Bcrhon is an R package that enables quick calibration of radiocarbon dates under various calibration curves (including user generated ones); Age-depth modelling as per the algorithm of Haslett and Panell (2008); Relative sea level rate estimation incoporating time uncertainty in polynomial regression models; and non-parametric phase modelling via Gaussian mixtures as a means to determine the activity of a site.
Bchron was developped by John Haslett and Andrew Parnell. To learn more about Bchron and its development, visit this page.
**NOTE**
When using outputs generated by Bchron, please cite the original publication:
Haslett, J., Parnell, A. (2008). A simple monotone process with application to radiocarbon-dated depth chronologies. Journal of the Royal Statistical Society: Series C (Applied Statistics), 57, 4, 1467-9876. doi: 10.1111/j.1467-9876.2008.00623.x
We provide a user-friendly interface where no R knowledge is required. The notebook will use a set of prompts to set up a run in Bchron, plot the results, and save the pertinent information to several text files.
Jupyter Notebook allows to intertwine explanation text and cells of code. To run the code within a cell, press the "play" button above or enter SHIFT+ENTER on your keyboard. We made this notebook as intuitive as possible using prompts and GUIs whenever possible (The GUI may hide behind your browser window). All you have to do is run the cells in order.
Let's start with something simple. Let's say 'Hello World!' using Python. Run the cell below and make sure the 'Hello World' prints underneath the code cell.
In [1]:
print("Hello, World!")
print('Congratulations, you have run your first Python code!')
Hello, World!
Congratulations, you have run your first Python code!
A record in LiPD format. This notebook assumes that the column are organized using this format. To learn more about LiPD and utilities to format your dataset, vist this page.
**NOTE**
A Welcome to LiPD guide is also available in a Jupyter Notebook, created in the root directory of your computer when you intalled the lipd module.
Now that you have a sense of how this workbook works, let's import a LiPD file and create a folder on your local computer where to save the chrocological information.
In [2]:
# Import the LiPD package and locate your files
from lipd.start import *
Where are your files stored?
1. Online URL
2. Browse Computer
3. Downloads folder
4. Notebooks folder
Option: 4
**NOTE**
Run the following cell everytime you restart the Jupyter Notebook!
In [3]:
# Load the LiPD files
loadLipds()
Found: 2 LiPD file(s)
processing: ODP1098B.lpd
Fetching update for lipdnames.csv
processing: SAm-LagunaChepicdeJong2013.lpd
Process Complete
Enter the name of the record you wish to analyze at the prompt
**NOTE**
Make sure you include the .lpd extension!
In [5]:
# Enter the name of the record you wish to analyze
record = input ("Which record would you like to analyze? ")
Which record would you like to analyze? ODP1098B.lpd
Enter the name of the folder in which you want to save the Bchron output and press enter. Note that if you wish to run alternative age models (for instance by changing the reservoir age correction), you need to enter a different folder name.
In [6]:
## Create a new folder in the current LiPD directory.
from ipywidgets import widgets
from IPython.display import display
newpath = widgets.Text(description = "Enter the name of your folder.\n")
display(newpath)
def createpath(sender):
import os
if not os.path.exists(newpath.value):
os.makedirs(newpath.value)
else:
print("Warning: All previous outputs will be overidden.\n \
To save in a new directory, please choose another name before continuing.")
newpath.on_submit(createpath)
Warning: All previous outputs will be overidden.
To save in a new directory, please choose another name before continuing.
In [7]:
# Get the metadata (To display lat/lon)
lipd_m = getMetadata("ODP1098B.lpd")
# Get the chronology data in a Panda DataFrame for ease of use
df_meta_l, df_data_l, df_chron_l = lipd_to_df("ODP1098B.lpd")
Process Complete
Process Complete
The Bchron run is set to use the most recent Marine calibration curve (Marine13 as of May 2016). Any correction to the standard marine reservoir age needs to be added to the 14C data prior import into Bchron. We recommend using data from the 14CHRONO website and using the mean and standard error of the mean as the reservoir age correction and associated error. Alternatively you can enter your own value for the reservoir age correction.
**NOTE**
**Only run the cell corresponding to your preferred option**.The first cell displays the latitude and longitude of the record of interest for use on the website.
In [8]:
print("Latitude: " + str(lipd_m.get('geo').get('geometry').get('coordinates')[0]))
print("Longitude: " + str(lipd_m.get('geo').get('geometry').get('coordinates')[1]))
Latitude: -64.86
Longitude: -64.2
**NOTE**
The cell below will automatically "paste" the reservoir age correction from the website. Make sure that the table is the last item copied to the clipboard. You will get a chance to check it before continuing.
A copy of the table is automatically saved as a text file as part of the routine.
In [9]:
import pandas as pd
import numpy as np
import math
data = pd.read_clipboard()
# Calculate the average DR and standard error of the mean
corr = data.mean()['DeltaR']
corr_err = data.std()['DeltaR']
datapoints = data.shape[0]
max_dist = data.max()['distance (km)']
# Save the data to a textfile and generate a log
dir = os.getcwd()+"/"+newpath.value
data.to_csv(os.path.join(dir,"Reservoir_Age.txt"),sep=' ', index=False,
header = True)
text_file = open(os.path.join(dir,"ReservoirAgeCorr_Info.txt"),"w")
text_file.write("The reservoir age correction was estimated from the \
chrono database.\n")
text_file.write("The reservoir age corrections was estimated on " +
str(datapoints) + " datapoints.\n")
text_file.write("DR = " + str(math.floor(corr)) + " years.\n")
text_file.write("DR sigma = " + str(math.floor(corr_err)) + " years, expressed as the standard \
error of the mean.\n")
text_file.write("The maximum distance between the core location and \
measured reservoir ages is " + str(max_dist) + "km.\n")
text_file.close()
# check your work
print("Don't forget to check your work!")
data
Don't forget to check your work!
Out[9]:
MapNo
Lon
Lat
DeltaR
DeltaRErr
Reference
Locality
ReservoirAge
ReservoirErr
distance (km)
NaN
522
-67.28
-67.87
956
40
Berkman, P A:1996
25-30 m depth
1267
41
362
NaN
521
-67.00
-68.65
1016
39
Berkman, P A:1996
25-30 m depth
1327
40
439
NaN
523
-56.98
-63.40
829
50
Bjorck, S, H:1991
Hope Bay
1160
51
386
NaN
368
-72.65
-51.70
221
40
Ingram, B L :1996
Puerto Natales, Chile
530
41
1541
NaN
516
-45.63
-61.72
831
50
Peck, L S, a:1996
Signy Island, Antarctica
1123
52
987
NaN
517
-45.63
-61.72
701
45
Peck, L S, a:1996
Signy Island, Antarctica
1009
47
987
NaN
518
-45.63
-61.72
872
45
Peck, L S, a:1996
Signy Island, Antarctica
1181
47
987
NaN
1081
-62.07
-38.92
143
40
Gomez, et al:2008
Parejas Creek
442
41
2888
NaN
1079
-62.10
-38.90
-229
40
Gomez, et al:2008
Belgrano Port
91
41
2890
NaN
1080
-62.10
-38.90
41
40
Gomez, et al:2008
Belgrano Port
356
41
2890
NaN
In [9]:
corr = input("Enter the DeltaR value (in years): ")
corr_err = input("Enter the DeltaR uncertainty (in years): ")
# Open a text file to store the relevant information
text_file = open(os.path.join(dir,"ReservoirAgeCorr_Info.txt"),"w")
text_file.write("The reservoir age correction was given by the user.\n")
text_file.write("DR = " + str(corr) + ".\n")
text_file.write("DR sigma = " + str(corr_err)+ ".\n")
text_file.close()
Enter the DeltaR value: 74
Enter the DeltaR uncertainty: 70
In [12]:
corr_14C = np.floor((df_chron_l.age14C - corr).values)
err_14C = np.floor(np.sqrt(df_chron_l.age14Cuncertainty**2 + corr_err**2).values)
In [13]:
# Get the Id column
Id = []
for i in np.arange(0,49,1):
Id.append(os.path.splitext(record)[0] + '_' + str(i+1))
# create the dataframe
radio = pd.DataFrame({'Id': Id,
'ages' : corr_14C,
'ageSds' : err_14C,
'position' : df_chron_l.depth,
'CalCurves' : 'marine13'})
# Save it to a text file
radio.to_csv(os.path.join((path+'/'+newpath.value),
(os.path.splitext(record)[0] + '_radiocarbon.txt')), sep = '\t',
index = False, header = True)
Then let's generate positions for the age model (i.e. where climate proxy observations are made.)
In [18]:
df_data_l.to_csv(os.path.join((path+'/'+newpath.value),(os.path.splitext(record)[0]
+ '_depth.txt')),
columns = ['depth'], index = False, header = False)
# Return some strings for use in Bchron
wd = path +'/'+newpath.value
radio_text = os.path.splitext(record)[0] + '_radiocarbon.txt'
depth_text = os.path.splitext(record)[0] + '_depth.txt'
record_name = os.path.splitext(record)[0]
In [16]:
%load_ext rpy2.ipython
Now let's setup some of Bchron optional arguments. Leave blank if you wish to use the default parameters.
In [17]:
iter = float(input("How many iterations do you wish to perform?: "))
max_t = float(input("When was the specimen collected (Years B.P.)?: "))
%R -i iter
%R -i wd
%R -i radio_text
%R -i depth_text
%R -i max_t
%R -i record_name
How many iterations do you wish to perform?: 10000
When was the specimen collected (Years B.P.)?: -58
Perform the Bchron run and return a plot
In [19]:
%%R
library(Bchron)
setwd(wd)
library("Bchron", lib.loc="/Library/Frameworks/R.framework/Versions/3.2/Resources/library")
radiocarbon_data <- read.delim(radio_text)
depth_data <- read.table(depth_text, quote="\"")
run=Bchronology(ages=radiocarbon_data$ages,ageSds=radiocarbon_data$ageSds,
calCurves=radiocarbon_data$CalCurves,positions=radiocarbon_data$position,
predictPositions=depth_data$V1,iterations=iter,extractDate=max_t)
plot(run,main=record_name,xlab='Age (cal years BP)',ylab='Depth (cm)',las=1)
//anaconda/lib/python3.5/site-packages/rpy2/robjects/functions.py:106: UserWarning: Loading required package: inline
res = super(Function, self).__call__(*new_args, **new_kwargs)
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Save the output of the chronology into text files
In [23]:
import pandas.rpy.common as com
run = com.load_data('run')
//anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:4: FutureWarning: The pandas.rpy module is deprecated and will be removed in a future version. We refer to external packages like rpy2.
See here for a guide on how to port your code to rpy2: http://pandas.pydata.org/pandas-docs/stable/r_interface.html
Look at the variables. Refresh the cell as you open/close/rerun the Notebook
In [22]:
whos
Variable Type Data/Info
-------------------------------------------------------
ACTIVE str active
ALL str all
ANCHOR str anchor
APPLICATION_NAME str lipd
ARC str arc
BASELINE str baseline
BEVEL str bevel
BOTH str both
BOTTOM str bottom
BROWSE str browse
BUTT str butt
BaseWidget type <class 'tkinter.BaseWidget'>
BitmapImage type <class 'tkinter.BitmapImage'>
BooleanVar type <class 'tkinter.BooleanVar'>
Button type <class 'tkinter.Button'>
CASCADE str cascade
CENTER str center
CHAR str char
CHECKBUTTON str checkbutton
CHORD str chord
CLIENT_SECRET_FILE str client_secret.json
COMMAND str command
CURRENT str current
CallWrapper type <class 'tkinter.CallWrapper'>
Canvas type <class 'tkinter.Canvas'>
Checkbutton type <class 'tkinter.Checkbutton'>
Convert type <class 'lipd.pkg_resource<...>eseries.Convert.Convert'>
DISABLED str disabled
DOI SRE_Pattern re.compile('\\b(10[.][0-9<...>?!["&\\\'<>,])\\S)+)\\b')
DOTBOX str dotbox
DoubleVar type <class 'tkinter.DoubleVar'>
E str e
EMPTY list n=9
END str end
EW str ew
EXCEPTION int 8
EXTENDED str extended
Entry type <class 'tkinter.Entry'>
Event type <class 'tkinter.Event'>
FALSE int 0
FIRST str first
FLAT str flat
Frame type <class 'tkinter.Frame'>
GROOVE str groove
Grid type <class 'tkinter.Grid'>
HIDDEN str hidden
HORIZONTAL str horizontal
INSERT str insert
INSIDE str inside
Id list n=49
Image module <module 'PIL.Image' from <...>e-packages/PIL/Image.py'>
ImageTk module <module 'PIL.ImageTk' fro<...>packages/PIL/ImageTk.py'>
IntVar type <class 'tkinter.IntVar'>
LAST str last
LEFT str left
Label type <class 'tkinter.Label'>
LabelFrame type <class 'tkinter.LabelFrame'>
LiPD type <class 'lipd.pkg_resources.lipd.LiPD.LiPD'>
LiPD_Library type <class 'lipd.pkg_resource<...>PD_Library.LiPD_Library'>
LiPD_to_df function <function LiPD_to_df at 0x10540fb70>
Listbox type <class 'tkinter.Listbox'>
MITER str miter
MOVETO str moveto
MULTIPLE str multiple
MapFrame type <class 'lipd.pkg_resource<...>lpers.MapFrame.MapFrame'>
Menu type <class 'tkinter.Menu'>
Menubutton type <class 'tkinter.Menubutton'>
Message type <class 'tkinter.Message'>
Misc type <class 'tkinter.Misc'>
N str n
NE str ne
NO int 0
NOAA_DATA_LINES list n=3
NOAA_EMPTY list n=5
NOAA_KEYS list n=3
NOAA_VAR_LINES list n=8
NONE str none
NORMAL str normal
NS str ns
NSEW str nsew
NUMERIC str numeric
NW str nw
NoDefaultRoot function <function NoDefaultRoot at 0x1044d22f0>
OFF int 0
ON int 1
OUTSIDE str outside
OptionMenu type <class 'tkinter.OptionMenu'>
PAGES str pages
PIESLICE str pieslice
PROJECTING str projecting
Pack type <class 'tkinter.Pack'>
PanedWindow type <class 'tkinter.PanedWindow'>
PhotoImage type <class 'tkinter.PhotoImage'>
Place type <class 'tkinter.Place'>
RADIOBUTTON str radiobutton
RAISED str raised
READABLE int 2
RE_VAR SRE_Pattern re.compile('#{2}(\\w)+')
RE_VAR_SPLIT SRE_Pattern re.compile('(\\w+)(\\s+)([\\w\\W\\s]+)')
RIDGE str ridge
RIGHT str right
ROUND str round
Radiobutton type <class 'tkinter.Radiobutton'>
S str s
SCOPES str https://www.googleapis.com/auth/drive
SCROLL str scroll
SE str se
SEL str sel
SEL_FIRST str sel.first
SEL_LAST str sel.last
SEPARATOR str separator
SINGLE str single
SOLID str solid
SUNKEN str sunken
SW str sw
Scale type <class 'tkinter.Scale'>
Scrollbar type <class 'tkinter.Scrollbar'>
Spinbox type <class 'tkinter.Spinbox'>
StringVar type <class 'tkinter.StringVar'>
TOP str top
TRUE int 1
TS function <function TS at 0x1128ba400>
TS_to_df function <function TS_to_df at 0x112728bf8>
Tcl function <function Tcl at 0x1044d2f28>
TclError type <class '_tkinter.TclError'>
TclVersion float 8.5
Text type <class 'tkinter.Text'>
TimeSeries type <class 'lipd.pkg_resource<...>s.TimeSeries.TimeSeries'>
TimeSeries_Library type <class 'lipd.pkg_resource<...>rary.TimeSeries_Library'>
Tk type <class 'tkinter.Tk'>
TkVersion float 8.5
Toplevel type <class 'tkinter.Toplevel'>
UNDERLINE str underline
UNITS str units
VERTICAL str vertical
Variable type <class 'tkinter.Variable'>
W str w
WORD str word
WRITABLE int 4
Widget type <class 'tkinter.Widget'>
Wm type <class 'tkinter.Wm'>
X str x
XView type <class 'tkinter.XView'>
Y str y
YES int 1
YView type <class 'tkinter.YView'>
age ndarray 1000x126: 126000 elems, type `float64`, 1008000 bytes (984.375 kb)
all_cap_re SRE_Pattern re.compile('([a-z0-9])([A-Z])')
browse_dialog function <function browse_dialog at 0x1044f9730>
check_file_age function <function check_file_age at 0x1044f96a8>
check_ts function <function check_ts at 0x1128ba378>
collections module <module 'collections' fro<...>collections/__init__.py'>
commondialog module <module 'tkinter.commondi<...>tkinter/commondialog.py'>
comparisons dict n=6
constants module <module 'tkinter.constant<...>.5/tkinter/constants.py'>
convert Convert <lipd.pkg_resources.times<...>rt object at 0x11274e208>
corr float64 538.1
corr_14C ndarray 49: 49 elems, type `float64`, 392 bytes
corr_err float64 447.669880852
cp module <module 'copy' from '//an<...>a/lib/python3.5/copy.py'>
create_tmp_dir function <function create_tmp_dir at 0x1044f9510>
createpath function <function createpath at 0x112be6ea0>
csv module <module 'csv' from '//ana<...>da/lib/python3.5/csv.py'>
data DataFrame MapNo Lon Lat <...> 41 2890 NaN
datapoints int 10
deepcopy function <function deepcopy at 0x101dbf950>
depth_text str ODP1098B_depth.txt
df_chron_l DataFrame Core age14C a<...> 1225.0 0.05
df_data_l DataFrame SST TEX86 a<...>n\n[126 rows x 5 columns]
df_meta_l DataFrame <...>036c2f712f4f01c50819e92
dialog module <module 'tkinter.dialog' <...>on3.5/tkinter/dialog.py'>
dir str /Users/deborahkhider/LiPD_notebooks/MyAgeModel
dir_cleanup function <function dir_cleanup at 0x1044f9620>
display function <function display at 0x1033b96a8>
doi function <function doi at 0x11274f9d8>
err_14C ndarray 49: 49 elems, type `float64`, 392 bytes
excel function <function excel at 0x11281bea0>
exportTimeSeries function <function exportTimeSeries at 0x1128ba158>
extractTimeSeries function <function extractTimeSeries at 0x1128ba0d0>
file_from_path function <function file_from_path at 0x1043f18c8>
filedialog module <module 'tkinter.filedial<...>5/tkinter/filedialog.py'>
find function <function find at 0x1128ba2f0>
first_cap_re SRE_Pattern re.compile('(.)([A-Z][a-z]+)')
getCsv function <function getCsv at 0x1128ba048>
getMetadata function <function getMetadata at 0x1128b7f28>
get_google_csv function <function get_google_csv at 0x10532b6a8>
get_matches function <function get_matches at 0x1128b7950>
get_numpy function <function get_numpy at 0x1128ba488>
get_static_google_map function <function get_static_google_map at 0x1053d66a8>
getboolean function <function getboolean at 0x1044d2ea0>
getdouble type <class 'float'>
getint type <class 'int'>
i int64 48
image_names function <function image_names at 0x1044e1ae8>
image_types function <function image_types at 0x1044f2e18>
io module <module 'io' from '//anac<...>nda/lib/python3.5/io.py'>
iter float 10000.0
lipd_lib LiPD_Library <lipd.pkg_resources.lipd.<...>ry object at 0x1043fb6d8>
lipd_m dict n=11
lipd_to_df function <function lipd_to_df at 0x1128b7bf8>
list_files function <function list_files at 0x1044f9598>
loadLipd function <function loadLipd at 0x1128b7ae8>
loadLipds function <function loadLipds at 0x1128b7b70>
mainloop function <function mainloop at 0x1044d2488>
math module <module 'math' from '//an<...>h.cpython-35m-darwin.so'>
max_dist float64 2890.0
max_t float -58.0
newpath Text <ipywidgets.widgets.widge<...>xt object at 0x112c018d0>
noaa function <function noaa at 0x1128a6ea0>
np module <module 'numpy' from '//a<...>kages/numpy/__init__.py'>
ntpath module <module 'ntpath' from '//<...>lib/python3.5/ntpath.py'>
os module <module 'os' from '//anac<...>nda/lib/python3.5/os.py'>
path str /Users/deborahkhider/LiPD_Notebooks
pd module <module 'pandas' from '//<...>ages/pandas/__init__.py'>
quit function <function quit at 0x1128ba730>
radio DataFrame CalCurves Id<...> 459.0 10026.0 47.46
radio_text str ODP1098B_radiocarbon.txt
re module <module 're' from '//anac<...>nda/lib/python3.5/re.py'>
re_calib_invalid SRE_Pattern re.compile('calibration(\<...>\w+)|calibration_(\\w+)')
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re_misc_fetch SRE_Pattern re.compile('(geo_(\\w+)|c<...>\\w+)|paleoData_(\\w+))')
re_paleo_invalid SRE_Pattern re.compile('paleodata(\\w<...>\w+)|measurement_(\\w+)')
re_pandas_x_num SRE_Pattern re.compile('(year\\d?|age\\d?|depth\\d?)\\b')
re_pandas_x_und SRE_Pattern re.compile('(year|age|depth){1}[_]{1}.*')
re_pub_cc SRE_Pattern re.compile('pub(\\w+)')
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re_pub_n SRE_Pattern re.compile('pub(\\d)(\\w+)')
re_pub_nh SRE_Pattern re.compile('pub(\\d)_(\\w+)')
re_pub_valid SRE_Pattern re.compile('pub(\\d)_(cit<...>DataUrl|abstract|pages)')
record str ODP1098B.lpd
record_name str ODP1098B
removeLipd function <function removeLipd at 0x1128ba620>
removeLipds function <function removeLipds at 0x1128ba6a8>
saveLipd function <function saveLipd at 0x1128ba510>
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wantobjects int 1
wd str /Users/deborahkhider/LiPD_Notebooks/MyAgeModel
widgets module <module 'ipywidgets.widge<...>ets/widgets/__init__.py'>
In [24]:
run
Out[24]:
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... ... ... ... ... ...
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999.0 11386.0 11392.0 11398.0 11400.0 11403.0
1000.0 11415.0 11419.0 11423.0 11425.0 11427.0
[1000 rows x 126 columns]}
In [ ]:
Content source: nickmckay/LiPD-utilities
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