import numpy as np
import pandas as pd
from pytides2.tide import Tide
import xarray as xr

from dask.distributed import Client, LocalCluster
cluster = LocalCluster(
    n_workers=128,          # one worker per thread
    threads_per_worker=1,   # HDF5/xarray likes this
    memory_limit="2GB"      # or ~ (503/256) GiB
)
client = Client(cluster)
client

2025-08-25 06:05:39,725 - distributed.shuffle._scheduler_plugin - WARNING - Shuffle fb588d4ed6c43ea1a205da0bba08d190 initialized by task ('rechunk-merge-rechunk-transfer-e2334a18789ec7046a7b7ca5a411b693', 0, 0, 87, 0) executed on worker tcp://127.0.0.1:43613
2025-08-25 07:44:33,300 - distributed.shuffle._scheduler_plugin - WARNING - Shuffle fb588d4ed6c43ea1a205da0bba08d190 deactivated due to stimulus 'task-finished-1756100673.2888749'
2025-08-25 07:44:35,185 - distributed.nanny.memory - WARNING - Worker tcp://127.0.0.1:36075 (pid=17985) exceeded 95% memory budget. Restarting...
2025-08-25 07:44:35,332 - distributed.scheduler - WARNING - Removing worker 'tcp://127.0.0.1:36075' caused the cluster to lose already computed task(s), which will be recomputed elsewhere: {('analyze_block-adfc206b715e7091324cca25aa48e84f', 707, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 697, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 699, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 709, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 703, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 715, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 708, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 694, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 717, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 695, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 711, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 705, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 704, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 701, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 714, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 702, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 713, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 716, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 700, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 710, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 698, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 712, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 706, 0, 0), ('analyze_block-adfc206b715e7091324cca25aa48e84f', 696, 0, 0)} (stimulus_id='handle-worker-cleanup-1756100675.328182')
2025-08-25 07:44:35,542 - distributed.nanny - WARNING - Restarting worker
2025-08-25 07:44:37,505 - distributed.shuffle._scheduler_plugin - WARNING - Shuffle fb588d4ed6c43ea1a205da0bba08d190 initialized by task ('rechunk-merge-rechunk-transfer-e2334a18789ec7046a7b7ca5a411b693', 0, 0, 102, 0) executed on worker tcp://127.0.0.1:43613
2025-08-25 08:35:18,293 - distributed.shuffle._scheduler_plugin - WARNING - Shuffle fb588d4ed6c43ea1a205da0bba08d190 deactivated due to stimulus 'task-finished-1756103718.2661896'

def pytides_to_df(pytides_tide: Tide) -> pd.DataFrame:
    constituent_names = [c.name.upper() for c in pytides_tide.model["constituent"]]
    return pd.DataFrame(pytides_tide.model, index=constituent_names).drop(
        "constituent",
        axis=1,
    )

def pytide_get_coefs(ts: pd.Series, resample: int = None) -> dict:
    if resample is not None:
        ts = ts.resample(f"{resample}min").mean()
        ts = ts.shift(freq=f"{resample / 2}min")  # Center the resampled points
    ts = ts.dropna()
    return Tide.decompose(ts.values, ts.index.to_pydatetime())[0]

def reduce_coef_to_fes(df: pd.DataFrame, cnst: list, verbose: bool = False):
    res = pd.DataFrame(0.0, index=cnst, columns=df.columns)
    common_constituents = df.index.intersection(cnst)
    res.loc[common_constituents] = df.loc[common_constituents]

    not_in_fes_df = df[~df.index.isin(cnst)]
    not_in_fes = not_in_fes_df.index.tolist()
    not_in_fes_amps = not_in_fes_df["amplitude"].round(3).tolist()
    missing_fes = set(cnst) - set(df.index)

    if verbose:
        print(f"Constituents found but not in FES: {not_in_fes}")
        print(f"Their amplitudes: {not_in_fes_amps}")
        if missing_fes:
            print(
                f"FES constituents missing from analysis (set to 0): {sorted(missing_fes)}",
            )

    return res

from natsort import natsorted
import glob

files = natsorted(glob.glob("/project/home/p200764/schism_runs/validation/schism3d/run4/20220*/outputs/out2d_*.nc"))
files[-5:]

['/project/home/p200764/schism_runs/validation/schism3d/run4/202204/outputs/out2d_176.nc',
 '/project/home/p200764/schism_runs/validation/schism3d/run4/202204/outputs/out2d_177.nc',
 '/project/home/p200764/schism_runs/validation/schism3d/run4/202204/outputs/out2d_178.nc',
 '/project/home/p200764/schism_runs/validation/schism3d/run4/202204/outputs/out2d_179.nc',
 '/project/home/p200764/schism_runs/validation/schism3d/run4/202204/outputs/out2d_180.nc']

ds = xr.open_mfdataset(
    files,
    concat_dim="time",
    combine="nested",
    parallel=True
)
# ds = ds.chunk({"time": -1, "nSCHISM_hgrid_node": 5000})  # now safe for Dask
ds

<xarray.Dataset> Size: 1TB
Dimensions:                  (time: 2880, one: 1, nSCHISM_hgrid_node: 2960823,
                              nSCHISM_hgrid_face: 5702272,
                              nMaxSCHISM_hgrid_face_nodes: 4,
                              nSCHISM_hgrid_edge: 8664341, two: 2)
Coordinates:
  * time                     (time) datetime64[ns] 23kB 2021-12-30T01:00:00 ....
    SCHISM_hgrid_node_x      (nSCHISM_hgrid_node) float64 24MB dask.array<chunksize=(2960823,), meta=np.ndarray>
    SCHISM_hgrid_node_y      (nSCHISM_hgrid_node) float64 24MB dask.array<chunksize=(2960823,), meta=np.ndarray>
    SCHISM_hgrid_face_x      (nSCHISM_hgrid_face) float64 46MB dask.array<chunksize=(5702272,), meta=np.ndarray>
    SCHISM_hgrid_face_y      (nSCHISM_hgrid_face) float64 46MB dask.array<chunksize=(5702272,), meta=np.ndarray>
    SCHISM_hgrid_edge_x      (nSCHISM_hgrid_edge) float64 69MB dask.array<chunksize=(8664341,), meta=np.ndarray>
    SCHISM_hgrid_edge_y      (nSCHISM_hgrid_edge) float64 69MB dask.array<chunksize=(8664341,), meta=np.ndarray>
Dimensions without coordinates: one, nSCHISM_hgrid_node, nSCHISM_hgrid_face,
                                nMaxSCHISM_hgrid_face_nodes,
                                nSCHISM_hgrid_edge, two
Data variables:
    minimum_depth            (time, one) float64 23kB dask.array<chunksize=(4, 1), meta=np.ndarray>
    SCHISM_hgrid             (time, one) |S1 3kB dask.array<chunksize=(4, 1), meta=np.ndarray>
    crs                      (time, one) int32 12kB dask.array<chunksize=(4, 1), meta=np.ndarray>
    depth                    (time, nSCHISM_hgrid_node) float32 34GB dask.array<chunksize=(4, 2960823), meta=np.ndarray>
    bottom_index_node        (time, nSCHISM_hgrid_node) int32 34GB dask.array<chunksize=(4, 2960823), meta=np.ndarray>
    SCHISM_hgrid_face_nodes  (time, nSCHISM_hgrid_face, nMaxSCHISM_hgrid_face_nodes) float64 526GB dask.array<chunksize=(4, 5702272, 4), meta=np.ndarray>
    SCHISM_hgrid_edge_nodes  (time, nSCHISM_hgrid_edge, two) float64 399GB dask.array<chunksize=(4, 8664341, 2), meta=np.ndarray>
    dryFlagNode              (time, nSCHISM_hgrid_node) float32 34GB dask.array<chunksize=(1, 2960823), meta=np.ndarray>
    elevation                (time, nSCHISM_hgrid_node) float32 34GB dask.array<chunksize=(1, 2960823), meta=np.ndarray>
    dryFlagElement           (time, nSCHISM_hgrid_face) float32 66GB dask.array<chunksize=(1, 2851136), meta=np.ndarray>
    dryFlagSide              (time, nSCHISM_hgrid_edge) float32 100GB dask.array<chunksize=(1, 2888114), meta=np.ndarray>

array(['2021-12-30T01:00:00.000000000', '2021-12-30T02:00:00.000000000',
       '2021-12-30T03:00:00.000000000', ..., '2022-04-28T22:00:00.000000000',
       '2022-04-28T23:00:00.000000000', '2022-04-29T00:00:00.000000000'],
      shape=(2880,), dtype='datetime64[ns]')

PandasIndex(DatetimeIndex(['2021-12-30 01:00:00', '2021-12-30 02:00:00',
               '2021-12-30 03:00:00', '2021-12-30 04:00:00',
               '2021-12-30 05:00:00', '2021-12-30 06:00:00',
               '2021-12-30 07:00:00', '2021-12-30 08:00:00',
               '2021-12-30 09:00:00', '2021-12-30 10:00:00',
               ...
               '2022-04-28 15:00:00', '2022-04-28 16:00:00',
               '2022-04-28 17:00:00', '2022-04-28 18:00:00',
               '2022-04-28 19:00:00', '2022-04-28 20:00:00',
               '2022-04-28 21:00:00', '2022-04-28 22:00:00',
               '2022-04-28 23:00:00', '2022-04-29 00:00:00'],
              dtype='datetime64[ns]', name='time', length=2880, freq=None))

elev = ds.elevation.chunk({"time": -1, "nSCHISM_hgrid_node": 500})
elev

<xarray.DataArray 'elevation' (time: 2880, nSCHISM_hgrid_node: 1480411)> Size: 17GB
dask.array<rechunk-p2p, shape=(2880, 1480411), dtype=float32, chunksize=(2880, 500), chunktype=numpy.ndarray>
Coordinates:
  * time                 (time) datetime64[ns] 23kB 2021-12-30T01:00:00 ... 2...
    SCHISM_hgrid_node_x  (nSCHISM_hgrid_node) float64 12MB dask.array<chunksize=(500,), meta=np.ndarray>
    SCHISM_hgrid_node_y  (nSCHISM_hgrid_node) float64 12MB dask.array<chunksize=(500,), meta=np.ndarray>
Dimensions without coordinates: nSCHISM_hgrid_node
Attributes:
    i23d:          1
    location:      node
    grid_mapping:  crs
    mesh:          SCHISM_hgrid

array(['2021-12-30T01:00:00.000000000', '2021-12-30T02:00:00.000000000',
       '2021-12-30T03:00:00.000000000', ..., '2022-04-28T22:00:00.000000000',
       '2022-04-28T23:00:00.000000000', '2022-04-29T00:00:00.000000000'],
      shape=(2880,), dtype='datetime64[ns]')

PandasIndex(DatetimeIndex(['2021-12-30 01:00:00', '2021-12-30 02:00:00',
               '2021-12-30 03:00:00', '2021-12-30 04:00:00',
               '2021-12-30 05:00:00', '2021-12-30 06:00:00',
               '2021-12-30 07:00:00', '2021-12-30 08:00:00',
               '2021-12-30 09:00:00', '2021-12-30 10:00:00',
               ...
               '2022-04-28 15:00:00', '2022-04-28 16:00:00',
               '2022-04-28 17:00:00', '2022-04-28 18:00:00',
               '2022-04-28 19:00:00', '2022-04-28 20:00:00',
               '2022-04-28 21:00:00', '2022-04-28 22:00:00',
               '2022-04-28 23:00:00', '2022-04-29 00:00:00'],
              dtype='datetime64[ns]', name='time', length=2880, freq=None))

import dask.array as da
FULL = [
    "M2", "S2", "N2", "K2", "2N2", "L2", "T2", "R2", "NU2", "MU2", "EPS2", "LAMBDA2",  # Semi-diurnal (twice daily)
    "K1", "O1", "P1", "Q1", "J1", "S1",  # Diurnal (once daily)
    "MF", "MM", "MSF", "SA", "SSA", "MSQM", "MTM",  # Long period (fortnightly to annual)
    "M4", "MS4", "M6", "MN4", "N4", "S4", "M8", "M3", "MKS2",  # Short period (higher harmonics)
]

metrics = ["amplitude","phase"]

def analyze_node(ts_np: np.ndarray, time_index: pd.DatetimeIndex) -> np.ndarray:

    ts = pd.Series(ts_np, index=time_index, name="elev")
    df = pytides_to_df(pytide_get_coefs(ts, 60))
    df = reduce_coef_to_fes(df, cnst=FULL)
    # df = df.loc[constituents, metrics]  # enforce order
    return df.to_numpy()

def analyze_block(block: np.ndarray, time_block: np.ndarray) -> np.ndarray:
    time_index = pd.DatetimeIndex(time_block)
    return np.stack(
        [analyze_node(block[:, i], time_index) for i in range(block.shape[1])],
        axis=0
    )

nconst = len(FULL)
nmetrics = len(metrics)

results = da.map_blocks(
    analyze_block,
    elev.data,            # (Nt, Nnodes)
    elev["time"],
    dtype=float,
    drop_axis=0,          # drop time axis
    new_axis=[1, 2],      # add constituent, metric axes
    chunks=(elev.chunks[1], nconst, nmetrics)
)
results

coef_da = xr.DataArray(
    results,
    dims=("nSCHISM_hgrid_node", "constituent", "metric"),
    coords={
        "nSCHISM_hgrid_node": elev.nSCHISM_hgrid_node,
        "constituent": FULL,
        "metric": metrics,
        "lon": elev.SCHISM_hgrid_node_x,
        "lat": elev.SCHISM_hgrid_node_y,
    },
    name="tidal_coefs"
)
coef_da

<xarray.DataArray 'tidal_coefs' (nSCHISM_hgrid_node: 1480411, constituent: 34,
                                 metric: 2)> Size: 805MB
dask.array<analyze_block, shape=(1480411, 34, 2), dtype=float64, chunksize=(500, 34, 2), chunktype=numpy.ndarray>
Coordinates:
  * nSCHISM_hgrid_node  (nSCHISM_hgrid_node) int64 12MB 0 1 ... 1480409 1480410
  * constituent         (constituent) <U7 952B 'M2' 'S2' 'N2' ... 'M3' 'MKS2'
  * metric              (metric) <U9 72B 'amplitude' 'phase'
    lon                 (nSCHISM_hgrid_node) float64 12MB dask.array<chunksize=(500,), meta=np.ndarray>
    lat                 (nSCHISM_hgrid_node) float64 12MB dask.array<chunksize=(500,), meta=np.ndarray>

array([      0,       1,       2, ..., 1480408, 1480409, 1480410],
      shape=(1480411,))

array(['M2', 'S2', 'N2', 'K2', '2N2', 'L2', 'T2', 'R2', 'NU2', 'MU2', 'EPS2',
       'LAMBDA2', 'K1', 'O1', 'P1', 'Q1', 'J1', 'S1', 'MF', 'MM', 'MSF', 'SA',
       'SSA', 'MSQM', 'MTM', 'M4', 'MS4', 'M6', 'MN4', 'N4', 'S4', 'M8', 'M3',
       'MKS2'], dtype='<U7')

array(['amplitude', 'phase'], dtype='<U9')

PandasIndex(RangeIndex(start=0, stop=1480411, step=1, name='nSCHISM_hgrid_node'))

PandasIndex(Index(['M2', 'S2', 'N2', 'K2', '2N2', 'L2', 'T2', 'R2', 'NU2', 'MU2', 'EPS2',
       'LAMBDA2', 'K1', 'O1', 'P1', 'Q1', 'J1', 'S1', 'MF', 'MM', 'MSF', 'SA',
       'SSA', 'MSQM', 'MTM', 'M4', 'MS4', 'M6', 'MN4', 'N4', 'S4', 'M8', 'M3',
       'MKS2'],
      dtype='object', name='constituent'))

PandasIndex(Index(['amplitude', 'phase'], dtype='object', name='metric'))

from dask.diagnostics import ProgressBar
import warnings
warnings.filterwarnings("ignore")

with ProgressBar():
    coef_result = coef_da.compute()

2025-08-25 07:44:35,066 - distributed.worker.memory - WARNING - Worker is at 83% memory usage. Pausing worker.  Process memory: 1.56 GiB -- Worker memory limit: 1.86 GiB
2025-08-25 08:35:20,083 - distributed.worker.memory - WARNING - Worker is at 81% memory usage. Pausing worker.  Process memory: 1.51 GiB -- Worker memory limit: 1.86 GiB
2025-08-25 08:35:21,141 - distributed.worker.memory - WARNING - Worker is at 63% memory usage. Resuming worker. Process memory: 1.19 GiB -- Worker memory limit: 1.86 GiB

coef_ds = coef_result.to_dataset(dim="metric")

coef_ds.to_netcdf("run4_tides.nc")

import hvplot.xarray

m2 = coef_ds.sel(constituent = "M2")
m2

<xarray.Dataset> Size: 59MB
Dimensions:             (nSCHISM_hgrid_node: 1480411)
Coordinates:
  * nSCHISM_hgrid_node  (nSCHISM_hgrid_node) int64 12MB 0 1 ... 1480409 1480410
    constituent         <U7 28B 'M2'
    lon                 (nSCHISM_hgrid_node) float64 12MB 179.1 178.8 ... -65.44
    lat                 (nSCHISM_hgrid_node) float64 12MB 69.3 69.4 ... 85.8
Data variables:
    amplitude           (nSCHISM_hgrid_node) float64 12MB 0.1405 ... 0.04601
    phase               (nSCHISM_hgrid_node) float64 12MB 58.31 56.19 ... 94.66

array([      0,       1,       2, ..., 1480408, 1480409, 1480410],
      shape=(1480411,))

array('M2', dtype='<U7')

array([179.057511, 178.779783, 178.954564, ..., -65.003016, -65.189936,
       -65.438228], shape=(1480411,))

array([69.299593, 69.402505, 69.359604, ..., 85.814531, 85.813495,
       85.800741], shape=(1480411,))

array([0.14053109, 0.13756482, 0.1398321 , ..., 0.04597918, 0.04595259,
       0.04601121], shape=(1480411,))

array([58.3126187 , 56.19087237, 56.83799297, ..., 94.80090538,
       94.74824717, 94.65566572], shape=(1480411,))

PandasIndex(RangeIndex(start=0, stop=1480411, step=1, name='nSCHISM_hgrid_node'))

# m2.hvplot.scatter(x='lon', y="lat",c="amplitude" )

Dashboard: http://127.0.0.1:8787/status	Workers: 128
Total threads: 128	Total memory: 238.42 GiB
Status: running	Using processes: True

Comm: tcp://127.0.0.1:39433	Workers: 0
Dashboard: http://127.0.0.1:8787/status	Total threads: 0
Started: Just now	Total memory: 0 B

Comm: tcp://127.0.0.1:37893	Total threads: 1
Dashboard: http://127.0.0.1:34979/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:38713
Local directory: /tmp/dask-scratch-space/worker-synalya4

Comm: tcp://127.0.0.1:40813	Total threads: 1
Dashboard: http://127.0.0.1:40233/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:46867
Local directory: /tmp/dask-scratch-space/worker-2_6nfnbn

Comm: tcp://127.0.0.1:40405	Total threads: 1
Dashboard: http://127.0.0.1:46427/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:44921
Local directory: /tmp/dask-scratch-space/worker-aqyymz3h

Connection method: Cluster object	Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:8787/status

Comm: tcp://127.0.0.1:45775	Total threads: 1
Dashboard: http://127.0.0.1:46393/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:36303
Local directory: /tmp/dask-scratch-space/worker-ikynb99o

Comm: tcp://127.0.0.1:44997	Total threads: 1
Dashboard: http://127.0.0.1:36633/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:45177
Local directory: /tmp/dask-scratch-space/worker-j28yzdsw

Comm: tcp://127.0.0.1:44291	Total threads: 1
Dashboard: http://127.0.0.1:44527/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:41515
Local directory: /tmp/dask-scratch-space/worker-hpbwogm0

Comm: tcp://127.0.0.1:38337	Total threads: 1
Dashboard: http://127.0.0.1:32851/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:34427
Local directory: /tmp/dask-scratch-space/worker-fxhuhz8l

Comm: tcp://127.0.0.1:42065	Total threads: 1
Dashboard: http://127.0.0.1:41413/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:44863
Local directory: /tmp/dask-scratch-space/worker-58m3s63h

Comm: tcp://127.0.0.1:46729	Total threads: 1
Dashboard: http://127.0.0.1:45719/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:44405
Local directory: /tmp/dask-scratch-space/worker-gj9z4rjr

Comm: tcp://127.0.0.1:37787	Total threads: 1
Dashboard: http://127.0.0.1:36051/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:33565
Local directory: /tmp/dask-scratch-space/worker-50kkmz1k

Comm: tcp://127.0.0.1:33265	Total threads: 1
Dashboard: http://127.0.0.1:36931/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:36007
Local directory: /tmp/dask-scratch-space/worker-z0od6n2z

Comm: tcp://127.0.0.1:44733	Total threads: 1
Dashboard: http://127.0.0.1:45237/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:44869
Local directory: /tmp/dask-scratch-space/worker-8p_h5s4w

Comm: tcp://127.0.0.1:36075	Total threads: 1
Dashboard: http://127.0.0.1:40369/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:38755
Local directory: /tmp/dask-scratch-space/worker-y79nikiq

Comm: tcp://127.0.0.1:38633	Total threads: 1
Dashboard: http://127.0.0.1:43199/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:34299
Local directory: /tmp/dask-scratch-space/worker-ubjza4bj

Comm: tcp://127.0.0.1:45729	Total threads: 1
Dashboard: http://127.0.0.1:35801/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:33807
Local directory: /tmp/dask-scratch-space/worker-ylgh_2j7

Comm: tcp://127.0.0.1:33929	Total threads: 1
Dashboard: http://127.0.0.1:42907/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:44111
Local directory: /tmp/dask-scratch-space/worker-2ke2a4su

Comm: tcp://127.0.0.1:40365	Total threads: 1
Dashboard: http://127.0.0.1:37263/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:46377
Local directory: /tmp/dask-scratch-space/worker-ep73_h13

Comm: tcp://127.0.0.1:36233	Total threads: 1
Dashboard: http://127.0.0.1:43411/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:35929
Local directory: /tmp/dask-scratch-space/worker-z4wnv3y7

Comm: tcp://127.0.0.1:44949	Total threads: 1
Dashboard: http://127.0.0.1:41425/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:38909
Local directory: /tmp/dask-scratch-space/worker-obksyfs1

Comm: tcp://127.0.0.1:45947	Total threads: 1
Dashboard: http://127.0.0.1:33091/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:41329
Local directory: /tmp/dask-scratch-space/worker-sovgnkic

Comm: tcp://127.0.0.1:33407	Total threads: 1
Dashboard: http://127.0.0.1:38645/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:33239
Local directory: /tmp/dask-scratch-space/worker-c68t8vxh

Comm: tcp://127.0.0.1:40099	Total threads: 1
Dashboard: http://127.0.0.1:40791/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:44439
Local directory: /tmp/dask-scratch-space/worker-d0kth6ap

Client

Cluster Info

LocalCluster

Scheduler Info

Scheduler

Workers

Worker: 0

Worker: 1

Worker: 2

Worker: 3

Worker: 4

Worker: 5

Worker: 6

Worker: 7

Worker: 8

Worker: 9

Worker: 10

Worker: 11

Worker: 12

Worker: 13

Worker: 14

Worker: 15

Worker: 16

Worker: 17

Worker: 18

Worker: 19

Worker: 20

Worker: 21

Worker: 22

Worker: 23

Worker: 24

Worker: 25

Worker: 26

Worker: 27

Worker: 28

Worker: 29

Worker: 30

Worker: 31

Worker: 32

Worker: 33

Worker: 34

Worker: 35

Worker: 36

Worker: 37

Worker: 38

Worker: 39

Worker: 40

Worker: 41

Worker: 42

Worker: 43

Worker: 44

Worker: 45

Worker: 46

Worker: 47

Worker: 48

Worker: 49

Worker: 50

Worker: 51

Worker: 52

Worker: 53

Worker: 54

Worker: 55

Worker: 56

Worker: 57

Worker: 58

Worker: 59

Worker: 60

Worker: 61

Worker: 62

Worker: 63

Worker: 64

Worker: 65

Worker: 66

Worker: 67

Worker: 68

Worker: 69

Worker: 70

Worker: 71

Worker: 72

Worker: 73

Comm: tcp://127.0.0.1:38901	Total threads: 1
Dashboard: http://127.0.0.1:34853/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:41059
Local directory: /tmp/dask-scratch-space/worker-rq0l9csa

Comm: tcp://127.0.0.1:41387	Total threads: 1
Dashboard: http://127.0.0.1:38535/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:43883
Local directory: /tmp/dask-scratch-space/worker-cfa_emtj

Comm: tcp://127.0.0.1:39491	Total threads: 1
Dashboard: http://127.0.0.1:38783/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:34419
Local directory: /tmp/dask-scratch-space/worker-mzqj57b4

Comm: tcp://127.0.0.1:35575	Total threads: 1
Dashboard: http://127.0.0.1:46305/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:40113
Local directory: /tmp/dask-scratch-space/worker-m3uszr6j

Comm: tcp://127.0.0.1:45761	Total threads: 1
Dashboard: http://127.0.0.1:40353/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:40889
Local directory: /tmp/dask-scratch-space/worker-p93vhzwx

Comm: tcp://127.0.0.1:41789	Total threads: 1
Dashboard: http://127.0.0.1:45143/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:37977
Local directory: /tmp/dask-scratch-space/worker-o6i_uq27

Comm: tcp://127.0.0.1:41877	Total threads: 1
Dashboard: http://127.0.0.1:43861/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:36485
Local directory: /tmp/dask-scratch-space/worker-z_doawy_

Comm: tcp://127.0.0.1:34123	Total threads: 1
Dashboard: http://127.0.0.1:40771/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:43343
Local directory: /tmp/dask-scratch-space/worker-exjg7y8t

Comm: tcp://127.0.0.1:39781	Total threads: 1
Dashboard: http://127.0.0.1:42809/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:39685
Local directory: /tmp/dask-scratch-space/worker-_vp5enb6

Comm: tcp://127.0.0.1:33321	Total threads: 1
Dashboard: http://127.0.0.1:39025/status	Memory: 1.86 GiB
Nanny: tcp://127.0.0.1:41765
Local directory: /tmp/dask-scratch-space/worker-fug09z0h