def make_plot(df, column_name, output_dir, filename_suffix):
# get datetime x-values
xs = get_datetime_xs(df)
# create a figure
fig, ax = plt.subplots(figsize=(15, 4.5))
plt.plot(xs, df[column_name])
# label axes
plt.xlabel('Time', fontsize=12)
plt.ylabel(column_name, fontsize=12)
# calculate Kendall taus and annotate plot
tau, p = get_kendell_tau(df[column_name].dropna())
textstr = f'Kendall $\\tau,~p = {tau:.3f}$, ${p:.4f}$'
ax.text(0.1,
0.95,
textstr,
transform=ax.transAxes,
fontsize=14,
verticalalignment='top')
# save the plot
output_filename = f'{column_name}' + filename_suffix + '.png'
collection_prefix = column_name.split('_')[0]
print(
f'Plotting {collection_prefix} correlation moving window analysis...'
)
plt.savefig(os.path.join(output_dir, output_filename), dpi=DPI)
plt.close(fig)
def make_plot(df, column, output_dir):
"""
Plot STL decomposition results.
Parameters
----------
df : DataFrame
The input time-series.
column : str
Column name to run STL on.
output_dir : str
Directory to save the plot in.
"""
# run fit
res = stl_decomposition(df[column], period)
# concert x values to datetime objects
xs = get_datetime_xs(df)
# formatting
default_figsize = plt.rcParams['figure.figsize']
default_fontsize = plt.rcParams['font.size']
plt.rc('figure', figsize=(20, 8))
plt.rc('font', size=15)
fig = res.plot()
ax_list = fig.axes
for ax in ax_list[:-1]:
ax.tick_params(labelbottom=False)
# set xlabel with datetime object
#ax_list[-1].set_xticklabels(xs, rotation=0, va="center")
# save plot
filename = os.path.join(output_dir, column + '_STL_decomposition.png')
plt.savefig(filename, dpi=DPI)
plt.close(fig)
# undo rc changes
plt.rc('figure', figsize=default_figsize)
plt.rc('font', size=default_fontsize)
def plot_ews_resiliance(series_name, EWSmetrics_df, Kendalltau_df, dates,
output_dir):
"""
Make early warning signals resiliance plots using the output
from the ewstools package.
Parameters
----------
series_name : str
String containing data collection and time series variable.
EWSmetrics_df : DataFrame
DataFrame from ewstools containing ews time series.
Kendalltau_df : DataFrame
DataFrame from ewstools containing Kendall tau values for EWSmetrics_df time series
output_dir: str
Output dir to save plot in.
"""
def zoom_out(ys):
ymin = ys.mean() - 2 * ((ys.mean() - ys).abs().max())
ymax = ys.mean() + 2 * ((ys.mean() - ys).abs().max())
return [ymin, ymax]
def annotate(text, xy=(6, 70), size=10):
if 'Kendall' in text:
xy = (xy[0], 60)
plt.gca().annotate(text,
xy=xy,
xycoords='axes points',
size=size,
ha='left',
va='top')
dates = get_datetime_xs(pd.DataFrame(dates).dropna())
fig, _ = plt.subplots(figsize=(4, 8), sharex='col')
ax1 = plt.subplot(611)
ys = EWSmetrics_df['State variable']
plt.plot(dates[-len(ys):], ys, color='black')
plt.plot(dates[-len(ys):],
EWSmetrics_df['Smoothing'],
color='red',
linestyle='dashed')
plt.ylim(zoom_out(ys))
annotate(series_name)
ax2 = plt.subplot(612, sharex=ax1)
ys = EWSmetrics_df['Residuals']
plt.plot(dates[-len(ys):], ys, color='black', label='Time Series')
plt.ylim(zoom_out(ys))
annotate('Residuals')
ax3 = plt.subplot(613, sharex=ax1)
ys = EWSmetrics_df['Lag-1 AC']
plt.plot(dates[-len(ys):], ys, color='black')
plt.ylim(zoom_out(ys))
annotate('Lag-1 AC')
tau = Kendalltau_df['Lag-1 AC'].iloc[0]
annotate(f'Kendall $\\tau = {tau:.2f}$', size=8)
"""ys = EWSmetrics_df['Lag-2 AC']
plt.plot(ys, color='navy')
plt.ylim(zoom_out(ys))
annotate('Lag-1 AC', xy=(8, 65))
tau = Kendalltau_df['Lag-1 AC'].iloc[0]
annotate(f'Kendall $\\tau = {tau:.2f}$', xy=(8, 55), size=8)"""
ax4 = plt.subplot(614, sharex=ax1)
ys = EWSmetrics_df['Standard deviation']
plt.plot(dates[-len(ys):], ys, color='black')
plt.ylim(zoom_out(ys))
annotate('Standard deviation')
tau = Kendalltau_df['Standard deviation'].iloc[0]
annotate(f'Kendall $\\tau = {tau:.2f}$', size=8)
ax5 = plt.subplot(615, sharex=ax1)
ys = EWSmetrics_df['Skewness']
plt.plot(dates[-len(ys):], ys, color='black')
plt.ylim(zoom_out(ys))
annotate('Skewness')
tau = Kendalltau_df['Skewness'].iloc[0]
annotate(f'Kendall $\\tau = {tau:.2f}$', size=8)
ax6 = plt.subplot(616, sharex=ax1)
ys = EWSmetrics_df['Kurtosis']
plt.plot(dates[-len(ys):], ys, color='black')
plt.ylim(zoom_out(ys))
annotate('Kurtosis')
tau = Kendalltau_df['Kurtosis'].iloc[0]
annotate(f'Kendall $\\tau = {tau:.2f}$', size=8)
plt.xlabel('Time')
# remove vertical space between plots
plt.subplots_adjust(hspace=0.0)
# tick formatting
for ax in [ax1, ax2, ax3, ax4, ax5]:
ax.tick_params(axis='both',
which='both',
bottom=True,
top=False,
labelbottom=False,
left=True,
labelleft=True,
direction='out',
labelsize=8)
# show x labels for bottom plot
ax6.tick_params(axis='both',
which='both',
bottom=True,
top=False,
labelbottom=True,
left=True,
labelleft=True,
direction='out',
labelsize=8)
# save the plot
output_filename = series_name.replace(' ', '-') + '-ews.png'
print(f'Plotting {series_name} ews plots...')
plt.savefig(os.path.join(output_dir, output_filename),
dpi=DPI,
bbox_inches='tight')
plt.close(fig)
def make_plot(df, column, output_dir, smoothing_option):
"""
Parameters
----------
df : DataFrame
The time-series results for variance and AR1.
column : str
Column name an offset50 variance column in df.
output_dir : str
Directory to save the plot in.
smoothing_option: str
Label for smoothing variable to be used
"""
# get short string prefix on column name
collection_prefix = column.split(
'_')[0] if 'offset50' in column else 'precipitation'
# hand mismatched NaNs
ar1_df = df.dropna(subset=[column.replace('var', 'ar1')])
var_df = df.dropna(subset=[column])
# extract x values and convert to datetime objects
ar1_xs = get_datetime_xs(ar1_df)
var_xs = get_datetime_xs(var_df)
# extract individual time series
variance = var_df[column]
ar1 = ar1_df[column.replace('var', 'ar1')]
ar1_se = ar1_df[column.replace('var', 'ar1_se')]
if any([smoothing_option in c for c in df.columns]):
variance_smooth = var_df[column.replace(
'offset50_mean', 'offset50_' + smoothing_option + '_mean')]
ar1_smooth = ar1_df[column.replace('var', 'ar1').replace(
'offset50_mean', 'offset50_' + smoothing_option + '_mean')]
ar1_se_smooth = ar1_df[column.replace('var', 'ar1_se').replace(
'offset50_mean', 'offset50_' + smoothing_option + '_mean')]
# create a figure
fig, ax = plt.subplots(figsize=(15, 5))
plt.xlabel('Time', fontsize=12)
# set up veg y axis
color = 'tab:blue'
ax.set_ylabel(f'{collection_prefix} AR1', color=color, fontsize=12)
ax.tick_params(axis='y', labelcolor=color)
# plot unsmoothed vegetation ar1 and std
ax.plot(ar1_xs, ar1, label='AR1', linewidth=2, color='tab:blue')
ax.fill_between(ar1_xs,
ar1 - ar1_se,
ar1 + ar1_se,
facecolor='blue',
alpha=0.1,
label='AR1 SE')
if any([smoothing_option in c for c in df.columns]):
# plot smoothed vegetation ar1 and std
ax.plot(ar1_xs,
ar1_smooth,
label='AR1 Smoothed',
linewidth=2,
color='tab:blue',
linestyle='dotted')
ax.fill_between(ar1_xs,
ar1_smooth - ar1_se_smooth,
ar1_smooth + ar1_se_smooth,
facecolor='none',
alpha=0.15,
label='AR1 SE Smoothed',
hatch='X',
edgecolor='tab:blue')
# set y lim
try: # in case there are no ar1 values, the array will be empty
ax.set_ylim([
min(ar1 - ar1_se) - 0.8 * max(ar1 + ar1_se),
1.8 * max(ar1 + ar1_se)
])
except:
return
# plot legend
plt.legend(loc='upper left')
# duplicate x-axis for variance
ax2 = ax.twinx()
color = 'tab:red'
ax2.set_ylabel(f'{collection_prefix} Variance',
color=color,
fontsize=12)
ax2.tick_params(axis='y', labelcolor=color)
# plot variance
ax2.plot(var_xs,
variance,
linewidth=2,
color=color,
alpha=0.75,
label='Variance')
if any([smoothing_option in c for c in df.columns]):
ax2.plot(var_xs,
variance_smooth,
linewidth=2,
color=color,
alpha=0.75,
linestyle='dotted',
label='Variance Smoothed')
# set y lim
ax2.set_ylim([0, 2 * max(variance)])
# add legend
plt.legend(loc='lower left')
# add Kendall tau
tau, p = get_kendell_tau(ar1)
tau_var, p_var = get_kendell_tau(variance)
if any([smoothing_option in c for c in df.columns]):
tau_smooth, p_smooth = get_kendell_tau(ar1_smooth)
tau_var_smooth, p_var_smooth = get_kendell_tau(variance_smooth)
# add to plot
textstr = ''
if any([smoothing_option in c for c in df.columns]):
textstr += f'AR1 Kendall $\\tau,~p$-$\\mathrm{{value}}={tau_smooth:.2f}$, ${p_smooth:.2f}$'
textstr += f' (${tau:.2f}$, ${p:.2f}$ unsmoothed)'
ax2.text(0.43,
0.95,
textstr,
transform=ax2.transAxes,
fontsize=14,
verticalalignment='top')
textstr = ''
if any([smoothing_option in c for c in df.columns]):
textstr += f'Variance Kendall $\\tau,~p$-$\\mathrm{{value}}={tau_var_smooth:.2f}$, ${p_var_smooth:.2f}$'
textstr += f' (${tau_var:.2f}$, ${p_var:.2f}$ unsmoothed)'
ax2.text(0.43,
0.85,
textstr,
transform=ax2.transAxes,
fontsize=14,
verticalalignment='top')
# layout
fig.tight_layout()
# save the plot
output_filename = collection_prefix + '-AR1-var-' + smoothing_option + '.png'
print(f'Plotting {collection_prefix} moving window time series...')
plt.savefig(os.path.join(output_dir, output_filename), dpi=DPI)
plt.close(fig)
def make_plot(df,
veg_prefix,
output_dir,
veg_prefix_b=None,
smoothing_option='smooth'):
# handle the case where vegetation and precipitation have mismatched NaNs
veg_df = df.dropna(subset=[veg_prefix + '_offset50_mean'])
# get vegetation x values to datetime objects
veg_xs = get_datetime_xs(veg_df)
# get vegetation y values
veg_means = veg_df[veg_prefix + '_offset50_mean']
veg_std = veg_df[veg_prefix + '_offset50_std']
# create a figure
fig, ax = plt.subplots(figsize=(15, 4.5))
plt.xlabel('Time', fontsize=14)
# set up veg y axis
color = 'tab:green'
ax.set_ylabel(f'{veg_prefix} Offset50', color=color, fontsize=14)
ax.tick_params(axis='y', labelcolor=color)
ax.set_ylim([
veg_means.min() - 1 * veg_std.max(),
veg_means.max() + 3 * veg_std.max()
])
# plot unsmoothed vegetation means
ax.plot(veg_xs,
veg_means,
label='Unsmoothed',
linewidth=1,
color='dimgray',
linestyle='dotted')
# add smoothed time series if availible
if any([
smoothing_option in c and veg_prefix in c
for c in veg_df.columns
]):
# get smoothed mean, std
veg_means_smooth = veg_df[veg_prefix + '_offset50_' +
smoothing_option + '_mean']
veg_stds_smooth = veg_df[veg_prefix + '_offset50_' +
smoothing_option + '_std']
# plot smoothed vegetation means and std
ax.plot(veg_xs,
veg_means_smooth,
marker='o',
markersize=7,
markeredgecolor=(0.9172, 0.9627, 0.9172),
markeredgewidth=2,
label='Smoothed',
linewidth=2,
color='green')
ax.fill_between(veg_xs,
veg_means_smooth - veg_stds_smooth,
veg_means_smooth + veg_stds_smooth,
facecolor='green',
alpha=0.1,
label='Std Dev')
# plot vegetation legend
plt.legend(loc='upper left')
# plot precipitation if availible
if 'total_precipitation' in df.columns:
# handle the case where vegetation and precipitation have mismatched NaNs
precip_df = df.dropna(subset=['total_precipitation'])
precip_ys = precip_df.total_precipitation
# get precipitation x values to datetime objects
precip_xs = get_datetime_xs(precip_df)
# duplicate axis for preciptation
ax2 = ax.twinx()
color = 'tab:blue'
ax2.set_ylabel(f'Precipitation [mm]', color=color, fontsize=14)
ax2.tick_params(axis='y', labelcolor=color)
ax2.set_ylim([
min(precip_ys) - 1 * np.array(precip_ys).std(),
max(precip_ys) + 2 * np.array(precip_ys).std()
])
# plot precipitation
ax2.plot(precip_xs,
precip_ys,
linewidth=2,
color=color,
alpha=0.75)
# add veg-precip correlation
max_corr_smooth, max_corr = get_max_lagged_cor(
os.path.dirname(output_dir), veg_prefix)
textstr = f'$r_{{t-{max_corr_smooth[1]}}}={max_corr_smooth[0]:.2f}$ '
textstr += f'($r_{{t-{max_corr[1]}}}={max_corr[0]:.2f}$ unsmoothed)'
# old correlation just calculates the 0-lag correlation
#raw_corr = veg_means.corr(precip_ys)
#smoothed_corr = veg_means_smooth.corr(precip_ys)
#textstr = f'$r={smoothed_corr:.2f}$ (${raw_corr:.2f}$ unsmoothed)'
ax2.text(0.13,
0.95,
textstr,
transform=ax2.transAxes,
fontsize=14,
verticalalignment='top')
# plot second vegetation time series if availible
if veg_prefix_b:
# handle the case where vegetation and precipitation have mismatched NaNs
veg_df_b = df.dropna(subset=[veg_prefix_b + '_offset50_mean'])
# get vegetation x values to datetime objects
veg_xs_b = get_datetime_xs(veg_df_b)
# get vegetation y values
veg_means_b = veg_df_b[veg_prefix_b + '_offset50_mean']
#veg_std_b = veg_df[veg_prefix_b+'_offset50_std']
veg_means_smooth_b = veg_df_b[veg_prefix_b +
'_offset50_smooth_mean']
veg_stds_smooth_b = veg_df_b[veg_prefix_b + '_offset50_smooth_std']
# plot secondary time series
ax3 = ax.twinx()
ax3.spines["left"].set_position(("axes", -0.08))
ax3.spines["left"].set_visible(True)
color = 'tab:purple'
ax3.set_ylabel(veg_prefix_b + ' Offset50',
color=color,
fontsize=14)
ax3.tick_params(axis='y', labelcolor=color)
ax3.yaxis.tick_left()
ax3.yaxis.set_label_position('left')
ax3.set_ylim([
veg_means.min() - 1 * veg_std.max(),
veg_means.max() + 3 * veg_std.max()
])
# plot unsmoothed vegetation means
ax.plot(veg_xs_b,
veg_means_b,
label='Unsmoothed',
linewidth=1,
color='indigo',
linestyle='dashed',
alpha=0.2)
# plot smoothed vegetation means and std
ax3.plot(veg_xs_b,
veg_means_smooth_b,
marker='o',
markersize=7,
markeredgecolor=(0.8172, 0.7627, 0.9172),
markeredgewidth=2,
label='Smoothed',
linewidth=2,
color=color)
ax3.fill_between(veg_xs_b,
veg_means_smooth_b - veg_stds_smooth_b,
veg_means_smooth_b + veg_stds_smooth_b,
facecolor='tab:purple',
alpha=0.1,
label='Std Dev')
# add veg-veg correlation
vegveg_corr = veg_means.corr(veg_means_b)
vegveg_corr_smooth = veg_means_smooth.corr(veg_means_smooth_b)
textstr = f'$r_{{vv}}={vegveg_corr_smooth:.2f}$ (${vegveg_corr:.2f}$ unsmoothed)'
ax2.text(0.55,
0.85,
textstr,
transform=ax2.transAxes,
fontsize=14,
verticalalignment='top')
# update prefix for filename use
veg_prefix = veg_prefix + '+' + veg_prefix_b
# add autoregression info
veg_means.index = veg_df.date
unsmoothed_ar1, unsmoothed_ar1_se = get_AR1_parameter_estimate(
veg_means)
if any(['smooth' in c and veg_prefix in c for c in veg_df.columns]):
veg_means_smooth.index = veg_df.date
smoothed_ar1, smoothed_ar1_se = get_AR1_parameter_estimate(
veg_means_smooth)
else:
smoothed_ar1, smoothed_ar1_se = np.NaN, np.NaN
ar1_dict = {}
ar1_dict['AR1'] = {
'unsmoothed': {
'param': unsmoothed_ar1,
'se': unsmoothed_ar1_se
},
'smoothed': {
'param': smoothed_ar1,
'se': smoothed_ar1_se
}
}
write_to_json(os.path.join(output_dir, veg_prefix + '_stats.json'),
ar1_dict)
textstr = f'AR$(1)={smoothed_ar1:.2f} \pm {smoothed_ar1_se:.2f}$ (${unsmoothed_ar1:.2f} \pm {unsmoothed_ar1_se:.2f}$ unsmoothed)'
ax.text(0.55,
0.95,
textstr,
transform=ax.transAxes,
fontsize=14,
verticalalignment='top')
# add Kendall tau
tau, p = get_kendell_tau(veg_means)
if any(['smooth' in c and veg_prefix in c for c in veg_df.columns]):
tau_smooth, p_smooth = get_kendell_tau(veg_means_smooth)
else:
tau_smooth, p_smooth = np.NaN, np.NaN
kendall_tau_dict = {}
kendall_tau_dict['Kendall_tau'] = {
'unsmoothed': {
'tau': tau,
'p': p
},
'smoothed': {
'tau': tau_smooth,
'p': p_smooth
}
}
write_to_json(os.path.join(output_dir, veg_prefix + '_stats.json'),
kendall_tau_dict)
textstr = f'$\\tau,~p$-$\\mathrm{{value}}={tau_smooth:.2f}$, ${p:.2f}$ (${tau:.2f}$, ${p_smooth:.2f}$ unsmoothed)'
ax.text(0.13,
0.85,
textstr,
transform=ax.transAxes,
fontsize=14,
verticalalignment='top')
# layout
sns.set_style('white')
fig.tight_layout()
filename_suffix = '_' + smoothing_option
# save the plot
output_filename = veg_prefix + '-time-series' + filename_suffix + '.png'
plt.savefig(os.path.join(output_dir, output_filename), dpi=DPI)
plt.close(fig)
def make_plot(df, veg_prefix, col_name, utput_dir):
veg_df = df.dropna(subset=[col_name])
# get vegetation x values to datetime objects
veg_xs = get_datetime_xs(veg_df)
# get vegetation y values
veg_means = veg_df[col_name]
#veg_means = veg_df[veg_prefix + '_ndvi_veg_mean']
if any([col_name.replace('mean', 'std') == c for c in df.columns]):
veg_std = veg_df[col_name.replace('mean', 'std')]
# create a figure
fig, ax = plt.subplots(figsize=(15, 4.5))
plt.xlabel('Time', fontsize=14)
# set up veg y axis
color = 'tab:green'
ax.set_ylabel(f'{veg_prefix} NDVI', color=color, fontsize=14)
ax.tick_params(axis='y', labelcolor=color)
if any([col_name.replace('mean', 'std') == c for c in df.columns]):
ax.set_ylim([
veg_means.min() - 1 * veg_std.max(),
veg_means.max() + 3 * veg_std.max()
])
# plot ndvi
#ax.plot(veg_xs, ndvi_means, label='Unsmoothed', linewidth=1, color='dimgray', linestyle='dotted')
ax.plot(veg_xs,
veg_means,
marker='o',
markersize=7,
markeredgecolor=(0.9172, 0.9627, 0.9172),
markeredgewidth=2,
label='Smoothed',
linewidth=2,
color='green')
if any([col_name.replace('mean', 'std') == c for c in df.columns]):
ax.fill_between(veg_xs,
veg_means - veg_std,
veg_means + veg_std,
facecolor='green',
alpha=0.1,
label='Std Dev')
# plot precipitation if availible
if 'total_precipitation' in df.columns:
# handle the case where vegetation and precipitation have mismatched NaNs
precip_df = df.dropna(subset=['total_precipitation'])
precip_ys = precip_df.total_precipitation
# get precipitation x values to datetime objects
precip_xs = get_datetime_xs(precip_df)
# duplicate axis for preciptation
ax2 = ax.twinx()
color = 'tab:blue'
ax2.set_ylabel(f'Precipitation [mm]', color=color, fontsize=14)
ax2.tick_params(axis='y', labelcolor=color)
ax2.set_ylim([
min(precip_ys) - 1 * np.array(precip_ys).std(),
max(precip_ys) + 2 * np.array(precip_ys).std()
])
# plot precipitation
ax2.plot(precip_xs,
precip_ys,
linewidth=2,
color=color,
alpha=0.75)
# add correlation information
correlations = get_corrs_by_lag(df[col_name],
df['total_precipitation'])
max_corr = np.max(np.array(correlations))
max_corr_lag = np.array(np.argmax(correlations))
textstr = f'$r_{{t-{max_corr_lag}}}={max_corr:.2f}$ '
ax2.text(0.13,
0.95,
textstr,
transform=ax2.transAxes,
fontsize=14,
verticalalignment='top')
# layout
sns.set_style('white')
fig.tight_layout()
# save the plot
output_filename = veg_prefix + '-ndvi-time-series.png'
plt.savefig(os.path.join(output_dir, output_filename), dpi=DPI)
plt.close(fig)
def make_plot(df, veg_prefix, output_dir, veg_prefix_b=None, smoothing_option='smooth'):
# handle the case where vegetation and precipitation have mismatched NaNs
veg_df = df.dropna(subset=[veg_prefix+'_offset50_mean'])
# get vegetation x values to datetime objects
veg_xs = get_datetime_xs(veg_df)
# get vegetation y values
veg_means = veg_df[veg_prefix + '_offset50_mean']
veg_std = veg_df[veg_prefix + '_offset50_std']
# create a figure
fig, ax = plt.subplots(figsize=(15, 4.5))
plt.xlabel('Time', fontsize=14)
# set up veg y axis
color = 'tab:green'
ax.set_ylabel(f'{veg_prefix} Offset50', color=color, fontsize=14)
ax.tick_params(axis='y', labelcolor=color)
ax.set_ylim([veg_means.min() - 1*veg_std.max(), veg_means.max() + 3*veg_std.max()])
# plot smoothed vegetation means and std
ax.plot(veg_xs, veg_means, marker='o', markersize=7,
markeredgecolor=(0.9172, 0.9627, 0.9172), markeredgewidth=2,
label='Offset50', linewidth=2, color='green')
ax.fill_between(veg_xs, veg_means - veg_std, veg_means + veg_std,
facecolor='green', alpha=0.1, label='Std Dev')
# # get smoothed mean, std
veg_means_smooth = veg_df[veg_prefix+'_offset50_'+smoothing_option+'_mean']
# plot vegetation legend
plt.legend(loc='upper left')
# plot precipitation if availible
if 'total_precipitation' in df.columns:
# handle the case where vegetation and precipitation have mismatched NaNs
precip_df = df.dropna(subset=['total_precipitation'])
precip_ys = precip_df.total_precipitation
# get precipitation x values to datetime objects
precip_xs = get_datetime_xs(precip_df)
# duplicate axis for preciptation
ax2 = ax.twinx()
color = 'tab:blue'
ax2.set_ylabel(f'Precipitation [mm]', color=color, fontsize=14)
ax2.tick_params(axis='y', labelcolor=color)
ax2.set_ylim([min(precip_ys)-1*np.array(precip_ys).std(), max(precip_ys)+2*np.array(precip_ys).std()])
# plot precipitation
ax2.plot(precip_xs, precip_ys, linewidth=2, color=color, alpha=0.75)
# add veg-precip correlation
max_corr_smooth, max_corr = get_max_lagged_cor(os.path.dirname(output_dir), veg_prefix)
textstr = f'$r_{{t-{max_corr[1]}}}={max_corr[0]:.2f}$'
# old correlation just calculates the 0-lag correlation
#raw_corr = veg_means.corr(precip_ys)
#smoothed_corr = veg_means_smooth.corr(precip_ys)
#textstr = f'$r={smoothed_corr:.2f}$ (${raw_corr:.2f}$ unsmoothed)'
ax2.text(0.13, 0.95, textstr, transform=ax2.transAxes, fontsize=14, verticalalignment='top')
# plot second vegetation time series if availible
if veg_prefix_b:
# handle the case where vegetation and precipitation have mismatched NaNs
veg_df_b = df.dropna(subset=[veg_prefix_b+'_offset50_mean'])
# get vegetation x values to datetime objects
veg_xs_b = get_datetime_xs(veg_df_b)
# get vegetation y values
veg_means_b = veg_df_b[veg_prefix_b+'_offset50_mean']
veg_means_smooth_b = veg_df_b[veg_prefix_b+'_offset50_smooth_mean']
veg_stds_smooth_b = veg_df_b[veg_prefix_b+'_offset50_smooth_std']
# plot secondary time series
ax3 = ax.twinx()
ax3.spines["left"].set_position(("axes", -0.08))
ax3.spines["left"].set_visible(True)
color = 'tab:purple'
ax3.set_ylabel(veg_prefix_b + ' Offset50', color=color, fontsize=14)
ax3.tick_params(axis='y', labelcolor=color)
ax3.yaxis.tick_left()
ax3.yaxis.set_label_position('left')
ax3.set_ylim([veg_means.min() - 1*veg_std.max(), veg_means.max() + 3*veg_std.max()])
# plot unsmoothed vegetation means
ax.plot(veg_xs_b, veg_means_b, label='Unsmoothed', linewidth=1, color='indigo', linestyle='dashed', alpha=0.2)
# plot smoothed vegetation means and std
ax3.plot(veg_xs_b, veg_means_smooth_b, marker='o', markersize=7,
markeredgecolor=(0.8172, 0.7627, 0.9172), markeredgewidth=2,
label='Smoothed', linewidth=2, color=color)
ax3.fill_between(veg_xs_b, veg_means_smooth_b - veg_stds_smooth_b, veg_means_smooth_b + veg_stds_smooth_b,
facecolor='tab:purple', alpha=0.1, label='Std Dev')
# add veg-veg correlation
vegveg_corr = veg_means.corr(veg_means_b)
vegveg_corr_smooth = veg_means_smooth.corr(veg_means_smooth_b)
textstr = f'$r_{{vv}} = {vegveg_corr:.2f}$'
ax2.text(0.55, 0.85, textstr, transform=ax2.transAxes, fontsize=14, verticalalignment='top')
# update prefix for filename use
veg_prefix = veg_prefix + '+' + veg_prefix_b
# add autoregression info
veg_means.index = veg_df.date
# layout
sns.set_style('white')
fig.tight_layout()
filename_suffix = '_' + smoothing_option
# save the plot
output_filename = veg_prefix + '-time-series' + filename_suffix + '.png'
plt.savefig(os.path.join(output_dir, output_filename), dpi=DPI)
