def smooth_gam(x, y, n_splines=100, lam=10):
from pygam import ExpectileGAM, LinearGAM, s, f
gam = LinearGAM(s(0, n_splines=n_splines), lam=lam).fit(x, y)
# gam = ExpectileGAM(s(0, n_splines=n_splines), expectile=0.5, lam=lam).gridsearch(x.values.reshape((-1,1)), y)
XX = gam.generate_X_grid(term=0)
confi = gam.confidence_intervals(XX)
# confi = gam.prediction_intervals(XX)
ym = gam.predict_mu(XX)
return XX[:, 0], ym, confi
# and a little thinner (0.5 instead of 1)
plt.rcParams['axes.edgecolor'] = almost_black
plt.rcParams['axes.labelcolor'] = almost_black
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
if plot_type == "GAM":
nsplines = 20
lct_1D = np.tile(np.arange(8), 22)
gam1 = LinearGAM(n_splines=nsplines).fit(
lct_1D, soilmoist_rn1.reshape(8 * 22))
x_pred = np.linspace(0, 7, num=100)
y_pred1 = gam1.predict(x_pred)
y_int1 = gam1.confidence_intervals(x_pred, width=.95)
np.savetxt('soilmoist_rn1.out',
soilmoist_rn1.reshape(8 * 22),
delimiter=',')
np.savetxt('soilmoist_rn2.out',
soilmoist_rn2.reshape(8 * 22),
delimiter=',')
np.savetxt('soilmoist_tdr_rn2.out',
soilmoist_tdr_rn2.reshape(8 * 22),
delimiter=',')
gam2 = LinearGAM(n_splines=nsplines).fit(
lct_1D, soilmoist_rn2.reshape(8 * 22))
y_pred2 = gam2.predict(x_pred)
y_int2 = gam2.confidence_intervals(x_pred, width=.95)
def main(flux_dir):
K_TO_C = 273.15
sites = ["AdelaideRiver","Calperum","CapeTribulation","CowBay",\
"CumberlandPlains","DalyPasture","DalyUncleared",\
"DryRiver","Emerald","Gingin","GreatWesternWoodlands",\
"HowardSprings","Otway","RedDirtMelonFarm","RiggsCreek",\
"Samford","SturtPlains","Tumbarumba","Whroo",\
"WombatStateForest","Yanco"]
pfts = ["SAV","SHB","TRF","TRF","EBF","GRA","SAV",\
"SAV","NA","EBF","EBF",\
"SAV","GRA","NA","GRA",\
"GRA","GRA","EBF","EBF",\
"EBF","GRA"]
d = dict(zip(sites, pfts))
id = dict(zip(sites, pd.factorize(pfts)[0]))
plot_dir = "plots"
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
flux_files = sorted(glob.glob(os.path.join(flux_dir, "*_flux.nc")))
met_files = sorted(glob.glob(os.path.join(flux_dir, "*_met.nc")))
data_qle = []
data_qh = []
data_tair = []
data_sw = []
pft_ids = []
# collect up data
for flux_fn, met_fn in zip(flux_files, met_files):
(site, df_flx, df_met) = open_file(flux_fn, met_fn)
if d[site] != "NA":
pft = d[site]
colour_id = id[site]
# Mask crap stuff
df_met.where(df_flx.Qle_qc == 1, inplace=True)
df_met.where(df_flx.Qh_qc == 1, inplace=True)
df_flx.where(df_flx.Qle_qc == 1, inplace=True)
df_flx.where(df_flx.Qh_qc == 1, inplace=True)
#df_flx.where(df_met.Tair_qc == 1, inplace=True)
#df_flx.where(df_met.SWdown == 1, inplace=True)
#df_met.where(df_met.SWdown == 1, inplace=True)
#df_met.where(df_met.Tair_qc == 1, inplace=True)
# Mask dew
df_met.where(df_flx.Qle > 0., inplace=True)
df_flx.where(df_flx.Qle > 0., inplace=True)
df_flx.dropna(inplace=True)
df_met.dropna(inplace=True)
df_flx = df_flx.between_time("09:00", "13:00")
df_met = df_met.between_time("09:00", "13:00")
if len(df_flx) > 0 and len(df_met) > 0:
#data_qle[pft].append(df_flx.Qle.values)
#data_qh[pft].append(df_flx.Qh.values)
#data_tair[pft].append(df_met.Tair.values - K_TO_C)
#data_sw[pft].append(df_met.SWdown.values)
data_qle.append(df_flx.Qle.values)
data_qh.append(df_flx.Qh.values)
data_tair.append(df_met.Tair.values - K_TO_C)
data_sw.append(df_met.SWdown.values)
pft_ids.append([pft] * len(df_flx))
pft_ids = list(itertools.chain(*pft_ids))
data_qle = list(itertools.chain(*data_qle))
data_qh = list(itertools.chain(*data_qh))
data_sw = list(itertools.chain(*data_sw))
data_tair = list(itertools.chain(*data_tair))
data_qle = np.asarray(data_qle)
data_qh = np.asarray(data_qh)
data_tair = np.asarray(data_tair)
data_sw = np.asarray(data_sw)
pft_ids = np.asarray(pft_ids)
colours = ["red", "green", "blue", "yellow", "pink"]
fig = plt.figure(figsize=(14, 4))
fig.subplots_adjust(hspace=0.1)
fig.subplots_adjust(wspace=0.1)
plt.rcParams['text.usetex'] = False
plt.rcParams['font.family'] = "sans-serif"
plt.rcParams['font.sans-serif'] = "Helvetica"
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['font.size'] = 14
plt.rcParams['legend.fontsize'] = 14
plt.rcParams['xtick.labelsize'] = 14
plt.rcParams['ytick.labelsize'] = 14
almost_black = '#262626'
# change the tick colors also to the almost black
plt.rcParams['ytick.color'] = almost_black
plt.rcParams['xtick.color'] = almost_black
# change the text colors also to the almost black
plt.rcParams['text.color'] = almost_black
# Change the default axis colors from black to a slightly lighter black,
# and a little thinner (0.5 instead of 1)
plt.rcParams['axes.edgecolor'] = almost_black
plt.rcParams['axes.labelcolor'] = almost_black
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)
colour_id = 0
for pft in np.unique(pfts):
if pft != "NA":
qle = data_qle[np.argwhere(pft_ids == pft)]
qh = data_qh[np.argwhere(pft_ids == pft)]
tair = data_tair[np.argwhere(pft_ids == pft)]
sw = data_sw[np.argwhere(pft_ids == pft)]
print(pft, len(qle), len(qh), len(tair), len(sw))
gam = LinearGAM(n_splines=20).gridsearch(sw, qh)
XX = generate_X_grid(gam)
CI = gam.confidence_intervals(XX, width=.95)
ax1.plot(XX,
gam.predict(XX),
color=colours[colour_id],
ls='-',
lw=2.0)
ax1.fill_between(XX[:, 0],
CI[:, 0],
CI[:, 1],
color=colours[colour_id],
alpha=0.7)
gam = LinearGAM(n_splines=20).gridsearch(sw, qle)
XX = generate_X_grid(gam)
CI = gam.confidence_intervals(XX, width=.95)
ax2.plot(XX,
gam.predict(XX),
color=colours[colour_id],
ls='-',
lw=2.0)
ax2.fill_between(XX[:, 0],
CI[:, 0],
CI[:, 1],
color=colours[colour_id],
alpha=0.7)
gam = LinearGAM(n_splines=20).gridsearch(tair, qh)
XX = generate_X_grid(gam)
CI = gam.confidence_intervals(XX, width=.95)
ax3.plot(XX,
gam.predict(XX),
color=colours[colour_id],
ls='-',
lw=2.0)
ax3.fill_between(XX[:, 0],
CI[:, 0],
CI[:, 1],
color=colours[colour_id],
alpha=0.7)
gam = LinearGAM(n_splines=20).gridsearch(tair, qle)
XX = generate_X_grid(gam)
CI = gam.confidence_intervals(XX, width=.95)
ax4.plot(XX,
gam.predict(XX),
color=colours[colour_id],
ls='-',
lw=2.0)
ax4.fill_between(XX[:, 0],
CI[:, 0],
CI[:, 1],
color=colours[colour_id],
alpha=0.7)
colour_id += 1
plt.setp(ax1.get_xticklabels(), visible=False)
plt.setp(ax2.get_xticklabels(), visible=False)
ax1.set_xlim(0, 1300)
ax1.set_ylim(0, 1000)
ax2.set_xlim(0, 45)
ax2.set_ylim(0, 1000)
ax3.set_xlabel("SW down (W m$^{-2}$)")
ax4.set_xlabel("Tair ($^\circ$C)")
ax1.set_ylabel("Qh flux (W m$^{-2}$)")
ax2.set_ylabel("Qle flux (W m$^{-2}$)")
#ax1.legend(numpoints=1, loc="best")
#fig.savefig(os.path.join(plot_dir, "%s.pdf" % (site)),
# bbox_inches='tight', pad_inches=0.1)
fig.savefig(os.path.join(plot_dir, "ozflux_by_pft.png"),
bbox_inches='tight',
pad_inches=0.1,
dpi=150)
def plot_LAI_MAP(df):
# Clean the df
df = df.drop(columns=['Potentially_erroneous_data', 'Reference_number',\
'Method'])
df.rename(columns={
'MAT_(Literature value)': 'MAT',
'MAP_(Literature value)': 'MAP'
},
inplace=True)
df_eucs = df[(df['PFT'] == "EB") & \
(df['MAP'] < 3000.0) & \
(df['Vegetation_status'] == "Natural") &\
(df['Dominant_species'].str.contains('Eucalyptus'))]
df_ebf = df[(df['PFT'] == "EB") &\
(df['MAP'] < 3000.0) & \
(df['Vegetation_status'] == "Natural") &\
(~ df['Dominant_species'].str.contains('Eucalyptus'))]
fig = plt.figure(figsize=(9, 6))
fig.subplots_adjust(hspace=0.1)
fig.subplots_adjust(wspace=0.05)
plt.rcParams['text.usetex'] = False
plt.rcParams['font.family'] = "sans-serif"
plt.rcParams['font.sans-serif'] = "Helvetica"
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['font.size'] = 14
plt.rcParams['legend.fontsize'] = 14
plt.rcParams['xtick.labelsize'] = 14
plt.rcParams['ytick.labelsize'] = 14
ax = fig.add_subplot(111)
colours = sns.color_palette("Set2", 8)
ax.plot(df_ebf.MAP,
df_ebf.Total_LAI,
color=colours[0],
ls=" ",
marker="o",
label="Global EBF")
nsplines = 7
x = df_ebf.MAP.values
y = df_ebf.Total_LAI.values
gam = LinearGAM(n_splines=nsplines).fit(x, y)
x_pred = np.linspace(min(x), max(x), num=100)
y_pred = gam.predict(x_pred)
y_int = gam.confidence_intervals(x_pred, width=.95)
ax.plot(x_pred, y_pred, color=colours[0], ls='-', lw=2.0, zorder=10)
ax.fill_between(x_pred,
y_int[:, 0],
y_int[:, 1],
alpha=0.2,
facecolor=colours[0],
zorder=10)
ax.plot(df_eucs.MAP,
df_eucs.Total_LAI,
color=colours[1],
ls=" ",
marker="o",
label="Eucalypts")
nsplines = 4
x = df_eucs.MAP.values
y = df_eucs.Total_LAI.values
gam = LinearGAM(n_splines=nsplines).fit(x, y)
x_pred = np.linspace(min(x), max(x), num=100)
y_pred = gam.predict(x_pred)
y_int = gam.confidence_intervals(x_pred, width=.95)
ax.plot(x_pred, y_pred, color=colours[1], ls='-', lw=2.0, zorder=10)
ax.fill_between(x_pred,
y_int[:, 0],
y_int[:, 1],
alpha=0.2,
facecolor=colours[1],
zorder=10)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.legend(numpoints=1, loc="best", frameon=False)
ax.set_xlabel("Mean annual precipitation (mm)")
ax.set_ylabel("Leaf area index (m$^{2}$ m$^{-2}$)")
plt.show()
odir = "plots"
fig.savefig(os.path.join(odir, "LAI_vs_MAP_Eucs_vs_EBF.pdf"),
bbox_inches='tight',
pad_inches=0.1)
plt.show()
def main(fname1, fname2, fname3, fname4):
df1 = pd.read_csv(fname1)
df1 = df1[df1.pft == "EBF"]
df2 = pd.read_csv(fname2)
df2 = df2[df2.pft == "EBF"]
# Add in Alice information from FLUXNET
df3 = pd.read_csv(fname3)
df3 = df3[df3.pft == "ENF"] # Alice misclassified by fluxnet
df3 = df3[~np.isnan(df3.gpp_sqrt_d)]
df1 = df1.append(df3, ignore_index=True)
df4 = pd.read_csv(fname4)
df4 = df4[df4.pft == "ENF"] # Alice misclassified by fluxnet
df4 = df4[~np.isnan(df4.gpp_sqrt_d)]
df2 = df2.append(df4, ignore_index=True)
df1.loc[df1.site == "AU-ASM", "site"] = "Alice Springs"
df2.loc[df2.site == "AU-ASM", "site"] = "Alice Springs"
width = 14
height = 9
fig = plt.figure(figsize=(width, height))
fig.subplots_adjust(hspace=0.05)
fig.subplots_adjust(wspace=0.02)
plt.rcParams['text.usetex'] = False
plt.rcParams['font.family'] = "sans-serif"
plt.rcParams['font.sans-serif'] = "Helvetica"
plt.rcParams['axes.labelsize'] = 16
plt.rcParams['font.size'] = 16
plt.rcParams['legend.fontsize'] = 12
plt.rcParams['xtick.labelsize'] = 16
plt.rcParams['ytick.labelsize'] = 16
labels = label_generator('lower', start="(", end=")")
count=0
sites = np.unique(df1.site)
for site in sites:
df_site1 = df1[df1.site == site]
df_site2 = df2[df2.site == site]
site_name = re.sub(r"(\w)([A-Z])", r"\1 \2", site)
ax = fig.add_subplot(3,2,1+count)
ax.plot(df_site2.gpp_sqrt_d, df_site2.et,
label="Heatwave days", color="red", ls="", marker=".",
lw=2, zorder=100, ms=9, mec="#ffb3b3")
ax.plot(df_site1.gpp_sqrt_d, df_site1.et,
label="Non-heatwave days", color="blue", ls="", marker=".",
lw=2, alpha=0.3, ms=9)
from scipy import stats
x = df_site2.gpp_sqrt_d
y = df_site2.et
y = y[~np.isnan(x)]
x = x[~np.isnan(x)]
x = x[~np.isnan(y)]
y = y[~np.isnan(y)]
if site != "Alice Springs":
nsplines = 20
else:
nsplines = 10
if site != "CumberlandPlains":
gam = LinearGAM(n_splines=nsplines).gridsearch(x, y)
x_pred = np.linspace(min(x), max(x), num=100)
y_pred = gam.predict(x_pred)
y_int = gam.confidence_intervals(x_pred, width=.95)
ax.plot(x_pred, y_pred, color="red", ls='-', lw=2.0, zorder=10)
ax.fill_between(x_pred, y_int[:, 0], y_int[:, 1], alpha=0.2,
facecolor='red', zorder=10)
x = df_site1.gpp_sqrt_d
y = df_site1.et
y = y[~np.isnan(x)]
x = x[~np.isnan(x)]
x = x[~np.isnan(y)]
y = y[~np.isnan(y)]
gam = LinearGAM(n_splines=nsplines).gridsearch(x, y)
x_pred = np.linspace(min(x), max(x), num=100)
y_pred = gam.predict(x_pred)
y_int = gam.confidence_intervals(x_pred, width=.95)
ax.plot(x_pred, y_pred, color="blue", ls='-', lw=2.0, zorder=10)
ax.fill_between(x_pred, y_int[:, 0], y_int[:, 1], alpha=0.2,
facecolor='blue', zorder=10)
if count < 4:
plt.setp(ax.get_xticklabels(), visible=False)
if count != 0 and count != 2 and count != 4:
plt.setp(ax.get_yticklabels(), visible=False)
props = dict(boxstyle='round', facecolor='white', alpha=1.0,
ec="white")
fig_label = "%s %s" % (next(labels), site_name)
ax.text(0.02, 0.95, fig_label,
transform=ax.transAxes, fontsize=14, verticalalignment='top',
bbox=props)
ax.set_xlim(0, 11)
ax.set_ylim(0, 4)
if count == 2:
ax.set_ylabel('E (mm d$^{-1}$)')
if count == 4:
#ax.set_xlabel('Temperature ($^\circ$C)', position=(1.0, 0.5))
ax.set_xlabel(r"GPP $\times$ D$^{0.5}$ (g C kPa$^{0.5}$ m$^{-2}$ d$^{-1}$)",
position=(1.0, 0.5))
else:
ax.set_xlabel(" ")
from matplotlib.ticker import MaxNLocator
ax.yaxis.set_major_locator(MaxNLocator(3))
ax.xaxis.set_major_locator(MaxNLocator(3))
ax.tick_params(direction='in', length=4)
if site == "Calperum":
#ax.legend(numpoints=1, ncol=1, frameon=False, loc=(1.6, 0.0))
ax.legend(numpoints=1, ncol=1, frameon=False, loc="best")
count+=1
ofdir = "/Users/mdekauwe/Dropbox/fluxnet_heatwaves_paper/figures/figs"
ofname = "ozflux_heatwave_vs_non_heatwave_gppsqrtd_et.pdf"
fig.savefig(os.path.join(ofdir, ofname),
bbox_inches='tight', pad_inches=0.1)
def calculate_gene_trends(session_ID, list_of_genes, branch_ID):
n_steps = 2 + len(list_of_genes)
#uns = cache_adata(session_ID, group="uns")
obs = cache_adata(session_ID, group="obs")
cache_progress(session_ID, progress=int(1 / n_steps * 100))
if (branch_ID == -1):
branch_probs = None
else:
branch_probs = obs["pseudotime_branch_" + str(branch_ID)]
pseudotime = obs["pseudotime"]
cache_progress(session_ID, progress=int(2 / n_steps * 100))
if ((branch_ID == -1) or (branch_probs is None)):
cells_in_branch = obs.index
else:
cells_in_branch = obs[obs["pseudotime_branch_" +
str(branch_ID)] > 0.2].index
print("[DEBUG] branch: " + str(branch_ID))
'''
gene_trends = palantir.presults.compute_gene_trends(pr_res,
imp_df.loc[:, genes],
lineages = [branch],
n_jobs=1)
'''
X_train = pseudotime.to_numpy()
# reduce the number of data points we fit to save computation time
max_samples_to_fit = 5000
if (len(X_train) <= max_samples_to_fit):
subsample_mask = np.ones_like(X_train)
else:
subsample_mask = np.zeros_like(X_train)
subsample_mask[0:max_samples_to_fit] = 1
np.random.shuffle(subsample_mask)
subsample_mask = np.array(subsample_mask, dtype=bool)
if ((branch_ID != -1) and not (branch_probs is None)):
weights = branch_probs.to_numpy()
else:
weights = np.ones_like(X_train)
X_train = X_train[subsample_mask]
weights = weights[subsample_mask]
X_train = np.reshape(X_train, (len(X_train), 1))
weights = np.reshape(weights, (len(weights), 1))
X_plot = np.linspace(np.min(obs["pseudotime"][cells_in_branch]),
np.max(obs["pseudotime"][cells_in_branch]), 125)
gene_trends = pd.DataFrame()
gene_trends["pseudotime"] = X_plot
step_number = 3
for gene in list_of_genes:
#Y_train = adata.obs_vector(gene, layer="imputed")
time_0 = datetime.now()
Y_train = get_obs_vector(session_ID, gene, layer="imputed")
print("[BENCH] time for get_obs_vector: " +
str(datetime.now() - time_0))
Y_train = Y_train[subsample_mask]
gam = LinearGAM(n_splines=5, spline_order=3)
time_0 = datetime.now()
gam.gridsearch(X_train, Y_train, weights=weights, progress=False)
print("[BENCH] time for gam fit: " + str(datetime.now() - time_0))
#gam = ExpectileGAM(terms="s(0)", expectile=0.5).gridsearch(X_train, Y_train)
#lam = gam.lam
#gam_upper = ExpectileGAM(expectile=0.75, lam=lam).fit(X_train, Y_train)
#gam_lower = ExpectileGAM(expectile=0.25, lam=lam).fit(X_train, Y_train)
gene_trends[gene] = gam.predict(X_plot)
#gene_trends[gene + "_ci_upper"] = gam_upper.predict(X_plot)
#gene_trends[gene + "_ci_lower"] = gam_lower.predict(X_plot)
ci = gam.confidence_intervals(X_plot, width=.95)
gene_trends[gene + "_ci_upper"] = ci[:, 1]
gene_trends[gene + "_ci_lower"] = ci[:, 0]
cache_progress(session_ID, progress=int(step_number / n_steps * 100))
step_number += 1
gene_trends = gene_trends.clip(lower=0)
return gene_trends
def make_plot(plot_dir, site, df_flx, df_met):
K_TO_C = 273.15
#golden_mean = 0.6180339887498949
#width = 6*2*(1/golden_mean)
#height = width * golden_mean
fig = plt.figure(figsize=(14, 4))
fig.subplots_adjust(hspace=0.1)
fig.subplots_adjust(wspace=0.1)
plt.rcParams['text.usetex'] = False
plt.rcParams['font.family'] = "sans-serif"
plt.rcParams['font.sans-serif'] = "Helvetica"
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['font.size'] = 14
plt.rcParams['legend.fontsize'] = 14
plt.rcParams['xtick.labelsize'] = 14
plt.rcParams['ytick.labelsize'] = 14
almost_black = '#262626'
# change the tick colors also to the almost black
plt.rcParams['ytick.color'] = almost_black
plt.rcParams['xtick.color'] = almost_black
# change the text colors also to the almost black
plt.rcParams['text.color'] = almost_black
# Change the default axis colors from black to a slightly lighter black,
# and a little thinner (0.5 instead of 1)
plt.rcParams['axes.edgecolor'] = almost_black
plt.rcParams['axes.labelcolor'] = almost_black
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
# Mask crap stuff
df_met.where(df_flx.Qle_qc == 1, inplace=True)
df_met.where(df_flx.Qh_qc == 1, inplace=True)
df_flx.where(df_flx.Qle_qc == 1, inplace=True)
df_flx.where(df_flx.Qh_qc == 1, inplace=True)
#df_flx.where(df_met.Tair_qc == 1, inplace=True)
#df_flx.where(df_met.SWdown == 1, inplace=True)
#df_met.where(df_met.SWdown == 1, inplace=True)
#df_met.where(df_met.Tair_qc == 1, inplace=True)
# Mask dew
df_met.where(df_flx.Qle > 0., inplace=True)
df_flx.where(df_flx.Qle > 0., inplace=True)
df_flx.dropna(inplace=True)
df_met.dropna(inplace=True)
if len(df_flx) > 0 and len(df_met) > 0:
print(site, len(df_flx), len(df_met))
alpha = 0.07
# < "Midday" data
df_flx = df_flx.between_time("09:00", "13:00")
df_met = df_met.between_time("09:00", "13:00")
ax1.plot(df_met.SWdown,
df_flx.Qle,
ls=" ",
marker=".",
mec="#FF0000",
color="#FF0000",
alpha=alpha)
ax1.plot(df_met.SWdown,
df_flx.Qh,
ls=" ",
marker=".",
mec="royalblue",
color="royalblue",
alpha=alpha)
gam = LinearGAM(n_splines=20).gridsearch(df_met.SWdown, df_flx.Qle)
XX = generate_X_grid(gam)
CI = gam.confidence_intervals(XX, width=.95)
ax1.plot(XX,
gam.predict(XX),
color="#FF0000",
ls='-',
lw=2.0,
label="Qle")
ax1.fill_between(XX[:, 0],
CI[:, 0],
CI[:, 1],
color='salmon',
alpha=0.7)
gam = LinearGAM(n_splines=20).gridsearch(df_met.SWdown, df_flx.Qh)
XX = generate_X_grid(gam)
CI = gam.confidence_intervals(XX, width=.95)
ax1.plot(XX,
gam.predict(XX),
color="royalblue",
ls='-',
lw=2.0,
label="Qh")
ax1.fill_between(XX[:, 0],
CI[:, 0],
CI[:, 1],
color='CornflowerBlue',
alpha=0.7)
ax2.plot(df_met.Tair - K_TO_C,
df_flx.Qle,
ls=" ",
marker=".",
color="#FF0000",
alpha=alpha,
mec="#FF0000")
ax2.plot(df_met.Tair - K_TO_C,
df_flx.Qh,
ls=" ",
marker=".",
color="royalblue",
alpha=alpha,
mec="royalblue")
gam = LinearGAM(n_splines=20).gridsearch(df_met.Tair - K_TO_C,
df_flx.Qle)
XX = generate_X_grid(gam)
CI = gam.confidence_intervals(XX, width=.95)
ax2.plot(XX, gam.predict(XX), color="#FF0000", ls='-', lw=2.0)
ax2.fill_between(XX[:, 0],
CI[:, 0],
CI[:, 1],
color='salmon',
alpha=0.7)
gam = LinearGAM(n_splines=20).gridsearch(df_met.Tair - K_TO_C,
df_flx.Qh)
XX = generate_X_grid(gam)
CI = gam.confidence_intervals(XX, width=.95)
ax2.plot(XX, gam.predict(XX), color="royalblue", ls='-', lw=2.0)
ax2.fill_between(XX[:, 0],
CI[:, 0],
CI[:, 1],
color='CornflowerBlue',
alpha=0.7)
plt.setp(ax2.get_yticklabels(), visible=False)
ax1.set_xlim(0, 1300)
ax1.set_ylim(0, 1000)
ax2.set_xlim(0, 45)
ax2.set_ylim(0, 1000)
ax1.set_xlabel("SW down (W m$^{-2}$)")
ax2.set_xlabel("Tair ($^\circ$C)")
ax1.set_ylabel("Daytime flux (W m$^{-2}$)")
#ax1.legend(numpoints=1, loc="best")
#fig.savefig(os.path.join(plot_dir, "%s.pdf" % (site)),
# bbox_inches='tight', pad_inches=0.1)
fig.savefig(os.path.join(plot_dir, "%s.png" % (site)),
bbox_inches='tight',
pad_inches=0.1,
dpi=100)
discrete=(False, False),
cbar=False,
cbar_kws=dict(shrink=.75),
ax=ax)
# sns.displot(df, x="WTD", y="deltaT",ax = ax)
x = df['WTD (m)'].values
y = df['ΔT (°C)'].values
xx = x.reshape(x.shape[0], 1)
yy = y.reshape(y.shape[0], 1)
print("I am OK 2")
# reshape for gam
gam = LinearGAM(n_splines=4).gridsearch(xx, yy) # n_splines=22
x_pred = np.linspace(min(x), max(x), num=100)
y_pred = gam.predict(x_pred)
y_int = gam.confidence_intervals(x_pred, width=.95)
ax.plot(x_pred, y_pred, color="red", ls='-', lw=2.0, zorder=10)
ax.fill_between(x_pred,
y_int[:, 0],
y_int[:, 1],
alpha=0.2,
facecolor='red',
zorder=10)
# ax.text(0.03, 0.95, '(f)', transform=ax.transAxes, fontsize=18, verticalalignment='top', bbox=props)
print("I am OK 3")
# plt.setp(ax.get_xticklabels(), visible=False)
# ax.set(xticks=xtickslocs, xticklabels=cleaner_dates) ####
ax.yaxis.tick_left()
ax.yaxis.set_label_position("left")
bwith = 2
