Welcome to pyFlapjack’s documentation!¶
pyFlapjack¶
pyFlapjack is our Python package that allows you to interface the Flapjack with Pandas and Numpy stack, so that you can easily implement it in your projects.
Instalation¶
Installing pyFlapjack is quite simple. Please refer to our Wiki for installation instructions, available here: Installation.
Tutorials¶
Now that you have pyFlapjack installed you can familiarize yourself with the tool using the Jupyter notebook tutorials designed for this purpose.
Reference this paper¶
Please reference our Flapjack’s paper–available here, using the following reference: > Guillermo Yáñez Feliú, Benjamín Earle Gómez, Verner Codoceo Berrocal, Macarena Muñoz Silva, Isaac N. Nuñez, Tamara F. Matute, Anibal Arce Medina, Gonzalo Vidal, Carlos Vidal Céspedes, Jonathan Dahlin, Fernán Federici, and Timothy J. Rudge ACS Synthetic Biology 2021 10 (1), 183-191 DOI: 10.1021/acssynbio.0c00554
API Reference¶
This page contains auto-generated API reference documentation 1.
flapjack¶
Submodules¶
flapjack.flapjack¶
Module Contents¶
- class Flapjack(url_base='localhost:8000')¶
- models = ['study', 'assay', 'sample', 'strain', 'media', 'vector', 'dna', 'signal', 'chemical',...¶
- __del__(self)¶
- async _analysis(self, **kwargs)¶
- async _measurements(self, **kwargs)¶
- async _plot(self, **kwargs)¶
- async _upload_measurements(self, df, **kwargs)¶
- analysis(self, **kwargs)¶
- create(self, model, confirm=True, overwrite=False, **kwargs)¶
- delete(self, model, id, confirm=True)¶
- get(self, model, **kwargs)¶
- handle_response(self, s)¶
- log_in(self, username, password)¶
- log_out(self)¶
- measurements(self, **kwargs)¶
- parse_params(self, **kwargs)¶
- patch(self, model, id, **kwargs)¶
- plot(self, **kwargs)¶
- refresh(self)¶
- upload_measurements(self, df, **kwargs)¶
- index_params = ['biomass_signal', 'ref_signal', 'analyte', 'analyte1', 'analyte2']¶
- plot_option_keys = ['normalize', 'subplots', 'markers', 'plot']¶
- replace_columns_with_ids = []¶
flapjack.simulator¶
Module Contents¶
- class Simulator(study_name='', assay_name='', study_description='', assay_description='', dna_name='', init_proteins=[0], concentrations=[0], n_signals=1, fluo_noise=0.01, od_noise=0.01)¶
- create_data(self, fj, step, n_samples, nt, dt, sim_steps)¶
- create_meta_objects(self, fj)¶
- colors = ['red', 'green', 'blue']¶
flapjack.util¶
Module Contents¶
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Layout figure optimized for print at 300dpi |
- exponential_growth(t, y0, k)¶
- exponential_growth_rate(t, y0, k)¶
- fit_curve(func, data, x, y, **kwargs)¶
- gompertz(t, y0, ymax, um, l)¶
- gompertz_growth_rate(t, y0, ymax, um, l)¶
- hill(x, a, b, k, n)¶
- layout_print(fig, width=3.3, height=1.5, font_size=6)¶
Layout figure optimized for print at 300dpi
fig = figure to layout width,height = size in inches font_size = font size in pts
Returns: fig = figure with correct layout
Package Contents¶
Classes¶
Functions¶
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Layout figure optimized for print at 300dpi |
Attributes¶
- class Flapjack(url_base='localhost:8000')¶
- models = ['study', 'assay', 'sample', 'strain', 'media', 'vector', 'dna', 'signal', 'chemical',...¶
- __del__(self)¶
- async _analysis(self, **kwargs)¶
- async _measurements(self, **kwargs)¶
- async _plot(self, **kwargs)¶
- async _upload_measurements(self, df, **kwargs)¶
- analysis(self, **kwargs)¶
- create(self, model, confirm=True, overwrite=False, **kwargs)¶
- delete(self, model, id, confirm=True)¶
- get(self, model, **kwargs)¶
- handle_response(self, s)¶
- log_in(self, username, password)¶
- log_out(self)¶
- measurements(self, **kwargs)¶
- parse_params(self, **kwargs)¶
- patch(self, model, id, **kwargs)¶
- plot(self, **kwargs)¶
- refresh(self)¶
- upload_measurements(self, df, **kwargs)¶
- class Simulator(study_name='', assay_name='', study_description='', assay_description='', dna_name='', init_proteins=[0], concentrations=[0], n_signals=1, fluo_noise=0.01, od_noise=0.01)¶
- create_data(self, fj, step, n_samples, nt, dt, sim_steps)¶
- create_meta_objects(self, fj)¶
- colors = ['red', 'green', 'blue']¶
- exponential_growth(t, y0, k)¶
- exponential_growth_rate(t, y0, k)¶
- fit_curve(func, data, x, y, **kwargs)¶
- gompertz(t, y0, ymax, um, l)¶
- gompertz_growth_rate(t, y0, ymax, um, l)¶
- hill(x, a, b, k, n)¶
- index_params = ['biomass_signal', 'ref_signal', 'analyte', 'analyte1', 'analyte2']¶
- layout_print(fig, width=3.3, height=1.5, font_size=6)¶
Layout figure optimized for print at 300dpi
fig = figure to layout width,height = size in inches font_size = font size in pts
Returns: fig = figure with correct layout
- plot_option_keys = ['normalize', 'subplots', 'markers', 'plot']¶
- replace_columns_with_ids = []¶
- 1
Created with sphinx-autoapi
pyFlapjack¶
pyFlapjack is our Python package that allows you to interface the Flapjack with Pandas and Numpy stack, so that you can easily implement it in your projects.
Instalation¶
Installing pyFlapjack is quite simple. Please refer to our Wiki for installation instructions, available here: Installation.
Tutorials¶
Now that you have pyFlapjack installed you can familiarize yourself with the tool using the Jupyter notebook tutorials designed for this purpose.
Reference this paper¶
Please reference our Flapjack’s paper–available here, using the following reference: > Guillermo Yáñez Feliú, Benjamín Earle Gómez, Verner Codoceo Berrocal, Macarena Muñoz Silva, Isaac N. Nuñez, Tamara F. Matute, Anibal Arce Medina, Gonzalo Vidal, Carlos Vidal Céspedes, Jonathan Dahlin, Fernán Federici, and Timothy J. Rudge ACS Synthetic Biology 2021 10 (1), 183-191 DOI: 10.1021/acssynbio.0c00554