#!/usr/bin/env python
"""
Example of plotting timeseries data from GEOS-Chem and saving
the output to a PDF file. You can modify this for your particular
diagnostic output. This also contains a good overview of
This example script creates a PDF file with 2 pages.
Page 1:
O3 from the first model layer (from the "SpeciesConc"
diagnostic collection is) plotted in blue.
O3 at 10 meter height (from the "SpeciesConc_10m"
diagnostic collection) is plotted in red.
Page 2:
HNO3 from the first model layer (from the SpeciesConc
diagnostic collection is) plotted in blue.
HNO3 at 10 meter height (from the SpeciesConc_10m
diagnostic collection) is plotted in red.
You can of course modify this for your own particular applications.
Examples
--------
1. Copy this file to a different folder and navigate to that folder.
2. Edit file paths, collection names, and coordinates in the main() routine.
3. Run the following commands.
.. code-block:: console
$ conda activate gcpy_env
(gcpy_env) $ ./plot_timeseries.py
References
----------
Author: Bob Yantosca (GitHub: @yantosca), 23 Aug 2019
"""
# Imports
import os
import warnings
import numpy as np
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import xarray as xr
from gcpy import constants
# Tell matplotlib not to look for an X-window, as we are plotting to
# a file and not to the screen. This will avoid some warning messages.
os.environ['QT_QPA_PLATFORM'] = 'offscreen'
# Suppress harmless run-time warnings (mostly about underflow in division)
warnings.filterwarnings('ignore', category=RuntimeWarning)
warnings.filterwarnings('ignore', category=UserWarning)
[docs]
def find_files_in_dir(path, substrs):
'''
Returns a list of all files in a directory that match one or more
substrings.
Parameters
----------
path : str
Path to the directory in which to search for files.
substrs : list of str
List of substrings used in the search for files.
Returns
-------
file_list : list of str
List of files in the directory (specified by path)
that match all substrings (specified in substrs).
'''
# Initialize
file_list = []
# Walk through the given data directory. Then for each file found,
# add it to file_list if it matches text in search_list.
for root, directory, files in os.walk(path):
for f in files:
for s in substrs:
if s in f:
file_list.append(os.path.join(root, f))
# Return an alphabetically sorted list of files
file_list.sort()
return file_list
[docs]
def find_value_index(seq, val):
'''
Finds the index of a numpy array that is close to a value.
Parameters
----------
seq : numpy ndarray
An array of numeric values.
val : number
The value to search for in seq.
Returns
-------
result : integer
The index of seq that has a value closest to val.
Notes
-----
This algorithm was found on this page:
https://stackoverflow.com/questions/48900977/find-all-indexes-of-a-numpy-array-closest-to-a-value
'''
r = np.where(np.diff(np.sign(seq - val)) != 0)
idx = r + (val - seq[r]) / (seq[r + np.ones_like(r)] - seq[r])
idx = np.append(idx, np.where(seq == val))
idx = np.sort(idx)
result = np.round(idx)
# NOTE: xarray needs integer values, so convert here!
return int(result[0])
[docs]
def read_geoschem_data(path, collections):
'''
Returns an xarray Dataset containing timeseries data.
Parameters
----------
path : str
Directory path where GEOS-Chem diagnostic output
files may be found.
collections : list of str
List of GEOS-Chem collections. Files for these
collections will be read into the xarray Dataset.
Returns
-------
ds : xarray Dataset
A Dataset object containing the GEOS-Chem diagnostic
output corresponding to the collections that were
specified.
'''
# Get a list of variables that GCPy should not read.
# These are mostly variables introduced into GCHP with the MAPL v1.0.0
# update. These variables contain either repeated or non-standard
# dimensions that can cause problems in xarray when combining datasets.
skip_vars = constants.SKIP_THESE_VARS
# Find all files in the given
file_list = find_files_in_dir(path, collections)
# Return a single xarray Dataset containing data from all files
# NOTE: Need to add combine="nested" for xarray 0.15 and higher
v = xr.__version__.split(".")
if int(v[0]) == 0 and int(v[1]) >= 15:
return xr.open_mfdataset(file_list,
drop_variables=skip_vars,
combine="nested",
concat_dim=None)
else:
return xr.open_mfdataset(file_list,
drop_variables=skip_vars)
[docs]
def plot_timeseries_data(ds, site_coords):
'''
Plots a timseries of data at a given (lat,lon) location.
Parameters
----------
ds : xarray Dataset
Dataset containing GEOS-Chem timeseries data.
site_coords : tuple
Contains the coordinate (lat, lon) of a site location
at which the timeseries data will be plotted.
'''
# ----------------------------------------------------------------------
# Get the GEOS-Chem data for O3 and HNO3 corresponding to the
# location of the observational station. We will save these into
# xarray DataArray objects, which we'll need for plotting.
#
# YOU CAN EDIT THIS FOR YOUR OWN PARTICULAR APPLICATION!
# ----------------------------------------------------------------------
# Find the indices corresponding to the site lon and lat
lat_idx = find_value_index(ds.lat.values, site_coords[0])
lon_idx = find_value_index(ds.lon.values, site_coords[1])
# Save O3 from the first level (~60m height) (ppb) into a DataArray
O3_L1 = ds['SpeciesConc_O3'].isel(lon=lon_idx, lat=lat_idx, lev=0)
O3_L1 *= 1.0e9
O3_L1.attrs['units'] = 'ppbv'
# Save O3 @ 10m height into a DataArray
O3_10m = ds['SpeciesConc10m_O3'].isel(lon=lon_idx, lat=lat_idx)
O3_10m *= 1.0e9
O3_10m.attrs['units'] = 'ppbv'
# Save HNO3 from the first level (~60m height) into a DataArray
HNO3_L1 = ds['SpeciesConc_HNO3'].isel(lon=lon_idx, lat=lat_idx, lev=0)
HNO3_L1 *= 1.0e9
HNO3_L1.attrs['units'] = 'ppbv'
# Save HNO3 @ 10m height into a DataArray
HNO3_10m = ds['SpeciesConc10m_HNO3'].isel(lon=lon_idx, lat=lat_idx)
HNO3_10m *= 1.0e9
HNO3_10m.attrs['units'] = 'ppbv'
# ----------------------------------------------------------------------
# Create a PDF file of the plots
# ----------------------------------------------------------------------
# Get min & max days of the plot span (for setting the X-axis range).
# To better center the plot, add a cushion of 12 hours on either end.
time = ds['time'].values
datemin = np.datetime64(time[0]) - np.timedelta64(12, 'h')
datemax = np.datetime64(time[-1]) + np.timedelta64(12, 'h')
# Define a PDF object so that we can save the plots to PDF
pdf = PdfPages('O3_and_HNO3.pdf')
# Loop over number of desired pages (in this case, 2)
for i in range(0, 2):
# Create a new figure: 1 plot per page, 2x as wide as high
figs, ax0 = plt.subplots(1, 1, figsize=[12, 6])
# -----------------------------
# Plot O3 on the first page
# -----------------------------
if i == 0:
# 1st model level
O3_L1.plot.line(ax=ax0, x='time', color='blue',
marker='o', label='O3 from 1st model level',
linestyle='-')
# 10 mheight
O3_10m.plot.line(ax=ax0, x='time', color='red',
marker='x', label='O3 at 10m height',
linestyle='-')
# Set title (has to be after the line plots are drawn)
ax0.set_title('O3 from the 1st model level and at 10m height')
# Set Y-axis minor tick marks at every 2 ppb (5 intervals)
ax0.yaxis.set_minor_locator(mticker.AutoMinorLocator(5))
# Set y-axis title
ax0.set_ylabel('O3 (ppbv)')
# -----------------------------
# Plot HNO3 on the second page
# -----------------------------
if i == 1:
# 1st model level
HNO3_L1.plot.line(ax=ax0, x='time', color='blue',
marker='o', label='HNO3 from 1st model level',
linestyle='-')
# 10m height
HNO3_10m.plot.line(ax=ax0, x='time', color='red',
marker='x', label='HNO3 at 10m height',
linestyle='-')
# Set title (has to be after the line plots are drawn
ax0.set_title('HNO3 from the 1st model level and at 10m height')
# Set Y-axis minor tick marks at every 0.05 ppb (4 intervals)
ax0.yaxis.set_minor_locator(mticker.AutoMinorLocator(4))
# Set y-axis title
ax0.set_ylabel('HNO3 (ppbv)')
# -----------------------------
# Set general plot parameters
# -----------------------------
# Add the plot legend
ax0.legend()
# Set the X-axis range
ax0.set_xlim(datemin, datemax)
# Set the X-axis major tickmarks
locator = mdates.DayLocator()
formatter = mdates.DateFormatter('%d')
ax0.xaxis.set_major_locator(locator)
ax0.xaxis.set_major_formatter(formatter)
# Set X-axis minor tick marks at noon of each day
# (i.e. split up the major interval into 2 bins)
ax0.xaxis.set_minor_locator(mticker.AutoMinorLocator(2))
# Don't rotate the X-axis jtick labels
ax0.xaxis.set_tick_params(rotation=0)
# Center the X-axis tick labels
for tick in ax0.xaxis.get_major_ticks():
tick.label1.set_horizontalalignment('center')
# Set X-axis and Y-axis labels
ax0.set_xlabel('Day of July (and August) 2016')
# -----------------------------
# Save this page to PDF
# -----------------------------
pdf.savefig(figs)
plt.close(figs)
# ----------------------------------------------------------------------
# Save the PDF file to disk
# ----------------------------------------------------------------------
pdf.close()
[docs]
def main():
'''
Main program.
'''
# Path where the data files live
# (YOU MUST EDIT THIS FOR YUR OWN PARTICULAR APPLICATION!)
path_to_data = '/path/to/GEOS-Chem/diagnostic/data/files'
# Get a list of files in the ConcAboveSfc and SpeciesConc collections
# (YOU CAN EDIT THIS FOR YOUR OWN PARTICULAR APPLICATION!)
collections = ['ConcAboveSfc', 'SpeciesConc']
# Read GEOS-Chem data into an xarray Dataset
ds = read_geoschem_data(path_to_data, collections)
# Plot timeseries data at Centerville, AL (32.94N, 87.18W)
# (YOU CAN EDIT THIS FOR YOUR OWN PARTICULAR APPLICATION!)
site_coords = (32.94, -87.18)
plot_timeseries_data(ds, site_coords)
if __name__ == "__main__":
main()