import numpy as np
import pandas as pd
def colorbar(mappable):
"""
properly add colorbar to mpl plots.
Parameters
----------
mappable : mpl.mappable
mappable
Returns
-------
mpl.colorbar
[description]
Credit
------
https://joseph-long.com/writing/colorbars/
"""
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib.pyplot as plt
last_axes = plt.gca()
ax = mappable.axes
fig = ax.figure
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cbar = fig.colorbar(mappable, cax=cax)
plt.sca(last_axes)
return cbar
# IQR filling plot:
[docs]def plot_day_clm(df_var, fig=None, ax=None, show_dif=False, col_ref="Obs"):
"""Produce a ensemble diurnal climatologies with uncertainties shown in inter-quartile ranges.
Parameters
----------
df_var : pd.DataFrame
DataFrame containing variables to plot with datetime as index.
show_dif: boolean
flag to determine if differences against `col_ref` should be plotted.
col_ref: str
name of column that is used as reference to show differences instead of original values.
Returns
-------
MPL.figure
figure showing median lines and IQR in shadings
"""
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
if fig is None and ax is None:
fig, ax = plt.subplots()
elif fig is None:
fig = ax.get_figure()
elif ax is None:
ax = fig.gca()
# group by hour and minute
grp_sdf_var = df_var.groupby(
[df_var.index.hour.rename("hr"), df_var.index.minute.rename("min")]
)
# get index
year = df_var.index.year.min()
month = df_var.index.month.min()
day = df_var.index.day.min()
idx = [
pd.datetime(year, month, day, h, m)
for h, m in sorted(grp_sdf_var.groups.keys())
]
idx = pd.date_range(idx[0], idx[-1], periods=len(idx))
idx = mdates.date2num(idx)
# calculate quartiles
quar_sel_pos_clm = grp_sdf_var.apply(lambda grp: grp.quantile([0.75, 0.5, 0.25]))
# rearrangement
quar_sel_pos_clm = quar_sel_pos_clm.unstack()
# indexing with proper datetime
quar_sel_pos_clm = quar_sel_pos_clm.set_index(idx)
for var in quar_sel_pos_clm.columns.levels[0]:
if show_dif:
df_ref = quar_sel_pos_clm.loc[:, col_ref]
df_x = quar_sel_pos_clm.loc[:, var] - df_ref
else:
df_x = quar_sel_pos_clm.loc[:, var]
if show_dif and var == col_ref:
y0 = df_ref[0.5]
y1, y2 = df_ref[0.75], df_ref[0.25]
ax2 = ax.twinx()
y0.plot(ax=ax2, label=var, linestyle="-.").fill_between(
quar_sel_pos_clm.index, y1, y2, alpha=0.0
)
else:
y0 = df_x[0.5]
y1, y2 = df_x[0.75], df_x[0.25]
y0.plot(ax=ax, label=var).fill_between(
quar_sel_pos_clm.index, y1, y2, alpha=(0.0 if show_dif else 0.3)
)
# add legend
ax.legend(title="variable")
# adjust xticks formar
ax.xaxis.set_major_locator(mdates.HourLocator(byhour=np.arange(0, 23, 3)))
ax.xaxis.set_major_formatter(mdates.DateFormatter("%H:%M"))
# return different objects based on `show_dif`
if show_dif:
return fig, ax, ax2
else:
return fig, ax
# comparison plot with 1:1 line added:
[docs]def plot_comp(
df_var,
scatter_kws={"alpha": 0.1, "s": 0.3, "color": "k"},
kde_kws={"shade": True, "shade_lowest": False, "levels": 4,},
show_pdf=False,
fig=None,
ax=None,
):
"""Produce a scatter plot with linear regression line to compare simulation results and observations.
Parameters
----------
df_var : pd.DataFrame
DataFrame containing variables to plot with datetime as index.
Two columns, 'Obs' and 'Sim' for observations and simulation results, respectively, must exist.
scatter_kws: dict
keyword arguments passed to `sns.regplot`. By default, `{"alpha": 0.1, "s": 0.3, "color": "k"}`.
show_pdf: boolean
if a PDF overlay should be added. By default, `False`.
kde_kws: dict
`kde_kws` passed to `sns.kdeplot` when `show_pdf=True`
Returns
-------
MPL.figure
figure showing 1:1 line plot
"""
# import when used for better performance in loading supy
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
if fig is None and ax is None:
fig, ax = plt.subplots()
elif fig is None:
fig = ax.get_figure()
elif ax is None:
ax = fig.gca()
# add regression expression
df_var_fit = df_var.dropna(how="any")
try:
val_x = df_var_fit["Obs"]
val_y = df_var_fit["Sim"]
except:
val_x = df_var_fit.iloc[:,0]
val_y = df_var_fit.iloc[:,1]
slope, intercept, r_value, p_value, std_err = stats.linregress(val_x, val_y)
mae = (val_y - val_x).abs().mean()
mbe = (val_y - val_x).mean()
sns.regplot(
x=val_x,
y=val_y,
# data=df_var_fit,
ax=ax,
fit_reg=True,
line_kws={
"label": "\n".join(
[
f"y={slope:.2f}x{'+' if intercept > 0 else ''}{intercept:.2f}",
f"$R^2$={r_value**2:.4f}",
f"MAE={mae:.2f}",
f"MBE={mbe:.2f}",
f"n={df_var_fit.shape[0]}",
]
)
},
scatter_kws=scatter_kws,
)
ax.legend()
color_last = ax.lines[0].get_color()
if show_pdf:
sns.kdeplot(
df_var.Obs, df_var.Sim, ax=ax, color=color_last, zorder=0, **kde_kws,
)
# set equal plotting range
x0, x1 = ax.get_xlim()
y0, y1 = ax.get_ylim()
lim_low, lim_high = np.min([x0, y0]), np.max([x1, y1])
ax.set_xlim(lim_low, lim_high)
ax.set_ylim(lim_low, lim_high)
# set 1:1 aspect ratio
ax.set_aspect("equal")
# add 1:1 line
ax.plot(
[lim_low, lim_high], [lim_low, lim_high], color="red", linewidth=1, zorder=0
)
return fig, ax
# several colour helper functions
# import seaborn as sns
# import matplotlib.pyplot as plt
def RGB_to_Hex(rgb):
RGB = rgb.split(",")
color = "#"
for i in RGB:
num = int(i)
color += str(hex(num))[-2:].replace("x", "0").upper()
return color
def RGB_list_to_Hex(RGB):
color = "#"
for i in RGB:
num = int(i)
color += str(hex(num))[-2:].replace("x", "0").upper()
return color
def Hex_to_RGB(hex):
r = int(hex[1:3], 16)
g = int(hex[3:5], 16)
b = int(hex[5:7], 16)
rgb = str(r) + "," + str(g) + "," + str(b)
return rgb, [r, g, b]
def gradient_color(color_list, color_sum=200):
color_center_count = len(color_list)
color_sub_count = int(color_sum / (color_center_count - 1))
color_index_start = 0
color_map = []
for color_index_end in range(1, color_center_count):
color_rgb_start = Hex_to_RGB(color_list[color_index_start])[1]
color_rgb_end = Hex_to_RGB(color_list[color_index_end])[1]
r_step = (color_rgb_end[0] - color_rgb_start[0]) / color_sub_count
g_step = (color_rgb_end[1] - color_rgb_start[1]) / color_sub_count
b_step = (color_rgb_end[2] - color_rgb_start[2]) / color_sub_count
now_color = color_rgb_start
color_map.append(RGB_list_to_Hex(now_color))
for color_index in range(1, color_sub_count):
now_color = [
now_color[0] + r_step,
now_color[1] + g_step,
now_color[2] + b_step,
]
color_map.append(RGB_list_to_Hex(now_color))
color_index_start = color_index_end
return color_map
def plot_colortable(colors, title, sort_colors=True, emptycols=0):
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
cell_width = 212
cell_height = 22
swatch_width = 48
margin = 12
topmargin = 40
# Sort colors by hue, saturation, value and name.
if sort_colors is True:
by_hsv = sorted(
(tuple(mcolors.rgb_to_hsv(mcolors.to_rgb(color))), name)
for name, color in colors.items()
)
names = [name for hsv, name in by_hsv]
else:
names = list(colors)
n = len(names)
ncols = 4 - emptycols
nrows = n // ncols + int(n % ncols > 0)
width = cell_width * 4 + 2 * margin
height = cell_height * nrows + margin + topmargin
dpi = 72
fig, ax = plt.subplots(figsize=(width / dpi, height / dpi), dpi=dpi)
fig.subplots_adjust(
margin / width,
margin / height,
(width - margin) / width,
(height - topmargin) / height,
)
ax.set_xlim(0, cell_width * 4)
ax.set_ylim(cell_height * (nrows - 0.5), -cell_height / 2.0)
ax.yaxis.set_visible(False)
ax.xaxis.set_visible(False)
ax.set_axis_off()
ax.set_title(title, fontsize=24, loc="left", pad=10)
for i, name in enumerate(names):
row = i % nrows
col = i // nrows
y = row * cell_height
swatch_start_x = cell_width * col
swatch_end_x = cell_width * col + swatch_width
text_pos_x = cell_width * col + swatch_width + 7
ax.text(
text_pos_x,
y,
name,
fontsize=14,
horizontalalignment="left",
verticalalignment="center",
)
ax.hlines(y, swatch_start_x, swatch_end_x, color=colors[name], linewidth=18)
return fig
# plotting RSL profiles
[docs]def plot_rsl(
df_output, var=None, fig=None, ax=None,
):
"""
Produce a quick plot of RSL results
Parameters
----------
df_output : pandas.DataFrame
SuPy output dataframe with RSL results.
var : str, optional
Varible to plot; must be one of 'U', 'T', or 'q'; or use `None` to plot all; by default None
Returns
-------
tuple
`(fig,ax)` of plot.
Raises
------
issue
If an invalid variable is specified, an issue will be raised.
"""
# import when used for better performance in loading supy
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
from .._env import logger_supy
from .._post import proc_df_rsl
# convert df_output to an easier format
df_rsl, df_rsl_debug = proc_df_rsl(df_output, debug=True)
# retrieve zH_RSL
zH_RSL = df_rsl_debug.zH_RSL
# retrive heights: the number of levels is fixed to 30 as set in SUEWS
ar_z = df_rsl.loc[:, "z"].iloc[:30].values
# retrieve times
df_x = df_rsl.droplevel("level")
ser_t = df_x.index.drop_duplicates()
# dict of x labels
dict_xlabel = {"U": "(m s$^{-1}$)", "T": r"($^\degree$C)", "q": "(g kg$^{-1}$)"}
if var is None:
# plot all variables in subplots
# force create a new figure to contain all variables
fig, axes = plt.subplots(1, 3, figsize=(12 * 0.68, 4), sharey=True)
for var, ax in zip(["U", "T", "q"], axes.flat):
for t in ser_t:
zH_RSL_t = zH_RSL.loc[t]
zh_diag = 10 if var == "U" else 2
_ = df_x.loc[t].plot(x=var, y="z", label=t, ax=ax)
_ = ax.axhline(y=zh_diag, linestyle="--", c="r", alpha=0.6)
_ = ax.axhline(y=zH_RSL_t, linestyle="-.", c="b", alpha=0.6)
_ = ax.set_ylabel("z (m)")
_ = ax.set_xlabel(var + " " + dict_xlabel[var])
fig.tight_layout()
return fig, axes
elif var in ["U", "T", "q"]:
if fig is None and ax is None:
fig, ax = plt.subplots()
elif fig is None:
fig = ax.get_figure()
elif ax is None:
ax = fig.gca()
for t in ser_t:
_ = df_x.loc[t].plot(x=var, y="z", label=t, ax=ax)
_ = ax.set_ylabel("z (m)")
_ = ax.set_xlabel(var + " " + dict_xlabel[var])
return fig, ax
else:
# raise issue
logger_supy.error(f"`var` cannoot be {var}: must be one of 'U', 'T', or 'q'. ")
