Trying out this Blue Bottle Opascope espresso w/ this hand grinder w/ my Kamira espresso maker. But the Kamira is not pulling the espresso for some reason. Comparing the Lavazza grounds to the Opascope grounds I can’t tell there’s a difference. Is it too fine? Not fine enough? This is the hand grinder. Here is what 2 out of 4 of the Blue Bottle pulls ended up like ...
The material source “Rabbit Rever” by Susan Orlean published in “New Yorker” in the “July 6 2020” edition The article in 3 sentences The author writes about how the rabbit, apparently domesticated for hundreds of years now, has been all of livestock, fur source and since mid century a third most popular choice of pet in the US. But a highly contagious and highly lethal “Rabbit Hemorrhagic Disease” ( RHD the “rabbit Ebola”) caused by a lagovirus from the Caliciviridae family, has been wiping out populations of rabbits outside the US since 1984, but in mid 2019, dead rabbits fitting the RHD signs were showing up in the islands near Seattle and in Texas, Arizona and New Mexico not long after. A protective vaccine was developed within five years of the initial 1984 emergence, but because the production of a “live genetically modified vaccine” is not yet allowed in the US, the vaccine (and another vaccine for a subsequent “RHDV2” variant ) cannot be used here except by rare limited emergency requests allowed by the USDA. ...
I was reading Team of Teams sometime around September 2019. I at least remember reading it in a really cold Starbucks in a mall in Tokyo haha. First, the standard template: The book in three sentences Team of Teams is former General Stanley McChrystal’s account of his time at the Joint Chiefs of Staff and in his role in the conflict with AQI (Al-Qaeda in Iraq) and Abu Musab al Zarqawi. He captures the information sharing struggles US forces in Iraq had that made them struggle in keeping up with AQI’s de-centralized un-command. And he shares the good news of how giving autonomy and decision making to teams stopped central command from being a bottle neck. ...
Made some Xero shoes.
Vitamin D questions There are a couple mini researach questions I have around Vitamin D. How different are humans with respect to Vitamin D, from other life forms or just other mammals. Also what does the synthesis pathway look like for Vitamin D (or more than one pathway if dietary is a separate pathway from skin). And how exactly does vitamin D enable immune function? The names. Per 1 , “plasma concentration of 25-hydroxyvitamin D” is one “vitamin D” biomarker. Fish Per 2, “Fish incorporates vitamin D2 and D3 through the diet.” Roles of “Vitamin D” Also per 2] , “In mammals, vitamin D forms are involved in mineral metabolism, cell growth, tissue differentiation, and antibacterial immune response.” Synthesis pathway for “Vitamin D” From 3, “The synthesis of vitamin D begins in the skin with activation of a precursor molecule by ultraviolet light from the sun. After that, the process moves to the liver for further chemical modification and then on to the kidneys for a final tweak.” And “Humans and other animals can synthesize this molecule right from cholesterol, something we always have plenty of” 3 . Dietary vitamin D Hmm ok so according to 3, “Many animals, including humans, can escape this paradox by simply ingesting the activated vitamin D precursor. Dietary vitamin D still requires the activation steps in the liver and kidney, but at least it lets us stay out of the sunlight. Such supplementation has allowed humans to live in frigid climates and it is no coincidence that Arctic diets tend toward fish and whale blubber, two very rich sources of vitamin D.” So I’m reading here that if you eat the vitamin D precursor, you can skip out on the sun. Dumb evolution Haha And from 3 , “it would make much more sense to house the enzymes for the synthesis of vitamin D within fat cells, which are often tucked right underneath the skin anyway, where the UV light is needed for the first step.” Indeed there is a list of these weird suboptimalities in life out there. Or do we just not understand what’s going on? References 1 here ...
Quick idea around more active content consumption My goal is to write about/ annotate what I read/ watch Just came up with a pattern of consumption to enable this. So on a youtube video for example, I can “Share” into “Dropbox” , specifically into a new folder say, of just metadata Then I found this youtube api doc where you can actually give the hyoutube api a youtube id and it will give you the metadata, like title, channel name, date, etc Then in a separate place, I can have my actual notes but they can now get linked together more easily, because I have the matching metadata. Anyway, just the beginning of a thought here.
We had a surprise Halloween pipe leak but luckily I got to our local hardware store just 15 minutes before they closed. And they had the pipe I needed. We had guests over that night, so this went much better than the worst case scenario!
This is a mini post part of this project. (Originally posted here ). Take a quick look at time of day distribution import matplotlib.pyplot as plt import numpy as np import pandas as pd datadir = '/opt/data' localdir = '/opt/program' tripsdf = pd.read_csv(f'{datadir}/2013-07 - Citi Bike trip data.csv') stationsdf = pd.read_csv(f'{localdir}/datas/stations/stations-2018-12-04-c.csv', index_col=0) tripsdf.iloc[0] tripduration 634 starttime 2013-07-01 00:00:00 stoptime 2013-07-01 00:10:34 start station id 164 start station name E 47 St & 2 Ave start station latitude 40.7532 start station longitude -73.9703 end station id 504 end station name 1 Ave & E 15 St end station latitude 40.7322 end station longitude -73.9817 bikeid 16950 usertype Customer birth year \N gender 0 Name: 0, dtype: object tripsdf['starttime'].map(lambda x: x[11:13]).iloc[:10] 0 00 1 00 2 00 3 00 4 00 5 00 6 00 7 00 8 00 9 00 Name: starttime, dtype: object tripsdf['hour'] = tripsdf['starttime'].map(lambda x: x[11:13]) # For all time looks like maybe two peaks fig = plt.figure(figsize=(6, 6)) fig.patch.set_facecolor('xkcd:mint green') ax = fig.add_subplot(111, ) ax.hist(tripsdf.hour.tolist(), bins=24) plt.grid(True) # Perhaps on weekdays different? import fresh.utils as fu fu.prepare_weekday_feature(tripsdf) fig = plt.figure(figsize=(6, 6)) fig.patch.set_facecolor('xkcd:mint green') ax = fig.add_subplot(111, ) ax.hist(tripsdf[tripsdf.weekday == True].hour.tolist(), bins=24) ax.set_title('Weekday hour histogram') plt.grid(True) # Weekend wow big difference. fig = plt.figure(figsize=(6, 6)) fig.patch.set_facecolor('xkcd:mint green') ax = fig.add_subplot(111, ) ax.hist(tripsdf[tripsdf.weekday == False].hour.tolist(), bins=24) ax.set_title('Weekend hour histogram') plt.grid(True) # Ok based on the above, going to create a version 2... # time_of_day_v2_peaky # Not sure how to account for the different peaks on weekends # # 0: 6-10, 1: 11-15, 2: 16-20, 3: 21-00, 00-5 Age Somehow I did not include age this time around but I should. tripsdf[['usertype', 'birth year']].iloc[:5] ...
Looking at hyperparameter tuning results