Facing weird problem when trying to plot simple lat/ lon points - matplotlib

Question

I have following dataframe (corresponding csv is hosted here: http://www.sharecsv.com/s/3795d862c1973efa311d8a770e978215/t.csv)
lat lon
count 6159.000000 6159.000000
mean 37.764859 -122.355491
std 0.028214 0.038874
min 37.742200 -122.482783
25% 37.746317 -122.360133
50% 37.746417 -122.333717
75% 37.785825 -122.331300
max 37.818133 -122.331167
Following code plots correctly:
test_ax = plt.axes(projection=ccrs.Mercator())
test_ax.plot(test_df['lon'], test_df['lat'], color="blue", linewidth=4, alpha=1.0,
transform=ccrs.Geodetic())
plt.show()
But if I take one subset, it doesn't:
test_ax = plt.axes(projection=ccrs.Mercator())
test_ax.plot(test_df['lon'][:1001], test_df['lat'][:1001], color="blue", linewidth=4, alpha=1.0,
transform=ccrs.Geodetic())
plt.show()
But does so with another subset.
test_ax = plt.axes(projection=ccrs.Mercator())
test_ax.plot(test_df['lon'][:3501], test_df['lat'][:3501], color="blue", linewidth=4, alpha=1.0,
transform=ccrs.Geodetic())
plt.show()
I am pretty sure I am doing something stupid, but I am just unable to figure the reason for this behaviour.
Edit:
On further experimentation I found that if I set the extent of map manually to include 0 meridian, the plot for the subset :1001, which wasn't showing earlier starts showing (the blue dot near San Francisco).
test_ax = plt.axes(projection=ccrs.Mercator())
test_ax.plot(test_df['lon'][:1001], test_df['lat'][:1001], color="blue", linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
test_ax.coastlines()
test_ax.set_extent([-130, 0, 30, 40], crs=ccrs.Geodetic())
test_ax.gridlines(draw_labels=True)
plt.show()
Edit: with reproducible example
(For jupyter notebook)
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import pandas as pd
df_csv_url = 'http://www.sharecsv.com/dl/76dd767525a37180ca54cd1d9314b9dc/t1.csv'
test_df = pd.read_csv(df_csv_url)
figure_params = { 'width': 9.6, 'height': 5.4 }
fig = plt.figure(
figsize=(figure_params["width"], figure_params["height"])
)
test_ax = fig.add_axes((0, 0.5, 0.5, 0.5), projection=ccrs.Mercator(), label="map1")
test_ax.plot(test_df['lon'], test_df['lat'], color="blue", linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
test_ax.coastlines()
test_ax.gridlines(draw_labels=True)
test_ax.set_title("Path doesn\'t show", y=1.5)
# Including 0 meridian in extent shows the path
test_ax1 = fig.add_axes((0, 0, 0.5, 0.5), projection=ccrs.Mercator(), label="map2")
test_ax1.plot(test_df['lon'], test_df['lat'], color="blue", linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
test_ax1.set_extent([-130, 0, 30, 40], crs=ccrs.Geodetic())
test_ax1.coastlines()
test_ax1.gridlines(draw_labels=True)
test_ax1.set_title("Path shows (blue dot near San Francisco)", y=1.1)
plt.show()
Edit
(with simplified reproducible example)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
test_df = pd.DataFrame()
test_df['lon'] = np.linspace(-120, -60, num=1000)
test_df['lat'] = 38
test_df1 = pd.DataFrame()
test_df1['lon'] = np.linspace(-120, -60, num=1001)
test_df1['lat'] = 38
fig = plt.figure()
meridian=0
test_ax = fig.add_axes((0, 0, 1, 0.6), projection=ccrs.Mercator())
test_ax.plot(test_df['lon'], test_df['lat'], color="blue", linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
test_ax.coastlines()
test_ax.set_extent((-125, meridian, 36, 38))
gl = test_ax.gridlines(draw_labels=True)
gl.xlabels_top = False
gl.ylabels_left = False
test_ax.set_title('Path with {} points, eastern edge={}'.format(len(test_df),meridian))
test_ax1 = fig.add_axes((0, 0.7, 1, 0.6), projection=ccrs.Mercator())
test_ax1.plot(test_df1['lon'], test_df1['lat'], color="red", linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
test_ax1.coastlines()
test_ax1.set_extent((-125, meridian, 36, 38))
gl1 = test_ax1.gridlines(draw_labels=True)
gl1.xlabels_top = False
gl1.ylabels_left = False
test_ax1.set_title('Path with {} points, eastern edge={}'.format(len(test_df1),meridian))
meridian=-10
test_ax2 = fig.add_axes((0, 1.4, 1, 0.6), projection=ccrs.Mercator())
test_ax2.plot(test_df['lon'], test_df['lat'], color="black", linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
test_ax2.coastlines()
test_ax2.set_extent((-125, -10, 36, 38))
gl2 = test_ax2.gridlines(draw_labels=True)
gl2.xlabels_top = False
gl2.ylabels_left = False
test_ax2.set_title('Path with {} points, eastern edge={}'.format(len(test_df),meridian))
test_ax3 = fig.add_axes((0, 2.1, 1, 0.6), projection=ccrs.Mercator())
test_ax3.plot(test_df1['lon'], test_df1['lat'], color="green", linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
test_ax3.coastlines()
test_ax3.set_extent((-125, -10, 36, 38))
gl3 = test_ax3.gridlines(draw_labels=True)
gl3.xlabels_top = False
gl3.ylabels_left = False
test_ax3.set_title('Path with {} points, eastern edge={}'.format(len(test_df1),meridian))
plt.show()

Answer 1

Given there seems to be a some issue with cartopy at play, the best work around I see is to split your data into chunks of less than 1000, and then plot all parts of it.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
test_df = pd.DataFrame()
test_df['lon'] = np.linspace(-120, -60, num=1001)
test_df['lat'] = 38
fig = plt.figure()
meridian=0
test_ax = fig.add_axes((0, 0.05, 1, 0.3), projection=ccrs.Mercator())
test_ax.plot(test_df['lon'], test_df['lat'], color="red",
linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
test_ax.coastlines()
test_ax.set_extent((-125, meridian, 36, 38))
gl = test_ax.gridlines(draw_labels=True)
gl.xlabels_top = False
gl.ylabels_left = False
test_ax.set_title('Path with {} points, eastern edge={}'.format(len(test_df),meridian))
meridian=-10
test_ax3 = fig.add_axes((0, 0.55, 1, 0.3), projection=ccrs.Mercator())
# plot first 500
test_ax3.plot(test_df['lon'][:500], test_df['lat'][:500], color="green",
linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
# plot to cover the gap
test_ax3.plot(test_df['lon'][499:501], test_df['lat'][499:501], color="blue",
linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
# plot last 501
test_ax3.plot(test_df['lon'][500:], test_df['lat'][500:], color="yellow",
linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
test_ax3.coastlines()
test_ax3.set_extent((-125, -10, 36, 38))
gl3 = test_ax3.gridlines(draw_labels=True)
gl3.xlabels_top = False
gl3.ylabels_left = False
test_ax3.set_title('Path with {} points, eastern edge={}'.format(len(test_df),meridian))
plt.show()
For the case of 1001 points, I just split it into a section of 500 points and a section of 501 points.
Since you are plotting the line, I also added the plot to cover the gap, shown in blue when you zoom in.
The reason to set up the gap filler instead of overlapping the two sections comes in if you are also plotting the points, like this:
test_ax3.plot(test_df['lon'][:500], test_df['lat'][:500], color="green",
linewidth=1, alpha=1.0, transform=ccrs.Geodetic(), marker='.')
# plot to cover the gap
test_ax3.plot(test_df['lon'][499:501], test_df['lat'][499:501], color="blue",
linewidth=1, alpha=1.0, transform=ccrs.Geodetic(), marker=None)
# plot last 501
test_ax3.plot(test_df['lon'][500:], test_df['lat'][500:], color="yellow",
linewidth=1, alpha=1.0, transform=ccrs.Geodetic(), marker='.')
By separating out the filler, you can make sure you aren't duplicating points, which could be an issue if you have an alpha value of less than 1.0.
Applying this to your original data, you can create a function to loop over the dataframe in chunks equal to whatever size you want.:
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import pandas as pd
PLOT_LIMIT = 1000
df_csv_url = 'http://www.sharecsv.com/dl/76dd767525a37180ca54cd1d9314b9dc/t1.csv'
test_df = pd.read_csv(df_csv_url)
figure_params = { 'width': 9.6, 'height': 5.4 }
fig = plt.figure(
figsize=(figure_params["width"], figure_params["height"])
)
print(len(test_df['lon']))
def ax_plot(test_ax, test_df):
# this function will loop over the dataframe in chunks equal to PLOT_LIMIT
len_df = len(test_df)
n=0
for i in range(len_df//PLOT_LIMIT):
test_ax.plot(test_df['lon'][1000*i:1000*(i+1)], test_df['lat'][1000*i:1000*(i+1)], color="blue",
linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
if (len_df-((n+1)*PLOT_LIMIT)) != 0:
test_ax.plot(test_df['lon'][(1000*i)-1:(1000*(i+1))+1], test_df['lat'][(1000*i)-1:(1000*(i+1))+1], color="blue",
linewidth=4, alpha=1.0, transform=ccrs.Geodetic(), marker='None')
n+=1
test_ax.plot(test_df['lon'][1000*n:], test_df['lat'][1000*n:], color="blue",
linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
test_ax1 = fig.add_axes((0, 0.55, 1, 0.45), projection=ccrs.Mercator(), label="map1")
ax_plot(test_ax1, test_df)
test_ax1.coastlines()
test_ax1.gridlines(draw_labels=True)
test_ax1.set_title("Path shows", y=1.5)
# Including 0 meridian in extent shows the path
test_ax2 = fig.add_axes((0, 0.1, 1, 0.45), projection=ccrs.Mercator(), label="map2")
ax_plot(test_ax2, test_df)
test_ax2.set_extent([-130, -30, 30, 40], crs=ccrs.Geodetic())
test_ax2.coastlines()
test_ax2.gridlines(draw_labels=True)
test_ax2.set_title("Path shows (blue dot near San Francisco)", y=1.1)
plt.show()
As you can see, you should now have flexibility in setting the viewing window on the map. I haven't checked edge cases like crossing the antimeridian, but in the cases presented it is working.

Answer 2

I have been able to find another workaround. If the points are transformed prior to using the plot function (instead of passing the transform parameter), it works.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
test_df = pd.DataFrame()
test_df['lon'] = np.linspace(-120, -60, num=1000)
test_df['lat'] = 38
test_df1 = pd.DataFrame()
test_df1['lon'] = np.linspace(-120, -60, num=1001)
test_df1['lat'] = 38
fig = plt.figure()
meridian=0
test_ax = fig.add_axes((0, 0, 1, 0.6), projection=ccrs.Mercator())
test_ax.plot(test_df['lon'], test_df['lat'], color="blue", linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
test_ax.coastlines()
test_ax.set_extent((-125, meridian, 36, 38))
gl = test_ax.gridlines(draw_labels=True)
gl.xlabels_top = False
gl.ylabels_left = False
test_ax.set_title('Path with {} points, eastern edge={}'.format(len(test_df),meridian))
test_ax1 = fig.add_axes((0, 0.7, 1, 0.6), projection=ccrs.Mercator())
test_ax1.plot(test_df1['lon'], test_df1['lat'], color="red", linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
test_ax1.coastlines()
test_ax1.set_extent((-125, meridian, 36, 38))
gl1 = test_ax1.gridlines(draw_labels=True)
gl1.xlabels_top = False
gl1.ylabels_left = False
test_ax1.set_title('Path with {} points, eastern edge={}'.format(len(test_df1),meridian))
meridian=-10
test_ax2 = fig.add_axes((0, 1.4, 1, 0.6), projection=ccrs.Mercator())
test_ax2.plot(test_df['lon'], test_df['lat'], color="black", linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
test_ax2.coastlines()
test_ax2.set_extent((-125, -10, 36, 38))
gl2 = test_ax2.gridlines(draw_labels=True)
gl2.xlabels_top = False
gl2.ylabels_left = False
test_ax2.set_title('Path with {} points, eastern edge={}'.format(len(test_df),meridian))
test_ax3 = fig.add_axes((0, 2.1, 1, 0.6), projection=ccrs.Mercator())
test_ax3.plot(test_df1['lon'], test_df1['lat'], color="green", linewidth=4, alpha=1.0, transform=ccrs.Geodetic())
test_ax3.coastlines()
test_ax3.set_extent((-125, -10, 36, 38))
gl3 = test_ax3.gridlines(draw_labels=True)
gl3.xlabels_top = False
gl3.ylabels_left = False
test_ax3.set_title('Path with {} points, eastern edge={}'.format(len(test_df1),meridian))
test_ax4 = fig.add_axes((0, 2.8, 1, 0.6), projection=ccrs.Mercator())
# Instead of transforming within the plot function, transform and then plot
transformed_points = ccrs.Mercator().transform_points(ccrs.Geodetic(), test_df1['lon'].values, test_df1['lat'].values)
test_ax4.plot([p[0] for p in transformed_points], [p[1] for p in transformed_points], color="green", linewidth=4, alpha=1.0)
test_ax4.coastlines()
test_ax4.set_extent((-125, -10, 36, 38))
gl3 = test_ax4.gridlines(draw_labels=True)
gl3.xlabels_top = False
gl3.ylabels_left = False
test_ax4.set_title('Path with {} prior transformed points, eastern edge={}'.format(len(test_df1),meridian))
plt.show()