theosanderson

# Example of how to run: tar -xJOf sequences_fasta_2024_04_25.tar.xz sequences.fasta | python translate.py | pv -l | zstd > seqs.fa.zst
import sys
import tarfile
import pandas as pd

def load_virus_to_accession_dict(tar_path):
    """ Load the virus to accession ID dictionary from a tar.xz file containing a .tsv. """
    with tarfile.open(tar_path, 'r:xz') as tar:
        # Find the tsv file within the archive, assuming there's only one .tsv
        tsv_file = [member for member in tar.getmembers() if member.name.endswith('.tsv')][0]

theosanderson

LOCUS       MN908947               29903 bp ss-RNA     linear   VRL 18-MAR-2020
DEFINITION  Severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1,
            complete genome.
ACCESSION   MN908947
VERSION     MN908947.3
KEYWORDS    .
SOURCE      Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
  ORGANISM  Severe acute respiratory syndrome coronavirus 2
            Viruses; Riboviria; Orthornavirae; Pisuviricota; Pisoniviricetes;
            Nidovirales; Cornidovirineae; Coronaviridae; Orthocoronavirinae;

theosanderson

theosanderson

cat ~/Desktop/hCoV-19_msa_1125.tar.xz | xz -dT10 |  tar -xJO msa_1125/msa_1125.fasta  | pv -c | zstd -c  > msa.fa.zst

cat ~/Desktop/metadata_tsv_2023_12_18.tar.xz | xz -dT10 |  tar -xJO metadata.tsv  | pv -c | zstd -c  > metadata.tsv.zst

theosanderson

```{r}

mutations_in_highly_mutated_seq = "A543G, G1068A, G1186A, G1264A, T1370C, G1743A, A2497G, C2583T, T2824C, G3287A, C3393T, A3430G, C3604T, G3794A, A4179G, G4300A, G4352A, G4444A, G4474A, A4501G, G4975A, T5117C, G5230A, G5720A, G5861A, C6031T, G6123A, C6198T, G6362A, A6565G, T6661C, G6759A, A6833G, C7051T, C7086T, T7114C, A7558G, C7749T, A7795G, G7934A, C8169T, T8779C, T8875C, T9148C, G9209A, A9409G, C9438T, C9521T, G9575A, A9984G, C10015T, G11198A, C11199T, G11330A, G11605A, T11701C, G11837A, G11944A, A12030G, A12271G, G12442A, C12540T, C12633T, T12703C, C13818T, C14216T, G14622A, C15026T, C15656T, C15783T, C16767T, G16861A, C17481T, T17505C, A17993G, C18201T, C18888T, A19574G, G20062A, T20502C, C21297T, C21588T, G21668A, A21717G, T21752C, A21996G, A22101G, T22189C, T22254C, G22770A, C22783T, A22893G, A23004G, C23013T, G23048A, T23425C, A23734G, T23948C, A24003G, A24062G, C24961T, G24977A, C25066T, G25537A, T25779C, C25872T, C26176T, C26195T, C26335T, G26428A, C26645T, T26819G, G27014A, A27146G, A27386

theosanderson

theosanderson

theosanderson

from fastapi import FastAPI, WebSocket
import openai
import time
import json
import asyncio



import os
#get the api key from the environment variable

theosanderson

You are LabGPT. You are in charge of conducting scientific experiments to achieve a stated goal. You have access to an OpenTrons OT2 robot and a lab helper

You have access to the following commands:

Request: enter a request to a user. you can use this for example to get them to set some labware up inside the robot in desired positions.

ExecuteRobotCommands: runs a Python script written in the OT2 API. This will be executed immediately using the Jupyter-notebook style implementation on the robot. State is maintained between calls to this.

The first time, set up like this:
```

theosanderson

```{r}
library(tidyverse)
library(gganimate)
library(tweenr)

# Read in the CSV file
df <- read_csv("~/Downloads/ambulance.csv", col_names = c("date","minutes"))

df$difftime =  as.difftime(df$minutes, units = 'mins')
p<-ggplot(df,aes(x=date,y=difftime))+geom_line(color="#142bdb") +coord_cartesian(ylim=c(0,NA)) + geom_hline(yintercept=18,linetype=2,color="#666666") +theme_bw() +scale_y_time(labels = function(l) strftime(l, '%M:%S')) +labs(x="Date",y="Response time\n(hours:minutes)",