This is a d3.js visualization of US zip codes.
Original zip code dataset from Geocommons.
5MB shapefile with properties such as zipcode, state, name, population, area, more.
http://geocommons.com/overlays/54893 (Thank you Bill Greer)
This converts it nicely:
| from dateutil import rrule | |
| import datetime | |
| # Generate ruleset for holiday observances on the NYSE | |
| def NYSE_holidays(a=datetime.date.today(), b=datetime.date.today()+datetime.timedelta(days=365)): | |
| rs = rrule.rruleset() | |
| # Include all potential holiday observances | |
| rs.rrule(rrule.rrule(rrule.YEARLY, dtstart=a, until=b, bymonth=12, bymonthday=31, byweekday=rrule.FR)) # New Years Day |
| <link href="/assets/threejs-cloth/styles/styles.css" rel="stylesheet"> | |
| <div class='container'> | |
| <div class='row'> | |
| </div> | |
| <h1> A quick demonstration - <small>writeup soon!</small></h1> | |
| <div class='row'> | |
| <h1 class='col-md-6'> Choose a pattern:</h1> | |
| </div> | |
| <div class='row controls'> |
| def nth_iterinterleave(*arrays, nth=1): | |
| """Interleave multiple lists. | |
| via https://github.com/dgilland/pydash/blob/develop/src/pydash/arrays.py | |
| Expanded to allow an nth parameter, where arrays[0] is treated as the primary and arrays[1:] are interleaved every n elements | |
| Example: | |
| given *arrays = [ [1,2,3,4,5,6,7,8,9,10], ['x', 'y', 'z'], ['a', 'b', 'c'] ] |
| echo "Downloading and installing go" | |
| wget https://dl.google.com/go/go1.11.2.linux-amd64.tar.gz /home/multipass/go1.11.2.linux-amd64.tar.gz | |
| tar -xvf go1.11.2.linux-amd64.tar.gz |
| CI_DOCKER_EXTRA_PARAMS="-e CI_BUILD_PYTHON=python3 -e CROSSTOOL_PYTHON_INCLUDE_PATH=/usr/include/python3.5" \ | |
| tensorflow/tools/ci_build/ci_build.sh PI-PYTHON3 \ | |
| tensorflow/tools/ci_build/pi/build_raspberry_pi.sh | |
| CC_TOOL=/tools/cross-pi-gcc-8.3.0-1/bin/arm-linux-gnueabihf-g++ \ | |
| bazel build -c opt \ | |
| --copt=-march=armv7-a \ | |
| --copt=-mfpu=neon-vfpv4 \ | |
| --copt=-std=c++11 \ | |
| --copt=-funsafe-math-optimizations --copt=-ftree-vectorize \ |
| WORKSPACE: /Users/ljohnson/repos/tensorflow | |
| CI_DOCKER_BUILD_EXTRA_PARAMS: | |
| CI_DOCKER_EXTRA_PARAMS: -e CI_BUILD_PYTHON=python3 -e CROSSTOOL_PYTHON_INCLUDE_PATH=/usr/include/python3.4 | |
| COMMAND: tensorflow/tools/ci_build/pi/build_raspberry_pi.sh | |
| CI_COMMAND_PREFIX: ./tensorflow/tools/ci_build/builds/with_the_same_user ./tensorflow/tools/ci_build/builds/configured pi-python3 | |
| CONTAINER_TYPE: pi-python3 | |
| BUILD_TAG: tf_ci | |
| (docker container name will be tf_ci.pi-python3) | |
| Building container (tf_ci.pi-python3)... |
| Model: "mobilenetv2_1.00_224" | |
| __________________________________________________________________________________________________ | |
| Layer (type) Output Shape Param # Connected to | |
| ================================================================================================== | |
| input_1 (InputLayer) [(None, 224, 224, 3) 0 | |
| __________________________________________________________________________________________________ | |
| Conv1_pad (ZeroPadding2D) (None, 225, 225, 3) 0 input_1[0][0] | |
| __________________________________________________________________________________________________ | |
| Conv1 (Conv2D) (None, 112, 112, 32) 864 Conv1_pad[0][0] | |
| __________________________________________________________________________________________________ |
