Update docs

cfr/proxy.py

@@ -10,6 +10,7 @@
 import copy
 from tqdm import tqdm
 from collections import OrderedDict
+from datetime import datetime
 import matplotlib.pyplot as plt
 from matplotlib import gridspec
 import cartopy.crs as ccrs
@@ -146,6 +147,11 @@ class ProxyRecord:
     def __init__(self, pid=None, time=None, value=None, lat=None, lon=None, elev=None, ptype=None, tags=None,
         value_name=None, value_unit=None, time_name=None, time_unit=None, seasonality=None):
         self.pid = pid
+        if time is not None:
+            if len(time) == 1:
+                time = [time]
+            if not utils.is_numeric(time):
+                time = utils.datetime2year_float(time)
         self.time = time
         self.value = value
         self.lat = lat
@@ -463,16 +469,25 @@ def get_pseudo(self, psm=None, signal=None, calibrate=True,
             time_max = np.min([proxy_time_max, pseudo_time_max])
             mask_proxy = (self.time>=time_min)&(self.time<=time_max)
             mask_pseudo = (self.pseudo.time>=time_min)&(self.pseudo.time<=time_max)
+            if verbose:
+                utils.p_header(f'>>> timespan: ({time_min}, {time_max})')
+
 
         value = self.pseudo.value
 
         if match_var:
             value = value / np.nanstd(value[mask_pseudo]) * np.nanstd(self.value[mask_proxy])
-            if verbose: utils.p_success(f'>>> Variance matched.')
+            if verbose:
+                utils.p_header(f'>>> Var(proxy)={np.nanvar(self.value[mask_proxy])}')
+                utils.p_header(f'>>> Var(pseudoproxy)={np.nanvar(value[mask_pseudo])}')
+                utils.p_success(f'>>> Variance matched.')
 
         if match_mean:
             value = value - np.nanmean(value[mask_pseudo]) + np.nanmean(self.value[mask_proxy])
-            if verbose: utils.p_success(f'>>> Mean matched.')
+            if verbose:
+                utils.p_header(f'>>> Mean(proxy)={np.nanmean(self.value[mask_proxy])}')
+                utils.p_header(f'>>> Mean(pseudoproxy)={np.nanmean(value[mask_pseudo])}')
+                utils.p_success(f'>>> Mean matched.')
 
         self.pseudo.value = value
 

cfr/psm.py

@@ -1,6 +1,7 @@
 from turtle import color
 import numpy as np
 import pandas as pd
+from datetime import datetime
 from scipy import integrate, signal, stats
 from tqdm import tqdm
 from multiprocessing import cpu_count

cfr/utils.py

@@ -648,4 +648,8 @@ def colored_noise_2regimes(alpha1, alpha2, f_break, t, f0=None, m=None, seed=Non
         sin_func = np.sin(2*np.pi*k*f0*t[j] + theta)
         y[j] = np.sum(coeff*sin_func)
 
-    return y
+    return y
+
+def is_numeric(obj):
+    attrs = ['__add__', '__sub__', '__mul__', '__truediv__', '__pow__']
+    return all(hasattr(obj, attr) for attr in attrs)

docs/_sources/notebooks/pp2k-pdb-load-viz.ipynb

@@ -1231,16 +1231,12 @@
     "One may ask \"How do I know the proxy IDs?\"\n",
     "\n",
     "A `cfr.ProxyDatabase` object also comes with a `.plotly()` method that can help us check **proxy IDs** on an interactive map.\n",
-    "\n",
-    "The below cell cannot be rendered on a webpage, but should execute in a local Jupyter notebook.\n",
-    "It should display an interactive map, and by hovering the mouse over each site marker, one may check the metadata of a specific site, including:\n",
+    "It will display an interactive map, and by hovering the mouse over each site marker, one may check the metadata of a specific site, including:\n",
     "\n",
     "- pid (proxy ID)\n",
     "- ptype\n",
     "- lat\n",
-    "- lon\n",
-    "\n",
-    "Take a try by yourself!"
+    "- lon"
    ]
   },
   {