I am trying to plot a scatter plot on a background using basemap. But it's overwriting the background. How do I retain the background?
I am using this code
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
m = Basemap(projection='merc',llcrnrlat=-80,urcrnrlat=80,llcrnrlon=-180,urcrnrlon=180,lat_ts=20,resolution='c')
m.bluemarble()
x, y = m(list(longitude), list(latitude))
plt.scatter(x,y,1,marker='o',color='Red')
plt.show()
But as soon as I run the scatter plot, its overwriting background image. How can I overlay the scatter plot on the image.
This is how to plot a series of points on top of a raster map. Note that the bluemarble image is huge, so a full scale (1.0 or default) plot of it should be avoided. The code is based on yours.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
# make up some data for scatter plot
lats = np.random.randint(-75, 75, size=20)
lons = np.random.randint(-179, 179, size=20)
fig = plt.gcf()
fig.set_size_inches(8, 6.5)
m = Basemap(projection='merc', \
llcrnrlat=-80, urcrnrlat=80, \
llcrnrlon=-180, urcrnrlon=180, \
lat_ts=20, \
resolution='c')
m.bluemarble(scale=0.2) # full scale will be overkill
m.drawcoastlines(color='white', linewidth=0.2) # add coastlines
x, y = m(lons, lats) # transform coordinates
plt.scatter(x, y, 10, marker='o', color='Red')
plt.show()
The resulting plot:
I realize it's an old question but in case anyone comes here with the same problem as I did.
The trick is to give a higher zorder for the scatter plot than for the .bluemarble().
m.scatter(x, y, 10, marker='o', color='Red', zorder=3)
More info here: https://matplotlib.org/3.1.0/gallery/misc/zorder_demo.html
I'm not entirely sure what you mean by "overwriting the background". When you use plt.scatter(), it will plot the points over the map, so it will display the points over the background.
Just based off the code provided, I think you're issue here is m(list(longitude), list(latitude)).
If you have multiple points in a list, you want to loop over them.
lats = [32, 38, 35]
lons = [-98, -79, -94]
x, y = m(lons, lats)
for i in range(len(lats)):
plt.scatter(x, y, marker = 'o')
If it's only one single point,
lat, lon = 32, -92
x, y = m(lon, lat)
plt.scatter(x, y, marker = 'o')
The styling of the points can be found in the matplotlib documentation.
Related
So, here is my code:
import pandas as pd
import scipy.stats as st
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorLocator
from fitter import Fitter, get_common_distributions
df = pd.read_csv("project3.csv")
bins = [282.33, 594.33, 906.33, 1281.33, 15030.33, 1842.33, 2154.33, 2466.33, 2778.33, 3090.33, 3402.33]
#declaring
facecolor = '#EAEAEA'
color_bars = '#3475D0'
txt_color1 = '#252525'
txt_color2 = '#004C74'
fig, ax = plt.subplots(1, figsize=(16, 6), facecolor=facecolor)
ax.set_facecolor(facecolor)
n, bins, patches = plt.hist(df.City1, color=color_bars, bins=10)
#grid
minor_locator = AutoMinorLocator(2)
plt.gca().xaxis.set_minor_locator(minor_locator)
plt.grid(which='minor', color=facecolor, lw = 0.5)
xticks = [(bins[idx+1] + value)/2 for idx, value in enumerate(bins[:-1])]
xticks_labels = [ "{:.0f}-{:.0f}".format(value, bins[idx+1]) for idx, value in enumerate(bins[:-1])]
plt.xticks(xticks, labels=xticks_labels, c=txt_color1, fontsize=13)
#beautify
ax.tick_params(axis='x', which='both',length=0)
plt.yticks([])
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
for idx, value in enumerate(n):
if value > 0:
plt.text(xticks[idx], value+5, int(value), ha='center', fontsize=16, c=txt_color1)
plt.title('Histogram of rainfall in City1\n', loc = 'right', fontsize = 20, c=txt_color1)
plt.xlabel('\nCentimeters of rainfall', c=txt_color2, fontsize=14)
plt.ylabel('Frequency of occurrence', c=txt_color2, fontsize=14)
plt.tight_layout()
#plt.savefig('City1_Raw.png', facecolor=facecolor)
plt.show()
city1 = df['City1'].values
f = Fitter(city1, distributions=get_common_distributions())
f.fit()
fig = f.plot_pdf(names=None, Nbest=4, lw=1, method='sumsquare_error')
plt.show()
print(f.get_best(method = 'sumsquare_error'))
The issue is with the plots it shows. The first histogram it generates is
Next I get another graph with best fitted distributions which is
Then an output statement
{'chi2': {'df': 10.692966790090342, 'loc': 16.690849400411103, 'scale': 118.71595997157786}}
Process finished with exit code 0
I have a couple of questions. Why is chi2, the best fitted distribution not plotted on the graph?
How do I plot these distributions on top of the histograms and not separately? The hist() function in fitter library can do that but there I don't get to control the bins and so I end up getting like 100 bins with some flat looking data.
How do I solve this issue? I need to plot the best fit curve on the histogram that looks like image1. Can I use any other module/package to get the work done in similar way? This uses least squares fit but I am OK with least likelihood or log likelihood too.
Simple way of plotting things on top of each other (using some properties of the Fitter class)
import scipy.stats as st
import matplotlib.pyplot as plt
from fitter import Fitter, get_common_distributions
from scipy import stats
numberofpoints=50000
df = stats.norm.rvs( loc=1090, scale=500, size=numberofpoints)
fig, ax = plt.subplots(1, figsize=(16, 6))
n, bins, patches = ax.hist( df, bins=30, density=True)
f = Fitter(df, distributions=get_common_distributions())
f.fit()
errorlist = sorted(
[
[f._fitted_errors[dist], dist]
for dist in get_common_distributions()
]
)[:4]
for err, dist in errorlist:
ax.plot( f.x, f.fitted_pdf[dist] )
plt.show()
Using the histogram normalization, one would need to play with scaling to generalize again.
I've been creating uneven subplots in matplotlib based on this question. The gridspec solution (third answer) worked a little better for me as it gives a bit more flexibility for the exact sizes of the subplots.
When I add a plot of a 2D array with imshow() the affected subplot is resized to the shape of the array. Is there any way to avoid that and keep the subplot-sizes (or rather aspect-ratio) fixed?
Here's the example code and the resulting image with the subplot-sizes I'm happy with:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec
# generate data
x = np.arange(0, 10, 0.2)
y = np.sin(x)
# plot
fig = plt.figure(figsize=(12, 9))
gs = gridspec.GridSpec(20, 20)
ax1 = fig.add_subplot(gs[0:5,0:11])
ax1.plot(x, y)
ax2 = fig.add_subplot(gs[6:11,0:11])
ax2.plot(y, x)
ax3 = fig.add_subplot(gs[12:20,0:11])
ax3.plot(y, x)
ax4 = fig.add_subplot(gs[0:9,13:20])
ax4.plot(x, y)
ax5 = fig.add_subplot(gs[11:20,13:20])
ax5.plot(y, x)
plt.show()
This is what happens if I additionally plot data from a 2D array with the following lines (insert before plt.show):
2Ddata = np.arange(0, 10, 0.1).reshape(10, 10)
im = ax3.imshow(2Ddata, cmap='rainbow')
How can I restore the original size of the subplot from ax3 (lower left corner)?
Including the line ax3.set_aspect('auto') seems to have solved the issue.
I am using matplotlib to draw a plot. What I want to achieve is to connect points if one condition is met. For instance, if I have a dataframe like the following:
import os
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
df=pd.DataFrame({'dates': [2001, 2002, 2003, 2004, 2005, 2006], 'census_people': [306,327,352,478,250, 566], 'census_houses': [150,200,249,263, 180, 475]}) #I changed the dates from strings to ints
I could create plots like this use the following codes:
plt.plot('dates','census_houses',data=df[df['dates'] < 2004] ,marker='o',color='orange', linewidth=2)
plt.plot('dates','census_houses',data=df[df['dates'] > 2002] ,marker='o',color='orange', linewidth=2, linestyle = '--')
The plot is like the following:
However, what I truely want is, for instance, use the dashed line to connect points if the census_houses is bigger than 250. How to achieve this using matplotlib? Any suggestions and insights are welcomed! Thank you~
This effect can be achieved by applying clipping paths. In this example I suppose the full line completely draws over the dashed line, so only clipping of the full line is needed.
In the example, the special value for the y-axis is set to 220, different colors and very thick lines are used, to better see what is happening. The parameters for Rectangle((x, y), width, height) are setting y to the desired cut-off value, x is some position far left, width makes sure that x + width is far right and height is a large positive number to clip above the line, negative to clip below the line.
This post has more information about clipping paths.
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib.patches import Rectangle
def do_clipping(patches, special_y, keep_below=True, ax=None):
ax = ax or plt.gca()
xmin, xmax = plt.xlim()
ymin, ymax = plt.ylim()
height = ymax - ymin
if keep_below:
height = -height
clip_rect = Rectangle((xmin, special_y), xmax - xmin, height,
transform=ax.transData)
for p in patches:
p.set_clip_path(clip_rect)
df = pd.DataFrame({'dates': [2001, 2002, 2003, 2004, 2005, 2006],
'census_houses': [150, 200, 249, 263, 180, 475]})
plt.plot('dates', 'census_houses', data=df, color='limegreen', linewidth=10, linestyle='--')
plot_patches = plt.plot('dates', 'census_houses', data=df, color='crimson', linewidth=10)
do_clipping(plot_patches, 220)
plt.show()
I have a subplot with a single legend entry. I am placing the legend at the bottom of the figure and using mode='expand'; however, the single legend entry is placed to the very left of the legend box. To my understanding, changing kwargs such as bbox_to_anchor changes the legend box parameters but not the parameters of the symbols/text within. Below is an example to reproduce my issue.
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-10, 10, 21)
y = np.exp(x)
z = x **2
fig, axes = plt.subplots(nrows=1, ncols=2)
axes[0].plot(x, y, color='r', label='exponential')
axes[1].plot(x, z, color='b')
# handles, labels = axes[0].get_legend_handles_labels()
plt.subplots_adjust(bottom=0.125)
fig.legend(mode='expand', loc='lower center')
plt.show()
plt.close(fig)
This code produces . How can I change the position of the symbol and text such that they are centered in the legend box?
PS: I am aware that exponential is a bad label for this subplot since it only describes the first subfigure. But, this is just for examples-sake so that I can apply it to my actual use-case.
The legend entries are placed using a HPacker object. This does not allow to be centered. The behaviour is rather that those HPackers are "justified" (similar to the "justify" option in common word processing software).
A workaround would be to create three (or any odd number of) legend entries, such that the desired entry is in the middle. This would be accomplished via the ncol argument and the use of "dummy" entries (which might be transparent and have no associated label).
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-10, 10, 21)
y = np.exp(x)
z = x **2
fig, axes = plt.subplots(nrows=1, ncols=2)
fig.subplots_adjust(bottom=0.125)
l1, = axes[0].plot(x, y, color='r', label='exponential')
axes[1].plot(x, z, color='b')
dummy = plt.Line2D([],[], alpha=0)
fig.legend(handles=[dummy, l1, dummy],
mode='expand', loc='lower center', ncol=3)
plt.show()
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()