I'm trying to make a Choropleth map from a GeoPandas data frame, rather than from a geojson file containing only geometry plus a pandas dataframe containing statistical data. Specifically, I would like to adapt this example, merging the shapefiles for US states with another dataset containing their respective unemployment numbers into a single GeoPandas data frame (merged), and then rendering it with folium.Choropleth.
The folium documentation says that the geo_data parameter can be a geopandas object. When I pass the geopandas_data_frame.geometry to it, the map renders. However, when I pass merged["Unemployment"] to the data parameter, each state renders in blue, despite the fact that the numbers vary.
m = folium.Map(location=[48, -102], zoom_start=3)
folium.Choropleth(
geo_data=merged,
name='choropleth',
data=merged["Unemployment"],
fill_color='YlGn',
fill_opacity=0.7,
line_opacity=1,
legend_name='Unemployment Rate (%)'
).add_to(m)
folium.LayerControl().add_to(m)
m
I have tried changing the data type of merged["Unemployment"] from float to int to str, as per this question.
Folium uses GeoJSON objects to plot the geometries (the geo_data param). You can use the geopandas but you'll have to convert it in the function call.
folium.Choropleth(geo_data=merged.to_json(),
name='choropleth',
data=merged,
columns=["id", "Unemployment"],
fill_color='YlGn',
fill_opacity=0.7,
line_opacity=1,
key_on="feature.properties.id",
legend_name='Unemployment Rate (%)').add_to(m)
The key_on parameter is the tricky one, it has to match the structure of the merged.to_json() file, just print it and check.
Related
How does one set the power spectral density (PSD) from file and is it possible to use a different PSD for generating the data and for likelihood evaluation?
Question asked by Vivien Raymond by email.
Setting the PSD from file
To set the PSD from a file, first initialise a list of interferometers, here we just use Hanford:
>>> ifos = bilby.gw.detector.InterferometerList(['H1'])
Every element of the list is initialised with a default PSD using the advanced LIGO noise curve, to check this
>>> ifos[0].power_spectral_density
PowerSpectralDensity(psd_file='/home/user1/miniconda3/lib/python3.6/site-packages/bilby-0.3.5-py3.6.egg/bilby/gw/noise_curves/aLIGO_ZERO_DET_high_P_psd.txt', asd_file='None')
Note, no data has yet been generated. To overwrite the PSD,simply create a new PowerSpectralDensity object and assign it (if you have multiple detectors, you'll need to do this for every element of the list)
ifos[0].power_spectral_density = bilby.gw.detector.PowerSpectralDensity(psd_file=PATH_TO_FILE)
Nest, generate an instance of the strain data from the PSD:
ifos.set_strain_data_from_power_spectral_densities(
sampling_frequency=4096, duration=4,
start_time=-3)
You can check what the data looks like by doing
ifos[0].plot_data()
Note, you can also inject signals using the ifos.inject_signal method.
Using a different PSD for likelihood evaluation
Each ifo in the ifos list contains both the data and a PSD (or equivalent ASD). For inference, we pass that list into the bilby.gw.GravitationalWaveLikelihood object as the first argument and the PSD for each element of the list is used in calculating the likelihood.
So, if you want to use a different PSD for likelihood estimate. First generate the data (as above). Then, assign the PSD you want to use for sampling to each element of ifos and pass that object into the likelihood instead. This won't overwrite the data (provided you don't call set_strain_data_from_power_spectral_densities of course).
I am using Paraview 5.0.1. If any solution requires updating, I can try.
I want to programmatically obtain field plots (and corresponding PlotOverLine) of displacements and stresses in rotated coordinate systems.
What are appropriate/convenient/possible ways of doing this?
So far, I have created one Calculator filter for each component of displacements and stresses.
For instance, I used Calculators in 2D with results
(displacement.iHat)*cos(0.7853981625)+(displacement.jHat)*sin(0.7853981625)
(stress_3-stress_0)*sin(45.0*3.14159265/180)*cos(45.0*3.14159265/180)+stress_1*((cos(45.0*3.14159265/180))^2-(sin(45.0*3.14159265/180))^2)
It works fine, but it is quite cumbersome, in several aspects:
Creating them (one filter per component).
Plotting several of them in a single XY plot
Exporting them (one export per component).
Is there a simple way to do this?
PS: The Transform filter does not accomplish this. It rotates the view, not the fields.
Two solutions:
Ugly, inneficient solution
Use Transform and check "Transform All Input vectors"
Add a calculator and add a dummy array
Use transform the other way around, without checking "Transform All Input vectors"
Correct solution :
Compute the transformation yourself in a programmable filter
input = self.GetUnstructuredGridInput();
output = self.GetUnstructuredGridOutput();
output.ShallowCopy(input)
data = input.GetPointData().GetArray("YourArray")
vec = vtk.vtkDoubleArray();
vec.SetNumberOfComponents(3);
vec.SetName("TransformedVectors");
numPoints = input.GetNumberOfPoints()
for i in xrange(0, numPoints):
tuple = data.GetTuple(i)
transform(tuple) # implement the transform in python
vec.InsertNextTuple(tuple)
output.GetPointData().AddArray(vec)
I am new to image processing and python coding.
I have detected a number of features in an image and have their respective pixel locations placed in a list format.
My_list = [(x1,y1),(x2,y2),......,(xn,yn)]
I would like to use DBSCAN algorithm to form clusters from the following points.
Currently using sklearn.cluster to import the build in DBSCAN function for python.
If the current format for the points is not compatible would like to know which is?
Error currently facing with the current format:
C:\Python\python.exe "F:/opencv_files/dbscan.py"
**Traceback (most recent call last):**
**File "**F:/opencv_files/dbscan.py**", line 83, in <module>
db = DBSCAN(eps=0.5, min_samples=5).fit(X) # metric=X)**
**File "**C:\Python\lib\site-packages\sklearn\cluster\dbscan_.py**", line 282, in fit
X = check_array(X, accept_sparse='csr')
File "**C:\Python\lib\site-packages\sklearn\utils\validation.py**", line 441, in check_array
"if it contains a single sample.".format(array))
ValueError: Expected 2D array, got 1D array instead:
array=[].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.**
Your data is a list of tuple. There is nothing in this structure that prevents you from doing crazy things with that, such as having different lengths in there. Plus, this is a very slow and memory inefficient way of keeping the data because everything is boxed as a Python object.
Just call data = numpy.array(data) to convert your data into an efficient multidimensional numeric array. This array will then have a shape.
I'm looking at implementing a Caffe CNN which accepts two input images and a label (later perhaps other data) and was wondering if anyone was aware of the correct syntax in the prototxt file for doing this? Is it simply an IMAGE_DATA layer with additional tops? Or should I use separate IMAGE_DATA layers for each?
Thanks,
James
Edit: I have been using the HDF5_DATA layer lately for this and it is definitely the way to go.
HDF5 is a key value store, where each key is a string, and each value is a multi-dimensional array. Thus, to use the HDF5_DATA layer, just add a new key for each top you want to use, and set the value for that key to store the image you want to use. Writing these HDF5 files from python is easy:
import h5py
import numpy as np
filelist = []
for i in range(100):
image1 = get_some_image(i)
image2 = get_another_image(i)
filename = '/tmp/my_hdf5%d.h5' % i
with hypy.File(filename, 'w') as f:
f['data1'] = np.transpose(image1, (2, 0, 1))
f['data2'] = np.transpose(image2, (2, 0, 1))
filelist.append(filename)
with open('/tmp/filelist.txt', 'w') as f:
for filename in filelist:
f.write(filename + '\n')
Then simply set the source of the HDF5_DATA param to be '/tmp/filelist.txt', and set the tops to be "data1" and "data2".
I'm leaving the original response below:
====================================================
There are two good ways of doing this. The easiest is probably to use two separate IMAGE_DATA layers, one with the first image and label, and a second with the second image. Caffe retrieves images from LMDB or LEVELDB, which are key value stores, and assuming you create your two databases with corresponding images having the same integer id key, Caffe will in fact load the images correctly, and you can proceed to construct your net with the data/labels of both layers.
The problem with this approach is that having two data layers is not really very satisfying, and it doesn't scale very well if you want to do more advanced things like having non-integer labels for things like bounding boxes, etc. If you're prepared to make a time investment in this, you can do a better job by modifying the tools/convert_imageset.cpp file to stack images or other data across channels. For example you could create a datum with 6 channels - the first 3 for your first image's RGB, and the second 3 for your second image's RGB. After reading this in using the IMAGE_DATA layer, you can split the stream into two images using a SLICE layer with a slice_point at index 3 along the slice_dim = 1 dimension. If further down the road, you decide that you want to load even more complex assortments of data, you'll understand the encoding scheme and can write your own decoding layer based off of src/caffe/layers/data_layer.cpp to gain full control of the pipeline.
You may also consider using HDF5_DATA layer with multiple "top"s
I am trying to save an image using opencv cvSaveImage function. The problem is that I am performing a DCT on the image and then changing the coefficients that are obtained after performing the DCT, after that I am performing an inverse DCT to get back the pixel values. But this time I get the pixel values in Decimals(e.g. 254.34576). So when I save this using cvSaveImage function it discards all the values after decimals(e.g. saving 254.34576 as 254) and saves the image. Due to this my result gets affected. Please Help
"The function cvSaveImage saves the image to the specified file. The image format is chosen depending on the filename extension, see cvLoadImage. Only 8-bit single-channel or 3-channel (with 'BGR' channel order) images can be saved using this function. If the format, depth or channel order is different, use cvCvtScale and cvCvtColor to convert it before saving, or use universal cvSave to save the image to XML or YAML format."
I'd suggest investigating the cvSave function.
HOWEVER, a much easier way is to just write your own save/load functions, this would be very easy:
f = fopen("image.dat","wb");
fprintf(f,"%d%d",width,height);
for (y=0 to height)
for (x=0 to width)
fprintf(f,"%f",pixelAt(x,y));
And a corresponding mirror function for reading.
P.S. Early morning and I can't remember for the life of me if fprintf works with binary files. But you get the idea. You could use fwrite() instead.