Matt usmcamp0811

## podman-shell.nix
{ pkgs ? import <nixpkgs> {} }:

let

  # To use this shell.nix on NixOS your user needs to be configured as such:
  # users.extraUsers.adisbladis = {
  #   subUidRanges = [{ startUid = 100000; count = 65536; }];
  #   subGidRanges = [{ startGid = 100000; count = 65536; }];
  # };

## dell-xps-15-9570.nix
###############################
## Dell XPS 15 9570 (0x087C) ##
###############################

{ config, lib, pkgs, ... }:

{
  boot.initrd.availableKernelModules = [ "xhci_pci" "ahci" "nvme" "usb_storage" "sd_mod" "rtsx_pci_sdmmc" ];
  boot.kernelModules = [ "kvm-intel" "i915" ];
  boot.kernelParams = [

## arch-base-install.sh
#!/bin/sh

echo "Installing Base Packages from the Arch main repositories"
sudo pacman -S $(curl https://raw.githubusercontent.com/usmcamp0811/dotfiles/wopr/pkglist-base | cut -d ' ')

echo "Installing Base Packages from AUR"
sudo pacman -S $(curl https://raw.githubusercontent.com/usmcamp0811/dotfiles/wopr/pkglist-aur-base | cut -d ' ')

## Benchy.gcode
;FLAVOR:Marlin
;TIME:7063
;Filament used: 4.92822m
;Layer height: 0.2
;MINX:108.627
;MINY:103.906
;MINZ:0.2
;MAXX:169.749
;MAXY:135.252
;MAXZ:48

## gmm_mean_visualization.py
import holoviews as hv
from sklearn.preprocessing import MinMaxScaler
from holoviews import opts
hv.extension('bokeh')
test = DiscretizerGMM(data=X["X"].values, merge_right_of_elbow=False, n_components=20, name="X")
gmm_df = test.gmm_df(sort="weights")
scale = MinMaxScaler()
gmm_df["weights"] = scale.fit_transform(np.reshape(gmm_df["weights"].values, (-1,1)))
def gmm_mean_plot(gmm_df, n_std=1):
    mean_lines = []

## input.ipynb

      
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                usmcamp0811
                / input.ipynb
            
            
              Last active
              February 22, 2019 00:56
            
              
                An attempt at getting papermill to work with Julia Notebooks. 
              
          
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## arma3.log
System Information:
OS Version:
	Linux Distribution:	Manjaro Linux
	Kernel Version:		4.19.14-1-MANJARO #1 SMP PREEMPT Wed Jan 9 17:17:37 UTC 2019

Hardware Info:
	Machine Type		Gigabyte Technology Co., Ltd. Z170X-Gaming 7 Default string
	CPU Type		GenuineIntel x86_64
	CPU Info		Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz
	CPU Speed		4200 MHz

## MorletWavelets.lgt.py
# ---
# jupyter:
#   jupytext:
#     text_representation:
#       extension: .lgt.py
#       format_name: light
#       format_version: '1.3'
#       jupytext_version: 0.8.4
#   kernelspec:
#     display_name: Python 3.6 (TF)

## ctable_append.py
def ctable_append(cts):
    """
    A function to append multiple ctables and clean up the disk entries along the 0 axis
    similar to pd.concat([df1, df2], axis=0)

    :param cts: a string containing the root directory path or a list of ctables
    :return: ctable
    """
    import shutil

## load_dfs_in_dask.py
import os
import feather
from tqdm import tqdm
import dask
import datetime
from dask import delayed
from dask import visualize
import pandas as pd
import dask.dataframe as dd
	{ pkgs ? import <nixpkgs> {} }:

	let

	# To use this shell.nix on NixOS your user needs to be configured as such:
	# users.extraUsers.adisbladis = {
	# subUidRanges = [{ startUid = 100000; count = 65536; }];
	# subGidRanges = [{ startGid = 100000; count = 65536; }];
	# };
	###############################
	## Dell XPS 15 9570 (0x087C) ##
	###############################

	{ config, lib, pkgs, ... }:

	{
	boot.initrd.availableKernelModules = [ "xhci_pci" "ahci" "nvme" "usb_storage" "sd_mod" "rtsx_pci_sdmmc" ];
	boot.kernelModules = [ "kvm-intel" "i915" ];
	boot.kernelParams = [
	#!/bin/sh

	echo "Installing Base Packages from the Arch main repositories"
	sudo pacman -S $(curl https://raw.githubusercontent.com/usmcamp0811/dotfiles/wopr/pkglist-base \| cut -d ' ')

	echo "Installing Base Packages from AUR"
	sudo pacman -S $(curl https://raw.githubusercontent.com/usmcamp0811/dotfiles/wopr/pkglist-aur-base \| cut -d ' ')
	;FLAVOR:Marlin
	;TIME:7063
	;Filament used: 4.92822m
	;Layer height: 0.2
	;MINX:108.627
	;MINY:103.906
	;MINZ:0.2
	;MAXX:169.749
	;MAXY:135.252
	;MAXZ:48
	import holoviews as hv
	from sklearn.preprocessing import MinMaxScaler
	from holoviews import opts
	hv.extension('bokeh')
	test = DiscretizerGMM(data=X["X"].values, merge_right_of_elbow=False, n_components=20, name="X")
	gmm_df = test.gmm_df(sort="weights")
	scale = MinMaxScaler()
	gmm_df["weights"] = scale.fit_transform(np.reshape(gmm_df["weights"].values, (-1,1)))
	def gmm_mean_plot(gmm_df, n_std=1):
	mean_lines = []
	System Information:
	OS Version:
	Linux Distribution: Manjaro Linux
	Kernel Version: 4.19.14-1-MANJARO #1 SMP PREEMPT Wed Jan 9 17:17:37 UTC 2019

	Hardware Info:
	Machine Type Gigabyte Technology Co., Ltd. Z170X-Gaming 7 Default string
	CPU Type GenuineIntel x86_64
	CPU Info Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz
	CPU Speed 4200 MHz
	# ---
	# jupyter:
	# jupytext:
	# text_representation:
	# extension: .lgt.py
	# format_name: light
	# format_version: '1.3'
	# jupytext_version: 0.8.4
	# kernelspec:
	# display_name: Python 3.6 (TF)
	def ctable_append(cts):
	"""
	A function to append multiple ctables and clean up the disk entries along the 0 axis
	similar to pd.concat([df1, df2], axis=0)

	:param cts: a string containing the root directory path or a list of ctables
	:return: ctable
	"""
	import shutil
	import os
	import feather
	from tqdm import tqdm
	import dask
	import datetime
	from dask import delayed
	from dask import visualize
	import pandas as pd
	import dask.dataframe as dd