Seaborn Facetgrid中热图的变化图

Question

Sorry in advance the number of images, but they help demonstrate the issue

I have built a dataframe which contains film thickness measurements, for a number of substrates, for a number of layers, as function of coordinates:

|    | Sub | Result | Layer | Row | Col |
|----|-----|--------|-------|-----|-----|
|  0 |   1 |   2.95 | 3 - H |   0 |  72 |
|  1 |   1 |   2.97 | 3 - V |   0 |  72 |
|  2 |   1 |   0.96 | 1 - H |   0 |  72 |
|  3 |   1 |   3.03 | 3 - H | -42 |  48 |
|  4 |   1 |   3.04 | 3 - V | -42 |  48 |
|  5 |   1 |   1.06 | 1 - H | -42 |  48 |
|  6 |   1 |   3.06 | 3 - H |  42 |  48 |
|  7 |   1 |   3.09 | 3 - V |  42 |  48 |
|  8 |   1 |   1.38 | 1 - H |  42 |  48 |
|  9 |   1 |   3.05 | 3 - H | -21 |  24 |
| 10 |   1 |   3.08 | 3 - V | -21 |  24 |
| 11 |   1 |   1.07 | 1 - H | -21 |  24 |
| 12 |   1 |   3.06 | 3 - H |  21 |  24 |
| 13 |   1 |   3.09 | 3 - V |  21 |  24 |
| 14 |   1 |   1.05 | 1 - H |  21 |  24 |
| 15 |   1 |   3.01 | 3 - H | -63 |   0 |
| 16 |   1 |   3.02 | 3 - V | -63 |   0 |

and this continues for >10 subs (per batch), and 13 sites per sub, and for 3 layers - this df is a composite. I am attempting to present the data as a facetgrid of heatmaps (adapting code from How to make heatmap square in Seaborn FacetGrid - thanks!)

I can plot a subset of the df quite happily:

spam = df.loc[df.Sub== 6].loc[df.Layer == '3 - H']
spam_p= spam.pivot(index='Row', columns='Col', values='Result')

sns.heatmap(spam_p, cmap="plasma")

BUT - there are some missing results, where the layer measurement errors (returns '10000') so I've replaced these with NaNs:

df.Result.replace(10000, np.nan)

To plot a facetgrid to show all subs/layers, I've written the following code:

def draw_heatmap(*args, **kwargs):
    data = kwargs.pop('data')
    d = data.pivot(columns=args[0], index=args[1], 
    values=args[2])
    sns.heatmap(d, **kwargs)

fig = sns.FacetGrid(spam, row='Wafer', 
col='Feature', height=5, aspect=1)

fig.map_dataframe(draw_heatmap, 'Col', 'Row', 'Result', cbar=False, cmap="plasma", annot=True, annot_kws={"size": 20})

which yields:

It has automatically adjusted axes to not show any positions where there is a NaN. I have tried masking (see https://github.com/mwaskom/seaborn/issues/375 ) but just errors out with Inconsistent shape between the condition and the input (got (237, 15) and (7, 7)) .

And the result of this is, when not using the cropped down dataset (ie df instead of spam , the code generates the following Facetgrid):

Plots featuring missing values at extreme (edge) coordinate positions make the plot shift within the axes - here all apparently to the upper left. Sub #5, layer 3-H should look like:

ie blanks in the places where there are NaN s.

Why is the facetgrid shifting the entire plot up and/or left? The alternative is dynamically generating subplots based on a sub/layer-count (ugh!).

Any help very gratefully received.

Full dataset for 2 layers of sub 5:

    Sub Result  Layer   Row     Col
0   5   2.987   3 - H   0       72
1   5   0.001   1 - H   0       72
2   5   1.184   3 - H   -42     48
3   5   1.023   1 - H   -42     48
4   5   3.045   3 - H   42      48 
5   5   0.282   1 - H   42      48
6   5   3.083   3 - H   -21     24 
7   5   0.34    1 - H   -21     24
8   5   3.07    3 - H   21      24
9   5   0.41    1 - H   21      24
10  5   NaN     3 - H   -63     0
11  5   NaN     1 - H   -63     0
12  5   3.086   3 - H   0       0
13  5   0.309   1 - H   0       0
14  5   0.179   3 - H   63      0
15  5   0.455   1 - H   63      0
16  5   3.067   3 - H   -21    -24
17  5   0.136   1 - H   -21    -24
18  5   1.907   3 - H   21     -24
19  5   1.018   1 - H   21     -24
20  5   NaN     3 - H   -42    -48
21  5   NaN     1 - H   -42    -48
22  5   NaN     3 - H   42     -48
23  5   NaN     1 - H   42     -48
24  5   NaN     3 - H   0      -72
25  5   NaN     1 - H   0      -72

Answer 1

You may create a list of unique column and row labels and reindex the pivot table with them.

cols = df["Col"].unique()
rows = df["Row"].unique()

pivot = data.pivot(...).reindex_axis(cols, axis=1).reindex_axis(rows, axis=0)

as seen in this answer .

Some complete code:

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

r = np.repeat([0,-2,2,-1,1,-3],2)
row = np.concatenate((r, [0]*2, -r[::-1]))
c = np.array([72]*2+[48]*4 + [24]*4 + [0]* 3)
col = np.concatenate((c,-c[::-1]))

df = pd.DataFrame({"Result" : np.random.rand(26),
                   "Layer" : list("AB")*13,
                   "Row" : row, "Col" : col})

df1 = df.copy()
df1["Sub"] = [5]*len(df1)
df1.at[10:11,"Result"] = np.NaN
df1.at[20:,"Result"] = np.NaN

df2 = df.copy()
df2["Sub"] = [3]*len(df2)
df2.at[0:2,"Result"] = np.NaN

df = pd.concat([df1,df2])

cols = np.unique(df["Col"].values)
rows = np.unique(df["Row"].values)

def draw_heatmap(*args, **kwargs):
    data = kwargs.pop('data')
    d = data.pivot(columns=args[0], index=args[1], 
                   values=args[2])
    d = d.reindex_axis(cols, axis=1).reindex_axis(rows, axis=0)
    print d
    sns.heatmap(d,  **kwargs)

grid = sns.FacetGrid(df, row='Sub', col='Layer', height=3.5, aspect=1 )

grid.map_dataframe(draw_heatmap, 'Col', 'Row', 'Result', cbar=False, 
                  cmap="plasma", annot=True)

plt.show()