def wind_rose(freqs,
sectors=12,
title='Wind Rose',
color=None,
all_ticks=False):
'''
Plots a wind rose using sectorwise frequencies
Parameters:
___________
freqs: numpy array, list, or pandas series of float or int
Array of frequencies to plot for wind rose
sectors: int
Number of sectors to plot. Must be the same as len(freqs) to
avoid error
title: string
Plot title
color: string, default None
Plot color, from standard matplotlib color library
all_ticks: boolean, default False
Enabling this parameter will plot ticks for every sector. Otherwise,
only 30 degree ticks are plotted
Returns:
________
Wind rose plot
'''
#Set up binned frequencies and labels
bins = 360 / sectors
theta = np.arange(0, 360, bins)
theta_rad = theta * math.pi / 180
if all_ticks:
ticklabs = [str(x) for x in theta]
ticks = theta
else:
ticklabs, ticks = np.arange(0, 360, 30), np.arange(0, 360, 30)
#Plot, with N correctly oriented
fig = plt.figure(figsize=(8, 8))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True)
ax.set_theta_zero_location('N')
ax.set_theta_direction(-1)
ax.set_thetagrids(ticks, labels=ticklabs)
ax.set_title(title)
ax.grid(True, 'major', color='w', linestyle='-', linewidth=0.7)
ax.patch.set_facecolor('0.90')
ax.set_axisbelow(True)
width = bins * math.pi / 180
adj_theta = theta_rad - width / 2
stylers.rbar(ax,
adj_theta,
freqs,
width=width,
bottom=0.0,
alpha=0.7,
color=color)
def wind_rose(freqs, sectors=12, title='Wind Rose', color=None,
all_ticks=False):
'''
Plots a wind rose using sectorwise frequencies
Parameters:
___________
freqs: numpy array, list, or pandas series of float or int
Array of frequencies to plot for wind rose
sectors: int
Number of sectors to plot. Must be the same as len(freqs) to
avoid error
title: string
Plot title
color: string, default None
Plot color, from standard matplotlib color library
all_ticks: boolean, default False
Enabling this parameter will plot ticks for every sector. Otherwise,
only 30 degree ticks are plotted
Returns:
________
Wind rose plot
'''
#Set up binned frequencies and labels
bins = 360/sectors
theta = np.arange(0, 360, bins)
theta_rad = theta*math.pi/180
if all_ticks:
ticklabs = [str(x) for x in theta]
ticks = theta
else:
ticklabs, ticks = np.arange(0, 360, 30), np.arange(0, 360, 30)
#Plot, with N correctly oriented
fig = plt.figure(figsize=(8, 8))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True)
ax.set_theta_zero_location('N')
ax.set_theta_direction(-1)
ax.set_thetagrids(ticks, labels=ticklabs)
ax.set_title(title)
ax.grid(True, 'major', color='w', linestyle='-', linewidth=0.7)
ax.patch.set_facecolor('0.90')
ax.set_axisbelow(True)
width = bins*math.pi/180
adj_theta = theta_rad-width/2
stylers.rbar(ax, adj_theta, freqs, width=width, bottom=0.0, alpha=0.7,
color=color)
def weibull(x, dist, binned=False, binned_x=None, binned_data=None, **kwargs):
'''
Plots a weibull distribution, both the pdf and binned values
Parameters:
___________
x: array of float or int
x-axis array, independent axis
dist: scipy.stats pdf
pdf of distribution, generated by scipy.stats
binned: boolean, default False
True if you want to plot both the continuous distribution and
the hourly distribution
binned_x: array of float or int, default None
x-axis array for binned data, optional
binned_data: array of float or int, default none
binned data, to plot over pdf
Returns:
________
Plot of weibull distribution
'''
fig, ax1 = plt.subplots()
ax2 = None
if binned:
bin_width = binned_x[1] - binned_x[0]
stylers.rbar(ax1, binned_x, binned_data, width=bin_width, **kwargs)
stylers.rstyle(ax1)
ax1.set_xlabel(r'Wind Speed [m/s]', fontsize=12)
ax1.set_ylabel(r'Hours', fontsize=12)
ax2 = ax1.twinx()
if ax2:
pdfax = ax2
else:
pdfax = ax1
pdfax.set_xlim((0, 40))
stylers.rfill(
pdfax,
x,
dist,
)
if not ax2:
stylers.rstyle(pdfax)
pdfax.set_ylabel(r'PDF', fontsize=12)
def weibull(x, dist, binned=False, binned_x=None, binned_data=None, **kwargs):
'''
Plots a weibull distribution, both the pdf and binned values
Parameters:
___________
x: array of float or int
x-axis array, independent axis
dist: scipy.stats pdf
pdf of distribution, generated by scipy.stats
binned: boolean, default False
True if you want to plot both the continuous distribution and
the hourly distribution
binned_x: array of float or int, default None
x-axis array for binned data, optional
binned_data: array of float or int, default none
binned data, to plot over pdf
Returns:
________
Plot of weibull distribution
'''
fig, ax1 = plt.subplots()
ax2 = None
if binned:
bin_width = binned_x[1]-binned_x[0]
stylers.rbar(ax1, binned_x, binned_data, width=bin_width, **kwargs)
stylers.rstyle(ax1)
ax1.set_xlabel(r'Wind Speed [m/s]', fontsize=12)
ax1.set_ylabel(r'Hours', fontsize=12)
ax2 = ax1.twinx()
if ax2:
pdfax = ax2
else:
pdfax = ax1
pdfax.set_xlim((0, 40))
stylers.rfill(pdfax, x, dist,)
if not ax2:
stylers.rstyle(pdfax)
pdfax.set_ylabel(r'PDF', fontsize=12)
