4. Plot the Climatology

4.1 Loading the data

import pandas as pd
import matplotlib.pyplot as plt

from smadi.climatology import Climatology
from smadi.data_reader import read_grid_point
from smadi.plot import plot_ts

plt.style.use("ggplot")
# Set display options
pd.set_option("display.max_columns", 8)  # Limit the number of columns displayed
pd.set_option("display.precision", 2)  # Set precision to 2 decimal places

# Define the path to the ASCAT data
data_path = "/home/m294/ascat_dataset"

# Example: A grid point in Morocco
lon = -7.382
lat = 33.348
gpid = 3611180

# Define the location of the observation point
loc = (lon, lat)

# Extract ASCAT soil moisture time series for the given location
data = read_grid_point(
    loc=loc, ascat_sm_path=data_path, read_bulk=False, era5_land_path=None
)  # Provide the path to the ERA5-Land data if you want mask snow
# and frozen soil conditions. For more information about
# the dataset see ERA5-Land data documentation and to download
# use the CDS API or https://ecmwf-models.readthedocs.io/en/latest/

# Get the ASCAT soil moisture time series
ascat_ts = data.get("ascat_ts")


# Display the first few rows of the time series data
ascat_ts.head()

Reading ASCAT soil moisture: /home/m294/ascat_dataset
ASCAT GPI: 3611180 - distance:   23.713 m
Warning: ERA5-Land not found: None
Warning: ERA5 Land not found - ASCAT soil moisture not masked!

	sm	sm_noise	as_des_pass	ssf	...	sigma40	sigma40_noise	num_sigma	sm_valid
2007-01-01 21:02:04.161	34.86	3.24	0	0	...	-12.27	0.19	3	True
2007-01-02 11:03:22.807	23.16	3.27	1	0	...	-13.05	0.19	3	True
2007-01-03 10:42:47.739	33.05	3.23	1	0	...	-12.39	0.19	3	True
2007-01-03 22:00:39.007	25.60	3.24	0	0	...	-12.88	0.19	3	True
2007-01-05 10:01:27.519	28.73	3.24	1	0	...	-12.67	0.19	3	True

5 rows × 16 columns

4.2 Plot the time series data

# Initialize Climatology object
clim = Climatology(
    df=ascat_ts,
    variable="sm",
    time_step="week",
    fillna=True,
    fillna_window_size=3,
    smoothing=True,
    smooth_window_size=31,
    normal_metrics=["mean", "median", "min", "max", "std"],
)

# Compute normals
clim_df = clim.compute_normals()
clim_df

	sm-mean	norm-mean	norm-median	norm-min	norm-max	norm-std
2007-01-01	33.29	56.55	51.97	33.29	75.79	13.60
2007-01-08	30.54	55.98	52.82	30.54	77.77	14.58
2007-01-15	34.77	56.58	57.21	32.46	79.92	14.96
2007-01-22	36.23	55.30	56.97	28.86	79.00	15.02
2007-01-29	36.65	54.32	54.77	31.44	78.50	14.62
...	...	...	...	...	...	...
2022-11-21	49.86	55.10	53.95	37.34	75.00	11.74
2022-11-28	63.00	58.17	59.25	36.70	74.45	11.60
2022-12-05	69.93	61.46	61.16	36.71	78.51	12.32
2022-12-12	73.97	61.43	59.37	35.26	83.27	12.69
2022-12-19	72.94	58.85	58.43	34.96	78.19	11.38

838 rows × 6 columns

# Set the plot options for each column
colms = {
    "sm-mean": {"color": "red", "linestyle": "-", "linewidth": 1.5, "label": "SM Avg"},
    "norm-mean": {
        "color": "blue",
        "linestyle": "--",
        "linewidth": 1.5,
        "label": "Normal Mean",
    },
    "norm-median": {"color": "green", "linewidth": 1.5, "label": "Normal Median"},
}

plot_ts(
    clim_df,
    clim_df.index,
    colmns_kwargs=colms,
    title=f"ASCAT Surface Soil Moisture CDR Climatology\nMorocco | GPI:  {gpid} | Longitude: {lon}, Latitude: {lat} | Frequency: Monthly",
    figsize=(17, 7),
    xlabel="Time",
    ylabel="Soil Moisture  Saturation (%)",
    legend=True,
    grid=False,
)

# Set the plot options for each metric
colms = {
    "sm-mean": {"color": "red", "linestyle": "-", "linewidth": 1.5, "label": "SSM Avg"},
    "norm-mean": {
        "color": "blue",
        "linestyle": "--",
        "linewidth": 1.5,
        "label": "Normal Mean",
    },
}

# To plot the raw data, set plot_raw=True and provide the raw_kwargs for customization
plot_ts(
    clim_df,
    clim_df.index,
    colmns_kwargs=colms,
    title=f"ASCAT Surface Soil Moisture CDR Climatology\nMorocco | GPI:  {gpid} | Longitude: {lon}, Latitude: {lat} | Frequency: Monthly",
    figsize=(17, 7),
    xlabel="Time",
    ylabel="Soil Moisture  Saturation (%)",
    plot_raw=True,
    raw_df= ascat_ts,
    raw_var="sm",
    raw_kwargs={"color": "green",  "label": "SSM", "alpha": 0.3},
    legend=True,
    grid=False,
)