How to plot discharge vectors in cross-section view in Flopy MF6?
I know this plots for plan view:
quiver = mapview.plot_specific_discharge(spdis[0])
I tried to get specific discharge using the following code but got error:
AttributeError: module 'flopy.utils.postprocessing' has no attribute 'get_specific_discharge'
Code:
hds_file = os.path.join(workspace, headfile)
hds = flopy.utils.binaryfile.HeadFile(hds_file)
cbb_file = os.path.join(workspace, budgetfile)
cbb = flopy.utils.CellBudgetFile(fname, precision='double')
q = flopy.utils.postprocessing.get_specific_discharge(gwf,cbb_file, precision='single', kstpkper=(0,1),
hdsfile=hds_file, position='centers')
For me (using mf6) plotting specific discharge on cross sections works like this:
Reading the cbc output:
cbcdobj = flopy.utils.binaryfile.CellBudgetFile(path, precision='double', verbose=True)
SPDIS = cbcdobj.get_data(kstpkper=kstpkper, text='DATA-SPDIS')[0]
You might need to use 'verbose=False' and 'precision=single' when using mf2005.
Then the cross section:
cros_mp=flopy.plot.PlotCrossSection(model=gwf, line={'row': 200})
cros_mp.plot_specific_discharge(SPDIS)
Remark that plotting specific discharges on an irregular cross section ('line', not 'row' nor 'column') is not possible.
Related
I am new to data science using python . While plotting two different barcharts I got into an problem. This is my code :
def compare_groups(field):
if field in less_equal_150_cal.columns:
less_equal_150_cal[field].plot.bar(color = 'blue',alpha =0.4, title = field )
more_150_cal[field].plot.bar(color = 'red', alpha =0.4)
else:
raise ValueError(f"{field} not found")
The resulting bargraphs are overlapping each other. I want two different bar graphs.
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 using seaborn to plot my dataset.
(i) Here is my first part of the code which shows the plot in the link below the code:
data = np.random.multivariate_normal([0,0],[[5,2],[2,2]],size=2000)
data = pd.DataFrame(data,columns=['x','y'])
for col in 'xy':
plt.hist(data[col],density=True,alpha=0.5)
Image of Histogram of the above data set
(ii)Second part of the code is here:
sns.kdeplot(data)
I am getting this error while running the second part:
ValueError: If using all scalar values, you must pass an index
In the second part of the code
Make this change:
sns.kdeplot(data=data,x='x',y='y')
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:
I want to plot many subplots in one figure using healpy. How to:
Set the position of the colorbar?
Set the tick and ticklabel of colorbar?
Set the position and size of the subplots?
I want to generate a plot such as figure 1, which is plotted in MATLAB, based on general coordinates
Right now, I only plot it as follows using healpy:
A similar code to produce figure 3 (similar to figure2) is as follow:
import numpy as np
import healpy as hp
degree = 4
nside = 2**degree
num_Pixel = hp.nside2npix(nside)
m = np.arange(num_Pixel)
margins = [[0.02,0,0,0],[0.01,0,0,0],[0.01,0,0.01,0],
[0.02,0,0,0],[0.01,0,0,0],[0.01,0,0.01,0],
[0.02,0.05,0,0],[0.01,0.05,0,0],[0.01,0.05,0.01,0]]
title = [
'Equinox', 'Jun. Solstice', 'Dec. Solstice',
'','','','','','']
for ifig in range(1,10):
if ifig < 7:
hp.cartview(
m, sub=330+ifig, margins=margins[ifig1],
cbar=False, title=title[ifig-1])
else:
hp.cartview(
m, sub=330+ifig, margins=margins[ifig-1],
cbar=True, title=title[ifig-1])
The code produced the figure 3
I'm afraid healpy doesn't come with a good way to handle the colorbar, ticks, ticklabels, the axes etc.
The best way forward would be to generate FITS images, based on your HEALPix map (e.g. using hp.cartview(..., return_projected_map=True) or using the reproject package).
You also need to generate the right FITS header for that, astropy would be the right tool for that (how-to manipulate FITS headers).
Once you have that, you can use the excellent WCSAxes framework within astropy, which gives you plenty of well-documented customization options.