Transformation of results

[gallego2286]

Dear Authors.

First I want to thank you for this very useful library that you have created for the community. Thank you very much for your efforts.

The question I asked, since I don't see that someone has asked it before in this section, is aimed at the result transformation obtained from the identification. I am a beginner and a question has arisen with the handling of this example: https://py-eddy-tracker.readthedocs.io/en/latest/grid_identification.html in the end I have been able to save the generated files here:

from netCDF import Dataset

with Dataset(date.strftime("share/Anticyclonic_%Y%m%d.nc"), "w") as h: a.tonetcdf(h). with Dataset(date.strftime("share/Cyclonic%Y%m%d.nc"), "w") as h: c.to_netcdf(h)

But from there, I have not been able to transform those files from .nc format to for example vector (shp) format, could you please give me a hand how can I do it. Thank you very much for your help.

[AntSimi]

To create shapefile you could use python module "shapefile", there is no routine in py eddy tracker to create shapefile. You will need to iterate on effective_contour_longitude /effective_contour_latitude or/and speed_contour_longitude/speed_contour_latitude in order to create shape for each eddy.

[gallego2286]

To create shapefile you could use python module "shapefile", there is no routine in py eddy tracker to create shapefile. You will need to iterate on effective_contour_longitude /effective_contour_latitude or/and speed_contour_longitude/speed_contour_latitude in order to create shape for each eddy.

Thank you very much for your timely and quick response. Best regards.

[AntSimi]

If you wrote a method to do this you are welcome to share it here

[gallego2286]

Of course, I hope this can help someone else if they find themselves in the same situation as I did a couple of days ago. Thank you very much for your help again.

# %%
import os
from datetime import datetime
from matplotlib import pyplot as plt
from py_eddy_tracker.dataset.grid import RegularGridDataset
from py_eddy_tracker.observations.observation import EddiesObservations
from netCDF4 import Dataset
import geopandas as gpd
from shapely import box, LineString

# %%
#path of the example images
directory = r'c:\Users\user\Desktop\py-eddy-tracker-master\src\py_eddy_tracker\data'

#sample file name
file_example = f'nrt_global_allsat_phy_l4_20190223_20190226.nc'
complete_path = os.path.join(directory, file_example)

# %%
from datetime import datetime

h = RegularGridDataset(complete_path, "longitude", "latitude")
h.bessel_high_filter("adt", 700, order=1)
date = datetime(2019, 2, 23)
a, c = h.eddy_identification(
    "adt",
    "ugos",
    "vgos",  # Variables used for identification
    date,  # Date of identification
    0.002,  # step between two isolines of detection (m)
    pixel_limit=(5, 2000),  # Min and max pixel count for valid contour
    shape_error=55,  # Error max (%) between ratio of circle fit and contour
)

# %%
fig = plt.figure(figsize=(15, 7))
ax = fig.add_axes([0.03, 0.03, 0.94, 0.94])
ax.set_title("Eddies detected -- Cyclonic(red) and Anticyclonic(blue)")
ax.set_ylim(-75, 75)
ax.set_xlim(0, 360)
ax.set_aspect("equal")
a.display(ax, color="b", linewidth=0.5)
c.display(ax, color="r", linewidth=0.5)
ax.grid()
#fig.savefig("share/png/eddies.png")

# %%
#saving path for processed images
stored_directory = r'c:\Users\user\Desktop\process'

#name of the example file
anticyclo = f'anticyclo_20190223_20190226.nc'

cyclonic = f'cyclonic_20190223_20190226.nc'

anticyclo_path = os.path.join(stored_directory, anticyclo)
cyclonic_path = os.path.join(stored_directory, cyclonic)

# %%
#Saving of processed files
with Dataset(anticyclo_path, "w") as h: a.to_netcdf(h)
with Dataset(cyclonic_path, "w") as h: c.to_netcdf(h)

# %%
# Extraction of the coordinates of the vertices
# of the detected cyclonic eddies

# Load cyclonic and anticyclonic files
Anticyclonic = EddiesObservations.load_file(anticyclo_path)
Cyclonic = EddiesObservations.load_file(cyclonic_path)

#Here you can see that I only do it with anticyclones.
lat_anti = Anticyclonic['contour_lat_e']
lon_anti = Anticyclonic['contour_lon_e']

# %%
# Create a list of polylines from coordinates
poli_anti = [LineString(zip(lon, lat))
              for lat, lon in zip(lat_anti, lon_anti)]

# GeoDataFrame creation
gdf = gpd.GeoDataFrame({'geometry': poli_anti})

#Field creation to distinguish the anticyclones
gdf['eddies'] = 'anticyclo'

gdf.plot()