def solar_irradiance(latitude, longitude, dt=None, optical_depth=0.118):
'''
Calculate the solar irradiance for the specified time and location.
latitude : scalar
The latitude of the location on the Earth in degrees
longitude : scalar
The longitude of the location on the Earth in degrees
dt : datetime.datetime instance
The date and time for which the irradiance should be calculated.
This defaults to the current date.
optical_depth : scalar
An overall optical depth value to assume for the atmosphere so
that the effects of atmospheric absorption can be accounted for.
Defaults to 0.118.
Returns : scalar or array
The solar irradiance in W / m^2 for each value in *hour*.
'''
if dt is None:
dt = datetime.utcnow()
if not iterable(dt):
dt = [dt]
lat_rad = latitude * degree
lon_rad = longitude * degree
cos_zenith = _get_cos_zenith(lat_rad, lon_rad, dt)
s = solar_constant(dt[0])
# Mask out values for cos < 0
mask = np.array(cos_zenith < 0.)
if mask.any():
cos_zenith = masked_array(cos_zenith, mask=mask)
return s * cos_zenith * np.exp(-optical_depth / cos_zenith)
def solar_irradiance(latitude, longitude, dt=None, optical_depth=0.118):
'''
Calculate the solar irradiance for the specified time and location.
latitude : scalar
The latitude of the location on the Earth in degrees
longitude : scalar
The longitude of the location on the Earth in degrees
dt : datetime.datetime instance
The date and time for which the irradiance should be calculated.
This defaults to the current date.
optical_depth : scalar
An overall optical depth value to assume for the atmosphere so
that the effects of atmospheric absorption can be accounted for.
Defaults to 0.118.
Returns : scalar or array
The solar irradiance in W / m^2 for each value in *hour*.
'''
if dt is None:
dt = datetime.utcnow()
if not iterable(dt):
dt = [dt]
lat_rad = latitude * degree
lon_rad = longitude * degree
cos_zenith = _get_cos_zenith(lat_rad, lon_rad, dt)
s = solar_constant(dt[0])
# Mask out values for cos < 0
mask = np.array(cos_zenith < 0.)
if mask.any():
cos_zenith = masked_array(cos_zenith, mask=mask)
return s * cos_zenith * np.exp(-optical_depth / cos_zenith)
def grid_data(ob_data, grid_x, grid_y, ob_x, ob_y, weight_func, params):
'''
Calculates a value at each grid point based on the observed data in
ob_data.
ob_data : 1D array
The observation data
grid_x : 2D array
The x locations of the grid points
grid_y : 2D array
The y locations of the grid points
ob_x : 1D array
The x locations of the observation points
ob_y : 1D array
The y locations of the observation points
weight_func : callable
Any function that returns weights for the observations given their
distance from a grid point.
params : any object or tuple of objects
Appropriate parameters to pass to *weight_func* after the distances.
Returns : 2D array
The values for the grid points
'''
if not iterable(params):
params = (params, )
# grid_point_dists calculates a 3D array containing the distance for each
# grid point to every observation.
weights = weight_func(grid_point_dists(grid_x, grid_y, ob_x, ob_y),
*params)
total_weights = weights.sum(axis=2)
final = (weights * ob_data).sum(axis=2) / total_weights
final = np.ma.masked_array(final, mask=(total_weights == 0.))
return final
def grid_data(ob_data, grid_x, grid_y, ob_x, ob_y, weight_func, params):
'''
Calculates a value at each grid point based on the observed data in
ob_data.
ob_data : 1D array
The observation data
grid_x : 2D array
The x locations of the grid points
grid_y : 2D array
The y locations of the grid points
ob_x : 1D array
The x locations of the observation points
ob_y : 1D array
The y locations of the observation points
weight_func : callable
Any function that returns weights for the observations given their
distance from a grid point.
params : any object or tuple of objects
Appropriate parameters to pass to *weight_func* after the distances.
Returns : 2D array
The values for the grid points
'''
if not iterable(params):
params = (params,)
# grid_point_dists calculates a 3D array containing the distance for each
# grid point to every observation.
weights = weight_func(grid_point_dists(grid_x, grid_y, ob_x, ob_y), *params)
total_weights = weights.sum(axis=2)
final = (weights * ob_data).sum(axis=2) / total_weights
final = np.ma.masked_array(final, mask=(total_weights==0.))
return final
def meteogram(data, fig=None, num_panels=3, time_range=None, layout=None,
styles=None, limits=None, units=None, tz=UTC):
'''
Plots a meteogram (collection of time series) for a data set. This
is broken down into a series of panels (defaults to 3), each of which
can plot multiple variables, with sensible defaults, but can also be
specified using *layout*.
*data* : numpy record array
A numpy record array containing time series for individual variables
in each field.
*fig* : :class:`matplotlib.figure.Figure` instance or None.
A matplotlib Figure on which to draw. If None, a new figure
will be created.
*num_panels* : int
The number of panels to use in the plot.
*time_range* : sequence, datetime.timedetla, or *None*
If a sequence, the starting and ending times for the x-axis. If
a :class:`datetime.timedelta` object, it represents the time span
to plot, which will end with the last data point. It defaults to
the last 24 hours of data.
*layout* : dictionary
A dictionary that maps panel numbers to lists of variables.
If a panel is not found in the dictionary, a default (up to panel 5)
will be used. *None* can be included in the list to denote that
:func:`pyplot.twinx` should be called, and the remaining variables
plotted.
*styles* : dictionary
A dictionary that maps variable names to dictionary of matplotlib
style arguments. Also, the keyword `fill` can be included, to
indicate that a filled plot should be used. Any variable not
specified will use a default (if available).
*limits* : dictionary
A dictionary that maps variable names to plot limits. These limits
are given by tuples with at least two items, which specify the
start and end limits. Either can be *None* which implies using the
automatically determined value. Optional third and fourth values
can be given in the tuple, which is a list of tick values and labels,
respectively.
*units* : dictionary
A dictionary that maps variable names to unit strings for axis labels.
*tz* : datetime.tzinfo instance
A :class:`datetime.tzinfo instance specifying the timezone to use
for plotting the x-axis. See the docs for :module:`datetime` and
:module:`pytz` for how to construct and use these objects. The
default is UTC.
Returns : list
A list of the axes objects that were created.
'''
if fig is None:
fig = plt.figure()
# Get the time variable
time = data['datetime']
# Process time_range.
major_ticker = AutoDateLocator(tz=tz)
minor_ticker = NullLocator()
major_formatter = DateFormatter('%H', tz=tz)
minor_formatter = NullFormatter()
if time_range is None:
time_range = timedelta(hours=24)
major_ticker = HourLocator(byhour=np.arange(0, 25, 3), tz=tz)
minor_ticker = HourLocator(tz=tz)
if not iterable(time_range):
end = time[-1]
start = end - time_range
time_range = (start, end)
#List of variables in each panel. None denotes that at that point, twinx
#should be called and the remaining variables plotted on the other axis
default_layout = {
0:['temperature', 'dewpoint'],
1:['wind gusts', 'wind speed', None, 'wind direction'],
2:['pressure'],
3:['rainfall'],
4:['solar radiation']}
if layout is not None:
default_layout.update(layout)
layout = default_layout
#Default styles for each variable
default_styles = {
'relative humidity':dict(color='#255425', linestyle='--'),
'dewpoint':dict(facecolor='#265425', edgecolor='None', fill=True),
'temperature':dict(facecolor='#C14F53', edgecolor='None', fill=True),
'pressure':dict(facecolor='#895125', edgecolor='None', fill=True),
'dewpoint':dict(facecolor='#265425', edgecolor='None', fill=True),
'wind speed':dict(facecolor='#1C2386', edgecolor='None', fill=True),
'wind gusts':dict(facecolor='#8388FC', edgecolor='None', fill=True),
'wind direction':dict(markeredgecolor='#A9A64B', marker='D',
linestyle='', markerfacecolor='None', markeredgewidth=1,
markersize=3),
'rainfall':dict(facecolor='#37CD37', edgecolor='None', fill=True),
'solar radiation':dict(facecolor='#FF8529', edgecolor='None',
fill=True),
'windchill':dict(color='#8388FC', linewidth=1.5),
'heat index':dict(color='#671A5C')}
if styles is not None:
default_styles.update(styles)
styles = default_styles
#Default data limits
default_limits = {
'wind direction':(0, 360, np.arange(0,400,45,),
['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW', 'N']),
'wind speed':(0, None),
'wind gusts':(0, None),
'rainfall':(0, None),
'solar radiation':(0, 1000, np.arange(0,1050,200))
}
if limits is not None:
default_limits.update(limits)
limits = default_limits
#Set data units
def_units = default_units.copy()
if units is not None:
def_units.update(units)
units = def_units
#Get the station name
site = data['site'][0]
#Get strings for the start and end times
start = time_range[0].astimezone(tz).strftime('%H%M %d %b %Y')
end = time_range[1].astimezone(tz).strftime('%H%M %d %b %Y %Z')
axes = []
for panel in range(num_panels):
if panel > 0:
ax = fig.add_subplot(num_panels, 1, panel+1, sharex=ax)
else:
ax = fig.add_subplot(num_panels, 1, panel+1)
ax.set_title('%s\n%s to %s' % (site, start, end))
panel_twinned = False
var_min = []
var_max = []
for varname in layout[panel]:
if varname is None:
_rescale_yaxis(ax, var_min + var_max)
ax = ax.twinx()
panel_twinned = True
var_min = []
var_max = []
continue
# Get the linestyle for this variable
style = styles.get(varname, dict())
#Get the variable from the data and plot
var = data[varname]
#Set special limits if necessary
lims = limits.get(varname, (None, None))
#Store the max and min for auto scaling
if var.max() is not np.ma.masked:
var_max.append(var.max())
var_min.append(var.min())
if style.pop('fill', False):
#Plot the filled area. Need latest Matplotlib for date support
#with fill_betweeen
lower = -500 if lims[0] is None else lims[0]
ax.fill_between(time, lower, var, where=~var.mask, **style)
#TODO: Matplotlib SVN has support for turning off the
#automatic scaling of the y-axis. Can we use that somehow
#to simplify our code??
_rescale_yaxis(ax, var_min + var_max)
else:
ax.plot(time, var, **style)
#If then length > 2, then we have ticks and (maybe) labels
if len(lims) > 2:
other = lims[2:]
lims = lims[:2]
#Separate out the ticks and perhaps labels
if len(other) == 1:
ax.set_yticks(other[0])
else:
ticks,labels = other
ax.set_yticks(ticks)
ax.set_yticklabels(labels)
ax.set_ylim(*lims)
# Set the label to the title-cased nice-version from the
# field info with units, if given.
if varname in units and units[varname]:
unit_str = ' (%s)' % units[varname]
if '^' in unit_str and '$' not in unit_str:
unit_str = '$' + unit_str + '$'
else:
unit_str = ''
descr = get_title(data, varname)
ax.set_ylabel(descr.title() + unit_str)
ax.xaxis.set_major_locator(major_ticker)
ax.xaxis.set_major_formatter(major_formatter)
ax.xaxis.set_minor_locator(minor_ticker)
ax.xaxis.set_minor_formatter(minor_formatter)
if not panel_twinned:
ax.yaxis.set_ticks_position('both')
for tick in ax.yaxis.get_major_ticks():
tick.label2On = True
axes.append(ax)
# Set the xlabel as appropriate depending on the timezone
ax.set_xlabel('Hour (%s)' % time[-1].astimezone(tz).tzname())
ax.set_xlim(*time_range)
return axes
def meteogram(data,
fig=None,
num_panels=3,
time_range=None,
layout=None,
styles=None,
limits=None,
units=None,
tz=UTC):
'''
Plots a meteogram (collection of time series) for a data set. This
is broken down into a series of panels (defaults to 3), each of which
can plot multiple variables, with sensible defaults, but can also be
specified using *layout*.
*data* : numpy record array
A numpy record array containing time series for individual variables
in each field.
*fig* : :class:`matplotlib.figure.Figure` instance or None.
A matplotlib Figure on which to draw. If None, a new figure
will be created.
*num_panels* : int
The number of panels to use in the plot.
*time_range* : sequence, datetime.timedetla, or *None*
If a sequence, the starting and ending times for the x-axis. If
a :class:`datetime.timedelta` object, it represents the time span
to plot, which will end with the last data point. It defaults to
the last 24 hours of data.
*layout* : dictionary
A dictionary that maps panel numbers to lists of variables.
If a panel is not found in the dictionary, a default (up to panel 5)
will be used. *None* can be included in the list to denote that
:func:`pyplot.twinx` should be called, and the remaining variables
plotted.
*styles* : dictionary
A dictionary that maps variable names to dictionary of matplotlib
style arguments. Also, the keyword `fill` can be included, to
indicate that a filled plot should be used. Any variable not
specified will use a default (if available).
*limits* : dictionary
A dictionary that maps variable names to plot limits. These limits
are given by tuples with at least two items, which specify the
start and end limits. Either can be *None* which implies using the
automatically determined value. Optional third and fourth values
can be given in the tuple, which is a list of tick values and labels,
respectively.
*units* : dictionary
A dictionary that maps variable names to unit strings for axis labels.
*tz* : datetime.tzinfo instance
A :class:`datetime.tzinfo instance specifying the timezone to use
for plotting the x-axis. See the docs for :module:`datetime` and
:module:`pytz` for how to construct and use these objects. The
default is UTC.
Returns : list
A list of the axes objects that were created.
'''
if fig is None:
fig = plt.figure()
# Get the time variable
time = data['datetime']
# Process time_range.
major_ticker = AutoDateLocator(tz=tz)
minor_ticker = NullLocator()
major_formatter = DateFormatter('%H', tz=tz)
minor_formatter = NullFormatter()
if time_range is None:
time_range = timedelta(hours=24)
major_ticker = HourLocator(byhour=np.arange(0, 25, 3), tz=tz)
minor_ticker = HourLocator(tz=tz)
if not iterable(time_range):
end = time[-1]
start = end - time_range
time_range = (start, end)
#List of variables in each panel. None denotes that at that point, twinx
#should be called and the remaining variables plotted on the other axis
default_layout = {
0: ['temperature', 'dewpoint'],
1: ['wind gusts', 'wind speed', None, 'wind direction'],
2: ['pressure'],
3: ['rainfall'],
4: ['solar radiation']
}
if layout is not None:
default_layout.update(layout)
layout = default_layout
#Default styles for each variable
default_styles = {
'relative humidity':
dict(color='#255425', linestyle='--'),
'dewpoint':
dict(facecolor='#265425', edgecolor='None', fill=True),
'temperature':
dict(facecolor='#C14F53', edgecolor='None', fill=True),
'pressure':
dict(facecolor='#895125', edgecolor='None', fill=True),
'dewpoint':
dict(facecolor='#265425', edgecolor='None', fill=True),
'wind speed':
dict(facecolor='#1C2386', edgecolor='None', fill=True),
'wind gusts':
dict(facecolor='#8388FC', edgecolor='None', fill=True),
'wind direction':
dict(markeredgecolor='#A9A64B',
marker='D',
linestyle='',
markerfacecolor='None',
markeredgewidth=1,
markersize=3),
'rainfall':
dict(facecolor='#37CD37', edgecolor='None', fill=True),
'solar radiation':
dict(facecolor='#FF8529', edgecolor='None', fill=True),
'windchill':
dict(color='#8388FC', linewidth=1.5),
'heat index':
dict(color='#671A5C')
}
if styles is not None:
default_styles.update(styles)
styles = default_styles
#Default data limits
default_limits = {
'wind direction': (0, 360, np.arange(
0,
400,
45,
), ['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW', 'N']),
'wind speed': (0, None),
'wind gusts': (0, None),
'rainfall': (0, None),
'solar radiation': (0, 1000, np.arange(0, 1050, 200))
}
if limits is not None:
default_limits.update(limits)
limits = default_limits
#Set data units
def_units = default_units.copy()
if units is not None:
def_units.update(units)
units = def_units
#Get the station name
site = data['site'][0]
#Get strings for the start and end times
start = time_range[0].astimezone(tz).strftime('%H%M %d %b %Y')
end = time_range[1].astimezone(tz).strftime('%H%M %d %b %Y %Z')
axes = []
for panel in range(num_panels):
if panel > 0:
ax = fig.add_subplot(num_panels, 1, panel + 1, sharex=ax)
else:
ax = fig.add_subplot(num_panels, 1, panel + 1)
ax.set_title('%s\n%s to %s' % (site, start, end))
panel_twinned = False
var_min = []
var_max = []
for varname in layout[panel]:
if varname is None:
_rescale_yaxis(ax, var_min + var_max)
ax = ax.twinx()
panel_twinned = True
var_min = []
var_max = []
continue
# Get the linestyle for this variable
style = styles.get(varname, dict())
#Get the variable from the data and plot
var = data[varname]
#Set special limits if necessary
lims = limits.get(varname, (None, None))
#Store the max and min for auto scaling
if var.max() is not np.ma.masked:
var_max.append(var.max())
var_min.append(var.min())
if style.pop('fill', False):
#Plot the filled area. Need latest Matplotlib for date support
#with fill_betweeen
lower = -500 if lims[0] is None else lims[0]
ax.fill_between(time, lower, var, where=~var.mask, **style)
#TODO: Matplotlib SVN has support for turning off the
#automatic scaling of the y-axis. Can we use that somehow
#to simplify our code??
_rescale_yaxis(ax, var_min + var_max)
else:
ax.plot(time, var, **style)
#If then length > 2, then we have ticks and (maybe) labels
if len(lims) > 2:
other = lims[2:]
lims = lims[:2]
#Separate out the ticks and perhaps labels
if len(other) == 1:
ax.set_yticks(other[0])
else:
ticks, labels = other
ax.set_yticks(ticks)
ax.set_yticklabels(labels)
ax.set_ylim(*lims)
# Set the label to the title-cased nice-version from the
# field info with units, if given.
if varname in units and units[varname]:
unit_str = ' (%s)' % units[varname]
if '^' in unit_str and '$' not in unit_str:
unit_str = '$' + unit_str + '$'
else:
unit_str = ''
descr = get_title(data, varname)
ax.set_ylabel(descr.title() + unit_str)
ax.xaxis.set_major_locator(major_ticker)
ax.xaxis.set_major_formatter(major_formatter)
ax.xaxis.set_minor_locator(minor_ticker)
ax.xaxis.set_minor_formatter(minor_formatter)
if not panel_twinned:
ax.yaxis.set_ticks_position('both')
for tick in ax.yaxis.get_major_ticks():
tick.label2On = True
axes.append(ax)
# Set the xlabel as appropriate depending on the timezone
ax.set_xlabel('Hour (%s)' % time[-1].astimezone(tz).tzname())
ax.set_xlim(*time_range)
return axes
