Hello!
I just only want to use KDE function to make a plot and get the Age peak because of lack of age error's data.But I tried many times, despite get the plot,but i can't get the age peak.Even i download the PeakUtils.I hope you can help me researcher!
Thank you very much.

Enter plot options below

whatToPlot = 'relative' # Options: cumulative, relative, or both
separateSubplots = False # Set to True to plot each relative age distribution in a separate subplot (allows histogram and pie)

Specify the age range (Myr) that you want to plot

x1 = 50
x2 = 3500
plotLog = False # Set to True to plot the x-axis as a log scale

Specify the plot dimensions

w = 10 # width of the plot
c = 4 # height of CDF panel
h = 'auto' # height of the relative panel (only required if separateSubplots is False). Options: 'auto' or an integer

Specify the interval (Myr) over which distributions are calculated

xdif = 1 # Note: an interval of 1 Myr is recommended

Cumulative distribution options

plotCDF = False # Plot the CDF discretized at xdif interval
plotCPDP = False # Plot the cumulative PDP
plotCKDE = False # Plot the cumulative KDE
plotDKW = False # Plot the 95% confidence interval of the CDF (Dvoretsky-Kiefer-Wolfowitz inequality)

Relative distribution options

normPlots = False # Will normalize the PDP/KDE if equals True (if separateSubplots is True)

plotKDE = True # Set to True if want to plot KDE
colorKDE = True # Will color KDE according to same coloration as used in CDF plotting
colorKDEbyAge = True # Will color KDE according to age populations if set to True
bw = 'optimizedFixed' # Specify the KDE bandwidth. Options are 'optimizedFixed', 'optimizedVariable', or a number (bandwidth in Myr)

plotPDP = False # Set to True if want to plot PDP
colorPDP = False # Will color PDP according to same coloration as used in CDF plotting
colorPDPbyAge = False # Will color PDP according to age populations if set to True

plotColorBar = False # Color age categories as vertical bars, can add white bars to create blank space between other colored bars

plotHist = False # Set to True to plot a histogram (only available when separateSubplots is True)
b = 5 # Specify the histogram bin size (Myr)

plotPIE = False # Will plot a pie diagram (only available when separateSubplots is True)

Specify age categories for colored KDE, PDP, and/or pie plots

Sharman et al. 2015 scheme

agebins = [0, 23, 65, 85, 100, 135, 200, 300, 500, 4500]
agebinsc = ['slategray','royalblue','gold','red','darkred','purple','navy','gray','saddlebrown']

plotAgePeaks = True # Will identify and plot age peaks
agePeakOptions = ['KDE', 0.05, 5, 2, True] # [distType, threshold, minDist, minPeakSize, labels]

fig = dFunc.plotAll(sampleList, ages, errors, numGrains, labels, whatToPlot, separateSubplots, plotCDF, plotCPDP, plotCKDE,
plotDKW, normPlots, plotKDE, colorKDE, colorKDEbyAge, plotPDP, colorPDP, colorPDPbyAge, plotColorBar,
plotHist, plotLog, plotPIE, x1, x2, b, bw, xdif, agebins, agebinsc, w, c, h, plotAgePeaks, agePeakOptions,
CDFlw=3, KDElw=1, PDPlw=1)

An update in matplotlib v3.8 that no longer allows auto-removal of overlapping axes has resulted in issues with some of the detritalPy plotting functions. Recommend using matplotlib v3.7 or earlier until plotting functions in detritalPy are updated.

ValueError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_31596\4126944053.py in
58 agePeakOptions = ['KDE', 0.05, 5, 2, True] # [distType, threshold, minDist, minPeakSize, labels]
59
---> 60 fig = dFunc.plotAll(sampleList, ages, errors, numGrains, labels, whatToPlot, separateSubplots, plotCDF, plotCPDP, plotCKDE,
61 plotDKW, normPlots, plotKDE, colorKDE, colorKDEbyAge, plotPDP, colorPDP, colorPDPbyAge, plotColorBar,
62 plotHist, plotLog, plotPIE, x1, x2, b, bw, xdif, agebins, agebinsc, w, c, h, plotAgePeaks, agePeakOptions,

d:\anaconda3\lib\site-packages\detritalpy\detritalFuncs.py in plotAll(sampleList, ages, errors, numGrains, labels, whatToPlot, separateSubplots, plotCDF, plotCPDP, plotCKDE, plotDKW, normPlots, plotKDE, colorKDE, colorKDEbyAge, plotPDP, colorPDP, colorPDPbyAge, plotColorBar, plotHist, plotLog, plotPIE, x1, x2, b, bw, xdif, agebins, agebinsc, w, c, h, plotAgePeaks, agePeakOptions, CDFlw, KDElw, PDPlw, plotDepoAge, depoAge, plotAgesOnCDF, plotHeatMap, heatMapType, heatMap, PDP_ymax, KDE_ymax, agebinsc_alpha, colors, bw_x)
392
393 if separateSubplots:
--> 394 fig = plotAll_1(sampleList, ages, errors, numGrains, labels, whatToPlot, plotCDF, plotCPDP, plotCKDE, plotDKW, normPlots, plotKDE,
395 colorKDE, colorKDEbyAge, plotPDP, colorPDP, colorPDPbyAge, plotColorBar, plotHist, plotLog, plotPIE, x1, x2, b, bw, xdif, agebins,
396 agebinsc, w, c, plotAgePeaks, agePeakOptions, CDFlw, KDElw, PDPlw, plotDepoAge, depoAge, plotAgesOnCDF, plotHeatMap, heatMapType, heatMap,

d:\anaconda3\lib\site-packages\detritalpy\detritalFuncs.py in plotAll_1(sampleList, ages, errors, numGrains, labels, whatToPlot, plotCDF, plotCPDP, plotCKDE, plotDKW, normPlots, plotKDE, colorKDE, colorKDEbyAge, plotPDP, colorPDP, colorPDPbyAge, plotColorBar, plotHist, plotLog, plotPIE, x1, x2, b, bw, xdif, agebins, agebinsc, w, c, plotAgePeaks, agePeakOptions, CDFlw, KDElw, PDPlw, plotDepoAge, depoAge, plotAgesOnCDF, plotHeatMap, heatMapType, heatMap, PDP_ymax, KDE_ymax, agebinsc_alpha, colors, bw_x)
828 else:
829 if isinstance(x1, list) == False:
--> 830 axPDP.set_ylim([0, max(PDP[i])+max(PDP[i])*0.05])
831 else:
832 axPDP.set_ylim([0, PDPmax[i]+PDPmax[i]*0.05])

d:\anaconda3\lib\site-packages\matplotlib_api\deprecation.py in wrapper(*args, **kwargs)
452 "parameter will become keyword-only %(removal)s.",
...
-> 3572 raise ValueError("Axis limits cannot be NaN or Inf")
3573 return converted_limit
3574

ValueError: Axis limits cannot be NaN or Inf
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...

The KDE algorithm (from statsmodels.nonparametric.kde import KDEUnivariate) returns an error if the "Best Age" values are not float.

I recently updated my python installation using conda (23.7.2) and I'm running numpy version 1.24.3 and detritalpy 1.3.36. I get the following error that np.float is deprecated when trying to plot KDE following the tutorial (when it worked prior to updating conda). The returned plot is empty.

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[4], line 52
     49 agePeakOptions = ['KDE', 0.05, 5, 2, True] # [distType, threshold, minDist, minPeakSize, labels]
     50 xdif=1
---> 52 fig = dFunc.plotAll(sampleList, ages, errors, numGrains, labels, whatToPlot, separateSubplots, plotCDF, plotCPDP, plotCKDE, 
     53                     plotDKW, normPlots, plotKDE, colorKDE, colorKDEbyAge, plotPDP, colorPDP, colorPDPbyAge, plotColorBar, 
     54                     plotHist, plotLog, plotPIE, x1, x2, b, bw, xdif, agebins, agebinsc, w, c, h, plotAgePeaks, agePeakOptions,
     55                     CDFlw=1, KDElw=1, PDPlw=1)

File ~\anaconda3\lib\site-packages\detritalpy\detritalFuncs.py:392, in plotAll(sampleList, ages, errors, numGrains, labels, whatToPlot, separateSubplots, plotCDF, plotCPDP, plotCKDE, plotDKW, normPlots, plotKDE, colorKDE, colorKDEbyAge, plotPDP, colorPDP, colorPDPbyAge, plotColorBar, plotHist, plotLog, plotPIE, x1, x2, b, bw, xdif, agebins, agebinsc, w, c, h, plotAgePeaks, agePeakOptions, CDFlw, KDElw, PDPlw, plotDepoAge, depoAge, plotAgesOnCDF, plotHeatMap, heatMapType, heatMap, PDP_ymax, KDE_ymax, agebinsc_alpha, colors, bw_x)
    389                 return None
    391 if separateSubplots:
--> 392     fig = plotAll_1(sampleList, ages, errors, numGrains, labels, whatToPlot, plotCDF, plotCPDP, plotCKDE, plotDKW, normPlots, plotKDE, 
    393         colorKDE, colorKDEbyAge, plotPDP, colorPDP, colorPDPbyAge, plotColorBar, plotHist, plotLog, plotPIE, x1, x2, b, bw, xdif, agebins, 
    394         agebinsc, w, c, plotAgePeaks, agePeakOptions, CDFlw, KDElw, PDPlw, plotDepoAge, depoAge, plotAgesOnCDF, plotHeatMap, heatMapType, heatMap,
    395         PDP_ymax, KDE_ymax, agebinsc_alpha, colors, bw_x)
    396 else:
    397     fig = plotAll_2(sampleList, ages, errors, numGrains, labels, whatToPlot, plotCDF, plotCPDP, plotCKDE, plotDKW, normPlots, plotKDE, 
    398         colorKDE, colorKDEbyAge, plotPDP, colorPDP, colorPDPbyAge, plotColorBar, plotHist, plotLog, plotPIE, x1, x2, b, bw, xdif, agebins, 
    399         agebinsc, w, c, h, CDFlw, KDElw, PDPlw, plotAgesOnCDF, agebinsc_alpha, colors, bw_x)

File ~\anaconda3\lib\site-packages\detritalpy\detritalFuncs.py:548, in plotAll_1(sampleList, ages, errors, numGrains, labels, whatToPlot, plotCDF, plotCPDP, plotCKDE, plotDKW, normPlots, plotKDE, colorKDE, colorKDEbyAge, plotPDP, colorPDP, colorPDPbyAge, plotColorBar, plotHist, plotLog, plotPIE, x1, x2, b, bw, xdif, agebins, agebinsc, w, c, plotAgePeaks, agePeakOptions, CDFlw, KDElw, PDPlw, plotDepoAge, depoAge, plotAgesOnCDF, plotHeatMap, heatMapType, heatMap, PDP_ymax, KDE_ymax, agebinsc_alpha, colors, bw_x)
    545 if (whatToPlot == 'both' or whatToPlot == 'relative'):
    546     # Cycle through each sample for normalized plots            
    547     if plotKDE or (plotHeatMap and heatMapType == 'KDE'):
--> 548         KDE_age, KDE = KDEcalcAges(ages=ages, x1=0, x2=4500, xdif=xdif, bw=bw, bw_x=bw_x, cumulative=False)          
    549     if plotPDP or (plotHeatMap and heatMapType == 'PDP'):
    550         PDP_age, PDP = PDPcalcAges(ages=ages, errors=errors, x1=0, x2=4500, xdif=xdif, cumulative=False)

File ~\anaconda3\lib\site-packages\detritalpy\detritalFuncs.py:3356, in KDEcalcAges(ages, x1, x2, xdif, bw, bw_x, cumulative)
   3353     if bw_x is not None:
   3354         print('Warning: If a bandwidth x-axis split is desired, must specify bandwidth values to use by placing bw in a list: e.g., bw=[2.5, 10]')
-> 3356     KDE_age, KDE = KDE_bw_selector(ages=ages, x1=x1, x2=x2, xdif=xdif, bw=bw, cumulative=cumulative)
   3357     return KDE_age, KDE
   3359 else:

File ~\anaconda3\lib\site-packages\detritalpy\detritalFuncs.py:3398, in KDE_bw_selector(ages, x1, x2, xdif, bw, cumulative)
   3396     KDE_age, KDE = KDEcalcAgesLocalAdapt(ages=ages, x1=x1, x2=x2, xdif=xdif, cumulative=cumulative)
   3397 if bw == 'optimizedFixed':
-> 3398     KDE_age, KDE = KDEcalcAgesGlobalAdapt(ages=ages, x1=x1, x2=x2, xdif=xdif, cumulative=cumulative)
   3400 return KDE_age, KDE

File ~\anaconda3\lib\site-packages\detritalpy\detritalFuncs.py:3542, in KDEcalcAgesGlobalAdapt(ages, x1, x2, xdif, cumulative)
   3540 KDE = np.zeros(shape=(len(ages),len(KDE_age)))
   3541 for i in range(len(ages)):
-> 3542     kde = akde.ssvkernel(np.asarray(ages[i]), tin=KDE_age, nbs=100)[0]
   3543     if cumulative:
   3544         kde = np.cumsum(kde)

File ~\anaconda3\lib\site-packages\detritalpy\adaptiveKDE.py:411, in ssvkernel(x, tin, M, nbs, WinFunc)
    409 y_hist = np.histogram(x_ab, thist-dt/2)[0] / dt
    410 L = y_hist.size
--> 411 N = sum(y_hist * dt).astype(np.float)
    413 # initialize window sizes
    414 W = logexpSSV(np.linspace(ilogexpSSV(5 * dt), ilogexpSSV(T), np.int(M)))

File ~\anaconda3\lib\site-packages\numpy\__init__.py:305, in __getattr__(attr)
    300     warnings.warn(
    301         f"In the future `np.{attr}` will be defined as the "
    302         "corresponding NumPy scalar.", FutureWarning, stacklevel=2)
    304 if attr in __former_attrs__:
--> 305     raise AttributeError(__former_attrs__[attr])
    307 # Importing Tester requires importing all of UnitTest which is not a
    308 # cheap import Since it is mainly used in test suits, we lazy import it
    309 # here to save on the order of 10 ms of import time for most users
    310 #
    311 # The previous way Tester was imported also had a side effect of adding
    312 # the full `numpy.testing` namespace
    313 if attr == 'testing':

AttributeError: module 'numpy' has no attribute 'float'.
`np.float` was a deprecated alias for the builtin `float`. To avoid this error in existing code, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
    https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations