# cdctool

**Repository Path**: hitdic/cdctool

## Basic Information

- **Project Name**: cdctool
- **Description**: A tool for the computation of the diffusion coefficients
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2016-04-03
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Computation of Diffusion Coefficient Toolkit
===============================================

This toolkit intends to compute the diffusion coefficients from experimental concentration profiles using the Matano-Boltzmann method for binary system and the Boltzmann-Kirkaldy method for ternary system.

Features
---------

- Fit the composition profiles with the superpositions of Matano functions
- Calculate the interdiffusion coefficients of binary and ternary diffusion couples

Dependencies
-------------

- numpy
- scipy
- matplotlib

Get Started
------------

1. Fake data
^^^^^^^^^^^^
Fake data is available in the cdctoolkit. One can visualize the fake data as following.
::
   import matplotlib.pyplot as plt
   from cdctool.faker import FakeBinary, FakeTernary
   fig = plt.figure()
   ax = fig.add_subplot(2, 1, 1)
   dist, c1 = FakeBinary()
   ax.plot(dist, c1)
   ax = fig.add_subplot(2, 1, 2)
   dist, c1, c2 = FakeTernary()
   ax.plot(dist, c1)
   ax.plot(dist, c2)
   plt.show()

2. Fitting the profiles
^^^^^^^^^^^^^^^^^^^^^^^^
The supperpositions of the Boltzmann functions are adopted as the fitting function in the cdctool. Two kinds of functions are considered, that is the fixed terminal composition and free terminal compositions. The fixed terminal fitting functions are proposed.

- FitFixedBoltzmann
- FitFixedDoubleBoltzmann
- FitFixedTripleBoltzmann
- AutoFitFixedBoltzmann
- AutoFitFixedDoubleBoltzmann
- AutoFitFixedTripleBoltzmann

The first categories with the prefix Auto means that the user has to chose the fitting shceme automatically. The second categories provide a more intelligent implementation, where a number schemes are iteratively selected and the one with the best fitness is returned.
::

    import matplotlib.pyplot as plt
    from cdctool.faker import FakeBinary, FakeTernary
    from cdctool import AutoFitFixedBoltzmann
    fig = plt.figure()
    ax = fig.add_subplot(111)
    dist, c1 = FakeBinary()
    ax.plot(dist, c1, "D", label="experiment")
    r = AutoFitFixedBoltzmann(dist, c1, 0.1, 0.3)
    res, func = r["res"], r["func"]
    plt.plot(dist, func(dist, res, xes=(0.1, 0.3)), lbel="Fitted")
    plt.show()

3. Properties of diffusion profiles
----------------------------------------
One can use the `DiffProfile` to retrieve other properties for a profile.
::

    from cdctool.profile import DiffProfile
    pro = DiffProfile(dist, c1, cleft=0.1, cright=0.3, atime=21600, fitmethod="fixedboltzmann")
    flux = pro.flux(dist)
    slope = pro.slope
    interd = flux / slope

4. Interdiffusion coefficients for ternary system
---------------------------------------------------
We use the experimental data imtimated by the cdctoolkit.
::

    import matplotlib.pyplot as plt
    import numpy as np
    from cdctool.faker import FakeBinary, FakeTernary, FakeTernaryAnother
    from cdctool.profile import DiffProfile
    from cdctool.solve import ternsolve

    dist1, c11, c12 = FakeTernary()
    dist2, c21, c22 = FakeTernaryAnother()

    p00 = DiffProfile(dist1, c11, 0.1, 0.3, atime=21600, fitmethod="fixedboltzmann")
    p01 = DiffProfile(dist1, c12, 0.2, 0.1, atime=21600, fitmethod="fixedboltzmann")
    p10 = DiffProfile(dist2, c21, 0.1, 0.3, atime=21600, fitmethod="fixedboltzmann")
    p11 = DiffProfile(dist2, c22, 0.1, 0.2, atime=21600, fitmethod="fixedboltzmann")

    d = ternsolve(p00, p01, p10, p11)
    print(d)