From bf87a1460545f6aac3e69fd92660ce04683cd016 Mon Sep 17 00:00:00 2001 From: Mendi33 Date: Thu, 20 Jun 2024 17:01:19 +0000 Subject: [PATCH 1/5] =?UTF-8?q?Am=C3=A9lioration=20de=20l'onglet=20Actions?= =?UTF-8?q?=20suite=20aux=20retours=20de=20MerTerre=20:=20-=20Wording=20-?= =?UTF-8?q?=20modification=20du=20popup=20de=20la=20map=20sur=20l'onglet?= =?UTF-8?q?=20'ramassages=20r=C3=A9alis=C3=A9s'=20-=20modification=20des?= =?UTF-8?q?=20tooltips=20des=20graphiques=20sur=20l'onglet=20'ramassages?= =?UTF-8?q?=20r=C3=A9alis=C3=A9s'=20-=20d=C3=A9placement=20dans=20home.py?= =?UTF-8?q?=20de=20la=20fonction=20load=5Fdf=5Fevents=5Fclean()=20pour=20c?= =?UTF-8?q?harger=20les=20evenements=20=C3=A0=20venir=20-=20suppression=20?= =?UTF-8?q?de=20la=20police=20Montserrat=20-=20conflit=20pyproject.toml=20?= =?UTF-8?q?OK?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- dashboards/app/home.py | 12 ++- dashboards/app/pages/actions.py | 148 +++++++++++++++++++++++++------- pyproject.toml | 1 - 3 files changed, 128 insertions(+), 33 deletions(-) diff --git a/dashboards/app/home.py b/dashboards/app/home.py index e43f5a0..3e42583 100644 --- a/dashboards/app/home.py +++ b/dashboards/app/home.py @@ -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: + 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() @@ -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 diff --git a/dashboards/app/pages/actions.py b/dashboards/app/pages/actions.py index fc3feb4..5183159 100644 --- a/dashboards/app/pages/actions.py +++ b/dashboards/app/pages/actions.py @@ -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\ @@ -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( @@ -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: @@ -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, @@ -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() @@ -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""" +
+

+

+ Evénement +
+
+ {row.NOM_EVENEMENT} +
+
+ Structure +
+
+ {row.NOM_STRUCTURE} +
+
+
+ Date : {row.DATE.strftime("%d/%m/%Y")} +
+
+ Nombre de participants : {format_participants} +
+

+
+ """ + + 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", @@ -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}
%{percent:.1%}" + textfont_size=12, + textfont_color="white", + textposition="inside", + textinfo="percent+label", + texttemplate="%{label}
%{percent:.1%}", + hovertemplate="Niveau %{label}
%{value} ramassages", ) # préparation du dataframe et figure releves types de déchets @@ -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="Type de déchets : %{label}
%{y} ramassages", + ) + + fig2_actions.update_yaxes(tickfont=dict(size=12)) l3_col1, l3_col2 = st.columns(2) cell4 = l3_col1.container(border=True) @@ -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="Type de milieux : %{label}
%{x} 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() @@ -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="Type de lieu : %{label}
%{y} 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) @@ -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") ] @@ -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="Mois : %{label}
%{y} 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) @@ -404,12 +487,12 @@ def load_df_events_clean() -> pd.DataFrame:
{row.TYPE_EVENEMENT}
-
+
{row.NOM_EVENEMENT}

- {str.capitalize(format_date(row.DATE, format="full", locale=locale))} + {str.capitalize(format_date(row.DATE, format="full", locale=bbl_locale))}

@@ -419,8 +502,11 @@ def load_df_events_clean() -> pd.DataFrame:

""" - 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], @@ -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"
{row.DATE.day}
{str.capitalize(locale.months['format']['wide'][row.DATE.month - 1])}
" + date_block = f"
{row.DATE.day}
{str.capitalize(bbl_locale.months['format']['abbreviated'][row.DATE.month])}
" # Bloc contenant le nom de l'événement event_block = ( f"
{row.NOM_EVENEMENT}
" diff --git a/pyproject.toml b/pyproject.toml index 7d1d9cb..f5d7dad 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,7 +21,6 @@ dash = "^2.16.1" duckdb = "^0.10.1" geopandas = "^0.14.3" folium = "^0.16.0" -folium = "^0.16.0" streamlit = "^1.32.2" plotly-express = "^0.4.1" streamlit-dynamic-filters = "^0.1.6" From 452556376874bd82bb2cce9282cbd21c50cb81b2 Mon Sep 17 00:00:00 2001 From: Mendi33 Date: Thu, 20 Jun 2024 18:43:03 +0000 Subject: [PATCH 2/5] update poetry.lock to solve precommit conflicts --- .gitignore | 2 +- dashboards/app/requirements.txt | 1 - poetry.lock | 1078 ++----------------------------- pyproject.toml | 4 +- 4 files changed, 56 insertions(+), 1029 deletions(-) diff --git a/.gitignore b/.gitignore index a21d144..ae45a6f 100644 --- a/.gitignore +++ b/.gitignore @@ -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. diff --git a/dashboards/app/requirements.txt b/dashboards/app/requirements.txt index 54ac0f9..b1aa314 100644 --- a/dashboards/app/requirements.txt +++ b/dashboards/app/requirements.txt @@ -7,7 +7,6 @@ streamlit==1.32.2 openpyxl==3.1.2 streamlit-folium==0.19.1 plotly==5.19.0 -streamlit-dynamic-filters==0.1.6 streamlit-authenticator==0.3.2 st-pages==0.4.5 babel==2.11.0 diff --git a/poetry.lock b/poetry.lock index 254c136..9bcff55 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,73 +1,10 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. - -[[package]] -name = "altair" -version = "5.3.0" -description = "Vega-Altair: A declarative statistical visualization library for Python." -optional = false -python-versions = ">=3.8" -files = [ - {file = "altair-5.3.0-py3-none-any.whl", hash = "sha256:7084a1dab4d83c5e7e5246b92dc1b4451a6c68fd057f3716ee9d315c8980e59a"}, - {file = "altair-5.3.0.tar.gz", hash = "sha256:5a268b1a0983b23d8f9129f819f956174aa7aea2719ed55a52eba9979b9f6675"}, -] - -[package.dependencies] -jinja2 = "*" -jsonschema = ">=3.0" -numpy = "*" -packaging = "*" -pandas = ">=0.25" -toolz = "*" -typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""} - -[package.extras] -all = ["altair-tiles (>=0.3.0)", "anywidget (>=0.9.0)", "pyarrow (>=11)", "vega-datasets (>=0.9.0)", "vegafusion[embed] (>=1.6.6)", "vl-convert-python (>=1.3.0)"] -dev = ["geopandas", "hatch", "ipython", "m2r", "mypy", "pandas-stubs", "pytest", "pytest-cov", "ruff (>=0.3.0)", "types-jsonschema", "types-setuptools"] -doc = ["docutils", "jinja2", "myst-parser", "numpydoc", "pillow (>=9,<10)", "pydata-sphinx-theme (>=0.14.1)", "scipy", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinxext-altair"] - -[[package]] -name = "attrs" -version = "23.2.0" -description = "Classes Without Boilerplate" -optional = false -python-versions = ">=3.7" -files = [ - {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"}, - {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"}, -] - -[package.extras] -cov = ["attrs[tests]", "coverage[toml] (>=5.3)"] -dev = ["attrs[tests]", "pre-commit"] -docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"] -tests = ["attrs[tests-no-zope]", "zope-interface"] -tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"] -tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"] - -[[package]] -name = "attrs" -version = "23.2.0" -description = "Classes Without Boilerplate" -category = "main" -optional = false -python-versions = ">=3.7" -files = [ - {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"}, - {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"}, -] - -[package.extras] -cov = ["attrs[tests]", "coverage[toml] (>=5.3)"] -dev = ["attrs[tests]", "pre-commit"] -docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"] -tests = ["attrs[tests-no-zope]", "zope-interface"] -tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"] -tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"] +# This file is automatically @generated by Poetry 1.4.0 and should not be changed by hand. [[package]] name = "blinker" version = "1.7.0" description = "Fast, simple object-to-object and broadcast signaling" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -75,25 +12,11 @@ files = [ {file = "blinker-1.7.0.tar.gz", hash = "sha256:e6820ff6fa4e4d1d8e2747c2283749c3f547e4fee112b98555cdcdae32996182"}, ] -[[package]] -name = "branca" -version = "0.7.1" -description = "Generate complex HTML+JS pages with Python" -category = "main" -optional = false -python-versions = ">=3.7" -files = [ - {file = "branca-0.7.1-py3-none-any.whl", hash = "sha256:70515944ed2d1ed2784c552508df58037ca19402a8a1069d57f9113e3e012f51"}, - {file = "branca-0.7.1.tar.gz", hash = "sha256:e6b6f37a37bc0abffd960c68c045a7fe025d628eff87fedf6ab6ca814812110c"}, -] - -[package.dependencies] -jinja2 = ">=3" - [[package]] name = "cachetools" version = "5.3.2" description = "Extensible memoizing collections and decorators" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -105,6 +28,7 @@ files = [ name = "certifi" version = "2024.2.2" description = "Python package for providing Mozilla's CA Bundle." +category = "main" optional = false python-versions = ">=3.6" files = [ @@ -116,6 +40,7 @@ files = [ name = "cfgv" version = "3.4.0" description = "Validate configuration and produce human readable error messages." +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -127,6 +52,7 @@ files = [ name = "chardet" version = "5.2.0" description = "Universal encoding detector for Python 3" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -138,6 +64,7 @@ files = [ name = "charset-normalizer" version = "3.3.2" description = "The Real First Universal Charset Detector. 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Developed by Plotly." +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -331,6 +225,7 @@ testing = ["beautifulsoup4 (>=4.8.2)", "cryptography (<3.4)", "dash-testing-stub name = "dash-core-components" version = "2.0.0" description = "Core component suite for Dash" +category = "main" optional = false python-versions = "*" files = [ @@ -342,6 +237,7 @@ files = [ name = "dash-html-components" version = "2.0.0" description = "Vanilla HTML components for Dash" +category = "main" optional = false python-versions = "*" files = [ @@ -353,6 +249,7 @@ files = [ name = "dash-table" version = "5.0.0" description = "Dash table" +category = "main" optional = false python-versions = "*" files = [ @@ -364,6 +261,7 @@ files = [ name = "distlib" version = "0.3.8" description = "Distribution utilities" +category = "dev" optional = false python-versions = "*" files = [ @@ -375,6 +273,7 @@ files = [ name = "duckdb" version = "0.10.1" description = "DuckDB in-process database" +category = "main" optional = false python-versions = ">=3.7.0" files = [ @@ -431,6 +330,7 @@ files = [ name = "exceptiongroup" version = "1.2.0" description = "Backport of PEP 654 (exception groups)" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -445,6 +345,7 @@ test = ["pytest (>=6)"] name = "filelock" version = "3.13.1" description = "A platform independent file lock." +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -457,58 +358,11 @@ docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1 testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"] typing = ["typing-extensions (>=4.8)"] -[[package]] -name = "fiona" -version = "1.9.6" -description = "Fiona reads and writes spatial data files" -category = "main" -optional = false -python-versions = ">=3.7" -files = [ - 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filtered_data = data_zds[data_zds['TYPE_MILIEU'] == selected_milieu] - return ["Sélectionnez un lieu..."] + list(filtered_data['TYPE_LIEU2'].dropna().unique()) + filtered_data = data_zds[data_zds["TYPE_MILIEU"] == selected_milieu] + return ["Sélectionnez un lieu..."] + list( + filtered_data["TYPE_LIEU2"].dropna().unique() + ) return ["Sélectionnez un lieu..."] + @st.cache_data def process_data(data_zds): # Filtering data to ensure surface area is not zero - data_zds = data_zds[data_zds['SURFACE'] > 0] + data_zds = data_zds[data_zds["SURFACE"] > 0] # Calculating density and filtering out anomalous values - data_zds['DENSITE'] = data_zds['VOLUME_TOTAL'] / data_zds['SURFACE'] - data_zds = data_zds[data_zds['DENSITE'] < 20] + data_zds["DENSITE"] = data_zds["VOLUME_TOTAL"] / data_zds["SURFACE"] + data_zds = data_zds[data_zds["DENSITE"] < 20] # Rounding values for better display - data_zds['DENSITE'] = data_zds['DENSITE'].round(4) - data_zds['SURFACE_ROND'] = data_zds['SURFACE'].round(2) + data_zds["DENSITE"] = data_zds["DENSITE"].round(4) + data_zds["SURFACE_ROND"] = data_zds["SURFACE"].round(2) return data_zds -#Zoom from admin level + +# Zoom from admin level if NIVEAU_ADMIN == "Commune": zoom_admin = 12 elif NIVEAU_ADMIN == "EPCI": @@ -469,14 +474,16 @@ def plot_density_map(data_zds: pd.DataFrame, filtered_data: pd.DataFrame) -> fol else: # Use processed data - processed_data = process_data(filtered_data if not filtered_data.empty else data_zds) + processed_data = process_data( + filtered_data if not filtered_data.empty else data_zds + ) m = folium.Map( location=[ - processed_data['LIEU_COORD_GPS_Y'].mean(), - processed_data['LIEU_COORD_GPS_X'].mean() + processed_data["LIEU_COORD_GPS_Y"].mean(), + processed_data["LIEU_COORD_GPS_X"].mean(), ], - zoom_start=zoom_admin + zoom_start=zoom_admin, ) # Loop over each row in the DataFrame to place markers @@ -491,26 +498,25 @@ def plot_density_map(data_zds: pd.DataFrame, filtered_data: pd.DataFrame) -> fol """ lgd_txt = '{txt}' - color = couleur_milieu(row['TYPE_MILIEU']) + color = couleur_milieu(row["TYPE_MILIEU"]) folium.CircleMarker( - fg = folium.FeatureGroup(name= lgd_txt.format( txt= ['TYPE_MILIEU'], col= color)), - location=[row['LIEU_COORD_GPS_Y'], row['LIEU_COORD_GPS_X']], - radius=np.log(row['DENSITE'] + 1)*15, + fg=folium.FeatureGroup( + name=lgd_txt.format(txt=["TYPE_MILIEU"], col=color) + ), + location=[row["LIEU_COORD_GPS_Y"], row["LIEU_COORD_GPS_X"]], + radius=np.log(row["DENSITE"] + 1) * 15, popup=folium.Popup(popup_html, max_width=300), color=color, fill=True, - ).add_to(m) folium_static(m) return m + # Function for 'milieu' density table -def density_table_milieu( - data_zds: pd.DataFrame, - filtered_data: pd.DataFrame -): +def density_table_milieu(data_zds: pd.DataFrame, filtered_data: pd.DataFrame): if data_zds.empty: st.write("Aucune donnée disponible pour la région sélectionnée.") @@ -554,10 +560,7 @@ def density_table_milieu( ) -def density_table_lieu( - data_zds: pd.DataFrame, - filtered_data: pd.DataFrame -): +def density_table_lieu(data_zds: pd.DataFrame, filtered_data: pd.DataFrame): if data_zds.empty: st.write("Aucune donnée disponible pour la région sélectionnée.") @@ -766,55 +769,74 @@ def create_contributors_table(data_zds: pd.DataFrame, multi_filter_dict: dict) - # Add a default "Select a milieu..." option selected_milieu = st.selectbox( "Sélectionnez un milieu:", - ["Sélectionnez un milieu..."] + list(pd.unique(data_zds_correct['TYPE_MILIEU'])) + ["Sélectionnez un milieu..."] + + list(pd.unique(data_zds_correct["TYPE_MILIEU"])), ) with right_column: # Update lieu options based on selected milieu lieu_options = update_lieu_options(selected_milieu) selected_lieu = st.selectbox("Sélectionnez un lieu:", lieu_options) - # Place the map centrally by using a wider column for the map and narrower ones on the sides col1, map_col, col3 = st.columns([4, 10, 1]) # Adjust column ratios as needed with map_col: st.markdown("### Carte des Densités") - if selected_milieu != "Sélectionnez un milieu..." and selected_lieu != "Sélectionnez un lieu...": - filtered_data = data_zds_correct[(data_zds_correct['TYPE_MILIEU'] == selected_milieu) & (data_zds_correct['TYPE_LIEU2'] == selected_lieu)] + if ( + selected_milieu != "Sélectionnez un milieu..." + and selected_lieu != "Sélectionnez un lieu..." + ): + filtered_data = data_zds_correct[ + (data_zds_correct["TYPE_MILIEU"] == selected_milieu) + & (data_zds_correct["TYPE_LIEU2"] == selected_lieu) + ] plot_density_map(data_zds_correct, filtered_data) else: - plot_density_map(data_zds_correct, data_zds_correct) # Show all data by default - + plot_density_map( + data_zds_correct, data_zds_correct + ) # Show all data by default col1, col2, col3 = st.columns([3, 3, 2]) with col1: st.markdown("#### Tableau des Densités par Milieu") - if selected_milieu != "Sélectionnez un milieu..." and selected_lieu != "Sélectionnez un lieu...": - filtered_data = data_zds_correct[(data_zds_correct['TYPE_MILIEU'] == selected_milieu) & (data_zds_correct['TYPE_LIEU2'] == selected_lieu)] + if ( + selected_milieu != "Sélectionnez un milieu..." + and selected_lieu != "Sélectionnez un lieu..." + ): + filtered_data = data_zds_correct[ + (data_zds_correct["TYPE_MILIEU"] == selected_milieu) + & (data_zds_correct["TYPE_LIEU2"] == selected_lieu) + ] density_table_milieu(data_zds_correct, filtered_data) else: density_table_milieu(data_zds_correct, data_zds_correct) with col2: st.markdown("#### Tableau des Densités par Lieu") - if selected_milieu != "Sélectionnez un milieu..." and selected_lieu != "Sélectionnez un lieu...": - filtered_data = data_zds_correct[(data_zds_correct['TYPE_MILIEU'] == selected_milieu) & (data_zds_correct['TYPE_LIEU2'] == selected_lieu)] + if ( + selected_milieu != "Sélectionnez un milieu..." + and selected_lieu != "Sélectionnez un lieu..." + ): + filtered_data = data_zds_correct[ + (data_zds_correct["TYPE_MILIEU"] == selected_milieu) + & (data_zds_correct["TYPE_LIEU2"] == selected_lieu) + ] density_table_lieu(data_zds_correct, filtered_data) else: density_table_lieu(data_zds_correct, data_zds_correct) with col3: - with st.expander("###### Notice ℹ️", expanded=True): - st.write( - """ + with st.expander("###### Notice ℹ️", expanded=True): + st.write( + """ **Milieu** désigne de grands types d'environnements comme le Littoral, les Cours d'eau ou la Montagne.\n Chaque Milieu est ensuite divisé en **Lieux** plus spécifiques. Par exemple, sous le Milieu Littoral, on trouve des Lieux comme les Plages, les Roches, les Digues, ou les Parkings. """ - ) + ) with tab2: # Use the selected filters From 17dcc63bf9f6ec202d11476aad1d2386f483b4e6 Mon Sep 17 00:00:00 2001 From: Mendi33 Date: Tue, 9 Jul 2024 10:12:31 +0000 Subject: [PATCH 4/5] =?UTF-8?q?Ajout=20docstring=20de=20:=20def=20load=5Fd?= =?UTF-8?q?f=5Fevents=5Fclean()=20Chargement=20des=20donn=C3=A9es=20des=20?= =?UTF-8?q?=C3=A9v=C3=A9nements=20=C3=A0=20venir?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- dashboards/app/home.py | 227 ----------------------------------------- 1 file changed, 227 deletions(-) diff --git a/dashboards/app/home.py b/dashboards/app/home.py index 3e42583..e69de29 100644 --- a/dashboards/app/home.py +++ b/dashboards/app/home.py @@ -1,227 +0,0 @@ -from pathlib import Path - -import pandas as pd -import streamlit as st -import streamlit_authenticator as stauth -import yaml -from st_pages import Page, show_pages -from yaml.loader import SafeLoader - -# Configuration de la page -st.set_page_config( - layout="wide", - page_title="Dashboard Zéro Déchet Sauvage", - page_icon=":dolphin:", - menu_items={ - "About": "https://www.zero-dechet-sauvage.org/", - }, -) - -# load and apply CSS styles -def load_css(file_name: str) -> None: - with Path(file_name).open() as f: - st.markdown(f"", unsafe_allow_html=True) - - -# Login -p_cred = Path(".credentials.yml") -with p_cred.open() as file: - config = yaml.load(file, Loader=SafeLoader) - -authenticator = stauth.Authenticate( - config["credentials"], - config["cookie"]["name"], - config["cookie"]["key"], - config["cookie"]["expiry_days"], - config["pre-authorized"], -) -authenticator.login( - fields={ - "Form name": "Connexion", - "Username": "Identifiant", - "Password": "Mot de passe", - "Login": "Connexion", - }, -) - -if st.session_state["authentication_status"]: - show_pages( - [ - Page("home.py", "Accueil", "🏠"), - ], - ) - - # Load and apply the CSS file at the start of your app - # local debug - load_css("style.css") - - st.markdown( - """ - # Bienvenue 👋 - #### Visualiser les collectes de déchets qui ont lieu sur votre territoire ! - """, - ) - - st.markdown("""# À propos""") - - # Chargement des données et filtre géographique à l'arrivée sur le dashboard - # Table des volumes par matériaux - @st.cache_data - def load_df_other() -> pd.DataFrame: - df = pd.read_csv( - "https://github.com/dataforgoodfr/12_zero_dechet_sauvage/raw/2-" - "nettoyage-et-augmentation-des-donn%C3%A9es/Exploration_visuali" - "sation/data/data_zds_enriched.csv", - ) - # Ajout des colonnes DEP_CODE_NOM et COMMUNE_CODE_NOM qui concatenent le numéro INSEE - # et le nom de l'entité géographique (ex : 13 - Bouches du Rhône) - df["DEP_CODE_NOM"] = df["DEP"] + " - " + df["DEPARTEMENT"] - df["COMMUNE_CODE_NOM"] = df["INSEE_COM"] + " - " + df["commune"] - return df - - # Table des structures - @st.cache_data - def load_structures() -> pd.DataFrame: - df = pd.read_csv( - "https://github.com/dataforgoodfr/12_zero_dechet_sauvage/raw/4-" - "onglet-structures/Exploration_visuali" - "sation/data/structures_export_cleaned.csv", - index_col=0, - ) - # Ajout des colonnes DEP_CODE_NOM et COMMUNE_CODE_NOM qui concatenent le numéro INSEE - # et le nom de l'entité géographique (ex : 13 - Bouches du Rhône) - df["DEP_CODE_NOM"] = df["dep"] + " - " + df["departement"] - df["COMMUNE_CODE_NOM"] = df["INSEE_COM"] + " - " + df["COMMUNE"] - df.columns = [c.upper() for c in df.columns] - return df - - # Table du nb de déchets - @st.cache_data - def load_df_nb_dechet() -> 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/data_releve_nb_dechet.csv", - ) - - @st.cache_data - # 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_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() - - # Création du filtre par niveau géographique : correspondance labels et variables du df - niveaux_admin_dict = { - "Région": "REGION", - "Département": "DEP_CODE_NOM", - "EPCI": "LIBEPCI", - "Commune": "COMMUNE_CODE_NOM", - } - - # 1ère étape : sélection du niveau administratif concerné (région, dép...) - # Si déjà saisi précédemment, initialiser le filtre avec les valeurs entrées précédemment - # Récupérer les index pour conserver la valeur des filtres au changement de pages - # Filtre niveau administratif - niveau_admin = st.session_state.get("niveau_admin", None) - index_admin = st.session_state.get("index_admin", None) - # Filtre collectivité - collectivite = st.session_state.get("collectivite", None) - index_collec = st.session_state.get("index_collec", None) - - # Initialiser la selectbox avec l'index récupéré - select_niveauadmin = st.selectbox( - "Niveau administratif : ", - niveaux_admin_dict.keys(), - index=index_admin, - placeholder="Choisir une option", - ) - - if select_niveauadmin is not None: - # Filtrer la liste des collectivités en fonction du niveau admin - liste_collectivites = df_other[niveaux_admin_dict[select_niveauadmin]] - liste_collectivites = liste_collectivites.sort_values().unique() - - # 2ème filtre : sélection de la collectivité concernée - select_collectivite = st.selectbox( - "Collectivité : ", - liste_collectivites, - index=index_collec, - placeholder="Choisir une collectivité", - ) - - button_disabled = not select_niveauadmin or not select_collectivite - if st.button("Enregistrer la sélection", disabled=button_disabled): - # Enregistrer les valeurs sélectionnées dans le session.state - st.session_state["niveau_admin"] = select_niveauadmin - st.session_state["index_admin"] = list(niveaux_admin_dict.keys()).index( - select_niveauadmin, - ) - - st.session_state["collectivite"] = select_collectivite - st.session_state["index_collec"] = list(liste_collectivites).index( - select_collectivite, - ) - - # Afficher la collectivité sélectionnée - st.write(f"Vous avez sélectionné : {select_niveauadmin} {select_collectivite}.") - show_pages( - [ - Page("home.py", "Accueil", "🏠"), - Page("pages/structures.py", "Structures", "🔭"), - Page("pages/actions.py", "Actions", "👊"), - Page("pages/data.py", "Data", "🔍"), - Page("pages/hotspots.py", "Hotspots", "🔥"), - ], - ) - - # Filtrer et enregistrer le DataFrame dans un session state pour la suite - colonne_filtre = niveaux_admin_dict[select_niveauadmin] - df_other_filtre = df_other[df_other[colonne_filtre] == select_collectivite] - st.session_state["df_other_filtre"] = df_other_filtre - - # Filtrer dataframe structures et enregistrer dans le session.state - df_structures_filtre = df_structures[ - df_structures[colonne_filtre] == select_collectivite - ] - 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 - id_releves = df_other_filtre["ID_RELEVE"].unique() - # Filtrer df_nb_dechets sur la liste des relevés - st.session_state["df_nb_dechets_filtre"] = df_nb_dechets[ - df_nb_dechets["ID_RELEVE"].isin(id_releves) - ] - - # Afficher le nombre de relevés disponibles - nb_releves = len(st.session_state["df_other_filtre"]) - st.write( - f"{nb_releves} relevés de collecte sont disponibles \ - pour l'analyse sur votre territoire.", - ) - - authenticator.logout() -elif st.session_state["authentication_status"] is False: - st.error("Mauvais identifiants ou mot de passe.") -elif st.session_state["authentication_status"] is None: - st.warning("Veuillez entrer votre identifiant et mot de passe") - - show_pages( - [ - Page("home.py", "Home", "🏠 "), - Page("pages/register.py", "S'enregistrer", "🚀"), - ], - ) From 1795f72d64400e87494c2651aec0df8b9dbc4d25 Mon Sep 17 00:00:00 2001 From: Mendi33 Date: Tue, 9 Jul 2024 13:59:01 +0000 Subject: [PATCH 5/5] =?UTF-8?q?Correction=20erreur=20dernier=20commit=20R?= =?UTF-8?q?=C3=A9solution=20des=20conflits=20de=20merge?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- dashboards/app/home.py | 236 +++++++ poetry.lock | 1427 ++++++++++++++++++++++++++++++++-------- pyproject.toml | 4 +- 3 files changed, 1375 insertions(+), 292 deletions(-) diff --git a/dashboards/app/home.py b/dashboards/app/home.py index e69de29..3e4743c 100644 --- a/dashboards/app/home.py +++ b/dashboards/app/home.py @@ -0,0 +1,236 @@ +from pathlib import Path + +import pandas as pd +import streamlit as st +import streamlit_authenticator as stauth +import yaml +from st_pages import Page, show_pages +from yaml.loader import SafeLoader + +# Configuration de la page +st.set_page_config( + layout="wide", + page_title="Dashboard Zéro Déchet Sauvage", + page_icon=":dolphin:", + menu_items={ + "About": "https://www.zero-dechet-sauvage.org/", + }, +) + +# load and apply CSS styles +def load_css(file_name: str) -> None: + with Path(file_name).open() as f: + st.markdown(f"", unsafe_allow_html=True) + + +# Login +p_cred = Path(".credentials.yml") +with p_cred.open() as file: + config = yaml.load(file, Loader=SafeLoader) + +authenticator = stauth.Authenticate( + config["credentials"], + config["cookie"]["name"], + config["cookie"]["key"], + config["cookie"]["expiry_days"], + config["pre-authorized"], +) +authenticator.login( + fields={ + "Form name": "Connexion", + "Username": "Identifiant", + "Password": "Mot de passe", + "Login": "Connexion", + }, +) + +if st.session_state["authentication_status"]: + show_pages( + [ + Page("home.py", "Accueil", "🏠"), + ], + ) + + # Load and apply the CSS file at the start of your app + # local debug + load_css("style.css") + + st.markdown( + """ + # Bienvenue 👋 + #### Visualiser les collectes de déchets qui ont lieu sur votre territoire ! + """, + ) + + st.markdown("""# À propos""") + + # Chargement des données et filtre géographique à l'arrivée sur le dashboard + # Table des volumes par matériaux + @st.cache_data + def load_df_other() -> pd.DataFrame: + df = pd.read_csv( + "https://github.com/dataforgoodfr/12_zero_dechet_sauvage/raw/2-" + "nettoyage-et-augmentation-des-donn%C3%A9es/Exploration_visuali" + "sation/data/data_zds_enriched.csv", + ) + # Ajout des colonnes DEP_CODE_NOM et COMMUNE_CODE_NOM qui concatenent le numéro INSEE + # et le nom de l'entité géographique (ex : 13 - Bouches du Rhône) + df["DEP_CODE_NOM"] = df["DEP"] + " - " + df["DEPARTEMENT"] + df["COMMUNE_CODE_NOM"] = df["INSEE_COM"] + " - " + df["commune"] + return df + + # Table des structures + @st.cache_data + def load_structures() -> pd.DataFrame: + df = pd.read_csv( + "https://github.com/dataforgoodfr/12_zero_dechet_sauvage/raw/4-" + "onglet-structures/Exploration_visuali" + "sation/data/structures_export_cleaned.csv", + index_col=0, + ) + # Ajout des colonnes DEP_CODE_NOM et COMMUNE_CODE_NOM qui concatenent le numéro INSEE + # et le nom de l'entité géographique (ex : 13 - Bouches du Rhône) + df["DEP_CODE_NOM"] = df["dep"] + " - " + df["departement"] + df["COMMUNE_CODE_NOM"] = df["INSEE_COM"] + " - " + df["COMMUNE"] + df.columns = [c.upper() for c in df.columns] + return df + + # Table du nb de déchets + @st.cache_data + def load_df_nb_dechet() -> 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/data_releve_nb_dechet.csv", + ) + + @st.cache_data + # Définition d'une fonction pour charger les evenements à venir + def load_df_events_clean() -> pd.DataFrame: + """Chargement du dataset 'export_event_cleaned.csv' + + Les 'évenements à venir' sont dans un autre dataset que data_zds + La liste des événements RAW est dans #2/explo/data/export_events_14032024.xlsx + Le script de nettoyage est #2/explo/cleaning_events.py + + Returns: + pd.DataFrame: DF des événements à venir nettoyé + """ + 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() + + # Création du filtre par niveau géographique : correspondance labels et variables du df + niveaux_admin_dict = { + "Région": "REGION", + "Département": "DEP_CODE_NOM", + "EPCI": "LIBEPCI", + "Commune": "COMMUNE_CODE_NOM", + } + + # 1ère étape : sélection du niveau administratif concerné (région, dép...) + # Si déjà saisi précédemment, initialiser le filtre avec les valeurs entrées précédemment + # Récupérer les index pour conserver la valeur des filtres au changement de pages + # Filtre niveau administratif + niveau_admin = st.session_state.get("niveau_admin", None) + index_admin = st.session_state.get("index_admin", None) + # Filtre collectivité + collectivite = st.session_state.get("collectivite", None) + index_collec = st.session_state.get("index_collec", None) + + # Initialiser la selectbox avec l'index récupéré + select_niveauadmin = st.selectbox( + "Niveau administratif : ", + niveaux_admin_dict.keys(), + index=index_admin, + placeholder="Choisir une option", + ) + + if select_niveauadmin is not None: + # Filtrer la liste des collectivités en fonction du niveau admin + liste_collectivites = df_other[niveaux_admin_dict[select_niveauadmin]] + liste_collectivites = liste_collectivites.sort_values().unique() + + # 2ème filtre : sélection de la collectivité concernée + select_collectivite = st.selectbox( + "Collectivité : ", + liste_collectivites, + index=index_collec, + placeholder="Choisir une collectivité", + ) + + button_disabled = not select_niveauadmin or not select_collectivite + if st.button("Enregistrer la sélection", disabled=button_disabled): + # Enregistrer les valeurs sélectionnées dans le session.state + st.session_state["niveau_admin"] = select_niveauadmin + st.session_state["index_admin"] = list(niveaux_admin_dict.keys()).index( + select_niveauadmin, + ) + + st.session_state["collectivite"] = select_collectivite + st.session_state["index_collec"] = list(liste_collectivites).index( + select_collectivite, + ) + + # Afficher la collectivité sélectionnée + st.write(f"Vous avez sélectionné : {select_niveauadmin} {select_collectivite}.") + show_pages( + [ + Page("home.py", "Accueil", "🏠"), + Page("pages/structures.py", "Structures", "🔭"), + Page("pages/actions.py", "Actions", "👊"), + Page("pages/data.py", "Data", "🔍"), + Page("pages/hotspots.py", "Hotspots", "🔥"), + ], + ) + + # Filtrer et enregistrer le DataFrame dans un session state pour la suite + colonne_filtre = niveaux_admin_dict[select_niveauadmin] + df_other_filtre = df_other[df_other[colonne_filtre] == select_collectivite] + st.session_state["df_other_filtre"] = df_other_filtre + + # Filtrer dataframe structures et enregistrer dans le session.state + df_structures_filtre = df_structures[ + df_structures[colonne_filtre] == select_collectivite + ] + 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 + id_releves = df_other_filtre["ID_RELEVE"].unique() + # Filtrer df_nb_dechets sur la liste des relevés + st.session_state["df_nb_dechets_filtre"] = df_nb_dechets[ + df_nb_dechets["ID_RELEVE"].isin(id_releves) + ] + + # Afficher le nombre de relevés disponibles + nb_releves = len(st.session_state["df_other_filtre"]) + st.write( + f"{nb_releves} relevés de collecte sont disponibles \ + pour l'analyse sur votre territoire.", + ) + + authenticator.logout() +elif st.session_state["authentication_status"] is False: + st.error("Mauvais identifiants ou mot de passe.") +elif st.session_state["authentication_status"] is None: + st.warning("Veuillez entrer votre identifiant et mot de passe") + + show_pages( + [ + Page("home.py", "Home", "🏠 "), + Page("pages/register.py", "S'enregistrer", "🚀"), + ], + ) diff --git a/poetry.lock b/poetry.lock index 9bcff55..616dacc 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,10 +1,93 @@ -# This file is automatically @generated by Poetry 1.4.0 and should not be changed by hand. +# This file is automatically 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