Mathematical Foundations and Note-Taking

Perfecting Mathematical Notation

After returning to academia following a decade in industry, I discovered the importance of clear mathematical notation. Here’s why it matters:

1. Context switching between work and academic projects requires crystal-clear notation to avoid misinterpretation and time waste.
2. Writing mathematics clearly helps crystallize problem-solving approaches, as pattern recognition often relies heavily on visual processing.
3. Practice writing challenging symbols (like Greek letters) until they become second nature. Even small practice sessions during downtime can make a significant difference.
4. This process often reveals knowledge gaps, creating opportunities for targeted learning and review.

Documentation and Organization

The PhD Notebook System

Maintain a dedicated notebook for your doctoral journey. Use it to:

• Document topics that need refreshing
• Record detailed notes from your research
• Capture valuable insights about study skills
• Note effective presentation techniques
• Track potential sponsor requirements

Task Management Strategy

The Two-Category System

Divide tasks into two distinct categories to maximize productivity:

Brain-Intensive Work:

• Homework assignments
• Literature reviews
• Debugging complex programs
• Algorithm generalization
• Technical paper reading and writing

Administrative/Mechanical Work:

• Note organization
• Plot refinement
• LaTeX formatting
• Basic code maintenance
• Email management
• Workspace organization
• Creating study materials

Essential Tips for Success

Organization and Time Management

• List Everything: Use TODO markers (searchable with ctrl+f) to track pending tasks
• Learn to Decline: Focus on two primary goals: sponsored project completion and PhD progress
• Maintain Your Timecard: Benefits include:
  • Keeping management satisfied
  • Tracking accomplishments
  • Ensuring legal compliance

Academic Success Strategies

• Embrace Memorization: While understanding principles is crucial, having key theorems readily available in memory is invaluable
• Create Reference Materials: Develop comprehensive cheat sheets combining text, equations, and diagrams
• Prioritize Learning: Focus on understanding rather than grades, while meeting program requirements
• Verbalize Your Work: Explain concepts to others, particularly non-experts, to solidify understanding and improve communication skills

Remember: Being able to communicate complex ideas clearly is fundamental to becoming an effective scientist. While work problems should stay at work, discussing academic challenges with others can provide valuable perspective and deepen understanding.

  
#!/bin/bash

## Required PBS Directives --------------------------------------
#PBS -A <account>
#PBS -q <queue>
#PBS -l select=4:ncpus=40:mpiprocs=1:nmlas=2
#PBS -l walltime=24:00:00

# Configure Cluster
export MASTER_PORT=8148
NODES=$(cat $PBS_NODEFILE | sort | uniq)
MASTER_ADDR=$(echo $NODES | cut -d ' ' -f 1)
export WORLD_SIZE=$(wc -l < $PBS_NODEFILE) 

# Debugging flags
export NCCL_P2P_DISABLE=0
export NCCL_DEBUG=WARN

echo "  MASTER_ADDR is: $MASTER_ADDR"
echo "  MASTER_PORT is: $MASTER_PORT"
echo "  WORLD_SIZE is : $WORLD_SIZE"

# Loop through all nodes and start the training.
NODE_RANK=0
for NODE in $NODES; do
    echo "Starting training on $NODE with NODE_RANK $NODE_RANK, MASTER_ADDR $MASTER_ADDR, MASTER_PORT $MASTER_PORT"
    # Ensure no spaces after the backslash ending each line.
    ssh $NODE "bash; \
        export MASTER_ADDR=$MASTER_ADDR; \
        export MASTER_PORT=$MASTER_PORT; \
        export WORLD_SIZE=$WORLD_SIZE; \
        export NODE_RANK=$NODE_RANK; \
        source ~/miniconda3/bin/activate <your env>; \
        cd <your dir> \
        echo \"   On $NODE_RANK with NODE_RANK $NODE_RANK, MASTER_ADDR $MASTER_ADDR, MASTER_PORT $MASTER_PORT, WORLD_SIZE $WORLD_SIZE\"; \
        python your_code.py fit --config configs/your_yaml.yaml --trainer.strategy=ddp --trainer.num_nodes=$WORLD_SIZE; \
    " &
    NODE_RANK=$((NODE_RANK + 1))
done

# Wait for all child processes to complete.
wait

This script fetches the node information from PBS. This information is only available on the master node. Next, it ssh’s into each node in the cluster allocated to your job, sets the appropriate environment variables (these tell Pytorch lightning how to communicate with the master node), and then launches your training script. The master node is automatically set when you set its environment variable, NODE_RANK = 0.

I left in the NCCL debugging flags which are helpful in debugging when the script hangs or nodes can’t communicate to each other. These can be easily commented out.

CTMVW26KR5KNSIBK

I don’t know why I still know this 30 years after learning it.

1 to 30MHz log mag, marker at 22.170MHz.

7 – 7.5 MHz, Marker at 7.155 MHz

20-23MHz,marker at 22.070MHz

50kHz – 50MHz, marker at 37.013MHz

7-30MHz, SWR, marker at 22.180MHz

1-30MHz, SWR, marker at 7.090MHz

I have noticed for year that tensorboard v2.x subscamples scalars when plotting.  I never tried to reproduce the problem but noticed if I had a longish training run, every epoch’s loss was not displayed on tensorboard.  This would produce a strange jaggy effect of the plot which I never liked, especially given my signal processing background.  It was also not obvious why or how it was subsampling.  For all I knew, it wasn’t even subsampling but doing some kind of filtering which resulted in the skipping of scalar values on the plots.

Today, I finally figured out how to fix this issue.  I was able to correct the issue by using a tensorboard command prompt of:

start “tensorboard” “e:\python_3.8\scripts\tensorboard” –samples_per_plugin scalars=9999999 –logdir .

The addition of the –samples_per_plugin scalars=999999 fixes the issue.  Now, all of the points I write out to tensorboard are displayed.

Ref: https://stackoverflow.com/questions/43763858/change-images-slider-step-in-tensorboard

This video is from the GOES Satellite imaging sensor over a 24 hour period.

I captured this using a SawBird LNA, a WiFi dish, an RTL-SDR, and the XRIT decoding software.

Screenshot of the “groundstation terminal.”

Source code to compile the video is below.

import os
import glob

import cv2
import matplotlib.pyplot as plt
import numpy as np
import tqdm

data_dir = r'C:\Users\laptop\Desktop\SDR\XRIT-Suite-Winter-20\images'
files = sorted(glob.glob(os.path.join(data_dir, '*full*fsclr*.png')), key=os.path.getmtime) # full disk
files = files[53:53+48+2]
N = len(files)

# Make mask
img = cv2.imread(os.path.join(data_dir, 'G16-Full Disk-FSCLR-202102101800.png'))
mask = np.mean(img, axis=2) == 0
mask[:162,:] = 0 
mask[5617:,:] = 0
mask = (~mask.astype('bool')).astype('uint8')

images = np.zeros((N, 5961//2, 5424//2, 3), dtype='uint8')
for fidx, f in enumerate(files):
   #img = cv2.cvtColor(cv2.imread(f), cv2.COLOR_BGR2RGB)
   img = cv2.imread(f) 
   if img.shape[0] != 5961:
      continue
   # Post process
   img = cv2.medianBlur(img, 7)
   img = img*mask[:,:,None]

   img = cv2.resize(img, (0,0), fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA)
   images[fidx,:,:,:,] = img

fourcc = cv2.VideoWriter_fourcc(*'MJPG')
out = cv2.VideoWriter('output.avi',fourcc, 30, (5424//2, 5961//2))
for k in tqdm.tqdm(np.linspace(0,N-2,N*15)):
   f = int(np.floor(k))
   offset = k - f
   print(offset)
   frame = ((1-offset)*images[f,:,:,:].astype('float32') + offset*images[f+1,:,:,:].astype('float32')).astype('uint8')
   out.write(frame)

out.release()
print('done')

I am writing a fairly complicated Numba code and kept getting a cryptic error which took me a few hours to figure out.  The error was the following:

numba.core.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
No implementation of function Function(<built-in function array>) found for signature:
>>> array(array(float64, 1d, C))
There are 2 candidate implementations:
- Of which 2 did not match due to:
Overload in function 'array': File: numba/core/typing/npydecl.py: Line 504.
With argument(s): '(array(float64, 1d, C))':
Rejected as the implementation raised a specific error:
... (truncated)

I discovered that the problem was the result of creating new numpy arrays using lists instead of tuples.  So instead of np.array([1,2,3]), I was supposed to do np.array((1,2,3)).  That’s it!

I found the solution by reading the Numba documentation (https://numba.pydata.org/numba-doc/latest/reference/numpysupported.html)

(nested lists are not yet supported by Numba)

As of Tensorflow 2.1, you cannot do the following when running in a windows 10 environment on the GPU (it does work fine if you are running on the CPU though):

r = tf.complex(magnitude, 0.0) * tf.math.exp(tf.complex(0.0,1.0)*tf.complex(phase, 0.0))

This results in a barrage of “CUDA_ERROR_LAUNCH_FAILED: unspecified launch failure” error messages. I filed a bug report here: https://github.com/tensorflow/tensorflow/issues/38443

You can see from the tensorflow code, I am doing a simple operation: converting the magnitude and phase representation of a complex number into real and imaginary representation. I am doing this by utilizing the phase rotation properties of the exponential function (i.e. e^{i phase}).

I did come up with a workaround which I am posting here to prevent someone else spending hours of their time on a similar error, questioning their judgement, and thinking this should trivially work. My workaround converts magnitude and phase to real and imaginary through Euler’s formula.

def mag_phase_2_real_imag(mag, phase):      
    cos_phase = tf.math.cos(phase)
    sin_phase = tf.math.sin(phase)
    r= tf.complex(mag*cos_phase, mag*sin_phase)
    return  r
#    

real_imag = tf.keras.layers.Lambda(lambda x:mag_phase_2_real_imag(x[0], x[1]))([mag, phase])  

We mountain bike quite a bit in Redarca, a little village behind Lerici, Italy. The trails are well maintained and mostly enduro types. I spend most of my time biking the trails around Monte Branzi and the surrounding area (Snake, Guercio, Branzi, Buck).

[momentopress url=https://momento360.com/e/u/1dc752ffe44e4088bac5c69df08b7a54?utm_campaign=embed&utm_source=other&utm_medium=other]

[momentopress url=https://momento360.com/e/u/ece1d6654dd74321be4be096ff559ef0?utm_campaign=embed&utm_source=other&utm_medium=other]

[momentopress url=https://momento360.com/e/u/eec5905447944bcaab74b415f3b48087?utm_campaign=embed&utm_source=other&utm_medium=other]

[momentopress url=https://momento360.com/e/u/a3b58bac643240148e611304e89a8c2c?utm_campaign=embed&utm_source=other&utm_medium=other]

Google Earth view of some of our favorite trails.

Photo courtesy of http://www.lericibike.com/2016/05/16/trail-branzi-2-2km/

Blurry shot of the Peocio fountain (peace fountain).

Figure adapted from Hawkins. Synthetic Aperture Imaging Algorithms:with application to wide bandwidth sonar.Phd Thesis. 1996. https://ir.canterbury.ac.nz/bitstream/handle/10092/1082/thesis_fulltext.pdf?sequence=1&isAllowed=y

Yesterday I blogged about the 17th-century ruins off of a mountain bike trail in Redarca.  I took Ryanne there this evening to see them.  She took some really great pics including some nice ones of the frescoes.  I wanted to share so here they are.

[envira-gallery id=”881″]

There’s a reasonable amount of mountain biking here in Lerici.  It’s not nearly as big as Rothrock, but it still beautiful and worth putting in the effort to see.  We bike in areas behind our flat called Monte Rochetta and Redarca.  It’s a 3-mile uphill ride to get to the trailheads.  There are quite a few gravel roads but most of the true mountain biking trails are enduro style quite so because it’s very hilly around here.  Many of the trails are built for a single direction of travel. Everything is well labeled though with the trail name and its difficulty level (they use the green, blue, black diamond system).  Many of the true MTB trails here are black diamonds.

I picked up a whip from a place called Eurobike in Romita Magra.  It’s a 2015 Specialized Stumpjumper Comp Evo (aluminum).  Slightly different feel from my Salsa Horsethief and El Mariachi back home.  I also rarely ride Specialized bikes.  I like the bike well enough though.  I ride 27.5″ wheels here with tubes which were recommended by the store owner given the terrain.  At home, I have been running 29″ wheels tubeless for as far as I can remember; that was a bit of a change for me.  The guy who sold me the bike was great.  He even dropped it off for me because I couldn’t take it on the bus.  In fact, we bought Ryanne’s bike (the yellow one in the pics below) from there as well.  Again, the guy offered to take the to our flat free of charge.  While we were waiting for the bus from Eurobikes to head home, we ran into the same guy and he stopped and let us hitch a ride home with him!

There’s a lot of history in the hills leading up to Monte Rochetta.  On the ascent, you can see old trails and terraces which are long abandoned.  Off of Trail Branzi (one of my favorite, http://www.lericibike.com/2016/05/16/trail-branzi-2-2km/), there are some ruins.  One prominent structure is a hunting lodge built in the 1600s by the Count at the time.  Around the 19th century, frescoes were pained inside which you can still see today. During WW2, this building was actually the home base of a clandestine print press which distributed anti-fascist literature.  The Germans came upon it in 1944 but someone had informed the occupants before they Germans got there to capture them and all that was left was the printing press and the abandoned building.  No one has occupied it since and it stands has pictured today.  Inside there is a plaque which reads (translated into English), “Here at the Rocchetta in the long clandestine silences, simple and courageous men, defying the Nazi-fascist anger, gave new ideas to the press and affirmed in the history the freedom of thought and the right democracy demanded by the resistance. “ [1].

The mountain biking community here is fairly organized and does a good bit of trail maintenance it seems.  There is somewhat of a gentlemen’s agreement whereby you don’t ride the trails for 2 days if it rains.  This is to help keep the trails in good shape.  They also organize beginner rides through the gravel roads and easy trails to help the youth develop their skills and foster an appreciation for the sport. [2]

Many of the trails here are always designated hiking trails which are marked with red and white horizontal lines.  I made the mistake once assuming the markings meant these trails were maintained.  I took a wrong turn once which let me out off a big drop onto the road.  No turning back to go the way I came.  So, I continued on until I found a red and white marked trail.  It was steep with quite a bit of scree — clearly not bikable.  I figured since the trail is marked it can’t be that bad.  Well, it was.  It was at least 100m of scree and I had to carry my bike the entire way.  The work paid off though because I was rewarded with a beautiful view once I reached the top and discovered a local rock climbing area.

Ok, on to the views!  There are lots of great views along the circuits of trails here.  Vineyards, old camps, panoramas of Lerici, and of course, spectacular views of the Carrara mountains. On trail La Serra, I once ran into a man and his son with a group of what looked like small donkeys.  You never know what you will find on the trails here.

Ciao!

[envira-gallery id=”872″]

References

1. Clandestine Printing House in Redarca. http://www.tramedilunigiana.it/it/risorsa/monumento/stamperia-clandestina-di-redarca. Fetched 22 April 2018.
2. Lerici Bike on Facebook. https://www.facebook.com/lericibike/
3. http://www.lericibike.com/
4. http://www.lericibike.com/2016/05/16/trail-branzi-2-2km/
5. https://www.facebook.com/Eurobikes-di-Piero-Arpesella-474552732565146/

I was doing an experiment that involved Monte-Carlo (MC) dropout. I had to change the rate of dropout and have it dropout during test time.

I was able to turn dropout on during test time by making a new function and passing in the learning_phase tensor.

# This code allows dropout to run during test time.
newLayer = K.function([model.layers[0].input, K.learning_phase()], [model.output])
layer_output = newLayer([x, 1])[0] # Dropout on

To change dropout from train time to test time I did the following:

# This code allows you to change the dropout
# Load model from .json
model.load_weights(filenameToModelWeights) # Load weights
model.layers[-2].rate = 0.04  # layer[-2] is my dropout layer, rate is dropout attribute
model = keras.models.clone(model) # If I do not clone, the new rate is never used. Weights are re-init now.
model.load_weights(filenameToModelWeights) # Load weights
model.predict(x)

https://youtu.be/m6xgQGJoBnk

In 2007,

• The first iPhone was release.
• GPU computing was coming into mainstream, CUDA 1.0 released.
• The Samsung Galaxy phone didn’t exist yet.
• The first year you longer need an invitation to get a gmail account.
• In February 2007, the Nintendo wii outsells the XBOX and Ps4 (https://www.technologyreview.com/s/408183/hack-the-nintendo-wii/)
• The first Amazon Kindle is released (https://en.wikipedia.org/wiki/Amazon_Kindle)
• There is no 4G network for cell phones.
• Deep Learning doesn’t exist.
• There is no imagenet benchmark for object recognition.
• In the Top 500 supercomputer list, entry to the top 50 requires 16 Teraflops. NVIDIA GPUs you purchase at Best Buy can do 10 Teraflops.(ttps://www.top500.org/lists/2007/06/highlights/, http://www.bestbuy.com/site/evga-geforce-gtx-titan-x-12gb-gddr5-pci-express-graphics-card-black/5550016.p?skuId=5550016)
• The NIH launches the Human  Microbiome Project (https://www.nih.gov/news-events/news-releases/nih-launches-human-microbiome-project)
• Wikipedia had 1.5 million English articles.  Today its around 5 million. (https://blog.wikimedia.org/2015/11/01/english-wikipedia-surpasses-five-million-articles/)

Here are my instructions to install Tensorflow (tf) in Windows 7 x64 for Python 3.5.  I have verified these instructions work to get tf working with Keras.  e sure to update your keras.json file to use the tf backend if you already use theano.  You do not need to purchase visual studio to get this to work.

I. Prerequisites

1. Install Cuda 8.0.  Note, you must use the release version of 8.0 and NOT the release candidate.
2. Install python35 to c:\python35 (no need for anaconda)
3. Download cudnn (record path)
4. Download swig (record path)
5. Install visual studio 2015 community edition with C++.  Had to manually install c++ in visual studio 2015 community edition. went into control panel, programs and features, and did a modify
6. Install git for windows

II. Getting the tf repository

1. git clone https://github.com/tensorflow/tensorflow.git
2. cd tensorflow\tensorflow\contrib\cmake
3. mkdir build
4. cd build

III. Begin the tf install

1.  “C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin\amd64\vcvarsall.bat” (make sure you are using the amd64 version)
2. cmake .. -A x64 -DCMAKE_BUILD_TYPE=Release -DSWIG_EXECUTABLE=d:/swigwin-3.0.10/swig.exe -DPYTHON_EXECUTABLE=c:/python35/python.exe -DPYTHON_LIBRARIES=C:/Python35/libspython35.lib -Dtensorflow_ENABLE_GPU=ON -DCUDNN_HOME=D:/ASASINATR/cudnn -Dtensorflow_ENABLE_GPU=ON
3. MSBuild /p:Configuration=Release tf_tutorials_example_trainer.vcxproj
4. Release\tf_tutorials_example_trainer.exe (this should work if everything built correctly)

IV. Make the python wheel

1. pip install travis pip setuptools wheel
2. MSBuild /p:Configuration=Release tf_python_build_pip_package.vcxproj
3. pip install D:\tensorflow\tensorflow\tensorflow\contrib\cmake\build\tf_python\dist\tensorflow-0.11.0rc2_cmake_experimental-py3-none-any.whl

If everything was successful you should be able to modify your keras.json file, load keras in python, and begin using tf.

Thanks to Derek Murray of Google for the help creating this.

I recently upgraded my machine learning toolchain to keras and thought it would be a good time to revisit the ongoing theano + Python 3.4 + Windows 7 saga.  I found a simple set of instructions here which I tweaked a bit to get to work with my setup.  Here are the full instructions:

1. Install python 64bit
2. Install precompiled whl from http://www.lfd.uci.edu/~gohlke/pythonlibs
   1. Numpy+MKL library
   2. scipy
3. Get GCC from http://tdm-gcc.tdragon.net/ (I usually have issues with GCC in Windows, however, this distribution works great out of the box believe it or not)
4. Install theano via pip and http://www.lfd.uci.edu/~gohlke/pythonlibs
5. At this point, when I tried to import theano into python 3.4, I would get linking errors.  I then installed libpython from http://www.lfd.uci.edu/~gohlke/pythonlibs and that resolved the issue.
6. At the python shell, type import theano, it should work
7. Configure your .theanorc.txt and you should be good to go.

I was fortunate enough to have 3 samples of what I will call “gut” bacteria (you fill in the blanks) examined by a new startup, uBiota.  I have to say, I am impressed with their visualization.  Its very interactive and easy to use.  I really like the search function.

A picture is worth a thousand words, but click here and you can test drive it for yourself.

The three samples were taken on:

1. 10-9-2015
2. 10-6-2015 – Also analyzed by Genova Diagnostics.
3. 10-1-2015

Apologies for not taking the samples in time order.  The second sample I also had analyzed by Genova Diagnostics for comparison. I will post results at a later date.

Full disclosure – I was given 3 sampling kits for free from uBiota.

SWB   SWA   Description
 0     1    Stable mode - position hold mode.
 0     2    Return to home mode
 1     1    Flight direction lock
 1     2    Altitude mode, drive at a fixed altitude (may run into hills).  Don't have to use the throttle (left paddle up-down)

Flight stability Calibration Left control pressed down left - red and green lights will blink - fly off ground for 20 seconds - lock and unlock on ground to save settings. Compass Calibration As soon as you power on the drone, turn the tx on and push the right paddle to the bottom right corner. The lights will eventually flicker yellow and green. Pick up the drone by the base keeping it level and spin yourself around 3-5 times. Now hold the drone so that front (opposite side of the battery pack access panel) is facing the floor and spin yourself again holding the drone 3-5 times. Now, place the drone on a level surface and disconnect the power. See this youtube for an example: https://www.youtube.com/watch?v=uz4_8RYXeD0 Switch controller off - drone will land auto land where tx is

Left control pressed down left - lock motors - red light blinks 
Left control pressed down right - lock motors - red light solid

GPS lock -    green light solid
No GPS lock - green light blinking

Bottom lights blinking and drone blinking - drone battery low
Beeping on the controller means the throttle (left paddle) is in the middle.

Aux knobs are for controlling a gimbal.

I took the images2gif.py class (https://bitbucket.org/bench/images2gif.py) and modified it to work with Python 3.  Most of the work involved moving from str type to bytes.

Code is on github (https://github.com/isaacgerg/images2gif).

Record of the plants around my yard because I am a science nerd.

(more…)

This is my first entry promoting something. I dislike when others do it but I am making an exception because this service has significantly helped me. The company is called PicnicHealth and their website is http://www.picnichealth.com.

Since returning from Italy last year, I have been plagued with chronic stomach dysfunction. I have seen several doctors including a specialist at John Hopkins Medical Center. I have had to go through all the pain of collating my notes, labs, imaging studies, etc.  to send to doctors and analyze myself. Its been a pain until I found this tool through an article about PicnicHeath and uBiome collaborating. I follow uBiome semi-regularly as a personal interest.

They have a legitimate DICOM viewer so all my medical imaging shows up great, including the adjustments for brightness, contrast, and movie playback controls.

Anyways, here’s a quick sample of what my health timeline looks like.

My instructions for installing Theano 0.6 with

• Windows 7-64 bit
• Anaconda 2.1.0 (Python 2.7).  This tutorial only works with 2.1.0.  I tested it with 2.2.0 and it did not work.  I have no plans to fix this issue.
• CUDA 7.0

Steps

1. Download Anaconda 2.1.0 from here.
2. Install pip via command line using “pip install https://pypi.python.org/packages/source/T/Theano/Theano-0.6.0.zip#md5=0a2211b250c358809014adb945dd0ba7“
3. Create a .theanorc.txt file in your user area (C:\Users\username\.theanorc.txt) with the specified text listed below.
4. Open Anaconda
5. Import and test/build theano by typeing import theano and then theano.test()
6. Sit back and relax while everything builds.

.theanorc.txt file contents (you must create at %USERDIR%/.theanorc.txt, for me this is c:\users\username\.theanorc.txt)

[global]
openmp=False
device = gpu0
floatX = float32

[blas] ldflags=

Notes

If you get an error about “CVM,” you must delete the cache files that are in C:\Users\MyUsername\AppData\Local\Theano. Once you delete everything, start python again and continue from there.

If you have path issues when trying to import theano, try using the Visual Studio 64-bit command prompt if you have it.  It sets a bunch of paths for you and “just works” for me.  For reference, the path I use is:

PATH=C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\BIN\amd64;C:\Windows\Microsoft.NET\Framework64\v4.0.30319;C:\Windows\Microsoft.NET\Framework64\v3.5;C:\Program Files (x86)\Microsoft Visual
Studio 10.0\VC\VCPackages;C:\Program Files (x86)\Microsoft Visual Studio 10.0\Common7\IDE;C:\Program Files (x86)\Microsoft Visual Studio 10.0\Common7\Tools;C:\Program Files (x86)\HTML Help Workshop;C:
\Program Files (x86)\Microsoft SDKs\Windows\v7.0A\bin\NETFX 4.0 Tools\x64;C:\Program Files (x86)\Microsoft SDKs\Windows\v7.0A\bin\x64;C:\Program Files (x86)\Microsoft SDKs\Windows\v7.0A\bin;C:\Program
 Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0\bin;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v7.0\libnvvp;C:\Python34\Lib\site-packages\PyQt5;C:\Program Files (x86)\NVIDIA Corporation\PhysX\Co
mmon;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v6.5\bin;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v6.5\libnvvp;C:\Program Files (x86)\Intel\iCLS Client\;C:\Program Files\Intel\iCLS C
lient\;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0\;C:\Program Files\Intel\Intel(R) Management Engine Components\DAL;C:\Program Files\Intel\Intel
(R) Management Engine Components\IPT;C:\Program Files (x86)\Intel\Intel(R) Management Engine Components\DAL;C:\Program Files (x86)\Intel\Intel(R) Management Engine Components\IPT;C:\Program Files\MATL
AB\R2014a\bin;C:\Program Files\TortoiseHg\;C:\Program Files (x86)\Microsoft SQL Server\100\Tools\Binn\;C:\Program Files\Microsoft SQL Server\100\Tools\Binn\;C:\Program Files\Microsoft SQL Server\100\D
TS\Binn\;C:\Users\username\AppData\Local\Continuum\Anaconda;C:\Users\username\AppData\Local\Continuum\Anaconda\Scripts

Update June 11, 2015
Added link to Ananconda download

Compute the number of blocks, k, given a window size, w, overlap size, p, and number of points, N.

[latex]k =\lfloor\frac{N-w}{w-p}\rfloor + 1[/latex]

Went out to Ridge Soaring Gliderport for a flight last Friday.  Doris Grove was my awesome pilot. Check it…

Would like to write something about this eventually.  Here are some pics.

This was a last minute trip literally — booked my plane tickets and a week later was in Casablanca.  Would like to write something about budget travel, traveling like you live there, and how you don’t need to have a plan to travel but still be safe.

Sexy title, I know!

Anyways, I’m developing using Visual Studio 10 (not my choice).  I have a project which involves the use of CUDA and Boost. The newest version of CUDA (at the time of this writing) won’t work out of the box with the  vs100 compiler that comes with Visual Studio 10.  CUDA can use nothing newer than the vs90 compiler.  So, I  install visual studio 9 and 10 and select v90 as my compiler for my Visual Studio 10 project.

Yesterday, I needed the Boost C++ libraries and began to install them.

The Boost installation is pretty good.  Its fairly automated which is nice.  You essentially call bootstrap and then bjam; they take care of all the configuring and building of the .libs.  However,  Boost’s scripts see that I have Visual Studio 10 installed and compile everything with the vs100 compiler.  This breaks my project in  Visual   Studio (I get all linking errors related to Boost) as I’m in  Visual Studio 10, but using the v90 compiler because of CUDA.

So, I figured out how to override Boost’s configuration to compile with the vn90 compiler instead of the vc100 compiler.  Here is how I fixed the problem.

 cd to BOOST_ROOT
 boostrap
 "C:\Program Files (x86)\Microsoft Visual Studio 9.0\VC\bin\vcvars64.bat"    // This invokes the vc90 environment variable set.
 bjam --toolset=msvc-9.0 --build-type=complete architecture=x86 address-model=64  // Makes Boost!

If you don’t run the 3rd line (vsvars64.bat), you will see tons of “cl” errors when running  bjam.  Essentially, Boost tries to call the vc90 compiler (cl.exe), but because the environment is hosed, it cant find it and bombs.

The missions trip I have previously taken to (and will take again this year very soon) Estonia was mentioned in State College, PA’s Town & Gown magazine.  The article is here.

Some Linux commands I find useful.

tar -xvjpf file.tar.bz2
bunzip2 file.bz2

tar -xzf file.tgz
find . -name file.txt -print

I use the wordpress plugin Counterize II to track who visits my websites.  I recently noticed the plugin did not report any information for one of my sites.  At first I thought it was a database issue, but upon looking into it, this was not the case.

I tried all sorts of methods to fix it: I deactivated and reactived the plugin, uninstalled and reinstalled the plugin, and messed around with the database tables.  None of these methods worked.

As a last resort, I thought it could be related to the theme I was using since I had made some changes to it.  I double checked all the changes I made and they all seemed benign.  So, I began to look through the Counterize II code to see what function actually gets called to record the site hits and how it gets called.

It turns out, that Counterize II gets called through a wordpress API hook.  Looking further into the problem, I realized that my theme never calls the function that is to invoke the hooks, specifically, the wp_head() function.

After some more searching, I found this website which helped me fix my problem.  For Counterize II to function, you have to have

<?php wp_head(); ?>

somewhere inside your <head> HTML section.  I added that line of code and Counterize II now works!