dplPy example using an entirely floating chronology¶

In [22]:

import numpy as np
import scipy as sp
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt

# nicely render figures in the notebook 
%config InlineBackend.figure_format = 'retina'
%matplotlib inline

# you can omit the line below if you'd like, but I really don't like the default fonts in Python, so I switch to Helvetica
plt.rcParams['font.family'] = 'Helvetica'
plt.rcParams['figure.figsize'] = (10, 5)

import numpy as np import scipy as sp import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt # nicely render figures in the notebook %config InlineBackend.figure_format = 'retina' %matplotlib inline # you can omit the line below if you'd like, but I really don't like the default fonts in Python, so I switch to Helvetica plt.rcParams['font.family'] = 'Helvetica' plt.rcParams['figure.figsize'] = (10, 5)

Let's import dplpy and check to make sure we've got the latest version and are using the Pypi installed version:

In [23]:

import dplpy

# print out the version number of dplPy you are using - should be v0.1.2
print('Currently using the following version of dplPy:', dplpy.__version__)

# You should see the path to your local Python installation
print('Currently using the following directory for dplPy:', dplpy.__file__)

import dplpy # print out the version number of dplPy you are using - should be v0.1.2 print('Currently using the following version of dplPy:', dplpy.__version__) # You should see the path to your local Python installation print('Currently using the following directory for dplPy:', dplpy.__file__)

Currently using the following version of dplPy: 0.1.2
Currently using the following directory for dplPy: /Users/kja/anaconda3/envs/dplpy/lib/python3.12/site-packages/dplpy/__init__.py

We'll use reader to get the raw ring width and we'll plot the series using Pandas build-in plotting.

In [24]:

df = dplpy.readers("wwr.rwl")
df.plot(legend=False);

df = dplpy.readers("wwr.rwl") df.plot(legend=False);

Attempting to read input file: wwr.rwl as .rwl format


SUCCESS!
File read as: .rwl file

Series names:
['WWRC501', 'WWRgq01', 'WWRa101', 'WWRa301', 'WWRgw01', 'WWRgw02', 'WWRl501', 'WWRgx01', 'WWRgy01', 'WWRbh01', 'WWRa601', 'WWRgs01', 'WWRc401', 'WWRc402', 'WWRst01a', 'WWRst01b', 'WWRst02a', 'WWRL601', 'WWRb201', 'WWRL801'] 

Get the series summary:

In [25]:

dplpy.summary(df)

dplpy.summary(df)

Out[25]:

	WWRC501	WWRgq01	WWRa101	WWRa301	WWRgw01	WWRgw02	WWRl501	WWRgx01	WWRgy01	WWRbh01	WWRa601	WWRgs01	WWRc401	WWRc402	WWRst01a	WWRst01b	WWRst02a	WWRL601	WWRb201	WWRL801
count	184.000000	199.000000	113.000000	123.000000	226.000000	172.000000	107.000000	220.000000	235.000000	223.000000	69.000000	226.000000	164.000000	165.000000	121.000000	85.000000	90.000000	147.000000	140.000000	135.000000
mean	1.417060	1.406482	2.523788	1.705341	1.146354	0.846506	2.167056	1.181932	1.094809	1.152668	2.368159	1.284230	1.500646	1.494570	2.458306	3.344494	3.916389	1.815898	1.640429	1.379948
std	0.713585	0.866385	1.123288	0.834921	1.040933	0.455078	1.081787	0.671113	0.976220	0.580281	1.304499	0.604241	0.862143	0.874111	1.814101	1.576738	1.560494	0.966497	1.180153	0.821949
min	0.208000	0.093000	0.211000	0.145000	0.000000	0.000000	0.167000	0.111000	0.000000	0.000000	0.160000	0.000000	0.000000	0.000000	0.325000	0.800000	0.821000	0.000000	0.100000	0.148000
25%	0.891000	0.789500	1.808000	1.066000	0.517000	0.512500	1.255500	0.696500	0.522000	0.776500	1.216000	0.856000	0.826750	0.811000	1.320000	2.161000	2.892250	1.074000	0.780750	0.769000
50%	1.352500	1.183000	2.472000	1.591000	0.827000	0.756500	2.073000	1.052500	0.777000	1.049000	2.285000	1.283000	1.291000	1.297000	2.019000	3.064000	3.847500	1.735000	1.315500	1.362000
75%	1.848250	1.916000	3.295000	2.300000	1.393500	1.127000	3.047000	1.473500	1.300000	1.412000	3.134000	1.684500	2.055750	2.001000	3.077000	4.061000	4.857250	2.396500	2.277750	1.761500
max	3.666000	4.165000	4.984000	4.166000	6.458000	2.236000	4.946000	3.836000	5.408000	3.612000	5.356000	2.992000	4.139000	4.549000	8.399000	7.962000	8.461000	4.835000	5.018000	4.550000

Get the series stats:

In [26]:

dplpy.stats(df)

dplpy.stats(df)

Out[26]:

	series	first	last	year	mean	median	stdev	skew	gini	ar1
1	WWRC501	1000	1183	184	1.417	1.35	0.714	0.612	0.282	0.782
2	WWRgq01	976	1174	199	1.406	1.18	0.866	0.906	0.337	0.858
3	WWRa101	1066	1178	113	2.524	2.47	1.123	0.051	0.253	0.753
4	WWRa301	1059	1181	123	1.705	1.59	0.835	0.454	0.275	0.788
5	WWRgw01	961	1186	226	1.146	0.83	1.041	2.478	0.420	0.871
6	WWRgw02	1011	1182	172	0.847	0.76	0.455	0.720	0.298	0.782
7	WWRl501	1079	1185	107	2.167	2.07	1.082	0.264	0.283	0.818
8	WWRgx01	960	1179	220	1.182	1.05	0.671	1.257	0.301	0.830
9	WWRgy01	946	1180	235	1.095	0.78	0.976	2.173	0.415	0.933
10	WWRbh01	962	1184	223	1.153	1.05	0.580	1.284	0.266	0.738
11	WWRa601	1116	1184	69	2.368	2.28	1.304	0.270	0.311	0.879
12	WWRgs01	957	1182	226	1.284	1.28	0.604	0.218	0.266	0.751
13	WWRc401	1020	1183	164	1.501	1.29	0.862	0.772	0.317	0.801
14	WWRc402	1020	1184	165	1.495	1.30	0.874	0.971	0.319	0.810
15	WWRst01a	956	1076	121	2.458	2.02	1.814	1.581	0.375	0.769
16	WWRst01b	956	1040	85	3.344	3.06	1.577	0.931	0.256	0.615
17	WWRst02a	954	1043	90	3.916	3.85	1.560	0.486	0.221	0.635
18	WWRL601	1015	1161	147	1.816	1.74	0.966	0.360	0.300	0.743
19	WWRb201	1044	1183	140	1.640	1.32	1.180	0.903	0.393	0.926
20	WWRL801	1050	1184	135	1.380	1.36	0.822	0.990	0.322	0.799

Make a segment plot:

In [27]:

dplpy.plot(df,type="seg")

dplpy.plot(df,type="seg")

Make a spaghetti plot:

In [28]:

dplpy.plot(df, type="spag")

dplpy.plot(df, type="spag")

Let's look at the crossdating:

In [29]:

dplpy.xdate(df, prewhiten=True, corr="Spearman", slide_period=50,bin_floor=100, p_val=0.05, show_flags=True)

dplpy.xdate(df, prewhiten=True, corr="Spearman", slide_period=50,bin_floor=100, p_val=0.05, show_flags=True)

Flags for WWRgy01
[B] Segment  High   -10    -9    -8    -7    -6    -5    -4    -3    -2    -1     0    +1    +2    +3    +4    +5    +6    +7    +8    +9   +10
   1125-1174   -1 -0.15 -0.16 -0.32 -0.05  0.12  0.04 -0.12  0.07  0.32  0.40  0.27  0.19  0.24  0.26  0.12 -0.02 -0.01  0.06 -0.07 -0.24 -0.26

Flags for WWRl501
[B] Segment  High   -10    -9    -8    -7    -6    -5    -4    -3    -2    -1     0    +1    +2    +3    +4    +5    +6    +7    +8    +9   +10
   1125-1174    1  0.14 -0.17 -0.05 -0.03 -0.10 -0.16 -0.01  0.02 -0.08  0.11  0.39  0.48  0.24 -0.02  0.18  0.15  0.08  0.03 -0.14 -0.23 -0.26

Out[29]:

	WWRC501	WWRL601	WWRL801	WWRa101	WWRa301	WWRa601	WWRb201	WWRbh01	WWRc401	WWRc402	WWRgq01	WWRgs01	WWRgw01	WWRgw02	WWRgx01	WWRgy01	WWRl501	WWRst01a	WWRst01b	WWRst02a
1000-1049	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.335510	NaN	NaN	0.399184	0.375750	0.499640	NaN	0.528836	0.466315	NaN	0.536230	NaN	NaN
1025-1074	0.487539	NaN	NaN	NaN	NaN	NaN	NaN	0.621417	NaN	NaN	0.602017	0.356447	0.605762	0.698920	0.443745	0.577623	NaN	0.455462	NaN	NaN
1050-1099	0.621128	0.286050	NaN	NaN	NaN	NaN	0.527683	0.594430	0.428956	0.478992	0.510972	0.506170	0.557167	0.614118	0.516927	0.629196	NaN	NaN	NaN	NaN
1075-1124	0.575894	0.421849	0.466987	0.609892	0.603649	NaN	0.487635	0.521441	0.502425	0.557551	0.484370	0.437791	0.560816	0.620360	0.660504	0.657719	NaN	NaN	NaN	NaN
1100-1149	0.469676	0.485426	0.480720	0.583385	0.655318	NaN	0.549772	0.585786	0.538343	0.654742	0.612101	0.358463	0.740024	0.762977	0.649748	0.620264	0.463721	NaN	NaN	NaN
1125-1174	0.530852	NaN	0.466218	0.369316	0.637647	0.618727	0.566291	0.537383	0.558319	0.545450	0.446915	0.509916	0.523457	0.515486	0.510012	0.267611	0.386218	NaN	NaN	NaN

Apply a 50 year spline and calculate the residuals (division) from that spline to detrend series:

In [30]:

df_rwi = dplpy.detrend(df, fit="spline", period=50, method="residual", plot=True)

df_rwi = dplpy.detrend(df, fit="spline", period=50, method="residual", plot=True)

Create a residual (AR-modeled) chronology:

In [32]:

crn_res = dplpy.chron(df_rwi, biweight=True, prewhiten=True, plot=True)

crn_res = dplpy.chron(df_rwi, biweight=True, prewhiten=True, plot=True)

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Last update: 2024-01-09