ror, scala, jetty, erlang, thrift, mongrel, comet server, my-sql, memchached, varnish, kestrel(mq), starling, gizzard, cassandra, hadoop, vertica, munin, nagios, awstats
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from scipy.stats.kde import gaussian_kde | |
import pymc | |
from math import log | |
from matplotlib import pyplot | |
def KernelSmoothing(name, dataset, bw_method=None, lower=float('-inf'), upper=float('inf'), observed=False, value=None): | |
'''Create a pymc node whose distribution comes from a kernel smoothing density estimate.''' | |
density = gaussian_kde(dataset, bw_method) | |
lower_tail = 0 | |
upper_tail = 0 |
This can reduce files to ~15% of their size (2.3M to 345K, in one case) with no obvious degradation of quality.
ghostscript -sDEVICE=pdfwrite -dCompatibilityLevel=1.4 -dPDFSETTINGS=/printer -dNOPAUSE -dQUIET -dBATCH -sOutputFile=output.pdf input.pdf
Other options for PDFSETTINGS:
- /screen selects low-resolution output similar to the Acrobat Distiller "Screen Optimized" setting.
- /ebook selects medium-resolution output similar to the Acrobat Distiller "eBook" setting.
- /printer selects output similar to the Acrobat Distiller "Print Optimized" setting.
- /prepress selects output similar to Acrobat Distiller "Prepress Optimized" setting.
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package main | |
import ( | |
"io" | |
"log" | |
"net" | |
"time" | |
) | |
func reader(r io.Reader) { |
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#!/bin/bash | |
name=$RANDOM | |
url='http://localhost:9093/api/v1/alerts' | |
echo "firing up alert $name" | |
# change url o | |
curl -XPOST $url -d "[{ | |
\"status\": \"firing\", |