Given the shape file available here: I know can produce the basic map that I need with county labels and even some points on the map (see below). The issue I'm having is that I cannot seem to control the size of the figure with figsize.
Here's what I have:
import geopandas as gpd
import matplotlib.pyplot as plt
%matplotlib inline
figsize=5,5
fig = plt.figure(figsize=(figsize),dpi=300)
shpfileshpfile=r'Y:\HQ\TH\Groups\NR\PSPD\Input\US_Counties\cb_2015_us_county_20m.shp'
c=gpd.read_file(shpfile)
c=c.loc[c['GEOID'].isin(['26161','26093','26049','26091','26075','26125','26163','26099','26115','26065'])]
c['coords'] = c['geometry'].apply(lambda x: x.representative_point().coords[:])
c['coords'] = [coords[0] for coords in c['coords']]
ax=c.plot()
#Control some attributes regarding the axis (for the plot above)
ax.spines['top'].set_visible(False);ax.spines['bottom'].set_visible(False);ax.spines['left'].set_visible(False);ax.spines['right'].set_visible(False)
ax.tick_params(axis='y',which='both',left='off',right='off',color='none',labelcolor='none')
ax.tick_params(axis='x',which='both',top='off',bottom='off',color='none',labelcolor='none')
for idx, row in c.iterrows():
ax.annotate(s=row['NAME'], xy=row['coords'],
horizontalalignment='center')
lat2=[42.5,42.3]
lon2=[-84,-83.5]
#Add another plot...
ax.plot(lon2,lat2,alpha=1,marker='o',linestyle='none',markeredgecolor='none',markersize=15,color='white')
plt.show()
As you can see, I opted to call the plots by the axis name because I need to control attributes of the axis, such as tick_params. I'm not sure if there is a better approach. This seems like a "no-brainer" but I can't seem to figure out why I can't control the figure size.
Thanks in advance!
I just had to do the following:
Use fig, ax = plt.subplots(1, 1, figsize = (figsize))
2.use the ax=ax argument in c.plot()
import geopandas as gpd
import matplotlib.pyplot as plt
%matplotlib inline
figsize=5,5
#fig = plt.figure(figsize=(figsize),dpi=300)
#ax = fig.add_subplot(111)
fig, ax = plt.subplots(1, 1, figsize = (figsize))
shpfileshpfile=r'Y:\HQ\TH\Groups\NR\PSPD\Input\US_Counties\cb_2015_us_county_20m.shp'
c=gpd.read_file(shpfile)
c=c.loc[c['GEOID'].isin(['26161','26093','26049','26091','26075','26125','26163','26099','26115','26065'])]
c['coords'] = c['geometry'].apply(lambda x: x.representative_point().coords[:])
c['coords'] = [coords[0] for coords in c['coords']]
c.plot(ax=ax)
ax.spines['top'].set_visible(False);ax.spines['bottom'].set_visible(False);ax.spines['left'].set_visible(False);ax.spines['right'].set_visible(False)
ax.tick_params(axis='y',which='both',left='off',right='off',color='none',labelcolor='none')
ax.tick_params(axis='x',which='both',top='off',bottom='off',color='none',labelcolor='none')
for idx, row in c.iterrows():
ax.annotate(s=row['NAME'], xy=row['coords'],
horizontalalignment='center')
lat2=[42.5,42.3]
lon2=[-84,-83.5]
ax.plot(lon2,lat2,alpha=1,marker='o',linestyle='none',markeredgecolor='none',markersize=15,color='white')
Related
I am trying to make an animation using ArtistAnimation like this:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig, ax = plt.subplots()
ims = []
for i in range(60):
x = np.linspace(0,i,1000)
y = np.sin(x)
im = ax.plot(x,y, color='black')
ims.append(im)
ani = animation.ArtistAnimation(fig, ims, interval=50, blit=True,
repeat_delay=1000)
plt.show()
This animates a sine wave growing across the figure. Currently I'm just adding the Lines2D object returned by ax.plot() to ims. However, I would like to potentially draw multiple overlapping plots on the Axes and adjust the title, legend and x-axis range for each frame. How do I get an object that I can add to ims after plotting and making all the changes I want for each frame?
The list you supply to ArtistAnimation should be a list of lists of artists, one list per frame.
artist_list = [[line1a, line1b, title1], [line2a, line2b, title2], ...]
where the first list is shown in the first frame, the second list in the second frame etc.
The reason your code works is that ax.plot returns a list of lines (in your case only a list of a single line).
In any case, the following might be a more understandable version of your code where an additional text is animated.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig, ax = plt.subplots()
artist_list = []
for i in range(60):
x = np.linspace(0,i,1000)
y = np.sin(x)
line, = ax.plot(x,y, color='black')
text = ax.text(i,0,i)
artist_list.append([line, text])
ani = animation.ArtistAnimation(fig, artist_list, interval=50, blit=True,
repeat_delay=1000)
plt.show()
In general, it will be hard to animate changing axes limits with ArtistAnimation, so if that is an ultimate goal consider using a FuncAnimation instead.
So currently learning how to import data and work with it in matplotlib and I am having trouble even tho I have the exact code from the book.
This is what the plot looks like, but my question is how can I get it where there is no white space between the start and the end of the x-axis.
Here is the code:
import csv
from matplotlib import pyplot as plt
from datetime import datetime
# Get dates and high temperatures from file.
filename = 'sitka_weather_07-2014.csv'
with open(filename) as f:
reader = csv.reader(f)
header_row = next(reader)
#for index, column_header in enumerate(header_row):
#print(index, column_header)
dates, highs = [], []
for row in reader:
current_date = datetime.strptime(row[0], "%Y-%m-%d")
dates.append(current_date)
high = int(row[1])
highs.append(high)
# Plot data.
fig = plt.figure(dpi=128, figsize=(10,6))
plt.plot(dates, highs, c='red')
# Format plot.
plt.title("Daily high temperatures, July 2014", fontsize=24)
plt.xlabel('', fontsize=16)
fig.autofmt_xdate()
plt.ylabel("Temperature (F)", fontsize=16)
plt.tick_params(axis='both', which='major', labelsize=16)
plt.show()
There is an automatic margin set at the edges, which ensures the data to be nicely fitting within the axis spines. In this case such a margin is probably desired on the y axis. By default it is set to 0.05 in units of axis span.
To set the margin to 0 on the x axis, use
plt.margins(x=0)
or
ax.margins(x=0)
depending on the context. Also see the documentation.
In case you want to get rid of the margin in the whole script, you can use
plt.rcParams['axes.xmargin'] = 0
at the beginning of your script (same for y of course). If you want to get rid of the margin entirely and forever, you might want to change the according line in the matplotlib rc file:
axes.xmargin : 0
axes.ymargin : 0
Example
import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset('tips')
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
tips.plot(ax=ax1, title='Default Margin')
tips.plot(ax=ax2, title='Margins: x=0')
ax2.margins(x=0)
Alternatively, use plt.xlim(..) or ax.set_xlim(..) to manually set the limits of the axes such that there is no white space left.
If you only want to remove the margin on one side but not the other, e.g. remove the margin from the right but not from the left, you can use set_xlim() on a matplotlib axes object.
import seaborn as sns
import matplotlib.pyplot as plt
import math
max_x_value = 100
x_values = [i for i in range (1, max_x_value + 1)]
y_values = [math.log(i) for i in x_values]
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
sn.lineplot(ax=ax1, x=x_values, y=y_values)
sn.lineplot(ax=ax2, x=x_values, y=y_values)
ax2.set_xlim(-5, max_x_value) # tune the -5 to your needs
I can generate an error-bar plot using the code below. The graph produced by the code shows vertical lines that represent the errors in y. I would like to have horizontal lines at the tips of these errors ("error bars") and am not sure how to do so.
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(1, 10, 10, dtype=int)
y = 2**x
yerr = np.sqrt(y)*10
fig, ax = plt.subplots()
ax.errorbar(x, y, yerr, solid_capstyle='projecting')
ax.grid(alpha=0.5, linestyle=':')
plt.show()
plt.close(fig)
The code generates the figure below. I've played with the solid_capstyle kwarg. Is there a specific kwarg that does what I am trying to do?
And as an example of what I'd like, the figure below:
In case it's relevant, I am using matplotlib 2.2.2
The argument you are looking for is capsize= in ax.errorbar(). The default is None so the length of the cap will default to the value of matplotlib.rcParams["errorbar.capsize"]. The number you give will be the length of the cap in points:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(1, 10, 10, dtype=int)
y = 2**x
yerr = np.sqrt(y)*10
fig, ax = plt.subplots()
ax.errorbar(x, y, yerr, solid_capstyle='projecting', capsize=5)
ax.grid(alpha=0.5, linestyle=':')
plt.show()
With the following code I create four histograms:
import numpy as np
import pandas as pd
data = pd.DataFrame(np.random.normal((1, 2, 3 , 4), size=(100, 4)))
data.hist(bins=10)
I want the histograms to look like this:
I know how to make it one graph at the time, see here
But how can I do it for multiple histograms without specifying each single one? Ideally I could use 'pd.scatter_matrix'.
Plot each histogram seperately and do the fit to each histogram as in the example you linked or take a look at the hist api example here. Essentially what should be done is
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)
for ax in [ax1, ax2, ax3, ax4]:
n, bins, patches = ax.hist(**your_data_here**, 50, normed=1, facecolor='green', alpha=0.75)
bincenters = 0.5*(bins[1:]+bins[:-1])
y = mlab.normpdf( bincenters, mu, sigma)
l = ax.plot(bincenters, y, 'r--', linewidth=1)
plt.show()
I'm trying to use seaborn to set axes properties for a (potentially large) number of matplotlib subfigures. What I would like to be able to do is generate all the plots with a single call to plt.subplots, and then set the subplot style when each actual plot is generated. Unfortunately it seems that the sns style only matters when the subplot is generated.
The code below is a minimum (non)working example. Ideally the two subfigures would have two different styles, but they do not.
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
def makeplt(sub, dat):
sub.contour(dat)
def makepltwith(sub, dat, style):
with sns.axes_style(style) as sty:
sub.contour(dat)
dat = np.arange(100).reshape(10, 10)
with sns.axes_style('ticks'):
fig, subs = plt.subplots(ncols=2)
makeplt(subs[0], dat)
makepltwith(subs[1], dat, 'darkgrid')
plt.show()
Is there a way to ensure that the second plot has the formatting I want it to have? The best idea I have on my own is to make some use of the sty object to manually reformat the sub object, but I can't come up with a pithy way of running through the formatting.
seaborn.__version__=0.7,
matplotlib.__version__=1.5 if that matters.
I encountered a similar problem and solved it like this:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
def add_sp_default(fig,pos):
ax = fig.add_subplot(pos)
return ax
def add_sp_image(fig,pos):
ax = fig.add_subplot(pos)
img=mpimg.imread('http://static.wixstatic.com/media/4afb41_998a1c7c0835c6eae5e159be3c2cfc07.png_1024')
ax.imshow(img)
ax.set_axis_off()
return ax
def add_sp_polar(fig,pos):
ax = fig.add_subplot(pos,projection='polar')
return ax
def add_sp_xkcd(fig,pos):
with plt.xkcd():
ax = fig.add_subplot(pos)
return ax
fig = plt.figure(figsize=(10,7))
ax1 = add_sp_default(fig,221)
ax2 = add_sp_image(fig,222)
ax3 = add_sp_polar(fig,223)
ax4 = add_sp_xkcd(fig,224)
plt.show()
No, it is not possible to do that. Axes styles are applied when the axes is created.
Of course, there are other ways to set up the subplots that don't involve making them all in one line of code, which would be more amenable to subplot-specific styles.