I am trying to plot locations in three states in the US in python with Altair. I saw the tutorial about the us map but I am wondering if there is anyway to zoom the image to the only three states of interest, i.e. NY,NJ and CT.
Currently, I have the following code:
from vega_datasets import data
states = alt.topo_feature(data.us_10m.url, 'states')
# US states background
background = alt.Chart(states).mark_geoshape(
fill='lightgray',
stroke='white',
limit=1000
).properties(
title='US State Capitols',
width=700,
height=400
).project("albers")
points=alt.Chart(accts).mark_point().encode(
longitude = "longitude",
latitude = "latitude",
color = "Group")
background+points
I inspected the us_10m.url data set and seems like there is no field which specifies the individual states. So I am hoping if I could just somehow change the xlim and ylim for the background to [-80,-70] and [35,45] for example. I want to zoom in to the regions where there are data points(blue dots).
Could someone kindly show me how to do that? Thanks!!
Update
There is a field called ID in the JSON file and I manually found out that NJ is 34, NY is 36 and CT is 9. Is there a way to filter on these IDs? That will get the job done!
Alright seems like the selection/zoom/xlim/ylim feature for geotype is not supported yet:
Document and add warning that geo-position doesn't support selection yet #3305
So I end up with a hackish way to solve this problem by first filtering based on the IDs using pure python. Basically, load the JSON file into a dictionary and then change the value field before converting the dictionary to topojson format. Below is an example for 5 states,PA,NJ,NY,CT,RI and MA.
import altair as alt
from vega_datasets import data
# Load the data, which is loaded as a dict object
us_10m = data.us_10m()
# Select the geometries under states under objects, filter on id (9,25,34,36,42,44)
us_10m['objects']['states']['geometries']=[item for item in us_10m['objects'] \
['states']['geometries'] if item['id'] in [9,25,34,36,42,44]]
# Make the topojson data
states = alt.Data(
values=us_10m,
format=alt.TopoDataFormat(feature='states',type='topojson'))
# Plot background (now only has 5 states)
background = alt.Chart(states).mark_geoshape(
fill='lightgray',
stroke='white',
limit=1000
).properties(
title='US State Capitols',
width=700,
height=400
).project("mercator")
# Plot the points
points=alt.Chart(accts).mark_circle(size=60).encode(
longitude = "longitude",
latitude = "latitude",
color = "Group").project("mercator")
# Overlay the two plots
background+points
The resulting plot looks ok:
Related
I have the following graph in Altair:
The code used to generate it is as follows:
data = pd.read_csv(data_csv)
display(data)
display(set(data['algo_score_raw']))
# First generate base graph
base = alt.Chart(data).mark_circle(opacity=1, stroke='#4c78a8').encode(
x=alt.X('Paragraph:N', axis=None),
y=alt.Y('Section:N', sort=list(OrderedDict.fromkeys(data['Section']))),
size=alt.Size('algo_score_raw:Q', title="Number of Matches"),
).properties(
width=900,
height=500
)
# Next generate the overlying graph with the lines
lines = alt.Chart(data).mark_rule(stroke='#4c78a8').encode(
x=alt.X('Paragraph:N', axis=alt.Axis(labelAngle=0)),
y=alt.Y('Section:N', sort=list(OrderedDict.fromkeys(data['Section'])))
).properties(
width=900,
height=500
)
if max(data['algo_score_raw']) == 0:
return lines # no circles if no matches
else:
return base + lines
However, I don't want the decimal values in my legend; I only want 1.0, 2.0, and 3.0, because those are the only values that are actually present in my data. However, Altair seems to default to what you see above.
The legend is generated based on how you specify your encoding. It sounds like your data are better represented as ordered categories than as a continuous quantitative scale. You can specify this by changing the encoding type to ordinal:
size=alt.Size('algo_score_raw:O')
You can read more about encoding types at https://altair-viz.github.io/user_guide/encoding.html
You can use alt.Legend(tickCount=2)) (labelExpr could also be helpful, see the docs for more):
import altair as alt
from vega_datasets import data
source = data.cars()
source['Acceleration'] = source['Acceleration'] / 10
chart = alt.Chart(source).mark_circle(size=60).encode(
x='Horsepower',
y='Miles_per_Gallon',
size='Acceleration',
)
chart
chart.encode(size=alt.Size('Acceleration', legend=alt.Legend(tickCount=2)))
I am trying to retrieve the coordinates of all nodes/corners/edges of each commercial building in a list. E.g. for the supermarket Aldi in Macclesfield (UK), I can get from the UI 10 nodes (all the corners/edges of the supermarket) but I can only retrieve from osmnx 2 of those 10 nodes. I would need to access to the complete list of nodes but it truncates the results giving only 2 nodes of 10 in this case.Using this code below:
import osmnx as ox
test = ox.geocode_to_gdf('aldi, Macclesfield, Cheshire, GB')
ax = ox.project_gdf(test).plot()
test.geometry
or
gdf = ox.geometries_from_place('Grosvenor, Macclesfield, Cheshire, GB', tags)
gdf.geometry
Both return just two coordinates and truncate other info/results that is available in openStreetMap UI (you can see it in the first column of the image attached geometry>POLYGON>only two coordinates and other results truncated...). I would appreciate some help on this, thanks in advance.
It's hard to guess what you're doing here because you didn't provide a reproducible example (e.g., tags is undefined). But I'll try to guess what you're going for.
I am trying to retrieve the coordinates of all nodes/corners/edges of commercial buildings
Here I retrieve all the tagged commercial building footprints in Macclesfield, then extract the first one's polygon coordinates. You could instead filter these by other attribute values as you see fit if you only want certain kinds of buildings. Proper usage of OSMnx's geometries module is described in the documentation.
import osmnx as ox
# get the building footprints in Macclesfield
place = 'Macclesfield, Cheshire, England, UK'
tags = {'building': 'commercial'}
gdf = ox.geometries_from_place(place, tags)
# how many did we get?
print(gdf.shape) # (57, 10)
# extract the coordinates for the first building's footprint
gdf.iloc[0]['geometry'].exterior.coords
Alternatively, if you want a specific building's footprint, you can look up its OSM ID and tell OSMnx to geocode that value:
gdf = ox.geocode_to_gdf('W251154408', by_osmid=True)
polygon = gdf.iloc[0]['geometry']
polygon.exterior.coords
gdf = ox.geocode_to_gdf('W352332709', by_osmid=True)
polygon = gdf.iloc[0]['geometry']
polygon.exterior.coords
list(polygon.exterior.coords)
The new capability in Cartopy 0.18.0 to add lat/lon labels for any map projection is excellent. It's a great addition to this package. For some maps, especially in polar regions, the lat/lon labels can be very crowded. Here is an example.
from matplotlib import pyplot as plt
import numpy as np
import cartopy.crs as ccrs
pcproj = ccrs.PlateCarree()
lon0 = -150
mapproj = ccrs.LambertAzimuthalEqualArea(
central_longitude=lon0,central_latitude=75,
)
XLIM = 600e3; YLIM=700e3
dm =5; dp=2
fig = plt.figure(0,(7,7))
ax = fig.add_axes([0.1,0.1,0.85,0.9],projection=mapproj)
ax.set_extent([-XLIM,XLIM,-YLIM,YLIM],crs=mapproj)
ax.coastlines(resolution='50m',color='.5',linewidth=1.5)
lon_grid = np.arange(-180,181,dm)
lat_grid = np.arange(-80,86,dp)
gl = ax.gridlines(draw_labels=True,
xlocs=lon_grid,ylocs=lat_grid,
x_inline=False,y_inline=False,
color='k',linestyle='dotted')
gl.rotate_labels = False
Here is the output plot: I can't embed image yet, so here is the link
What I am looking for is to have lat labels on the left and right sides and lon labels at the bottom, with no labels at the top. This can be easily done in Basemap using a list of flags. I am wondering if this is possible with cartopy now.
Several failed attempts:
I came across a Github open issue for cartopy on a similar topic, but the suggested method does not work for this case. Adding gl.ylocator = mticker.FixedLocator(yticks) does nothing and adding gl.xlocator = mticker.FixedLocator(xticks) gets rid of most of lon labels except the 180 line on left and right sides but all the other lon labels are missing. The 80N lat label is still on the top, see here. After a more careful read of that thread, it seems it is still an ongoing effort for future cartopy releases.
Using gl.top_labels=False does not work either.
Setting y_inline to True makes the lat labels completely gone. I guess this might be because of axes extent I used. The lat labels might be on some longitude lines outside of the box. This is a separate issue, about how to specify the longitude lines/locations of the inline labels.
Right now, I have chosen to turn off the labels. Any suggestions and temporary solutions will be appreciated. At this point, the maps such as the examples above are useful for quicklooks but not ready for any formal use.
UPDATE:
Based on #swatchai 's suggestion, there is a temporary workaround below:
# --- add _labels attribute to gl
plt.draw()
# --- tol is adjusted based on the positions of the labels relative to the borders.
tol = 20
for ea in gl._labels:
pos = ea[2].get_position()
t_label = ea[2].get_text()
# --- remove lon labels on the sides
if abs(abs(pos[0])-XLIM)<tol:
if 'W' in t_label or 'E' in t_label or '180°' in t_label:
print(t_label)
ea[2].set_text('')
# --- remove labels on top
if abs(pos[1]-YLIM)<tol:
ea[2].set_text('')
This is almost what I wanted except that the 74N labels are missing because it is close to the 170W labels on the sides and cartopy chose 170W label instead of 74N. So I need a little more simple tweaks to put it back there.
This could be a workaround for your project until a better solution comes up.
# more code above this line
# this suppresses drawing labels on top edges
# only longitude_labels disappear, some latitude_labels persist
gl.top_labels=False
# workaround here to manipulate the remaining labels
plt.draw() #enable the use of ._lables()
for ea in gl._labels:
#here, ea[2] is a Text object
#print(ea)
if '80°N'==ea[2].get_text():
# set it a blank string
ea[2].set_text("")
ax.set_title("No Labels on Top Edge");
plt.show()
The output plot:
I am trying to construct a grouped vertical bar chart in Bokeh from a pandas dataframe. I'm struggling with understanding the use of factor_cmap and how the color mapping works with this function. There's an example in the documentation (https://docs.bokeh.org/en/latest/docs/user_guide/categorical.html#pandas) that was helpful to follow, here:
from bokeh.io import output_file, show
from bokeh.palettes import Spectral5
from bokeh.plotting import figure
from bokeh.sampledata.autompg import autompg_clean as df
from bokeh.transform import factor_cmap
output_file("bar_pandas_groupby_nested.html")
df.cyl = df.cyl.astype(str)
df.yr = df.yr.astype(str)
group = df.groupby(by=['cyl', 'mfr'])
index_cmap = factor_cmap('cyl_mfr', palette=Spectral5, factors=sorted(df.cyl.unique()), end=1)
p = figure(plot_width=800, plot_height=300, title="Mean MPG by # Cylinders and Manufacturer",
x_range=group, toolbar_location=None, tooltips=[("MPG", "#mpg_mean"), ("Cyl, Mfr", "#cyl_mfr")])
p.vbar(x='cyl_mfr', top='mpg_mean', width=1, source=group,
line_color="white", fill_color=index_cmap, )
p.y_range.start = 0
p.x_range.range_padding = 0.05
p.xgrid.grid_line_color = None
p.xaxis.axis_label = "Manufacturer grouped by # Cylinders"
p.xaxis.major_label_orientation = 1.2
p.outline_line_color = None
show(p)
This yields the following (again, a screen shot from the documentation):
Grouped Vbar output
I understand how factor_cmap is working here, I think. The index for the dataframe has multiple factors and we're only taking the first by slicing (as seen with the end = 1). But when I try to instead set coloring based on the second index level, mfr, (setting start = 1 , end = 2) , the index mapping breaks and I get this. I based this change on my assumption that the factors were hierarchical and I needed to slice them to get the second level.
I think I must be thinking about the indexing with these categorical factors wrong, but I'm not sure what I'm doing wrong. How do I get a categorical mapper to color by the second level of the factor? I assumed the format of the factors was ('cyl', 'mfr') but maybe that assumption is wrong?
Here's the documentation for factor_cmap, although it wasn't very helpful: https://docs.bokeh.org/en/latest/docs/reference/transform.html#bokeh.transform.factor_cmap .
If you mean you are trying this:
index_cmap = factor_cmap('cyl_mfr',
palette=Spectral5,
factors=sorted(df.cyl.unique()),
start=1, end=2)
Then there are at least two issues:
2 is out of bounds for the length of the list of sub-factors ('cyl', 'mfr'). You would just want start=1 and leave end with its default value of None (which means to the end of the list, as usual for any Python slice).
In this specific case, with start=1 that means "colormap based on mfr sub-factors of the values", but you are still configuring the cololormapper with the cylinders as the factors for the map:
factors=sorted(df.cyl.unique())
When the colormapper goes to look up a value with mfr="mazda" in the mapping, it does not find anything (because you only put cylinder values in the mapping) so it gets shaded the default color grey (as expected).
So you could do something like this:
index_cmap = factor_cmap('cyl_mfr',
palette=Spectral5,
factors=sorted(df.mfr.unique()),
start=1)
Which "works" modulo the fact that there are way more manufacturer values than there are colors in the Spectral5 palette:
In the real situation you'll need to make sure you use a palette as least as big as the number of (sub-)factors that you configure.
I'm currently working in my final project for my Coding class (my first coding class, so kind of an amateur).
My idea is for a code to search every newspaper in the world for a specific word within the titles (using bs4) and then obtaining a dictionary with the average mentions by country, taking into account the number of newspaper in each country. Afterwards, and this is the part where I'm stuck, I want to put this in a map.
The whole program is already working properly, until the part where I have a CSV with the following form:
'Country','Average'
'Afghanistan',10
'Albania',5
'Algeria',0
'Andorra',2
'Antigua and Barbuda',7
'Argentina',0
'Armenia',4
Now, I want to create a worldmap where the higher the number, the redder (or any other color) the whole polygon of the country. So far I've found many codes that work well placing points in space, but I haven't found one that "appends" the CSV data presented above and then fills each country accordingly. Below is the part of the code that currently created the worldmap:
# Now we proceed with the creation of the map
fig, ax = plt.subplots(figsize=(15,10)) # We define the size of the map
m = Basemap(resolution='c', # c, l, i, h, f or None
projection='merc', # Mercator projection
lat_0=24.20, lon_0=-6.67, # The center of the mas, so that the whole world is shown without splitting Asia
llcrnrlon=-180, llcrnrlat= -85,urcrnrlon=180, urcrnrlat=85) # The coordinates of the whole world
m.drawmapboundary(fill_color='#46bcec') # We choose a color for the boundary of the map
m.fillcontinents(color='#f2f2f2',lake_color='#46bcec') # We choose a color for the land and one for the lakes
m.drawcoastlines() # We choose to draw the lines of the map
m.readshapefile('Final project\\vincent_map_data-master\\ne_110m_admin_0_countries\\ne_110m_admin_0_countries', 'areas') # We import the shape file of the whole world
df_poly = pd.DataFrame({ # We define the polygon structure
'shapes': [Polygon(np.array(shape), True) for shape in m.areas],
'area': [area['name'] for area in m.areas_info]
})
cmap = plt.get_cmap('Oranges')
pc = PatchCollection(df_poly.shapes, zorder=2)
norm = Normalize()
mapper = matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap)
# We show the map
plt.show(m)
I opened the shapefile of the countries and the way to identify the countries is with the variable "sovereignty". There might be some non-sensical things within my code, since I've extracted things from many places. Sorry about that.
If someone could help me out, I would really appreciated.
Thanks