alicanb

"""
Python is a dynamic language, and it is relatively easy to dynamically create
and modify things such as classes and objects. Functions, however, are quite
challenging to create dynamically.
One area where we might want to do this is in an RPC library, where a function
defined on a server needs to be available remotely on a client.
The naive solution is to simply pass arguments to a generic function that
accepts `*args` and `**kwargs`. A lot of information is lost with this approach,
however, in particular the number of arguments taken. Used in an RPC
implementation, this also delays any error feedback until after the arguments

alicanb

alicanb

alicanb

#! /bin/bash
# Usage:
# ./git-move.sh path/to/file/or/dir path/to/destination/repo
echo "creating patch for path ${1}"
git log --name-only --pretty="format:" --follow  "${1}" \
 | sort -u | \
 xargs git log --pretty=email --patch-with-stat --reverse --full-index --binary -m --first-parent -- > "${2}/_patch_" \
&& echo "moving to destination repo at ${2}" \
&& cd "${2}" \
&& echo "applying patch" \

alicanb

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
	<key>Ansi 0 Color</key>
	<dict>
		<key>Alpha Component</key>
		<real>1</real>
		<key>Blue Component</key>
		<real>0.0</real>

alicanb

alicanb

#target photoshop;
main();
function main(){
var ref = new ActionReference();
ref.putProperty(charIDToTypeID('Prpr'),stringIDToTypeID("countClass"));
ref.putEnumerated( charIDToTypeID("Dcmn"), charIDToTypeID("Ordn"), charIDToTypeID("Trgt") ); 
var Count = executeActionGet(ref).getList(stringIDToTypeID("countClass")).count
for(var z =0; z < Count; z++){
var X = executeActionGet(ref).getList(stringIDToTypeID("countClass")).getObjectValue(z).getUnitDoubleValue(stringIDToTypeID( "x" ));
var Y = executeActionGet(ref).getList(stringIDToTypeID("countClass")).getObjectValue(z).getUnitDoubleValue(stringIDToTypeID( "y" ));

alicanb

from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D, Dropout
from keras import backend as K
from keras.optimizers import RMSprop

train_datagen = ImageDataGenerator(
        rotation_range=180,
        vertical_flip=True,