added author to file

guide.ipynb

@@ -15,13 +15,12 @@
  ]
  },
  {
- "cell_type": "code",
- "execution_count": 2,
+ "cell_type": "markdown",
  "metadata": {},
- "outputs": [],
  "source": [
- "%load_ext autoreload\n",
- "%autoreload 2"
+ "Reproducible code for \"A machine learning emulator for Lagrangian particle dispersion model footprints: A case study using NAME\" by Elena Fillola, Raul Santos-Rodriguez, Alistair Manning, Simon O'Doherty and Matt Rigby (2022)\n",
+ "\n",
+ "Author: Elena Fillola ([email protected])"
  ]
  },
  {
@@ -57,7 +56,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
  "metadata": {},
  "outputs": [
  {
@@ -80,7 +79,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 6,
+ "execution_count": 4,
  "metadata": {},
  "outputs": [],
  "source": [
@@ -96,7 +95,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 7,
+ "execution_count": 5,
  "metadata": {},
  "outputs": [],
  "source": [
@@ -116,7 +115,7 @@
  },
  {
  "cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
  "metadata": {},
  "outputs": [],
  "source": [
@@ -129,11 +128,13 @@
  },
  {
  "cell_type": "code",
- "execution_count": 8,
+ "execution_count": 14,
  "metadata": {},
  "outputs": [],
  "source": [
- "# Save the predictions\n",
+ "# Save the predictions as a .nc file\n",
+ "## Pass save_dir argument to specify path and filename format (year+month+.txt will be appended)\n",
+ "## Otherwise default is used, saved in the data/emulated_fps folder\n",
  "predictions.save_predictions(info)"
  ]
  },

train_emulator.py

@@ -5,6 +5,13 @@
 
 import argparse
 
+"""
+Reproducible code for "A machine learning emulator for Lagrangian particle dispersion model footprints: A case study using NAME" 
+by Elena Fillola, Raul Santos-Rodriguez, Alistair Manning, Simon O'Doherty and Matt Rigby (2022)
+
+Author: Elena Fillola ([email protected])
+"""
+
 parser = argparse.ArgumentParser(prog = "tree_emulator", description='Train footprint emulator')
 parser.add_argument('site', type=str, help='Site to train on, as string (eg "MHD")')
 parser.add_argument('year', help='Time period to train on. Can be int (2016) or str ("201[4-5]")')
@@ -61,4 +68,3 @@
 
 
 
-

trees_emulator/load_data.py

@@ -3,6 +3,13 @@
 import glob
 import dask
 
+"""
+Reproducible code for "A machine learning emulator for Lagrangian particle dispersion model footprints: A case study using NAME" 
+by Elena Fillola, Raul Santos-Rodriguez, Alistair Manning, Simon O'Doherty and Matt Rigby (2022)
+
+Author: Elena Fillola ([email protected])
+"""
+
 class LoadData:
  """
  Load data for training and testing, for a particular site

trees_emulator/predicting.py

@@ -15,6 +15,14 @@
 import sklearn.metrics as metrics
 import xarray as xr
 
+"""
+Reproducible code for "A machine learning emulator for Lagrangian particle dispersion model footprints: A case study using NAME" 
+by Elena Fillola, Raul Santos-Rodriguez, Alistair Manning, Simon O'Doherty and Matt Rigby (2022)
+
+Author: Elena Fillola ([email protected])
+"""
+
+
 class MakePredictions:
  """
  Make footprint predictions
@@ -99,9 +107,12 @@ def save_predictions(self, info, save_dir=None, year=None):
 
 
  def predict_fluxes(self, flux, units_transform = "default"):
- ## convolute predicted footprints and fluxes, returns two np arrays, one with the true flux and one with the emulated flux, of shape (n_footprints,)
- ## flux is an array, regridded and cut to the same resolution and size of the footprints
- ## units_transform can be None (use fluxes directly), "default" (performs flux*1e3 / CH4molarmass) or another function (which should return an array of the same shape as the original flux)
+ """
+ convolute predicted footprints and fluxes.
+ Returns two np arrays, one with the true flux and one with the emulated flux, both of shape (n_footprints,)
+ input flux should be an array, regridded and cut to the same resolution and size of the footprints
+ units_transform can be None (use fluxes directly), "default" (performs flux*1e3 / CH4molarmass) or another function (which should return an array of the same shape as the original flux)
+ """
  shape = self.size 
  if units_transform != None:
  if units_transform == "default":

trees_emulator/training.py

@@ -1,10 +1,25 @@
 import numpy as np
 from sklearn.ensemble import GradientBoostingRegressor
 
+"""
+Reproducible code for "A machine learning emulator for Lagrangian particle dispersion model footprints: A case study using NAME" 
+by Elena Fillola, Raul Santos-Rodriguez, Alistair Manning, Simon O'Doherty and Matt Rigby (2022)
+
+Author: Elena Fillola ([email protected])
+"""
+
 def train_tree(data, inputs, frequency, hours_back, tree):
  """
- Train a GBRT (ie the regressor for a single cell) using parameters provided. Returns a trained sklearn regressor
-
+ Train a GBRT (ie the regressor for a single cell) using parameters provided following method from paper. 
+ Returns a trained sklearn regressor
+ 
+ Requires inputs:
+ - data (LoadData object)
+ - inputs (meteorological inputs)
+ - frequency (freq to sample footprints)
+ - hours_back (same parameter as used to produce inputs)
+ - tree (int ID of cell to train, if all cells are flattened. tree is then unraveled into x,y coordinates within the domain, eg tree 0 is at loc 0,0)
+ 
  Parallel predicting function is predict_tree. Edit/replace both of these for custom training/predicting 
  """