def plot_sector_ratio(sec_ratio, wdir, sec_ratio_dist, col_names, boom_dir_1=-1, boom_dir_2=-1):
"""
Accepts a DataFrame table, along with 2 anemometer names, and one wind vane name and plots the speed ratio
by sector. Optionally can include anemometer boom directions also.
:param sec_ratio: Series of sector_ratios
:type sec_ratio: pandas.Series
:param wdir: Direction series
:type wdir: pandas.Series
:param sec_ratio_dist: DataFrame from SectorRatio.by_sector()
:type sec_ratio_dist; pandas.Series
:param boom_dir_1: Boom direction in degrees of speed_col_name_1. Defaults to -1.
:type boom_dir_1: float
:param boom_dir_2: Boom direction in degrees of speed_col_name_2. Defaults to -1.
:type boom_dir_2: float
:param col_names: A list of strings containing column names of wind speeds, first string is divisor and second is
dividend
:type col_names: list(str)
:returns A speed ratio plot showing average speed ratio by sector and scatter of individual data points.
"""
radians = np.radians(utils._get_dir_sector_mid_pts(sec_ratio_dist.index))
fig = plt.figure(figsize=(10, 10))
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(utils._get_dir_sector_mid_pts(sec_ratio_dist.index))
ax.plot(np.append(radians, radians[0]), sec_ratio_dist['Mean_Sector_Ratio'].append(sec_ratio_dist.iloc[0]),
color=bw_colors('green'), linewidth=4)
# plt.title('Speed Ratio by Direction')
# Get max and min levels and set chart axes
max_level = sec_ratio_dist['Mean_Sector_Ratio'].max() + 0.05
min_level = sec_ratio_dist['Mean_Sector_Ratio'].min() - 0.1
ax.set_ylim(min_level, max_level)
# Add boom dimensions to chart, if required
width = np.pi / 108
radii = max_level
annotate = False
annotation_text = '* Plot generated using '
if boom_dir_1 >= 0:
boom_dir_1 = np.radians(boom_dir_1)
ax.bar(boom_dir_1, radii, width=width, bottom=min_level, color='#659CEF')
if boom_dir_2 == -1:
annotation_text += col_names[1] + ' (top mounted) divided by ' + col_names[0] + ' (blue boom)'
annotate = True
if boom_dir_2 >= 0:
boom_dir_2 = np.radians(boom_dir_2)
ax.bar(boom_dir_2, radii, width=width, bottom=min_level, color='#DCF600')
if boom_dir_1 == -1:
annotation_text += col_names[1] + ' (yellow green boom) divided by ' + col_names[0] + ' (top mounted)'
annotate = True
if boom_dir_2 >= 0 and boom_dir_1 >= 0:
annotation_text += col_names[1] + ' (yellow green boom) divided by ' + col_names[0] + ' (blue boom)'
annotate = True
if annotate:
ax.annotate(annotation_text, xy=(0.5, 0.035), xycoords='figure fraction', horizontalalignment='center')
ax.scatter(np.radians(wdir), sec_ratio, color=bw_colors('asphault'), alpha=0.3, s=1)
return ax.get_figure()
def plot_TI_by_sector(turbulence, wdir, ti):
radians = np.radians(utils._get_dir_sector_mid_pts(ti.index))
fig = plt.figure(figsize=(10, 10))
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(utils._get_dir_sector_mid_pts(ti.index))
ax.plot(np.append(radians, radians[0]), ti.append(ti.iloc[0])['Mean_TI'], color=bw_colors('green'), linewidth=4,
figure=fig)
maxlevel = ti['Mean_TI'].max() + 0.1
ax.set_ylim(0, maxlevel)
ax.scatter(np.radians(wdir), turbulence, color=bw_colors('asphault'), alpha=0.3, s=1)
ax.legend(loc=8, framealpha=1)
return ax.get_figure()
def plot_shear_by_sector(shear, wdir, shear_dist):
radians = np.radians(utils._get_dir_sector_mid_pts(shear_dist.index))
fig = plt.figure(figsize=(10, 10))
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(utils._get_dir_sector_mid_pts(shear_dist.index))
ax.plot(np.append(radians, radians[0]), shear_dist.append(shear_dist.iloc[0])['Mean_Shear'],
color=bw_colors('green'), linewidth=4)
# ax.set_title('Shear by Direction')
maxlevel = shear_dist['Mean_Shear'].max() + 0.1
ax.set_ylim(0, maxlevel)
ax.scatter(np.radians(wdir), shear, color=bw_colors('asphault'), alpha=0.3, s=1)
ax.legend(loc=8, framealpha=1)
return ax.get_figure()
def plot_sector_ratio(sec_ratio,
wddir,
sec_ratio_dist,
col_names,
boom_dir_1=-1,
boom_dir_2=-1):
"""
Accepts a dataframe table, along with 2 anemometer names, and one wind vane name and plots the speed ratio
by sector. Optionally can include anemometer boom directions also.
:param sec_ratio:
:param wddir:
:param sec_ratio_dist: Dataframe from SectorRation.by_speed()
:param boom_dir_1: Boom direction in degrees of speed_col_name_1. Defaults to 0.
:param boom_dir_2: Boom direction in degrees of speed_col_name_2. Defaults to 0.
:param col_names: A list of strings containing column names of wind speeds
:returns A speed ratio plot showing average speed ratio by sector and scatter of individual datapoints.
"""
radians = np.radians(utils._get_dir_sector_mid_pts(sec_ratio_dist.index))
fig = plt.figure(figsize=(10, 10))
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(utils._get_dir_sector_mid_pts(sec_ratio_dist.index))
ax.plot(np.append(radians, radians[0]),
sec_ratio_dist['Mean_Sector_Ratio'].append(sec_ratio_dist.iloc[0]),
c=bw_colors('green'),
linewidth=4)
# plt.title('Speed Ratio by Direction')
# Get max and min levels and set chart axes
maxlevel = sec_ratio_dist['Mean_Sector_Ratio'].max() + 0.05
minlevel = sec_ratio_dist['Mean_Sector_Ratio'].min() - 0.1
ax.set_ylim(minlevel, maxlevel)
# Add boom dimensions to chart, if required
width = np.pi / 108
radii = maxlevel
ctr = 0
if boom_dir_1 >= 0:
boom_dir_1 = np.radians(boom_dir_1)
ax.bar(boom_dir_1,
radii,
width=width,
bottom=minlevel,
color='#659CEF')
ctr += 1
if boom_dir_2 >= 0:
boom_dir_2 = np.radians(boom_dir_2)
ax.bar(boom_dir_2,
radii,
width=width,
bottom=minlevel,
color='#DCF600')
ctr += 1
if ctr == 2:
ax.annotate('*Plot generated using ' + col_names[1] +
' (yellow green boom) divided by' + col_names[0] +
' (blue boom)',
xy=(20, 10),
xycoords='figure pixels')
ax.scatter(np.radians(wddir),
sec_ratio,
c=bw_colors('asphault'),
alpha=0.3,
s=1)
return ax.get_figure()
def plot_shear_by_sector(scale_variable,
wind_rose_data,
calc_method='power_law'):
result = wind_rose_data.copy(deep=False)
radians = np.radians(utils._get_dir_sector_mid_pts(scale_variable.index))
sectors = len(result)
fig = plt.figure(figsize=(12, 12), )
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)
bin_edges = pd.Series([])
for i in range(sectors):
bin_edges[i] = float(
re.findall(r"[-+]?\d*\.\d+|\d+", wind_rose_data.index[i])[0])
if i == sectors - 1:
bin_edges[i + 1] = abs(
float(
re.findall(r"[-+]?\d*\.\d+|\d+",
wind_rose_data.index[i])[1]))
label = ''
if calc_method == 'power_law':
label = 'Mean_Shear'
if calc_method == 'log_law':
label = 'Mean_Roughness_Coefficient'
scale_variable_y = np.append(scale_variable, scale_variable[0])
plot_x = np.append(radians, radians[0])
scale_to_fit = max(scale_variable) / max(result / 100)
wind_rose_r = (result / 100) * scale_to_fit
bin_edges = np.array(bin_edges)
width = pd.Series([])
for i in range(len(bin_edges) - 1):
if bin_edges[i + 1] == 0:
width[i] = 2 * np.pi * (360 - bin_edges[i]) / 360 - (np.pi / 180)
elif bin_edges[i + 1] > bin_edges[i]:
width[i] = 2 * np.pi * (
(bin_edges[i + 1] - bin_edges[i]) / 360) - (np.pi / 180)
else:
width[i] = 2 * np.pi * ((
(360 + bin_edges[i + 1]) - bin_edges[i]) / 360) - (np.pi / 180)
ax.bar(radians,
wind_rose_r,
width=width,
color=COLOR_PALETTE.secondary,
align='center',
edgecolor=[COLOR_PALETTE.secondary for i in range(len(result))],
alpha=0.8,
label='Wind_Directional_Frequency')
maxlevel = (max(scale_variable_y)) + max(scale_variable_y) * .1
ax.set_thetagrids(radians * 180 / np.pi)
ax.plot(plot_x,
scale_variable_y,
color=COLOR_PALETTE.primary,
linewidth=4,
label=label)
ax.set_ylim(0, top=maxlevel)
ax.legend(loc=8, framealpha=1)
return ax.get_figure()
