def plot_2d():
positions = [[10, 90], [60, 5]]
extents = [[30, -30], [30, 20]]
colors = ['dodgerblue', 'crimson']
fig = plt.figure()
fig.set_size_inches(5.5, 2.5)
ax = fig.add_subplot(121)
ax.set_xlim([0, 100])
ax.set_xticks([0, 25, 50, 75, 100])
ax.set_xlabel('width [mm]')
ax.set_ylim([0, 100])
ax.set_ylabel('height [mm]')
ax.set_yticks([0, 25, 50, 75, 100])
rect = patches.Rectangle((10, 90), 30, -30, alpha=0.25, facecolor="dodgerblue", edgecolor='k',
lw=0.75, ls='--')
ax.add_patch(rect)
rect = patches.Rectangle((60, 5), 30, 20, alpha=0.25, facecolor="crimson", edgecolor='k',
lw=0.75, ls='--')
ax.add_patch(rect)
ax.scatter([10, 60], [90, 5], marker=".", s=50, lw=0.25, facecolor="r", edgecolor='k')
ax.plot([10, 40], [90, 90], lw=1.5, color='r')
ax.plot([10, 10], [90, 60], lw=1.5, color='r')
ax.plot([60, 60], [5, 25], lw=1.5, color='r')
ax.plot([60, 90], [5, 5], lw=1.5, color='r')
col_labels = ["Pos 1", "Pos 2"]
rows_2d = ["width", "height"]
cell_text = [[positions[0][0], positions[1][0]],
[positions[0][1], positions[1][1]]]
table = matplotlib.table.table(ax, cellText=cell_text, rowLabels=rows_2d,
colLabels=col_labels,
cellLoc="center", bbox=[1.6, 0.6, 0.4, 0.3],
colWidths=[0.2 for c in col_labels],
colColours=colors)
ax.text(130, 100, "Positions DataArray") #, transform=ax.transAxes , fontsize=10)
table.auto_set_font_size(False)
table.set_fontsize(8)
col_labels = ["Ext 1", "Ext 2"]
cell_text = [[extents[0][0], extents[1][0]],
[extents[0][1], extents[1][1]]]
table = matplotlib.table.table(ax, cellText=cell_text, rowLabels=rows_2d,
colLabels=col_labels,
cellLoc="center", bbox=[1.6, 0.1, 0.4, 0.3],
colWidths=[0.2 for c in col_labels],
colColours=colors)
ax.text(130, 100, "Positions DataArray") #, transform=ax.transAxes , fontsize=10)
table.auto_set_font_size(False)
table.set_fontsize(8)
ax.text(130, 45, "Extents DataArray") #, transform=ax.transAxes , fontsize=10)
ax.spines["top"].set_visible(False)
ax.spines['right'].set_visible(False)
fig.subplots_adjust(bottom=0.175, left=0.15, right=0.95, top=0.95)
fig.savefig("../images/2d_mtag.png")
def make_water_quality_confidence_score_guide(png_path):
""" Create table explaining confidence scores for water quality forecasts. This table is
static.
Args:
png_path: Raw str. Path for PNG to be created
Returns:
None. Table is saved as a PNG to the specified path
"""
matplotlib.table.CustomCell = MyCell
# Data for table grid
data = [
["None", "-", "< 0.2"],
["Very low", "< 25", "-"],
["Low", "25 - 50", "0.2 - 0.4"],
["Medium", "50 - 75", "0.4 - 0.6"],
["High", "> 75", "> 0.6"],
]
cell_colours = [
["lightgrey", "white", "lightgrey"],
["lightsalmon", "lightsalmon", "white"],
["yellow", "yellow", "yellow"],
["yellowgreen", "yellowgreen", "yellowgreen"],
["lightsteelblue", "lightsteelblue", "lightsteelblue"],
]
# Build table
col_labels = [
"Label",
"Likelihood\n(%)",
"Historic skill\n(MCC³)",
]
table = plt.table(
cellText=data,
colLabels=col_labels,
cellColours=cell_colours,
loc="center",
cellLoc="center",
)
# Layout and basic formatting
table.auto_set_font_size(False)
table.auto_set_column_width(col=range(len(col_labels)))
table.scale(1, 2.5)
for (row, col), cell in table.get_celld().items():
if row == 0:
cell.set_text_props(fontproperties=FontProperties(weight="bold"))
cell.set_height(0.25)
table.set_fontsize(20)
plt.axis("off")
plt.savefig(png_path, dpi=300, bbox_inches="tight")
def plot_3d():
positions = [[10, 10, 90], [60, 5, 20]]
extents = [[30, 40, -30], [30, 60, 20]]
colors = ['dodgerblue', 'crimson']
fig = plt.figure()
fig.set_size_inches(5.5, 2.5)
ax = plt.subplot2grid((2, 5), (0, 0), colspan=3, rowspan=2, projection='3d')
ax.set_xlim([0, 100])
ax.set_xticks([0, 25, 50, 75, 100])
ax.set_xlabel('width [mm]')
ax.set_ylim([0, 100])
ax.set_yticks([0, 25, 50, 75, 100])
ax.set_ylabel('depth [mm]')
ax.set_zlabel('height [mm]')
ax.set_zlim([0, 100])
ax.set_zticks([0, 25, 50, 75, 100])
for i in range(len(positions)):
plotcubus(ax, positions[i], extents[i], colors[i])
col_labels = ["Pos 1", "Pos 2"]
rows_3d = ["width", "height", "depth"]
cell_text = [[positions[0][0], positions[1][0]], [positions[0][2], positions[1][2]],
[positions[0][1], positions[1][1]]]
table = matplotlib.table.table(ax, cellText=cell_text, rowLabels=rows_3d, colLabels=col_labels,
cellLoc="center", bbox=[1.4, 0.5, 0.25, 0.3],
colWidths=[0.1 for c in col_labels],
colColours=colors)
ax.text(170, 100, 130.0, "Positions DataArray") #, transform=ax.transAxes , fontsize=10)
table.auto_set_font_size(False)
table.set_fontsize(8)
col_labels = ["Ext 1", "Ext 2"]
cell_text = [[extents[0][0], extents[1][0]], [extents[0][2], extents[1][2]],
[extents[0][1], extents[1][1]]]
table = matplotlib.table.table(ax, cellText=cell_text, rowLabels=rows_3d, colLabels=col_labels,
cellLoc="center", bbox=[1.4, -.1, 0.25, 0.3],
colWidths=[0.1 for c in col_labels],
colColours=colors)
table.auto_set_font_size(False)
table.set_fontsize(8)
ax.text(180, 100, 5.0, "Extents DataArray") #, transform=ax.transAxes , fontsize=10)
fig.subplots_adjust(bottom=0.1, left=0., right=0.95, top=0.95)
fig.savefig("../images/3d_mtag.png")
def plot_3d():
positions = [[10, 10, 90], [60, 5, 20]]
extents = [[30, 40, -30], [30, 60, 20]]
colors = ['dodgerblue', 'crimson']
fig = plt.figure()
fig.set_size_inches(5.5, 2.5)
ax = plt.subplot2grid((2, 5), (0, 0),
colspan=3,
rowspan=2,
projection='3d')
ax.set_xlim([0, 100])
ax.set_xticks([0, 25, 50, 75, 100])
ax.set_xlabel('width [mm]')
ax.set_ylim([0, 100])
ax.set_yticks([0, 25, 50, 75, 100])
ax.set_ylabel('depth [mm]')
ax.set_zlabel('height [mm]')
ax.set_zlim([0, 100])
ax.set_zticks([0, 25, 50, 75, 100])
for i in range(len(positions)):
plotcubus(ax, positions[i], extents[i], colors[i])
col_labels = ["Pos 1", "Pos 2"]
rows_3d = ["width", "height", "depth"]
cell_text = [[positions[0][0], positions[1][0]],
[positions[0][2], positions[1][2]],
[positions[0][1], positions[1][1]]]
table = matplotlib.table.table(ax,
cellText=cell_text,
rowLabels=rows_3d,
colLabels=col_labels,
cellLoc="center",
bbox=[1.4, 0.5, 0.25, 0.3],
colWidths=[0.1 for c in col_labels],
colColours=colors)
ax.text(170, 100, 130.0,
"Positions DataArray") #, transform=ax.transAxes , fontsize=10)
table.auto_set_font_size(False)
table.set_fontsize(8)
col_labels = ["Ext 1", "Ext 2"]
cell_text = [[extents[0][0],
extents[1][0]], [extents[0][2], extents[1][2]],
[extents[0][1], extents[1][1]]]
table = matplotlib.table.table(ax,
cellText=cell_text,
rowLabels=rows_3d,
colLabels=col_labels,
cellLoc="center",
bbox=[1.4, -.1, 0.25, 0.3],
colWidths=[0.1 for c in col_labels],
colColours=colors)
table.auto_set_font_size(False)
table.set_fontsize(8)
ax.text(180, 100, 5.0,
"Extents DataArray") #, transform=ax.transAxes , fontsize=10)
fig.subplots_adjust(bottom=0.1, left=0., right=0.95, top=0.95)
fig.savefig("../images/3d_mtag.png")
def plot_2d():
positions = [[10, 90], [60, 5]]
extents = [[30, -30], [30, 20]]
colors = ['dodgerblue', 'crimson']
fig = plt.figure()
fig.set_size_inches(5.5, 2.5)
ax = fig.add_subplot(121)
ax.set_xlim([0, 100])
ax.set_xticks([0, 25, 50, 75, 100])
ax.set_xlabel('width [mm]')
ax.set_ylim([0, 100])
ax.set_ylabel('height [mm]')
ax.set_yticks([0, 25, 50, 75, 100])
rect = patches.Rectangle((10, 90),
30,
-30,
alpha=0.25,
facecolor="dodgerblue",
edgecolor='k',
lw=0.75,
ls='--')
ax.add_patch(rect)
rect = patches.Rectangle((60, 5),
30,
20,
alpha=0.25,
facecolor="crimson",
edgecolor='k',
lw=0.75,
ls='--')
ax.add_patch(rect)
ax.scatter([10, 60], [90, 5],
marker=".",
s=50,
lw=0.25,
facecolor="r",
edgecolor='k')
ax.plot([10, 40], [90, 90], lw=1.5, color='r')
ax.plot([10, 10], [90, 60], lw=1.5, color='r')
ax.plot([60, 60], [5, 25], lw=1.5, color='r')
ax.plot([60, 90], [5, 5], lw=1.5, color='r')
col_labels = ["Pos 1", "Pos 2"]
rows_2d = ["width", "height"]
cell_text = [[positions[0][0], positions[1][0]],
[positions[0][1], positions[1][1]]]
table = matplotlib.table.table(ax,
cellText=cell_text,
rowLabels=rows_2d,
colLabels=col_labels,
cellLoc="center",
bbox=[1.6, 0.6, 0.4, 0.3],
colWidths=[0.2 for c in col_labels],
colColours=colors)
ax.text(130, 100,
"Positions DataArray") #, transform=ax.transAxes , fontsize=10)
table.auto_set_font_size(False)
table.set_fontsize(8)
col_labels = ["Ext 1", "Ext 2"]
cell_text = [[extents[0][0], extents[1][0]],
[extents[0][1], extents[1][1]]]
table = matplotlib.table.table(ax,
cellText=cell_text,
rowLabels=rows_2d,
colLabels=col_labels,
cellLoc="center",
bbox=[1.6, 0.1, 0.4, 0.3],
colWidths=[0.2 for c in col_labels],
colColours=colors)
ax.text(130, 100,
"Positions DataArray") #, transform=ax.transAxes , fontsize=10)
table.auto_set_font_size(False)
table.set_fontsize(8)
ax.text(130, 45,
"Extents DataArray") #, transform=ax.transAxes , fontsize=10)
ax.spines["top"].set_visible(False)
ax.spines['right'].set_visible(False)
fig.subplots_adjust(bottom=0.175, left=0.15, right=0.95, top=0.95)
fig.savefig("../images/2d_mtag.png")
def make_chla_forecast_table(fcst, rmse, cls_err, mcc, png_path):
""" Make a summary table for chl-a.
Args:
fcst: Float. Predicted value
rmse: Float. Hindcast root mean squared error
cls_err: Float. Hindcast classification error (%)
mcc: Float. Hindcast Matthews Correlation Coefficient
png_path: Raw str. Path for PNG to be created
Returns:
None. Table is saved as a PNG to the specified path
"""
assert 0 <= cls_err <= 100, "'cls_err' must be between 0 and 100."
assert mcc <= 1, "'mcc' cannot be greater than 1."
matplotlib.table.CustomCell = MyCell
# Get most likely class
if fcst < 20:
pred_class = "lower"
else:
pred_class = "upper"
# Data for table grid (historic skill is "None" for all climate vars)
data = [
[
wfd_class_dict[("chla", "lower")],
"",
"",
"",
"",
"",
"",
],
[
wfd_class_dict[("chla", "upper")],
"",
"",
"",
"",
"",
"",
],
]
# Build table
col_labels = [
"WFD class",
"Likelihood\nof class",
f"Forecasted\nvalue\n({units_dict['chla']})",
"RMSE\n(mg/l)¹",
"Classification\nerror (%)²",
"MCC³",
"Forecast summary",
]
table = plt.table(
cellText=data,
colLabels=col_labels,
loc="center",
cellLoc="center",
)
# Layout and basic formatting
table.auto_set_font_size(False)
table.auto_set_column_width(col=range(len(col_labels)))
table.scale(1, 6)
for (row, col), cell in table.get_celld().items():
if row == 0:
cell.set_text_props(fontproperties=FontProperties(weight="bold"))
# Fake merged cells
# Historic skill
h = table.get_celld()[(0, 0)].get_height()
w = table.get_celld()[(0, 0)].get_width()
header = [table.add_cell(-1, pos, w, h, loc="center") for pos in [3, 4, 5]]
[
cell.set_text_props(fontproperties=FontProperties(weight="bold"))
for cell in header
]
[cell.set_height(0.15) for cell in header]
header[0].visible_edges = "TBL"
header[1].visible_edges = "TB"
header[2].visible_edges = "TBR"
header[1].get_text().set_text("Historic skill*")
# Stat cols
lik = "Not available"
for col, stat in enumerate([lik, fcst, rmse, cls_err, mcc], start=1):
patch = [
table.add_cell(pos, col, w, h, loc="center") for pos in [1, 2]
]
patch[0].visible_edges = "TLR"
patch[1].visible_edges = "BLR"
patch[0].get_text().set_text(f"\n\n{stat}")
# Set colour just for MCC
[cell.set_facecolor(assign_mcc_class(mcc)[1]) for cell in patch]
# Forecast summary
class_label = wfd_class_dict[("chla", pred_class)].split("(")[0][:-1]
summary = (f"{names_dict['chla']} is expected\nto be " + "$\\bf{" +
class_label.replace(" ", "\\ ") + "}$")
conf = "Confidence level: $\\bf{Medium}$" # Always 'Medium'; See guidance doc
patch = [table.add_cell(pos, 6, w, h, loc="center") for pos in [1, 2]]
patch[0].visible_edges = "TLR"
patch[1].visible_edges = "BLR"
patch[0].get_text().set_text(summary)
patch[1].get_text().set_text(conf)
table.set_fontsize(20)
plt.axis("off")
plt.savefig(png_path, dpi=300, bbox_inches="tight")
def make_tp_cyano_colour_forecast_table(variable, lik_low, lik_hi, fcst, rmse,
cls_err, mcc, png_path):
""" Make a summary table for TP, cyano or colour.
Args:
variable: Str. Variable of interest. One of ['tp', 'cyano', 'colour']
lik_low: Float. Likelihood of lower class (probability between 0 and 1)
lik_hi: Float. Likelihood of upper class (probability between 0 and 1)
fcst: Float. Predicted value
rmse: Float. Hindcast root mean squared error
cls_err: Float. Hindcast classification error (%)
mcc: Float. Hindcast Matthews Correlation Coefficient
png_path: Raw str. Path for PNG to be created
Returns:
None. Table is saved as a PNG to the specified path
"""
assert 0 <= lik_low <= 1, "'lik_low' must be a probability between 0 and 1."
assert 0 <= lik_hi <= 1, "'lik_hi' must be a probability between 0 and 1."
assert 0 <= cls_err <= 100, "'cls_err' must be between 0 and 100."
assert mcc <= 1, "'mcc' cannot be greater than 1."
matplotlib.table.CustomCell = MyCell
# Get most likely class
if lik_low > lik_hi:
pred_class = "lower"
max_lik = lik_low
else:
pred_class = "upper"
max_lik = lik_hi
# Data for table grid (historic skill is "None" for all climate vars)
data = [
[
wfd_class_dict[(variable, "lower")],
assign_likelihood_label_water_quality(lik_low),
"",
"",
"",
"",
"",
],
[
wfd_class_dict[(variable, "upper")],
assign_likelihood_label_water_quality(lik_hi),
"",
"",
"",
"",
"",
],
]
# Build table
col_labels = [
"WFD class",
"Likelihood\nof class",
f"Forecasted\nvalue\n({units_dict[variable]})",
f"RMSE\n({units_dict[variable]})¹",
"Classification\nerror (%)²",
"MCC³",
"Forecast summary",
]
table = plt.table(
cellText=data,
colLabels=col_labels,
loc="center",
cellLoc="center",
)
# Layout and basic formatting
table.auto_set_font_size(False)
table.auto_set_column_width(col=range(len(col_labels)))
table.scale(1, 6)
for (row, col), cell in table.get_celld().items():
if row == 0:
cell.set_text_props(fontproperties=FontProperties(weight="bold"))
table[(1, 1)].set_facecolor(
colour_dict[assign_likelihood_label_water_quality(lik_low)[0]])
table[(2, 1)].set_facecolor(
colour_dict[assign_likelihood_label_water_quality(lik_hi)[0]])
# Fake merged cells
# Historic skill
h = table.get_celld()[(0, 0)].get_height()
w = table.get_celld()[(0, 0)].get_width()
header = [table.add_cell(-1, pos, w, h, loc="center") for pos in [3, 4, 5]]
[
cell.set_text_props(fontproperties=FontProperties(weight="bold"))
for cell in header
]
[cell.set_height(0.15) for cell in header]
header[0].visible_edges = "TBL"
header[1].visible_edges = "TB"
header[2].visible_edges = "TBR"
header[1].get_text().set_text("Historic skill*")
# Stat cols
for col, stat in enumerate([fcst, rmse, cls_err, mcc], start=2):
patch = [
table.add_cell(pos, col, w, h, loc="center") for pos in [1, 2]
]
patch[0].visible_edges = "TLR"
patch[1].visible_edges = "BLR"
patch[0].get_text().set_text(f"\n\n{stat}")
# Set colour just for MCC
[cell.set_facecolor(assign_mcc_class(mcc)[1]) for cell in patch]
# Forecast summary
class_label = wfd_class_dict[(variable, pred_class)].split("(")[0][:-1]
summary = (f"{names_dict[variable]} is expected\nto be " + "$\\bf{" +
class_label.replace(" ", "\\ ") + "}$")
conf = get_overall_confidence(
assign_likelihood_label_water_quality(max_lik),
assign_mcc_class(mcc)[0])
conf = "Confidence level: $\\bf{" + conf + "}$"
patch = [table.add_cell(pos, 6, w, h, loc="center") for pos in [1, 2]]
patch[0].visible_edges = "TLR"
patch[1].visible_edges = "BLR"
patch[0].get_text().set_text(summary)
patch[1].get_text().set_text(conf)
table.set_fontsize(20)
plt.axis("off")
plt.savefig(png_path, dpi=300, bbox_inches="tight")