Develop Reference

Is there a way to plot lines over a datashader plot in Bokeh (Python)?

I am working with relatively large datasets (approximately 10x20.000.000 data point), for which Datashader is a useful visualisation tool. To give more information in these visualisations, I would like to add lines showing averages/standarddeviations on top of this datashade figure. Does anyone know how this would be possible?
My current code:
from bokeh.plotting import figure
from bokeh.io import show
x = 'xcol'
y= 'ycol'
data = dataframe
fig = figure(x_axis_label=x, y_axis_label=y)
points = hv.Points(data[[x, y]], label=('Title'))
hd.datashade(points, cmap='crest')
What I would like to do is for example add the following line to the figure generated with the code above:
fig.line([1,10,20], [0, 1000,2000], line_width=4)
Thanks in advance.

plotting/graphing excel file data using python

I'm new to Python and there is a piece of Python coding that I am having trouble getting to graph.
Specifically how to parse data from an Excel spreadsheet in order to generate and plot some basic value comparison graphs.
I'm using the Spyder IDE with Python 3.6.3.
The file 'foc' location is:
C:\Users\Murphy\Desktop\WinPython-64bit-3.6.3.0Qt5\PYWorkFiles\foc.csv
I have more than one version of the excel spreadsheet foc file as I have attempted to graph it in more than one format. The two formats it is stored in at present are csv and xlsx
The code scraps I have put together at present are:
import xlrd
workbook = xlrd.open_workbook('foc.xlsx')
from csv import reader
import matplotlib.pyplot as plt
with open('foc.csv', 'r') as f:
data = list(reader(f))
taste = [i[6] for i in data]
plt.plot(range(len(taste)), taste)
plt.show()
plt.plot()
All these pieces of code generate is two useless graphs (I've attached them below) with only the first one even showing any of the foc spreadsheet data.
Can I get any help with this? I have very little knowledge of how to use Python.
graph1
graph2

To make it as simple as possible, I recommend using numpy(pip install numpy to install it). Using numpy we can do this:
import matplotlib.pyplot as plt
import numpy as np
x, y = np.loadtxt('foc.csv', delimiter=',', unpack=True)
plt.plot(x,y, label='Loaded from file!')
plt.xlabel('x')
plt.ylabel('y')
plt.title('Test')
plt.legend()
plt.show()
and this is our result
foc.csv:
1, 10
2, 20
3, 30
4, 40
5, 50
If you still need more help or want to get into more complex things using matplotlib, I recommend checking out sentdex's tutorials

Cartopy Image from Grib2 file does not align with Coastlines and Borders in same CRS

This seems like it's going to be something simple that will fix my code but I think I've just looked at the code too much at the moment and need to get some fresh eyes on it. I'm simply trying to bring in a Grib2 file that I've downloaded from NCEP for the HRRR model. According to their information the grid type is Lambert Conformal with the extents of (21.13812, 21.14055, 47.84219, 47.83862) for the latitudes of the corners and (-122.7195, -72.28972, -60.91719, -134.0955) for the longitudes of the corners for the models domain.
Before even trying to zoom into my area of interest I just wanted to simply display an image in the appropriate CRS however when I try to do this for the domain of the model I get the borders and coastlines to fall within that extent but the actual image produced from the Grib2 file is just zoomed into. I've tried to use extent=[my domain extent] but it always seems to crash the notebook I'm testing it in. Here is my code and the associated image that I get from it.
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy
from mpl_toolkits.basemap import Basemap
from osgeo import gdal
gdal.SetConfigOption('GRIB_NORMALIZE_UNITS', 'NO')
plt.figure()
filename='C:\\Users\\Public\\Documents\\GRIB\\hrrr.t18z.wrfsfcf00.grib2'
grib = gdal.Open(filename, gdal.GA_ReadOnly)
z00 = grib.GetRasterBand(47)
meta00 = z00.GetMetadata()
band_description = z00.GetDescription()
bz00 = z00.ReadAsArray()
latitude_south = 21.13812 #38.5
latitude_north = 47.84219 #50
longitude_west = -134.0955 #-91
longitude_east = -60.91719 #-69
fig = plt.figure(figsize=(20, 20))
title= meta00['GRIB_COMMENT']+' at '+meta00['GRIB_SHORT_NAME']
fig.set_facecolor('white')
ax = plt.axes(projection=ccrs.LambertConformal())
ax.add_feature(cartopy.feature.BORDERS, linestyle=':')
ax.coastlines(resolution='110m')
ax.imshow(bz00,origin='upper',transform=ccrs.LambertConformal())
plt.title(title)
plt.show()
Returns Just Grib File
If I change:
ax = plt.axes(projection=ccrs.LambertConformal()
to
ax = plt.axes(projection=ccrs.LambertConformal(central_longitude=-95.5,
central_latitude=38.5,cutoff=21.13)
I get my borders but my actual data is not aligned and it creates what I'm dubbing a Batman plot.
Batman Plot
A similar issue occurs even when I do zoom into the domain and still have my borders present. The underlying data from the Grib file doesn't change to correspond to what I'm trying to get.
So as I've already said this is probably something that is an easy fix that I'm just missing but if not, it would be nice to know what step or what process I'm screwing up that I can learn from so that I don't do it in the future!
Updated 1:
I've added and changed some code and am back to getting only the image to show without the borders and coastlines showing up.
test_extent = [longitude_west,longitude_east,latitude_south,latitude_north]
ax.imshow(bz00,origin='upper',extent=test_extent)
This gives me the following image.
Looks exactly like image 1.
The other thing that I'm noticing which maybe the root cause of all of this is that when I'm printing out the value for plt.gca().get_ylim() and plt.gca().get_xlim() I'm getting hugely different values depending on what is being displayed.

It seems that my problem arises from the fact that the Grib file regardless of whether or not it can be displayed properly in other programs just doesn't play nicely with Matplotlib and Cartopy out of the box. Or at the very least does not with the Grib files that I was using. Which for sake of this perhaps helping others in the future are from the NCEP HRRR model that you can get here or here.
Everything seems to work nicely if you convert the file from Grib2 format to NetCDF format and I was able to get what I wanted with the borders, coastlines, etc. on the map. I've attached the code and the output below to show how it worked. Also I hand picked a single dataset that I wanted to display to test versus my previous code so incase you want to look at the rest of datasets available in the file you'll need to utilize ncdump or something similar to view the information on the datasets.
import numpy as np
from netCDF4 import Dataset
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy
import cartopy.feature as cfeature
from osgeo import gdal
gdal.SetConfigOption('GRIB_NORMALIZE_UNITS', 'NO')
nc_f = 'C:\\Users\\Public\\Documents\\GRIB\\test.nc' # Your filename
nc_fid = Dataset(nc_f, 'r') # Dataset is the class behavior to open the
# file and create an instance of the ncCDF4
# class
# Extract data from NetCDF file
lats = nc_fid.variables['gridlat_0'][:]
lons = nc_fid.variables['gridlon_0'][:]
temp = nc_fid.variables['TMP_P0_L1_GLC0'][:]
fig = plt.figure(figsize=(20, 20))
states_provinces = cfeature.NaturalEarthFeature(category='cultural', \
name='admin_1_states_provinces_lines',scale='50m', facecolor='none')
proj = ccrs.LambertConformal()
ax = plt.axes(projection=proj)
plt.pcolormesh(lons, lats, temp, transform=ccrs.PlateCarree(),
cmap='RdYlBu_r', zorder=1)
ax.add_feature(cartopy.feature.BORDERS, linestyle=':', zorder=2)
ax.add_feature(states_provinces, edgecolor='black')
ax.coastlines()
plt.show()
Final Preview of Map

putting some text to a python plot

I'm trying to do a correlation plot using python, so I'm starting with this basic example
import numpy as np
import matplotlib.pyplot as plt
image=np.random.rand(10,10)
plt.imshow(image)
plt.colorbar()
plt.show()
ok, this script give to me an image like this
so the next step is to put my dataset and not a random matrix, i know it, but I want to put some axis or text in this plot, and to get something like this image
It is a very pretty image using paint (lol), but someone can say me what way I need to follow to do something like thik please (how to search it in google).
Before to post it I think in labels, but also I think that I can assign only one label to each axis
cheers

As #tcaswell said in the comments, the function you want to use is annotate, and the documentation can be found here.
I've given an example below using your code above:
import numpy as np
import matplotlib.pyplot as plt
def annotate_axes(x1,y1,x2,y2,x3,y3,text):
ax.annotate('', xy=(x1, y1),xytext=(x2,y2), #draws an arrow from one set of coordinates to the other
arrowprops=dict(arrowstyle='<->'), #sets style of arrow
annotation_clip=False) #This enables the arrow to be outside of the plot
ax.annotate(text,xy=(0,0),xytext=(x3,y3), #Adds another annotation for the text
annotation_clip=False)
fig, ax = plt.subplots()
image=np.random.rand(10,10)
plt.imshow(image)
plt.colorbar()
#annotate x-axis
annotate_axes(-0.5,10,4.5,10,2.5,10.5,'A') # changing these changes the position of the arrow and the text
annotate_axes(5,10,9.5,10,7.5,10.5,'B')
#annotate y-axis
annotate_axes(-1,0,-1,4,-1.5,2,'A')
annotate_axes(-1,4.5,-1,9.5,-1.5,7.5,'B')
plt.show()
This give the image shown below:

(Matplotlib, python) long subplots, scrolling

I would like to create a pdf file [by using plt.savefig("~~~.pdf")]
containing lots of (about 20) subplots
each of which is drawing timeseries data.
I am using a matplotlib library with python language.
Each subplot may be long, and I want to put the subplots
horizontally.
Therefore, the figure should be very long (horizontally), so the horizontal scroll bar should be needed!
Is there any way to do this?
some example code will be appreciated!
The following is my sample code.
I just wanted to draw 10 sine graphs arranged horizontally
and save it as pdf file.
(but I'm not pretty good at this. so the code may looks to be weird to you.. :( )
from matplotlib import pyplot as plt
import numpy as np
x=np.linspace(0,100,1000)
y=np.sin(x)
numplots=10
nr=1
nc=numplots
size_x=nc*50
size_y=size_x*3/4
fig=plt.figure(1,figsize=(size_x,size_y))
for i in range(nc):
ctr=i+1
ax=fig.add_subplot(nr,nc,ctr)
ax.plot(x,y)
plt.savefig("longplot.pdf")
plt.clf()
Thank you!

You should do that using the backend "matplotlib.backends.backend_pdf". This enables you to save matplotlib graphs in pdf format.
I have simplified your code a bit, here is a working example:
from matplotlib import pyplot as plt
import numpy as np
from matplotlib.backends.backend_pdf import PdfPages
x = np.linspace(0,100,1000)
y = np.sin(x)
nr = 10
nc = 1
for i in range(nr):
plt.subplot(nr, nc, i + 1)
plt.plot(x, y)
pdf = PdfPages('longplot.pdf')
pdf.savefig()
pdf.close()
I hope this helps.

In the link below there is a solution, which can help you, since it was helpful to me either.
Scrollbar on Matplotlib showing page
But if you have many subplots, I am afraid your problem won't be solved. Since it will shrink each graph anyway. In that case it will be better to break your graphs into smaller and separate parts.