A set of scripts for visualizing data for/from the dvm-dos-tem ecosystem model.
Each script has some decent help with the --help flag.
(Python scripts) I am not certain that all of these are necessary, but this is the envrionment I have:
$ pip freeze
GDAL==1.9.1
PyMySQL==0.5
basemap==1.0.6
distribute==0.6.34
git-remote-helpers==0.1.0
ipython==0.13.1
matplotlib==1.2.0
netCDF4==1.0.2
numpy==1.6.2
pandas==0.10.1
python-dateutil==2.1
pytz==2012j
scipy==0.11.0
six==1.2.0
virtualenv==1.8.4
wsgiref==0.1.2
yolk==0.4.3
(R scripts) Again, this is my environment, not sure what other verisons may work
- R, v3.0.0
- ncdf4
- argparse (which requires python 2.7 I think?)
These are really rough. I have not had time to clean up the plots, or deal with labeling the axes, titles, etc., but this should give an idea of what the scripts can generate.
There are designed to work with ouputs from a single site run. I have not tested anything but the yearly timestep and the 'eq' run stage.
First:
$ ./output-extract.R --help
Loading required package: proto
usage: ./output-extract.R [-h] --nc-files-dir PATH
[--generated-files-dir DIRNAME]
[--run-mode M [M ...]] [--timestep T [T ...]]
Reads a bunch of cmt***_***-**.nc files that are generated by a run of dvm-
dos-tem in single site mode and generates csv files for the specified
variables.
optional arguments:
-h, --help show this help message and exit
--nc-files-dir PATH A path (relative or absolute) to a folder containing
some cmt***_***-**.nc files.
--generated-files-dir DIRNAME
Name of directory for storing the generated file(s)
Defaults to making a subdirectory withing the nc-
files-dir named "output-extract-generated-files"
--run-mode M [M ...] Which run mode(s) you are processing. (default: eq)
--timestep T [T ...] Which timestep you are processing. (default: yearly)
And then:
$ ./plotting-output.R --help
Loading required package: proto
usage: ./plotting-output.R [-h] --generated-csv-dir PATH [--output-id ID]
This script reads a whole bunch of csv files that werte generated with output-
extract.R. This script plots the variables and places the resulting plots in a
folder (identified by the "--output-id" option).
optional arguments:
-h, --help show this help message and exit
--generated-csv-dir PATH
A path (absolute or relative) to a directory of csv
files that were generated using output-extract.R.
--output-id ID A unique name to indentify this set of generated plots
(pdfs) All the generated plots will end up in a folder
with this name in the current directory (wherever you
call this script from).
Examples:
C Plot
N Plot
Env Variable Plot
Looks like this only deals with outputs from the multi site run?
$ ./plot_general_diagnostics.py --help
usage: plot_general_diagnostics.py [-h] [-ls] [-d] [-s] [-cix N] [-p N]
[--compare COMPARE]
inputfile
Make a generally useful selection of plots...
positional arguments:
inputfile path to a NetCDF file (A) to read from.
optional arguments:
-h, --help show this help message and exit
-ls, --list List the cohorts and PFTs in this file and then exit.
-d, --display Display the plot
-s, --save Save the plot with generic name. Warning: will
overwrite existing file with same name!!
-cix N, --cohortindex N
The *index* of the cohort to plot. NOTE: this may or
may not be the same as the COHORTID!
-p N, --pft N Which PFT to plot
--compare COMPARE path to a NetCDF file (B) to compare.
Example of file list function (obvisously something is tweaked with my output files as it looks like there are 6 cohorts in the file that are all the same!):
./plot_general_diagnostics.py -ls output-archives/before-phen/output-sp.nc \
--compare output-archives/after-phen/output-tr.nc
Loading dataset(s)...
Summary
-----------------------------------
(A): output-archives/before-phen/output-sp.nc
Available CHTIDs: 6 6 6 6 6 6
# of PFTs (zero indexed): 10
length of YYYYMM: 120
(B): output-archives/after-phen/output-tr.nc
Available CHTIDs: 6 6 6 6 6 6
# of PFTs (zero indexed): 10
length of YYYYMM: 1272
-----------------------------------
Example of plotting call:
./plot_general_diagnostics.py --save \
--pft 0 \
--cohortindex 0 \
output-archives/before-phen/output-sp.nc
./heatmap_year_vs_month.py --help
usage: heatmap_year_vs_month.py [-h] [-d] [-s SAVE] [-m {rect,polar}] -c
COHORT [-v VARIABLE] [-pft PFT]
[--compare COMPARE [COMPARE ...]]
inputfiles [inputfiles ...]
Makes a heatmap for a single variable, and a single PFT. The heatmap is a time
vs. time heatmap with years on the vertical axis and months on the horizontal
axis. The color of each pixel in this grid corresponds to the variables value
at that month, and year. A similar to the example here: http://stat-
computing.org/dataexpo/2009/posters/wicklin-allison.pdf
positional arguments:
inputfiles path to one or more NetCDF file(s) to read from (A).
optional arguments:
-h, --help show this help message and exit
-d, --display Display the plot
-s SAVE, --save SAVE Save the plot to the file name you provide
-m {rect,polar}, --mode {rect,polar}
Whether to use rectangular or polar coords
-c COHORT, --cohort COHORT
Which cohort to plot
-v VARIABLE, --variable VARIABLE
Which variable to plot
-pft PFT Which PFT to display data for.
--compare COMPARE [COMPARE ...]
path to one or more NetCDF file(s) to compare to (B).
Call:
$ ./heatmap_year_vs_month.py -s "examples/hm-rect1.png" \
-m rect \
-c 1 \
-pft 1 \
-v VEGC \
output-archives/after-phen/output-tr.nc
Call:
$ ./heatmap_year_vs_month.py -s "examples/hm-polar1.png" \
-m polar \
-c 1 \
-pft 1 \
-v VEGC \
output-archives/after-phen/output-sp.nc
$ ./plot_pft_variable_mly.py --help
usage: plot_pft_variable_mly.py [-h] [-n | -e] [-d] [-s SAVE] -c COHORT
[-v VARIABLE] [--compare COMPARE]
inputfile
Plots a variable vs. time for
each PFT in a cohort (left axis). The right axis plots the PFT's percent
coverage. If there are 10 PFTs, there will be 10 plots generated.
There are two modes: "normal" and "explorer".
In "normal" mode, the min and max for each plot are determined by the min and
max among all PFTs. In "explorer" mode, the y axis of each PFT plot will auto-
scale to the range for only that PFT.
Plots like this (not showing right y axis and coverage for clarity):
cohort x, variable
^ x xx
PFT1 | x x x x x xxx>
| x x x x x xx
| x xxxx xx x
+-x----------------------------->
time (months)
^ x xx
| x x x x x
PFT2 | x x x x x xx
| x xxxx xx x xx >
+-x--------------------------x-->
time (months)
.
.
^ xxx xxx x xx
PFTn | xx x x x x x xxx>
| x xx x xx
| x x
+-x----------------------------->
time (months)
Link to continue working on ascii representation of plots:
http://www.asciiflow.com/#Draw1054136659174578755/54679995
positional arguments:
inputfile path to a NetCDF file to read from (A).
optional arguments:
-h, --help show this help message and exit
-n, --normal
-e, --explorer
-d, --display Display the plot
-s SAVE, --save SAVE Save the plot to simple-plot.png
-c COHORT, --cohort COHORT
Which cohort to plot
-v VARIABLE, --variable VARIABLE
Which variable to plot
--compare COMPARE path to a NetCDF file to compare to (B).
Call:
$ ./plot_pft_variable_mly.py --explorer \
-s "examples/plot_pft_var1.png"
--cohort 1 \
--variable VEGC \
output-archives/before-phen/output-sp.nc
$ ./map_cohort_locations.py --help
usage: map_cohort_locations.py [-h] [-d] [-s SAVE] -c COHORTID -g GRIDID
Displays a dot on a map for each cohort's location. Looks ok for a few cohorts
in Alaska, but nothing has been done to automatically format things like
choosing the best projection.
optional arguments:
-h, --help show this help message and exit
-d, --display Display the plot
-s SAVE, --save SAVE Save the plot to simple-plot.png
-c COHORTID, --cohortid COHORTID
A cohortid.nc file with cohort list.
-g GRIDID, --gridid GRIDID
A grid.nc file that maps cohort ids to lat/lons
Call
$ ./map_cohort_locations.py --display \
-c ../dvm-dos-tem/DATA/test_06_cohorts/datacht/cohortid.nc \
-g ../dvm-dos-tem/DATA/test_06_cohorts/datagrid/grid.nc
Maybe good for representing fluxes??
This is an experiment...I think this might be useful, but I have not made it plot real data yet...Not even sure if the concept is right? Are these appropriate variables??
$ ./sankeyA.py
