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 (i.e. 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:
i.e. 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.
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
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()