cwood1967

cwood1967

import os
import warnings

import numpy as np
import torch
import tifffile

from cellfinder import train, infer

warnings.filterwarnings('ignore')

cwood1967

cwood1967

import numpy as np
import pandas as pd
from holoviews import opts
import holoviews as hv
from holoviews import streams
import datashader
import xarray as xr
import hvplot.xarray 
hv.extension('bokeh','matplotlib')

cwood1967

import pandas as pd
import autoencoder.hover as hover
from bokeh.plotting import figure, ColumnDataSource
from bokeh.models import HoverTool, ZoomInTool, ZoomOutTool
from bokeh.io import output_notebook, show
from bokeh.palettes import Spectral6, inferno

## this uses the pandas dataframe df that is already full of data
df['agc'] =agc ## this put results from clustering into the dataframe
output_notebook()

cwood1967

## Using tensorflow freeze graph to read a graph saved with
## tf.train.write_graph

incheck=autoencoder-2048x-10000
ingraph=saved_10000.pb
outgraph=frozen_faces_10000.pb
outnodes=decoder/Sigmoid

python -m tensorflow.python.tools.freeze_graph \
	--input_graph=$ingraph \

cwood1967

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

cwood1967

short[] image = new short[size];

        int index = 0;
        for (int i = 0; i < fileBytes.length; i += 5) {

            int s = 2;
            int t0 = (fileBytes[i] & 0x00FF) << s;
            int t1 = (fileBytes[i + 1] & 0x00FF) << s;
            int t2 = (fileBytes[i + 2] & 0x00FF) << s;
            int t3 = (fileBytes[i + 3] & 0x00FF) << s;

cwood1967

  <properties>
    <main-class>org.stowers.microscopy.ij1plugins.Main</main-class>
  </properties>

<build>
      
    <plugins>
        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-jar-plugin</artifactId>

cwood1967

/* fast floating point exp function
 * must initialize table with buildexptable before using
 
Based on 
 A Fast, Compact Approximation of the Exponential Function
 Nicol N. Schraudolph 1999

Adapted to single precision to improve speed and added adjustment table to improve accuracy.
Alrecenk 2014