#@title Révision archéostratigraphique de Combe-Cullier import matplotlib.pyplot as plt #%matplotlib inline import seaborn as sns import numpy as np import pandas as pd import math import datetime from mpl_toolkits import mplot3d import plotly.express as px import plotly.graph_objects as go plt.rcParams.update(plt.rcParamsDefault) file_path = "https://raw.githubusercontent.com/magdantho/pycocu/master/pyth_carnets.csv" carnets = pd.read_csv(file_path, sep=";", decimal=",", index_col=0, header='infer') carnets.drop(["Remarque", 'X', 'Y', 'Z', 'Xmoy', 'Ymoy', 'Zmoy', 'Ma_strati', "Travee", "Colonne"], axis=1, inplace=True) carnets["Datation"].fillna(0,inplace=True) dates = carnets[carnets["Datation"]!=0] filepath = "https://raw.githubusercontent.com/magdantho/pycocu/master/pyth_raccords.csv" raccords = pd.read_csv(filepath, sep=",") df_raccords = pd.merge(raccords, carnets.loc[carnets.index.isin(raccords.ID), ["Xabs", "Yabs", "Zabs", "Couche", "UA"]].reset_index(), on="ID").sort_values(by=["id_raccord", "ID"]) color_dict_ua = dict({'UA 1a': 'sienna', 'UA 1b': 'darkorange', 'UA 2': '#FFF82C', 'UA 3': 'limegreen', 'UA 4a': 'salmon', 'UA 4b': 'dodgerblue', 'UA 4c': 'skyblue', 'UA 5a': 'sandybrown', 'UA 5b': 'hotpink', 'UA ?': 'gainsboro'}) color_dict_nature = dict({'Lithique': 'mediumblue', 'Galet': 'dodgerblue', 'Plaquette': 'skyblue', 'Gres': 'cyan', 'Calcaire': 'grey', 'Faune': 'lightcoral', 'Indos': 'limegreen', 'Esquille': 'gold', 'Coquille': 'deeppink', 'Ocre/Oxydes/Colorants': 'darkred', 'Charbon': 'black', 'Débris': 'gainsboro', 'indet': 'gainsboro', }) def points_proj(x, ep, t): if t == 's': df = carnets.loc[(carnets["Xabs"] >= x) & (carnets["Xabs"] < x+ep)] elif t == 'f': df = carnets.loc[(carnets["Yabs"] >= x) & (carnets["Yabs"] < x+ep)] elif t == 'p': df = carnets.loc[(carnets["Zabs"] >= x) & (carnets["Zabs"] < x+ep)] return df def title_fig(x, ep, t): if t == 's': title = "Coupe sagittale " + str(x) + " ≤ x < " + str(x + ep) elif t == 'f': title = "Coupe frontale " + str(x) + " ≤ y < " + str(x + ep) elif t == 'p': title = "Vue planaire " + str(x) + " ≤ z < " + str(x + ep) return title def nom_fig(x, ep, t): if t == 's': nom = "Coupe sagittale_" + str(x) + "-" + str(x + ep) elif t == 'f': nom = "Coupe frontale_" + str(x) + "-" + str(x + ep) elif t == 'p': nom = "Vue plan_" + str(x) + "-" + str(x + ep) return nom def type_proj(): tp = input("Type de coupe à afficher : 's' pour sagittale, 'f' pour frontale et 'p' pour planaire : ") if tp.lower() in ['s', 'f', 'p']: return tp.lower() else: return "Valeur incorrecte, recommencez !" def type_color(): tc = input("Choisir entre 'ua' (colorer les points selon leur UA) ou 'nat' (selon leur nature) : ") if tc.lower() == 'ua': cat = ("UA", color_dict_ua) return cat elif tc.lower() == 'nat': cat = ("Nature", color_dict_nature) return cat else: return "Valeur incorrecte, recommencez !" def coloration(tc): if tc.lower() == 'ua': cat = ("UA", color_dict_ua) return cat elif tc.lower() == 'nat': cat = ("Nature", color_dict_nature) return cat else: pass def tabl_dates(): carnets["Datation"].fillna(0,inplace=True) dates = carnets[carnets["Datation"]!=0] return dates.sort_values(by='Datation') def plan_proj(x1, ep, t): """ Cette fonction affiche une vue planaire de la projection donnée en paramètres. x1 = valeur à partir de laquelle commence la projection ep = épaisseur de la projection (en cm) t = type de projection : 's' pour sagittale, 'f' pour frontale et 'p' pour planaire """ plt.rcParams.update(plt.rcParamsDefault) # plt.figure(figsize=(6.693,5.12)) #format en inch (17 x 13 cm) dfp = points_proj(x1, ep, t) plt.title("Position de la coupe") sns.scatterplot(x="Xabs", y="Yabs", s=3, marker='o', color='navy', data=dfp, edgecolor='dimgrey', linewidth = 0.3 ) axes = plt.gca() axes.set_xticks(range(0, 800, 100), minor=False) axes.set_xticks(range(0, 800, 50), minor=True) axes.xaxis.set_ticklabels(['G', 'H', 'I', 'J', 'K', 'L', 'M'], minor=True, rotation=0, style='normal') axes.xaxis.set_tick_params(which='major', length=5, width=1, labelcolor='k') axes.xaxis.set_tick_params(which='minor', length=5, color='w', labelsize=12, labelcolor='dimgray') axes.set_yticks(range(0, 800, 100), minor=False) axes.set_yticks(range(0, 800, 50), minor=True) axes.yaxis.set_ticklabels(['27','26', '25', '24', '23', '22', '21', '20'], minor=True, rotation=0, style='normal') axes.yaxis.set_tick_params(which='major', length=5, width=1, labelcolor='k') axes.yaxis.set_tick_params(which='minor', length=8, color='w', labelsize=12, labelcolor='dimgray') plt.axis('equal') plt.xlabel("X (cm)") plt.ylabel("Y (cm)") plt.grid(color='silver', linestyle='--', linewidth=0.75) def i_plan_proj(x1, ep, t): """ Cette fonction affiche une vue planaire et interactive de la projection donnée en paramètres. x1 = valeur à partir de laquelle commence la projection ep = épaisseur de la projection (en cm) t = type de projection : 's' pour sagittale, 'f' pour frontale et 'p' pour planaire """ dfp = points_proj(x1, ep, t) fig = px.scatter(dfp.reset_index(), x="Xabs", y="Yabs", hover_data=["ID", "Carre", "Numero", "Couche", "Nature", "UA"], labels={"Xabs": "X (cm)", "Yabs": "Y (cm)"}, title="Position de la coupe" ) fig.layout.update(dict(coloraxis_showscale=False, showlegend=True, template="plotly_dark", paper_bgcolor='gray', plot_bgcolor='white', title_x=0.5 )) fig.update_traces(marker_size=3, marker_color='navy') fig.update_xaxes(dtick=100, range=[50, 801], constrain="domain", showline=True, linewidth=1, linecolor='white', gridcolor="grey", zerolinecolor="grey") fig.update_yaxes(dtick=100, scaleanchor="x", scaleratio=1, showline=True, linewidth=1, linecolor='white', gridcolor="grey", zerolinecolor="grey") fig.layout.margin.update({'t': 30, 'l': 5}) fig.show(config = {'displayModeBar': True}) def projection(x1,ep,t,color): """ x1 = valeur à partir de laquelle commence la projection ep = épaisseur de la projection (en cm) t = type de projection : 's' pour sagittale, 'f' pour frontale et 'p' pour planaire colortype = type de coloration : 'ua' (selon leur UA) ou 'nat' (selon leur nature) f = format de sortie ('eps', 'jpg', 'pdf', 'png', 'ps', 'raw', 'svg', 'svgz', 'tif', 'tiff') """ plt.rcParams.update(plt.rcParamsDefault) #plt.figure(figsize=(6.693,4.855)) #format en inch (17 x 12.332 cm) df = points_proj(x1,ep,t) titlefig = title_fig(x1,ep,t) plt.title(titlefig) colortype = coloration(color) plt.grid(color='silver', linestyle='--', linewidth=0.75) axes = plt.gca() axes.set_xticks(range(0,800,50), minor = True) axes.set_xticks(range(0,800,100), minor = False) if t =='s': print("La projection souhaitée, d'une épaisseur de", ep, "cm, se fera entre x=", x1, "et x=", (x1 + ep)) sns.scatterplot(x="Yabs", y="Zabs", s=15, marker='o', hue=colortype[0], palette=colortype[1], data=df.sort_values(by=colortype[0]), edgecolor = "dimgrey", linewidth = 0.40) plt.xlabel("Y (cm)") plt.ylabel("Z (cm)") axes.set_yticks(range(-600,0,100), minor = False) axes.set_yticks(range(-650,-50,50), minor = True) axes.xaxis.set_ticklabels(['27','26','25','24','23','22','21','20'], minor = True, rotation = 0, style = 'normal') axes.xaxis.set_tick_params(which = 'major', length = 5, width = 1, labelcolor = 'k') axes.xaxis.set_tick_params(which = 'minor', length = 5, color = 'w', labelsize = 12, labelcolor = 'dimgray') elif t == "f": print("La projection souhaitée, d'une épaisseur de", ep, "cm, se fera entre y=", x1, "et y=", (x1 + ep)) sns.scatterplot(x="Xabs", y="Zabs", s=15, marker='o', hue=colortype[0], palette=colortype[1], data=df.sort_values(by=colortype[0]), edgecolor = "dimgrey", linewidth = 0.40) plt.xlabel("X (cm)") plt.ylabel("Z (cm)") axes.set_yticks(range(-600,0,100), minor = False) axes.set_yticks(range(-650,-50,50), minor = True) axes.xaxis.set_ticklabels(['G','H','I','J','K','L','M'], minor = True, rotation = 0, style = 'normal') axes.xaxis.set_tick_params(which = 'major', length = 5, width = 1, labelcolor = 'k') axes.xaxis.set_tick_params(which = 'minor', length = 5, color = 'w', labelsize = 12, labelcolor = 'dimgray') elif t=='p': print("La plan souhaité, d'une épaisseur de", ep, "cm, se fera entre z=", x1, "et z=", (x1 + ep)) sns.scatterplot(x="Xabs", y="Yabs", s=15, marker='o', hue=colortype[0], palette=colortype[1], data=df.sort_values(by=colortype[0]), edgecolor = "dimgrey", linewidth = 0.40) plt.xlabel("X (cm)") plt.ylabel("Y (cm)") axes.xaxis.set_ticklabels(['G','H','I','J','K','L','M'], minor = True, rotation = 0, style = 'normal') axes.xaxis.set_tick_params(which = 'major', length = 5, width = 1, labelcolor = 'k') axes.xaxis.set_tick_params(which = 'minor', length = 5, color = 'w', labelsize = 12, labelcolor = 'dimgray') axes.set_yticks(range(0,800,100), minor = False) axes.set_yticks(range(0,800,50), minor = True) axes.yaxis.set_ticklabels(['27','26','25','24','23','22','21','20'], minor = True, rotation = 0, style = 'normal') axes.yaxis.set_tick_params(which = 'major', length = 5, width = 1, labelcolor = 'k') axes.yaxis.set_tick_params(which = 'minor', length = 8, color = 'w', labelsize = 12, labelcolor = 'dimgray') else: pass plt.axis('equal') plt.legend(loc="best", fontsize='small', facecolor='white', framealpha=1, edgecolor='0.9', shadow=True, labelspacing=0.3) def dates_proj(x1,ep,t): """ Projection des objets datés. x1 = valeur à partir de laquelle commence la projection ep = épaisseur de la projection (en cm) t = type de projection : 's' pour sagittale, 'f' pour frontale et 'p' pour planaire """ dates = tabl_dates() if t =='s': df = dates.loc[(dates["Xabs"]>=x1) & (dates["Xabs"]=x1) & (dates["Yabs"]=x1) & (dates["Zabs"]= x1) & (dates["Xabs"] < x1+ep)] elif t == "f": ax01.set_xlabel("X (cm)") ax01.set_ylabel("Z (cm)") df = dates.loc[(dates["Yabs"] >= x1) & (dates["Yabs"] < x1+ep)] elif t == 'p': ax01.set_xlabel("X (cm)") ax01.set_ylabel("Y (cm)") ax02 = fig.add_subplot(2, 1, 2) ax02.set_xlabel("X (cm)") ax02.set_ylabel("Y (cm)") plan_proj(x1, ep, t) sns.scatterplot(x="Xabs", y="Yabs", s=70, marker='*', color='red', data=df, edgecolor="darkred", linewidth=0.40, label="Datation") if f != "": format_sortie = f.lower() date = datetime.datetime.now() figname = nom_fig(x1, ep, t)+'_'+str(date.year)+str(date.month)+str(date.day)+'_'+str(2+date.hour)+'h'+str(date.minute)+'.'+format_sortie if format_sortie in ['png', 'jpg', 'jpeg','tif', 'tiff'] : plt.savefig(figname, dpi=300) else: plt.savefig(figname) print("La figure a été enregistrée sous le nom {}.".format(figname)) plt.show() def proj_3d(): """ x1 = valeur à partir de laquelle commence la projection ep = épaisseur de la projection (en cm) t = type de projection : 's' pour sagittale, 'f' pour frontale et 'p' pour planaire colortype = type de coloration : 'ua' (selon leur UA) ou 'nat' (selon leur nature) """ df = carnets[["Xabs", "Yabs", "Zabs", "UA","Nature", "Couche"]].sort_values(by="UA") fig = px.scatter_3d(df, x='Xabs', y='Yabs', z='Zabs', hover_data=["Nature", "Couche", df.index], color='UA', color_discrete_map=color_dict_ua, width=900, height=900 ) fig.update_traces(marker=dict(size=1, line=dict(width=0, color='DarkSlateGrey')), selector=dict(mode='markers')) fig.update_layout(title="Figure interactive", coloraxis_showscale=False, showlegend=True, template='plotly_dark', paper_bgcolor='gray', plot_bgcolor='white', legend={'itemsizing': 'constant', 'traceorder': 'normal'} ) fig.update_xaxes(showline=True, linewidth=2, linecolor='white', gridcolor="grey", zerolinecolor="grey" ) fig.update_yaxes(showline=True, linewidth=2, linecolor='white', gridcolor="grey", zerolinecolor="grey" ) fig.show() def ou_es_tu(ID, ep, t, color, f=""): """ Cette fonction permet d'afficher une projection autour de l'objet dont l'ID est donné en paramètre. ID : identifiant de l'objet (carré-numéro) entre guillemet double "". Exemple : "K23-24" ep = épaisseur de la projection (en cm) t = type de projection : 's' pour sagittale, 'f' pour frontale et 'p' pour planaire color = type de coloration : 'ua' (selon leur UA) ou 'nat' (selon leur nature) """ # Enregistrement date = datetime.datetime.now() format_sortie = f.lower() # Extraction des coordonnées df = pd.DataFrame(carnets.loc[ID]).T x_id = float(carnets.loc[ID, ["Xabs"]]) y_id = float(carnets.loc[ID, ["Yabs"]]) z_id = float(carnets.loc[ID, ["Zabs"]]) print(carnets.loc[ID, ["Carre", "Numero", "Couche", "Nature", "Annee", "Xabs", "Yabs", "Zabs", "UA", "Datation"]], end='\n\n') fig = plt.figure(figsize=(7.677, 10.852)) # format en inch # Condition selon type de coupe if t == 's': plt.subplot(2, 1, 1) projection(x_id-ep/2, ep, 's', color) sns.scatterplot(x="Yabs", y="Zabs", s=100, marker='X', color='m', data=df, edgecolor="black", linewidth=0.70, label=ID) plt.xlabel("Y (cm)") plt.ylabel("Z (cm)") plt.subplot(2, 1, 2) plan_proj(x_id-ep/2, ep, 's') sns.scatterplot(x="Xabs", y="Yabs", s=100, marker='X', color='m', data=df, edgecolor="black", linewidth=0.70, label=ID) plt.xlabel("X (cm)") plt.ylabel("Y (cm)") figname = nom_fig(x_id-ep/2, ep, t)+'_'+str(date.year)+str(date.month)+str(date.day)+'_'+str(date.hour+2)+'h'+str(date.minute)+'.'+format_sortie if t == 'f': plt.subplot(2, 1, 1) projection(y_id-ep/2, ep, 'f', color) sns.scatterplot(x="Xabs", y="Zabs", s=100, marker='X', color='m', data=df, edgecolor="black", linewidth=0.70, label=ID) plt.xlabel("X (cm)") plt.ylabel("Z (cm)") plt.subplot(2, 1, 2) plan_proj(y_id-ep/2, ep, 'f') sns.scatterplot(x="Xabs", y="Yabs", s=100, marker='X', color='m', data=df, edgecolor="black", linewidth=0.70, label=ID) plt.xlabel("X (cm)") plt.ylabel("Z (cm)") figname = nom_fig(y_id-ep/2, ep, t)+'_'+str(date.year)+str(date.month)+str(date.day)+'_'+str(date.hour+2)+'h'+str(date.minute)+'.'+format_sortie if t == 'p': plt.subplot(2, 1, 1) projection(z_id-ep/2, ep, 'p', color) sns.scatterplot(x="Xabs", y="Yabs", s=100, marker='X', color='m', data=df, edgecolor="black", linewidth=0.70, label=ID) plt.xlabel("X (cm)") plt.ylabel("Y (cm)") figname = nom_fig(z_id-ep/2, ep, t)+'_'+str(date.year)+str(date.month)+str(date.day)+'_'+str(date.hour+2)+'h'+str(date.minute)+'.'+format_sortie if format_sortie in ['png', 'jpg', 'jpeg','tif', 'tiff'] : plt.savefig(figname, dpi=300) print("La figure a été enregistrée sous le nom {}.".format(figname)) elif format_sortie in ['svg', 'pdf'] : plt.savefig(figname) print("La figure a été enregistrée sous le nom {}.".format(figname)) else : pass plt.show() def i_ou_es_tu(ID, ep, t, color): """ Cette fonction permet d'afficher une projection interactive autour de l'objet dont l'ID est donné en paramètre. ID : identifiant de l'objet (carré-numéro) entre guillemet double "". Exemple : "K23-24" ep = épaisseur de la projection (en cm) t = type de projection : 's' pour sagittale, 'f' pour frontale et 'p' pour planaire color = type de coloration : 'ua' (selon leur UA) ou 'nat' (selon leur nature) """ # Paramétrage de la palette de couleur if color == 'ua': palette = color_dict_ua elif color == 'nat': palette = color_dict_nature # Récupération des données sur l'objet recherché df = pd.DataFrame(carnets.loc[ID]).T x_id = float(carnets.loc[ID, ["Xabs"]]) y_id = float(carnets.loc[ID, ["Yabs"]]) z_id = float(carnets.loc[ID, ["Zabs"]]) # Condition selon type de coupe if t == 's': data = carnets.loc[(carnets.Xabs > x_id - ep/2) & (carnets.Xabs < x_id + ep/2)].sort_values(by="UA").reset_index() fig = px.scatter(data, x="Yabs", y="Zabs", color="UA", color_discrete_map=palette, hover_data=["ID", "Carre", "Numero", "Couche", "Nature", "UA", "Datation"], labels={"Yabs": "Y (cm)", "Zabs": "Z (cm)"}, title=title_fig(x_id, ep, t)) fig.add_trace(go.Scatter(x=carnets.loc[ID, ["Yabs"]], y=carnets.loc[ID, ["Zabs"]], mode='markers', marker_size=12, marker_symbol='x-dot', marker_color="#bf00bf", marker_line_color='black', marker_line_width=0.8, hovertext="{}, couche {}, {}, {} BP".format( df.Nature[0], df.Couche[0], df.UA[0], df.Datation[0]), hoverinfo='all', name=ID)) elif t == 'f': data = carnets.loc[(carnets.Yabs > y_id - ep/2) & (carnets.Yabs < y_id + ep/2)].sort_values(by="UA").reset_index() fig = px.scatter(data, x="Xabs", y="Zabs", color="UA", color_discrete_map=palette, hover_data=["ID", "Carre", "Numero", "Couche", "Nature", "UA"], labels={"Xabs": "X (cm)", "Zabs": "Z (cm)"}, title=title_fig(y_id, ep, t)) fig.add_trace(go.Scatter(x=carnets.loc[ID, ["Xabs"]], y=carnets.loc[ID, ["Zabs"]], mode='markers', marker_size=12, marker_symbol='x-dot', marker_color="#bf00bf", marker_line_color='black', marker_line_width=0.8, hovertext="{}, couche {}, {}, {} BP".format( df.Nature[0], df.Couche[0], df.UA[0], df.Datation[0]), hoverinfo='all', name=ID)) elif t == 'p': data = carnets.loc[(carnets.Zabs > z_id - ep/2) & (carnets.Zabs < z_id + ep/2)].sort_values(by="UA").reset_index() fig = px.scatter(data, x="Xabs", y="Yabs", color="UA", color_discrete_map=palette, hover_data=["ID", "Carre", "Numero", "Couche", "Nature", "UA"], labels={"Xabs": "X (cm)", "Yabs": "Y (cm)"}, title=title_fig(z_id, ep, t)) fig.add_trace(go.Scatter(x=carnets.loc[ID, ["Xabs"]], y=carnets.loc[ID, ["Yabs"]], mode='markers', marker_size=12, marker_symbol='x-dot', marker_color="#bf00bf", marker_line_color='black', marker_line_width=0.8, hovertext="{}, couche {}, {}, {} BP".format( df.Nature[0], df.Couche[0], df.UA[0], df.Datation[0]), hoverinfo='all', name=ID)) # Amélioration de la figure fig.layout.update(dict(coloraxis_showscale=False, showlegend=True, template="plotly_dark", paper_bgcolor='gray', plot_bgcolor='white', title_x=0.5, height= 600, width = 900 )) fig.update_xaxes(dtick=100, range=[50, 801], constrain="domain", showline=True, linewidth=1, linecolor='white', gridcolor="grey", zerolinecolor="grey") fig.update_yaxes(dtick=100, scaleanchor="x", scaleratio=1, showline=True, linewidth=1, linecolor='white', gridcolor="grey", zerolinecolor="grey") fig.layout.margin.update({'t': 30, 'l': 5}) fig.show(config = {'scrollZoom': True, 'displayModeBar': True}) if t == 's': dfp = points_proj(x_id-ep/2, ep, t) figp = px.scatter(dfp.reset_index(), x="Xabs", y="Yabs", hover_data=["ID", "Carre", "Numero", "Couche", "Nature", "UA"], labels={"Xabs": "X (cm)", "Yabs": "Y (cm)"}, title="Position de la coupe" ) figp.update_traces(marker_size=3, marker_color='navy') elif t == 'f': dfp = points_proj(y_id-ep/2, ep, t) figp = px.scatter(dfp.reset_index(), x="Xabs", y="Yabs", hover_data=["ID", "Carre", "Numero", "Couche", "Nature", "UA"], labels={"Xabs": "X (cm)", "Yabs": "Y (cm)"}, title="Position de la coupe" ) figp.update_traces(marker_size=3, marker_color='navy') else: pass figp.add_trace(go.Scatter(x=carnets.loc[ID, ["Xabs"]], y=carnets.loc[ID, ["Yabs"]], mode='markers', marker_size=12, marker_symbol='x-dot', marker_color="#bf00bf", marker_line_color='black', marker_line_width=0.8, hovertext="{}, couche {}, {}, {} BP".format( df.Nature[0], df.Couche[0], df.UA[0], df.Datation[0]), hoverinfo='all', name=ID)) figp.layout.update(dict(coloraxis_showscale=False, showlegend=True, template="plotly_dark", paper_bgcolor='gray', plot_bgcolor='white', title_x=0.5, height= 600, width = 900 )) figp.update_xaxes(dtick=100, range=[50, 801], constrain="domain", showline=True, linewidth=1, linecolor='white', gridcolor="grey", zerolinecolor="grey") figp.update_yaxes(dtick=100, scaleanchor="x", scaleratio=1, showline=True, linewidth=1, linecolor='white', gridcolor="grey", zerolinecolor="grey") figp.layout.margin.update({'t': 30, 'l': 5}) figp.show(config = {'scrollZoom': True, 'displayModeBar': True}) def i_proj(x1, ep, t, color): """ Cette fonction permet d'afficher une projection interactive selon une tranche donnée. x1 : borne inférieure de la projection ; ep = épaisseur de la projection (en cm) ; t = type de projection : 's' pour sagittale, 'f' pour frontale et 'p' pour planaire ; color = type de coloration : 'ua' (selon leur UA) ou 'nat' (selon leur nature) """ # Paramétrage de la palette de couleur if color == 'ua': palette = color_dict_ua colonne_color = "UA" elif color == 'nat': palette = color_dict_nature colonne_color = "Nature" # Récupération des données df = points_proj(x1,ep,t).sort_values(by="UA") # Condition selon type de coupe if t == 's': fig = px.scatter(df, x="Yabs", y="Zabs", color=colonne_color, color_discrete_map=palette, hover_data=["Carre", "Numero", "Couche", "Nature", "UA", "Datation", "Xabs"], labels={"Yabs": "Y (cm)", "Zabs": "Z (cm)"}, title=title_fig(x1, ep, t)) elif t == 'f': fig = px.scatter(df, x="Xabs", y="Zabs", color=colonne_color, color_discrete_map=palette, hover_data=["Carre", "Numero", "Couche", "Nature", "UA", "Datation", "Yabs"], labels={"Xabs": "X (cm)", "Zabs": "Z (cm)"}, title=title_fig(x1, ep, t)) elif t == 'p': fig = px.scatter(df, x="Xabs", y="Yabs", color=colonne_color, color_discrete_map=palette, hover_data=["Carre", "Numero", "Couche", "Nature", "UA", "Datation", "Zabs"], labels={"Xabs": "X (cm)", "Yabs": "Y (cm)"}, title=title_fig(x1, ep, t)) # Amélioration de la figure fig.layout.update(dict(coloraxis_showscale=False, showlegend=True, template="plotly_dark", paper_bgcolor='gray', plot_bgcolor='white', title_x=0.5, height= 600, width = 900 )) fig.update_xaxes(dtick=100, range=[50, 801], constrain="domain", showline=True, linewidth=1, linecolor='white', gridcolor="grey", zerolinecolor="grey") fig.update_yaxes(dtick=100, scaleanchor="x", scaleratio=1, showline=True, linewidth=1, linecolor='white', gridcolor="grey", zerolinecolor="grey") fig.layout.margin.update({'t': 30, 'l': 5}) fig.show(config = {'scrollZoom': True, 'displayModeBar': True}) if t == 's': figp = px.scatter(df, x="Xabs", y="Yabs", hover_data=["Carre", "Numero", "Couche", "Nature", "UA", "Datation", "Zabs"], labels={"Xabs": "X (cm)", "Yabs": "Y (cm)"}, title="Position de la coupe" ) figp.update_traces(marker_size=3, marker_color='navy') elif t == 'f': figp = px.scatter(df, x="Xabs", y="Yabs", hover_data=["Carre", "Numero", "Couche", "Nature", "UA", "Datation", "Zabs"], labels={"Xabs": "X (cm)", "Yabs": "Y (cm)"}, title="Position de la coupe" ) figp.update_traces(marker_size=3, marker_color='navy') else: pass if t in ['s', 'f']: figp.layout.update(dict(coloraxis_showscale=False, showlegend=True, template="plotly_dark", paper_bgcolor='gray', plot_bgcolor='white', title_x=0.5, height= 600, width = 900 )) figp.update_xaxes(dtick=100, range=[50, 801], constrain="domain", showline=True, linewidth=1, linecolor='white', gridcolor="grey", zerolinecolor="grey") figp.update_yaxes(dtick=100, scaleanchor="x", scaleratio=1, showline=True, linewidth=1, linecolor='white', gridcolor="grey", zerolinecolor="grey") figp.layout.margin.update({'t': 30, 'l': 5}) figp.show(config = {'scrollZoom': True, 'displayModeBar': True}) def raccords_3d(nature='all', opac=35): """ nature : 'indos', 'lithique' ou 'all' [par défaut : 'all'] opac : opacité en % (0 = transparent ; 100 = opaque) [par défaut : 35] """ # Import des données de raccords filepath = "https://raw.githubusercontent.com/magdantho/pycocu/master/pyth_raccords.csv" raccords = pd.read_csv(filepath, sep=",") data = pd.merge(raccords, carnets.loc[carnets.index.isin(raccords.ID), ["Xabs", "Yabs", "Zabs", "Couche", "UA"]].reset_index(), on="ID").sort_values(by=["id_raccord", "ID"]) # Nuage de tous les points df = carnets[["Xabs", "Yabs", "Zabs", "UA", "Couche"]].sort_values(by="UA") fig = px.scatter_3d(df, x='Xabs', y='Yabs', z='Zabs', hover_data=["Couche", "UA"], labels=None, color='UA', color_discrete_map=color_dict_ua, opacity=opac/100, width=900, height=900 ) fig.update_traces(marker=dict(size=1, line=dict(width=0, color='DarkSlateGrey')), selector=dict(mode='markers') ) fig.update_layout(title="Projection 3D des raccords, remontages et rapprochements", coloraxis_showscale=False, showlegend=True, template='plotly_dark', paper_bgcolor='gray', plot_bgcolor='white', legend=dict(itemsizing='constant', traceorder='normal', # x=0, y=1, orientation='v') ) fig.update_xaxes(showline=True, linewidth=2, linecolor='white', gridcolor="grey", zerolinecolor="grey" ) fig.update_yaxes(showline=True, linewidth=2, linecolor='white', gridcolor="grey", zerolinecolor="grey" ) if nature == "indos": data = data.loc[data.nature == "indos"] elif nature == "lithique": data = data.loc[data.nature == "lithique"] else: pass # Projection des objets raccordés/rapprochés for objet in data.ID: obj_rac = data.loc[data.ID == objet] fig.add_trace(go.Scatter3d(x=obj_rac.Xabs, y=obj_rac.Yabs, z=obj_rac.Zabs, name=objet, mode="markers", marker=dict(size=2.5, color=color_dict_ua[obj_rac.UA.iloc[0]], symbol='square'), hovertext=[obj_rac.UA.iloc[0], obj_rac.ID.iloc[0]], showlegend=False)) # Projection des liaisons entre ces objets for i in data.id_raccord.unique(): rac = data.loc[data.id_raccord == i] if rac.type.iloc[0] == 'raccord': fig.add_trace(go.Scatter3d(x=rac.Xabs, y=rac.Yabs, z=rac.Zabs, mode="lines", line=dict( width=8, color='red', dash='solid'), name=i, hovertext=str(rac.ID.values), showlegend=True )) if rac.type.iloc[0] == 'rapprochement': fig.add_trace(go.Scatter3d(x=rac.Xabs, y=rac.Yabs, z=rac.Zabs, mode="lines", line=dict( width=8, color='magenta', dash="solid"), name=i, hovertext=str(rac.ID.values), showlegend=True )) fig.show(config={'displaylogo': False, 'displayModeBar': True, 'toImageButtonOptions': {'format': 'png', 'filename': 'raccords_3D', 'height':1500, 'width':1500, 'scale': 2 }}) print(data.loc[:,["id_raccord","ID","type","nature","Couche","UA"]].reset_index(drop=True)) def proj_raccords(liste, ep_min=15, f=""): """ liste : écrire entre [] les identifiants des objets raccordés, entre guillemets et séparés par une virgule. Par exemple : ["J26-79", "J26-85"] ep_min : épaisseur minimum en cm de la projection [par défaut : 15 cm] f : format d'enregistrement ("png", "svg", "pdf", "tiff", "jpg"). Si vide, la figure ne s'enregistre pas [par défaut : ""] """ # Import des données de raccords filepath = "https://raw.githubusercontent.com/magdantho/pycocu/master/pyth_raccords.csv" raccords = pd.read_csv(filepath, sep=",") df_rac = pd.merge(raccords, carnets.loc[carnets.index.isin(raccords.ID), ["Xabs", "Yabs", "Zabs", "Couche", "UA"]].reset_index(), on="ID").sort_values(by=["id_raccord", "ID"]) # Choix des raccords à projeter liste.sort() data = df_rac.loc[df_rac.ID.isin(liste), :] # Projection du reste du matériel autour du raccord fig = plt.figure(figsize=(10.852, 10)) # format A4 en inch # Projection sagittale ax01 = fig.add_subplot(2, 2, 1) x1 = data.Xabs.min() ep1 = data.Xabs.max() - data.Xabs.min() if ep1 < ep_min: ep1 = ep_min projection(x1, ep1, 's', 'ua') sns.lineplot(x="Yabs", y="Zabs", color='red', markers=True, data=data) ax01.set_xlabel("Y (cm)") ax01.set_ylabel("Z (cm)") ax01.legend(loc='best', fontsize='small', facecolor='white', framealpha=1, edgecolor='0.9', shadow=True, labelspacing=0.3) # Vue planaire de la sagittale ax02 = fig.add_subplot(2, 2, 2) plan_proj(x1, ep1, 's') sns.lineplot(x="Xabs", y="Yabs", color='red', markers=True, label=data.type.iloc[0]+" "+str(", ".join(liste)), data=data) ax02.set_xlabel("X (cm)") ax02.set_ylabel("Y (cm)") ax02.legend(loc='best', fontsize='small', facecolor='white', framealpha=1, edgecolor='0.9', shadow=True, labelspacing=0.3) # Projection frontale ax03 = fig.add_subplot(2, 2, 3) y3 = data.Yabs.min() ep3 = data.Yabs.max()- data.Yabs.min() if ep3 < ep_min: ep3 = ep_min projection(y3, ep3, 'f', 'ua') sns.lineplot(x="Xabs", y="Zabs", color='red', markers=True, data=data) ax03.set_xlabel("X (cm)") ax03.set_ylabel("Z (cm)") ax03.legend(loc='best', fontsize='small', facecolor='white', framealpha=1, edgecolor='0.9', shadow=True, labelspacing=0.3) # Vue planaire de la frontale ax04 = fig.add_subplot(2, 2, 4) plan_proj(y3, ep3, 'f') sns.lineplot(x="Xabs", y="Yabs", color='red', markers=True, label=data.type.iloc[0]+" "+str(", ".join(liste)), data=data) ax04.set_xlabel("X (cm)") ax04.set_ylabel("Y (cm)") ax04.legend(loc='best', fontsize='small', facecolor='white', framealpha=1, edgecolor='0.9', shadow=True, labelspacing=0.3) if f in ['png', 'jpg', 'jpeg','tif', 'tiff'] : plt.savefig(str(data.id_raccord.iloc[0])+"."+f, dpi=300) print("le fichier a été enregistré sous le nom {}.".format(str(data.id_raccord.iloc[0])+"."+f)) elif f in ['svg','pdf'] : plt.savefig(str(data.id_raccord.iloc[0])+"."+f) print("le fichier a été enregistré sous le nom {}.".format(str(data.id_raccord.iloc[0])+"."+f)) else: pass plt.show()