def monthlyhourlyplot(metdat, catinfo, category=None, basecolor='span'):
"""**Get Monthly Hourly Averaged Profile**.
Plot the monthly hourly averaged profile of a given variable (or category of variables) grouped by a given condition (or set of conditions).
Parameters:
1. metdat (Pandas DataFrame): The desired input data (Met Mast).
2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names.
3. category (string) [default: None]: Specifies the category of information that is desired for plotting.
4. basecolor (string) [default: 'span']: Provides the color code information to get from "utils.py".
Returns:
1. fig (Matplotlib Figure): The figure object for the desired input data and categories.
2. ax (Matplotlib Axes): The axes object for the desired input data and categories.
"""
if category is None:
print('not sure what to plot...')
pass
months = utils.monthnames()
colors = utils.get_colors(len(catinfo['columns'][category]),
basecolor=basecolor,
reverse=True)
colnames, vertlocs, ind = utils.get_vertical_locations(
catinfo['columns'][category], reverse=True)
plotdat = metdat[colnames].groupby(
[metdat.index.month.rename('month'),
metdat.index.hour.rename('hour')]).mean()
fig, ax = plt.subplots(4, 3, figsize=(9, 11), sharex=True, sharey=True)
for iax in range(len(months)):
for catitem in range(len(colnames)):
ax.flatten()[iax].plot(plotdat[colnames[catitem]].xs(iax + 1),
color=colors[catitem])
ax.flatten()[iax].set_title(months[iax], fontsize=12)
fig.text(0.5, 0.2, 'Time of Day [hour]', ha='center', va='center')
leg = fig.legend([str(v) + ' m' for v in vertlocs],
loc='upper center',
bbox_to_anchor=(0, -0.825, 1, 1),
bbox_transform=plt.gcf().transFigure,
frameon=False,
ncol=2)
fig.tight_layout()
fig.subplots_adjust(bottom=0.25)
fig.text(0,
0.6125,
catinfo['labels'][category],
ha='center',
va='center',
rotation='vertical')
return fig, ax
def monthly_hist(metdat, catinfo, category, vertloc=80, basecolor='blue'):
###########################################
"""
Histogram of a given field without any sorting.
Parameters:
metdat:
Pandas dataframe containing met mast data
catinfo:
dict containing categorization info for the metmast data. Fore each category,
catinfo holds column names, labels, units, and save names
category:
string specifying category of information to plot (e.g. 'speed', 'stability', etc.)
vertloc:
int or float describing the exact or approximate height of interest along the tower
basecolor:
string with the color code info to get from utils.
"""
colors = utils.get_nrelcolors()
color = colors[basecolor][0]
months = utils.monthnames()
# set up data
varcol, vertloc, _ = utils.get_vertical_locations(
catinfo['columns'][category], location=vertloc)
temp = metdat.groupby(metdat.index.month)
temp = temp[varcol]
binwidth = (metdat[varcol].dropna().max() -
metdat[varcol].dropna().min()) / 35
bins = np.arange(metdat[varcol].dropna().min(),
metdat[varcol].dropna().max(), binwidth)
fig, ax = plt.subplots(4, 3, figsize=(9, 9), sharex=True, sharey=True)
for im, month in enumerate(months):
data = temp.get_group(im + 1).dropna()
ax.flatten()[im].hist(data,
bins=bins,
color=color,
edgecolor='k',
weights=np.ones(len(data)) / len(data) * 100)
ax.flatten()[im].set_title(month, fontsize=12)
fig.tight_layout()
fig.text(0,
0.5,
'Frequency [%]',
rotation='vertical',
ha='center',
va='center')
fig.text(0.5, 0, catinfo['labels'][category], ha='center', va='center')
return fig, ax
def monthly_hist(metdat, catinfo, category, vertloc=80, basecolor='blue'):
"""**Get Monthly Histogram Figure**.
Plot the monthly histogram of a given variable (or category of variables) grouped by a given condition (or set of conditions).
Parameters:
1. metdat (Pandas DataFrame): The desired input data (Met Mast).
2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names.
3. category (string): Specifies the category of information that is desired for plotting.
4. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis.
5. basecolor (string) [default: 'blue']: Provides the color code information to get from "utils.py".
Returns:
1. fig (Matplotlib Figure): The figure object for the desired input data and categories.
2. ax (Matplotlib Axes): The axes object for the desired input data and categories.
"""
colors = utils.get_nrelcolors()
color = colors[basecolor][0]
months = utils.monthnames()
# set up data
varcol, vertloc, _ = utils.get_vertical_locations(
catinfo['columns'][category], location=vertloc)
temp = metdat.groupby(metdat.index.month)
temp = temp[varcol]
binwidth = (metdat[varcol].dropna().max() -
metdat[varcol].dropna().min()) / 35
bins = np.arange(metdat[varcol].dropna().min(),
metdat[varcol].dropna().max(), binwidth)
fig, ax = plt.subplots(4, 3, figsize=(9, 9), sharex=True, sharey=True)
for im, month in enumerate(months):
data = temp.get_group(im + 1).dropna()
ax.flatten()[im].hist(data,
bins=bins,
color=color,
edgecolor='k',
weights=np.ones(len(data)) / len(data) * 100)
ax.flatten()[im].set_title(month, fontsize=12)
fig.tight_layout()
fig.text(0,
0.5,
'Frequency [%]',
rotation='vertical',
ha='center',
va='center')
fig.text(0.5, 0, catinfo['labels'][category], ha='center', va='center')
return fig, ax
def monthlyhourlyplot(metdat, catinfo, category=None, basecolor='span'):
###########################################
"""
Plot monthly average profiles against one another.
Parameters:
metdat:
Pandas dataframe containing met mast data
catinfo:
dict containing categorization info for the metmast data. Fore each category,
catinfo holds column names, labels, units, and save names
category:
string specifying category of information to plot (e.g. 'speed', 'stability', etc.)
basecolor:
string with the color code info to get from utils.
"""
if category is None:
print('not sure what to plot...')
pass
months = utils.monthnames()
colors = utils.get_colors(len(catinfo['columns'][category]),
basecolor=basecolor,
reverse=True)
colnames, vertlocs, ind = utils.get_vertical_locations(
catinfo['columns'][category], reverse=True)
plotdat = metdat[colnames].groupby([metdat.index.month,
metdat.index.hour]).mean()
fig, ax = plt.subplots(4, 3, figsize=(9, 11), sharex=True, sharey=True)
for iax in range(len(months)):
for catitem in range(len(colnames)):
ax.flatten()[iax].plot(plotdat[colnames[catitem]].xs(iax + 1),
color=colors[catitem])
ax.flatten()[iax].set_title(months[iax], fontsize=12)
fig.text(0.5, 0.2, 'Time of Day [hour]', ha='center', va='center')
leg = fig.legend([str(v) + ' m' for v in vertlocs],
loc='upper center',
bbox_to_anchor=(0, -0.825, 1, 1),
bbox_transform=plt.gcf().transFigure,
frameon=False,
ncol=2)
fig.tight_layout()
fig.subplots_adjust(bottom=0.25)
fig.text(0,
0.6125,
catinfo['labels'][category],
ha='center',
va='center',
rotation='vertical')
return fig, ax
def monthly_profile(metdat, catinfo, category=None, basecolor='cycle'):
"""**Get Monthly Profile**.
Plot the monthly profile of a given variable (or category of variables) grouped by a given condition (or set of conditions).
Parameters:
1. metdat (Pandas DataFrame): The desired input data (Met Mast).
2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names.
3. category (string) [default: None]: Specifies the category of information that is desired for plotting.
4. basecolor (string) [default: 'cycle']: Provides the color code information to get from "utils.py".
Returns:
1. fig (Matplotlib Figure): The figure object for the desired input data and categories.
2. ax (Matplotlib Axes): The axes object for the desired input data and categories.
"""
if category is None:
print('not sure what to plot...')
pass
months = utils.monthnames()
colors = utils.get_colors(len(months), basecolor=basecolor)
colnames, vertlocs, ind = utils.get_vertical_locations(
catinfo['columns'][category])
plotdat = metdat[colnames].groupby(metdat.index.month).mean()
fig, ax = plt.subplots(figsize=(3.5, 5), sharex=True, sharey=True)
for iax in range(len(months)):
ax.plot(plotdat.xs(iax + 1), vertlocs, color=colors[iax])
leg = ax.legend(months, loc=7, bbox_to_anchor=(1.75, 0.5), edgecolor='w')
ax.set_ylabel('Probe Height [m]')
ax.set_xlabel(catinfo['labels'][category])
fig.tight_layout()
return fig, ax
def monthly_profile(metdat, catinfo, category=None, basecolor='cycle'):
###########################################
"""
Plot monthly average profiles against one another.
Parameters:
metdat:
Pandas dataframe containing met mast data
catinfo:
dict containing categorization info for the metmast data. Fore each category,
catinfo holds column names, labels, units, and save names
category:
string specifying category of information to plot (e.g. 'speed', 'stability', etc.)
basecolor:
string with the color code info to get from utils.
"""
if category is None:
print('not sure what to plot...')
pass
months = utils.monthnames()
colors = utils.get_colors(len(months), basecolor=basecolor)
colnames, vertlocs, ind = utils.get_vertical_locations(
catinfo['columns'][category])
plotdat = metdat[colnames].groupby(metdat.index.month).mean()
fig, ax = plt.subplots(figsize=(3.5, 5), sharex=True, sharey=True)
for iax in range(len(months)):
ax.plot(plotdat.xs(iax + 1), vertlocs, color=colors[iax])
leg = ax.legend(months, loc=7, bbox_to_anchor=(1.75, 0.5), edgecolor='w')
ax.set_ylabel('Probe Height [m]')
ax.set_xlabel(catinfo['labels'][category])
fig.tight_layout()
return fig, ax
import matplotlib.pyplot as plt
from colour import Color
from windrose import WindroseAxes
from calendar import monthrange, month_name
import pylab as p
import vis
import utils
import met_funcs
# Place input files here
#inputfiles_here = ['2013_January.csv','2013_February.csv','2013_March.csv','2013_April.csv','2013_May.csv','2013_June.csv','2013_July.csv','2013_August.csv','2013_September.csv','2013_October.csv','2013_November.csv','2013_December.csv']
#inputfiles_here = ['2012_August.csv']
#inputfiles_here = ['2013_September.csv']
year = 2013
inputfiles_here = [str(year) + '_' + s + '.csv' for s in utils.monthnames()]
# Load and filter data
actual_data = met_funcs.load_met_data(inputfiles_here)
actual_data = met_funcs.drop_nan_cols(actual_data)
actual_data = met_funcs.qc_mask(actual_data)
# Extract categorical information
keep_cats = met_funcs.categories_to_keep()
ex_cats = met_funcs.categories_to_exclude()
var_cats, var_units, var_labels, var_save = met_funcs.categorize_fields(
actual_data, keep_cats, ex_cats)
# Extract more information
met_funcs.groom_data(actual_data, var_cats)
stab_conds, stab_cats = met_funcs.flag_stability(actual_data)
def monthly_stacked_hist_by_stability(metdat, catinfo, category, vertloc=80):
"""**Get Monthly Stacked Stability Grouped Histogram Figure**.
Plot the monthly stacked stability grouped histogram of a given variable (or category of variables) grouped by a given condition (or set of conditions).
Parameters:
1. metdat (Pandas DataFrame): The desired input data (Met Mast).
2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names.
3. category (string): Specifies the category of information that is desired for plotting.
4. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis.
Returns:
1. fig (Matplotlib Figure): The figure object for the desired input data and categories.
2. ax (Matplotlib Axes): The axes object for the desired input data and categories.
"""
stabconds = utils.get_stabconds()
stabcol, _, _ = utils.get_vertical_locations(
catinfo['columns']['stability flag'], location=vertloc)
varcol, vertloc, _ = utils.get_vertical_locations(
catinfo['columns'][category], location=vertloc)
colors = utils.get_colors(len(stabconds), basecolor='span')
months = utils.monthnames()
plotdat = metdat.groupby([metdat.index.month, stabcol])
plotdat = plotdat[varcol]
fig, ax = plt.subplots(4, 3, figsize=(9, 10), sharex=True, sharey=True)
for iax, month in enumerate(months):
temp = pd.DataFrame(
{cond: plotdat.get_group((iax + 1, cond))
for cond in stabconds})
temp.plot.hist(
ax=ax.flatten()[iax],
stacked=True,
color=colors,
bins=35,
edgecolor='k',
legend=False,
# weights = np.ones(temp.dropna().shape) / np.prod(temp.shape),
density=True)
ax.flatten()[iax].set_title(month)
ax.flatten()[iax].set_ylabel('')
# fig.legend(stabconds, loc=8, bbox_to_anchor=(0, -0.1), edgecolor='w')
fig.text(0,
0.58,
'Frequency',
ha='center',
va='center',
fontsize=14,
rotation='vertical')
leg = fig.legend(stabconds,
loc=9,
bbox_to_anchor=(0.55, 0.15),
frameon=False)
fig.tight_layout()
fig.subplots_adjust(bottom=0.21)
fig.text(0.5,
0.16,
catinfo['labels'][category],
ha='center',
va='center',
fontsize=14)
return fig, ax #, leg
def monthly_rose_fig(metdat,
catinfo,
category=None,
vertloc=80,
bins=6,
nsector=36,
ylim=None,
noleg=False):
"""**Get Monthly Wind Rose Figure**.
Plot the monthly wind rose of a given variable (or category of variables) grouped by a given condition (or set of conditions).
Parameters:
1. metdat (Pandas DataFrame): The desired input data (Met Mast).
2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names.
3. category (string) [default: None]: Specifies the category of information that is desired for plotting.
4. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis.
5. bins (integer, list) [default: 6]: Indicates the number of equally spaced bins to divide the variable.
6. nsector (integer) [default: 36]: Indicated the number of sector directions to divide the rose figure.
7. ylim (float) [default: None]: Provides the maximum value for the frequency of observations and is used to plot different roses with uniform limits.
8. noleg (Boolean) [default: False]: Determines whether or not there will be a legend to the figure.
Returns:
1. fig (Matplotlib Figure): The figure object for the desired input data and categories.
2. ax (Matplotlib Axes): The axes object for the desired input data and categories.
3. leg (Matplotlib Legend): The legend object for the desired input data and categories.
"""
# set up data
dircol, _, _ = utils.get_vertical_locations(
catinfo['columns']['direction'], location=vertloc)
varcol, vertloc, _ = utils.get_vertical_locations(
catinfo['columns'][category], location=vertloc)
plotdat = metdat.groupby(metdat.index.month)
winddir = plotdat[dircol]
var = plotdat[varcol]
months = utils.monthnames()
# wind speed bins to use in wind roses
# get var divisions set up
if isinstance(bins, int):
nbins = bins
else:
nbins = len(bins)
# set up plotting colors
colors = utils.get_colors(nbins - 1, basecolor='span')
colors += ['#3A4246'] # add something dark to the end.
colors = tuple(colors[0:nbins])
fig = plt.figure(figsize=(9, 13))
for iax, month in enumerate(months):
ax = fig.add_subplot(4, 3, iax + 1, projection="windrose")
ax.bar(winddir.get_group(iax + 1),
var.get_group(iax + 1),
bins=bins,
nsector=36,
colors=colors,
linewidth=0.35,
normed=True)
# Set the tick labels font
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontname('Arial')
label.set_fontsize(12)
ax.set_title(month, fontsize=12, y=1.15)
if iax == 10:
leg = plt.legend(loc=8,
ncol=2,
bbox_to_anchor=(0.5, -0.65),
frameon=False)
leg.set_title(catinfo['labels'][category])
fig.text(0.5,
-0.085,
r'$z={}$ m'.format(vertloc),
ha='center',
va='center')
axes = fig.get_children()[1:]
# adjust plot for specified max frequency
if ylim is None:
ylim = 0.0
for iax, month in enumerate(months):
ylim = np.max([ylim, axes[iax].get_ylim()[-1]])
for iax, month in enumerate(months):
axes[iax].set_ylim(0, ylim)
axes[iax].set_yticks(np.linspace(0.0, ylim, 4))
# print(axes[iax].get_yticks())
axes[iax].set_yticklabels(
[str(np.round(x, decimals=1)) for x in axes[iax].get_yticks()])
fig.tight_layout()
return fig, axes, leg
def monthly_stability_profiles(metdat,
catinfo,
category=None,
vertloc=80,
basecolor='span'):
"""**Get Monthly Stability Profile**.
Plot the monthly stability profile of a given variable (or category of variables) grouped by a given condition (or set of conditions).
Parameters:
1. metdat (Pandas DataFrame): The desired input data (Met Mast).
2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names.
3. category (string) [default: None]: Specifies the category of information that is desired for plotting.
4. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis.
5. basecolor (string) [default: 'span']: Provides the color code information to get from "utils.py".
Returns:
1. fig (Matplotlib Figure): The figure object for the desired input data and categories.
2. ax (Matplotlib Axes): The axes object for the desired input data and categories.
"""
if category is None:
print('not sure what to plot...')
pass
stab, stabloc, ind = utils.get_vertical_locations(
catinfo['columns']['stability flag'], location=vertloc)
plotdat = metdat.groupby([metdat.index.month, stab])
colors = utils.get_colors(5, basecolor='span')
months = utils.monthnames()
stabconds = utils.get_stabconds()
# extract vertical locations of data from variable names
_, vertlocs, ind = utils.get_vertical_locations(
catinfo['columns'][category])
fig, ax = plt.subplots(4, 3, figsize=(8, 13), sharex=True, sharey=True)
for iax, month in enumerate(months):
for ii, cond in enumerate(stabconds):
pdat = plotdat[catinfo['columns'][category]].get_group(
(iax + 1, cond)).mean()
ax.flatten()[iax].plot(pdat[ind], vertlocs, color=colors[ii])
ax.flatten()[iax].set_title(month)
fig.text(0,
0.58,
'Probe Height [m]',
ha='center',
va='center',
fontsize=14,
rotation='vertical')
leg = fig.legend(stabconds,
loc=9,
bbox_to_anchor=(0.55, 0.12),
frameon=False)
fig.tight_layout()
fig.subplots_adjust(bottom=0.175)
fig.text(0.525,
0.135,
catinfo['labels'][category],
ha='center',
va='center',
fontsize=14)
return fig, ax
def normalized_monthly_hist_by_stability(metdat, catinfo, vertloc=80):
"""**Get Normalized Monthly Stability Grouped Histogram Figure**.
Plot the normalized monthly stability grouped histogram of a given variable (or category of variables) grouped by a given condition (or set of conditions).
Parameters:
1. metdat (Pandas DataFrame): The desired input data (Met Mast).
2. catinfo (dictionary): Categorization information for the desired input data. Holds column names, labels, units, and save names.
3. vertloc (integer, float) [default: 80]: Describes the desired vertical location alond the tower for analysis.
Returns:
1. fig (Matplotlib Figure): The figure object for the desired input data and categories.
2. ax (Matplotlib Axes): The axes object for the desired input data and categories.
"""
months = utils.monthnames()
hours = np.arange(24)
stabcol, _, _ = utils.get_vertical_locations(
catinfo['columns']['stability flag'], location=vertloc)
stabconds = utils.get_stabconds()
colors = utils.get_colors(5, basecolor='span')
temp = metdat[stabcol].dropna()
plotdata = temp.groupby(
[temp.index.month.rename('month'),
temp.index.hour.rename('hour')]).value_counts(normalize=True)
plotdata.index.names = ['month', 'hour', 'stabclass']
temp = plotdata.reorder_levels(['month', 'stabclass', 'hour'])
indexvals = [np.arange(1, 13), stabconds, np.arange(24)]
indx = pd.MultiIndex.from_product(indexvals,
names=['month', 'stabclass', 'hour'])
temp = temp.reindex(index=indx).fillna(0.0)
fig, ax = plt.subplots(4, 3, figsize=(9, 10), sharex=True, sharey=True)
for ii, month in enumerate(months):
newbottom = np.zeros(24)
for jj, cond in enumerate(stabconds):
pdat = temp.loc[ii + 1, cond]
ax.flatten()[ii].bar(hours,
pdat,
color=colors[jj],
bottom=newbottom)
newbottom += pdat
# fig.legend(stabconds, loc=8, bbox_to_anchor=(0, -0.1), edgecolor='w')
fig.text(-0.02,
0.58,
'Probability [%]',
ha='center',
va='center',
rotation='vertical')
leg = fig.legend(stabconds,
loc=9,
bbox_to_anchor=(0.55, 0.125),
frameon=False)
fig.tight_layout()
fig.subplots_adjust(bottom=0.21)
fig.text(0.5, 0.165, 'Time of Day [Hour]', ha='center', va='center')
return fig, ax
###########################################
# End of Code
###########################################
def monthly_rose_fig(metdat,
catinfo,
category=None,
vertloc=80,
bins=6,
nsector=36,
ylim=None,
noleg=False):
###########################################
"""
make wind rose from pandas.Series wind direction and some other value of the same size.
Parameters:
metdat:
Pandas dataframe containing met mast data
catinfo:
dict containing categorization info for the metmast data. Fore each category,
catinfo holds column names, labels, units, and save names
category:
string specifying category of information to plot (e.g. 'speed', 'stability', etc.)
vertloc:
int or float describing the exact or approximate height of interest along the tower
bins:
int specifying number of equally spaced bins to divide var.
OR
list of bin division limits (eg [0,4,8,12,16])
nsector:
number or direction sectors to divide rose
ylim:
optional float with maximum value for frequency of observations, use to
plot different roses with uniform limits
noleg:
bool switch to turn legend off
"""
# set up data
dircol, _, _ = utils.get_vertical_locations(
catinfo['columns']['direction'], location=vertloc)
varcol, vertloc, _ = utils.get_vertical_locations(
catinfo['columns'][category], location=vertloc)
plotdat = metdat.groupby(metdat.index.month)
winddir = plotdat[dircol]
var = plotdat[varcol]
months = utils.monthnames()
# wind speed bins to use in wind roses
# get var divisions set up
if isinstance(bins, int):
nbins = bins
else:
nbins = len(bins)
# set up plotting colors
colors = utils.get_colors(nbins - 1, basecolor='span')
colors += ['#3A4246'] # add something dark to the end.
colors = tuple(colors[0:nbins])
fig = plt.figure(figsize=(9, 13))
for iax, month in enumerate(months):
ax = fig.add_subplot(4, 3, iax + 1, projection="windrose")
ax.bar(winddir.get_group(iax + 1),
var.get_group(iax + 1),
bins=bins,
nsector=36,
colors=colors,
linewidth=0.35,
normed=True)
# Set the tick labels font
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontname('Arial')
label.set_fontsize(12)
ax.set_title(month, fontsize=12, y=1.15)
if iax == 10:
leg = plt.legend(loc=8,
ncol=2,
bbox_to_anchor=(0.5, -0.65),
frameon=False)
leg.set_title(catinfo['labels'][category])
fig.text(0.5,
-0.085,
r'$z={}$ m'.format(vertloc),
ha='center',
va='center')
axes = fig.get_children()[1:]
# adjust plot for specified max frequency
if ylim is None:
ylim = 0.0
for iax, month in enumerate(months):
ylim = np.max([ylim, axes[iax].get_ylim()[-1]])
for iax, month in enumerate(months):
axes[iax].set_ylim(0, ylim)
axes[iax].set_yticks(np.linspace(0.0, ylim, 4))
# print(axes[iax].get_yticks())
axes[iax].set_yticklabels(
[str(np.round(x, decimals=1)) for x in axes[iax].get_yticks()])
fig.tight_layout()
return fig, axes, leg
def monthly_stability_profiles(metdat,
catinfo,
category=None,
vertloc=80,
basecolor='span'):
###########################################
"""
Plot monthly average profiles against one another.
Parameters:
metdat:
Pandas dataframe containing met mast data
catinfo:
dict containing categorization info for the metmast data. Fore each category,
catinfo holds column names, labels, units, and save names
category:
string specifying category of information to plot (e.g. 'speed', 'stability', etc.)
vertloc:
int or float describing the exact or approximate height of interest along the tower
basecolor:
string with the color code info to get from utils.
"""
if category is None:
print('not sure what to plot...')
pass
stab, stabloc, ind = utils.get_vertical_locations(
catinfo['columns']['stability flag'], location=vertloc)
plotdat = metdat.groupby([metdat.index.month, stab])
colors = utils.get_colors(5, basecolor=basecolor)
months = utils.monthnames()
stabconds = utils.get_stabconds()
# extract vertical locations of data from variable names
_, vertlocs, ind = utils.get_vertical_locations(
catinfo['columns'][category])
fig, ax = plt.subplots(4, 3, figsize=(8, 13), sharex=True, sharey=True)
for iax, month in enumerate(months):
for ii, cond in enumerate(stabconds):
pdat = plotdat[catinfo['columns'][category]].get_group(
(iax + 1, cond)).mean()
ax.flatten()[iax].plot(pdat[ind],
vertlocs,
color=colors[ii],
label=cond)
ax.flatten()[iax].set_title(month)
fig.text(0,
0.58,
'Probe Height [m]',
ha='center',
va='center',
fontsize=14,
rotation='vertical')
leg = fig.legend(stabconds,
loc=9,
bbox_to_anchor=(0.55, 0.12),
frameon=False)
fig.tight_layout()
fig.subplots_adjust(bottom=0.175)
fig.text(0.525,
0.135,
catinfo['labels'][category],
ha='center',
va='center',
fontsize=14)
return fig, ax
def normalized_monthly_hist_by_stability(metdat, catinfo, vertloc=80):
###########################################
"""
make a normlizec histogram of data separated by stability class.
Parameters:
metdat:
Pandas dataframe containing met mast data
catinfo:
dict containing categorization info for the metmast data. Fore each category,
catinfo holds column names, labels, units, and save names
vertloc:
int or float describing the exact or approximate height of interest along the tower
"""
months = utils.monthnames()
hours = np.arange(24)
stabcol, _, _ = utils.get_vertical_locations(
catinfo['columns']['stability flag'], location=vertloc)
stabconds = utils.get_stabconds()
colors = utils.get_colors(5, basecolor='span')
temp = metdat[stabcol].dropna()
plotdata = temp.groupby([temp.index.month,
temp.index.hour]).value_counts(normalize=True)
plotdata.index.names = ['month', 'hour', 'stabclass']
temp = plotdata.reorder_levels(['month', 'stabclass', 'hour'])
indexvals = [np.arange(1, 13), stabconds, np.arange(24)]
indx = pd.MultiIndex.from_product(indexvals,
names=['month', 'stabclass', 'hour'])
temp = temp.reindex(index=indx).fillna(0.0)
fig, ax = plt.subplots(4, 3, figsize=(9, 10), sharex=True, sharey=True)
for ii, month in enumerate(months):
newbottom = np.zeros(24)
for jj, cond in enumerate(stabconds):
pdat = temp.loc[ii + 1, cond]
ax.flatten()[ii].bar(hours,
pdat,
color=colors[jj],
bottom=newbottom)
newbottom += pdat
ax.flatten()[ii].set_title(month)
# fig.legend(stabconds, loc=8, bbox_to_anchor=(0, -0.1), edgecolor='w')
fig.text(-0.02,
0.58,
'Probability [%]',
ha='center',
va='center',
rotation='vertical')
leg = fig.legend(stabconds,
loc=9,
bbox_to_anchor=(0.55, 0.145),
frameon=False)
fig.tight_layout()
fig.subplots_adjust(bottom=0.21)
fig.text(0.5, 0.165, 'Time of Day [Hour]', ha='center', va='center')
return fig, ax
###########################################
def monthly_stacked_hist_by_stability(metdat, catinfo, category, vertloc=80):
###########################################
"""
make histograms of data separated by month and stability class.
Parameters:
metdat:
Pandas dataframe containing met mast data
catinfo:
dict containing categorization info for the metmast data. Fore each category,
catinfo holds column names, labels, units, and save names
category:
string specifying category of information to plot (e.g. 'speed', 'stability', etc.)
vertloc:
int or float describing the exact or approximate height of interest along the tower
"""
stabconds = utils.get_stabconds()
stabcol, _, _ = utils.get_vertical_locations(
catinfo['columns']['stability flag'], location=vertloc)
varcol, vertloc, _ = utils.get_vertical_locations(
catinfo['columns'][category], location=vertloc)
colors = utils.get_colors(len(stabconds), basecolor='span')
months = utils.monthnames()
plotdat = metdat.groupby([metdat.index.month, stabcol])
plotdat = plotdat[varcol]
fig, ax = plt.subplots(4, 3, figsize=(9, 10), sharex=True, sharey=True)
for iax, month in enumerate(months):
temp = pd.DataFrame(
{cond: plotdat.get_group((iax + 1, cond))
for cond in stabconds})
temp.plot.hist(
ax=ax.flatten()[iax],
stacked=True,
color=colors,
bins=35,
edgecolor='k',
legend=False,
# weights = np.ones(temp.dropna().shape) / np.prod(temp.shape),
density=True)
ax.flatten()[iax].set_title(month)
ax.flatten()[iax].set_ylabel('')
# fig.legend(stabconds, loc=8, bbox_to_anchor=(0, -0.1), edgecolor='w')
fig.text(0,
0.58,
'Frequency',
ha='center',
va='center',
fontsize=14,
rotation='vertical')
leg = fig.legend(stabconds,
loc=9,
bbox_to_anchor=(0.55, 0.15),
frameon=False)
fig.tight_layout()
fig.subplots_adjust(bottom=0.21)
fig.text(0.5,
0.16,
catinfo['labels'][category],
ha='center',
va='center',
fontsize=14)
return fig, ax #, leg
