def statsTs(timeseries=""):
""" Calculate simple statistics of a timeseries
Args:
timeseries: sytem will prompt for one if not given
Returns:
the mean, median, min, max, standard deviation and the
inter-quartile range (IQR) of a timeseries.
Examples:
>>> mean, median, min_, max_, std, IQR = pyleo.statsTs(timeseries)
"""
if not 'timeseries_list' in globals():
openLipds()
# Get the timeseries if not present
if not timeseries:
timeseries = LipdUtils.getTs(timeseries_list)
# get the values
y = np.array(timeseries['paleoData_values'], dtype='float64')
mean, median, min_, max_, std, IQR = Stats.simpleStats(y)
return mean, median, min_, max_, std, IQR
def interpTs(timeseries="", x_axis="", interp_step="", start="", end=""):
"""Simple linear interpolation
Simple linear interpolation of the data using the numpy.interp method
Args:
timeseries. Default is blank, will prompt for it
x-axis (str): The representation against which to plot the paleo-data.
Options are "age", "year", and "depth". Default is to let the
system choose if only one available or prompt the user.
interp_step (float): the step size. By default, will prompt the user.
start (float): Start year/age/depth. Default is the minimum
end (float): End year/age/depth. Default is the maximum
Returns:
interp_age - the interpolated age/year/depth according to the end/start
and time step, \n
interp_values - the interpolated values
"""
if not 'timeseries_list' in globals():
openLipds()
# get a timeseries if none provided
if not timeseries:
timeseries = LipdUtils.getTs(timeseries_list)
# Get the values
y = np.array(timeseries['paleoData_values'], dtype='float64')
x, label = LipdUtils.checkXaxis(timeseries, x_axis=x_axis)
#remove nans
index = np.where(~np.isnan(y))[0]
x = x[index]
y = y[index]
#Interpolate the timeseries
interp_age, interp_values = Timeseries.interp(x,y,interp_step = interp_step,\
start= start, end=end)
return interp_age, interp_values
def binTs(timeseries="", x_axis="", bin_size="", start="", end=""):
"""Bin the paleoData values of the timeseries
Args:
timeseries. By default, will prompt the user for one.
x-axis (str): The representation against which to plot the paleo-data.
Options are "age", "year", and "depth". Default is to let the
system choose if only one available or prompt the user.
bin_size (float): the size of the bins to be used. By default,
will prompt for one
start (float): Start time/age/depth. Default is the minimum
end (float): End time/age/depth. Default is the maximum
Returns:
binned_values- the binned output,\n
bins- the bins (centered on the median, i.e. the 100-200 bin is 150),\n
n- number of data points in each bin,\n
error- the standard error on the mean in each bin\n
"""
if not 'timeseries_list' in globals():
openLipds()
# get a timeseries if none provided
if not timeseries:
timeseries = LipdUtils.getTs(timeseries_list)
# Get the values
y = np.array(timeseries['paleoData_values'], dtype='float64')
x, label = LipdUtils.checkXaxis(timeseries, x_axis=x_axis)
#remove nans
index = np.where(~np.isnan(y))[0]
x = x[index]
y = y[index]
#Bin the timeseries:
bins, binned_values, n, error = Timeseries.bin(x,y, bin_size = bin_size,\
start = start, end = end)
return bins, binned_values, n, error
def standardizeTs(timeseries="", scale=1, ddof=0, eps=1e-3):
""" Centers and normalizes the paleoData values of a given time series.
Constant or nearly constant time series not rescaled.
Args:
x (array): vector of (real) numbers as a time series, NaNs allowed
scale (real): a scale factor used to scale a record to a match a given variance
axis (int or None): axis along which to operate, if None, compute over the whole array
ddof (int): degress of freedom correction in the calculation of the standard deviation
eps (real): a threshold to determine if the standard deviation is too close to zero
Returns:
- z (array): the standardized time series (z-score), Z = (X - E[X])/std(X)*scale, NaNs allowed \n
- mu (real): the mean of the original time series, E[X] \n
- sig (real): the standard deviation of the original time series, std[X] \n
References:
1. Tapio Schneider's MATLAB code: http://www.clidyn.ethz.ch/imputation/standardize.m
2. The zscore function in SciPy: https://github.com/scipy/scipy/blob/master/scipy/stats/stats.py
@author: fzhu
"""
if not 'timeseries_list' in globals():
openLipds()
# get a timeseries if none provided
if not timeseries:
timeseries = LipdUtils.getTs(timeseries_list)
# get the values
y = np.array(timeseries['paleoData_values'], dtype='float64')
# Remove NaNs
index = np.where(~np.isnan(y))[0]
y = y[index]
#Standardize
z, mu, sig = Timeseries.standardize(y,
scale=1,
axis=None,
ddof=0,
eps=1e-3)
return z, mu, sig
def agemodelData(timeseries):
"""Get the necessary information for the agemodel plot
Args:
timeseries: a single timeseries object. By default, will
prompt the user
Returns:
depth - the depth values \n
age - the age values \n
x_label - the label for the x-axis \n
y_label - the label for the y-axis \n
archiveType - the archiveType (for default plot settings)
"""
if not "age" in timeseries.keys() and not "year" in timeseries.keys():
sys.exit("No time information")
elif not "depth" in timeseries.keys():
sys.exit("No depth information")
else:
if "age" in timeseries.keys() and "year" in timeseries.keys():
print("Do you want to use age or year?")
choice = int(input("Enter 0 for age and 1 for year: "))
if choice == 0:
age = timeseries['age']
if "ageUnits" in timeseries.keys():
age_label = "Calendar Age (" +\
timeseries["ageUnits"] +")"
else:
age_label = "Calendar Age"
elif choice == 1:
age = timeseries['year']
if "yearUnits" in timeseries.keys():
age_label = "Year (" +\
timeseries["yearUnits"] +")"
else:
age_label = "Year"
else:
sys.exit("Enter 0 or 1")
if "age" in timeseries.keys():
age = timeseries['age']
if "ageUnits" in timeseries.keys():
age_label = "Calendar Age (" +\
timeseries["ageUnits"] +")"
else:
age_label = "Calendar Age"
if "year" in timeseries.keys():
age = timeseries['year']
if "yearUnits" in timeseries.keys():
age_label = "Year (" +\
timeseries["ageUnits"] +")"
else:
age_label = "Year"
depth = timeseries['depth']
if "depthUnits" in timeseries.keys():
depth_label = "Depth (" + timeseries["depthUnits"] + ")"
else:
depth_label = "Depth"
# Get the archiveType and make sure it aligns with the ontology
archiveType = LipdUtils.LipdToOntology(timeseries["archiveType"])
return depth, age, depth_label, age_label, archiveType
def TsData(timeseries, x_axis=""):
""" Get the PaleoData with age/depth information
Get the necessary information for the TS plots/necessary to allow for
axes specification
Args:
timeseries: a single timeseries object.
By default, will prompt the user
x-axis (str): The representation against which to plot the
paleo-data. Options are "age", "year", and "depth".
Default is to let the system choose if only one available
or prompt the user.
Returns:
x - the x-valus \n
y - the y-values \n
archiveType - the archiveType (for plot settings) \n
x_label - the label for the x-axis \n
y_label - the label for the y-axis \n
label - the results of the x-axis query. Either depth, year, or age
"""
# Grab the x and y values
y = np.array(timeseries['paleoData_values'], dtype='float64')
x, label = LipdUtils.checkXaxis(timeseries, x_axis=x_axis)
# Remove NaNs
index = np.where(~np.isnan(y))[0]
x = x[index]
y = y[index]
# Grab the archiveType
archiveType = LipdUtils.LipdToOntology(timeseries["archiveType"])
# x_label
if label + "Units" in timeseries.keys():
x_label = label[0].upper() + label[1:] + " (" + timeseries[
label + "Units"] + ")"
else:
x_label = label[0].upper() + label[1:]
# ylabel
if "paleoData_InferredVariableType" in timeseries.keys():
if "paleoData_units" in timeseries.keys():
y_label = timeseries["paleoData_InferredVariableType"] + \
" (" + timeseries["paleoData_units"]+")"
else:
y_label = timeseries["paleoData_InferredVariableType"]
elif "paleoData_ProxyObservationType" in timeseries.keys():
if "paleoData_units" in timeseries.keys():
y_label = timeseries["paleoData_ProxyObservationType"] + \
" (" + timeseries["paleoData_units"]+")"
else:
y_label = timeseries["paleoData_ProxyObservationType"]
else:
if "paleoData_units" in timeseries.keys():
y_label = timeseries["paleoData_variableName"] + \
" (" + timeseries["paleoData_units"]+")"
else:
y_label = timeseries["paleoData_variableName"]
return x, y, archiveType, x_label, y_label
def mapAllArchive(markersize = 50, background = 'shadedrelief',\
saveFig = False, dir="", format='eps'):
"""Map all the available records loaded into the workspace by archiveType.
Map of all the records into the workspace by archiveType.
Uses the default color palette. Enter pyleoclim.plot_default for detail.
Args:
markersize (int): The size of the markers. Default is 50
background (str): Plots one of the following images on the map:
bluemarble, etopo, shadedrelief, or none (filled continents).
Default is shadedrelief.
saveFig (bool): Default is to not save the figure
dir (str): The absolute path of the directory in which to save the
figure. If not provided, creates a default folder called 'figures'
in the LiPD working directory (lipd.path).
format (str): One of the file extensions supported by the active
backend. Default is "eps". Most backend support png, pdf, ps, eps,
and svg.
Returns:
The figure
"""
# Make sure there are LiPD files to plot
if not 'timeseries_list' in globals():
openLipds()
#Get the lipd files
lipd_in_directory = lpd.getLipdNames()
# Initialize the various lists
lat = []
lon = []
archiveType = []
# Loop ang grab the metadata
for lipd in lipd_in_directory:
d = lpd.getMetadata(lipd)
lat.append(d['geo']['geometry']['coordinates'][1])
lon.append(d['geo']['geometry']['coordinates'][0])
archiveType.append(LipdUtils.LipdToOntology(d['archiveType']).lower())
# append the default palette for other category
plot_default.update({'other': ['k', 'o']})
# make sure criteria is in the plot_default list
for idx, val in enumerate(archiveType):
if val not in plot_default.keys():
archiveType[idx] = 'other'
# Make the map
fig = Map.mapAll(lat,lon,archiveType,lat_0=0,lon_0=0,palette=plot_default,\
background = background, markersize = markersize)
# Save the figure if asked
if saveFig == True:
LipdUtils.saveFigure('mapLipds_archive', format, dir)
else:
plt.show
return fig
def corrSigTs(timeseries1 = "", timeseries2 = "", x_axis = "", \
interp_step = "", start = "", end = "", nsim = 1000, \
method = 'isospectral', alpha = 0.5):
""" Estimates the significance of correlations between non IID timeseries.
Function written by. F. Zhu.
Args:
timeseries1, timeseries2: timeseries object. Default is blank.
x-axis (str): The representation against which to express the
paleo-data. Options are "age", "year", and "depth".
Default is to let the system choose if only one available
or prompt the user.
interp_step (float): the step size. By default, will prompt the user.
start (float): Start time/age/depth. Default is the maximum of
the minima of the two timeseries
end (float): End time/age/depth. Default is the minimum of the
maxima of the two timeseries
nsim (int): the number of simulations. Default is 1000
method (str): method use to estimate the correlation and significance.
Available methods include:
- 'ttest': T-test where the degrees of freedom are corrected for
the effect of serial correlation \n
- 'isopersistant': AR(1) modeling of the two timeseries \n
- 'isospectral' (default): phase randomization of original
inputs.
The T-test is parametric test, hence cheap but usually wrong
except in idyllic circumstances.
The others are non-parametric, but their computational
requirements scales with nsim.
alpha (float): significance level for critical value estimation. Default is 0.05
Returns:
r (float) - correlation between the two timeseries \n
sig (bool) - Returns True if significant, False otherwise \n
p (real) - the p-value
"""
if not 'timeseries_list' in globals():
openLipds()
# Get the timeseries
if not timeseries1:
timeseries1 = LipdUtils.getTs(timeseries_list)
if not timeseries2:
timeseries2 = LipdUtils.getTs(timeseries_list)
# Get the first time and paleoData values
y1 = np.array(timeseries1['paleoData_values'], dtype='float64')
x1, label = LipdUtils.checkXaxis(timeseries1, x_axis=x_axis)
# Get the second one
y2 = np.array(timeseries2['paleoData_values'], dtype='float64')
x2, label = LipdUtils.checkXaxis(timeseries2, x_axis=label)
# Remove NaNs
index1 = np.where(~np.isnan(y1))[0]
x1 = x1[index1]
y1 = y1[index1]
index2 = np.where(~np.isnan(y2))[0]
x2 = x2[index2]
y2 = y2[index2]
#Check that the two timeseries have the same lenght and if not interpolate
if len(y1) != len(y2):
print("The two series don't have the same length. Interpolating ...")
xi, interp_values1, interp_values2 = Timeseries.onCommonAxis(
x1, y1, x2, y2, interp_step=interp_step, start=start, end=end)
elif min(x1) != min(x2) and max(x1) != max(x2):
print("The two series don't have the same length. Interpolating ...")
xi, interp_values1, interp_values2 = Timeseries.onCommonAxis(
x1, y1, x2, y2, interp_step=interp_step, start=start, end=end)
else:
#xi = x1
interp_values1 = y1
interp_values2 = y2
r, sig, p = Stats.corrsig(interp_values1,
interp_values2,
nsim=nsim,
method=method,
alpha=alpha)
return r, sig, p
def summaryTs(timeseries="", x_axis="", saveFig=False, dir="", format="eps"):
"""Basic summary plot
Plots the following information: the time series, a histogram of
the PaleoData_values, location map, age-depth profile if both are
available from the paleodata, metadata about the record.
Args:
timeseries: a timeseries object. By default, will prompt for one
x_axis (str): The representation against which to plot the paleo-data.
Options are "age", "year", and "depth". Default is to let the
system choose if only one available or prompt the user.
saveFig (bool): default is to not save the figure
dir (str): the full path of the directory in which to save the figure.
If not provided, creates a default folder called 'figures' in the
LiPD working directory (lipd.path).
format (str): One of the file extensions supported by the active
backend. Default is "eps". Most backend support png, pdf, ps, eps,
and svg.
Returns:
The figure
"""
if not 'timeseries_list' in globals():
openLipds()
# get a timeseries if none provided
if not timeseries:
timeseries = LipdUtils.getTs(timeseries_list)
# get the necessary metadata
metadata = SummaryPlots.getMetadata(timeseries)
# get the information about the timeseries
x, y, archiveType, x_label, y_label = SummaryPlots.TsData(timeseries,
x_axis=x_axis)
# get the age model information if any
if "age" and "depth" in timeseries.keys(
) or "year" and "depth" in timeseries.keys():
depth, age, depth_label, age_label, archiveType = SummaryPlots.agemodelData(
timeseries)
flag = ""
else:
flag = "no age or depth info"
# Make the figure
fig = plt.figure(figsize=(11, 8))
gs = gridspec.GridSpec(2, 5)
gs.update(left=0, right=1.1)
# Plot the timeseries
ax1 = fig.add_subplot(gs[0, :-3])
marker = [plot_default[archiveType][0], plot_default[archiveType][1]]
markersize = 50
ax1.scatter(x,
y,
s=markersize,
facecolor='none',
edgecolor=marker[0],
marker=marker[1],
label='original')
ax1.plot(x, y, color=marker[0], linewidth=1, label='interpolated')
plt.xlabel(x_label)
plt.ylabel(y_label)
axes = plt.gca()
ymin, ymax = axes.get_ylim()
plt.title(timeseries['dataSetName'], fontsize=14, fontweight='bold')
plt.legend(loc=3, scatterpoints=1, fancybox=True, shadow=True, fontsize=10)
# Plot the histogram and kernel density estimates
ax2 = fig.add_subplot(gs[0, 2])
sns.distplot(y, vertical = True, color = marker[0], \
hist_kws = {"label":"Histogram"},
kde_kws = {"label":"KDE fit"})
plt.xlabel('PDF')
ax2.set_ylim([ymin, ymax])
ax2.set_yticklabels([])
# Plot the Map
lat = timeseries["geo_meanLat"]
lon = timeseries["geo_meanLon"]
ax3 = fig.add_subplot(gs[1, 0])
map = Basemap(projection='ortho', lon_0=lon, lat_0=lat)
map.drawcoastlines()
map.shadedrelief(scale=0.5)
map.drawcountries()
X, Y = map(lon, lat)
map.scatter(X, Y, s=150, color=marker[0], marker=marker[1])
# Plot Age model if any
if not flag:
ax4 = fig.add_subplot(gs[1, 2])
plt.style.use("ggplot")
ax4.plot(depth, age, color=marker[0], linewidth=1.0)
plt.xlabel(depth_label)
plt.ylabel(age_label)
else:
print("No age or depth information available, skipping age model plot")
#Add the metadata
textstr = "archiveType: " + metadata["archiveType"]+"\n"+"\n"+\
"Authors: " + metadata["authors"]+"\n"+"\n"+\
"Year: " + metadata["Year"]+"\n"+"\n"+\
"DOI: " + metadata["DOI"]+"\n"+"\n"+\
"Variable: " + metadata["Variable"]+"\n"+"\n"+\
"units: " + metadata["units"]+"\n"+"\n"+\
"Climate Interpretation: " +"\n"+\
" Climate Variable: " + metadata["Climate_Variable"] +"\n"+\
" Detail: " + metadata["Detail"]+"\n"+\
" Seasonality: " + metadata["Seasonality"]+"\n"+\
" Direction: " + metadata["Interpretation_Direction"]+"\n \n"+\
"Calibration: \n" + \
" Equation: " + metadata["Calibration_equation"] + "\n" +\
" Notes: " + metadata["Calibration_notes"]
plt.figtext(0.7, 0.4, textstr, fontsize=12)
#Save the figure if asked
if saveFig == True:
name = 'plot_timeseries_'+timeseries["dataSetName"]+\
"_"+y_label
LipdUtils.saveFigure(name, format, dir)
else:
plt.show()
return fig
def histTs(timeseries = "", bins = None, hist = True, \
kde = True, rug = False, fit = None, hist_kws = {"label":"Histogram"},\
kde_kws = {"label":"KDE fit"}, rug_kws = {"label":"Rug"}, \
fit_kws = {"label":"Fit"}, color = "default", vertical = False, \
norm_hist = True, saveFig = False, format ="eps",\
dir = ""):
""" Plot a univariate distribution of the PaleoData values
This function is based on the seaborn displot function, which is
itself a combination of the matplotlib hist function with the
seaborn kdeplot() and rugplot() functions. It can also fit
scipy.stats distributions and plot the estimated PDF over the data.
Args:
timeseries: A timeseries. By default, will prompt the user for one.
bins (int): Specification of hist bins following matplotlib(hist),
or None to use Freedman-Diaconis rule
hist (bool): Whether to plot a (normed) histogram
kde (bool): Whether to plot a gaussian kernel density estimate
rug (bool): Whether to draw a rugplot on the support axis
fit: Random variable object. An object with fit method, returning
a tuple that can be passed to a pdf method of positional
arguments following a grid of values to evaluate the pdf on.
{hist, kde, rug, fit}_kws: Dictionaries. Keyword arguments for
underlying plotting functions. If modifying the dictionary, make
sure the labels "hist", "kde", "rug" and "fit" are still passed.
color (str): matplotlib color. Color to plot everything but the
fitted curve in. Default is to use the default paletter for each
archive type.
vertical (bool): if True, oberved values are on y-axis.
norm_hist (bool): If True (default), the histrogram height shows
a density rather than a count. This is implied if a KDE or
fitted density is plotted
saveFig (bool): default is to not save the figure
dir (str): the full path of the directory in which to save the figure.
If not provided, creates a default folder called 'figures' in the
LiPD working directory (lipd.path).
format (str): One of the file extensions supported by the active
backend. Default is "eps". Most backend support png, pdf, ps, eps,
and svg.
Returns
fig - The figure
"""
if not 'timeseries_list' in globals():
openLipds()
# Get the timeseries if not present
if not timeseries:
timeseries = LipdUtils.getTs(timeseries_list)
# Get the values
y = np.array(timeseries['paleoData_values'], dtype='float64')
# Remove NaNs
index = np.where(~np.isnan(y))[0]
y = y[index]
# Get the y_label
if "paleoData_InferredVariableType" in timeseries.keys():
if "paleoData_units" in timeseries.keys():
y_label = timeseries["paleoData_InferredVariableType"] + \
" (" + timeseries["paleoData_units"]+")"
else:
y_label = timeseries["paleoData_InferredVariableType"]
elif "paleoData_ProxyObservationType" in timeseries.keys():
if "paleoData_units" in timeseries.keys():
y_label = timeseries["paleoData_ProxyObservationType"] + \
" (" + timeseries["paleoData_units"]+")"
else:
y_label = timeseries["paleoData_ProxyObservationType"]
else:
if "paleoData_units" in timeseries.keys():
y_label = timeseries["paleoData_variableName"] + \
" (" + timeseries["paleoData_units"]+")"
else:
y_label = timeseries["paleoData_variableName"]
# Grab the color
if color == "default":
archiveType = LipdUtils.LipdToOntology(timeseries["archiveType"])
color = plot_default[archiveType][0]
# Make this histogram
fig = Plot.plot_hist(y, bins = bins, hist = hist, \
kde = kde, rug = rug, fit = fit, hist_kws = hist_kws,\
kde_kws = kde_kws, rug_kws = rug_kws, \
fit_kws = fit_kws, color = color, vertical = vertical, \
norm_hist = norm_hist, label = y_label)
#Save the figure if asked
if saveFig == True:
name = 'plot_timeseries_'+timeseries["dataSetName"]+\
"_"+y_label
LipdUtils.saveFigure(name, format, dir)
else:
plt.show()
return fig
def plotTs(timeseries = "", x_axis = "", markersize = 50,\
marker = "default", saveFig = False, dir = "",\
format="eps"):
"""Plot a single time series.
Args:
A timeseries: By default, will prompt the user for one.
x_axis (str): The representation against which to plot the paleo-data.
Options are "age", "year", and "depth". Default is to let the
system choose if only one available or prompt the user.
markersize (int): default is 50.
marker (str): a string (or list) containing the color and shape of the
marker. Default is by archiveType. Type pyleo.plot_default to see
the default palette.
saveFig (bool): default is to not save the figure
dir (str): the full path of the directory in which to save the figure.
If not provided, creates a default folder called 'figures' in the
LiPD working directory (lipd.path).
format (str): One of the file extensions supported by the active
backend. Default is "eps". Most backend support png, pdf, ps, eps,
and svg.
Returns:
The figure.
"""
# Get the timeseries dictionary if not in global variables
if not 'timeseries_list' in globals():
openLipds()
# Get a timeseries if not already provided
if not timeseries:
timeseries = LipdUtils.getTs(timeseries_list)
y = np.array(timeseries['paleoData_values'], dtype='float64')
x, label = LipdUtils.checkXaxis(timeseries, x_axis=x_axis)
# remove nans
index = np.where(~np.isnan(y))[0]
x = x[index]
y = y[index]
# get the markers
if marker == "default":
archiveType = LipdUtils.LipdToOntology(timeseries["archiveType"])
marker = [plot_default[archiveType][0], plot_default[archiveType][1]]
# Get the labels
# title
title = timeseries['dataSetName']
# x_label
if label + "Units" in timeseries.keys():
x_label = label[0].upper() + label[1:] + " (" + timeseries[
label + "Units"] + ")"
else:
x_label = label[0].upper() + label[1:]
# ylabel
if "paleoData_InferredVariableType" in timeseries.keys():
if "paleoData_units" in timeseries.keys():
y_label = timeseries["paleoData_InferredVariableType"] + \
" (" + timeseries["paleoData_units"]+")"
else:
y_label = timeseries["paleoData_InferredVariableType"]
elif "paleoData_ProxyObservationType" in timeseries.keys():
if "paleoData_units" in timeseries.keys():
y_label = timeseries["paleoData_ProxyObservationType"] + \
" (" + timeseries["paleoData_units"]+")"
else:
y_label = timeseries["paleoData_ProxyObservationType"]
else:
if "paleoData_units" in timeseries.keys():
y_label = timeseries["paleoData_variableName"] + \
" (" + timeseries["paleoData_units"]+")"
else:
y_label = timeseries["paleoData_variableName"]
# make the plot
fig = Plot.plot(x,y,markersize=markersize,marker=marker,x_label=x_label,\
y_label=y_label, title=title)
#Save the figure if asked
if saveFig == True:
name = 'plot_timeseries_'+timeseries["dataSetName"]+\
"_"+y_label
LipdUtils.saveFigure(name, format, dir)
else:
plt.show()
return fig
def mapLipd(timeseries="", countries = True, counties = False, \
rivers = False, states = False, background = "shadedrelief",\
scale = 0.5, markersize = 50, marker = "ro", \
saveFig = False, dir = "", format="eps"):
""" Create a Map for a single record
Orthographic projection map of a single record.
Args:
timeseries: a LiPD timeseries object. Will prompt for one if not given
countries (bool): Draws the country borders. Default is on (True).
counties (bool): Draws the USA counties. Default is off (False).
rivers (bool): Draws the rivers. Default is off (False).
states (bool): Draws the American and Australian states borders.
Default is off (False)
background (str): Plots one of the following images on the map:
bluemarble, etopo, shadedrelief, or none (filled continents).
Default is shadedrelief
scale (float): useful to downgrade the original image resolution to
speed up the process. Default is 0.5.
markersize (int): default is 50
marker (str): a string (or list) containing the color and shape of the
marker. Default is by archiveType. Type pyleo.plot_default to see
the default palette.
saveFig (bool): default is to not save the figure
dir (str): the full path of the directory in which to save the figure.
If not provided, creates a default folder called 'figures' in the
LiPD working directory (lipd.path).
format (str): One of the file extensions supported by the active
backend. Default is "eps". Most backend support png, pdf, ps, eps,
and svg.
Returns:
The figure
"""
# Make sure there are LiPD files to plot
if not 'timeseries_list' in globals():
openLipds()
if not timeseries:
timeseries = LipdUtils.getTs(timeseries_list)
# Get latitude/longitude
lat = timeseries['geo_meanLat']
lon = timeseries['geo_meanLon']
# Get the marker color and shape
archiveType = LipdUtils.LipdToOntology(timeseries['archiveType']).lower()
# Make sure it's in the palette
if marker == 'default':
archiveType = LipdUtils.LipdToOntology(timeseries["archiveType"])
marker = plot_default[archiveType]
else:
marker = 'ro'
fig = Map.mapOne(lat,lon,marker=marker,markersize=markersize,\
countries = countries, counties = counties,rivers = rivers, \
states = states, background = background, scale =scale)
# Save the figure if asked
if saveFig == True:
LipdUtils.saveFigure(timeseries['dataSetName'] + '_map', format, dir)
else:
plt.show
return fig