2A. Monsoon (Wang)
This notebook demonstrates how to use the PCDMI Monsoon (Wang) driver.
It is expected that you have downloaded the sample data as demonstrated in the download notebook
The following cell reads in the choices you made during the download data step.
[1]:
from user_choices import demo_data_directory, demo_output_directory
[ ]:
# To open and display one of the graphics
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from matplotlib import rcParams
import os
import glob
%matplotlib inline
[18]:
# To open and display one of the graphics
from IPython.display import display_png, JSON, Image
Basic Example
The Monsoon (Wang) driver is simple and does not require many settings. These are the required parameters:
modnames
test_data_path
reference_data_path
results_dir
For a full look at the driver options available, use monsoon_wang_driver.py --help in the command line.
Important note about threshold: The default threshold for the threat score is 2.5 mm/day, but the Monsoon (Wang) driver assumes that the precipitation inputs are in units of kg m-2 s-1 and adjust the threshold value accordingly. If your precipitation data uses units of mm/day, set ``threshold = 2.5``.
First, display the parameter file used for this example:
[2]:
with open("basic_monsoon_wang_param.py") as f:
print(f.read())
import os
#
# OPTIONS ARE SET BY USER IN THIS FILE AS INDICATED BELOW BY:
#
#
# LIST OF MODEL VERSIONS TO BE TESTED
modnames = ['CanCM4']
# ROOT PATH FOR MODELS CLIMATOLOGIES
test_data_path = 'demo_data_tmp/CMIP5_demo_clims/cmip5.historical.%(model).r1i1p1.mon.pr.198101-200512.AC.v20200426.nc'
# ROOT PATH FOR OBSERVATIONS
reference_data_path = 'demo_data_tmp/obs4MIPs_PCMDI_monthly/NOAA-NCEI/GPCP-2-3/mon/pr/gn/v20210727/pr_mon_GPCP-2-3_PCMDI_gn_197901-201907.nc'
# DIRECTORY WHERE TO PUT RESULTS
results_dir = 'demo_output_tmp/monsoon_wang'
# Threshold
threshold = 2.5 / 86400
The following command is used to run the Monsoon (Wang) metrics driver via the command line. Bash cell magic is used to run this command as a subprocess in the next cell.
monsoon_wang_driver.py -p basic_monsoon_wang_param.py
Note: the following old method is now deprecated
# (deprecated old method) mpindex_compute.py -p basic_monsoon_wang_param.py
[3]:
%%bash
monsoon_wang_driver.py -p basic_monsoon_wang_param.py
/Users/lee1043/mambaforge/envs/pmp_devel_20241126/lib/python3.10/site-packages/pcmdi_metrics/utils/string_constructor.py:43: UserWarning: Keyword 'model' not provided for filling the template.
warnings.warn(f"Keyword '{k}' not provided for filling the template.")
modelFile = demo_data_tmp/CMIP5_demo_clims/cmip5.historical.CanCM4.r1i1p1.mon.pr.198101-200512.AC.v20200426.nc
dom = AllMW
Figure(1200x1000)
dom = AllM
Figure(1200x1000)
dom = NAMM
Figure(1200x1000)
dom = SAMM
Figure(1200x1000)
dom = NAFM
Figure(1200x1000)
dom = SAFM
Figure(1200x1000)
dom = ASM
Figure(1200x1000)
dom = AUSM
Figure(1200x1000)
INFO::2024-11-28 13:44::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/monsoon_wang/monsoon_wang.json
2024-11-28 13:44:06,152 [INFO]: base.py(write:422) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/monsoon_wang/monsoon_wang.json
2024-11-28 13:44:06,152 [INFO]: base.py(write:422) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/monsoon_wang/monsoon_wang.json
[21]:
# Find all PNG files in the output directory
list_files = glob.glob(os.path.join(demo_output_directory, "monsoon_wang/historical_CMIP5_wang-monsoon/*.png"))
[22]:
a = Image(list_files[0])
display_png(a)
[23]:
# figure size in inches optional
rcParams['figure.figsize'] = 16, 12
# Count the number of PNG files
num_files = len(list_files)
print(f"Number of PNG files: {num_files}")
# Display all PNG files in 2 rows
fig, axes = plt.subplots(2, num_files // 2, figsize=(20, 10))
axes = axes.flatten()
for i, file in enumerate(list_files):
img = mpimg.imread(file)
axes[i].imshow(img)
axes[i].axis('off')
plt.show()
Number of PNG files: 8
The metrics are saved to monsoon_wang.json, opened below.
[4]:
import json
import os
with open(os.path.join(demo_output_directory, "monsoon_wang/monsoon_wang.json")) as f:
metric = json.load(f)["RESULTS"]
print(json.dumps(metric, indent=2))
{
"CanCM4": {
"AllMW": {
"cor": "0.735",
"rmsn": "0.550",
"threat_score": "0.479"
},
"AllM": {
"cor": "0.735",
"rmsn": "0.562",
"threat_score": "0.479"
},
"NAMM": {
"cor": "0.586",
"rmsn": "0.694",
"threat_score": "0.497"
},
"SAMM": {
"cor": "0.766",
"rmsn": "0.660",
"threat_score": "0.441"
},
"NAFM": {
"cor": "0.864",
"rmsn": "0.452",
"threat_score": "0.691"
},
"SAFM": {
"cor": "0.748",
"rmsn": "0.467",
"threat_score": "0.646"
},
"ASM": {
"cor": "0.758",
"rmsn": "0.555",
"threat_score": "0.394"
},
"AUSM": {
"cor": "0.728",
"rmsn": "0.631",
"threat_score": "0.525"
}
}
}
Command line options
The following example shows how to use the command line to specify one model, increase the threshold to 3 mm/day (for data in kg m-2 s-1), change the name of the output json (outnj), and specify the model experiment and MIP:
[6]:
%%bash
monsoon_wang_driver.py -p basic_monsoon_wang_param.py \
--modnames "['CanCM4']" \
--results_dir 'demo_output_tmp/monsoon_wang_ex2' \
--outnj "monsoon_wang_ex2" \
--experiment historical \
--MIP cmip5 \
--threshold 0.00003472222
/Users/lee1043/mambaforge/envs/pmp_devel_20241126/lib/python3.10/site-packages/pcmdi_metrics/utils/string_constructor.py:43: UserWarning: Keyword 'model' not provided for filling the template.
warnings.warn(f"Keyword '{k}' not provided for filling the template.")
modelFile = demo_data_tmp/CMIP5_demo_clims/cmip5.historical.CanCM4.r1i1p1.mon.pr.198101-200512.AC.v20200426.nc
dom = AllMW
Figure(1200x1000)
dom = AllM
Figure(1200x1000)
dom = NAMM
Figure(1200x1000)
dom = SAMM
Figure(1200x1000)
dom = NAFM
Figure(1200x1000)
dom = SAFM
Figure(1200x1000)
dom = ASM
Figure(1200x1000)
dom = AUSM
Figure(1200x1000)
INFO::2024-11-28 13:51::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/monsoon_wang_ex2/monsoon_wang_ex2.json
2024-11-28 13:51:24,087 [INFO]: base.py(write:422) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/monsoon_wang_ex2/monsoon_wang_ex2.json
2024-11-28 13:51:24,087 [INFO]: base.py(write:422) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output_tmp/monsoon_wang_ex2/monsoon_wang_ex2.json
Opening the new results file, you will find that the threat_scores have changed slightly as a result of changing the threshold.
[8]:
with open(os.path.join(demo_output_directory, "monsoon_wang_ex2/monsoon_wang_ex2.json")) as f:
metric = json.load(f)["RESULTS"]
print(json.dumps(metric, indent=2))
{
"CanCM4": {
"AllMW": {
"cor": "0.730",
"rmsn": "0.547",
"threat_score": "0.457"
},
"AllM": {
"cor": "0.730",
"rmsn": "0.559",
"threat_score": "0.457"
},
"NAMM": {
"cor": "0.540",
"rmsn": "0.732",
"threat_score": "0.492"
},
"SAMM": {
"cor": "0.700",
"rmsn": "0.661",
"threat_score": "0.432"
},
"NAFM": {
"cor": "0.858",
"rmsn": "0.425",
"threat_score": "0.715"
},
"SAFM": {
"cor": "0.774",
"rmsn": "0.433",
"threat_score": "0.665"
},
"ASM": {
"cor": "0.773",
"rmsn": "0.542",
"threat_score": "0.370"
},
"AUSM": {
"cor": "0.676",
"rmsn": "0.666",
"threat_score": "0.417"
}
}
}