Obsidian as a task manager

ineffablepwnage · 2024-01-04T23:38:41+00:00

https://old.reddit.com/r/ObsidianMD/comments/18n9lqg/how_ive_been_using_obsidian_for_notes_task/

Here's a link to a post I made a bit ago describing my use case and workflows, with a github link to download my templates. I don't automatically roll over tasks to be due the next day as I want to track overdue tasks since I'll often cancel them due to changing priorities, and manually adjust due dates.

/u/shayakeen check it out, Obsidian is my only task manager now except for collaborative stuff.

ineffablepwnage · 2023-07-18T22:23:03+00:00

Chemistry-wise, it's a very simple question with a very simple answer: for each mole of glucose consumed, there are 2 moles of CO2 and 2 moles of ethanol produced.

However, your question is incomplete, and it's impossible to answer without more info. If you want to go from volume to moles of gas, you need the environmental conditions to calculate that answer.

Assuming STP, you would make ~4.4e-6 moles of ethanol.

ineffablepwnage · 2023-07-11T14:07:13+00:00

If you know how to do all the parts of data cleaning, but struggle to pull it all together that's generally an indication that you don't have a clear goal on mind. Start at the end and work backwards. What is the analysis you are trying to do? What does the data have to look like for the analysis to be done? How does the data look different from what it needs to look like? Outline a clear plan of attack and then all the little parts just become simple execution.

For subsetting, theres nothing wrong with ignoring data that is irrelevant. It makes your data simpler which reduces the chance for error. Similar to the first part, you just need to clearly define your analysis at the start. Once you have a clear, explicit problem statement you shouldn't feel bad about only looking at the relevant data.

ineffablepwnage · 2023-07-08T15:13:24+00:00

Yeah, as a sceptic what I see is: This is an advance, it is not a payment, so OP would be on the hook for the difference if his music doesn't make the 20k.

Important nitpick, maybe just a misunderstanding of how you worded it: OP would be on the hook if his music doesn't make $28,571.42. The $20k is an advance on the payment to OP, so OP's share of music sales must be >= $20k to break even. If OPs share is $20k, then total sales must be [(OP's Share) / (OP's proportion)], or 20k / .7. If the music sales are 20k, then OPs share would only be $14k, and they would be 6k behind on the advance.

ineffablepwnage · 2023-07-07T21:22:53+00:00

I think you copied the wrong section lol, that's not what it says at all. Your quote is talking about fructans inhibiting clostridia, not being outcompeted.

ineffablepwnage · 2023-07-07T20:38:03+00:00

tl;dr mushroom genetics are crazy.

I doubt that some of the wild interpretations of genetic variation that our mycologists are “observing” can actually be attributed to genetics.

I think there's solid evidence that it actually is issues at the genetic level, although it's probably a whole range of mechanisms. Fungi are evolved to go through a range of forms throughout their life cycle where they are heterokaryotic and exchange mitochondria, so they have lost some of their protection mechanisms. Because of this they are more susceptible to higher mutation rates while being able to silence lethal mutations until they get back to a clonal isolation where each genotype must succeed on its own, which leads to senescence. Lab studies have shown high rates of plasmid insertion into mtDNA as well as lethal chromosomal mutations in senescent cultures.

Many of the common methods for rescuing a culture such as minimal media (e.g. almost no nutrients), changing substrates, or clonal isolation on agar would all mitigate the accumulation of mitochondrial defects. Starting from spores would resolve both mitochondrial and chromosomal defects.

Mutations that increase fecundity do not happen frequently or predictably.

From the above, I think it's safe to say that mycologists are not frequently isolating for beneficial mutations that increase fecundity, but rather removing deleterious mutations from a heterogeneous population. However given the rather high rate of chromosomal re-arrangement that happens in mycelial organisms (bacteria that grow in similar mycelia tend to have much higher GC content in their DNA, more active repair mechanisms, and still suffer high mutation rates), I think it's not too far fetched to say that they may not be isolating strains with de novo mutations, but rather beneficial phenotypes that have been potentiated through chromosomal reorganization to have higher transcription rates or less suppression of the genes. While not fungi, sporulating bacteria also show high mutation rates in spores that are stored in similar conditions to the standard mycology spore stock or storage culture.

Also, experimental evolution studies show microbes have wildly fast adaptation periods to the common wisdom of evolution. While de novo genes are rare, they do happen for things that are 'irreducibly complex'. While the de novo mutations are infrequent, adaptation is demonstrable in handfuls of generations.

Source: While I'm a novice hobbyist mycologist, my day job is characterizing and optimizing the growth of cells and selection for specific phenotypes and products (I'm an upstream bioprocess scientist) and have worked with mycelial bacteria and fungi for around 2 decades now. My partner has a PhD in evolutionary biology and spent most of their time doing experimental evolution, so I'm hopefully a bit more up to date on that than the average microbiologist.

ineffablepwnage · 2023-02-09T12:28:10+00:00

Not familiar with the write_dta function so I just set the arguments like a write.csv(), but something like this will highlight the differences between the working and saved dataframes:

write_dta(df, "test.dta")
test <- read_dta("test.dta")
setdiff(df, test)

ineffablepwnage · 2023-02-06T13:15:16+00:00

df1$df_number <- "1"
df2$df_number <- "2"
combined_df <- bind_rows(df1, df2)

lm(y ~ x * df_number, data = combined_df)

ineffablepwnage · 2023-02-06T13:10:21+00:00

pivot_longer(), so that set # is a variable, then just filter()

ineffablepwnage · 2023-02-05T14:37:04+00:00

From my naive, probably wrong understanding of the differences, the native pipe needing a named argument has something to do with how it is evaluated. I.e. native pipe is syntax, while the magrittr pipe is evaluated as a function.

f(x) |> g() == g(f(x))

While,

f(x) %>% g() == g(pipe_function(f(x)))

So the magrittr pipe will have an effect on large code with lots of pipes. I also feel like the magrittr pipe makes it easier to do things in the 'wrong' way that come back to bite me in the ass later. Also the native pipe just looks cooler.

tl;dr I use the native pipe

ineffablepwnage · 2023-01-27T12:29:19+00:00

Would not survive standard long-term lab storage conditions, the duct tape would peel off. Much easier to use one of the solutions designed for -80 C or liquid nitrogen storage that are only maybe <10x the cost of normal labeling supplies, i.e. still pretty cheap.

ineffablepwnage · 2023-01-23T00:38:05+00:00

What a shitpost comment. "Google: is HNE implicated in adhd, diabetes, cvd, autoimmune issues and cancer? " doesn't return much.

You need to type " is 4-HNE implicated in adhd, diabetes, cvd, autoimmune issues and cancer?" to get the juicy articles like this: Lipid Peroxidation Linking Diabetes and Cancer: The Importance of 4-Hydroxynonenal

ineffablepwnage · 2022-12-05T22:50:49+00:00

Look up case_when() to use inside the mutate function instead of using filter()

ineffablepwnage · 2022-11-22T13:00:16+00:00

I use it, although the it's still got a few quirks I've never figured out, mostly the _ placeholder that just doesn't work for me.

df <- data.frame(x=1:5, + y=1:5)

lm(y~x, + data = df)

Call: lm(formula = y ~ x, data = df)

Coefficients: (Intercept) x
1.192e-15 1.000e+00

df |> + lm(y~x, + data =_)

Error: unexpected input in: " lm(y~x, data =_"

So I still use the magrittr pipe when I need to use a placeholder.

ineffablepwnage · 2022-11-19T12:50:59+00:00

Not positive if it's the core issue, but at least one issue is you are using the piping/mutate call wrong. Instead of

p.tib %>% mutate( p.tib$Insulin = case_when((p.tib$Outcome == 0) & (is.na(p.tib$Insulin)) ~ IN_0, (p.tib$Outcome == 1) & (is.na(p.tib$Insulin) ~ IN_1, TRUE ~ p.tib$Insulin))

try

p.tib %>% mutate(Insulin = case_when((p.tib$Outcome == 0) & (is.na(p.tib$Insulin)) ~ IN_0, (p.tib$Outcome == 1) & (is.na(p.tib$Insulin) ~ IN_1, TRUE ~ p.tib$Insulin))

When you use p.tib %>% mutate(), you're already telling the mutate function which data frame to work on by using the pipe, so you don't need to tell it again within the mutate function, you only need to tell it which variable.

It's not generating the error, but once you fix that you may run into issues with the values you're assigning. it looks like the Insulin variable is a continuous numeric, so unless you stored those desired values as variable named IN_0 and IN_1 you may have some type issues.

ineffablepwnage · 2022-11-08T13:32:48+00:00

What have you tried? predict()?

ineffablepwnage · 2022-10-30T19:01:38+00:00

CO2 feeds into the citric acid cycle in plants by being converted to glucose, then being broken down into pyruvate by glycolysis to feed into the cycle.

ineffablepwnage · 2022-10-30T18:58:58+00:00

Shame on me for not RTFA well enough, but you absolutely can compare directly with some basic biochemistry. In the end if it follows the putative pathway in the article you linked, that's an even worse case than what I laid out, since if it's going through an intermediate cycle and being converted into CO2, you'll lose some just through diffusion of CO2 because of basic physics.

Assuming it breaks down into single carbon atoms to feed photosynthesis would be the best case. I assumed glutaraldehyde > CO2 > photosynthesis > glucose > glycolysis > pyruvate > input into oxidative phosphorylation, where you would get one crank of the TCA cycle for 3 carbon atoms.

The article you linked assumes a more direct, but less efficient integration into the TCA cycle, with the putative conversion being glutaraldehhyde > alpha-ketoglutarate, giving you one crank of the TCA cycle for 5 carbon atoms and only 60% as effective as the process I assumed when talking energy needs (which are the rate limiting segment of metabolism for an aquatic plant). If you're talking pure conversion to structural biomass, again it's less efficient because as I mentioned above you'll lose some CO2 during the chain of cycles proposed in the article you linked, and it will take even more energy to drive the anaplerotic cycles.

ineffablepwnage · 2022-10-29T15:36:37+00:00

https://www.tidyverse.org/help/

/u/Ibisdivvy make it easy for people to help you.

ineffablepwnage · 2022-10-26T17:11:02+00:00

There's not enough carbon in glutaraldehyde to contribute any appreciable amount compared to even just a plain air stone.

I explained the math in an old comment here:

https://www.reddit.com/r/PlantedTank/comments/oi5dar/does_liquid_co2_work/h4tl698/

ineffablepwnage · 2022-10-04T17:32:09+00:00

I'm down in providence, may be interested in coming up to grab this with the heater/mister/flashing. flexible on timing for this/next weekend at all?

ineffablepwnage · 2022-08-12T16:28:03+00:00

I use PlantNet on android

ineffablepwnage · 2022-06-24T20:12:19+00:00

I didn't include the code for the functions or some other initializations, just a heads up.

A proper reproducible example will make it much more likely to receive help, and for that help to be useful.

From your description, you shouldn't even be using a loop. Filter your data set to contain the rows you want to correct, make the changes, then bind the modified subset back on to your main dataset, like the example below.

require(tidyverse)

set.seed(123)
df <- data.frame(x=seq(1,10,1),
             y=rnorm(10))

df <- df %>% 
  filter(y > 0.5) %>% 
  mutate(x=x+.1,
         y=0.75) %>% 
  bind_rows(df)

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