Choosing a map for an adventure is easy right? The standard map for hiking for many years has been the Ordnance Survey 1:50000 set of waterproof laminated maps which retail for about €25. More recently, though the company has apparently been inexistance for over 30 years, EastWest mapping have produced €9.99 maps of specific areas of interest at a scale of 1:25000 and are water resistant. I am aware that the OS issue some 1:25000 maps but I don’t have one to compare with so here’s my thoughts on what map you should get out of the two I have.
Continue reading Choosing a map for your adventures
The simple things in Astrophotography
How to take good astrophotography photos:
- Go to Kerry International Dark Sky Reserve
- Wander outside at night and take a photo with your camera.
Slieve Carr
Ever since I first got interested in Summits On the Air, one mountain in the west of Ireland has been calling me. In the Nephin range in Co. Mayo lies Slieve Carr. At 721m it is not even in the top 40 highest mountains in Ireland, but as it is often referred to as ‘the most remote mountain in Ireland’ it had remained un activated.
Continue reading Slieve CarrPassing REMOTE_USER through mod_WSGI
I need to change the way we authenticate a django application.
Currently we authenticate by an apache module which sets the REMOTE_USER header and passes it to the python app in the environment of mod_wsgi.
The new approach will be to use an app gateway. The gateway works like a reverse proxy and intercept requests to the application. The app gateway authenticates the user and if succesful forwards the request to our application with the REMOTE_USER header set.
The problem with this approach is that mod_wsgi will drop that header.
Continue reading Passing REMOTE_USER through mod_WSGIMars imaging notes
Some notes on how I’ve been processing recent mars images.
OMNIRIG AND the ICOM 7300 FIL2 problem
I came across an odd problem getting my shiny new IC-7300 working for digital modes using Omnirig.
I set the filter to FIL1, the default widest filter, and the software (JTDX in this case) would transmit correctly. But after the transmission period when it went back to receive it would activate the narrower FIL2.
Here’s what’s going on…
Omnirig sends this instruction to the ICOM to go back into receive:
[pmDIG_U]
; These lines select USB-D for USB digital mode
Command=FEFE94E0.2600.01.01.FD
ReplyLength=15
Validate=FEFE94E026000101FD.FEFEE094FBFD
And according to the ICOM manual that should set the filter back to the “default filter”.
Trouble is nowhere in the manual can I see how to actually set the default filter for the mode.
So for now the best solution is to have Omnirig explicitly set us back to filter 1. I could just change FIL2 to be wider but then the filters get more confusing. Having Omnirig explictly set a filter when all I want the radio to do is go back revieve isnt ideal, but it works. To do this you’ll need to change this section in the IC-7300-DATA.ini Omnirig file:
[pmDIG_U]
; These lines select USB-D for USB digital mode
Command=FEFE94E0.2600.01.01.01.FD
ReplyLength=16
Validate=FEFE94E02600010101FD.FEFEE094FBFD
This is identical to the original except we explicitly set the filter as per the manual and update the replylength and validate string accordingly.
Installing Jupyter Notebooks on Solaris
pip installing jupyterlab may give you the following error (somewhere in the error output anyway!):
|
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4 | #include "zmq.h" | ^~~~~~~ compilation terminated. error: command 'gcc' failed with exit status 1 Failed with default libzmq, trying again with /usr/local {'libraries': ['zmq'], 'include_dirs': ['/usr/local/include'], 'library_dirs': ['/usr/local/lib/amd64', '/usr/local/lib'], 'runtime_library_dirs': ['/usr/local/lib/amd64', '/usr/local/lib'], 'extra_link_args': ['-m64']} |
The simplest solution I’ve found to this is just to install libzmq. We can pull the latest code from github and install it. I usually install things in /opt but as the pip process will look for libzmq in /usr/local, we’ll install it in there this time.
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pkg install git libtool git clone https://github.com/zeromq/libzmq.git /export/libzmq cd /export/libzmq ./autogen.sh ./configure --prefix=/usr/local MAKE=gmake gmake gmake install pip install jupyterlab |
If you do put it somewhere other than /usr/local you might need to let pkg-config know. E.g.:
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1 |
export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/opt/libzmq/lib/pkgconfig/ |
Tested on a SPARC-M7 T7-1 kernel zone running 11.4.24.0.1.75.0
Install scipy on Solaris
When trying to install scipy via pip on Solaris you may encounter this error:
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1 |
numpy.distutils.system_info.NotFoundError: No lapack/blas resources found. |
There’s a simple way to resolve this but it’s not immediately obvious. BLAS (Basic Linear Algebra Subprograms) and LAPACK (Linear Algebra Something Something…?) are actually provided by the Solaris math and perf libraries, so installing those and setting a couple of environment variables will get scipy and numpy up and running for us on Solaris.
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pkg install sunperf system/library/math virtualenv gcc system/header virtualenv-3.7 /export/venv . /export/venv/bin/activate BLAS=/usr/lib/libsunperf.so; export BLAS LAPACK=/usr/lib/libsunmath.so ; export LAPACK pip install numpy scipy |
Python 3.7 is in more SRU’s. It’s best practice to use virtualenv to create a virtual environment to work on rather than installing into the system site-packages. I’ve seen a few users make a mess of the packaging system by updating the system python libraries with pip so please just don’t do it 🙂
Tested on a SPARC-M7 T7-1 kernel zone running 11.4.24.0.1.75.0
One-hot encoding labels
Another little snippet that I sometimes forget 🙂
Suppose you have a dataframe with a column that has data in a string format and you need to transform that into a way that a machine learning algorithm can use. One good way is with one-hot encoding which will take the values in the column and create new columns with 1’s & 0’s representing the original data.
Have a look at this dataframe snippet:
| prod | rev | |
|---|---|---|
| 0 | Solaris 11.3 | 2 |
| 1 | Solaris 11.3 | 1 |
| 2 | Solaris 11.4 | 4 |
| 3 | Solaris 11.4 | 5 |
| 4 | Solaris 11.3 | 1 |
One-hot encoding can be use to transform that:
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OH = pd.DataFrame(OH_encoder.fit_transform(mydf[['prod']])) display(OH.head()) pd.concat([mydf, OH], axis=1) |
| prod | rev | 0 | 1 | 2 | 3 | |
|---|---|---|---|---|---|---|
| 0 | Solaris 11.3 | 2 | 0.0 | 1.0 | 0.0 | 0.0 |
| 1 | Solaris 11.3 | 1 | 0.0 | 1.0 | 0.0 | 0.0 |
| 2 | Solaris 11.4 | 4 | 0.0 | 0.0 | 1.0 | 0.0 |
| 3 | Solaris 11.4 | 5 | 0.0 | 0.0 | 1.0 | 0.0 |
| 4 | Solaris 11.3 | 1 | 0.0 | 1.0 | 0.0 | 0.0 |
And that’s fine for machine learning. Your models and pipelines will just handle it once it added to the original dataframe. But what if you actually want to poke around in the dataframe and use the data yourself? Then it would be really useful to have the label of the column reflect what the data in it actually is. No problem just get the feature names from the encoder and rename the columns on the one-hot dataframe before concating it to the original frame:
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column_name = OH.get_feature_names(['prod']) OH_cols_train.columns=column_name df3 = pd.concat([df2, OH_cols_train], axis=1) |
| prod | rev | prod_Solaris 11.1 | prod_Solaris 11.3 | prod_Solaris 11.4 | |
|---|---|---|---|---|---|
| 0 | Solaris 11.3 | 2 | 0.0 | 1.0 | 0.0 |
| 1 | Solaris 11.3 | 1 | 0.0 | 1.0 | 0.0 |
| 2 | Solaris 11.4 | 4 | 0.0 | 0.0 | 1.0 |
| 3 | Solaris 11.4 | 5 | 0.0 | 0.0 | 1.0 |
Cheap radio audio interface
This turned into a surprisingly tricky project… I have a signalink as my main audio interface box to my radios. It’s basically a USB sound card that connects to the data port of my radios and lets me use digital modes. It also has a couple of knobs to control levels. It’s great, I love it, but as it costs about €130 I wasn’t going to bu another one just for the convenience of not having to move cables around when I want to use it on a different radio.
