• Went to Kings@Nets.
• Private work.
• The multigame view for march madness is very clean: https://www.ncaa.com/march-madness-live/multigame

• SBSC.
• Observers can observe columns or entire objects. Any dot-notated path, eg observer on game within Score would work (straight object), and observer on game.opponent1.prediction.name within Score would also work (nested col through many other objects).
• Merged the old lines and scores tables. It’s now a single table (games) with datetime/line/week/status and opponent1/2, then the opponents objects each have score/margin/team/prediction/location/coverage.
• Made the picks objs FK to opponents, not teams.
• Moved coverage and score into Opponent, rather than Score. Also added margin there.

• Private work.
• BoA got more than 15B in deposits over the last couple days as people move to bigger (safer?) banks.
• NSU advanced to the DII elite 8 last night and got reseeded 1st: https://www.ncaa.com/brackets/basketball-men/d2/2023
• FB cutting another 13%. Anchorage 20% (they’re only a few hundred strong).
• Finished Community.

• SBSC.
• Be careful with the team/prediction/location schema change; although historically team1=favorite and team2=underdog in the database, that’s not the case. Either team can have any of the 3 predictions (favorite/pickem/underdog). I’ve written all logic in the app to respect that (look it up where necessary).
• Added observers for basically every stat calculation (eg overperformance margin). The only exception is pick%. It’s an SBSC stat. I intentionally just calculate it in the stats module – don’t want this being a Team.pick_counts col observing the Pick table (for example), then it would update on every pick (frequent). Just calc when the stats view is requested/read by a user (infrequent).
• Basically rewrote the whole app on this ticket. Data, backend, and frontend.
• Renamed line/lines to game/games to be more clear about what the table is.
• Instead of associating team/prediction/location on each game row, duplicated, I split it out so each game just has two objects, opponent1 and opponent2. They FK to the Opponents table, which has cols for team/prediction/location (nested FKs to their respective tables).

• Signed into desktop app with nvidia, updated graphics drivers for the first time in a LONG time. Hopefully this fixes the issue of the right monitor lagging.
• Private work.
• Roasted the 7lb pork butt.
• Cumin is the dried/ground seeds of a plant in the parsley family.

• SBSC. Made a pretty drastic change to split favored_team* and underdog_team* cols in the lines and scores table into just team1* and team2*, then separate cols for *_prediction and *_location and *_coverage.
• This cleans a lot of the logic, and normalizes a bit further.
• It also spills into the home_team and coverer logic, cleaning it up there as well. It also makes them non-nullable, so much easier to manage.
• If something is able to be looked up via table relationships, leave it that way. Example: A pick obj has a team obj and a line obj, the line obj has a score obj, the score obj has a coverage obj (for that picked team obj), the coverage obj has a points col.
• If something requires a calculation, add an observer. Example: A score obj has to compare the score cols of team1/2 against its line obj and set each team’s coverage if cover/push/noncover.

• Private work.
• Remember USDC depegged to $0.87 briefly in the wake of SVB.
• Continued melodysheep (after end of time) with their 3-part series Life Beyond.
  • Inevitable that aliens exist. There are more habitable (distance to star, elements, liquid) planets/moons in our universe than grains of sand on earth.
  • Big bang ~14B years ago. Life on earth started developing ~4B years ago. We have ~100T years until the last star dies and life is extinguished. We’re only at the very beginning. Much more time for primal life to develop, intelligent life to get better at travel/communication, etc.
  • Also gets into the museum of alien life, what they could be like. Our planet (earth) has ~10B people.
  • Our solar system has 8 planets. Our galaxy (milky way) has ~100B stars. Our universe has ~1T galaxies.
  • Kardashev scale, type 1/2/3 civilization. 1 = you can capture all the energy available to your planet. 2 = you can harvest all the energy from your star. 3 = your entire galaxy.
  • If the whole universe is an ocean, we’ve searched about a cup of it. There are some corners we can never reach because of the expanding universe.

• Pandas refresher.
• Tabular data. Rows and cols.
• Read and write. Import and export.
• Select and query. Some for iterating/dev. Faster alts for prod.
• Join.
• Plot.
• Computed columns.
• Aggregations. Summaries. Statistics.
• Reshaping. Lots of functions to moved data and relations around.
• Numpy is a bit faster and more memory efficient. Pandas has more high-level functionality. Numpy does support named axes (structured arrays), but in general pandas is better at multidimensional.

• SBSC.
• Little annoying that you can’t just set the width of the bars in plotly for horizontal bar charts, and then let height automatically adjust: https://community.plotly.com/t/can-you-scale-the-dcc-graph-height-automatically-to-content/45471/3. Computing height is easy, but you have to include the top section for title and such.
• Added vlines for the average of ALL traces, not just the first.
• categoryorder is pretty cool (https://plotly.com/python/reference/layout/xaxis/#layout-xaxis-categoryorder), but I’m doing a sort across all traces so I can’t just do a single sort with knowledge of only the keys.
• As much as possible, frontend views try to match database views. Little to zero modification. So the results module is mostly just querying the sqla objects, very slight calc wrappers on the data (rollups, payouts, etc), then serializing to the app. The stats module, on the other hand, is purely meant for statistical analysis. So it uses numpy and pandas, then serializes to json (with layout/config) and passes to the frontend for plotly to render.

• Just 3 devices in my home aren’t smart and need updating for DST: oven, coffee, aquarium autofeeder.
• There isn’t a leak in my coffee maker. It has an overflow hole on the back.
• All homemade mealprep: smoothies, powders, peanut butter, oat milk, liver, protein bars.
• Cool video: https://www.youtube.com/watch?v=uD4izuDMUQA. Stars extinguishing into white dwarfs, then black dwarfs. Life become extinct very quickly. Then the cold dark universe exists a long long long time after. Black holes absorb a lot of stuff. But the (accelerating) expansion of the universe impossible to catch up with. Potential for multiverse.
• Little bit of SBSC work, little bit of private work.

• SVB summary.
  • 40 years. Working with mostly startups and VCs.
  • Midquarter update on wednesday. Announced losses and the need to raise.
    • Like all other balance sheets, they raise X by offering Y and use X for Z. In most cases, X is money from investors/depositors. Y is usually equity or interest. Z can be a product/service they’re building/offering (a device, a software app, loans, other investments, whatever the business model is).
    • In this case, X is depositor money, Y is interest, and Z is a mix of investments. Z was mostly split into long-term loans for startups as well as bonds (investment). X was ~175b, Z was ~210b, hence losses.
    • Problem #1: Markets have been bad for a while, so deposits were slowing while investments did not slow as much. More out than in. This was mostly concentrated in bay area tech, so little diversification.
    • Problem #2: Markets have been bad for a while, so the bond investments of Z are down bad (bonds move inversely with interest rates of course). Again, little diversification.
  • SIVB lost a ton of value after the announcement. Confirmed lots of fears depositors already had. Everyone pulled. Bank run. Liquidity was need for withdrawals, so SVB had to sell assets, and all bonds were sold at large loss (+ early sale before maturity), exacerbating the problem (reducing confidence in SVB balances/liquidity). Death spiral. Trading on SIVB Halted.
  • Friday, 2 days later, FDIC places SVB into receivership and takes control.
  • FDIC insurance limit is $250k, which each depositor will receive on monday. Plus certificates for the remainder (the majority). Then the FDIC will sell the remaining SBV assets over time and pay depositors back (maybe in full, maybe not).
  • Another big financial institution can rescue SVB in the next couple days, provide the backstop/balance/liquidity, and take over the business.
• Remember to use tuple[str, ...] and include ... so that python knows to match size. Python can do the type expansion for mutable iterables like list[str], but tuples have immutable length and so we enforce type on all elements of the collection; tuple[str] by itself implies a tuple with one element.

• Lots of private work.
• https://rotatingsandwiches.com/
• Finished a first pass of all 20 fragrances in the humidifier. My rankings (best -> worst): sandalwood, cedar, lavendar, lemongrass, chamomile, vanilla, frankincense, eucalyptus, peppermint, lemon, grapefruit, orange, rosemary, rose, bergamot, jasmine, vetiver, tea tree, geranium, cinnamon. General order of categories: woods -> fruits -> flowers.
• SBSC. Modularized the plotting function for stacked bars and regular bars, inferring a ton about the passed data. Really clean now, but took some time. Worth it; I need the inference in order to properly make the data analysis dynamically looped based on variables (rather than hardcoded as variable relationships).
• Lots of other cleanup in the apps.