{"templateId":"markdown","sharedDataIds":{"sidebar":"sidebar-guides/sidebars.yaml"},"props":{"metadata":{"markdoc":{"tagList":[]},"type":"markdown"},"seo":{"title":"Introduction","description":"Accelerate E&P application development and protect your innovation by consuming our Data and Domain APIs / Platform APIs.","lang":"en-US","meta":[{"name":"robots","content":"noindex"}],"llmstxt":{"hide":true,"excludeFiles":[]}},"dynamicMarkdocComponents":[],"compilationErrors":[],"ast":{"$$mdtype":"Tag","name":"article","attributes":{},"children":[{"$$mdtype":"Tag","name":"Heading","attributes":{"level":1,"id":"introduction","__idx":0},"children":["Introduction"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["In this tutorial we will explain:"]},{"$$mdtype":"Tag","name":"ul","attributes":{},"children":[{"$$mdtype":"Tag","name":"li","attributes":{},"children":["How to ",{"$$mdtype":"Tag","name":"MarkdownLink","attributes":{"href":"#write-bulk-data---all-at-once"},"children":["write bulk data"]}," using ",{"$$mdtype":"Tag","name":"MarkdownLink","attributes":{"href":"/solutions/wellbore-dms/apis/wdms-osdu-da-v3"},"children":["Wellbore DDMS chunking APIs"]}]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["How to ",{"$$mdtype":"Tag","name":"MarkdownLink","attributes":{"href":"#welllog-record-versioning"},"children":["read and write a given version"]}," of a WellLog"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["How to ",{"$$mdtype":"Tag","name":"MarkdownLink","attributes":{"href":"#read-bulk-data"},"children":["read bulk data"]}," with filtering options as columns, offset and limit"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["How is ensured meta (record) and bulk ",{"$$mdtype":"Tag","name":"MarkdownLink","attributes":{"href":"#welllog-consistency-rules"},"children":["data consistency for WellLogs"]}]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["How is ensured meta (record) and bulk ",{"$$mdtype":"Tag","name":"MarkdownLink","attributes":{"href":"#trajectory-consistency-rules"},"children":["data consistency for Wellbore Trajectories"]}]}]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":1,"id":"prerequisites","__idx":1},"children":["Prerequisites"]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":2,"id":"required-python-packages","__idx":2},"children":["Required Python packages"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["Before to start to write bulk data through Wellbore DDMS API's, you will need to install the Python packages below:"]},{"$$mdtype":"Tag","name":"ul","attributes":{},"children":[{"$$mdtype":"Tag","name":"li","attributes":{},"children":["The pandas module and its Pandas.Dataframe json format to structure log bulk data to be written to the Wellbore DDMS."]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["The pyarrow module to transform Pandas.Dataframe to parquet file through the pyarrow engine."]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["The httpx module that allows to post request to the Wellbore DDMS."]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Prerequisite to run this notebook\n!python -m pip install pip --upgrade\n!pip install pandas numpy httpx pyarrow\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":2,"id":"authorization","__idx":3},"children":["Authorization"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["For any call to Wellbore DDMS API's you need to pass into the header of the request a valid bearer token. This token can be obtained from any API catalog on the developer portal. You will need first to request a developer base subscription. Then from the developer base subscription pick any API and execute it. A valid bearer token is returns in the Curl section of the response. Copy this token value and assign it to the TOKEN variable below."]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"TOKEN = '' # Paste here the token without the bearer prefix\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":2,"id":"utility-methods","__idx":4},"children":["Utility methods"]},{"$$mdtype":"Tag","name":"details","attributes":{},"children":[{"$$mdtype":"Tag","name":"summary","attributes":{},"children":[" Helper functions used in the different sample scripts of this tutorial. "]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"from typing import List\nimport httpx\nimport pandas as pd\nimport numpy as np\nimport io\nfrom IPython.display import display_html, display, HTML\nfrom itertools import chain, cycle\n\ndef generate_df_typed(columns, index):\n def gen_values(col_name, size):\n if col_name.startswith('float'):\n return np.random.random_sample(size=size)\n if col_name.startswith('str'):\n return [f'string_value_{i}' for i in range(size)]\n if col_name.startswith('bool'):\n return np.random.choice(a=[False, True], size=size) \n if col_name.startswith('date'):\n return (np.datetime4('2021-01-01') + days for days in range(size))\n return np.random.randint(-100, 1000, size=size)\n\n df = pd.DataFrame({c: gen_values(c, len(index))\n for c in columns}, index=index)\n return df\n\ndef multi_table(table_list):\n '''Acceps a list of IpyTable objects and returns a table which contains each IpyTable in a cell'''\n return HTML(\n '' + \n ''.join(['' for table in table_list]) +\n '
' + table._repr_html_() + '
'\n )\n\ndef gen_color(color):\n def fct(val=None):\n return f'color: {color}'\n return fct\n\ndef display_operation(before, sent, after):\n colors = ['blue', 'green', 'orange', 'red']\n color_fct = [gen_color(c) for c in colors]\n sent_st = [sent[i].style.set_caption(f'chunk {i+1} sent').applymap(color_fct[i]) for i in range(len(sent))]\n def color_output(s):\n res = []\n for r in s.index:\n c = ''\n for i in range(len(sent)):\n if s.name in sent[i] and int(r) in sent[i][s.name]:\n c = color_fct[i]()#f'color: {colors[i]}'\n res.append(c)\n return res\n\n margin = '65'\n after_st = after.style.set_table_attributes(f\"style='margin-left:{margin}px'\").apply(color_output).highlight_null(null_color='lightyellow').set_caption('Final data - After session commit') \n display(multi_table([before.style.set_table_attributes(f\"style='margin-right:{margin}px'\").set_caption('Initial data - Before session'), *sent_st, after_st]))\n \ndef display_side_by_side(dfs:list, captions:list):\n \"\"\"Display tables side by side to save vertical space\n Input:\n dfs: list of pandas.DataFrame\n captions: list of table captions\n \"\"\"\n output = \"\"\n combined = dict(zip(captions, dfs))\n for caption, df in combined.items():\n output += df.style.set_table_attributes(\"style='display:inline'\").set_caption(caption)._repr_html_()\n output += \"\\xa0\\xa0\\xa0\"\n display(HTML(output))\n \ndef generate_df(columns: List[str], index):\n nbrows = len(index)\n df = pd.DataFrame(\n np.random.randint(-100, 1000, size=(nbrows, len(columns))), index=index)\n df.columns= columns\n return df\n\n\ndef print_response(resp):\n print(f'{resp.request.method} : {resp.url} -> {resp.status_code}')\n if resp.status_code != httpx.codes.OK:\n display(resp.content)\n\n \ndef create_df_from_response(response):\n \"\"\"Returns a dataframe created from the WellLog bulk data response\n Input:\n response: a httpx.response object\n Output:\n dataframe: a pandas.dataframe object\n \"\"\"\n content_type = response.headers.get('content-type')\n \n if content_type == 'application/json':\n return pd.DataFrame.from_dict(response.json())\n \n elif content_type == 'application/x-parquet':\n f = io.BytesIO(response.content)\n f.seek(0)\n return pd.read_parquet(f)\n \n raise ValueError(f\"Unknown content-type: '{content_type}'\")\n \ndef display_previous_and_current_well_log_data_versions(record_id):\n \"\"\"Display the previous and current WellLog data versions for a given record id and highlight differences between them.\n Input:\n record_id: a WellLog record id\n \"\"\"\n # list record version\n results_response = client.get(f'{welllog_dms_url}/{record_id}/versions')\n wellLog_versions_response = results_response.json()\n versions = wellLog_versions_response['versions']\n \n is_previous_results = False\n is_current_results = False\n if len(versions) >= 2:\n previous_version_id = versions[len(versions)-2]\n curl = f'{welllog_dms_url}/{record_id}/versions/{previous_version_id}/data'\n results_response = client.get(curl)\n if results_response.status_code == 200:\n previous_results = create_df_from_response(results_response)\n is_previous_results = True\n \n current_version_id = versions[len(versions)-1]\n curl = f'{welllog_dms_url}/{record_id}/versions/{current_version_id}/data'\n results_response = client.get(curl)\n if results_response.status_code == 200:\n current_results = create_df_from_response(results_response)\n is_current_results = True\n \n colors = ['blue', 'red']\n color_fct = [gen_color(c) for c in colors]\n def color_output(s):\n res = [] \n for r in s.index:\n c = ''\n if s.name in previous_results and int(r) in previous_results[s.name]:\n c = color_fct[0]()\n else:\n c = color_fct[1]()\n res.append(c)\n return res\n \n margin = '65'\n tables = []\n if is_previous_results:\n previous_results_st = previous_results.style.set_table_attributes(f\"style='margin-left:{margin}px'\").highlight_null(null_color='lightyellow').set_caption('Previous WellLog data version').applymap(color_fct[0]) \n tables.append(previous_results_st)\n \n if is_current_results:\n if is_previous_results:\n current_results_st = current_results.style.set_table_attributes(f\"style='margin-left:{margin}px'\").apply(color_output).highlight_null(null_color='lightyellow').set_caption('Current WellLog data version with data chunks added in red')\n tables.append(current_results_st)\n else:\n current_results_st = current_results.style.set_table_attributes(f\"style='margin-left:{margin}px'\").highlight_null(null_color='lightyellow').set_caption('Current WellLog data version') \n tables.append(current_results_st)\n \n display(multi_table(tables))\n","lang":"python"},"children":[]}]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":2,"id":"settings","__idx":5},"children":["Settings"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["Several settings as the base url end-point and the data partition id to create a WellLog to the Wellbore DDMS. Please change those settings accordingly to the environment settings that you want to target."]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"base_url = \"\" # set a base URL value\ndata_partition_id = \"\" # set a data partition id\nlegal_tag = \"\" # set a valid legal tag in the data partition \nacl_domain = \"\" # set an Access Control Lists (ACL) domain\n\nwelllog_dms_url = f'{base_url}/api/os-wellbore-ddms/ddms/v3/welllogs'\n\nclient = httpx.Client(verify=False,\n headers={\n \"data-partition-id\": f\"{data_partition_id}\",\n \"Authorization\": f\"Bearer {TOKEN}\",\n },\n timeout=120\n)\n\n# Create a new WellLog. Here is a fake body just to illustrate the API use\nrecord = {\n \"kind\": \"osdu:wks:work-product-component--WellLog:1.0.0\",\n \"acl\": {\n \"viewers\": [f\"data.default.viewers@{data_partition_id}.{acl_domain}\"],\n \"owners\": [f\"data.default.owners@{data_partition_id}.{acl_domain}\"]\n },\n \"legal\": {\n \"legaltags\": [f\"{legal_tag}\"],\n \"otherRelevantDataCountries\": [\"US\"],\n },\n \"data\": {\"\"\n \"WellboreID\": \"namespace:master-data--Wellbore:SomeUniqueWellboreID:\",\n \"Curves\": [\n {\n \"CurveID\": \"MD\",\n },\n {\n \"CurveID\": \"X\",\n }\n ]\n },\n \"version\" : 0\n}\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":2,"id":"create-a-welllog-record","__idx":6},"children":["Create a WellLog record"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["The script below is creating a WellLog record that is used in this tutorial to demonstrate how to write WellLog bulk data to the Wellbore DDMS."]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"response = client.post(welllog_dms_url, json=[record])\nprint_response(response)\nrecord_id = response.json()[\"recordIds\"][0]\nrecord_id\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":1,"id":"write-bulk-data---all-at-once","__idx":7},"children":["Write bulk data - all at once",{"$$mdtype":"Tag","name":"a","attributes":{"name":"write-bulk-data---all-at-once"},"children":[]}]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["Each time that data are written to the WellLog, a new version is created to the Wellbore DDMS. This is true when writting the entire bulk data at once or even by chunks (cover in a next section of this tutorial)."," ","So when writting all bulk data at once, the payload is expected to contain the entire bulk data that replaces the previous bulk version by creating a new version. This new bulk version becomes the latest one and the current version that is returned by the GET WellLog bulk data API for the given record id."]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["The Wellbore DDMS bulk data API supports both Parquet and JSON formats. In order to target one of this format the 'Content-Type' must be set accordingly in the headers of the HTTP POST request. Wellbore DDMS API supports HTTP chunked encoding as well."]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["First of all let's generate a Pandas.Dataframe through the code below with 2 columns and 5 rows."]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"generated_dataframe = generate_df(['COLUMN_MD', 'COLUMN_X'], range(5))\ngenerated_dataframe\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"div","attributes":{},"children":[{"$$mdtype":"Tag","name":"table","attributes":{"border":"0","className":"dataframe"},"children":[{"$$mdtype":"Tag","name":"thead","attributes":{},"children":[{"$$mdtype":"Tag","name":"tr","attributes":{"style":{"textAlign":"right"}},"children":[{"$$mdtype":"Tag","name":"th","attributes":{},"children":[]},{"$$mdtype":"Tag","name":"th","attributes":{},"children":["COLUMN_MD"]},{"$$mdtype":"Tag","name":"th","attributes":{},"children":["COLUMN_X"]}]}]},{"$$mdtype":"Tag","name":"tbody","attributes":{},"children":[{"$$mdtype":"Tag","name":"tr","attributes":{},"children":[{"$$mdtype":"Tag","name":"th","attributes":{},"children":["0"]},{"$$mdtype":"Tag","name":"td","attributes":{},"children":["986"]},{"$$mdtype":"Tag","name":"td","attributes":{},"children":["712"]}]},{"$$mdtype":"Tag","name":"tr","attributes":{},"children":[{"$$mdtype":"Tag","name":"th","attributes":{},"children":["1"]},{"$$mdtype":"Tag","name":"td","attributes":{},"children":["311"]},{"$$mdtype":"Tag","name":"td","attributes":{},"children":["348"]}]},{"$$mdtype":"Tag","name":"tr","attributes":{},"children":[{"$$mdtype":"Tag","name":"th","attributes":{},"children":["2"]},{"$$mdtype":"Tag","name":"td","attributes":{},"children":["-27"]},{"$$mdtype":"Tag","name":"td","attributes":{},"children":["339"]}]},{"$$mdtype":"Tag","name":"tr","attributes":{},"children":[{"$$mdtype":"Tag","name":"th","attributes":{},"children":["3"]},{"$$mdtype":"Tag","name":"td","attributes":{},"children":["230"]},{"$$mdtype":"Tag","name":"td","attributes":{},"children":["191"]}]},{"$$mdtype":"Tag","name":"tr","attributes":{},"children":[{"$$mdtype":"Tag","name":"th","attributes":{},"children":["4"]},{"$$mdtype":"Tag","name":"td","attributes":{},"children":["162"]},{"$$mdtype":"Tag","name":"td","attributes":{},"children":["740"]}]}]}]}]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"all-at-once---parquet","__idx":8},"children":["All at once - Parquet"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["Sending the whole dataframe to the WellLog bulk data."]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"data_to_send_parquet = generated_dataframe.to_parquet(path=None, engine=\"pyarrow\")\nheaders = { 'content-type': 'application/x-parquet'}\n\nprint_response(client.post(f'{welllog_dms_url}/{record_id}/data', data=data_to_send_parquet, headers=headers))\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"all-at-once---json","__idx":9},"children":["All at once - JSON"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["With the JSON format the orient parameter has to be set accordingly to the Pandas.Dataframe orientation."," ","This orient value can be passed through the params argument of the HTTP POST request."," ","Supported orient values are split and columns. The default orient value is set to split."]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["Here are examples of the same Pandas.Dataframe (5 rows and 2 columns) with different orientation:"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["split:"," ","{\"columns\":[\"COLUMN_MD\",\"COLUMN_X\"],\"index\":[0,1,2,3,4],\"data\":[[0.0,1001],[0.5,1002],[1.0,1003],[1.5,1004],[2.0,1005]]}"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["columns:"," ","{\"COLUMN_MD\":{\"0\":0.0,\"1\":0.5,\"2\":1.0,\"3\":1.5,\"4\":2.0},\"COLUMN_X\":{\"0\":1001,\"1\":1002,\"2\":1003,\"3\":1004,\"4\":1005}}"]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"data_to_send_json = {\n 'index': [0, 1, 2, 3, 4],\n 'columns': ['COLUMN_MD', 'COLUMN_X'],\n 'data': [[265, 845], [92, 246], [804, 268], [645, 877], [-20, -28]]\n}\n\nparams = {'orient':'split'}\nprint_response(client.post(f'{welllog_dms_url}/{record_id}/data', params=params, json=data_to_send_json))\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":1,"id":"write-bulk-data---by-chunk","__idx":10},"children":["Write bulk data - by chunk"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["In order to write WellLog bulk data by chunks to the Wellbore DDMS you have to follow those 3 steps:"]},{"$$mdtype":"Tag","name":"ol","attributes":{},"children":[{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Create a WellLog session - POST /ddms/v3/welllogs/{record_id}/sessions"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Send data by chunk in the session - POST /ddms/v3/welllogs/{record_id}/sessions/{session_id}/data"]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["Commit the session once all chunks are sent - PATCH /ddms/v3/welllogs/{welllog_id}/sessions/{session_id}"]}]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["In step 3 you can also update the session or abandon. This is controlled by the state attribute that is passed in the JSON of the PATCH HTTP session API."]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["{"," ","\"state\": \"commit\", \"abandon\" or \"update\""," ","}"]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":2,"id":"flow-to-send-json","__idx":11},"children":["Flow to send json"]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":2,"id":"open-a-new-session--send-json-chunks--commit-the-session","__idx":12},"children":["Open a new session > Send json chunks > Commit the session"]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":3,"id":"session-mode-update-or-overwrite","__idx":13},"children":["Session mode: update or overwrite"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["A session can be created with two different modes:"]},{"$$mdtype":"Tag","name":"ul","attributes":{},"children":[{"$$mdtype":"Tag","name":"li","attributes":{},"children":["update: existing data in previous WellLog version is merged with the data sent during the session when the session is committed."]},{"$$mdtype":"Tag","name":"li","attributes":{},"children":["overwrite: existing data in previous WellLog version is ignored, the final result only contains data sent during the session when the session is committed. In this case the only way to retrieve the previous data is querying the previous WellLog version."]}]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"SESSION_MODE = 'update' # 'update' | 'overwrite'\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":2,"id":"add-data-by-rows","__idx":14},"children":["Add data by rows"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["In the sample script below the WellLog data is ingested by chunk of row data."," ","In the same session it is possible to liberate WellLog data with both JSON and Parquet formats as shown below:"]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Create a session\ncreate_session_response = client.post(f'{welllog_dms_url}/{record_id}/sessions', json={'mode': SESSION_MODE})\n\nprint_response(create_session_response)\nsession_data = create_session_response.json()\nsession_id = session_data['id']\nprint(f\"Session created: {session_data['state']} with id {session_id}\\n\")\n \n# append first chunk - JSON\nchunk_1 = generate_df(['COLUMN_MD', 'COLUMN_X'], range(5,10))\nresponse_chunk_1 = client.post(f'{welllog_dms_url}/{record_id}/sessions/{session_id}/data', json=chunk_1.to_dict(orient='split'))\nprint_response(response_chunk_1)\n\n# append second chunk - JSON\nchunk_2 = generate_df(['COLUMN_MD', 'COLUMN_X'], range(10,15))\nresponse_chunk_2 = client.post(f'{welllog_dms_url}/{record_id}/sessions/{session_id}/data', json=chunk_2.to_dict(orient='split'))\nprint_response(response_chunk_2)\n\n\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["Once the whole WellLog data has been sent through the session, then the session needs to be committed using a session PATCH API call with the 'state' attribute sets to 'commit' value."]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Commit session\ncommit_session_response = client.patch(f'{welllog_dms_url}/{record_id}/sessions/{session_id}', json={'state': 'commit'})\n\nprint_response(commit_session_response)\nsession = commit_session_response.json()\nprint('Session after commit =', session['state'])\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["Or the session can be abandonned calling the session PATCH API with the 'state' attribute sets to 'abandon' value."]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# OR else, ABANDON session\nabandon_session_response = client.patch(f'{welllog_dms_url}/{record_id}/sessions/{session_id}', json={'state': 'abandon'})\nprint_response(abandon_session_response)\nif abandon_session_response.status_code == httpx.codes.OK:\n print('Session after commit =', abandon_session_response.json()['state'])\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":2,"id":"flow-to-send-parquet","__idx":15},"children":["Flow to send parquet"]},{"$$mdtype":"Tag","name":"Heading","attributes":{"level":2,"id":"open-a-new-session--send-parquet-chunks--commit-the-session","__idx":16},"children":["Open a new session > Send parquet chunks > Commit the session"]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"SESSION_MODE = 'update'\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Create a session to send parquet\ncreate_session_response = client.post(f'{wellbore_dms_url}/{record_id}/sessions', json={'mode': SESSION_MODE})\n\nprint_response(create_session_response)\nsession_data = create_session_response.json()\nsession_id = session_data['id']\nprint(f\"Session created: {session_data['state']} with id {session_id}\\n\")\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# append first chunk - PARQUET\nchunk_3 = generate_df(['COLUMN_MD', 'COLUMN_X'], range(15,20))\nheaders = {'content-type': 'application/x-parquet'}\nresponse_chunk_3 = client.post(f'{wellbore_dms_url}/{record_id}/sessions/{session_id}/data', data=chunk_3.to_parquet(engine=\"pyarrow\"), headers=headers)\nprint_response(response_chunk_3)\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# append second chunk - PARQUET\nchunk_4 = generate_df(['COLUMN_MD', 'COLUMN_X'], range(20,25))\nheaders = {'content-type': 'application/x-parquet'}\nresponse_chunk_4 = client.post(f'{wellbore_dms_url}/{record_id}/sessions/{session_id}/data', data=chunk_4.to_parquet(engine=\"pyarrow\"), headers=headers)\nprint_response(response_chunk_4)\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# commit session for parquet\nprint_response(client.patch(f'{wellbore_dms_url}/{record_id}/sessions/{session_id}', json={'state': 'commit'}))\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["The code below shows initial WellLog data before the session and chunks by rows inserted to the final WellLog data version after the session has been committed."]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Display result\nresults_response = client.get(f'{welllog_dms_url}/{record_id}/data')\nresults_cols_md_x = create_df_from_response(results_response) 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update or overwrite mode is clearly illustrated through WellLog data previous and current versions returned by the function."]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"display_previous_and_current_well_log_data_versions(record_id)\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"div","attributes":{},"children":[{"$$mdtype":"Tag","name":"style","attributes":{"type":"text/css"},"children":["\n#T_f8ed1_row0_col0,#T_f8ed1_row0_col1,#T_f8ed1_row1_col0,#T_f8ed1_row1_col1,#T_f8ed1_row2_col0,#T_f8ed1_row2_col1,#T_f8ed1_row3_col0,#T_f8ed1_row3_col1,#T_f8ed1_row4_col0,#T_f8ed1_row4_col1{\n color: blue;\n }\n",{"$$mdtype":"Tag","name":"p","attributes":{},"children":["#T_360e3_row0_col0,#T_360e3_row0_col1,#T_360e3_row1_col0,#T_360e3_row1_col1,#T_360e3_row2_col0,#T_360e3_row2_col1,#T_360e3_row3_col0,#T_360e3_row3_col1,#T_360e3_row4_col0,#T_360e3_row4_col1{"," ","color: blue;"," ","}"," 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Previous WellLog data version
COLUMN_MD COLUMN_X
0265845
192246
2804268
3645877
4-20-28
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Current WellLog data version with data chunks added in red
COLUMN_MD COLUMN_X
0265845
192246
2804268
3645877
4-20-28
5-29832
6-15107
7339212
8823240
9-97349
1018389
11194276
12-7-7
13446829
1432706
15914740
16593279
17304-57
18697145
19775247
\n",{"$$mdtype":"Tag","name":"Heading","attributes":{"level":2,"id":"add-data-by-columns","__idx":17},"children":["Add data by columns"]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["In the sample script below the WellLog data is ingested column per column. This is a typical action when we create new curves in a WellLog."," ","If the new columns added by chunks are sent in a session created with update mode then the new columns are appended to the list of columns present in the current version of the WellLog bulk data."," ","The columns posted in a session created with overwrite mode are only remaining in the latest version of the bulk data and the previous columns aren't preserved."]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"SESSION_MODE = 'update' # 'update' | 'overwrite'\n\n# Create a session\ncreate_session_response = client.post(f'{welllog_dms_url}/{record_id}/sessions', json={'mode': SESSION_MODE})\n\nprint_response(create_session_response)\nsession_id = create_session_response.json()['id']\n\n\n# Send data for Y\ngenerated_Y_dataframe = generate_df(['COLUMN_Y'], range(5, 25)) # variable has different indexes\nprint_response(client.post(f'{welllog_dms_url}/{record_id}/sessions/{session_id}/data', json=generated_Y_dataframe.to_dict(orient='split')))\n\n# Send data for Z\ngenerated_Z_dataframe = generate_df(['COLUMN_Z'], range(10, 15)) # variable has different indexes\nprint_response(client.post(f'{welllog_dms_url}/{record_id}/sessions/{session_id}/data', json=generated_Z_dataframe.to_dict(orient='split')))\n\n\n# Commit session\nprint_response(client.patch(f'{welllog_dms_url}/{record_id}/sessions/{session_id}', json={'state': 'commit'}))\n","lang":"python"},"children":[]},{"$$mdtype":"Tag","name":"p","attributes":{},"children":["The code below shows initial WellLog data before the session and chunks by columns inserted to the final WellLog data version after the session has been committed."]},{"$$mdtype":"Tag","name":"CodeBlock","attributes":{"data-language":"python","header":{"controls":{"copy":{}}},"source":"# Display result\nfull_data_response = client.get(f'{welllog_dms_url}/{record_id}/data')\nprint_response(full_data_response)\nwith_new_col = create_df_from_response(full_data_response)\n\ndisplay_operation(results_cols_md_x, [generated_Y_dataframe, generated_Z_dataframe], with_new_col)\n","lang":"python"},"children":[]},"
\n