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README.md

pyDataView

"A graphical user interface for people who hate graphical user interfaces"

If you know how to read your data by writing Python code, PyDataView simply provides a way to quickly explore the data with a slider/file viewer. Once you see something you like, click Save Script to generate a starting point for further analysis.

Introduction

A Data Viewer GUI written in python, wxpython and matplotlib.

This is a lightweight interface for quick insight into scientific data. Data can be explored as a matplotlib lineplot or contour using sliders to traverse the range of existing records and bins. For more detailed analysis, a figure can be saved, the data output as a csv file or a minimal python script generated. The emphasis of this project is the provision of a simple reader to get data into python, a minimal GUI to get quick insight into that data and generation of python scripts for more detailed analysis.

Quickstart

pyDataView uses fields of five dimensional data: three spatial, one temporal and one for dimensionality of data. Currently, support is provided for field data written by a number of binary format MPI/Fortran codes, OpenFOAM, Channelflow (h5 format) and LAMMPS. To use pyDataView, from the command line, simply point it at directory which contains the data,

pyDataView.py -d ./path/to/dir

or you can run pyDataView.py and choosing the directory. Any files which can be converted to fields are displayed on the left hand side.

alt tag

As well as the raw data fields, derived fields are also supported, for example, dividing momentum by density to get velocity.

If the code/datatype is already supported, all available data should be displayed. In order to add new datatypes, the user must create a raw data reader in newreader.py,

from rawdata import RawData

class SomeNewReader(RawData):

    def __init__(self,fdir,fname,dtype,nperbin):
        if (fdir[-1] != '/'): fdir += '/' 
        self.fdir = fdir
        self.fname = fname
        self.dtype = dtype
        self.nperbin = nperbin
        self.filepath = self.fdir + self.fname + '/'
        self.header = self.read_header(fdir)  #The user should write these
        self.grid = self.get_gridtopology()   #The user should write these
        self.maxrec = self.get_maxrec()

    def read(self, startrec, endrec):

        # Read a 5D array [nx, ny, nz, nperbin, nrecs]
        # where nrecs=endrec-startrec+1 and nperbin is 1 for scalar field
        # and 3 for vector field, etc
        
        return bindata
    

To use this raw data, a field datatype can then be added to newfield.py,

from field import Field
from newreader import SomeNewReader

class SomeNewVectorField(Field):
    
    dtype = 'd'
    nperbin = 3
    
    def __init__(self,fdir):
        Raw = SomeNewReader(fdir, self.fname, self.dtype, 
                         self.nperbin)
        Field.__init__(self,Raw)
        self.header = self.Raw.header
        self.axislabels = ['x','y','z']

This field type can now be called in any python script, allowing 5D data fields to be read as well as data preparation for plots, including profiles, contours, fft, etc. To add these new fields to the GUI, they should be added to newpostproc.py

from postproc import PostProc
from newfield import SomeNewVectorField

class New_PostProc(PostProc):

    def __init__(self,resultsdir,**kwargs):
        self.resultsdir = resultsdir
        self.plotlist = {} 

        possibles = {'New Field': SomeNewVectorField}

        self.plotlist = {}
        for key, field in possibles.items(): 
            try:
                self.plotlist[key] = field(self.resultsdir)
            except AssertionError:
                pass 

which must be instantiated in allpostproc.py allowing pyDataView to find and display the new field format.

There are many examples of different data formats in the postproclib file which can be used as a template.

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