format with black; use loguru for status updates

conda_environment.yaml

@@ -0,0 +1,47 @@
+name: pyVPRM
+dependencies:
+  - dask
+  - netCDF4
+  - esmf
+  - esmpy
+
+  - tqdm
+  - gitsummary
+  - loguru
+
+  # plotting
+  - hvplot
+  - geoviews
+  - nzgeom
+  - matplotlib
+
+  # development
+  - conda-build  # for `mamba develop`
+  - isort
+  - jupyterlab
+  - jupyter_console
+  - jupyterlab_code_formatter
+  - tox
+  - pytest
+  - black
+  - pylint
+  - mypy
+  - pyright
+  - ipdb
+  - sphinx
+  - sphinx_rtd_theme
+  - sphinx-autodoc-typehints
+  - m2r2
+
+  # # github dependencies
+  # - pip
+  # - pip:
+  #    - "--editable=git+https://github.com/Timothy-W-Hilton/pyVPRM"
+
+  # - pip
+  # - pip:
+  #     - "git+https://github.com/Timothy-W-Hilton/pyVPRM"
+
+  - pip
+  - pip:
+      - "--editable=/home/timh/Code/pyVPRM"

fit_vprm_parameters/fit_params_draft.py

@@ -1,42 +1,44 @@
 import sys
 import os
 import pathlib
-sys.path.append(os.path.join(pathlib.Path(__file__).parent.resolve(), '..', 'lib'))
+
+sys.path.append(os.path.join(pathlib.Path(__file__).parent.resolve(), "..", "lib"))
 from sat_manager import VIIRS, modis, copernicus_land_cover_map
-from VPRM import vprm 
+from VPRM import vprm
 import yaml
 import glob
 import time
 import numpy as np
 import pandas as pd
 import pickle
 import argparse
+from loguru import logger
 
 p = argparse.ArgumentParser(
-        description = "Commend Line Arguments",
-        formatter_class = argparse.RawTextHelpFormatter)
+    description="Commend Line Arguments", formatter_class=argparse.RawTextHelpFormatter
+)
 p.add_argument("--h", type=int)
 p.add_argument("--v", type=int)
 p.add_argument("--config", type=str)
 p.add_argument("--n_cpus", type=int, default=1)
 args = p.parse_args()
-print(args)
+logger.info(str(args))
 
 
 with open(args.config, "r") as stream:
     try:
-        cfg  = yaml.safe_load(stream)
+        cfg = yaml.safe_load(stream)
     except yaml.YAMLError as exc:
-        print(exc)
+        logger.info(exc)
 
 h = args.h
 v = args.v
 
-if not os.path.exists(cfg['out_path']):
-    os.makedirs(cfg['out_path'])
+if not os.path.exists(cfg["out_path"]):
+    os.makedirs(cfg["out_path"])
 
-outfile = os.path.join(cfg['out_path'], 'h{}v{}_{}.pickle'.format(h, v, cfg['year']))
-print(outfile)
+outfile = os.path.join(cfg["out_path"], "h{}v{}_{}.pickle".format(h, v, cfg["year"]))
+logger.info(outfile)
 
 
 ### ToDo: Provide a list of lats and lons for the data extraction, i.e. the flux tower positions
@@ -51,25 +53,37 @@
 
 vprm_inst = vprm(n_cpus=args.n_cpus)
 
-for c, i in enumerate(glob.glob(os.path.join(cfg['sat_image_path'], '*h{:02d}v{:02d}*.h*'.format(h, v)))):
-    print(i)
-    if cfg['satellite'] == 'modis':
+for c, i in enumerate(
+    glob.glob(os.path.join(cfg["sat_image_path"], "*h{:02d}v{:02d}*.h*".format(h, v)))
+):
+    logger.info(i)
+    if cfg["satellite"] == "modis":
         handler = modis(sat_image_path=i)
         handler.load()
-        vprm_inst.add_sat_img(handler, b_nir='B02', b_red='B01',
-                              b_blue='B03', b_swir='B06',
-                              which_evi='evi',
-                              drop_bands=True)
+        vprm_inst.add_sat_img(
+            handler,
+            b_nir="B02",
+            b_red="B01",
+            b_blue="B03",
+            b_swir="B06",
+            which_evi="evi",
+            drop_bands=True,
+        )
     else:
         handler = VIIRS(sat_image_path=i)
         handler.load()
-        vprm_inst.add_sat_img(handler, b_nir='SurfReflect_I2', b_red='SurfReflect_I1',
-                          b_blue='no_blue_sensor', b_swir='SurfReflect_I3',
-                          which_evi='evi2',
-                          drop_bands=True)
+        vprm_inst.add_sat_img(
+            handler,
+            b_nir="SurfReflect_I2",
+            b_red="SurfReflect_I1",
+            b_blue="no_blue_sensor",
+            b_swir="SurfReflect_I3",
+            which_evi="evi2",
+            drop_bands=True,
+        )
 
 # Uncomment if you want to do a spatial smearing with size=(3,3). Otherwise give size manually.
-#vprm_inst.smearing(lonlats=lonlats)
+# vprm_inst.smearing(lonlats=lonlats)
 
 # Sort the satellite images by time and merge internally for easier computations
 vprm_inst.sort_and_merge_by_timestamp()
@@ -84,15 +98,18 @@
 
 # Loop over the year and get the vprm_variables
 
-time_range = pd.date_range(start="{}-01-01".format(cfg['year']),
-                           end="{}-01-01".format(cfg['year'] + 1),
-                           freq='H')
+time_range = pd.date_range(
+    start="{}-01-01".format(cfg["year"]),
+    end="{}-01-01".format(cfg["year"] + 1),
+    freq="H",
+)
 
-for c,t in enumerate(time_range):
+for c, t in enumerate(time_range):
     t0 = time.time()
     # Returns dict with VPRM input variables for the different lats and lons
     # Additional ERA5 variables can be added trough the  add_era_variables key
-    vrbls = vprm_inst.get_vprm_variables(t, lat=lats, lon=lons,
-                                         add_era_variables=['svwl1'])
-
+    vrbls = vprm_inst.get_vprm_variables(
+        t, lat=lats, lon=lons, add_era_variables=["svwl1"]
+    )
+
 # ToDo: write to your own format and store for further analysis