plotly plot for standard curve of yeast growth that is part of cell density estimator

link to live plot.md

here is an example of what the code will make when you add your plot credentials and run

UPDATE: The former plot.ly site for the plots shas been moved to https://chart-studio.plot.ly/ , see here, but for some reason this plot no longer shows there?!? That's okay because now seems to work in Jupyter without credentials and that was why I originally didn't post the code where it worked. Now it has been added back in to https://github.com/fomightez/methods_in_yeast_genetics/blob/master/cell_density_estimator/cell_density_estimator_for_multiple_samples.ipynb and https://github.com/fomightez/methods_in_yeast_genetics/blob/master/cell_density_estimator/cell_density_estimator_for_single_sample.ipynb. (Although I need to see if resulting plot show in nbviewer still.)

plotly plot for standard curve of yeast growth.py

# PASTE ALL OF THIS IN A CELL IN THE NOTEBOOK AT https://github.com/fomightez/methods_in_yeast_genetics/blob/master/cell_density_estimator/cell_density_estimator_for_multiple_samples.ipynb
# and then run after running the code above. IN FACT IT IS NOW ADDED TO THE NOTEBOOK!
import plotly.graph_objects as go
import pandas as pd

od660_values_list, cells_per_ml_values_list = unzip(yeast_cell_density_by_OD660_tuples)
cells_per_ml_values_list = [(x/1.0e7) for x in cells_per_ml_values_list] # trying to get them in scale, see http://stackoverflow.com/questions/32542957/control-tick-labels-in-python-seaborn-package
data_dict = {'od':od660_values_list, 'cells_per_ml':cells_per_ml_values_list} # see http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/
data_df = pd.DataFrame(data_dict,columns=['od','cells_per_ml']) # see http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/

data = [
    go.Scatter(
        x=data_df['cells_per_ml'], # assign x as the dataframe column 'x'
        y=data_df['od']
    )
]

layout = go.Layout(
title='Optical Density vs. Cell Density',
xaxis=dict(
    title='$\\text{cells per ml (}\\times 10^7{)}$'  #latex help from https://plot.ly/python/LaTeX/
    ),
yaxis=dict(
    title='$\\text{OD}_{660}$'
    )
)

# IPython notebook
pfig=go.Figure(data=data,layout=layout)
pfig.show()

##BELOW IS OLD 2016 CODE BEFORE IT WORKS RIGHT IN JUPYTER WITHOUT CREDENTIALS #########################
'''
# PASTE ALL OF THIS IN A CELL IN THE NOTEBOOK AT https://github.com/fomightez/methods_in_yeast_genetics/blob/master/cell_density_estimator/cell_density_estimator_for_multiple_samples.ipynb AND 
# THEN EDIT TO INCLUDE YOUR PLOTLY CREDENTIALS.
import plotly
import plotly.plotly as py
py.sign_in('YOUR_PLOTLY_USERNAME', 'YOUR_PLOTLY_API_KEY')
import plotly.graph_objs as go

# based on example at https://plot.ly/pandas/line-charts/

import pandas as pd

od660_values_list, cells_per_ml_values_list = unzip(yeast_cell_density_by_OD660_tuples)
cells_per_ml_values_list = [(x/1.0e7) for x in cells_per_ml_values_list] # trying to get them in scale, see http://stackoverflow.com/questions/32542957/control-tick-labels-in-python-seaborn-package
data_dict = {'od':od660_values_list, 'cells_per_ml':cells_per_ml_values_list} # see http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/
data_df = pd.DataFrame(data_dict,columns=['od','cells_per_ml']) # see http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/

data = [
    go.Scatter(
        x=data_df['cells_per_ml'], # assign x as the dataframe column 'x'
        y=data_df['od']
    )
]

layout = go.Layout(
title='Optical Density vs. Cell Density',
xaxis=dict(
    title='$\\text{cells per ml (}\\times 10^7{)}$'  #latex help from https://plot.ly/python/LaTeX/
    ),
yaxis=dict(
    title='$\\text{OD}_{660}$'
    )
)

# IPython notebook
figure=go.Figure(data=data,layout=layout) # this and next line from https://plot.ly/python/user-guide/ , under 'What is Plotly?' since differs from basic example above in that need layout pointed to.
py.iplot(figure, filename='od_vs_cell_density')
'''