API Reference
Below API docs are auto-generated by mkdocstrings.
num_heatmap(df, value, row='MAP_ROW', col='MAP_COL', cmap='jet', title=None, vlim=None, vsigma=None, vrange=None, ax=None) ¶
Create Wafer Heatmap for Numerical Variable
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
df | pd.DataFrame | Wafer Data | required |
value | str | Column name of the numeric variable | required |
row | str | Wafer Map Row (Y Coordinate) | 'MAP_ROW' |
col | str | Wafer Map Col (X Coordinate) | 'MAP_COL' |
cmap | str | ColorMap | 'jet' |
title | str | Title | None |
vlim | tuple | (zmin,zmax) limits of the colorbar, will ignore the vsigma/vrange if provided | None |
vsigma | float | colorbar range is center mean±3*vsigma if vsigma is provided | None |
vrange | float | Range of the colorbar, works when vlim is not available and ignore the vsigma | None |
ax | matplotlib.axes | Axe to plot on | None |
Returns:
| Name | Type | Description |
|---|---|---|
ax | matplotlib.axes | Matplotlib Axes |
cat_heatmap(df, item, row='MAP_ROW', col='MAP_COL', title=None, code_dict=None, qty_limit=10, colors=DEFECT_COLORS, verbose=False, ax=None) ¶
Create Wafer Heatmap for Categorical Variable
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
df | pd.DataFrame | Wafer Data | required |
item | str | Column name of the categorical variable | required |
row | str | Wafer Map Row (Y Coordinate) | 'MAP_ROW' |
col | str | Wafer Map Col (X Coordinate) | 'MAP_COL' |
title | str | Title | None |
code_dict | dict | {‘orignal’:’new_code’} Replace original code with code_dict | None |
qty_limit | int | add restrictions on the total unique variables to plot | 10 |
colors | list | [‘lightgreen’, ‘red’, ‘orange’, ‘blue’, ‘purple’, ‘cyan’, ‘pink’, ‘yellow’, ‘lightblue’, ‘gold’, ‘darkblue’, ‘gray’, ‘darkred’, ‘black’] | DEFECT_COLORS |
verbose | bool | Return the summary of categorical data or not. | False |
ax | matplotlib.axes | Axe to plot on | None |
Returns:
| Name | Type | Description |
|---|---|---|
ax | matplotlib.axes | Matplotlib Axes and additional data if verbose is True |
wafermap(df, value, row='MAP_ROW', col='MAP_COL', title=None, vrange=None, vsigma=None, wftype=None) ¶
Create Wafer Heatmap
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
df | pd.DataFrame | Wafer Data | required |
value | str | Column name of the numeric variable | required |
row | str | Wafer Map Row (Y Coordinate) | 'MAP_ROW' |
col | str | Wafer Map Col (X Coordinate) | 'MAP_COL' |
title | str | Title | None |
vrange | float | Range of Y-axis/Colorbar, will overide the vsigma setting | None |
vsigma | float | color bar range is center mean±3*vsigma if vsigma is provided | None |
wftype | str | Wafer Layout Type, Plot additional trend chart if provided | None |
Returns:
| Name | Type | Description |
|---|---|---|
fig | matplotlib.figure.Figure | Figure |
defectmap(df, defect_col, ok_codes=['OK', 'BINA'], code_dict=None, row='MAP_ROW', col='MAP_COL', qty_limit=10, colors=DEFECT_COLORS, title=None) ¶
Create Wafer DefectMap
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
df | pd.DataFrame | Wafer Data | required |
defect_col | str | Column name of the defect code | required |
ok_codes | list | list of code being treated as ‘OK’ | ['OK', 'BINA'] |
code_dict | dict | {‘orignal’:’new_code’} Replace original code with code_dict | None |
row | str | Wafer Map Row (Y Coordinate) | 'MAP_ROW' |
col | str | Wafer Map Col (X Coordinate) | 'MAP_COL' |
qty_limit | int | add restrictions on the total unique variables to show in the plot | 10 |
colors | list | [‘lightgreen’, ‘red’, ‘orange’, ‘blue’, ‘purple’, ‘cyan’, ‘pink’, ‘yellow’, ‘lightblue’, ‘gold’, ‘darkblue’, ‘gray’, ‘darkred’, ‘black’] | DEFECT_COLORS |
title | str | Title | None |
Returns:
| Name | Type | Description |
|---|---|---|
fig | matplotlib.figure.Figure | Figure |
wif_trend(df, y, x='WIF_COL', yn='FF_ROW', xn='FF_COL', wftype='UP2', majority=90, method='median', title=None, ylim=None, yrange=None, color='b', style='.') ¶
Create Wafer Trend Chart by Flash Field
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
df | pd.DataFrame | Wafer Data | required |
y | str | Column name of the numeric variable to plot | required |
x | str | Column name(and x_label) of x-axis | 'WIF_COL' |
yn | str | Y Coordinate of Flash Field/Subplot | 'FF_ROW' |
xn | str | X Coordinate of Flash Field/Subplot | 'FF_COL' |
wftype | str | Wafer Layout Type [‘UP’|’UP2’|’UP3’|’UP2E’|’UP3E’] | 'UP2' |
majority | int | Center Percentage of Population used for Estimation | 90 |
method | str | ‘mean’ or ‘median’ Trend | 'median' |
title | str | Title | None |
ylim | tuple | (ymin:float,ymax:float) | None |
yrange | float | Range of Y-axis, ignored if ylim is provided | None |
color | str | Color of the trend line | 'b' |
style | str | Style of the trend line | '.' |
Returns:
| Name | Type | Description |
|---|---|---|
fig | matplotlib.figure.Figure | Figure |
wif_trends(df, ys, x='WIF_COL', yn='FF_ROW', xn='FF_COL', wftype='UP2', method='median', title=None, ylim=None, yrange=None) ¶
Create Wafer Trend Charts by Flash Field (when numerical variables share similar ranges)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
df | pd.DataFrame | Wafer Data | required |
ys | list | List of column names to plot | required |
x | str | Column name(and x_label) of x-axis | 'WIF_COL' |
yn | str | Y Coordinate of Flash Field/Subplot | 'FF_ROW' |
xn | str | X Coordinate of Flash Field/Subplot | 'FF_COL' |
wftype | str | Wafer Layout Type [‘UP’|’UP2’|’UP3’|’UP2E’|’UP3E’] | 'UP2' |
method | str | ‘mean’ or ‘median’ Trend | 'median' |
title | str | Title | None |
ylim | tuple | (ymin:float,ymax:float) | None |
yrange | float | Range of Y-axis, ignored if ylim is provided | None |
Returns:
| Name | Type | Description |
|---|---|---|
fig | matplotlib.figure.Figure | Figure |
twin_trends(df, y, ty, x='WIF_COL', yn='FF_ROW', xn='FF_COL', wftype='UP2', method='median', title=None, yrange=None, tyrange=None, fix_scale=True) ¶
Create Wafer Trend Charts by Flash Field (when two variables have very different ranges)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
df | pd.DataFrame | Wafer Data | required |
y | str | Column name of the variable to plot on the primary axis | required |
ty | str | Column name of the variable to plot on the secondary axis | required |
x | str | Column name(and x_label) of x-axis | 'WIF_COL' |
yn | str | Y Coordinate of Flash Field/Subplot | 'FF_ROW' |
xn | str | X Coordinate of Flash Field/Subplot | 'FF_COL' |
wftype | str | Wafer Layout Type [‘UP’|’UP2’|’UP3’|’UP2E’|’UP3E’] | 'UP2' |
method | str | ‘mean’ or ‘median’ Trend | 'median' |
title | str | Title | None |
yrange | float | Range of Y-axis | None |
tyrange | float | Range of 2nd Y-axis | None |
fix_scale | bool | Keep the same scale or Not | True |
Returns:
| Name | Type | Description |
|---|---|---|
fig | matplotlib.figure.Figure | Figure |
wif_corrplot(df, x, y, yn='FF_ROW', xn='FF_COL', wftype='UP2', fit_deg=1, title=None) ¶
Create Correlation Plot between two continuous variables by Flash Field
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
df | pd.DataFrame | Wafer Data | required |
x | str | Column name(and x_label) of x-axis | required |
y | str | Column name of the variable to plot on the primary axis | required |
yn | str | Y Coordinate of Flash Field/Subplot | 'FF_ROW' |
xn | str | X Coordinate of Flash Field/Subplot | 'FF_COL' |
wftype | str | Wafer Layout Type [‘UP’|’UP2’|’UP3’|’UP2E’|’UP3E’] | 'UP2' |
fit_deg | int | Polynomial fit degree | 1 |
title | str | Title | None |
Returns:
| Name | Type | Description |
|---|---|---|
fig | matplotlib.figure.Figure | Figure |