Gireesh Bogu gireeshkbogu

## images.sh
s

## mtcars.csv

          
            model
            mpg
            cyl
            disp
            hp
            drat
            wt
            qsec
            vs
            am
            gear
            carb

            
              Mazda RX4
              21
              6
              160
              110
              3.9
              2.62
              16.46
              0
              1
              4
              4

            
              Mazda RX4 Wag
              21
              6
              160
              110
              3.9
              2.875
              17.02
              0
              1
              4
              4

            
              Datsun 710
              22.8
              4
              108
              93
              3.85
              2.32
              18.61
              1
              1
              4
              1

            
              Hornet 4 Drive
              21.4
              6
              258
              110
              3.08
              3.215
              19.44
              1
              0
              3
              1

            
              Hornet Sportabout
              18.7
              8
              360
              175
              3.15
              3.44
              17.02
              0
              0
              3
              2

            
              Valiant
              18.1
              6
              225
              105
              2.76
              3.46
              20.22
              1
              0
              3
              1

            
              Duster 360
              14.3
              8
              360
              245
              3.21
              3.57
              15.84
              0
              0
              3
              4

            
              Merc 240D
              24.4
              4
              146.7
              62
              3.69
              3.19
              20
              1
              0
              4
              2

            
              Merc 230
              22.8
              4
              140.8
              95
              3.92
              3.15
              22.9
              1
              0
              4
              2

## ffmpeg-watermark.md

      
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                gireeshkbogu
                / ffmpeg-watermark.md
            
            
              Created
              April 6, 2022 17:50
                — forked from bennylope/ffmpeg-watermark.md
            
              
                FFmpeg add a watermark to video
              
          
    How to Add a Watermark to Video

FFMPEG filters provide a powerful way to programmatically enhance or alter videos, and itâ€™s fairly simple to add a watermark to a video using the overlay filter. The easiest way to install ffmpeg is to download a pre-built binary for your specific platform. Then you donâ€™t have to worry about including and installing all the right dependencies and codecs you will be using.
Once you have ffmpeg installed, adding a watermark is as easy as passing your existing source through an overlay filter like so:
ffmpeg -i test.mp4 -i watermark.png -filter_complex "overlay=10:10" test1.mp4
Basically, weâ€™re passing in the original video, and an overlay image as inputs, then passing it through the filter, and saving the output as test1.mp4.

  
## simple_visualization_streamlit.py
# With this you cann upload a CSV file and plot a scatterplot.

import pandas as pd
import streamlit as st
import seaborn as sns
import matplotlib.pyplot as plt

st.set_option('deprecation.showPyplotGlobalUse', False)
st.title("Basic plotting..")

## rhrad_online_24hr_alerts_v5.py
# RHR Online Anomaly Detection & Alert Monitoring
######################################################
# Author: Gireesh K. Bogu                            #
# Email: gbogu17@stanford.edu                        #
# Location: Dept.of Genetics, Stanford University    #
# Date: Oct 29 2020                                #
######################################################


## simple command

## ASFODQR_steps.csv
"user","datetime","steps"
"ASFODQR","2024-07-07 00:00:00",0
"ASFODQR","2024-07-07 00:01:00",0
"ASFODQR","2024-07-07 00:02:00",9
"ASFODQR","2024-07-07 00:03:00",0
"ASFODQR","2024-07-07 00:04:00",0
"ASFODQR","2024-07-07 00:05:00",26
"ASFODQR","2024-07-07 00:06:00",0
"ASFODQR","2024-07-07 00:07:00",12
"ASFODQR","2024-07-07 00:08:00",0

## test.txt
	steps
2020-02-01 00:00:00	0
2020-02-01 00:01:00	0
2020-02-01 00:02:00	0
2020-02-01 00:03:00	0
2020-02-01 00:04:00	0
2020-02-01 00:05:00	0
2020-02-01 00:06:00	0
2020-02-01 00:07:00	0
2020-02-01 00:08:00	0

## reproducible.examples.txt
Subject	datetime	Type	Count
User1	4/16/20 15:00	A1	1
User2	3/28/20 13:00	A1	1
User3	4/29/20 15:00	A1	1
User4	5/2/20 9:00	A1	1
User5	2/19/20 18:00	A1	1
User6	4/20/20 16:00	A1	1
User7	5/4/20 20:00	A1	1
User8	4/4/20 16:00	A1	1
User9	4/5/20 8:00	A1	1

## nii_to_png.sh
# source: https://pypi.org/project/med2image/
# https://stackoverflow.com/questions/29640641/how-to-convert-nii-format-file-into-2d-image

# requirements
#pip3 install pydicom
#pip3 install med2image
#pip3 install pudb
#pip3 install pfmisc

#$1 = input.nii

## add_prefix_to_filenames.sh
for filename in *.jpg; do mv "$filename" "prefix_$filename"; done;
model	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21	6	160	110	3.9	2.62	16.46	0	1	4	4
Mazda RX4 Wag	21	6	160	110	3.9	2.875	17.02	0	1	4	4
Datsun 710	22.8	4	108	93	3.85	2.32	18.61	1	1	4	1
Hornet 4 Drive	21.4	6	258	110	3.08	3.215	19.44	1	0	3	1
Hornet Sportabout	18.7	8	360	175	3.15	3.44	17.02	0	0	3	2
Valiant	18.1	6	225	105	2.76	3.46	20.22	1	0	3	1
Duster 360	14.3	8	360	245	3.21	3.57	15.84	0	0	3	4
Merc 240D	24.4	4	146.7	62	3.69	3.19	20	1	0	4	2
Merc 230	22.8	4	140.8	95	3.92	3.15	22.9	1	0	4	2
	# With this you cann upload a CSV file and plot a scatterplot.

	import pandas as pd
	import streamlit as st
	import seaborn as sns
	import matplotlib.pyplot as plt

	st.set_option('deprecation.showPyplotGlobalUse', False)
	st.title("Basic plotting..")
	# RHR Online Anomaly Detection & Alert Monitoring
	######################################################
	# Author: Gireesh K. Bogu #
	# Email: gbogu17@stanford.edu #
	# Location: Dept.of Genetics, Stanford University #
	# Date: Oct 29 2020 #
	######################################################


	## simple command
	"user","datetime","steps"
	"ASFODQR","2024-07-07 00:00:00",0
	"ASFODQR","2024-07-07 00:01:00",0
	"ASFODQR","2024-07-07 00:02:00",9
	"ASFODQR","2024-07-07 00:03:00",0
	"ASFODQR","2024-07-07 00:04:00",0
	"ASFODQR","2024-07-07 00:05:00",26
	"ASFODQR","2024-07-07 00:06:00",0
	"ASFODQR","2024-07-07 00:07:00",12
	"ASFODQR","2024-07-07 00:08:00",0
	steps
	2020-02-01 00:00:00 0
	2020-02-01 00:01:00 0
	2020-02-01 00:02:00 0
	2020-02-01 00:03:00 0
	2020-02-01 00:04:00 0
	2020-02-01 00:05:00 0
	2020-02-01 00:06:00 0
	2020-02-01 00:07:00 0
	2020-02-01 00:08:00 0
	Subject datetime Type Count
	User1 4/16/20 15:00 A1 1
	User2 3/28/20 13:00 A1 1
	User3 4/29/20 15:00 A1 1
	User4 5/2/20 9:00 A1 1
	User5 2/19/20 18:00 A1 1
	User6 4/20/20 16:00 A1 1
	User7 5/4/20 20:00 A1 1
	User8 4/4/20 16:00 A1 1
	User9 4/5/20 8:00 A1 1
	# source: https://pypi.org/project/med2image/
	# https://stackoverflow.com/questions/29640641/how-to-convert-nii-format-file-into-2d-image

	# requirements
	#pip3 install pydicom
	#pip3 install med2image
	#pip3 install pudb
	#pip3 install pfmisc

	#$1 = input.nii