[SN-145] removed buffered stream mentioning (#1662)

Author: Gabefire

examples/annotation_import/pdf.ipynb

@@ -282,7 +282,7 @@
     },
     {
       "metadata": {},
-      "source": "client.enable_experimental = True\ntask = lb.DataRow.export(client=client, global_keys=[global_key])\ntask.wait_till_done()\nstream = task.get_stream()\n\ntext_layer = \"\"\nfor output in stream:\n    output_json = json.loads(output.json_str)\n    text_layer = output_json[\"media_attributes\"][\"text_layer_url\"]\nprint(text_layer)",
+      "source": "client.enable_experimental = True\ntask = lb.DataRow.export(client=client, global_keys=[global_key])\ntask.wait_till_done()\nstream = task.get_buffered_stream()\n\ntext_layer = \"\"\nfor output in stream:\n    output_json = json.loads(output.json)\n    text_layer = output_json[\"media_attributes\"][\"text_layer_url\"]\nprint(text_layer)",
       "cell_type": "code",
       "outputs": [],
       "execution_count": null

examples/basics/batches.ipynb

@@ -121,7 +121,7 @@
     },
     {
       "metadata": {},
-      "source": "client.enable_experimental = True\n\nexport_task = dataset.export()\nexport_task.wait_till_done()\n\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+      "source": "client.enable_experimental = True\n\nexport_task = dataset.export()\nexport_task.wait_till_done()\n\ndata_rows = []\n\n\ndef json_stream_handler(output: lb.BufferedJsonConverterOutput):\n    data_row = json.loads(output.json)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_buffered_stream(stream_type=lb.StreamType.ERRORS).start(\n        stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_buffered_stream(\n        stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
       "cell_type": "code",
       "outputs": [],
       "execution_count": null
@@ -254,7 +254,7 @@
     },
     {
       "metadata": {},
-      "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+      "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.BufferedJsonConverterOutput):\n    data_row = json.loads(output.json)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_buffered_stream(stream_type=lb.StreamType.ERRORS).start(\n        stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_buffered_stream(\n        stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
       "cell_type": "code",
       "outputs": [],
       "execution_count": null

examples/basics/custom_embeddings.ipynb

@@ -102,7 +102,7 @@
     },
     {
       "metadata": {},
-      "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+      "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.BufferedJsonConverterOutput):\n    data_row = json.loads(output.json)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_buffered_stream(stream_type=lb.StreamType.ERRORS).start(\n        stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_buffered_stream(\n        stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
       "cell_type": "code",
       "outputs": [],
       "execution_count": null

examples/basics/data_rows.ipynb

@@ -113,7 +113,7 @@
     },
     {
       "metadata": {},
-      "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.JsonConverterOutput):\n    data_row = json.loads(output.json_str)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_stream(converter=lb.JsonConverter(),\n                           stream_type=lb.StreamType.ERRORS).start(\n                               stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_stream(\n        converter=lb.JsonConverter(), stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
+      "source": "data_rows = []\n\n\ndef json_stream_handler(output: lb.BufferedJsonConverterOutput):\n    data_row = json.loads(output.json)\n    data_rows.append(data_row)\n\n\nif export_task.has_errors():\n    export_task.get_buffered_stream(stream_type=lb.StreamType.ERRORS).start(\n        stream_handler=lambda error: print(error))\n\nif export_task.has_result():\n    export_json = export_task.get_buffered_stream(\n        stream_type=lb.StreamType.RESULT).start(\n            stream_handler=json_stream_handler)",
       "cell_type": "code",
       "outputs": [],
       "execution_count": null
@@ -236,7 +236,7 @@
     },
     {
       "metadata": {},
-      "source": "# Fetch a data row from the first dataset example\nts = dataset.export()\nts.wait_till_done()\nDATA_ROW_ID = [json.loads(output.json_str) for output in ts.get_stream()\n              ][0][\"data_row\"][\"id\"]\nGLOBAL_KEY = [json.loads(output.json_str) for output in ts.get_stream()\n             ][0][\"data_row\"][\"global_key\"]\n\nprint(f\"Pick either a data row id : {DATA_ROW_ID} or global key: {GLOBAL_KEY}\")",
+      "source": "# Fetch a data row from the first dataset example\nts = dataset.export()\nts.wait_till_done()\nDATA_ROW_ID = [json.loads(output.json) for output in ts.get_buffered_stream()\n              ][0][\"data_row\"][\"id\"]\nGLOBAL_KEY = [json.loads(output.json) for output in ts.get_buffered_stream()\n             ][0][\"data_row\"][\"global_key\"]\n\nprint(f\"Pick either a data row id : {DATA_ROW_ID} or global key: {GLOBAL_KEY}\")",
       "cell_type": "code",
       "outputs": [],
       "execution_count": null

examples/exports/composite_mask_export.ipynb

@@ -211,7 +211,7 @@
     },
     {
       "metadata": {},
-      "source": "stream = export_task.get_stream()\n\nmask_tool_rgb_mapping = {}\n\nfor output in stream:\n    # Parse the JSON string from the output\n    output_json = json.loads(output.json_str)\n\n    # Get the labels for the specified project ID or an empty list if the project ID is not found\n    project_labels = (output_json[\"projects\"].get(PROJECT_ID,\n                                                  {}).get(\"labels\", []))\n\n    # Iterate through each label\n    for label in project_labels:\n        # Get the list of annotations (objects) for the label\n        annotations = label[\"annotations\"].get(\"objects\", [])\n\n        # Iterate through each annotation\n        for annotation in annotations:\n            # Check if the annotation is of type \"ImageSegmentationMask\"\n            if annotation.get(\"annotation_kind\") == \"ImageSegmentationMask\":\n                # Add the color RGB information to the mapping dictionary\n                mask_tool_rgb_mapping.setdefault(annotation[\"name\"], []).append(\n                    annotation[\"composite_mask\"][\"color_rgb\"])\n\nprint(mask_tool_rgb_mapping)",
+      "source": "stream = export_task.get_buffered_stream()\n\nmask_tool_rgb_mapping = {}\n\nfor output in stream:\n    # Parse the JSON string from the output\n    output_json = json.loads(output.json)\n\n    # Get the labels for the specified project ID or an empty list if the project ID is not found\n    project_labels = (output_json[\"projects\"].get(PROJECT_ID,\n                                                  {}).get(\"labels\", []))\n\n    # Iterate through each label\n    for label in project_labels:\n        # Get the list of annotations (objects) for the label\n        annotations = label[\"annotations\"].get(\"objects\", [])\n\n        # Iterate through each annotation\n        for annotation in annotations:\n            # Check if the annotation is of type \"ImageSegmentationMask\"\n            if annotation.get(\"annotation_kind\") == \"ImageSegmentationMask\":\n                # Add the color RGB information to the mapping dictionary\n                mask_tool_rgb_mapping.setdefault(annotation[\"name\"], []).append(\n                    annotation[\"composite_mask\"][\"color_rgb\"])\n\nprint(mask_tool_rgb_mapping)",
       "cell_type": "code",
       "outputs": [],
       "execution_count": null
@@ -246,7 +246,7 @@
     },
     {
       "metadata": {},
-      "source": "tools_frames_color = {}\nstream = export_task_video.get_stream()\n\n# Iterate over each output in the stream\nfor output in stream:\n    output_json = json.loads(output.json_str)\n\n    # Iterate over the labels in the specific project\n    for dr in output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]:\n        frames_data = dr[\"annotations\"][\"frames\"]\n\n        # Iterate over each frame in the frames data\n        for frame_key, frame_value in frames_data.items():\n\n            # Iterate over each annotation in the frame\n            for annotation_key, annotation_value in frame_value.items():\n                if \"objects\" in annotation_key and annotation_value.values():\n\n                    # Iterate over each object in the annotation\n                    for object_key, object_value in annotation_value.items():\n                        if (object_value[\"annotation_kind\"] ==\n                                \"VideoSegmentationMask\"):\n                            # Update tools_frames_color with object information\n                            tools_frames_color.setdefault(\n                                object_value[\"name\"], []).append({\n                                    frame_key:\n                                        object_value[\"composite_mask\"]\n                                        [\"color_rgb\"]\n                                })\n\nprint(tools_frames_color)",
+      "source": "tools_frames_color = {}\nstream = export_task_video.get_buffered_stream()\n\n# Iterate over each output in the stream\nfor output in stream:\n    output_json = json.loads(output.json)\n\n    # Iterate over the labels in the specific project\n    for dr in output_json[\"projects\"][VIDEO_PROJECT_ID][\"labels\"]:\n        frames_data = dr[\"annotations\"][\"frames\"]\n\n        # Iterate over each frame in the frames data\n        for frame_key, frame_value in frames_data.items():\n\n            # Iterate over each annotation in the frame\n            for annotation_key, annotation_value in frame_value.items():\n                if \"objects\" in annotation_key and annotation_value.values():\n\n                    # Iterate over each object in the annotation\n                    for object_key, object_value in annotation_value.items():\n                        if (object_value[\"annotation_kind\"] ==\n                                \"VideoSegmentationMask\"):\n                            # Update tools_frames_color with object information\n                            tools_frames_color.setdefault(\n                                object_value[\"name\"], []).append({\n                                    frame_key:\n                                        object_value[\"composite_mask\"]\n                                        [\"color_rgb\"]\n                                })\n\nprint(tools_frames_color)",
       "cell_type": "code",
       "outputs": [],
       "execution_count": null