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"description": "====\npytc\n====\nA python software package for analyzing Isothermal Titration Calorimetry data.\nThe name is a `portmanteau `_ of Python and ITC. \n\nIntroduction\n============\n`pytc `_ is python software used to extract\nthermodynamic information from isothermal titration calorimetry (ITC)\nexperiments. It fits arbitrarily complex thermodynamic models to multiple ITC\nexperiments simultaneously. We built it with three design principles:\n\n + **Open source and cross platform**. The full source code should be available.\n The program should not require proprietary software to run. \n + **Ease of use**. Fitting basic models should be easy. Implementing completely\n new thermodynamic models should be straightforward. \n + **Accessible for users and programmers**. It should have both a GUI and a \n well-documented API. \n\nOur implementation is built on `python3 `_ extended with `numpy `_,\n`scipy `_, `matplotlib `_ and\n`emcee `_. The GUI is built on\n`pytq5 `_.\n\nFeatures\n========\n\n + Clean, pythonic API\n + Simple, cross-platform GUI based on `PyQt5 `_.\n + New models can be defined using a few lines of python code.\n + Easy integration with `jupyter `_ notebooks for \n writing custom fitting scripts.\n\nDocumentation\n=============\n\n + `Installation `_\n + `Fitting models using the API `_.\n + `Fitting models using the GUI `_.\n + `Fitting and statistics `_.\n + `Description of individual experiment models included in package `_.\n + `Description of global fits included in package `_.\n + `Defining new models `_.\n\n.. warning::\n **pytc** will fit all sorts of complicated models to your data. It is up to\n you to make sure the fit is justified by the data. See the \n `Fitting and statistics `_ section to see what **pytc** \n reports to help in this decision making.\n\nAPI Quick Start\n===============\n\nIf you already have a python3-based scientific computing environment installed, \nyou can start using the API by:\n\n.. sourcecode:: bash\n\n # Install pytc\n pip3 install pytc-fitter\n\n # Clone the demos repo\n git clone https://github.com/harmslab/pytc-demos\n\n # Fire up jupyter \n cd pytc-demos\n jupyter notebook\n\n\nExample code using the API\n==========================\n\nFit a :math:`Ca^{2+}/EDTA` binding experiment to a single-site binding model.\n\n.. sourcecode:: python\n\n import pytc\n\n # Load in integrated heats from an ITC experiment\n e = pytc.ITCExperiment(\"demos/ca-edta/tris-01.DH\",\n pytc.indiv_models.SingleSite)\n\n # Create the global fitter, add the experiment, and fit\n g = pytc.GlobalFit()\n g.add_experiment(e)\n g.fit()\n\n # Print the results out\n g.plot()\n print(g.fit_as_csv)\n\nGUI\n===\n\nThe GUI is installed separately from the API. Instructions are \n`here `_. The GUI\ndocs are `here `_. A few \nscreenshots showing the GUI in action are below.\n\n.. image:: images/gui_01.png\n.. image:: images/gui_02.png\n\n\n.. toctree::\n :maxdepth: 2\n :caption: Contents:\n\n _api/pytc.rst\n\nIndices and tables\n==================\n\n* :ref:`genindex`\n* :ref:`modindex`\n* :ref:`search`",
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