I'm trying to use a combination of geopandas, Pandas and Folium to create a polygon map that I can embed incorporate into a web page.
For some reason, it's not displaying.
The steps I've taken:
Grabbed a .shp from the UK's OS for Parliamentary boundaries.
I've then used geopandas to change the projection to epsg=4326 and then exported as GeoJSON which takes the following format:
{ "type": "Feature", "properties": { "PCON13CD": "E14000532", "PCON13CDO": "A03", "PCON13NM": "Altrincham and Sale West" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -2.313999519326579, 53.357408280545918 ], [ -2.313941776174758, 53.358341455420039 ], [ -2.31519699483377, 53.359035664493433 ], [ -2.317953152796459, 53.359102954309151 ], [ -2.319855973429864, 53.358581917200119 ],... ] ] ] } },...
Then what I'd like to do is mesh this with a dataframe of constituencies in the following format, dty:
constituency count
0 Burton 667
1 Cannock Chase 595
2 Cheltenham 22
3 Cheshire East 2
4 Congleton 1
5 Derbyshire Dales 1
6 East Staffordshire 4
import folium
mapf = folium.Map(width=700, height=370, tiles = "Stamen Toner", zoom_start=8, location= ["53.0219392","-2.1597434"])
mapf.geo_json(geo_path="geo_json_shape2.json",
data_out="data.json",
data=dty,
columns=["constituency","count"],
key_on="feature.properties.PCON13NM.geometry.type.Polygon",
fill_color='PuRd',
fill_opacity=0.7,
line_opacity=0.2,
reset="True")
The output from mapf looks like:
mapf.json_data
{'../../Crime_data/staffs_data92.json': [{'Burton': 667,
'Cannock Chase': 595,
'Cheltenham': 22,
'Cheshire East': 2,
'Congleton': 1,
'Derbyshire Dales': 1,
'East Staffordshire': 4,
'Lichfield': 438,
'Newcastle-under-Lyme': 543,
'North Warwickshire': 1,
'Shropshire': 17,
'South Staffordshire': 358,
'Stafford': 623,
'Staffordshire Moorlands': 359,
'Stoke-on-Trent Central': 1053,
'Stoke-on-Trent North': 921,
'Stoke-on-Trent South': 766,
'Stone': 270,
'Tamworth': 600,
'Walsall': 1}]}
Although the mapf.create_map() function successfully creates a map, the polygons don't render.
What debugging steps should I take?
#elksie5000, Try mplleaflet it is extremely straightforward.
pip install mplleaflet
in Jupyter/Ipython notebook:
import mplleaflet
ax = geopandas_df.plot(column='variable_to_plot', scheme='QUANTILES', k=9, colormap='YlOrRd')
mplleaflet.show(fig=ax.figure)
Related
I am trying to make custom text appear when I hover over the points in the scatter chart.
This is my code to generate the chart.
import plotly.io as pio
#data
fig = dict({
"data": [{"type": "scatter3d",
"x": [4.5, 2, 3, 18.8],
"y": [1, 3, 2, 23],
"z": [1, 4, 3, 2]}],
"layout": {"title": {"text": "A map of Human Space"}}
})
#Generate graph
pio.show(fig)
I have a data set that combines two temporal measurement series with one row per measurement
time: 1, measurement: a, value: 5
time: 2, measurement: b, value: false
time: 10, measurement: a, value: 2
time: 13, measurement: b, value: true
time: 20, measurement: a, value: 4
time: 24, measurement: b, value: true
time: 30, measurement: a, value: 6
time: 32, measurement: b, value: false
in a visualization using Vega lite, I'd like to combine the measurement series and encode measurement a and b in a single visualization without simply layering their representation on a temporal axis but representing their value in a single encoding spec.
either measurement a values need to be interpolated and added as a new value to rows of measurement b
eg:
time: 2, measurement: b, value: false, interpolatedMeasurementA: 4.6667
or the other way around, which leaves the question of how to interpolate a boolean. maybe closest value by time, or simpler: last value
eg:
time: 30, measurement: a, value: 6, lastValueMeasurementB: true
I suppose this could be done either query side in which case this question would be regarding indexDB Flux query language
or this could be done on the visualization side in which case this would be regarding vega-lite
There's not any true linear interpolation schemes built-in to Vega-Lite (though the loess transform comes close), but you can achieve roughly what you wish with a window transform.
Here is an example (view in editor):
{
"data": {
"values": [
{"time": 1, "measurement": "a", "value": 5},
{"time": 2, "measurement": "b", "value": false},
{"time": 10, "measurement": "a", "value": 2},
{"time": 13, "measurement": "b", "value": true},
{"time": 20, "measurement": "a", "value": 4},
{"time": 24, "measurement": "b", "value": true},
{"time": 30, "measurement": "a", "value": 6},
{"time": 32, "measurement": "b", "value": false}
]
},
"transform": [
{
"calculate": "datum.measurement == 'a' ? datum.value : null",
"as": "measurement_a"
},
{
"window": [
{"op": "mean", "field": "measurement_a", "as": "interpolated"}
],
"sort": [{"field": "time"}],
"frame": [1, 1]
},
{"filter": "datum.measurement == 'b'"}
],
"mark": "line",
"encoding": {
"x": {"field": "time"},
"y": {"field": "interpolated"},
"color": {"field": "value"}
}
}
This first uses a calculate transform to isolate the values to be interpolated, then a window transform that computes the mean over adjacent values (frame: [1, 1]), then a filter transform to isolate interpolated rows.
If you wanted to go the other route, you could do a similar sequence of transforms targeting the boolean value instead.
I made a model in python and exported it to tensorflow js.
The model is classifying facial images to emotions, and there should be 7 categories.
I made a tensor from the image and used predict like so:
const prediction = model.predict(imageTensor);
my prediction is :
Tensor {
"dataId": Object {},
"dtype": "float32",
"id": 415,
"isDisposedInternal": false,
"kept": false,
"rankType": "2",
"scopeId": 197,
"shape": Array [
1,
7,
],
"size": 7,
"strides": Array [
7,
],
}
how do I extract the result from here?
Prediction is a tensor. If you want to access the data, you need to use prediction.dataSync()
found the answer, I was using typescript and it was needed:
const prediction = (model.predict(
imageTensor
) as tf.Tensor).dataSync();
Hello i have followed all the steps to make an inference and successfully run it on the model in this link : https://pjreddie.com/media/files/yolov3.weights
but when i tried it on a model i trained with darknet i get this error :
[ INFO ] Creating Inference Engine...
[ INFO ] Loading network files:
newyolo.xml
newyolo.bin
[ INFO ] Preparing inputs
[ INFO ] Loading model to the plugin
[ INFO ] Starting inference...
To close the application, press 'CTRL+C' here or switch to the output window and press ESC key
To switch between sync/async modes, press TAB key in the output window
yolo_original.py:280: DeprecationWarning: shape property of IENetLayer is deprecated. Please use shape property of DataPtr instead objects returned by in_data or out_data property to access shape of input or output data on corresponding ports
out_blob = out_blob.reshape(net.layers[net.layers[layer_name].parents[0]].shape)
[ INFO ] Layer detector/yolo-v3/Conv_14/BiasAdd/YoloRegion parameters:
[ INFO ] classes : 10
[ INFO ] num : 3
[ INFO ] coords : 4
[ INFO ] anchors : [55.0, 56.0, 42.0, 87.0, 68.0, 81.0]
Traceback (most recent call last):
File "yolo_original.py", line 363, in <module>
sys.exit(main() or 0)
File "yolo_original.py", line 286, in main
args.prob_threshold)
File "yolo_original.py", line 153, in parse_yolo_region
h_scale=orig_im_h, w_scale=orig_im_w))
File "yolo_original.py", line 99, in scale_bbox
xmin = int((x - w / 2) * w_scale)
ValueError: cannot convert float NaN to integer
knowing that i have provided the right shape and changed yolo_v3.json to match my model
here is the content of my yolo_v3.json:
[
{
"id": "TFYOLOV3",
"match_kind": "general",
"custom_attributes": {
"classes": 10,
"anchors": [18,22,31,33,33,50,55, 56,42,87,68,81,111,98,73,158,156,202],
"coords": 4,
"num": 9,
"masks":[[6, 7, 8], [3, 4, 5], [0, 1, 2]],
"entry_points": ["detector/yolo-v3/Reshape", "detector/yolo-v3/Reshape_4", "detector/yolo-v3/Reshape_8"]
}
}
]
i have tried multiple things to debug this like not providing the jsonfile ....etc
ps : yolo_original.py is the same demo that comes with openvino just renamed,
i'm using openvino version 2020.1
transforming NaN to float or skipping values with Nan didn't slove the problem.
There is a similar question here: Pandas using row labels in boolean indexing
But that one uses a simple index and I can't figure out how to generalize it to a MultiIndex:
df = DataFrame( { 'ssn' : [ 489, 489, 220, 220 ],
'year': [ 2009, 2010, 2009, 2010 ],
'tax' : [ 300, 600, 800, 900 ],
'flag': [ 0, 0, 0, 0 ] } )
df.set_index( ['ssn','year'], inplace=True )
Semi-solutions:
df.flag[ (df.year ==2010) & (df.tax<700) ] = 9 (works if drop=False in set_index)
df.flag[ (df.index==2010) & (df.tax<700) ] = 9 (works for a simple index)
I've tried several things but I just can't figure out how to generalize from simple index to multi. E.g. df.index.year=2010 and 20 other guesses...
You can use index.get_level_values(), e.g.
df.flag[(df.index.get_level_values('year') == 2010) & (df.tax < 700)] = 9