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Merge pull request #87 from digital-science:v1.4
vV1.4
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‎CHANGELOG.md

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# Changelog
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## v 1.4
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* Fix CLI [history bug](https://github.com/digital-science/dimcli/issues/80)
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* Added DSL grammar for DSL [V2.10](https://docs.dimensions.ai/dsl/releasenotes.html#version-2-10-0-2024-april-week-of-29th)
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## v 1.3
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* Function `extract_affiliations` return an extra column for unstructured data, with the input string.
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## v 1.2
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* Added DSL grammar for DSL V2.8
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* Added DSL grammar for DSL [V2.8](https://docs.dimensions.ai/dsl/releasenotes.html#version-2-8-0-2023-september-week-of-25th)
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* New utility function: [utils.explode_nested_repeated_field](https://digital-science.github.io/dimcli/modules.html#dimcli.utils.misc_utils.explode_nested_repeated_field)
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## v 1.1
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## v 0.9.9
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* New parameters for [DslDataset.as_dataframe()](https://digital-science.github.io/dimcli/modules.html#dimcli.core.api.DslDataset.as_dataframe)
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* Improvements to the `--nice` and `--links` rendering of dataframes with Dimesions data
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* Improvements to the `--nice` and `--links` rendering of dataframes with Dimensions data
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- with hyperlinks, drop IDs if title is present
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- affiliations as a list with GRID links
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- generic 'default_transform' method for extra column not in transformations

‎dimcli/VERSION.py

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# !/usr/bin/env python
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# -*- coding: UTF-8 -*-
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__version__ = "1.3" # LATEST? => https://pypi.org/project/dimcli/
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__version__ = "1.4" # LATEST? => https://pypi.org/project/dimcli/
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__copyright__ = "CopyRight (C) 2018-2024 by Digital Science"
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__license__ = "MIT"
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__author__ = "Michele Pasin"

‎dimcli/core/dsl_grammar_core.py

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@@ -577,11 +577,11 @@
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"fieldsets": ["basics", "extras", "categories", "studies"],
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"metrics": {"count": {"description": "Total count", "name": "count"}},
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"search_fields": [
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"title_only",
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"raw_affiliations",
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"full_data",
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"title_only",
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"investigators",
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"title_abstract_only",
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"raw_affiliations",
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],
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},
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"datasets": {
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"type": "string",
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},
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"repository": {
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"description": "Repository associtated with the dataset.",
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"description": "Repository associated with the dataset.",
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"is_entity": True,
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"is_facet": True,
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"is_filter": True,
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},
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"fieldsets": ["basics", "categories"],
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"metrics": {"count": {"description": "Total count", "name": "count"}},
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"search_fields": ["full_data", "title_only", "title_abstract_only"],
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"search_fields": ["title_abstract_only", "title_only", "full_data"],
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},
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"funder_groups": {
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"fields": {
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"long_description": None,
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"type": "string",
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},
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"active_status": {
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"description": None,
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"is_entity": False,
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"is_facet": True,
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"is_filter": True,
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"is_multivalue": False,
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"long_description": None,
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"type": "string",
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},
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"active_year": {
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"description": "List of active years for a grant.",
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"is_entity": False,
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"long_description": None,
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"type": "states",
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},
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"research_org_types": {
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"description": "Type of organisation to which investigators are affiliated. This field only contains information for organisations with GRID IDs.",
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"is_entity": False,
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"is_facet": True,
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"is_filter": True,
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"is_multivalue": False,
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"long_description": None,
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"type": "string",
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},
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"research_orgs": {
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"description": "GRID organisations receiving the grant (note: identifiers are automatically extracted from the source text and can be missing in some cases).",
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"is_entity": True,
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},
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"search_fields": [
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"concepts",
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"title_only",
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"raw_affiliations",
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"full_data",
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"title_only",
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"investigators",
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"title_abstract_only",
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"raw_affiliations",
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],
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},
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"organizations": {
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"is_entity": False,
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"is_facet": False,
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"is_filter": True,
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"is_multivalue": False,
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"is_multivalue": True,
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"long_description": None,
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"type": "json",
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},
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"inventors",
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"title_only",
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"assignees",
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"full_data",
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"title_abstract_only",
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"full_data",
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],
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},
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"policy_documents": {
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},
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"fieldsets": ["basics", "categories"],
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"metrics": {"count": {"description": "Total count", "name": "count"}},
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"search_fields": ["full_data", "title_only"],
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"search_fields": ["title_only", "full_data"],
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},
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"publications": {
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"fields": {
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"long_description": None,
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"type": "string",
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},
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"document_type": {
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"description": "Document type prediction based on the content and metadata of a publication.",
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"is_entity": False,
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"is_facet": True,
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"is_filter": True,
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"is_multivalue": False,
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"long_description": None,
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"type": "string",
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},
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"doi": {
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"description": "Digital object identifier.",
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"is_entity": False,
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"long_description": None,
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"type": "string",
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},
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"research_org_types": {
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"description": "Types of organisation associated with the publication. This field only contains information for organisations with GRID IDs.",
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"is_entity": False,
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"is_facet": True,
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"is_filter": True,
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"is_multivalue": True,
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"long_description": None,
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"type": "string",
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},
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"research_orgs": {
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"description": "GRID organisations associated to a publication. Identifiers are automatically extracted from author affiliations text, so they can be missing in some cases (note: this field supports :ref:`filter-functions`: ``count``).",
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"is_entity": True,
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},
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"search_fields": [
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"acknowledgements",
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"authors",
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"concepts",
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"full_data_exact",
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"full_data",
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"terms",
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"title_abstract_only",
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"raw_affiliations",
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"authors",
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"title_only",
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"full_data",
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],
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},
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"reports": {
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},
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"fieldsets": ["basics", "categories"],
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"metrics": {"count": {"description": "Total count", "name": "count"}},
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"search_fields": ["full_data", "concepts", "title_abstract_only"],
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"search_fields": ["concepts", "title_abstract_only", "full_data"],
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},
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"research_org_groups": {
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"fields": {

‎dimcli/repl/history.py

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# print("HISTORY: ", filename)
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super(SelectiveFileHistory, self).__init__(filename)
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def append_string(self, string):
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" Add string to the history only if it is a valid DSL query"
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def append_string(self, string: str) -> None:
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"DIMCLI override: add string to the history only if it is a valid DSL query"
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l = G.allowed_starts_dsl_query()
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for x in l:
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if string.startswith(x):
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self._loaded_strings.append(string)
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self.store_string(string)
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return
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if string.startswith(tuple(l)):
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self._loaded_strings.insert(0, string)
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self.store_string(string)
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‎dimcli/repl/repl.py

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>>> .export_as_html: save results from last query as HTML file.
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>>> .export_as_bar_chart: save results from last query as Plotly bar chart.
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>>> .export_as_jupyter: save results from last query as Jupyter notebook.
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>>> .export_as_gsheets: save results from last query as Google Sheets (requires gpread credentials).
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>>> .export_as_gsheets: save results from last query as Google Sheets (requires gspread credentials).
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>>> .show [optional: N]: print N results from last query, trying to build URLs for objects. Default N=10.
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>>> .json_compact: print results of last query as single-line JSON.
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>>> .json_full: print results of last query as formatted JSON.
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click.secho(f"Using endpoint: {CLIENT._url} - DSL version: {_info}", dim=True)
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# history
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session = PromptSession(history=SelectiveFileHistory(USER_HISTORY_FILE))
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session = PromptSession(history=SelectiveFileHistory(USER_HISTORY_FILE)) ### December 30, 2024
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# session = PromptSession()
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databuffer = DslResultsBuffer()
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