00:23 into ring buffer offsets 000:023 setup: - read 24 time steps in 0.1 sec, from ../demos/demo_hindcast/schsim3D setup: ---------------------------------------------------------------------- setup: - Starting time stepping: 2017-01-01T00:30:00 to 2017-01-01T23:30:00 , duration 0 days 23 hrs 0 min 0 sec S: 0000: 00%:H0000b00-01 Day +00 00:00 2017-01-01 00:30:00: Rel:1,000: Active:1,000 Move:1,000 Bottom: 0 Strand:0 Dead: 0 Out: 0 Buffer: 1% step time = 5.0 ms S: 0006: 04%:H0001b01-02 Day +00 01:00 2017-01-01 01:30:00: Rel:4,000: Active:4,000 Move:4,000 Bottom: 0 Strand:0 Dead: 0 Out: 0 Buffer: 5% step time = 3.3 ms S: 0012: 09%:H0002b02-03 Day +00 02:00 2017-01-01 02:30:00: Rel:7,000: Active:7,000 Move:7,000 Bottom: 0 Strand:0 Dead: 0 Out: 0 Buffer:10% step time = 2.7 ms S: 0018: 13%:H0003b03-04 Day +00 03:00 2017-01-01 03:30:00: Rel:10,000: Active:10,000 Move:10,000 Bottom: 0 Strand:0 Dead: 0 Out: 0 Buffer:14% step time = 2.6 ms S: 0024: 17%:H0004b04-05 Day +00 04:00 2017-01-01 04:30:00: Rel:13,000: Active:13,000 Move:12,999 Bottom: 1 Strand:0 Dead: 0 Out: 0 Buffer:18% step time = 2.7 ms S: 0030: 22%:H0005b05-06 Day +00 05:00 2017-01-01 05:30:00: Rel:16,000: Active:16,000 Move:15,998 Bottom: 2 Strand:0 Dead: 0 Out: 0 Buffer:22% step time = 2.9 ms S: 0036: 26%:H0006b06-07 Day +00 06:00 2017-01-01 06:30:00: Rel:19,000: Active:19,000 Move:18,998 Bottom: 2 Strand:0 Dead: 0 Out: 0 Buffer:27% step time = 2.8 ms S: 0042: 30%:H0007b07-08 Day +00 07:00 2017-01-01 07:30:00: Rel:22,000: Active:22,000 Move:21,993 Bottom: 7 Strand:0 Dead: 0 Out: 0 Buffer:31% step time = 3.1 ms S: 0048: 35%:H0008b08-09 Day +00 08:00 2017-01-01 08:30:00: Rel:25,000: Active:25,000 Move:24,984 Bottom: 16 Strand:0 Dead: 0 Out: 0 Buffer:35% step time = 3.7 ms S: 0054: 39%:H0009b09-10 Day +00 09:00 2017-01-01 09:30:00: Rel:28,000: Active:28,000 Move:27,977 Bottom: 23 Strand:0 Dead: 0 Out: 0 Buffer:40% step time = 3.9 ms S: 0060: 43%:H0010b10-11 Day +00 10:00 2017-01-01 10:30:00: Rel:31,000: Active:31,000 Move:30,973 Bottom: 27 Strand:0 Dead: 0 Out: 0 Buffer:44% step time = 4.1 ms S: 0066: 48%:H0011b11-12 Day +00 11:00 2017-01-01 11:30:00: Rel:34,000: Active:34,000 Move:33,981 Bottom: 19 Strand:0 Dead: 0 Out: 0 Buffer:48% step time = 3.2 ms S: 0072: 52%:H0012b12-13 Day +00 12:00 2017-01-01 12:30:00: Rel:37,000: Active:37,000 Move:36,985 Bottom: 15 Strand:0 Dead: 0 Out: 0 Buffer:52% step time = 4.2 ms S: 0078: 57%:H0012b12-13 Day +00 13:00 2017-01-01 13:30:00: Rel:40,000: Active:40,000 Move:39,989 Bottom: 11 Strand:0 Dead: 0 Out: 0 Buffer:57% step time = 4.5 ms S: 0084: 61%:H0014b14-15 Day +00 14:00 2017-01-01 14:30:00: Rel:43,000: Active:43,000 Move:42,994 Bottom: 6 Strand:0 Dead: 0 Out: 0 Buffer:61% step time = 3.5 ms S: 0090: 65%:H0015b15-16 Day +00 15:00 2017-01-01 15:30:00: Rel:46,000: Active:46,000 Move:45,967 Bottom: 7 Strand:26 Dead: 0 Out: 0 Buffer:65% step time = 3.3 ms S: 0096: 70%:H0016b16-17 Day +00 16:00 2017-01-01 16:30:00: Rel:49,000: Active:49,000 Move:48,965 Bottom: 9 Strand:26 Dead: 0 Out: 0 Buffer:70% step time = 3.5 ms S: 0102: 74%:H0017b17-18 Day +00 17:00 2017-01-01 17:30:00: Rel:52,000: Active:52,000 Move:51,970 Bottom: 4 Strand:26 Dead: 0 Out: 0 Buffer:74% step time = 3.4 ms S: 0108: 78%:H0018b18-19 Day +00 18:00 2017-01-01 18:30:00: Rel:55,000: Active:55,000 Move:54,970 Bottom: 4 Strand:26 Dead: 0 Out: 0 Buffer:78% step time = 3.8 ms S: 0114: 83%:H0019b19-20 Day +00 19:00 2017-01-01 19:30:00: Rel:58,000: Active:58,000 Move:57,969 Bottom: 5 Strand:26 Dead: 0 Out: 0 Buffer:82% step time = 3.3 ms S: 0120: 87%:H0020b20-21 Day +00 20:00 2017-01-01 20:30:00: Rel:61,000: Active:61,000 Move:60,967 Bottom: 7 Strand:26 Dead: 0 Out: 0 Buffer:87% step time = 3.6 ms S: 0126: 91%:H0021b21-22 Day +00 21:00 2017-01-01 21:30:00: Rel:64,000: Active:64,000 Move:63,980 Bottom: 20 Strand:0 Dead: 0 Out: 0 Buffer:91% step time = 3.9 ms S: 0132: 96%:H0022b22-23 Day +00 22:00 2017-01-01 22:30:00: Rel:67,000: Active:67,000 Move:66,985 Bottom: 15 Strand:0 Dead: 0 Out: 0 Buffer:95% step time = 3.9 ms S: 0138: 100%:H0023b23-00 Day +00 23:00 2017-01-01 23:30:00: Rel:69,000: Active:69,000 Move:68,993 Bottom: 7 Strand:0 Dead: 0 Out: 0 Buffer:98% step time = 6.8 ms end: ---------------------------------------------------------------------- end: >>> Warning: Deleted contents of existing output dir end: end: >>> Warning: Numba has already been imported, some numba options may not be used (ignore SVML warning) end: hint: Ensure any code using Numba is imported after Oceantracker is run, eg Oceantrackers "load_output_files.py" and "read_ncdf_output_files.py" end: end: ---------------------------------------------------------------------- end: Error counts - 0 errors, 2 warnings, 1 notes, check above end: end: - Finished "heat_map_example" started: 22859.6871497, ended: 2025-02-20 13:09:31.321585 end: Computational time =0:00:04.726820 end: Output in f:H_Local_driveParticleTrackingoceantrackertutorials_how_tooutputheat_map_example end: end: --- Finished Oceantracker run ---------------------------------------- end:
Read and plot heat maps¶
The statistics output from the above run is in file output:raw-latex:heat_map_example:raw-latex:heat_map_example_stats_gridded_time_my_heatmap.nc
This netcdf file can be read and organized as a python dictionary by directly with read_ncdf_output_files.read_stats_file.
To plot use, load_output_files.load_stats_data, which also loads grid etc for plotting
# read stats files
from read_oceantracker.python import read_ncdf_output_files, load_output_files
from plot_oceantracker import plot_statistics
from IPython.display import HTML
from os import path
from oceantracker.util import json_util
# basic read of net cdf, first get file name from case_info.json
case_info = json_util.read_JSON(case_info_file_name)
stats_file = path.join(case_info['output_files']['run_output_dir'], case_info['output_files']['particle_statistics']['my_heatmap'])
raw_stats = read_ncdf_output_files.read_stats_file(stats_file)
print('raw_stats', raw_stats.keys())
# better, load netcdf plus grid and other data useful in plotting
# uses case_info name returned from run above
stats_data = load_output_files.load_stats_data(case_info_file_name,'my_heatmap')
print('stats',stats_data.keys())
# use stats_data variable to plot heat map at last time step, by default plots var= "count"
ax= [1591000, 1601500, 5478500, 5491000]
anim= plot_statistics.animate_heat_map(stats_data, release_group='my_release_point', axis_lims=ax,
heading='Particle count heatmap built on the fly, no tracks recorded', fps=1)
HTML(anim.to_html5_video())# this is slow to build!
# animate the pollutant
anim= plot_statistics.animate_heat_map(stats_data, var='a_pollutant',release_group= 'my_release_point', axis_lims=ax,
heading='Decaying particle property , a_pollutant built on the fly, no tracks recorded', fps=1)
# this line only used in note books, in python scripts use show = True above and set moive file name
# this is slow to build!
HTML(anim.to_html5_video())# this is slow to build!
# static heat map
plot_statistics.plot_heat_map(stats_data, var='a_pollutant',release_group= 'my_release_point', axis_lims=ax, heading='a_pollutant at last time step depth built on the fly, no tracks recorded')
raw_stats dict_keys(['total_num_particles_released', 'particle_status_values_counted', 'dimensions', 'limits', 'variable_attributes', 'time', 'y', 'count', 'num_released_total', 'grid_cell_area', 'number_released_each_release_group', 'sum_a_pollutant', 'count_all_particles', 'x', 'global_attributes', 'time_var', 'date', 'stats_type', 'connectivity_matrix', 'a_pollutant'])
stats dict_keys(['total_num_particles_released', 'particle_status_values_counted', 'dimensions', 'limits', 'variable_attributes', 'time', 'y', 'count', 'num_released_total', 'grid_cell_area', 'number_released_each_release_group', 'sum_a_pollutant', 'count_all_particles', 'x', 'global_attributes', 'time_var', 'date', 'stats_type', 'connectivity_matrix', 'a_pollutant', 'particle_status_flags', 'particle_release_groups', 'grid'])
animate_heat_map> colour axis limits [0, 1031] [0, 1031]
animate_heat_map> colour axis limits [1.026187963170189e-07, 1000.0] [1.026187963170189e-07, 1000.0]
Polygon example¶
add polygon stats example with plotting¶
# Polygon Statistics example.py run using dictionary of parameters
#------------------------------------------------
# make instance of oceantracker to use to set parameters using code, then run
ot = OceanTracker()
# ot.settings method use to set basic settings
ot.settings(output_file_base='heat_map_example', # name used as base for output files
root_output_dir='output', # output is put in dir 'root_output_dir'\\'output_file_base'
time_step= 600., # 10 min time step as seconds
write_tracks = False # particle tracks not needed for on fly
)
# ot.set_class, sets parameters for a named class
ot.add_class('reader',input_dir= '../demos/demo_hindcast/schsim3D', # folder to search for hindcast files, sub-dirs will, by default, also be searched
file_mask= 'demo_hindcast_schisim3D*.nc') # hindcast file mask
# add one release locations
ot.add_class('release_groups',
name='my_release_point', # optional name used to refere to group in plotting
points= [ [1599000, 5486200]], # ust be 1 by N list pairs of release locations
release_interval= 900, # seconds between releasing particles
pulse_size= 1000, # number of particles released each release_interval
)
# add a decaying particle property
# add and Age decay particle property, with exponential decay based on age, with time scale 1 hour
ot.add_class('particle_properties', # add a new property to particle_properties role
name ='a_pollutant', # must have a user given name to
class_name='oceantracker.particle_properties.age_decay.AgeDecay', # class_role is resuspension
initial_value= 1000,
decay_time_scale = 3600.) # time scale of age decay ie decays initial_value* exp(-age/decay_time_scale)
# add a gridded particle statistic
ot.add_class('particle_statistics',
name='my_polygon',
class_name= 'PolygonStats2D_timeBased',
polygon_list = [
dict(points= [ [1597682.1237, 5489972.7479],# list of one or more polygons
[1598604.1667, 5490275.5488],
[1598886.4247, 5489464.0424],
[1597917.3387, 5489000],
[1597300, 5489000], [1597682.1237, 5489972.7479]]
)]
)
# run oceantracker
poly_case_info_file_name = ot.run()
helper: ---------------------------------------------------------------------- helper: Starting OceanTrackerhelper class, version 0.50.0030-2025-01-28 helper: Python version: 3.10.9 | packaged by conda-forge | (main, Jan 11 2023, 15:15:40) [MSC v.1916 64 bit (AMD64)] helper: ---------------------------------------------------------------------- helper: OceanTracker version 0.50.0030-2025-01-28 starting setup helper "main.py": helper: >>> Warning: Deleted contents of existing output dir helper: Output is in dir "f:H_Local_driveParticleTrackingoceantrackertutorials_how_tooutputheat_map_example" helper: hint: see for copies of screen output and user supplied parameters, plus all other output helper: >>> Note: to help with debugging, parameters as given by user are in "user_given_params.json" helper: >>> Warning: Numba has already been imported, some numba options may not be used (ignore SVML warning) helper: hint: Ensure any code using Numba is imported after Oceantracker is run, eg Oceantrackers "load_output_files.py" and "read_ncdf_output_files.py" helper: ---------------------------------------------------------------------- helper: Numba setup: applied settings, max threads = 32, physical cores = 32 helper: hint: cache code = False, fastmath= False helper: ---------------------------------------------------------------------- helper: - Built OceanTracker package tree, 0.016 sec helper: - Built OceanTracker sort name map, 0.000 sec helper: - Done package set up to setup ClassImporter, 0.016 sec setup: ---------------------------------------------------------------------- setup: OceanTracker version 0.50.0030-2025-01-28 setup: Starting user param. runner: "heat_map_example" at 2025-02-20T13:09:19.758302 setup: ---------------------------------------------------------------------- setup: - Start field group manager and readers setup setup: - Found input dir "../demos/demo_hindcast/schsim3D" setup: - Detected reader class_name = "oceantracker.reader.SCHISM_reader.SCHISMreader" setup: Hydro-model is "3D", type "SCHISMreader" setup: hint: Files found in dir and sub-dirs of "../demos/demo_hindcast/schsim3D" setup: Geographic coords = "False" setup: Hindcast start: 2017-01-01T00:30:00 end: 2017-01-01T23:30:00 setup: time step = 0 days 1 hrs 0 min 0 sec, number of time steps= 24 setup: grid bounding box = [1589789.000 5479437.000] to [1603398.000 5501640.000] setup: - Starting grid setup setup: - built node to triangles map, 0.000 sec setup: - built triangle adjacency matrix, 0.000 sec setup: - found boundary triangles, 0.000 sec setup: - built domain and island outlines, 0.652 sec setup: - calculated triangle areas, 0.000 sec setup: - Finished grid setup setup: - built barycentric-transform matrix, 0.000 sec setup: - Finished field group manager and readers setup, 0.777 sec setup: - Added release groups and found run start and end times, 0.002 sec setup: - Done initial setup of all classes, 1.209 sec setup: ---------------------------------------------------------------------- setup: - Starting heat_map_example, duration: 0 days 23 hrs 0 min 0 sec setup: From 2017-01-01T00:30:00 to 2017-01-01T23:30:00 setup: - Reading 24 time steps, for hindcast time steps 00:23 into ring buffer offsets 000:023 setup: - read 24 time steps in 0.1 sec, from ../demos/demo_hindcast/schsim3D setup: ---------------------------------------------------------------------- setup: - Starting time stepping: 2017-01-01T00:30:00 to 2017-01-01T23:30:00 , duration 0 days 23 hrs 0 min 0 sec S: 0000: 00%:H0000b00-01 Day +00 00:00 2017-01-01 00:30:00: Rel:1,000: Active:1,000 Move:1,000 Bottom: 0 Strand:0 Dead: 0 Out: 0 Buffer: 1% step time = 836.3 ms S: 0006: 04%:H0001b01-02 Day +00 01:00 2017-01-01 01:30:00: Rel:4,000: Active:4,000 Move:4,000 Bottom: 0 Strand:0 Dead: 0 Out: 0 Buffer: 5% step time = 2.9 ms S: 0012: 09%:H0002b02-03 Day +00 02:00 2017-01-01 02:30:00: Rel:7,000: Active:7,000 Move:7,000 Bottom: 0 Strand:0 Dead: 0 Out: 0 Buffer:10% step time = 2.3 ms S: 0018: 13%:H0003b03-04 Day +00 03:00 2017-01-01 03:30:00: Rel:10,000: Active:10,000 Move:9,999 Bottom: 1 Strand:0 Dead: 0 Out: 0 Buffer:14% step time = 2.3 ms S: 0024: 17%:H0004b04-05 Day +00 04:00 2017-01-01 04:30:00: Rel:13,000: Active:13,000 Move:12,999 Bottom: 1 Strand:0 Dead: 0 Out: 0 Buffer:18% step time = 2.4 ms S: 0030: 22%:H0005b05-06 Day +00 05:00 2017-01-01 05:30:00: Rel:16,000: Active:16,000 Move:15,999 Bottom: 1 Strand:0 Dead: 0 Out: 0 Buffer:22% step time = 2.5 ms S: 0036: 26%:H0006b06-07 Day +00 06:00 2017-01-01 06:30:00: Rel:19,000: Active:19,000 Move:18,999 Bottom: 1 Strand:0 Dead: 0 Out: 0 Buffer:27% step time = 2.5 ms S: 0042: 30%:H0007b07-08 Day +00 07:00 2017-01-01 07:30:00: Rel:22,000: Active:22,000 Move:21,990 Bottom: 10 Strand:0 Dead: 0 Out: 0 Buffer:31% step time = 2.6 ms S: 0048: 35%:H0008b08-09 Day +00 08:00 2017-01-01 08:30:00: Rel:25,000: Active:25,000 Move:24,978 Bottom: 22 Strand:0 Dead: 0 Out: 0 Buffer:35% step time = 2.6 ms S: 0054: 39%:H0009b09-10 Day +00 09:00 2017-01-01 09:30:00: Rel:28,000: Active:28,000 Move:27,975 Bottom: 25 Strand:0 Dead: 0 Out: 0 Buffer:40% step time = 3.0 ms S: 0060: 43%:H0010b10-11 Day +00 10:00 2017-01-01 10:30:00: Rel:31,000: Active:31,000 Move:30,978 Bottom: 22 Strand:0 Dead: 0 Out: 0 Buffer:44% step time = 2.8 ms S: 0066: 48%:H0011b11-12 Day +00 11:00 2017-01-01 11:30:00: Rel:34,000: Active:34,000 Move:33,975 Bottom: 25 Strand:0 Dead: 0 Out: 0 Buffer:48% step time = 2.9 ms S: 0072: 52%:H0012b12-13 Day +00 12:00 2017-01-01 12:30:00: Rel:37,000: Active:37,000 Move:36,983 Bottom: 17 Strand:0 Dead: 0 Out: 0 Buffer:52% step time = 4.1 ms S: 0078: 57%:H0012b12-13 Day +00 13:00 2017-01-01 13:30:00: Rel:40,000: Active:40,000 Move:39,993 Bottom: 7 Strand:0 Dead: 0 Out: 0 Buffer:57% step time = 3.0 ms S: 0084: 61%:H0014b14-15 Day +00 14:00 2017-01-01 14:30:00: Rel:43,000: Active:43,000 Move:42,991 Bottom: 8 Strand:1 Dead: 0 Out: 0 Buffer:61% step time = 2.9 ms S: 0090: 65%:H0015b15-16 Day +00 15:00 2017-01-01 15:30:00: Rel:46,000: Active:46,000 Move:45,968 Bottom: 12 Strand:20 Dead: 0 Out: 0 Buffer:65% step time = 3.0 ms S: 0096: 70%:H0016b16-17 Day +00 16:00 2017-01-01 16:30:00: Rel:49,000: Active:49,000 Move:48,975 Bottom: 5 Strand:20 Dead: 0 Out: 0 Buffer:70% step time = 3.1 ms S: 0102: 74%:H0017b17-18 Day +00 17:00 2017-01-01 17:30:00: Rel:52,000: Active:52,000 Move:51,978 Bottom: 2 Strand:20 Dead: 0 Out: 0 Buffer:74% step time = 3.1 ms S: 0108: 78%:H0018b18-19 Day +00 18:00 2017-01-01 18:30:00: Rel:55,000: Active:55,000 Move:54,977 Bottom: 3 Strand:20 Dead: 0 Out: 0 Buffer:78% step time = 2.9 ms S: 0114: 83%:H0019b19-20 Day +00 19:00 2017-01-01 19:30:00: Rel:58,000: Active:58,000 Move:57,978 Bottom: 2 Strand:20 Dead: 0 Out: 0 Buffer:82% step time = 4.2 ms S: 0120: 87%:H0020b20-21 Day +00 20:00 2017-01-01 20:30:00: Rel:61,000: Active:61,000 Move:60,970 Bottom: 10 Strand:20 Dead: 0 Out: 0 Buffer:87% step time = 3.1 ms S: 0126: 91%:H0021b21-22 Day +00 21:00 2017-01-01 21:30:00: Rel:64,000: Active:64,000 Move:63,981 Bottom: 19 Strand:0 Dead: 0 Out: 0 Buffer:91% step time = 3.2 ms S: 0132: 96%:H0022b22-23 Day +00 22:00 2017-01-01 22:30:00: Rel:67,000: Active:67,000 Move:66,991 Bottom: 9 Strand:0 Dead: 0 Out: 0 Buffer:95% step time = 3.2 ms S: 0138: 100%:H0023b23-00 Day +00 23:00 2017-01-01 23:30:00: Rel:69,000: Active:69,000 Move:68,990 Bottom: 10 Strand:0 Dead: 0 Out: 0 Buffer:98% step time = 5.1 ms end: ---------------------------------------------------------------------- end: >>> Warning: Deleted contents of existing output dir end: end: >>> Warning: Numba has already been imported, some numba options may not be used (ignore SVML warning) end: hint: Ensure any code using Numba is imported after Oceantracker is run, eg Oceantrackers "load_output_files.py" and "read_ncdf_output_files.py" end: end: ---------------------------------------------------------------------- end: Error counts - 0 errors, 2 warnings, 1 notes, check above end: end: - Finished "heat_map_example" started: 22852.8185402, ended: 2025-02-20 13:09:26.410383 end: Computational time =0:00:06.679164 end: Output in f:H_Local_driveParticleTrackingoceantrackertutorials_how_tooutputheat_map_example end: end: --- Finished Oceantracker run ---------------------------------------- end:
Read polygon/connectivity statistics¶
#Read polygon stats and calculate connectivity matrix
from read_oceantracker.python import load_output_files
poly_stats_data = load_output_files.load_stats_data(poly_case_info_file_name,'my_polygon')
print('stats',poly_stats_data.keys())
import matplotlib.pyplot as plt
plt.plot(poly_stats_data['date'], poly_stats_data['connectivity_matrix'][:,0,0])
plt.title('Connectivity time series between release point and polygon')
#print(poly_stats_data['date'])
stats dict_keys(['total_num_particles_released', 'particle_status_values_counted', 'dimensions', 'limits', 'variable_attributes', 'time', 'count', 'num_released_total', 'number_released_each_release_group', 'count_all_particles', 'global_attributes', 'time_var', 'date', 'stats_type', 'polygon_list', 'connectivity_matrix', 'particle_status_flags', 'particle_release_groups', 'grid'])
Text(0.5, 1.0, 'Connectivity time series between release point and polygon')
Time verses Age statistics¶
Both gridded and polygon statistics come in two types, “time” and “age”.
“time” statistics are time series, or snapshots, of particle numbers and particle properties at a time interval given by “calculation_interval” parameter. Eg. gridded stats showing how the heat map of a source’s plume evolves over time.
“age” statistics are particle counts and properties binned by particle age. The result are age based histograms of counts or particle proprieties. This is useful to give numbers in each age band arriving at a given grid cell or polygon, from each release group. Eg. counting how many larvae are old enough to settle in a polygon or grid cell from each potential source location.