Revert changes to tests that check data field

Author: marthacryan

packages/python/plotly/plotly/tests/test_optional/test_figure_factory/test_figure_factory.py

@@ -7,7 +7,6 @@
 import base64
 import datetime
 from plotly.express.imshow_utils import rescale_intensity
-from plotly.tests.b64 import _b64
 
 img_rgb = np.array([[[255, 0, 0], [0, 255, 0], [0, 0, 255]]], dtype=np.uint8)
 img_gray = np.arange(100, dtype=float).reshape((10, 10))
@@ -182,7 +181,7 @@ def test_imshow_xarray(binary_string):
     assert fig.layout.xaxis.title.text == "dim_cols"
     assert fig.layout.yaxis.title.text == "dim_rows"
     if not binary_string:
-        assert np.all(np.array(fig.data[0].x) == _b64(da.coords["dim_cols"]))
+        assert np.all(np.array(fig.data[0].x) == np.array(da.coords["dim_cols"]))
 
 
 def test_imshow_xarray_slicethrough():
@@ -192,7 +191,7 @@ def test_imshow_xarray_slicethrough():
     # Dimensions are used for axis labels and coordinates
     assert fig.layout.xaxis.title.text == "dim_2"
     assert fig.layout.yaxis.title.text == "dim_1"
-    assert np.all(np.array(fig.data[0].x) == _b64(da.coords["dim_2"]))
+    assert np.all(np.array(fig.data[0].x) == np.array(da.coords["dim_2"]))
 
 
 def test_imshow_xarray_facet_col_string():
@@ -204,7 +203,7 @@ def test_imshow_xarray_facet_col_string():
     # Dimensions are used for axis labels and coordinates
     assert fig.layout.xaxis.title.text == "dim_2"
     assert fig.layout.yaxis.title.text == "dim_1"
-    assert np.all(np.array(fig.data[0].x) == _b64(da.coords["dim_2"]))
+    assert np.all(np.array(fig.data[0].x) == np.array(da.coords["dim_2"]))
 
 
 def test_imshow_xarray_animation_frame_string():
@@ -216,7 +215,7 @@ def test_imshow_xarray_animation_frame_string():
     # Dimensions are used for axis labels and coordinates
     assert fig.layout.xaxis.title.text == "dim_2"
     assert fig.layout.yaxis.title.text == "dim_1"
-    assert np.all(np.array(fig.data[0].x) == _b64(da.coords["dim_2"]))
+    assert np.all(np.array(fig.data[0].x) == np.array(da.coords["dim_2"]))
 
 
 def test_imshow_xarray_animation_facet_slicethrough():
@@ -226,7 +225,7 @@ def test_imshow_xarray_animation_facet_slicethrough():
     # Dimensions are used for axis labels and coordinates
     assert fig.layout.xaxis.title.text == "dim_3"
     assert fig.layout.yaxis.title.text == "dim_2"
-    assert np.all(np.array(fig.data[0].x) == _b64(da.coords["dim_3"]))
+    assert np.all(np.array(fig.data[0].x) == np.array(da.coords["dim_3"]))
 
 
 def test_imshow_labels_and_ranges():
@@ -292,7 +291,8 @@ def test_imshow_dataframe():
     assert fig.data[0].x[0] == df.columns[0]
     assert fig.data[0].x[0] == "nation"
     assert fig.layout.xaxis.title.text is None
-    assert fig.data[0].y == _b64(df.index)
+    assert fig.data[0].y[0] == df.index[0]
+    assert fig.data[0].y[0] == 0
     assert fig.layout.yaxis.title.text is None
 
     df = px.data.medals_wide(indexed=True)

packages/python/plotly/plotly/tests/test_optional/test_px/test_imshow.py

@@ -3,15 +3,14 @@
 import numpy as np
 import pytest
 from itertools import permutations
-from plotly.tests.b64 import _b64
 
 
 def test_scatter():
     iris = px.data.iris()
     fig = px.scatter(iris, x="sepal_width", y="sepal_length")
     assert fig.data[0].type == "scatter"
-    assert np.all(fig.data[0].x == _b64(iris.sepal_width))
-    assert np.all(fig.data[0].y == _b64(iris.sepal_length))
+    assert np.all(fig.data[0].x == iris.sepal_width)
+    assert np.all(fig.data[0].y == iris.sepal_length)
     # test defaults
     assert fig.data[0].mode == "markers"
 
@@ -29,11 +28,8 @@ def test_custom_data_scatter():
         color="species",
         hover_data=["petal_length", "petal_width"],
     )
-    assert fig.data[0].customdata == {
-        "dtype": "f8",
-        "bdata": "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",
-        "shape": "50, 2",
-    }
+    for data in fig.data:
+        assert np.all(np.in1d(data.customdata[:, 1], iris.petal_width))
     # Hover and custom data, no repeated arguments
     fig = px.scatter(
         iris,

packages/python/plotly/plotly/tests/test_optional/test_px/test_px.py

@@ -4,7 +4,6 @@
 import numpy as np
 import pandas as pd
 import pytest
-from plotly.tests.b64 import _b64
 
 
 def _compare_figures(go_trace, px_fig):
@@ -18,9 +17,9 @@ def _compare_figures(go_trace, px_fig):
     del go_fig["layout"]["template"]
     del px_fig["layout"]["template"]
     for key in go_fig["data"][0]:
-        assert_array_equal(_b64(go_fig["data"][0][key]), _b64(px_fig["data"][0][key]))
+        assert_array_equal(go_fig["data"][0][key], px_fig["data"][0][key])
     for key in go_fig["layout"]:
-        assert _b64(go_fig["layout"][key]) == _b64(px_fig["layout"][key])
+        assert go_fig["layout"][key] == px_fig["layout"][key]
 
 
 def test_pie_like_px():
@@ -150,11 +149,11 @@ def test_sunburst_treemap_with_path():
     # Values passed
     fig = px.sunburst(df, path=path, values="values")
     assert fig.data[0].branchvalues == "total"
-    assert fig.data[0].values == {"bdata": "AQMCBAICAQQGBQQECgkT", "dtype": "i1"}
+    assert fig.data[0].values[-1] == np.sum(values)
     # Values passed
     fig = px.sunburst(df, path=path, values="values")
     assert fig.data[0].branchvalues == "total"
-    assert fig.data[0].values == {"bdata": "AQMCBAICAQQGBQQECgkT", "dtype": "i1"}
+    assert fig.data[0].values[-1] == np.sum(values)
     # Error when values cannot be converted to numerical data type
     df["values"] = ["1 000", "3 000", "2", "4", "2", "2", "1 000", "4 000"]
     msg = "Column `values` of `df` could not be converted to a numerical data type."
@@ -167,11 +166,9 @@ def test_sunburst_treemap_with_path():
     # Continuous colorscale
     df["values"] = 1
     fig = px.sunburst(df, path=path, values="values", color="values")
-    # assert "coloraxis" in fig.data[0].marker
-    assert fig.data[0].values == {"bdata": "AQEBAQEBAQECAgICBAQI", "dtype": "i1"}
-    # depending on pandas version we get different dtype for marker.colors
-    assert fig.data[0].marker.colors["bdata"] is not None
-    assert fig.data[0].marker.colors["dtype"] is not None
+    assert "coloraxis" in fig.data[0].marker
+    assert np.all(np.array(fig.data[0].marker.colors) == 1)
+    assert fig.data[0].values[-1] == 8
 
 
 def test_sunburst_treemap_with_path_and_hover():
@@ -228,16 +225,10 @@ def test_sunburst_treemap_with_path_color():
     path = ["total", "regions", "sectors", "vendors"]
     fig = px.sunburst(df, path=path, values="values", color="calls")
     colors = fig.data[0].marker.colors
-    assert colors == {
-        "bdata": "AAAAAAAAIEAAAAAAAAAAQAAAAAAAAPA/AAAAAAAACEAAAAAAAAAAQAAAAAAAAABAAAAAAAAAEEAAAAAAAADwP6uqqqqqqgJAmpmZmZmZ+T8AAAAAAAAMQAAAAAAAAABAZmZmZmZmBkAcx3Ecx3H8P2wor6G8hgJA",
-        "dtype": "f8",
-    }
+    assert np.all(np.array(colors[:8]) == np.array(calls))
     fig = px.sunburst(df, path=path, color="calls")
     colors = fig.data[0].marker.colors
-    assert colors == {
-        "bdata": "AAAAAAAAIEAAAAAAAAAAQAAAAAAAAPA/AAAAAAAACEAAAAAAAAAAQAAAAAAAAABAAAAAAAAAEEAAAAAAAADwPwAAAAAAAABAAAAAAAAABEAAAAAAAAAUQAAAAAAAAABAAAAAAAAADEAAAAAAAAACQAAAAAAAAAdA",
-        "dtype": "f8",
-    }
+    assert np.all(np.array(colors[:8]) == np.array(calls))
 
     # Hover info
     df["hover"] = [el.lower() for el in vendors]
@@ -261,10 +252,7 @@ def test_sunburst_treemap_with_path_color():
     path = ["total", "regions", "sectors", "vendors"]
     fig = px.sunburst(df, path=path, values="values", color="calls")
     colors = fig.data[0].marker.colors
-    assert colors == {
-        "bdata": "AAAAAAAAIEAAAAAAAAAAQAAAAAAAAPA/AAAAAAAACEAAAAAAAAAAQAAAAAAAAABAAAAAAAAAEEAAAAAAAADwP6uqqqqqqgJAmpmZmZmZ+T8AAAAAAAAMQAAAAAAAAABAZmZmZmZmBkAcx3Ecx3H8P2wor6G8hgJA",
-        "dtype": "f8",
-    }
+    assert np.all(np.array(colors[:8]) == np.array(calls))
 
 
 def test_sunburst_treemap_column_parent():
@@ -337,7 +325,7 @@ def test_sunburst_treemap_with_path_non_rectangular():
         fig = px.sunburst(df, path=path, values="values")
     df.loc[df["vendors"].isnull(), "sectors"] = "Other"
     fig = px.sunburst(df, path=path, values="values")
-    assert fig.data[0].values == {"bdata": "AQMCBAICAQQGBQEBBAQLChU=", "dtype": "i1"}
+    assert fig.data[0].values[-1] == np.sum(values)
 
 
 def test_pie_funnelarea_colorscale():

packages/python/plotly/plotly/tests/test_optional/test_px/test_px_functions.py

@@ -7,7 +7,6 @@
 import unittest.mock as mock
 from plotly.express._core import build_dataframe
 from pandas.testing import assert_frame_equal
-from plotly.tests.b64 import b64, _b64
 import sys
 import warnings
 
@@ -29,9 +28,8 @@ def add_interchange_module_for_old_pandas():
 
 def test_numpy():
     fig = px.scatter(x=[1, 2, 3], y=[2, 3, 4], color=[1, 3, 9])
-
-    assert np.all(fig.data[0].x == b64(np.array([1, 2, 3])))
-    assert np.all(fig.data[0].y == b64(np.array([2, 3, 4])))
+    assert np.all(fig.data[0].x == np.array([1, 2, 3]))
+    assert np.all(fig.data[0].y == np.array([2, 3, 4]))
     assert np.all(fig.data[0].marker.color == np.array([1, 3, 9]))
 
 
@@ -103,16 +101,16 @@ def test_several_dataframes():
     df = pd.DataFrame(dict(x=[0, 1], y=[3, 4]))
     df2 = pd.DataFrame(dict(x=[3, 5], y=[23, 24]))
     fig = px.scatter(x=df.y, y=df2.y)
-    assert np.all(fig.data[0].x == b64(np.array([3, 4])))
-    assert np.all(fig.data[0].y == b64(np.array([23, 24])))
+    assert np.all(fig.data[0].x == np.array([3, 4]))
+    assert np.all(fig.data[0].y == np.array([23, 24]))
     assert fig.data[0].hovertemplate == "x=%{x}<br>y=%{y}<extra></extra>"
 
     df = pd.DataFrame(dict(x=[0, 1], y=[3, 4]))
     df2 = pd.DataFrame(dict(x=[3, 5], y=[23, 24]))
     df3 = pd.DataFrame(dict(y=[0.1, 0.2]))
     fig = px.scatter(x=df.y, y=df2.y, size=df3.y)
-    assert np.all(fig.data[0].x == b64(np.array([3, 4])))
-    assert np.all(fig.data[0].y == b64(np.array([23, 24])))
+    assert np.all(fig.data[0].x == np.array([3, 4]))
+    assert np.all(fig.data[0].y == np.array([23, 24]))
     assert (
         fig.data[0].hovertemplate
         == "x=%{x}<br>y=%{y}<br>size=%{marker.size}<extra></extra>"
@@ -122,8 +120,8 @@ def test_several_dataframes():
     df2 = pd.DataFrame(dict(x=[3, 5], y=[23, 24]))
     df3 = pd.DataFrame(dict(y=[0.1, 0.2]))
     fig = px.scatter(x=df.y, y=df2.y, hover_data=[df3.y])
-    assert np.all(fig.data[0].x == b64(np.array([3, 4])))
-    assert np.all(fig.data[0].y == b64(np.array([23, 24])))
+    assert np.all(fig.data[0].x == np.array([3, 4]))
+    assert np.all(fig.data[0].y == np.array([23, 24]))
     assert (
         fig.data[0].hovertemplate
         == "x=%{x}<br>y=%{y}<br>hover_data_0=%{customdata[0]}<extra></extra>"
@@ -133,8 +131,8 @@ def test_several_dataframes():
 def test_name_heuristics():
     df = pd.DataFrame(dict(x=[0, 1], y=[3, 4], z=[0.1, 0.2]))
     fig = px.scatter(df, x=df.y, y=df.x, size=df.y)
-    assert np.all(fig.data[0].x == b64(np.array([3, 4])))
-    assert np.all(fig.data[0].y == b64(np.array([0, 1])))
+    assert np.all(fig.data[0].x == np.array([3, 4]))
+    assert np.all(fig.data[0].y == np.array([0, 1]))
     assert fig.data[0].hovertemplate == "y=%{marker.size}<br>x=%{y}<extra></extra>"
 
 
@@ -407,27 +405,27 @@ def test_splom_case():
     assert len(fig.data[0].dimensions) == len(iris.columns)
     dic = {"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}
     fig = px.scatter_matrix(dic)
-    assert np.all(fig.data[0].dimensions[0].values == b64(np.array(dic["a"])))
+    assert np.all(fig.data[0].dimensions[0].values == np.array(dic["a"]))
     ar = np.arange(9).reshape((3, 3))
     fig = px.scatter_matrix(ar)
-    assert np.all(fig.data[0].dimensions[0].values == b64(ar[:, 0]))
+    assert np.all(fig.data[0].dimensions[0].values == ar[:, 0])
 
 
 def test_int_col_names():
     # DataFrame with int column names
     lengths = pd.DataFrame(np.random.random(100))
     fig = px.histogram(lengths, x=0)
-    assert np.all(b64(np.array(lengths).flatten()) == fig.data[0].x)
+    assert np.all(np.array(lengths).flatten() == fig.data[0].x)
     # Numpy array
     ar = np.arange(100).reshape((10, 10))
     fig = px.scatter(ar, x=2, y=8)
-    assert np.all(fig.data[0].x == b64(ar[:, 2]))
+    assert np.all(fig.data[0].x == ar[:, 2])
 
 
 def test_data_frame_from_dict():
     fig = px.scatter({"time": [0, 1], "money": [1, 2]}, x="time", y="money")
     assert fig.data[0].hovertemplate == "time=%{x}<br>money=%{y}<extra></extra>"
-    assert np.all(fig.data[0].x == _b64([0, 1]))
+    assert np.all(fig.data[0].x == [0, 1])
 
 
 def test_arguments_not_modified():
@@ -491,11 +489,13 @@ def test_identity_map():
 
 def test_constants():
     fig = px.scatter(x=px.Constant(1), y=[1, 2])
-    assert fig.data[0].x == _b64([1, 1])
+    assert fig.data[0].x[0] == 1
+    assert fig.data[0].x[1] == 1
     assert "x=" in fig.data[0].hovertemplate
 
     fig = px.scatter(x=px.Constant(1, label="time"), y=[1, 2])
-    assert fig.data[0].x == _b64([1, 1])
+    assert fig.data[0].x[0] == 1
+    assert fig.data[0].x[1] == 1
     assert "x=" not in fig.data[0].hovertemplate
     assert "time=" in fig.data[0].hovertemplate
 
@@ -519,12 +519,15 @@ def test_constants():
 
 def test_ranges():
     fig = px.scatter(x=px.Range(), y=[1, 2], hover_data=[px.Range()])
-    assert fig.data[0].x == _b64([0, 1])
-    assert fig.data[0].customdata == _b64([[0], [1]])
+    assert fig.data[0].x[0] == 0
+    assert fig.data[0].x[1] == 1
+    assert fig.data[0].customdata[0][0] == 0
+    assert fig.data[0].customdata[1][0] == 1
     assert "x=" in fig.data[0].hovertemplate
 
     fig = px.scatter(x=px.Range(label="time"), y=[1, 2])
-    assert fig.data[0].x == _b64([0, 1])
+    assert fig.data[0].x[0] == 0
+    assert fig.data[0].x[1] == 1
     assert "x=" not in fig.data[0].hovertemplate
     assert "time=" in fig.data[0].hovertemplate
 
@@ -614,55 +617,55 @@ def test_x_or_y(fn):
     categorical_df = pd.DataFrame(dict(col=categorical), index=index)
 
     fig = fn(x=numerical)
-    assert fig.data[0].x == _b64(numerical)
-    assert fig.data[0].y == _b64(range_4)
+    assert list(fig.data[0].x) == numerical
+    assert list(fig.data[0].y) == range_4
     assert fig.data[0].orientation == "h"
     fig = fn(y=numerical)
-    assert fig.data[0].x == _b64(range_4)
-    assert fig.data[0].y == _b64(numerical)
+    assert list(fig.data[0].x) == range_4
+    assert list(fig.data[0].y) == numerical
     assert fig.data[0].orientation == "v"
     fig = fn(numerical_df, x="col")
-    assert fig.data[0].x == _b64(numerical)
-    assert fig.data[0].y == _b64(index)
+    assert list(fig.data[0].x) == numerical
+    assert list(fig.data[0].y) == index
     assert fig.data[0].orientation == "h"
     fig = fn(numerical_df, y="col")
-    assert fig.data[0].x == _b64(index)
-    assert fig.data[0].y == _b64(numerical)
+    assert list(fig.data[0].x) == index
+    assert list(fig.data[0].y) == numerical
     assert fig.data[0].orientation == "v"
 
     if fn != px.bar:
         fig = fn(x=categorical)
         assert list(fig.data[0].x) == categorical
-        assert fig.data[0].y == _b64(range_4)
+        assert list(fig.data[0].y) == range_4
         assert fig.data[0].orientation == "h"
         fig = fn(y=categorical)
-        assert fig.data[0].x == _b64(range_4)
+        assert list(fig.data[0].x) == range_4
         assert list(fig.data[0].y) == categorical
         assert fig.data[0].orientation == "v"
         fig = fn(categorical_df, x="col")
         assert list(fig.data[0].x) == categorical
-        assert fig.data[0].y == _b64(index)
+        assert list(fig.data[0].y) == index
         assert fig.data[0].orientation == "h"
         fig = fn(categorical_df, y="col")
-        assert fig.data[0].x == _b64(index)
+        assert list(fig.data[0].x) == index
         assert list(fig.data[0].y) == categorical
         assert fig.data[0].orientation == "v"
 
     else:
         fig = fn(x=categorical)
         assert list(fig.data[0].x) == categorical
-        assert fig.data[0].y == _b64(constant)
+        assert list(fig.data[0].y) == constant
         assert fig.data[0].orientation == "v"
         fig = fn(y=categorical)
-        assert fig.data[0].x == _b64(constant)
+        assert list(fig.data[0].x) == constant
         assert list(fig.data[0].y) == categorical
         assert fig.data[0].orientation == "h"
         fig = fn(categorical_df, x="col")
         assert list(fig.data[0].x) == categorical
-        assert fig.data[0].y == _b64(constant)
+        assert list(fig.data[0].y) == constant
         assert fig.data[0].orientation == "v"
         fig = fn(categorical_df, y="col")
-        assert fig.data[0].x == _b64(constant)
+        assert list(fig.data[0].x) == constant
         assert list(fig.data[0].y) == categorical
         assert fig.data[0].orientation == "h"