Altair

[quinn-dougherty]

https://github.com/pennsignals/chime/issues/43

• factored out matplotlib
• charts are now calls to pure functions
• current interactivity is zoom in/out & scroll side to side, mouseover tooltip showing values

[quinn-dougherty]

i do not have https://github.com/pennsignals/chime/issues/29 in here i actually think that should be a separate PR after this one

[mdbecker]

Looks like there's a regression in the census graph when I change some of the input values
Input values I tested with to produce this issue:

• Currently Known Regional Infections: 100
• Currently Hospitalized COVID-19 Patients: 20
• Doubling Time (days): 12
  Old:
  

New:

Both the y and x axis seem off

[jlubken]

Add the altair dependency to requirements.txt, and environment.yml too. Remove matplotlib if it is no longer needed.

[quinn-dougherty]

Add the altair dependency to requirements.txt, and environment.yml too. Remove matplotlib if it is no longer needed.

done. thanks @jlubken!

[quinn-dougherty]

@mdbecker this is what i got currently. correct numbers but less granularity

• Currently Known Regional Infections: 100
• Currently Hospitalized COVID-19 Patients: 20
• Doubling Time (days): 12

do you want to merge now or wait until i crack this?

the thing is, this whole block might need to be refactored

for k, los in los_dict.items():
    census = (
        projection_admits.cumsum().iloc[:-los, :]
        - projection_admits.cumsum().shift(los).fillna(0)
    ).apply(np.ceil)
    census_dict[k] = census[k]

census_df = pd.DataFrame(census_dict)
census_df["day"] = census_df.index
census_df = census_df[["day", "hosp", "icu", "vent"]]

census_table = census_df[np.mod(census_df.index, 7) == 0].copy()
census_table.index = range(census_table.shape[0])
census_table.loc[0, :] = 0
census_table = census_table.dropna().astype(int)

to pull out the census[k]s into a solitary dataframe. I don't want to add too much complexity and force yall to verify the model correctness of a PR if i don't have to

[cjbayesian]

This is great. I am looking at it and is giving me the right numbers. @quinn-dougherty can you help me understand why the granularity is lower for the census plots?

[cjbayesian]

Ahh, I see I think. You can plot census_df instead of census_table .

[quinn-dougherty]

This is great. I am looking at it and is giving me the right numbers. @quinn-dougherty can you help me understand why the granularity is lower for the census plots?

line 264 of my branch census_table = census_df[np.mod(census_df.index, 7) == 0].copy() suggests to me that the correct label is weeks from today, but in the original it's days from today.

I got the correct numbers by passing census_table into my chart building function, and i get incorrect numbers by passing either census_df or census into it. (passing census was clearly incorrect and i shouldn't have had that in my first draft). The trouble is due to the mod operation there's less data in census_table.

the original called ax.plot(census.head(plot_projection_days)[k], ".-", label=k + " census") from inside the for loop, but it's not fully clear to me why it results in something different than building a chart off of census_df.

[quinn-dougherty]

Ahh, I see I think. You can make plot census_df instead of census_table .

calling my chart build func on census_df gives the right granularity and the wrong numbers.

[cjbayesian]

Trying to figure it out now. I suggest we go with the 7 day granularity for now to get the stability and submit an issue to fix the granularity.