Mark Wronkiewicz wronk

## epochsToNpy.py
"""
epochsToStc

@author:wronk

Convert epochs to stc and save as array for use in Blender.
"""

import mne
import numpy as np

## vertexColors_v1.py
"""
vertexColors_v1.py

@author: wronk

Script meant to be run within blender to load source estimate data and create a series of images
(one for each time point) that can be converted into a movie. This requires that the brain surface
first be loaded into blender.
"""
# Blender specific libraries:

## genPythonInv.py
#Generate an inverse solution via python

import mne
import os

fwdName = "fwd.fif"
rawName = "raw.fif"
covName = "noiseCov.fif"

fSaveInv = os.path.join(os.getcwd(), "invPython.fif")

## postUpdate_TODO_missingEEGReference.html
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<!-- saved from url=(0021)file:///tmp/tmpnc7vEt -->
<html><head><meta http-equiv="Content-Type" content="text/html; charset=ISO-8859-1">

    <title></title>
    <style type="text/css">
        table.diff {font-family:Courier; border:medium;}
        .diff_header {background-color:#e0e0e0}
        td.diff_header {text-align:right}
        .diff_next {background-color:#c0c0c0}

## Tutorial_EC2_with_Python.ipynb

      
              1 file
            
          
              0 forks
            
          
              1 comment
            
          
              0 stars
            
          
                wronk
                / Tutorial_EC2_with_Python.ipynb
            
            
              Last active
              July 17, 2017 19:26
            
              
                A basic tutorial on getting an Amazon EC2 instance running with Python for cloud computing
              
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## TF_servingPost_export_estimator.py
# Define a place to save your compiled model
export_dir = '/path/to/my_exported_models/001'

# Define a path to your trained keras model and load it in as a `tf.keras.models.Model`
# If you just trained your model, you may already have it in memory and can skip the below 2 lines
model_save_fpath = '/path/to/my_model.h5'
keras_model = tf.keras.models.load_model(model_save_fpath)

# Create an Estimator object
estimator_save_dir = '/path/to/save/estimator'

## TF_servingPost_docker_image.sh
######################################
# Pseudocode for creating Docker image

# Get the Docker TF Serving image we'll use as a foundation to build our custom image
docker pull tensorflow/serving

# Start up this TF Docker image as a container named `serving_base`
docker run -d --name serving_base tensorflow/serving

# Copy the Estimator from our local folder to the Docker container

## TF_servingPost_inference_payload.py
import json
import base64
import requests

# Modify the name of your model (`hv_grid` here) to match what you used in Section 2
server_endpoint = 'http://localhost:8501/v1/models/hv_grid:predict'
img_fpaths = ['path/to/my_image_1.png', 'path/to/my_image_2.png']

# Load and Base64 encode images
data_samples = []

## TF_servingPost_payload_format.json
{
  "instances": [
    {
      "image_bytes": {"b64": "iVBO...Oxs6"}
    },
    {
      "image_bytes": {"b64": "0KGg...Pyg8"}
    },
    {
      "image_bytes": {"b64": "AABr...EKA0"}

## TF_servingPost_send_inference_payload.py
# Send prediction request
r = requests.post(server_endpoint, data=payload)
print(json.loads(r.content)['predictions'])
	"""
	epochsToStc

	@author:wronk

	Convert epochs to stc and save as array for use in Blender.
	"""

	import mne
	import numpy as np
	"""
	vertexColors_v1.py

	@author: wronk

	Script meant to be run within blender to load source estimate data and create a series of images
	(one for each time point) that can be converted into a movie. This requires that the brain surface
	first be loaded into blender.
	"""
	# Blender specific libraries:
	#Generate an inverse solution via python

	import mne
	import os

	fwdName = "fwd.fif"
	rawName = "raw.fif"
	covName = "noiseCov.fif"

	fSaveInv = os.path.join(os.getcwd(), "invPython.fif")
	<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
	<!-- saved from url=(0021)file:///tmp/tmpnc7vEt -->
	<html><head><meta http-equiv="Content-Type" content="text/html; charset=ISO-8859-1">

	<title></title>
	<style type="text/css">
	table.diff {font-family:Courier; border:medium;}
	.diff_header {background-color:#e0e0e0}
	td.diff_header {text-align:right}
	.diff_next {background-color:#c0c0c0}
	# Define a place to save your compiled model
	export_dir = '/path/to/my_exported_models/001'

	# Define a path to your trained keras model and load it in as a `tf.keras.models.Model`
	# If you just trained your model, you may already have it in memory and can skip the below 2 lines
	model_save_fpath = '/path/to/my_model.h5'
	keras_model = tf.keras.models.load_model(model_save_fpath)

	# Create an Estimator object
	estimator_save_dir = '/path/to/save/estimator'
	######################################
	# Pseudocode for creating Docker image

	# Get the Docker TF Serving image we'll use as a foundation to build our custom image
	docker pull tensorflow/serving

	# Start up this TF Docker image as a container named `serving_base`
	docker run -d --name serving_base tensorflow/serving

	# Copy the Estimator from our local folder to the Docker container
	import json
	import base64
	import requests

	# Modify the name of your model (`hv_grid` here) to match what you used in Section 2
	server_endpoint = 'http://localhost:8501/v1/models/hv_grid:predict'
	img_fpaths = ['path/to/my_image_1.png', 'path/to/my_image_2.png']

	# Load and Base64 encode images
	data_samples = []
	{
	"instances": [
	{
	"image_bytes": {"b64": "iVBO...Oxs6"}
	},
	{
	"image_bytes": {"b64": "0KGg...Pyg8"}
	},
	{
	"image_bytes": {"b64": "AABr...EKA0"}
	# Send prediction request
	r = requests.post(server_endpoint, data=payload)
	print(json.loads(r.content)['predictions'])