Package: geomagpy
MagPy (or GeomagPy) is a Python package for analysing and displaying
geomagnetic data.Version Info: (please note: this package is still in a development state
with frequent modifcations) please check the release notes.MagPy provides tools for geomagnetic data analysis with special focus on
typical data processing routines in observatories. MagPy provides
methods for data format conversion, plotting and mathematical procedures
with specifically geomagnetic analysis routines such as basevalue and
baseline calculation and database handling. Among the supported data
formats areImagCDF, IAGA-02, WDC, IMF, IAF, BLV, and many more. Full
installation also provides a graphical user interface,xmagpy. You
will find a complete manual forxmagpyin the docs.Typical usage of the basic MagPy package for reading and visualising
data looks like this:#!/usr/bin/env python

from magpy.stream import read
import magpy.mpplot as mp
stream = read('filename_or_url')
mp.plot(stream)Below you will find a quick guide to usage of the basic MagPy package.
For instructions onxmagpyplease refer to the document “An
introduction to
XMagPy”
in the docs. You can also subscribe to our information channel atTelegramfor further information on updates
and current issues.1. INSTALLATIONPleas note that with the publication of MagPy 1.0 the recommended python
enironment is >= 3.6. The following installation instructions will
assume such an environment. Particularly if you are using Python2.7
please go to the end of this sections for help.This section is currently updated and will be ready with the publication
of MagPy 1.0.1.1 Linux installation (Ubuntu,Debian)1.1.1 Complete InstallTested for Ubuntu 18.04 and Debian Stretch (full installation with all
optional packages). Please note that installation requires python 3.x.$ sudo pip3 install geomagpy    #Will install MagPy and all dependencies
$ sudo pip3 install wxpython    #Will install WX graphics systemIf wxpython installation via pip3 fails you can try$ sudo apt-get install python3-wxgtk4.0You can now run XMagPy by using the following command$ xmagpy1.1.2 UpdatesTo upgrade to the most recent version:$ sudo pip3 install -U geomagpy1.1.3 Creating a desktop linkIn order to create a desktop link on linux systems please refer to
instruction too be found your distribution. For Ubunutu and other Debian
systems such links are created as follows:Firstly create a file “xmagpy.desktop” which contains:[Desktop Entry]
Type=Application
Name=XMagPy
GenericName=GeoMagPy User Interface
Exec=xmagpy
Icon=/usr/local/lib/python3.7/dist-packages/magpy/gui/magpy128.xpm
Terminal=false
Categories=Application;Development;Then copy this file to the systems application folder:sudo cp xmagpy.desktop /usr/share/applications/1.2 MacOs installation1.2.1 Install a python3 interpreterwe recommendMinicondaorAnacondasee e.g.https://docs.continuum.io/anaconda/installfor more detailsbefore continuiung, test whether python is working. Open a terminal
and run python1.2.2 Install MagPyOpen a terminal and use the following commands:$ pip install geomagpy    #Will install MagPy and all dependencies
$ pip install wxpython    #Will install WX graphics system for XMagPyYou can now run XMagPy from the terminal by using the following command$ xmagpyw1.2.3 Creating a desktop linkOpen Finder and search for xmagpyw. Copy it to the desktop. To change
the icon, click on the xmagpyw link, open information and replace the
image on the upper left with e.g. magpy128.jpg (also to be found using
finder).1.3 Windows installation - WinPython Package1.3.1 Install MagPy for Windowsget theMagPy Windows
installerhere (under Downloads):https://cobs.zamg.ac.atdownload and execute magpy-x.x.x.exeall required packages are included in the installer1.3.2 Post-installation informationMagPy will have a sub-folder in the Start menu. Here you will find
three items:* command -> opens a DOS shell within the Python environment e.g. for updates
* python  -> opens a python shell ready for MagPy
* xmagpy  -> opens the MagPy graphical user interface1.3.3 Update an existing MagPy installation on Windowsright-click on subfolder “command” in the start menuselect “run as administrator”issue the following command “pip install -U geomagpy” (you can also
specify the version e.g. pip install geomagpy==0.x.x)1.3.4 Installation with user priviledges onlyDownload a most recent version of WinPython3.xUnpack in your home directoryGo to the WinPython Folder and run WinPython command promptissue the same commands as for MacOS installationto run XMagPy: use xmagpy from the WinPython command promt.1.4 Installation instructions for Python 2.7The current version of magpy is still supporting python 2.7, although it
is highly recommended to switch to python >= 3.6. Installation on python
2.7 is more complex, as some packages for graphical user interface and
CDF support not as well supported. Please note: None of the addtional
steps is necessary for python 3.x.1.4.1 Pre-installation workGet a recent version of NasaCDF for your platform, enables CDF support
for formats like ImagCDF. Package details and files can be found athttp://cdf.gsfc.nasa.gov/On Linux such installation will look like
(http://cdf.gsfc.nasa.gov/html/sw_and_docs.html)$ tar -zxvf cdf37_0-dist-all.tar.gz
$ cd cdf37...
$ make OS=linux ENV=gnu CURSES=yes FORTRAN=no UCOPTIONS=-O2 SHARED=yes all
$ sudo make INSTALLDIR=/usr/local/cdf installInstall the following additional compilers before continuing (required
for spacepy): Linux: install gcc MacOs: install gcc and gfortranInstall coordinate system transformation support:$ sudo apt-get install libproj-dev proj-data proj-bin1.4.2 Install MagPy and dependenciesOn Linux this will look like:$ sudo apt-get install python-matplotlib python-scipy python-h5py cython python-pip
$ sudo apt-get install python-wxgtk3.0 # or python-wxgtk2.8 (Debian Stretch)
$ sudo apt-get install python-twisted
$ sudo pip install ffnet
$ sudo pip install pyproj==1.9.5
$ sudo pip install pyserial
$ sudo pip install service_identity
$ sudo pip install ownet
$ sudo pip install spacepy
$ sudo pip install geomagpyOn Mac and Windows you need to download a python interpreter likeAnacondaor [WinPython] and
then install similar packages, particluarly the old wxpython 3.x.1.5 Platform independent container - Docker1.5.1 InstallDocker(toolbox) on your operating system- https://docs.docker.com/engine/installation/1.5.2 Get the MagPy Image- open a docker shell

       >>> docker pull geomagpy/magpy:latest
       >>> docker run -d --name magpy -p 8000:8000 geomagpy/magpy:latest1.5.3 Open a browser- open address http://localhost:8000 (or http://"IP of your VM":8000)
- NEW: first time access might require a token or passwd

       >>> docker logs magpy

     will show the token
- run python shell (not conda)
- in python shell

       >>> %matplotlib inline
       >>> from magpy.stream import read
       >>> ...1.6 Install from sourceRequirements: - Python 2.7, 3.x (recommended is >=3.6)Recommended: - Python packages: * wxpython (for python2.7 it needs to
be 3.x or older) * NasaCDF (python 2.7 only) * SpacePy (python 2.7
only)Other useful Software:pyproj (for geographic coordinate systems)MySQL (database features)Webserver (e.g. Apache2, PHP)git clone git://github.com/GeomagPy/MagPy.git cd magpy* sudo
python setup.py install2. A quick guide to MagPywritten by R. Leonhardt, R. Bailey (April 2017)MagPy’s functionality can be accessed basically in three different ways:
1) Directly import and use the magpy package into a python environment
2) Run the graphical user interface xmagpy (xmagpyw for Mac) 3) Use
predefined applications “Scripts”The following section will primarily deal with way 1. For 2 - xmagpy -
we refer to the video tutorials whcih can be found here: Section 3
contains examples for predefined applications/scripts2.1 Getting started with the python packageStart python. Import all stream methods and classes using:from magpy.stream import *Please note that this import will shadow any already existingreadmethod.2.2 Reading and writing dataMagPy supports the following data formats and thus conversions between
them: - WDC: World Data Centre format - JSON: JavaScript Object Notation
- IMF: Intermagnet Format - IAF: Intermagnet Archive Format - NEIC: WGET
data from USGS - NEIC - IAGA: IAGA 2002 text format - IMAGCDF:
Intermagnet CDF Format - GFZKP: GeoForschungsZentrum KP-Index format -
GSM19/GSM90: Output formats from GSM magnetometers - POS1: POS-1 binary
output - BLV: Baseline format Intermagnet - IYFV: Yearly mean format
Intermagnet… and many others. To get a full list, use:from magpy.stream import *
print(PYMAG_SUPPORTED_FORMATS)You will find several example files provided with MagPy. Thecdffile is stored along with meta information in NASA’s common data format
(cdf). Reading this file requires a working installation of Spacepy cdf.If you do not have any geomagnetic data file you can access example data
by using the following command (afterimport *):data = read(example1)The data fromexample1has been read into a MagPyDataStream(orstream) object. Most data processing routines in MagPy are applied to
data streams.Several example data sets are provided within the MagPy package:example1:IAGAZIP (IAGA2002, zip
compressed) file with 1 second HEZ dataexample2:MagPyArchive (CDF) file with 1 sec F dataexample3:MagPyBasevalue (TXT) ascii file with DI
and baseline dataexample4:INTERMAGNETImagCDF
(CDF) file with one week of 1 second dataexample5:MagPyArchive (CDF) raw data file with xyz
and supporting dataexample6a:MagPyDI (txt) raw data file with DI
measurementexample6b:MagPylike 6a to be used with example4flagging_example:MagPyFlagDictionary (JSON)
flagging info to be used with example1recipe1_flags:MagPyFlagDictionary (JSON) to be
used with cookbook recipe 12.2.1 ReadingFor a file in the same directory:data = read(r'myfile.min')… or for specific paths in Linux:data = read(r'/path/to/file/myfile.min')… or for specific paths in Windows:data = read(r'c:\path\to\file\myfile.min')Pathnames are related to your operating system. In this guide we will
assume a Linux system. Files that are read in are uploaded to the memory
and each data column (or piece of header information) is assigned to an
internal variable (key). To get a quick overview of the assigned keys in
any given stream (data) you can use the following method:print(data._get_key_headers() )2.2.2 WritingAfter loading data from a file, we can save the data in the standard
IAGA02 and IMAGCDF formats with the following commands.To create an IAGA-02 format file, use:data.write(r'/path/to/diretory/',format_type='IAGA')To create anINTERMAGNETCDF (ImagCDF)
file:data.write(r'/path/to/diretory/',format_type='IMAGCDF')The filename will be created automatically according to the defined
format. By default, daily files are created and the date is added to the
filename in-between the optional parametersfilenamebeginsandfilenameends. Iffilenameendsis missing,.txtis used as
default.2.2.3 Other possibilities for reading filesTo read all local files ending with .min within a directory (creates a
single stream of all data):data = read(r'/path/to/file/*.min')Getting magnetic data directly from an online source such as the WDC:data = read(r'ftp://thewellknownaddress/single_year/2011/fur2011.wdc')Gettingkpdata from the GFZ Potsdam:data = read(r'http://www-app3.gfz-potsdam.de/kp_index/qlyymm.tab')(Please note: data access and usage is subjected to the terms and
conditions of the individual data provider. Please make sure to read
them before accessing any of these products.)No format specifications are required for reading. If MagPy can handle
the format, it will be automatically recognized.Getting data for a specific time window for local files:data = read(r'/path/to/files/*.min',starttime="2014-01-01", endtime="2014-05-01")… and remote files:data = read(r'ftp://address/fur2013.wdc',starttime="2013-01-01", endtime="2013-02-01")Reading data from the INTERMAGNET Webservice (starting soon):data = read('http://www.intermagnet.org/test/ws/?id=WIC')2.2.4 Selecting timerangeThe stream can be trimmed to a specific time interval after reading by
applying the trim method, e.g. for a specific month:data = data.trim(starttime="2013-01-01", endtime="2013-02-01")2.3 Getting help on options and usage2.3.1 Python’s help functionInformation on individual methods and options can be obtained as
follows:For basic functions:help(read)For specific methods related to e.g. a stream object “data”:help(data.fit)Note that this requires the existence of a “data” object, which is
obtained e.g. by data = read(…). The help text can also be shown by
directly calling theDataStreamobject method using:help(DataStream.fit)2.3.2 MagPy’s logging systemMagPy automatically logs many function options and runtime information,
which can be useful for debugging purposes. This log is saved by default
in the temporary file directory of your operating system, e.g. for Linux
this would be/tmp/magpy.log. The log is formatted as follows with
the date, module and function in use and the message leve
(INFO/WARNING/ERROR):2017-04-22 09:50:11,308 INFO - magpy.stream - Initiating MagPy...Messages on the WARNING and ERROR level will automatically be printed to
shell. Messages for more detailed debugging are written at the DEBUG
level and will not be printed to the log unless an additional handler
for printing DEBUG is added.Custom loggers can be defined by creating a logger object after
importing MagPy and adding handlers (with formatting):from magpy.stream import *
import logging

logger = logging.getLogger()
hdlr = logging.FileHandler('testlog.log')
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
hdlr.setFormatter(formatter)
logger.addHandler(hdlr)The logger can also be configured to print to shell (stdout, without
formatting):import sys
logger = logging.getLogger()
stdoutlog = logging.StreamHandler(sys.stdout)
logger.addHandler(stdoutlog)2.4 PlottingYou will find some example plots at theConrad
Observatory.2.4.1 Quick (and not dirty)import magpy.mpplot as mp
mp.plot(data)2.4.2 Some optionsSelect specific keys to plot:mp.plot(data,variables=['x','y','z'])Defining a plot title and specific colors (seehelp(mp.plot)for
list and all options):mp.plot(data,variables=['x','y'],plottitle="Test plot",
        colorlist=['g', 'c'])2.4.3 Data from multiple streamsVarious datasets from multiple data streams will be plotted above one
another. Provide a list of streams and an array of keys:mp.plotStreams([data1,data2],[['x','y','z'],['f']])2.5 Flagging dataThe flagging procedure allows the observer to mark specific data points
or ranges. Falgs are useful for labelling data spikes, storm onsets,
pulsations, disturbances, lightning strikes, etc. Each flag is asociated
with a comment and a type number. The flagtype number ranges between 0
and 4:0: normal data with comment (e.g. “Hello World”)1: data marked by automated analysis (e.g. spike)2: data marked by observer as valid geomagnetic signature (e.g. storm
onset, pulsation). Such data cannot be marked invalid by automated
procedures3: data marked by observer as invalid (e.g. lightning, magnetic
disturbance)4: merged data (e.g. data inserted from another source/instrument as
defined in the comment)Flags can be stored along with the data set (requires CDF format output)
or separately in a binary archive. These flags can then be applied to
the raw data again, ascertaining perfect reproducibility.2.5.1 Mark data spikesLoad a data record with data spikes:datawithspikes = read(example1)Mark all spikes using the automated functionflag_outlierwith
default options:flaggeddata = datawithspikes.flag_outlier(timerange=timedelta(minutes=1),threshold=3)Show flagged data in a plot:mp.plot(flaggeddata,['f'],annotate=True)2.5.2 Flag time rangeFlag a certain time range:flaglist = flaggeddata.flag_range(keys=['f'], starttime='2012-08-02T04:33:40',
                                  endtime='2012-08-02T04:44:10',
                                  flagnum=3, text="iron metal near sensor")Apply these flags to the data:flaggeddata = flaggeddata.flag(flaglist)Show flagged data in a plot:mp.plot(flaggeddata,['f'],annotate=True)2.5.3 Save flagged dataTo save the data together with the list of flags to a CDF file:flaggeddata.write('/tmp/',filenamebegins='MyFlaggedExample_', format_type='PYCDF')To check for correct save procedure, read and plot the new file:newdata = read("/tmp/MyFlaggedExample_*")
mp.plot(newdata,annotate=True, plottitle='Reloaded flagged CDF data')2.5.4 Save flags separatelyTo save the list of flags seperately from the data in a pickled binary
file:fullflaglist = flaggeddata.extractflags()
saveflags(fullflaglist,"/tmp/MyFlagList.pkl"))These flags can be loaded in and then reapplied to the data set:data = read(example1)
flaglist = loadflags("/tmp/MyFlagList.pkl")
data = data.flag(flaglist)
mp.plot(data,annotate=True, plottitle='Raw data with flags from file')2.5.5 Drop flagged dataFor some analyses it is necessary to use “clean” data, which can be
produced by dropping data flagged as invalid (e.g. spikes). By default,
the following method removes all data marked with flagtype numbers 1 and
3.cleandata = flaggeddata.remove_flagged()
mp.plot(cleandata, ['f'], plottitle='Flagged data dropped')2.6 Basic methods2.6.1 FilteringMagPy’sfilteruses the settings recommended byIAGA/INTERMAGNET.
Ckeckhelp(data.filter)for further options and definitions of
filter types and pass bands.First, get the sampling rate before filtering in seconds:print("Sampling rate before [sec]:", cleandata.samplingrate())Filter the data set with default parameters (filterautomatically
chooses the correct settings depending on the provided sanmpling rate):filtereddata = cleandata.filter()Get sampling rate and filtered data after filtering (please note that
all filter information is added to the data’s meta information
dictionary (data.header):print("Sampling rate after [sec]:", filtereddata.samplingrate())
print("Filter and pass band:", filtereddata.header.get('DataSamplingFilter',''))2.6.2 Coordinate transformationAssuming vector data in columns [x,y,z] you can freely convert between
xyz, hdz, and idf coordinates:cleandata = cleandata.xyz2hdz()2.6.3 Calculate delta FIf the data file contains xyz (hdz, idf) data and an independently
measured f value, you can calculate delta F between the two instruments
using the following:cleandata = cleandata.delta_f()
mp.plot(cleandata,plottitle='delta F')2.6.4 Calculate MeansMean values for certain data columns can be obtained using themeanmethod. The mean will only be calculated for data with the percentage of
valid data (in contrast to missing data) points not falling below the
value given by the percentage option (default 95). If too much data is
missing, then no mean is calulated and the function returns NaN.print(cleandata.mean('df', percentage=80))The median can be calculated by defining themeanfunctionoption:print(cleandata.mean('df', meanfunction='median'))2.6.5 Applying offsetsConstant offsets can be added to individual columns using theoffsetmethod with a dictionary defining the MagPy stream column keys and the
offset to be applied (datetime.timedelta object for time column, float
for all others):offsetdata = cleandata.offset({'time':timedelta(seconds=0.19),'f':1.24})2.6.6 Scaling dataIndividual columns can also be multiplied by values provided in a
dictionary:multdata = cleandata.multiply({'x':-1})2.6.7 Fit functionsMagPy offers the possibility to fit functions to data using either
polynomial functions or cubic splines (default):func = cleandata.fit(keys=['x','y','z'],knotstep=0.1)
mp.plot(cleandata,variables=['x','y','z'],function=func)2.6.8 DerivativesTime derivatives, which are useful to identify outliers and sharp
changes, are calculated as follows:diffdata = cleandata.differentiate(keys=['x','y','z'],put2keys = ['dx','dy','dz'])
mp.plot(diffdata,variables=['dx','dy','dz'])2.6.9 All methods at a glanceFor a summary of all supported methods, see the sectionList of all
MagPy methodsbelow.2.7 Geomagnetic analysis2.7.1 Determination of K indicesMagPy supports the FMI method for determination of K indices. Please
consult the MagPy publication for details on this method and
application.A month of one minute data is provided inexample2, which
corresponds to anINTERMAGNETIAF
archive file. Reading a file in this format will load one minute data by
default. Accessing hourly data and other information is described below.data2 = read(example2)
kvals = data2.k_fmi()The determination of K values will take some time as the filtering
window is dynamically adjusted. In order to plot the original data (H
component) and K values together, we now use the multiple stream
plotting methodplotStreams. Here you need to provide a list of
streams and an array containing variables for each stream. The
additional options determine the appearance of the plot (limits, bar
chart):mp.plotStreams([data2,kvals],[['x'],['var1']],
               specialdict = [{},{'var1':[0,9]}],
               symbollist=['-','z'],
               bartrange=0.06)'z'insymbollistrefers to the second subplot (K), which should
be plotted as bars rather than the standard line ('-').2.7.2 Automated geomagnetic storm detectionGeomagnetic storm detection is supported by MagPy using two procedures
based on wavelets and the Akaike Information Criterion (AIC) as outlined
in detail in Bailey and Leonhardt (2016). A basic example of usage to
find an SSC using a Discrete Wavelet Transform (DWT) is shown below:from magpy.stream import read
from magpy.opt.stormdet import seekStorm
stormdata = read("LEMI025_2015-03-17.cdf")      # 1s variometer data
stormdata = stormdata.xyz2hdz()
stormdata = stormdata.smooth('x', window_len=25)
detection, ssc_list = seekStorm(stormdata, method="MODWT")
print("Possible SSCs detected:", ssc_list)The methodseekStormwill return two variables:detectionis
True if any detection was made, whilessc_listis a list of
dictionaries containing data on each detection. Note that this method
alone can return a long list of possible SSCs (most incorrectly
detected), particularly during active storm times. It is most useful
when additional restrictions based on satellite solar wind data apply
(currently only optimised for ACE data, e.g. from the NOAA website):satdata_ace_1m = read('20150317_ace_swepam_1m.txt')
satdata_ace_5m = read('20150317_ace_epam_5m.txt')
detection, ssc_list, sat_cme_list = seekStorm(stormdata,
            satdata_1m=satdata_ace_1m, satdata_5m=satdata_ace_5m,
            method='MODWT', returnsat=True)
print("Possible CMEs detected:", sat_cme_list)
print("Possible SSCs detected:", ssc_list)2.7.3 Sq analysisMethods are currently in preparation.2.7.4 Validity check of dataA common and important application used in the geomagnetism community is
a general validity check of geomagnetic data to be submitted to the
official data repositoriesIAGA, WDC, orINTERMAGNET. Please note: this is
currently under development and will be extended in the near future. A
‘one-click’ test method will be included in xmagpy in the future,
checking:Validity of data formats, e.g.:data = read('myiaffile.bin', debug=True)Completeness of meta-informationConformity of applied techniques to respective rulesInternal consistency of dataOptional: regional consistency2.7.5 Spectral Analysis and NoiseFor analysis of the spectral content of data, MagPy provides two basic
plotting methods.plotPSwill calculate and display a power spectrum
of the selected component.plotSpectrogramwill plot a spectrogram
of the time series. As usual, there are many options for plot window and
processing parameters that can be accessed using the help method.data = read(example1)
mp.plotPS(data,key='f')
mp.plotSpectrogram(data,['f'])2.8 Handling multiple streams2.8.1 Merging streamsMerging data comprises combining two streams into one new stream. This
includes adding a new column from another stream, filling gaps with data
from another stream or replacing data from one column with data from
another stream. The following example sketches the typical usage:print("Data columns in data2:", data2._get_key_headers())
newstream = mergeStreams(data2,kvals,keys=['var1'])
print("Data columns after merging:", data2._get_key_headers())
mp.plot(newstream, ['x','y','z','var1'],symbollist=['-','-','-','z'])If columnvar1does not existing in data2 (as above), then this
column is added. If columnvar1had already existed, then missing
data would be inserted from streamkvals. In order to replace any
existing data, use optionmode='replace'.2.8.2 Differences between streamsSometimes it is necessary to examine the differences between two data
streams e.g. differences between the F values of two instruments running
in parallel at an observatory. The methodsubtractStreamsis
provided for this analysis:diff = subtractStreams(data1,data2,keys=['f'])2.9 The art of meta-informationEach data set is accompanied by a dictionary containing meta-information
for this data. This dictionary is completely dynamic and can be filled
freely, but there are a number of predefined fields that help the user
provide essential meta-information as requested byIAGA,INTERMAGNETand other data providers.
All meta information is saved only to MagPy-specific archive formats
PYCDF and PYSTR. All other export formats save only specific information
as required by the projected format.The current content of this dictionary can be accessed by:data = read(example1)
print(data.header)Information is added/changed by using:data.header['SensorName'] = 'FGE'Individual information is obtained from the dictionary using standard
key input:print(data.header.get('SensorName'))If you want to have a more readable list of the header information, do:for key in data.header:
    print ("Key: {} \t Content: {}".format(key,data.header.get(key)))2.9.1 Conversion to ImagCDF - Adding meta-informationTo convert data fromIAGAor IAF formats
to the newINTERMAGNETCDF format, you
will usually need to add additional meta-information required for the
new format. MagPy can assist you here, firstly by extracting and
correctly adding already existing meta-information into newly defined
fields, and secondly by informing you of which information needs to be
added for producing the correct output format.Example of IAGA02 to ImagCDF:mydata = read('IAGA02-file.min')
mydata.write('/tmp',format_type='IMAGCDF')The console output of the write command (see below) will tell you which
information needs to be added (and how) in order to obtain correct
ImagCDF files. Please note, MagPy will store the data in any case and
will be able to read it again even if information is missing. Before
submitting to a GIN, you need to make sure that the appropriate
information is contained. Attributes that relate to publication of the
data will not be checked at this point, and might be included later.>>>Writing IMAGCDF Format /tmp/wic_20150828_0000_PT1M_4.cdf
>>>writeIMAGCDF: StandardLevel not defined - please specify by yourdata.header['DataStandardLevel'] = ['None','Partial','Full']
>>>writeIMAGCDF: Found F column
>>>writeIMAGCDF: given components are XYZF. Checking F column...
>>>writeIMAGCDF: analyzed F column - values are apparently independend from vector components - using column name 'S'Now add the missing information. Selecting ‘Partial’ will require
additional information. You will get a ‘reminder’ if you forget this.
Please check IMAGCDF instructions on specific codes:mydata.header['DataStandardLevel'] = 'Partial'
mydata.header['DataPartialStandDesc'] = 'IMOS-01,IMOS-02,IMOS-03,IMOS-04,IMOS-05,IMOS-06,IMOS-11,IMOS-12,IMOS-13,IMOS-14,IMOS-15,IMOS-21,IMOS-22,IMOS-31,IMOS-41'Similar reminders to fill out complete header information will be shown
for other conversions like:mydata.write('/tmp',format_type='IAGA')
mydata.write('/tmp',format_type='IMF')
mydata.write('/tmp',format_type='IAF',coverage='month')
mydata.write('/tmp',format_type='WDC')2.9.2 Providing location dataProviding location data usually requires information on the reference
system (ellipsoid,…). By default MagPy assumes that these values are
provided in WGS84/WGS84 reference system. In order to facilitate most
easy referencing and conversions, MagPy supportsEPSGcodes for coordinates. If you
provide the geodetic references as follows, and provided that theproj4Python package is available,
MagPy will automatically convert location data to the requested output
format (currently WGS84).mydata.header['DataAcquisitionLongitude'] = -34949.9
mydata.header['DataAcquisitionLatitude'] = 310087.0
mydata.header['DataLocationReference'] = 'GK M34, EPSG: 31253'

>>>...
>>>writeIMAGCDF: converting coordinates to epsg 4326
>>>...2.9.3 Special meta-information fieldsThe meta-information fields can hold much more information than required
by most output formats. This includes basevalue and baseline parameters,
flagging details, detailed sensor information, serial numbers and much
more. MagPy makes use of these possibilities. In order to save this
meta-information along with your data set you can use MagPy internal
archiving format,PYCDF, which can later be converted to any of the
aforementioned output formats. You can even reconstruct a full data
base. Any upcoming meta-information or output request can be easily
added/modified without disrupting already existing data sets and the
ability to read and analyse old data. This data format is also based on
Nasa CDF. ASCII outputs are also supported by MagPy, of which thePYSTRformat also contains all meta information andPYASCIIis
the most compact. Please consider that ASCII formats require a lot of
memory, especially for one second and higher resolution data.mydata.write('/tmp',format_type='PYCDF',coverage='year')2.10 Data transferMagPy contains a number of methods to simplify data transfer for
observatory applications. Methods within the basic Python functionality
can also be very useful. Using the implemented methods requires:from magpy import transfer as mt2.10.1 DownloadsUse thereadmethod as outlined above. No additional imports are
required.2.10.2 FTP uploadFiles can also be uploaded to an FTP server:mt.ftpdatatransfer(localfile='/path/to/data.cdf',ftppath='/remote/directory/',myproxy='ftpaddress or address of proxy',port=21,login='user',passwd='passwd',logfile='/path/mylog.log')The upload methods using FTP, SCP and GIN support logging. If the data
file failed to upload correctly, the path is added to a log file and,
when called again, upload of the file is retried. This option is useful
for remote locations with unstable network connections.2.10.3 Secure communication protocol (SCP)To transfer via SCP:mt.scptransfer('user@address:/remote/directory/','/path/to/data.cdf',passwd,timeout=60)2.10.4 Upload data to GINUse the following command:mt.ginupload('/path/to/data.cdf', ginuser, ginpasswd, ginaddress, faillog=True, stdout=True)2.10.5 Avoiding real-text passwords in scriptsIn order to avoid using real-text password in scripts, MagPy comes along
with a simple encryption routine.from magpy.opt import cred as mpcredCredentials will be saved to a hidden file with encrypted passwords. To
add information for data transfer to a machine called ‘MyRemoteFTP’ with
an IP of 192.168.0.99:mpcred.cc('transfer', 'MyRemoteFTP', user='user', passwd='secure', address='192.168.0.99', port=21)Extracting passwd information within your data transfer scripts:user = mpcred.lc('MyRemoteFTP', 'user')
password = mpcred.lc('MyRemoteFTP','passwd')2.11 DI measurements, basevalues and baselinesThese procedures require an additional import:from magpy import absolutes as di2.11.1 Data structure of DI measurementsPlease checkexample3, which is an example DI file. You can create
these DI files by using the input sheet from xmagpy or the online input
sheet provided by the Conrad Observatory. If you want to use this
service, please contact the Observatory staff. Also supported are
DI-files from the AUTODIF.2.11.2 Reading DI dataReading and analyzing DI data requires valid DI file(s). For correct
analysis, variometer data and scalar field information needs to be
provided as well. Checkouthelp(di.absoluteAnalysis)for all
options. The analytical procedures are outlined in detail in the MagPy
article (citation). A typical analysis looks like:diresult = di.absoluteAnalysis('/path/to/DI/','path/to/vario/','path/to/scalar/')Path to DI can either point to a single file, a directory or even use
wildcards to select data from a specific observatory/pillar. Using the
examples provided along with MagPy, the analysis line looks likediresult = di.absoluteAnalysis(example3,example2,example2)Calling this method will provide terminal output as follows and a stream
objectdiresultwhich can be used for further analyses.>>>...
>>>Analyzing manual measurement from 2015-03-25
>>>Vector at: 2015-03-25 08:18:00+00:00
>>>Declination: 3:53:46, Inclination: 64:17:17, H: 21027.2, Z: 43667.9, F: 48466.7
>>>Collimation and Offset:
>>>Declination:    S0: -3.081, delta H: -6.492, epsilon Z: -61.730
>>>Inclination:    S0: -1.531, epsilon Z: -60.307
>>>Scalevalue: 1.009 deg/unit
>>>Fext with delta F of 0.0 nT
>>>Delta D: 0.0, delta I: 0.0Fext indicates that F values have been used from a separate file and not
provided along with DI data. Delta values for F, D, and I have not been
provided either.diresultis a stream object containing average D, I
and F values, the collimation angles, scale factors and the base values
for the selected variometer, beside some additional meta information
provided in the data input form.2.11.3 Reading BLV filesBasevalues:blvdata = read('/path/myfile.blv')
mp.plot(blvdata, symbollist=['o','o','o'])Adopted baseline:bldata = read('/path/myfile.blv',mode='adopted')
mp.plot(bldata)2.11.4 Basevalues and baselinesBasevalues as obtained in (2.11.2) or (2.11.3) are stored in a normal
data stream object, therefore all analysis methods outlined above can be
applied to this data. Thediresultobject contains D, I, and F
values for each measurement in columns x,y,z. Basevalues for H, D and Z
related to the selected variometer are stored in columns dx,dy,dz. Inexample4, you will find some more DI analysis results. To plot these
basevalues we can use the following plot command, where we specify the
columns, filled circles as plotsymbols and also define a minimum spread
of each y-axis of +/- 5 nT for H and Z, +/- 0.05 deg for D.basevalues = read(example4)
mp.plot(basevalues, variables=['dx','dy','dz'], symbollist=['o','o','o'], padding=[5,0.05,5])Fitting a baseline can be easily accomplished with thefitmethod.
First we test a linear fit to the data by fitting a polynomial function
with degree 1.func = basevalues.fit(['dx','dy','dz'],fitfunc='poly', fitdegree=1)
mp.plot(basevalues, variables=['dx','dy','dz'], symbollist=['o','o','o'], padding=[5,0.05,5], function=func)We then fit a spline function using 3 knotsteps over the timerange (the
knotstep option is always related to the given timerange).func = basevalues.fit(['dx','dy','dz'],fitfunc='spline', knotstep=0.33)
mp.plot(basevalues, variables=['dx','dy','dz'], symbollist=['o','o','o'], padding=[5,0.05,5], function=func)Hint: a good estimate on the necessary fit complexity can be obtained by
looking at delta F values. If delta F is mostly constant, then the
baseline should also not be very complex.2.11.5 Applying baselinesThe baseline method provides a number of options to assist the observer
in determining baseline corrections and realted issues. The basic
building block of the baseline method is the fit function as discussed
above. Lets first load raw vectorial geomagnetic data, the absevalues of
which are contained in above example:rawdata = read(example5)Now we can apply the basevalue information and the spline function as
tested above:func = rawdata.baseline(basevalues, extradays=0, fitfunc='spline',
                        knotstep=0.33,startabs='2015-09-01',endabs='2016-01-22')Thebaselinemethod will determine and return a fit function between
the two given timeranges based on the provided basevalue datablvdata. The optionextradaysallows for adding days before and
after start/endtime for which the baseline function will be
extrapolated. This option is useful for providing quasi-definitive data.
When applying this method, a number of new meta-information attributes
will be added, containing basevalues and all functional parameters to
describe the baseline. Thus, the stream object still contains
uncorrected raw data, but all baseline correction information is now
contained within its meta data. To apply baseline correction you can use
thebcmethod:corrdata = rawdata.bc()If baseline jumps/breaks are necessary due to missing data, you can call
the baseline function for each independent segment and combine the
resulting baseline functions to a list:stream = read(mydata,starttime='2016-01-01',endtime='2016-03-01')
basevalues = read(mybasevalues)
adoptedbasefunc = []
adoptedbasefunc.append(stream.baseline(basevalues, extradays=0, fitfunc='poly', fitdegree=1,startabs='2016-01-01',endabs='2016-02-01')
adoptedbasefunc.append(stream.baseline(basevalues, extradays=0, fitfunc='spline', knotstep=0.33,startabs='2016-01-02',endabs='2016-01-03')

corr = stream.bc()The combined baseline can be plotted accordingly. Extend the function
parameters with each additional segment.mp.plot(basevalues, variables=['dx','dy','dz'], symbollist=['o','o','o'], padding=[5,0.05,5], function=adoptedbasefunc)Adding a baseline for scalar data, which is determined from the delta F
values provided within the basevalue data stream:scalarbasefunc = []
scalarbasefunc.append(basevalues.baseline(basevalues, keys=['df'], extradays=0, fitfunc='poly', fitdegree=1,startabs='2016-01-01',endabs='2016-03-01'))
plotfunc = adoptedbasefunc
plotfunc.extend(scalarbasefunc)
mp.plot(basevalues, variables=['dx','dy','dz','df'], symbollist=['o','o','o','o'], padding=[5,0.05,5,5], function=plotfunc)Getting dailymeans and correction for scalar baseline can be acomplished
by:meanstream = stream.dailymeans()
meanstream = meanstream.func2stream(scalarbasefunc,mode='sub',keys=['f'],fkeys=['df'])
meanstream = meanstream.delta_f()Please note that here the function originally determined from the deltaF
(df) values of the basevalue data needs to be applied to the F column
(f) from the data stream. Before saving we will also extract the
baseline parameters from the meta information, which is automatically
generated by thebaselinemethod.absinfo = stream.header.get('DataAbsInfo','')
fabsinfo = basevalues.header.get('DataAbsInfo','')2.11.6 Saving basevalue and baseline informationThe following will create a BLV file:basevalues.write('/my/path', coverage='all', format_type='BLV', diff=meanstream, year='2016', absinfo=absinfo, deltaF=fabsinfo)Information on the adopted baselines will be extracted from optionabsinfo. If several functions are provided, baseline jumps will be
automatically inserted into the BLV data file. The output of adopted
scalar baselines is configured by optiondeltaF. If a number is
provided, this value is assumed to represent the adopted scalar
baseline. If either ‘mean’ or ‘median’ are given (e.g.deltaF='mean'), then the mean/median value of all delta F values in
thebasevaluesstream is used, requiring that such data is
contained. Providing functional parameters as stored in aDataAbsInfometa information field, as shown above, will calculate
and use the scalar baseline function. Themeanstreamstream contains
daily averages of delta F values between variometer and F measurements
and the baseline adoption data in the meta-information. You can,
however, provide all this information manually as well. The typical way
to obtain such ameanstreamis sketched above.2.12 Database supportMagPy supports database access and many methods for optimizing data
treatment in connection with databases. Among many other benefits, using
a database simplifies many typical procedures related to
meta-information. Currently, MagPy supportsMySQLdatabases. To use these features, you
need to have MySQL installed on your system. In the following we provide
a brief outline of how to set up and use this optional addition. Please
note that a proper usage of the database requires sensor-specific
information. In geomagnetism, it is common to combine data from
different sensors into one file structure. In this case, such data needs
to remain separate for database usage and is only combined when
producingIAGA/INTERMAGNETdefinitive data. Furthermore, unique sensor information such as type and
serial number is required.import magpy import database as mdb2.12.1 Setting up a MagPy database (using MySQL)Open mysql (e.g. Linux:mysql-uroot-pmysql) and create a new
database. Replace#DB-NAMEwith your database name (e.g.MyDB).
After creation, you will need to grant priviledges to this database to a
user of your choice. Please refer to official MySQL documentations for
details and further commands.mysql> CREATE DATABASE #DB-NAME;
mysql> GRANT ALL PRIVILEGES ON #DB-NAME.* TO '#USERNAME'@'%' IDENTIFIED BY '#PASSWORD';2.12.2 Initializing a MagPy databaseConnecting to a database using MagPy is done using following command:db = mdb.mysql.connect(host="localhost",user="#USERNAME",passwd="#PASSWORD",db="#DB-NAME")
mdb.dbinit(db)2.12.3 Adding data to the databaseExamples of useful meta-information:iagacode = 'WIC'
data = read(example1)
gsm = data.selectkeys(['f'])
fge = data.selectkeys(['x','y','z'])
gsm.header['SensorID'] = 'GSM90_12345_0002'
gsm.header['StationID'] = iagacode
fge.header['SensorID'] = 'FGE_22222_0001'
fge.header['StationID'] = iagacode
mdb.writeDB(db,gsm)
mdb.writeDB(db,fge)All available meta-information will be added automatically to the
relevant database tables. The SensorID scheme consists of three parts:
instrument (GSM90), serial number (12345), and a revision number (0002)
which might change in dependency of maintenance, calibration, etc. As
you can see in the example above, we separate data from different
instruments, which we recommend particularly for high resolution data,
as frequency and noise characteristics of sensor types will differ.2.12.4 Reading dataTo read data from an established database:data = mdb.readDB(db,'GSM90_12345_0002')Options e.g. starttime=’’ and endtime=’’ are similar as for normalread.2.12.5 Meta dataAn often used application of database connectivity with MagPy will be to
apply meta-information stored in the database to data files before
submission. The following command demostrates how to extract all missing
meta-information from the database for the selected sensor and add it to
the header dictionary of the data object.rawdata = read('/path/to/rawdata.bin')
rawdata.header = mdb.dbfields2dict(db,'FGE_22222_0001')
rawdata.write(..., format_type='IMAGCDF')2.13 Monitoring scheduled scriptsAutomated analysis can e easily accomplished by adding a series of MagPy
commands into a script. A typical script could be:# read some data and get means
data = read(example1)
mean_f = data.mean('f')

# import monitor method
from magpy.opt import Analysismonitor
analysisdict = Analysismonitor(logfile='/var/log/anamon.log')
analysisdict = analysisdict.load()
# check some arbitray threshold
analysisdict.check({'data_threshold_f_GSM90': [mean_f,'>',20000]})If provided criteria are invalid, then the logfile is changed
accordingly. This method can assist you particularly in checking data
actuality, data contents, data validity, upload success, etc. In
combination with an independent monitoring tool likeNagios, you can easily create mail/SMS
notfications of such changes, in addition to monitoring processes, live
times, disks etc.MARCOScomes
along with some instructions on how to use Nagios/MagPy for data
acquisition monitoring.2.14 Data acquisition supportMagPy contains a couple of packages which can be used for data
acquisition, collection and organization. These methods are primarily
contained in two applications:MARTASandMARCOS. MARTAS (Magpy Automated
Realtime Acquisition System) supports communication with many common
instruments (e.g. GSM, LEMI, POS1, FGE, and many non-magnetic
instruments) and transfers serial port signals toWAMP(Web Application Messaging Protocol),
which allows for real-time data access using e.g. WebSocket
communication through the internet. MARCOS (Magpy’s Automated Realtime
Collection and Organistaion System) can access such real-time streams
and also data from many other sources and supports the observer by
storing, analyzing, archiving data, as well as monitoring all processes.
Details on these two applications can be found elsewhere.2.15 Graphical user interfaceMany of the above mentioned methods are also available within the
graphical user interface of MagPy. To use this check the installation
instructions for your operating system. You will find Video Tutorials
online (to be added) describing its usage for specific analyses.2.16 Current developments2.16.1 Exchange data objects withObsPyMagPy supports the exchange of data with ObsPy, the seismological
toolbox. Data objects of both python packages are very similar. Note:
ObsPy assumes regular spaced time intervals. Please be careful if this
is not the case with your data. The example below shows a simple import
routine, on how to read a seed file and plot a spectrogram (which you
can identically obtain from ObsPy as well). Conversions to MagPy allow
for vectorial analyses, and geomagnetic applications. Conversions to
ObsPy are useful for effective high frequency analysis, requiring evenly
spaced time intervals, and for exporting to seismological data formats.from obspy import read as obsread
seeddata = obsread('/path/to/seedfile')
magpydata = obspy2magpy(seeddata,keydict={'ObsPyColName': 'x'})
mp.plotSpectrogram(magpydata,['x'])2.16.2 Flagging in ImagCDFdatawithspikes = read(example1)
flaggeddata = datawithspikes.flag_outlier(keys=['f'],timerange=timedelta(minutes=1),threshold=3)
mp.plot(flaggeddata,['f'],annotate=True)
flaggeddata.write(tmpdir,format_type='IMAGCDF',addflags=True)Theaddflagsoption denotes that flagging information will be added
to the ImagCDF format. Please note that this is still under development
and thus content and format specifications may change. So please use it
only for test purposes and not for archiving. To read and view flagged
ImagCDF data, just use the normal read command, and activate annotation
for plotting.new = read('/tmp/cnb_20120802_000000_PT1S_1.cdf')
mp.plot(new,['f'],annotate=True)3. Predefined scriptsMagPy comes with a steadily increasing number of applications for
various purposes. These applications can be run from some command prompt
and allow to simplify/automize some commonly used applications of MagPy.
All applications have the same syntax, consisting of the name of
application and options. The option -h is available for all applications
and provides an overview about purpose and options of the application:$> application -h3.1 Running applications in Linux/MacOsOn Linux Systems all applications are added the bin directory and can be
run directly from any command interface/terminal after installation of
MagPy:$> application -h3.2 Running applications in WindowsAfter installing MagPy/GeomagPy on Windows, three executables are found
in the MagPy program folder. For running applications you have to start
the MagPy “command prompt”. In this terminal you will have to go to the
Scripts directory:.../> cd ScriptsAnd here you now can run the application of your choice using the python
environment:.../Scripts>python application -h3.3 ApplicationsThe available applications are briefly intruduced in the following.
Please refer to “application -h” for all available options for each
application.3.3.1 mpconvertmpconvert converts bewteen data formats based on MagPy. Typical
applications are the conversion of binary data formats to readable ASCII
data sets or the conversion.Typical applications includeConvert IAGA seconds to IMAGCDF and include obligatory meta
information:mpconvert -r "/iagaseconds/wic201701*" -f IMAGCDF -c month -w "/tmp"
             -m "DataStandardLevel:Full,IAGACode:WIC,DataReferences:myref"Convert IMAGCDF seconds to IAF minute (using IAGA/IM filtering
procedures):mpconvert -r "/imagcdf/wic_201701_000000_PT1S_4.cdf" -f IAF -i -w "/tmp"mpconvert -r
“/srv/products/data/magnetism/definitive/wic2017/ImagCDF/wic_201708_000000_PT1S_4.cdf”
-f IAF -i -w “/tmp”3.3.2 addcredUsed to store encrypted credential information for automatic data
transfer. So that sensitive information has not to be written in plain
text in scripts or cron jobs.Add information for ftp data transfer. This information is encrypted
and can be accessed by referring to the shortcut “zamg”.addcred -t transfer -c zamg -u max -p geheim
          -a "ftp://ftp.remote.ac.at" -l 21