add impresso-team as author

src/content/notebooks/impresso-py-maps.mdx

@@ -2,41 +2,48 @@
 title: Exploring impresso with maps
 githubUrl: https://github.com/impresso/impresso-datalab-notebooks/blob/main/4-impresso-py/maps_explore.ipynb
 authors:
+  - impresso-team
   - RomanKalyakin
 sha: 168c669246385a2ec6c3e088b0081364f129d11c
 date: 2024-09-27T12:54:12Z
 googleColabUrl: https://colab.research.google.com/github/impresso/impresso-datalab-notebooks/blob/main/4-impresso-py/maps_explore.ipynb
 ---
 
 {/* cell:0 cell_type:markdown */}
+
 ## Install dependencies
 
 We need the following packages:
 
- * [impresso-py](https://impresso-project.ch/)
- * [ipyleaflet](https://ipyleaflet.readthedocs.io/en/latest/index.html)
+- [impresso-py](https://impresso-project.ch/)
+- [ipyleaflet](https://ipyleaflet.readthedocs.io/en/latest/index.html)
 
 {/* cell:1 cell_type:code */}
+
 ```python
 %pip install git+https://github.com/impresso/impresso-py.git ipyleaflet
 ```
 
 {/* cell:2 cell_type:markdown */}
+
 ## Connect to Impresso
 
 {/* cell:3 cell_type:code */}
+
 ```python
 from impresso import connect, OR, DateRange
 
 impresso = connect(public_api_url="https://dev.impresso-project.ch/public-api")
 ```
 
 {/* cell:4 cell_type:markdown */}
+
 ## Search and collect entities
 
 Find top 100 location entities mentioned in articles that talk about nuclear power plants in the first three decades following the second world war.
 
 {/* cell:5 cell_type:code */}
+
 ```python
 locations = impresso.search.facet(
     "location",
@@ -52,6 +59,7 @@ locations
 Get entities details, including wikidata details
 
 {/* cell:7 cell_type:code */}
+
 ```python
 entities_ids = locations.df.index.tolist()
 entities = impresso.entities.find(entity_id=OR(*entities_ids), load_wikidata=True, limit=len(entities_ids))
@@ -62,6 +70,7 @@ entities
 Filter out entities that have no coordinates and add a country tag.
 
 {/* cell:9 cell_type:code */}
+
 ```python
 df = entities.df
 entities_with_coordinates = df[df['wikidata.coordinates.latitude'].notna() & df['wikidata.coordinates.longitude'].notna()]
@@ -74,6 +83,7 @@ entities_with_coordinates
 Add counts of mentions to the entities dataframe.
 
 {/* cell:11 cell_type:code */}
+
 ```python
 entities_with_coordinates['mentions_count'] = entities_with_coordinates.index.map(locations.df['count'])
 ```
@@ -82,6 +92,7 @@ entities_with_coordinates['mentions_count'] = entities_with_coordinates.index.ma
 Plot entities on the map.
 
 {/* cell:13 cell_type:markdown */}
+
 ### Utility methods
 
 Functions used to calculate extra details needed to plot data on a map.
@@ -90,6 +101,7 @@ Functions used to calculate extra details needed to plot data on a map.
 Find geo bounds of a group of items.
 
 {/* cell:15 cell_type:code */}
+
 ```python
 def find_bounds(coordinates):
   """
@@ -124,6 +136,7 @@ def find_bounds(coordinates):
 Create an HTML used for rendering the hover pop-up.
 
 {/* cell:17 cell_type:code */}
+
 ```python
 from ipywidgets import HTML
 from ipyleaflet import Popup
@@ -150,9 +163,11 @@ def build_hover_popup(title: str, subtitle: str, mentions: int) -> Popup:
 ```
 
 {/* cell:18 cell_type:markdown */}
+
 ### Plot
 
 {/* cell:19 cell_type:code */}
+
 ```python
 from ipyleaflet import Map, Marker, AwesomeIcon, CircleMarker
 

src/content/notebooks/impresso-py-network.mdx

@@ -2,41 +2,49 @@
 title: Network graph with Impresso Py
 githubUrl: https://github.com/impresso/impresso-datalab-notebooks/blob/main/4-impresso-py/network_graph.ipynb
 authors:
+  - impresso-team
   - RomanKalyakin
 sha: 168c669246385a2ec6c3e088b0081364f129d11c
 date: 2024-09-27T12:54:12Z
 googleColabUrl: https://colab.research.google.com/github/impresso/impresso-datalab-notebooks/blob/main/4-impresso-py/network_graph.ipynb
 ---
 
 {/* cell:0 cell_type:markdown */}
+
 ## Install dependencies
 
 {/* cell:1 cell_type:code */}
+
 ```python
 %pip install git+https://github.com/impresso/impresso-py.git ipysigma
 ```
 
 {/* cell:2 cell_type:markdown */}
+
 ## Connect to Impresso
 
 {/* cell:3 cell_type:code */}
+
 ```python
 from impresso import connect, OR, AND
 
 impresso = connect(public_api_url="https://dev.impresso-project.ch/public-api")
 ```
 
 {/* cell:4 cell_type:markdown */}
+
 ## Part 1: Get entities and their co-occurrences
 
 Find all persons mentioned in all articles that talk about the [Prague Spring](https://en.wikipedia.org/wiki/Prague_Spring).
 
 {/* cell:5 cell_type:code */}
+
 ```python
 query = OR("Prague Spring", "Prager Frühling", "Printemps de Prague")
 ```
 
 {/* cell:6 cell_type:code */}
+
 ```python
 persons = impresso.search.facet(
   facet="person",
@@ -51,6 +59,7 @@ persons
 Get all combinations of all entities with a mention count higher than `N`.
 
 {/* cell:8 cell_type:code */}
+
 ```python
 import itertools
 
@@ -66,6 +75,7 @@ print(f"Total combinations: {len(persons_ids_combinations)}")
 ```
 
 {/* cell:9 cell_type:code */}
+
 ```python
 if len(persons_ids_combinations) > 500:
   msg = (
@@ -81,6 +91,7 @@ if len(persons_ids_combinations) > 500:
 Get timestamps and counts of all articles where persons pairs appear.
 
 {/* cell:11 cell_type:code */}
+
 ```python
 from impresso.util.error import ImpressoError
 from time import sleep
@@ -115,6 +126,7 @@ for idx, combo in enumerate(persons_ids_combinations):
 Put them all into a dataframe
 
 {/* cell:13 cell_type:code */}
+
 ```python
 import pandas as pd
 
@@ -132,14 +144,17 @@ connections_df
 ```
 
 {/* cell:14 cell_type:code */}
+
 ```python
 connections_df.to_csv("connections.csv")
 ```
 
 {/* cell:15 cell_type:markdown */}
+
 ## Part 2: visualise
 
 {/* cell:16 cell_type:code */}
+
 ```python
 import pandas as pd
 
@@ -148,6 +163,7 @@ connections_df
 ```
 
 {/* cell:17 cell_type:code */}
+
 ```python
 grouped_connections_df = connections_df.groupby(['node_a', 'node_b']) \
     .agg({'timestamp': lambda x: ', '.join(list(x)), 'count': 'sum', 'url': lambda x: list(set(x))[0]}) \
@@ -156,6 +172,7 @@ grouped_connections_df
 ```
 
 {/* cell:18 cell_type:code */}
+
 ```python
 import networkx as nx
 
@@ -172,12 +189,14 @@ G.nodes
 ```
 
 {/* cell:19 cell_type:code */}
+
 ```python
 filename = input("Enter the filename: ")
 filename = f"{filename.replace(' ', '_')}.gefx"
 ```
 
 {/* cell:20 cell_type:code */}
+
 ```python
 nx.write_gexf(G, filename)
 ```
@@ -186,6 +205,7 @@ nx.write_gexf(G, filename)
 If running in Colab - activate custom widgets to allow Sigma to render the graph.
 
 {/* cell:22 cell_type:code */}
+
 ```python
 try:
     from google.colab import output
@@ -198,6 +218,7 @@ except:
 Render the graph.
 
 {/* cell:24 cell_type:code */}
+
 ```python
 import networkx as nx
 from ipysigma import Sigma