Merge pull request #2166 from plotly/imshow-xarray

xarray support in imshow

doc/python/datashader.md

@@ -5,8 +5,8 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: "1.2"
-      jupytext_version: 1.3.1
+      format_version: '1.2'
+      jupytext_version: 1.3.0
   kernelspec:
     display_name: Python 3
     language: python
@@ -20,10 +20,9 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.8
+    version: 3.7.3
   plotly:
-    description:
-      How to use datashader to rasterize large datasets, and visualize
+    description: How to use datashader to rasterize large datasets, and visualize
       the generated raster data with plotly.
     display_as: scientific
     language: python
@@ -98,30 +97,19 @@ fig.show()
 ```
 
 ```python
-import plotly.graph_objects as go
+import plotly.express as px
 import pandas as pd
 import numpy as np
 import datashader as ds
 df = pd.read_parquet('https://raw.githubusercontent.com/plotly/datasets/master/2015_flights.parquet')
 
 cvs = ds.Canvas(plot_width=100, plot_height=100)
 agg = cvs.points(df, 'SCHEDULED_DEPARTURE', 'DEPARTURE_DELAY')
-x = np.array(agg.coords['SCHEDULED_DEPARTURE'])
-y = np.array(agg.coords['DEPARTURE_DELAY'])
-
-# Assign nan to zero values so that the corresponding pixels are transparent
-agg = np.array(agg.values, dtype=np.float)
-agg[agg<1] = np.nan
-
-fig = go.Figure(go.Heatmap(
-    z=np.log10(agg), x=x, y=y,
-    hoverongaps=False,
-    hovertemplate='Scheduled departure: %{x:.1f}h <br>Depature delay: %{y} <br>Log10(Count): %{z}',
-    colorbar=dict(title='Count (Log)', tickprefix='1.e')))
-fig.update_xaxes(title_text='Scheduled departure')
-fig.update_yaxes(title_text='Departure delay')
+agg.values = np.log10(agg.values)
+fig = px.imshow(agg, origin='lower', labels={'color':'Log10(count)'})
+fig.update_traces(hoverongaps=False)
+fig.update_layout(coloraxis_colorbar=dict(title='Count', tickprefix='1.e'))
 fig.show()
-
 ```
 
 ```python

doc/python/heatmaps.md

@@ -36,9 +36,7 @@ jupyter:
 
 ### Heatmap with `plotly.express` and `px.imshow`
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly. With `px.imshow`, each value of the input array is represented as a heatmap pixel.
-
-`px.imshow` makes opiniated choices for representing heatmaps, such as using square pixels. To override this behaviour, you can use `fig.update_layout` or use the `go.Heatmap` trace from `plotly.graph_objects` as described below. 
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/). With `px.imshow`, each value of the input array is represented as a heatmap pixel.
 
 For more examples using `px.imshow`, see the [tutorial on displaying image data with plotly](/python/imshow).
 
@@ -51,6 +49,22 @@ fig = px.imshow([[1, 20, 30],
 fig.show()
 ```
 
+### Customizing the axes and labels on a heatmap
+
+You can use the `x`, `y` and `labels` arguments to customize the display of a heatmap, and use `.update_xaxes()` to move the x axis tick labels to the top:
+
+```python
+import plotly.express as px
+data=[[1, 25, 30, 50, 1], [20, 1, 60, 80, 30], [30, 60, 1, 5, 20]]
+fig = px.imshow(data,
+                labels=dict(x="Day of Week", y="Time of Day", color="Productivity"),
+                x=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
+                y=['Morning', 'Afternoon', 'Evening']
+               )
+fig.update_xaxes(side="top")
+fig.show()
+```
+
 ### Basic Heatmap with `plotly.graph_objects`
 
 If Plotly Express does not provide a good starting point, it is also possible to use the more generic `go.Heatmap` function from `plotly.graph_objects`.
@@ -67,7 +81,7 @@ fig.show()
 
 ### Heatmap with Categorical Axis Labels
 
-In this example we also show how to ignore [hovertext](https://plot.ly/python/hover-text-and-formatting/) when we have [missing values](https://plot.ly/python/missing_values) in the data by setting the [hoverongaps](https://plot.ly/python/reference/#heatmap-hoverongaps) to False. 
+In this example we also show how to ignore [hovertext](https://plot.ly/python/hover-text-and-formatting/) when we have [missing values](https://plot.ly/python/missing_values) in the data by setting the [hoverongaps](https://plot.ly/python/reference/#heatmap-hoverongaps) to False.
 
 ```python
 import plotly.graph_objects as go

doc/python/imshow.md

@@ -6,7 +6,7 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.2'
-      jupytext_version: 1.3.0
+      jupytext_version: 1.3.1
   kernelspec:
     display_name: Python 3
     language: python
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.7.3
+    version: 3.6.8
   plotly:
     description: How to display image data in Python with Plotly.
     display_as: scientific
@@ -74,7 +74,7 @@ fig = px.imshow(img)
 fig.show()
 ```
 
-### Display single-channel 2D image as grayscale
+### Display single-channel 2D data as a heatmap
 
 For a 2D image, `px.imshow` uses a colorscale to map scalar data to colors. The default colorscale is the one of the active template (see [the tutorial on templates](/python/templates/)).
 
@@ -88,6 +88,17 @@ fig.show()
 
 ### Choose the colorscale to display a single-channel image
 
+You can customize the [continuous color scale](/python/colorscales/) just like with any other Plotly Express function:
+
+```python
+import plotly.express as px
+import numpy as np
+img = np.arange(100).reshape((10, 10))
+fig = px.imshow(img, color_continuous_scale='Viridis')
+fig.show()
+```
+
+You can use this to make the image grayscale as well:
 
 ```python
 import plotly.express as px
@@ -97,16 +108,60 @@ fig = px.imshow(img, color_continuous_scale='gray')
 fig.show()
 ```
 
-### Hiding the colorbar when displaying a single-channel image
+### Hiding the colorbar and axis labels
 
-See [the tutorial on coloraxis](/python/colorscales/#share-color-axis) for more details on coloraxis.
+See the [continuous color](/python/colorscales/) and [cartesian axes](/python/axes/) pages for more details.
 
 ```python
 import plotly.express as px
 from skimage import data
 img = data.camera()
 fig = px.imshow(img, color_continuous_scale='gray')
 fig.update_layout(coloraxis_showscale=False)
+fig.update_xaxes(showticklabels=False)
+fig.update_yaxes(showticklabels=False)
+fig.show()
+```
+
+### Customizing the axes and labels on a single-channel image
+
+You can use the `x`, `y` and `labels` arguments to customize the display of a heatmap, and use `.update_xaxes()` to move the x axis tick labels to the top:
+
+```python
+import plotly.express as px
+data=[[1, 25, 30, 50, 1], [20, 1, 60, 80, 30], [30, 60, 1, 5, 20]]
+fig = px.imshow(data,
+                labels=dict(x="Day of Week", y="Time of Day", color="Productivity"),
+                x=['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
+                y=['Morning', 'Afternoon', 'Evening']
+               )
+fig.update_xaxes(side="top")
+fig.show()
+```
+
+### Display an xarray image with px.imshow
+
+[xarrays](http://xarray.pydata.org/en/stable/) are labeled arrays (with labeled axes and coordinates). If you pass an xarray image to `px.imshow`, its axes labels and coordinates will be used for axis titles. If you don't want this behavior, you can pass `img.values` which is a NumPy array if `img` is an xarray. Alternatively, you can override axis titles hover labels and colorbar title using the `labels` attribute, as above.
+
+```python
+import plotly.express as px
+import xarray as xr
+# Load xarray from dataset included in the xarray tutorial
+airtemps = xr.tutorial.open_dataset('air_temperature').air.sel(lon=250.0)
+fig = px.imshow(airtemps.T, color_continuous_scale='RdBu_r', origin='lower')
+fig.show()
+```
+
+### Display an xarray image with square pixels
+
+For xarrays, by default `px.imshow` does not constrain pixels to be square, since axes often correspond to different physical quantities (e.g. time and space), contrary to a plain camera image where pixels are square (most of the time). If you want to impose square pixels, set the parameter `aspect` to "equal" as below.
+
+```python
+import plotly.express as px
+import xarray as xr
+airtemps = xr.tutorial.open_dataset('air_temperature').air.isel(time=500)
+colorbar_title = airtemps.attrs['var_desc'] + '<br>(%s)'%airtemps.attrs['units']
+fig = px.imshow(airtemps, color_continuous_scale='RdBu_r', aspect='equal')
 fig.show()
 ```
 
@@ -201,7 +256,7 @@ fig.show()
 ### imshow and datashader
 
 Arrays of rasterized values build by datashader can be visualized using
-imshow. See the [plotly and datashader tutorial](/python/datashader/) for 
+imshow. See the [plotly and datashader tutorial](/python/datashader/) for
 examples on how to use plotly and datashader.
 
 

packages/python/plotly/plotly/express/_doc.py

@@ -6,13 +6,6 @@
 except AttributeError:  # python 2
     getfullargspec = inspect.getargspec
 
-# TODO contents of columns
-# TODO explain categorical
-# TODO handle color
-# TODO handle details of box/violin/histogram
-# TODO handle details of column selection with `dimensions`
-# TODO document "or `None`, default `None`" in various places
-# TODO standardize positioning and casing of 'default'
 
 colref_type = "str or int or Series or array-like"
 colref_desc = "Either a name of a column in `data_frame`, or a pandas Series or array_like object."
@@ -325,11 +318,11 @@
     ],
     title=["str", "The figure title."],
     template=[
-        "or dict or plotly.graph_objects.layout.Template instance",
-        "The figure template name or definition.",
+        "str or dict or plotly.graph_objects.layout.Template instance",
+        "The figure template name (must be a key in plotly.io.templates) or definition.",
     ],
     width=["int (default `None`)", "The figure width in pixels."],
-    height=["int (default `600`)", "The figure height in pixels."],
+    height=["int (default `None`)", "The figure height in pixels."],
     labels=[
         "dict with str keys and str values (default `{}`)",
         "By default, column names are used in the figure for axis titles, legend entries and hovers.",