title: Genextreme fit not working for some datasets
body: <p>I'm trying to fit a GEV distribution to temperature data to help identify extreme values. I have data sets for different regions - for some regions the fit works fine but for others it breaks down. It appears that it is setting the location parameter close to the maximum of the distribution range. All data sets are large, of the same size, complete and have no particularly strange values.</p>
  
  <p>Could you please suggest what might be happening or how I can investigate the genextreme function process to work out what the problem is?</p>
  
  <p>Here's the relevant bits of code (values are read in from NetCDF without any problem):</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  import netCDF4 as nc
  import matplotlib.pyplot as plt
  from scipy import stats
  from scipy.stats import genextreme as gev
  
  # calculate GEV fit
  fit = gev.fit(season_temp)
  
  # GEV parameters from fit
  c, loc, scale = fit
  fit_mean= loc
  min_extreme,max_extreme = gev.interval(0.99,c,loc,scale) 
  
  # evenly spread x axis values for pdf plot
  x = np.linspace(min(season_temp),max(season_temp),200)
  
  # plot distribution
  fig,ax = plt.subplots(1, 1)
  plt.plot(x, gev.pdf(x, *fit))
  plt.hist(season_temp,30,normed=True,alpha=0.3)
  </code></pre>
  
  <p>And here are two examples of outputs from different regions, successful and not:</p>
  
  <p><a href="https://i.stack.imgur.com/2jrxZ.png" rel="nofollow noreferrer">Successful fit</a></p>
  
  <p><a href="https://i.stack.imgur.com/OWRdI.png" rel="nofollow noreferrer">Unsuccessful fit</a></p>
  
  <p>The successfully fitted distribution has mean location parameter of 1.066 compared to data mean of 2.395. The one that failed has calculated a location parameter of 12.202 compared to data mean of 2.138.</p>
  
  <p>Thanks in advance for your help!</p>