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\"\n@@ -1316,34 +1316,34 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:37.540234Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:37.539772Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:37.552081Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:37.550981Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:56.947840Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:56.947380Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:56.960119Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:56.959372Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"%config InlineBackend.figure_formats = ['png']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:37.556837Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:37.556524Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:37.636335Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:37.635308Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:56.964128Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:56.963841Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:57.051344Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:57.050493Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n@@ -1367,34 +1367,34 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:37.641367Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:37.641034Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:37.652937Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:37.651825Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:57.055581Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:57.055290Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:57.079341Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:57.078496Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"%config InlineBackend.figure_formats = ['png2x']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:37.657691Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:37.657335Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:37.762469Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:37.761386Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:57.083469Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:57.083175Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:57.219255Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:57.218484Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n@@ -1426,35 +1426,35 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:37.767981Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:37.767671Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:38.143979Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:38.142827Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:57.222805Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:57.222534Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:57.995268Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:57.994489Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:38.151282Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:38.149664Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:38.173750Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:38.172088Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:57.999144Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:57.998739Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:58.027266Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:58.026483Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n@@ -1552,18 +1552,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:38.179316Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:38.177957Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:38.325578Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:38.324470Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:58.030709Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:58.030439Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:58.227277Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:58.226481Z\"\n }\n },\n \"outputs\": [\n {\n \"ename\": \"OptionError\",\n \"evalue\": \"No such keys(s): 'display.latex.repr'\",\n \"output_type\": \"error\",\n@@ -1597,18 +1597,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:38.330551Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:38.330256Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:38.369551Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:38.368386Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:58.231040Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:58.230778Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:58.307297Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:58.306548Z\"\n }\n },\n \"outputs\": [\n {\n \"ename\": \"OptionError\",\n \"evalue\": \"No such keys(s): 'display.latex.longtable'\",\n \"output_type\": \"error\",\n@@ -1638,18 +1638,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:38.374298Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:38.374002Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:38.412821Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:38.411761Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:58.311029Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:58.310766Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:58.391263Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:58.390486Z\"\n }\n },\n \"outputs\": [\n {\n \"ename\": \"OptionError\",\n \"evalue\": \"No such keys(s): 'display.latex.escape'\",\n \"output_type\": \"error\",\n@@ -1676,18 +1676,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:38.417554Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:38.417233Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:38.427769Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:38.426687Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:58.395324Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:58.395035Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:58.415252Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:58.414480Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n@@ -1823,34 +1823,34 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:38.433017Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:38.432722Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:38.437854Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:38.436750Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:58.419031Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:58.418766Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:58.427232Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:58.426479Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from IPython.display import Markdown\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:38.442628Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:38.442332Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:38.449398Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:38.448280Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:58.431005Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:58.430743Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:58.443257Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:58.442483Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/markdown\": [\n \"\\n\",\n@@ -1922,18 +1922,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:38.454499Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:38.454189Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:38.473184Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:38.472131Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:58.447116Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:58.446852Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:58.483256Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:58.482483Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n@@ -1944,15 +1944,15 @@\n \" frameborder=\\\"0\\\"\\n\",\n \" allowfullscreen\\n\",\n \" \\n\",\n \" >\\n\",\n \" \"\n ],\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n@@ -2039,18 +2039,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:38.478134Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:38.477833Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:38.484242Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:38.483241Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:58.487493Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:58.487231Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:58.499241Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:58.498479Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"application/javascript\": [\n \"\\n\",\n@@ -2085,18 +2085,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:38.489363Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:38.489040Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:38.494246Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:38.493483Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:58.503025Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:58.502762Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:58.515254Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:58.514499Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/x-haskell\": [\n \"main = putStrLn \\\"Hello, world!\\\"\"\n@@ -2126,18 +2126,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:38.500247Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:38.499923Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:38.506358Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:38.505428Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:58.519061Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:58.518799Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:58.525435Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:58.524808Z\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n@@ -2167,18 +2167,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:38.512840Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:38.512488Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:38.521523Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:38.520182Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:58.528750Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:58.528490Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:58.547234Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:58.546484Z\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n@@ -2225,18 +2225,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:38.526677Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:38.526315Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:38.535699Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:38.534219Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:58.551003Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:58.550745Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:58.567233Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:58.566480Z\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n@@ -2271,18 +2271,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:38.541109Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:38.540734Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:38.549817Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:38.548444Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:27:58.570957Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:27:58.570692Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:27:58.583232Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:27:58.582479Z\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n"}]}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/configuring-kernels.ipynb", "source2": "./usr/share/doc/python-nbsphinx/html/configuring-kernels.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.999609375%", "Differences: {\"'cells'\": \"{5: {'metadata': {'execution': {'iopub.execute_input': '2025-06-04T10:28:13.534121Z', \"", " \"'iopub.status.busy': '2025-06-04T10:28:13.533818Z', 'iopub.status.idle': \"", " \"'2025-06-04T10:28:13.563384Z', 'shell.execute_reply': \"", " \"'2025-06-04T10:28:13.562621Z'}}}}\"}"], "unified_diff": "@@ -63,18 +63,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:42.766006Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:42.764705Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:42.794780Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:42.793119Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:28:13.534121Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:28:13.533818Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:28:13.563384Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:28:13.562621Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'Hello from conf.py!'\"\n"}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/custom-css.ipynb", "source2": "./usr/share/doc/python-nbsphinx/html/custom-css.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9994791666666667%", "Differences: {\"'cells'\": \"{5: {'metadata': {'execution': {'iopub.execute_input': '2025-06-04T10:28:17.815615Z', \"", " \"'iopub.status.busy': '2025-06-04T10:28:17.815303Z', 'iopub.status.idle': \"", " \"'2025-06-04T10:28:17.859198Z', 'shell.execute_reply': \"", " \"'2025-06-04T10:28:17.858497Z'}}}}\"}"], "unified_diff": "@@ -118,18 +118,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:45.028267Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:45.027611Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:45.057176Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:45.055611Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:28:17.815615Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:28:17.815303Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:28:17.859198Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:28:17.858497Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"42\"\n"}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/gallery/cell-metadata.html", "source2": "./usr/share/doc/python-nbsphinx/html/gallery/cell-metadata.html", "unified_diff": "@@ -369,15 +369,15 @@\n
\n
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[5]:\n 
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\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -24,15 +24,15 @@\n z = (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)\n [4]:\n zmax = np.max(np.abs(z))\n [5]:\n fig, ax = plt.subplots(figsize=[5, 3.5])\n ax.imshow(z, vmin=-zmax, vmax=zmax)\n [5]:\n-\n+\n [../_images/gallery_cell-metadata_7_1.svg]\n *\b**\b**\b**\b**\b**\b* _\bn\bn_\bb\bb_\bs\bs_\bp\bp_\bh\bh_\bi\bi_\bn\bn_\bx\bx *\b**\b**\b**\b**\b**\b*\n *\b**\b**\b**\b* N\bNa\bav\bvi\big\bga\bat\bti\bio\bon\bn *\b**\b**\b**\b*\n * _\bI_\bn_\bs_\bt_\ba_\bl_\bl_\ba_\bt_\bi_\bo_\bn\n * _\bU_\bs_\ba_\bg_\be\n * _\bM_\ba_\br_\bk_\bd_\bo_\bw_\bn_\b _\bC_\be_\bl_\bl_\bs\n * _\bC_\bo_\bd_\be_\b _\bC_\be_\bl_\bl_\bs\n"}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/gallery/cell-metadata.ipynb.gz", "source2": "./usr/share/doc/python-nbsphinx/html/gallery/cell-metadata.ipynb.gz", "unified_diff": null, "details": [{"source1": "cell-metadata.ipynb", "source2": "cell-metadata.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9981915020489239%", "Differences: {\"'cells'\": \"{2: {'metadata': {'execution': {'iopub.execute_input': '2025-06-04T10:28:24.527670Z', \"", " \"'iopub.status.busy': '2025-06-04T10:28:24.527335Z', 'iopub.status.idle': \"", " \"'2025-06-04T10:28:25.667331Z', 'shell.execute_reply': \"", " \"'2025-06-04T10:28:25.666503Z'}}}, 3: {'metadata': {'execution': \"", " \"{'iopub.execute_input': '2025-06-04T10:28:25.671461Z', 'iopub.status.busy': \"", " \"'2025-06-04T10:28:25.671083Z', 'iopub.status.idle': '2025-06-04T10:28:2 [\u2026]"], "unified_diff": "@@ -35,35 +35,35 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:47.527389Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:47.527074Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:48.033186Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:48.032482Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:28:24.527670Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:28:24.527335Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:28:25.667331Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:28:25.666503Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:48.036703Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:48.036328Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:48.041122Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:48.040466Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:28:25.671461Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:28:25.671083Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:28:25.679350Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:28:25.678563Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"plt.rcParams['image.cmap'] = 'coolwarm'\\n\",\n \"plt.rcParams['image.origin'] = 'lower'\"\n ]\n@@ -77,61 +77,61 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:48.044741Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:48.043991Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:48.048905Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:48.048318Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:28:25.683247Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:28:25.682971Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:28:25.699250Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:28:25.698489Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"x, y = np.meshgrid(np.arange(-3, 3, 0.1), np.arange(-2, 2, 0.1))\\n\",\n \"z = (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:48.052476Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:48.051772Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:48.055742Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:48.055146Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:28:25.703083Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:28:25.702806Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:28:25.711667Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:28:25.710526Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"zmax = np.max(np.abs(z))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:48.059208Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:48.058929Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:48.419090Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:48.418486Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:28:25.715638Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:28:25.715357Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:28:26.491455Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:28:26.490666Z\"\n },\n \"nbsphinx-thumbnail\": {\n \"tooltip\": \"This tooltip message was defined in cell metadata\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n@@ -173,28 +173,28 @@\n \" \\n\",\n \" \\n\",\n- \" \\n\",\n+ \" \\n\",\n \" \\n\",\n+ 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\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n- \" \\n\",\n+ \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n- \" \\n\",\n+ \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n- \" \\n\",\n+ \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n- \" \\n\",\n+ \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n@@ -554,15 +554,15 @@\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n- \" \\n\",\n+ \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"\\n\"\n ],\n \"text/plain\": [\n \"
\"\n"}]}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/gallery/cell-tag.html", "source2": "./usr/share/doc/python-nbsphinx/html/gallery/cell-tag.html", "unified_diff": "@@ -334,15 +334,15 @@\n
\n
\n
[2]:\n 
\n
\n
\n
\n-[<matplotlib.lines.Line2D at 0xffff65d35690>]\n+[<matplotlib.lines.Line2D at 0xffff77f1c610>]\n 
\n
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\n
\n
\n \"../_images/gallery_cell-tag_4_1.svg\"
\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -9,15 +9,15 @@\n [1]:\n import matplotlib.pyplot as plt\n The following cell has the nbsphinx-thumbnail tag:\n [2]:\n fig, ax = plt.subplots(figsize=[6, 3])\n ax.plot([4, 9, 7, 20, 6, 33, 13, 23, 16, 62, 8])\n [2]:\n-[]\n+[]\n [../_images/gallery_cell-tag_4_1.svg]\n *\b**\b**\b**\b**\b**\b* _\bn\bn_\bb\bb_\bs\bs_\bp\bp_\bh\bh_\bi\bi_\bn\bn_\bx\bx *\b**\b**\b**\b**\b**\b*\n *\b**\b**\b**\b* N\bNa\bav\bvi\big\bga\bat\bti\bio\bon\bn *\b**\b**\b**\b*\n * _\bI_\bn_\bs_\bt_\ba_\bl_\bl_\ba_\bt_\bi_\bo_\bn\n * _\bU_\bs_\ba_\bg_\be\n * _\bM_\ba_\br_\bk_\bd_\bo_\bw_\bn_\b _\bC_\be_\bl_\bl_\bs\n * _\bC_\bo_\bd_\be_\b _\bC_\be_\bl_\bl_\bs\n"}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/gallery/cell-tag.ipynb.gz", "source2": "./usr/share/doc/python-nbsphinx/html/gallery/cell-tag.ipynb.gz", "unified_diff": null, "details": [{"source1": "cell-tag.ipynb", "source2": "cell-tag.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9989821257872245%", "Differences: {\"'cells'\": \"{2: {'metadata': {'execution': {'iopub.execute_input': '2025-06-04T10:28:30.946069Z', \"", " \"'iopub.status.busy': '2025-06-04T10:28:30.945753Z', 'iopub.status.idle': \"", " \"'2025-06-04T10:28:32.327318Z', 'shell.execute_reply': \"", " \"'2025-06-04T10:28:32.326501Z'}}}, 4: {'metadata': {'execution': \"", " \"{'iopub.execute_input': '2025-06-04T10:28:32.335660Z', 'iopub.status.busy': \"", " \"'2025-06-04T10:28:32.335287Z', 'iopub.status.idle': '2025-06-04T10:28:3 [\u2026]"], "unified_diff": "@@ -27,18 +27,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:50.425063Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:50.424423Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:50.942311Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:50.941163Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:28:30.946069Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:28:30.945753Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:28:32.327318Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:28:32.326501Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\"\n ]\n },\n@@ -50,28 +50,28 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:50.947815Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:50.947421Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:51.256144Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:51.255138Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:28:32.335660Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:28:32.335287Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:28:33.315473Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:28:33.314633Z\"\n },\n \"tags\": [\n \"nbsphinx-thumbnail\"\n ]\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n- \"[]\"\n+ \"[]\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n@@ -117,20 +117,20 @@\n \"z\\n\",\n \"\\\" style=\\\"fill: #ffffff\\\"/>\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n- \" \\n\",\n \" \\n\",\n \" \\n\",\n- \" \\n\",\n+ \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n- \" \\n\",\n+ \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n- \" \\n\",\n+ \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n- \" \\n\",\n+ \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" 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\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n- \" \\n\",\n+ \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n- \" \\n\",\n+ \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n- \" \\n\",\n+ \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n@@ -522,15 +522,15 @@\n \"L 163.888636 160.746207 \\n\",\n \"L 194.325 90.36 \\n\",\n \"L 224.761364 142.497931 \\n\",\n \"L 255.197727 116.428966 \\n\",\n \"L 285.634091 134.677241 \\n\",\n \"L 316.070455 14.76 \\n\",\n \"L 346.506818 155.532414 \\n\",\n- \"\\\" clip-path=\\\"url(#p1766de88b6)\\\" style=\\\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\\\"/>\\n\",\n+ \"\\\" clip-path=\\\"url(#pde1264ac56)\\\" style=\\\"fill: none; stroke: #1f77b4; stroke-width: 1.5; stroke-linecap: square\\\"/>\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n@@ -547,15 +547,15 @@\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n- \" \\n\",\n+ \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"\\n\"\n ],\n \"text/plain\": [\n \"
\"\n"}]}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/gallery/multiple-outputs.ipynb.gz", "source2": "./usr/share/doc/python-nbsphinx/html/gallery/multiple-outputs.ipynb.gz", "unified_diff": null, "details": [{"source1": "multiple-outputs.ipynb", "source2": "multiple-outputs.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9994791666666667%", "Differences: {\"'cells'\": \"{2: {'metadata': {'execution': {'iopub.execute_input': '2025-06-04T10:28:38.695635Z', \"", " \"'iopub.status.busy': '2025-06-04T10:28:38.695294Z', 'iopub.status.idle': \"", " \"'2025-06-04T10:28:40.603479Z', 'shell.execute_reply': \"", " \"'2025-06-04T10:28:40.602496Z'}}}}\"}"], "unified_diff": "@@ -37,18 +37,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:53.274645Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:53.274009Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:54.053359Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:54.052184Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:28:38.695635Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:28:38.695294Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:28:40.603479Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:28:40.602496Z\"\n },\n \"nbsphinx-thumbnail\": {\n \"output-index\": 2\n }\n },\n \"outputs\": [\n {\n"}]}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/gallery/thumbnail-from-conf-py.ipynb.gz", "source2": "./usr/share/doc/python-nbsphinx/html/gallery/thumbnail-from-conf-py.ipynb.gz", "unified_diff": null, "details": [{"source1": "thumbnail-from-conf-py.ipynb", "source2": "thumbnail-from-conf-py.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9986607142857142%", "Differences: {\"'cells'\": \"{3: {'metadata': {'execution': {'iopub.execute_input': '2025-06-04T10:28:47.178148Z', \"", " \"'iopub.status.busy': '2025-06-04T10:28:47.177805Z', 'iopub.status.idle': \"", " \"'2025-06-04T10:28:48.679315Z', 'shell.execute_reply': \"", " \"'2025-06-04T10:28:48.678496Z'}}}, 4: {'metadata': {'execution': \"", " \"{'iopub.execute_input': '2025-06-04T10:28:48.687655Z', 'iopub.status.busy': \"", " \"'2025-06-04T10:28:48.687287Z', 'iopub.status.idle': '2025-06-04T10:28:4 [\u2026]"], "unified_diff": "@@ -49,50 +49,50 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:56.349835Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:56.349499Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:56.818782Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:56.817456Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:28:47.178148Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:28:47.177805Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:28:48.679315Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:28:48.678496Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib agg\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:56.823361Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:56.821837Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:56.826475Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:56.825810Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:28:48.687655Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:28:48.687287Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:28:48.699260Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:28:48.698487Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:56.829659Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:56.829303Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:56.934161Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:56.933238Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:28:48.702884Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:28:48.702613Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:28:49.039277Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:28:49.038490Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"fig, ax = plt.subplots()\\n\",\n \"ax.plot([4, 8, 15, 16, 23, 42])\\n\",\n \"fig.savefig('a-local-file.png')\"\n"}]}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/hidden-cells.ipynb", "source2": "./usr/share/doc/python-nbsphinx/html/hidden-cells.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9994140625%", "Differences: {\"'cells'\": \"{2: {'metadata': {'execution': {'iopub.execute_input': '2025-06-04T10:28:54.665333Z', \"", " \"'iopub.status.busy': '2025-06-04T10:28:54.665004Z', 'iopub.status.idle': \"", " \"'2025-06-04T10:28:54.683443Z', 'shell.execute_reply': \"", " \"'2025-06-04T10:28:54.682616Z'}}}, 4: {'metadata': {'execution': \"", " \"{'iopub.execute_input': '2025-06-04T10:28:54.687406Z', 'iopub.status.busy': \"", " \"'2025-06-04T10:28:54.687132Z', 'iopub.status.idle': '2025-06-04T10:28:5 [\u2026]"], "unified_diff": "@@ -27,18 +27,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:59.206982Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:59.206669Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:59.215484Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:59.214799Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:28:54.665333Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:28:54.665004Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:28:54.683443Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:28:54.682616Z\"\n },\n \"nbsphinx\": \"hidden\"\n },\n \"outputs\": [],\n \"source\": [\n \"answer = 6 * 7\"\n ]\n@@ -52,18 +52,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-05-02T03:53:59.219312Z\",\n- \"iopub.status.busy\": \"2024-05-02T03:53:59.219048Z\",\n- \"iopub.status.idle\": \"2024-05-02T03:53:59.231053Z\",\n- \"shell.execute_reply\": \"2024-05-02T03:53:59.230323Z\"\n+ \"iopub.execute_input\": \"2025-06-04T10:28:54.687406Z\",\n+ \"iopub.status.busy\": \"2025-06-04T10:28:54.687132Z\",\n+ \"iopub.status.idle\": \"2025-06-04T10:28:54.711310Z\",\n+ \"shell.execute_reply\": \"2025-06-04T10:28:54.710496Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"42\"\n"}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/searchindex.js", "source2": "./usr/share/doc/python-nbsphinx/html/searchindex.js", "unified_diff": null, "details": [{"source1": "js-beautify {}", "source2": "js-beautify {}", "unified_diff": "@@ -1118,18 +1118,18 @@\n \"exp\": 8,\n \"zmax\": 8,\n \"max\": 8,\n \"imshow\": 8,\n \"vmin\": 8,\n \"vmax\": 8,\n \"axesimag\": 8,\n- \"0xffff84bf17d0\": 8,\n+ \"0xffff601389d0\": 8,\n \"appropri\": [9, 16, 18, 23, 28],\n \"line2d\": 9,\n- \"0xffff65d35690\": 9,\n+ \"0xffff77f1c610\": 9,\n \"thumbnail\": [10, 13, 15, 28, 29],\n \"what\": [10, 14, 21, 25],\n \"type\": [10, 15, 21, 23, 28],\n \"via\": [12, 22],\n \"nbsphinx_thumbnail\": [12, 15],\n \"dictionari\": [12, 28],\n \"wildcard\": [12, 28],\n"}]}, {"source1": "./usr/share/doc/python-nbsphinx/html/subdir/a-notebook-in-a-subdir.ipynb.gz", "source2": "./usr/share/doc/python-nbsphinx/html/subdir/a-notebook-in-a-subdir.ipynb.gz", "unified_diff": null, "details": [{"source1": "a-notebook-in-a-subdir.ipynb", "source2": "a-notebook-in-a-subdir.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 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\",\n \"text/plain\": [\n"}]}]}, {"source1": "./usr/share/doc/python-nbsphinx/nbsphinx.pdf", "source2": "./usr/share/doc/python-nbsphinx/nbsphinx.pdf", "unified_diff": null, "details": [{"source1": "pdftotext {} -", "source2": "pdftotext {} -", "unified_diff": "@@ -2066,15 +2066,15 @@\n False\n True\n \n 4.3.7 YouTube Videos\n \n [31]: from IPython.display import YouTubeVideo\n YouTubeVideo('9_OIs49m56E')\n-[31]: \n+[31]: \n \n 40\n \n \f4.3.8 Interactive Widgets (HTML only)\n The basic widget infrastructure is provided by the ipywidgets152 module. More advanced widgets are\n available in separate packages, see for example https://jupyter.org/widgets.\n The JavaScript code which is needed to display Jupyter widgets is loaded automatically (using RequireJS). If you want to use non-default URLs or local files, you can use the nbsphinx_widgets_path\n@@ -2882,15 +2882,15 @@\n Metadata to Select a Thumbnail (page 57).\n \n [1]: import matplotlib.pyplot as plt\n The following cell has the nbsphinx-thumbnail tag:\n \n [2]: fig, ax = plt.subplots(figsize=[6, 3])\n ax.plot([4, 9, 7, 20, 6, 33, 13, 23, 16, 62, 8])\n-[2]: []\n+[2]: []\n \n 60\n 50\n 40\n 30\n 20\n 10\n@@ -2937,15 +2937,15 @@\n html:\n \n [3]: x, y = np.meshgrid(np.arange(-3, 3, 0.1), np.arange(-2, 2, 0.1))\n z = (1 - x / 2 + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)\n [4]: zmax = np.max(np.abs(z))\n [5]: fig, ax = plt.subplots(figsize=[5, 3.5])\n ax.imshow(z, vmin=-zmax, vmax=zmax)\n-[5]: \n+[5]: \n \n 35\n 30\n 25\n 20\n 15\n 10\n"}]}]}]}]}]}