André F. Rendeiro afrendeiro

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                afrendeiro
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              April 3, 2024 17:02
            
          
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## transcribe.py
#! python3.7

import argparse
import os
import numpy as np
import speech_recognition as sr
import whisper
import torch

from datetime import datetime, timedelta

## rendeiro_Nature_2021_covid19_paper.geomx_diff_expression.py
"""
Re-analyze GeoMx data from Rendeiro et al. 2021 doi:s41586-021-03475-6

Requirements (Python 3.10+):
pip install \
    "anndata" \
    "matplotlib>=3.8.3" \
    "numpy>=1.26.4" \
    "pandas>=2.1.0" \
    "scanpy" \

## gdrive_download.py
"""
Download file from Google Drive URL
"""
import os
import time

import click
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.firefox.options import Options as FirefoxOptions

## oils.csv

          
            oil
            saturated
            polyunsaturated (omega 3 & 6)
            monounsaturated
            trans fat
            other fat
            omega 3
            omega 6
            yield, litres per hectare
            smoke point
            flavour
            cooking conditions

            
              almond
              0.08
              0.17
              0.7
              0.0
              0.05
              0.0
              0.17
              1380
              230
              2
              >230 °C (Deep-frying)

            
              beef tallow
              0.5
              0.04
              0.42
              0.0
              0.04
              0.01
              0.03
              -
              220
              1
              200-229 °C (Stir-frying)

            
              flaxseed
              0.09
              0.66
              0.2
              0.0
              0.05
              0.59
              0.13
              506
              107
              2
              <150 °C (Dressings)

            
              apricot kernel
              0.06
              0.29
              0.6
              0.0
              0.05
              0.0
              0.29
              680
              257
              0
              >230 °C (Deep-frying)

            
              argan
              0.18
              0.37
              0.43
              0.0
              0.02
              0.02
              0.35
              0.06
              218
              2
              200-229 °C (Stir-frying)

            
              avocado
              0.12
              0.14
              0.71
              0.0
              0.03
              0.01
              0.13
              2638
              269
              1
              >230 °C (Deep-frying)

            
              brazil nut
              0.25
              0.36
              0.39
              0.0
              0.0
              0.0
              0.36
              2392
              195
              2
              150-199 °C (Light saute)

            
              butter
              0.51
              0.03
              0.21
              0.033
              0.25
              0.0
              0.03
              -
              167
              2
              150-199 °C (Light saute)

            
              butter, clarified (ghee)
              0.65
              0.03
              0.32
              0.0
              0.0
              0.0
              0.03
              -
              235
              2
              >230 °C (Deep-frying)

## test_nuclei_dataloaders.py
"""
Use dataloaders with nuclei coordinates for training.
"""

from functools import partial

import requests
import h5py
from tqdm import tqdm
import numpy as np

## rasterize_pdf.sh
#!/bin/bash

# Terminate on first error
set -e

# Check if input file is provided
if [ -z "$1" ]; then
  echo "Usage: $0 <input_file.pdf>"
  exit 1
fi

## test_gpus_accelerate.py
#!/usr/bin/env python

"""
A simple example of how to use the Accelerator API
to train a vision model on a dummy dataset.

Accelerator enables training on a single or multiple GPUs.

Run once `accelerate config` to set up your configuration file.

## quantization_experiments.py
"""
Run the experiments for the quantization speedup and plot results.
"""

import subprocess

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

## fix_indica_tiff.py
"""
Convert a TIFF file generated by IndicaLabs (HALO) to a usable TIFF file.
"""

import typing as tp
from pathlib import Path
from xml.etree import ElementTree

import tifffile
from tifffile.tiffcomment import tiffcomment
	#! python3.7

	import argparse
	import os
	import numpy as np
	import speech_recognition as sr
	import whisper
	import torch

	from datetime import datetime, timedelta
	"""
	Re-analyze GeoMx data from Rendeiro et al. 2021 doi:s41586-021-03475-6

	Requirements (Python 3.10+):
	pip install \
	"anndata" \
	"matplotlib>=3.8.3" \
	"numpy>=1.26.4" \
	"pandas>=2.1.0" \
	"scanpy" \
	"""
	Download file from Google Drive URL
	"""
	import os
	import time

	import click
	from selenium import webdriver
	from selenium.webdriver.common.by import By
	from selenium.webdriver.firefox.options import Options as FirefoxOptions
oil	saturated	polyunsaturated (omega 3 & 6)	monounsaturated	trans fat	other fat	omega 3	omega 6	yield, litres per hectare	smoke point	flavour	cooking conditions
almond	0.08	0.17	0.7	0.0	0.05	0.0	0.17	1380	230	2	>230 °C (Deep-frying)
beef tallow	0.5	0.04	0.42	0.0	0.04	0.01	0.03	-	220	1	200-229 °C (Stir-frying)
flaxseed	0.09	0.66	0.2	0.0	0.05	0.59	0.13	506	107	2	<150 °C (Dressings)
apricot kernel	0.06	0.29	0.6	0.0	0.05	0.0	0.29	680	257	0	>230 °C (Deep-frying)
argan	0.18	0.37	0.43	0.0	0.02	0.02	0.35	0.06	218	2	200-229 °C (Stir-frying)
avocado	0.12	0.14	0.71	0.0	0.03	0.01	0.13	2638	269	1	>230 °C (Deep-frying)
brazil nut	0.25	0.36	0.39	0.0	0.0	0.0	0.36	2392	195	2	150-199 °C (Light saute)
butter	0.51	0.03	0.21	0.033	0.25	0.0	0.03	-	167	2	150-199 °C (Light saute)
butter, clarified (ghee)	0.65	0.03	0.32	0.0	0.0	0.0	0.03	-	235	2	>230 °C (Deep-frying)
	"""
	Use dataloaders with nuclei coordinates for training.
	"""

	from functools import partial

	import requests
	import h5py
	from tqdm import tqdm
	import numpy as np
	#!/bin/bash

	# Terminate on first error
	set -e

	# Check if input file is provided
	if [ -z "$1" ]; then
	echo "Usage: $0 <input_file.pdf>"
	exit 1
	fi
	#!/usr/bin/env python

	"""
	A simple example of how to use the Accelerator API
	to train a vision model on a dummy dataset.

	Accelerator enables training on a single or multiple GPUs.

	Run once `accelerate config` to set up your configuration file.
	"""
	Run the experiments for the quantization speedup and plot results.
	"""

	import subprocess

	import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt
	import seaborn as sns
	"""
	Convert a TIFF file generated by IndicaLabs (HALO) to a usable TIFF file.
	"""

	import typing as tp
	from pathlib import Path
	from xml.etree import ElementTree

	import tifffile
	from tifffile.tiffcomment import tiffcomment