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plot_ablation.py
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# -*- coding: utf-8 -*-
"""
Created on %(date)s
@author: [email protected]
tip list:
%matplotlib inline
%matplotlib qt
import pdb; pdb.set_trace()
"""
import matplotlib.pyplot as plt
import pandas as pd
import os
import matplotlib
import matplotlib.dates as mdates
from datetime import datetime
matplotlib.use('Agg')
import tocgen
import numpy as np
# Initialize
data_type = 'sites'
path_new = '../aws-l3-dev/' + data_type + '/'
filename = 'plot_compilations/ablation_' + data_type + '.md'
df_meta = pd.read_csv(path_new + '../AWS_' + data_type + '_metadata.csv')
df_meta = df_meta.set_index(data_type[:-1] + '_id')
f = open(filename, "w")
def Msg(txt):
with open(filename, "a") as f:
print(txt)
f.write(txt + "\n")
plt.close('all')
# Loop over each station
for station in df_meta.index:
# for station in ['QAS_M']:
Msg('## ' + station)
# Check if the file exists
if not os.path.isfile(path_new + station + '/' + station + '_day.csv'):
continue
# Read the station data
df_new = pd.read_csv(path_new + station + '/' + station + '_day.csv')
if df_new.loc[df_new.z_surf_combined.last_valid_index(), 'z_surf_combined'] >0:
Msg('accumulation site')
continue
df_new.time = pd.to_datetime(df_new.time)
df_new = df_new.set_index('time')
# Create a figure with two panels
fig, ax_list = plt.subplots(2, 1, sharex=False, figsize=(10, 10))
plt.subplots_adjust(right=0.75, left=0.08, hspace=0.15)
# Top panel: z_surf_combined
if 'z_surf_combined' in df_new.columns and not df_new['z_surf_combined'].isnull().all():
ax_list[0].plot(df_new.index, df_new['z_surf_combined'],
marker='.', markeredgecolor='None', linestyle='None', label='z_surf_combined')
ax_list[0].set_ylabel('Surface Height (m)')
ax_list[0].set_title(station)
ax_list[0].grid()
ax_list[0].legend(loc='center left', bbox_to_anchor=(1, 0.5))
# caluclating ice surface
df_new['z_ice_surf'] = df_new['z_surf_combined'].cummin()
# removing years with too many NaNs in JJA
mask = df_new[df_new.index.month.isin([6, 7, 8])]['z_surf_combined'].isnull().resample('Y').sum().to_frame()
for index, count in mask.iterrows():
if count.iloc[0]>15:
df_new.loc[str(index.year), 'z_ice_surf'] = np.NaN
# Bottom panel: z_ice_surface as function of day of year
for year in df_new.index.year.unique():
# Filter data for the year range September to August Y
start_date = pd.Timestamp(f'{year}-04-01')
end_date = pd.Timestamp(f'{year}-10-31')
df_year = df_new.loc[(df_new.index >= start_date) & (df_new.index < end_date), :].copy()
# Calculate the day of the year
df_year['day_of_year'] = df_year.index.dayofyear.values
# df_year.loc[df_year['day_of_year'] < 0, 'day_of_year'] = 365 + df_year.loc[df_year['day_of_year'] < 0, 'day_of_year']
# Plot z_surf_combined adjusted by first valid value
if df_year['z_surf_combined'].notnull().any():
first_valid_value = df_year['z_ice_surf'].loc[
slice(df_year['z_ice_surf'].first_valid_index(),
df_year['z_ice_surf'].first_valid_index()+pd.to_timedelta('10 days'))].mean()
if year == 2024:
ax_list[1].plot(df_year['day_of_year'],
df_year['z_ice_surf'] - first_valid_value,
label='_no_legend_', linestyle='-', color='w',lw=10, alpha=0.7)
ax_list[1].plot(df_year['day_of_year'],
df_year['z_ice_surf'] - first_valid_value,
label=str(year), linestyle='-', color='k',lw=4)
else:
ax_list[1].plot(df_year['day_of_year'],
df_year['z_ice_surf'] - first_valid_value,
label=str(year), linestyle='-')
# Set x-axis limits
# Major ticks and labels every 30 days
labels = []
tick_positions = []
for i in np.cumsum(np.array([30, 31, 30, 31, 31, 29, 31, 30, 31, 30, 31, 31])):
tick_positions.append(i)
# Determine the corresponding month and year for the label
month_label = ''
if i < 30: month_label = 'Jan.'
elif i < 60: month_label = 'Feb.'
elif i < 90: month_label = 'Mar.'
elif i < 120: month_label = 'Apr.'
elif i < 150: month_label = 'May'
elif i < 180: month_label = 'Jun.'
elif i < 210: month_label = 'Jul.'
elif i < 240: month_label = 'Aug.'
elif i < 270: month_label = 'Sept.'
elif i < 300: month_label = 'Oct.'
elif i < 330: month_label = 'Nov.'
elif i < 360: month_label = 'Dec.'
labels.append(month_label)
ax_list[1].set_xticks(tick_positions)
ax_list[1].set_xticklabels(labels, rotation=45, ha='right')
ax_list[1].set_xlim(150, 290)
ax_list[1].set_ylabel('Snow Height (m)')
ax_list[1].grid()
ax_list[1].legend(title='Year', loc='center left', bbox_to_anchor=(1, 0.5), ncol=1)
# Save the figure
fig.savefig('figures/snow_height/%s/%s_ice_surface.png' % (data_type, station), dpi=300)
Msg(f'')
Msg(' ')
tocgen.processFile(filename, filename[:-3] + "_toc.md")
f.close()
# %%
import matplotlib.pyplot as plt
import pandas as pd
import os
import numpy as np
import matplotlib
matplotlib.use('Agg')
# Initialize
data_type = 'sites'
path_new = '../aws-l3-dev/' + data_type + '/'
filename = 'plot_compilations/ablation_' + data_type + '.md'
df_meta = pd.read_csv(path_new + '../AWS_' + data_type + '_metadata.csv')
df_meta = df_meta.set_index(data_type[:-1] + '_id')
f = open(filename, "w")
# Load the ablation data
df_ablation = pd.read_csv('ancil/promice_ice_ablation_2024.txt', delim_whitespace=True, na_values=-999).set_index('Year')
def Msg(txt):
with open(filename, "a") as f:
print(txt)
f.write(txt + "\n")
plt.close('all')
# Loop over each station
for station in df_meta.index:
# for station in ['KAN_L']:
Msg('## ' + station)
# Check if the file exists
if not os.path.isfile(path_new + station + '/' + station + '_day.csv'):
continue
# Read the station data
df_new = pd.read_csv(path_new + station + '/' + station + '_day.csv')
if df_new.loc[df_new.z_surf_combined.last_valid_index(), 'z_surf_combined'] > 0:
Msg('accumulation site')
continue
df_new.time = pd.to_datetime(df_new.time)
df_new = df_new.set_index('time')
# Filter the ablation data for the current station
if station not in df_ablation.columns:
continue
df_selec = df_ablation[station].dropna()
# Set up subplots
years = df_new.index.year.unique()
num_years = len(years)
fig, ax_list = plt.subplots(round(num_years**0.5)+1, round(num_years**0.5), sharex=True, sharey=True, figsize=(15, 10))
ax_list = ax_list.flatten()
plt.subplots_adjust(right=0.85, left=0.04, hspace=0.3,wspace=0.1)
if num_years == 1:
ax_list = [ax_list] # To keep it consistent for single year
# Iterate over each year
for i, year in enumerate(years):
start_date = pd.Timestamp(f'{year}-04-01')
end_date = pd.Timestamp(f'{year}-10-31')
df_year = df_new.loc[(df_new.index >= start_date) & (df_new.index < end_date), :].copy()
if df_year.empty:
continue
# Calculate the day of the year and first valid value
df_year['day_of_year'] = df_year.index.dayofyear.values
df_year = df_year.loc[df_year.day_of_year > 150]
first_valid_value = (df_year['z_surf_combined'].loc[
slice(df_year['z_surf_combined'].first_valid_index(),
df_year['z_surf_combined'].first_valid_index() + pd.to_timedelta('10 days'))
] - df_year['snow_height'].loc[
slice(df_year['z_surf_combined'].first_valid_index(),
df_year['z_surf_combined'].first_valid_index() + pd.to_timedelta('10 days'))
]).mean()
# Plot z_surf_combined adjusted by first valid value
ax_list[i].plot(df_year['day_of_year'], df_year['z_surf_combined'] - first_valid_value,
label="Surface height", linestyle='-')
# Plot snow_height
ax_list[i].plot(df_year['day_of_year'], df_year['snow_height'],
label="Snow height", linestyle='-')
ax_list[i].hlines(0, xmin=0, xmax=350, color='k')
if year in df_selec.index:
ax_list[i].annotate('', xy=(255, -df_selec[year] * 1000 / 917),
xytext=(255, 0),
arrowprops=dict(facecolor='black', shrink=0.05, width=2, headwidth=10),
label=f'{year}: {df_selec[year]:.2f} m')
if i % round(num_years**0.5) == 0:
ax_list[i].set_ylabel('Height (m)')
ax_list[i].set_title(f'{station} - {year}')
ax_list[i].grid()
# Set x-axis limits to show from DOY 150 to 250
tick_positions = np.arange(150, 270, 20)
ax_list[i].set_xticks(tick_positions)
ax_list[i].set_xlim(150, 270)
from matplotlib.lines import Line2D # Import for custom legend marker
# Add a custom legend with arrow marker for "Expert assessment"
arrow_marker = Line2D([0], [0], color='k', marker='v', markersize=3, linestyle='None', label='Expert assessment')
# Add a single legend on the right side with custom arrow marker
handles, labels = ax_list[0].get_legend_handles_labels()
handles.append(arrow_marker) # Add custom arrow marker to legend
labels.append('Expert assessment') # Add custom arrow marker to legend
fig.legend(handles, labels, loc='center right', bbox_to_anchor=(0.99, 0.5), title='Legend')
# Save the figure
fig.savefig(f'figures/snow_height/{data_type}/{station}_ablation.png', dpi=300)
Msg(f'')
Msg(' ')
f.close()