How to iteratively populate matplotlib gridspec with a multi-dense sea plot?

Question

Some kind of minimal code of what I'm working on. Some parameters may seem redundant, but I did not delete them.

import matplotlib import matplotlib.gridspec as gridspec matplotlib.use("macosx") import matplotlib.pyplot as plt import pandas as pd import seaborn as sns def plot_overlaid_2d_hist(data, plot_axis_x, plot_axis_y, plot_axis_x_lab, plot_axis_y_lab, group_by = "group_name"): # don't mind this for now df = data # Figure aspect w, h = plt.figaspect(1) fig = plt.figure(figsize = (w, h)) # Count the number of groups to make plots for n_groups = len(df.groupby(group_by)) gs = gridspec.GridSpec(nrows = n_groups, ncols = 1) subplot_id = 0 # Reshape data to make it work for name, group in df.groupby(group_by, sort = False): # Initialize subplot fig.add_subplot(gs[subplot_id, 0]) # Check if we get subplots with pyplot if subplot_id == 0: col = "red" else: col = "blue" plt.plot(x, y, color = col) # instantiate JointGrid # g = sns.JointGrid(group[plot_axis_x], # group[plot_axis_y], # data = group, # space = 0, # xlim = (0, 1.2), # ylim = (0, 1)) # # # Fix labels # g = g.set_axis_labels(xlabel = str(plot_axis_x_lab), # ylabel = str(plot_axis_y_lab)) # # # center scatter plot on top # g = g.plot_joint(plt.scatter, # s = 0.5, # alpha = 1, # linewidth = 1) # # # marginals plot # g = g.plot_marginals(sns.distplot, # kde = True, # kde_kws = dict(linewidth = 2, # alpha = 1, # bw = "Scott"), # hist_kws = dict(alpha = 1)) # Next plot in row +1 subplot_id += 1 # Output plt.tight_layout() # Attempts to fix alignment of subplot layout and axis titles plt.show() # quick data to check if the plots end up where they should x = [0.5, 0.5, 0.4, 0.4] y = [0.6, 0.4, 0.3, 0.4] grp = ["a", "a", "b", "b"] df = pd.DataFrame({"x":x, "y":y, "grp": grp}) plot_overlaid_2d_hist(data = df, group_by = "grp", plot_axis_x_lab = "x", plot_axis_y_lab = "x", plot_axis_y = "x", plot_axis_x = "x") 

Running the code with all the marine charts (g) commented that it works great for the native pip, but when I add to the multi-part marine charts, they appear on separate figures. I would like every 2D histogram with marginals and scatter to fill their own row / column gridspec.

Answer 1

Seeing that this question has already been asked before, I moved this answer to the old question. I would like to delete it here, but I can’t do it because it has already been accepted.

As indicated in several places ( this question , also this question ), several teams from the sea belt automatically create their own figure. This is hardcoded in the marine code, so there is currently no way to produce such graphs in existing numbers. These are PairGrid , FacetGrid , JointGrid , pairplot , jointplot and lmplot .

There is a sea ​​fork that will allow you to equip the seatpost with the appropriate classes so that the plot was created in a preexisting figure. To use this, you need to copy axisgrid.py from the fork to the folder with sea transport. Note that this limitation is currently used for use with matplotlib 2.1 (possibly also 2.0).

An alternative would be to create a sea shore shape and copy the axes to another shape. The principle of this is shown in this answer and can be extended to Searborn stories. The implementation is a bit more complicated than I originally expected. Below is the SeabornFig2Grid class, which can be called using an instance of the north grid (returning any of the above commands), the matplotlib shape and a subplot_spec , which is the gridspec grid gridspec .

 import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec import seaborn as sns import numpy as np class SeabornFig2Grid(): def __init__(self, seaborngrid, fig, subplot_spec): self.fig = fig self.sg = seaborngrid self.subplot = subplot_spec if isinstance(self.sg, sns.axisgrid.FacetGrid) or \ isinstance(self.sg, sns.axisgrid.PairGrid): self._movegrid() elif isinstance(self.sg, sns.axisgrid.JointGrid): self._movejointgrid() self._finalize() def _movegrid(self): """ Move PairGrid or Facetgrid """ self._resize() n = self.sg.axes.shape[0] m = self.sg.axes.shape[1] self.subgrid = gridspec.GridSpecFromSubplotSpec(n,m, subplot_spec=self.subplot) for i in range(n): for j in range(m): self._moveaxes(self.sg.axes[i,j], self.subgrid[i,j]) def _movejointgrid(self): """ Move Jointgrid """ h= self.sg.ax_joint.get_position().height h2= self.sg.ax_marg_x.get_position().height r = int(np.round(h/h2)) self._resize() self.subgrid = gridspec.GridSpecFromSubplotSpec(r+1,r+1, subplot_spec=self.subplot) self._moveaxes(self.sg.ax_joint, self.subgrid[1:, :-1]) self._moveaxes(self.sg.ax_marg_x, self.subgrid[0, :-1]) self._moveaxes(self.sg.ax_marg_y, self.subgrid[1:, -1]) def _moveaxes(self, ax, gs): #/questions/110685/matplotlib-can-i-create-axessubplot-objects-then-add-them-to-a-figure-instance/689918#689918 ax.remove() ax.figure=self.fig self.fig.axes.append(ax) self.fig.add_axes(ax) ax._subplotspec = gs ax.set_position(gs.get_position(self.fig)) ax.set_subplotspec(gs) def _finalize(self): plt.close(self.sg.fig) self.fig.canvas.mpl_connect("resize_event", self._resize) self.fig.canvas.draw() def _resize(self, evt=None): self.sg.fig.set_size_inches(self.fig.get_size_inches()) 

Using this class will look like this:

 import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec import seaborn as sns; sns.set() import SeabornFig2Grid as sfg iris = sns.load_dataset("iris") tips = sns.load_dataset("tips") # An lmplot g0 = sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips, palette=dict(Yes="g", No="m")) # A PairGrid g1 = sns.PairGrid(iris, hue="species") g1.map(plt.scatter, s=5) # A FacetGrid g2 = sns.FacetGrid(tips, col="time", hue="smoker") g2.map(plt.scatter, "total_bill", "tip", edgecolor="w") # A JointGrid g3 = sns.jointplot("sepal_width", "petal_length", data=iris, kind="kde", space=0, color="g") fig = plt.figure(figsize=(13,8)) gs = gridspec.GridSpec(2, 2) mg0 = sfg.SeabornFig2Grid(g0, fig, gs[0]) mg1 = sfg.SeabornFig2Grid(g1, fig, gs[1]) mg2 = sfg.SeabornFig2Grid(g2, fig, gs[3]) mg3 = sfg.SeabornFig2Grid(g3, fig, gs[2]) gs.tight_layout(fig) #gs.update(top=0.7) plt.show() 

Please note that there may be several drawbacks from the copy axes, and the above has not been fully tested.