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Amélioration de l'onglet Actions suite aux retours de MerTerre : #30

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Jul 9, 2024
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Amélioration de l'onglet Actions suite aux retours de MerTerre : #30

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bf87a14
Amélioration de l'onglet Actions suite aux retours de MerTerre :
Mendi33 Jun 20, 2024
4525563
update poetry.lock to solve precommit conflicts
Mendi33 Jun 20, 2024
301fc1a
upload hotspot.py from data
Mendi33 Jun 20, 2024
17dcc63
Ajout docstring de :
Mendi33 Jul 9, 2024
1795f72
Correction erreur dernier commit
Mendi33 Jul 9, 2024
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2 changes: 1 addition & 1 deletion .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -102,7 +102,7 @@ ipython_config.py
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
poetry.lock

# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
Expand Down
12 changes: 11 additions & 1 deletion dashboards/app/home.py
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Original file line number Diff line number Diff line change
Expand Up @@ -104,10 +104,19 @@ def load_df_nb_dechet() -> pd.DataFrame:
"sation/data/data_releve_nb_dechet.csv",
)

# Appel des fonctions pour charger les données
@st.cache_data
# Définition d'une fonction pour charger les evenements à venir
def load_df_events_clean() -> pd.DataFrame:
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Pourquoi définir une fonction pour charger les données ?

  • penser au docstrings pour aider les copains (et l'IDE) à comprendre la fonction ^^

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Done

return pd.read_csv(
"https://github.com/dataforgoodfr/12_zero_dechet_sauvage/raw/2-"
"nettoyage-et-augmentation-des-donn%C3%A9es/Exploration_visuali"
"sation/data/export_events_cleaned.csv",
)

# Appel des fonctions pour charger les données
df_other = load_df_other()
df_structures = load_structures()
df_events = load_df_events_clean()

# Création du filtre par niveau géographique : correspondance labels et variables
df_nb_dechets = load_df_nb_dechet()
Expand Down Expand Up @@ -187,6 +196,7 @@ def load_df_nb_dechet() -> pd.DataFrame:
]
st.session_state["structures_filtre"] = df_structures_filtre
st.session_state["structures"] = df_structures
st.session_state["events"] = df_events

# Filtrer et enregistrer le dataframe nb_dechets dans session.State
# Récuperer la liste des relevés
Expand Down
148 changes: 117 additions & 31 deletions dashboards/app/pages/actions.py
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Rien à dire !
Comme pour #29 les html pourraient être dans des fichiers dédiés

Original file line number Diff line number Diff line change
Expand Up @@ -30,19 +30,10 @@
else:
st.write(f"Votre territoire : {filtre_niveau} {filtre_collectivite}")

# Définition d'une fonction pour charger les evenements à venir
def load_df_events_clean() -> pd.DataFrame:
return pd.read_csv(
"https://github.com/dataforgoodfr/12_zero_dechet_sauvage/raw/2-"
"nettoyage-et-augmentation-des-donn%C3%A9es/Exploration_visuali"
"sation/data/export_events_cleaned.csv"
)

# Appel des fonctions pour charger les données
df_events = load_df_events_clean()

# Appeler les dataframes volumes et nb_dechets filtré depuis le session state
if "df_other_filtre" not in st.session_state:
if ("df_other_filtre" not in st.session_state) or (
"events" not in st.session_state
):
st.write(
"""
### :warning: Merci de sélectionner une collectivité\
Expand All @@ -52,6 +43,7 @@ def load_df_events_clean() -> pd.DataFrame:
st.stop()
else:
df_other = st.session_state["df_other_filtre"].copy()
df_events = st.session_state["events"].copy()

# 2 Onglets : Evènements, Evènements à venir
tab1, tab2 = st.tabs(
Expand All @@ -62,7 +54,7 @@ def load_df_events_clean() -> pd.DataFrame:
)

# Locale du package Babel
locale = Locale("fr", "FR")
bbl_locale = Locale("fr", "FR")

# Onglet 1 : Evènements
with tab1:
Expand Down Expand Up @@ -119,7 +111,7 @@ def load_df_events_clean() -> pd.DataFrame:
format_date(
datetime(2022, mois_dict[mois_liste[x - 1]], 1),
format="MMMM",
locale=locale,
locale=bbl_locale,
)
),
index=0,
Expand All @@ -141,8 +133,6 @@ def load_df_events_clean() -> pd.DataFrame:
df_ramassages = df_other_filtre.copy()

# Calcul des indicateurs clés de haut de tableau avant transformation
volume_total = df_ramassages["VOLUME_TOTAL"].sum()
poids_total = df_ramassages["POIDS_TOTAL"].sum()
nombre_participants = df_ramassages["NB_PARTICIPANTS"].sum()
nb_collectes = len(df_ramassages)
nombre_structures = df_ramassages["ID_STRUCTURE"].nunique()
Expand Down Expand Up @@ -201,10 +191,40 @@ def load_df_events_clean() -> pd.DataFrame:
# Application d'une limite minimale pour le rayon si nécessaire
radius = max(radius, 5)

format_participants = "{:.0f}".format(row.NB_PARTICIPANTS)

html = f"""
<div style="font-family: LATO REGULAR, sans-serif; font-size: 12px;">
<p>
<div style="color: gray; font-size: 10px;">
Evénement
</div>
<div style="font-weight: bold; font-size: 14px;">
{row.NOM_EVENEMENT}
</div>
<div style="color: gray; font-size: 10px;">
Structure
</div>
<div style="font-weight: bold; font-size: 14px;">
{row.NOM_STRUCTURE}
</div>
<br>
<div style="font-weight: bold;">
Date : <span style="color: gray;">{row.DATE.strftime("%d/%m/%Y")}</span>
</div>
<div style="font-weight: bold;">
Nombre de participants : <span style="color: gray;">{format_participants}</span>
</div>
</p>
</div>
"""

popup = folium.Popup(html, max_width=300)

folium.CircleMarker(
location=(row["LIEU_COORD_GPS_Y"], row["LIEU_COORD_GPS_X"]),
radius=radius, # Utilisation du rayon ajusté
popup=f"{row['NOM_ZONE']}, {row['LIEU_VILLE']}, {row['NOM_EVENEMENT']}, {row['DATE']} : nombre de participants : {row['NB_PARTICIPANTS']}",
popup=popup,
color="#3186cc",
fill=True,
fill_color="#3186cc",
Expand Down Expand Up @@ -235,8 +255,14 @@ def load_df_events_clean() -> pd.DataFrame:
color_discrete_sequence=colors,
category_orders={"NIVEAU_CARAC": [0, 1, 2, 3, 4]},
)

fig1_actions.update_traces(
textposition="inside", texttemplate="%{label}<br>%{percent:.1%}"
textfont_size=12,
textfont_color="white",
textposition="inside",
textinfo="percent+label",
texttemplate="%{label}<br>%{percent:.1%}",
hovertemplate="<b>Niveau</b> %{label}<br><b>%{value}</b> ramassages",
)

# préparation du dataframe et figure releves types de déchets
Expand All @@ -248,14 +274,29 @@ def load_df_events_clean() -> pd.DataFrame:
df_type_dechet_counts_sorted = df_type_dechet_counts.sort_values(
by="counts", ascending=False
)

fig2_actions = px.bar(
df_type_dechet_counts_sorted,
y="counts",
x="TYPE_DECHET",
title="Nombre de relevés par types de déchets",
title="Nombre de relevés par type de déchets",
text="counts",
)
fig2_actions.update_layout(xaxis_title="", yaxis_title="")

fig2_actions.update_layout(
uniformtext_minsize=8,
yaxis_title=None,
xaxis_title=None,
separators=", ", # Formatte les nombres en français (séparateur décimale, séparateur milliers)
)

fig2_actions.update_traces(
texttemplate="%{text:,.0f}",
textfont_size=12,
hovertemplate="<b>Type de déchets</b> : %{label}<br><b>%{y}</b> ramassages",
)

fig2_actions.update_yaxes(tickfont=dict(size=12))

l3_col1, l3_col2 = st.columns(2)
cell4 = l3_col1.container(border=True)
Expand Down Expand Up @@ -288,11 +329,25 @@ def load_df_events_clean() -> pd.DataFrame:
df_milieux_counts_sorted,
y="TYPE_MILIEU",
x="counts",
title="Nombre de relevés par types de milieux",
title="Nombre de relevés par type de milieux",
text="counts",
orientation="h",
)
fig3_actions.update_layout(xaxis_title="", yaxis_title="")

fig3_actions.update_layout(
uniformtext_minsize=8,
yaxis_title=None,
xaxis_title=None,
separators=", ", # Formatte les nombres en français (séparateur décimale, séparateur milliers)
)

fig3_actions.update_traces(
texttemplate="%{text:,.0f}",
textfont_size=12,
hovertemplate="<b>Type de milieux</b> : %{label}<br><b>%{x}</b> ramassages",
)

fig3_actions.update_yaxes(tickfont=dict(size=12))

# préparation du dataframe et figure releves types de lieux 2
df_type_lieu2 = df_other_filtre.copy()
Expand All @@ -313,11 +368,25 @@ def load_df_events_clean() -> pd.DataFrame:
df_type_lieu2_counts_sorted,
y="counts",
x="TYPE_LIEU2",
title="Nombre de relevés par types de lieu",
title="Nombre de relevés par type de lieu",
text="counts",
)
fig4_actions.update_layout(xaxis_title="", yaxis_title="")

fig4_actions.update_layout(
uniformtext_minsize=8,
yaxis_title=None,
xaxis_title=None,
separators=", ", # Formatte les nombres en français (séparateur décimale, séparateur milliers)
)

fig4_actions.update_traces(
texttemplate="%{text:,.0f}",
textfont_size=12,
hovertemplate="<b>Type de lieu</b> : %{label}<br><b>%{y}</b> ramassages",
)

fig4_actions.update_xaxes(tickangle=45)
fig4_actions.update_yaxes(tickfont=dict(size=12))

l4_col1, l4_col2 = st.columns(2)
cell6 = l4_col1.container(border=True)
Expand All @@ -333,7 +402,7 @@ def load_df_events_clean() -> pd.DataFrame:
# préparation du dataframe et figure volume + nb collectes volume + nb collectes par mois
# Créer une liste ordonnée des noms de mois dans l'ordre souhaité
mois_ordre = [
str.capitalize(format_date(dt, format="MMMM", locale=locale))
str.capitalize(format_date(dt, format="MMMM", locale=bbl_locale))
for dt in pd.date_range(start="2022-01-01", end="2022-12-01", freq="MS")
]

Expand All @@ -350,7 +419,21 @@ def load_df_events_clean() -> pd.DataFrame:
title="Nombre de relevés par mois",
text="counts",
)
fig5_actions.update_layout(xaxis_title="", yaxis_title="")

fig5_actions.update_layout(
uniformtext_minsize=8,
yaxis_title=None,
xaxis_title=None,
separators=", ", # Formatte les nombres en français (séparateur décimale, séparateur milliers)
)

fig5_actions.update_traces(
texttemplate="%{text:,.0f}",
textfont_size=12,
hovertemplate="<b>Mois</b> : %{label}<br><b>%{y}</b> ramassages",
)

fig5_actions.update_yaxes(tickfont=dict(size=12))
# Utiliser la liste mois_ordre comme étiquettes sur l'axe x
fig5_actions.update_xaxes(tickvals=list(range(1, 13)), ticktext=mois_ordre)

Expand Down Expand Up @@ -404,12 +487,12 @@ def load_df_events_clean() -> pd.DataFrame:
<div style="color: gray; font-size: 10px;">
{row.TYPE_EVENEMENT}
</div>
<div style="font-family: MONTSERRAT BOLD; font-weight: bold; font-size: 14px;">
<div style="font-weight: bold; font-size: 14px;">
{row.NOM_EVENEMENT}
</div>
<br>
<div style="font-weight: bold; color: gray;">
{str.capitalize(format_date(row.DATE, format="full", locale=locale))}
{str.capitalize(format_date(row.DATE, format="full", locale=bbl_locale))}
</div>
</p>
<p>
Expand All @@ -419,8 +502,11 @@ def load_df_events_clean() -> pd.DataFrame:
</div>
"""

iframe = folium.IFrame(html=html, width=300, height=120)
popup = folium.Popup(iframe, parse_html=True, max_width=300)
# Adapte la hauteur du popup par iFrame
iframe_height = 140 if event_envg else 120

iframe = folium.IFrame(html=html, width=300, height=iframe_height)
popup = folium.Popup(iframe, parse_html=True)

folium.Marker(
location=[row.COORD_GPS_Y, row.COORD_GPS_X],
Expand Down Expand Up @@ -451,7 +537,7 @@ def load_df_events_clean() -> pd.DataFrame:
for idx, row in df_events_a_venir.iterrows():
with st.container(border=True):
# Bloc contenant la date
date_block = f"<div style='font-weight:bold; color:{color_ZDS_rouge}; text-align: center;'>{row.DATE.day}<br>{str.capitalize(locale.months['format']['wide'][row.DATE.month - 1])}</div>"
date_block = f"<div style='font-weight:bold; color:{color_ZDS_rouge}; text-align: center;'>{row.DATE.day}<br>{str.capitalize(bbl_locale.months['format']['abbreviated'][row.DATE.month])}</div>"
# Bloc contenant le nom de l'événement
event_block = (
f"<div style='font-weight:bold;'>{row.NOM_EVENEMENT}</div>"
Expand Down
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