Remove Lightning_Report_Automatic.json file, eliminating the associated n8n workflow and its components for report generation and token management. Update Lightning_Report_Manual.json to reflect changes in node positions and IDs, ensuring consistency across workflows. Enhance report generation logic by adding detailed strike list sections for cloud-to-ground and intercloud lightning events, improving clarity and user experience in report outputs.

2026-06-18 16:34:52 +03:00 · 2026-06-18 16:34:52 +03:00 · 8152e76d05
commit 8152e76d05
parent 7fd27ffed8
10 changed files with 898 additions and 3568 deletions
--- a/Import_Wind_Turbine_Farm.json
+++ b/Import_Wind_Turbine_Farm.json
--- a/Lightning_Report_Automatic.json
+++ b/Lightning_Report_Automatic.json
--- a/Lightning_Report_Manual.json
+++ b/Lightning_Report_Manual.json
--- a/examples/wind_turbines_sample.csv
+++ b/examples/wind_turbines_sample.csv
@ -0,0 +1,4 @@
+name,latitude,longitude,farm_name
+WTG-01,39.854321,32.765432,Sample Wind Farm
+WTG-02,39.855100,32.766200,Sample Wind Farm
+WTG-03,39.855880,32.766968,Sample Wind Farm
--- a/report_service/adapter.py
+++ b/report_service/adapter.py
@ -31,6 +31,7 @@ _LIGHTNING_COLUMN_ALIASES: dict[str, list[str]] = {
        "time",
        "timestamp",
    ],
+    "ic_height": ["ic_height", "inCloudHeight", "in_cloud_height", "InCloudHeight"],
 }

 _TURBINE_COLUMN_ALIASES: dict[str, list[str]] = {
@ -158,6 +159,9 @@ def _build_lightning_df(
    if "current_abs" not in df.columns:
        df["current_abs"] = df["current"].abs()

+    if "ic_height" in df.columns:
+        df["ic_height"] = pd.to_numeric(df["ic_height"], errors="coerce")
+
    return df


--- a/src/reporting/docx.py
+++ b/src/reporting/docx.py
@ -4,7 +4,7 @@ import io
 import os
 from dataclasses import dataclass
 from datetime import datetime
-from typing import Any, Iterable
+from typing import Any, Iterable, Literal

 import pandas as pd
 import matplotlib
@ -163,6 +163,24 @@ def _add_paragraph(doc: Document, text: str, size_pt: int = 11, align: WD_ALIGN_
 	r.font.size = Pt(size_pt)


+def _add_multiline_paragraph(
+	doc: Document,
+	text: str,
+	size_pt: int = 11,
+	align: WD_ALIGN_PARAGRAPH = WD_ALIGN_PARAGRAPH.LEFT,
+) -> None:
+	lines = [line.strip() for line in text.splitlines() if line.strip()]
+	if not lines:
+		return
+	p = doc.add_paragraph()
+	p.alignment = align
+	for index, line in enumerate(lines):
+		if index > 0:
+			p.add_run().add_break()
+		run = p.add_run(line)
+		run.font.size = Pt(size_pt)
+
+
 def _add_bullets(doc: Document, items: Iterable[str], size_pt: int = 10) -> None:
 	for item in items:
 		p = doc.add_paragraph(style="List Bullet")
@ -499,6 +517,56 @@ def _fit_one_line_table_layout(
 	return font_pt, [w * scale for w in required]


+def _add_detailed_strike_list_section(
+	doc: Document,
+	title: str,
+	description: str,
+	empty_message: str,
+	centroid_lat: float,
+	centroid_lng: float,
+	lightning_df: pd.DataFrame,
+	strike_type: Literal["cg", "ic"],
+	content_width: float,
+	lang,
+) -> None:
+	_add_title(doc, title, size_pt=14)
+	_add_multiline_paragraph(doc, description, size_pt=10)
+	table_data, row_colors = build_lightning_event_table_data(
+		centroid_lat,
+		centroid_lng,
+		lightning_df,
+		lang,
+		strike_type=strike_type,
+	)
+	if len(table_data) <= 1:
+		_add_paragraph(doc, empty_message, size_pt=10)
+		return
+
+	table_data[0] = [
+		str(h).replace(" ", "\u00A0", 1) if " " in str(h) else str(h) for h in table_data[0]
+	]
+	available_width_cm = float(content_width) * 2.54
+	if strike_type == "ic":
+		min_col_widths_cm = [1, 3.2, 1.6, 1.6, 1.9, 1.4, 1.8]
+	else:
+		min_col_widths_cm = [1, 3.2, 1.6, 1.6, 1.9, 1.8]
+	font_pt, col_widths_cm = _fit_one_line_table_layout(
+		table_data=table_data,
+		available_width_cm=available_width_cm,
+		min_col_widths_cm=min_col_widths_cm,
+		max_font_pt=15,
+		min_font_pt=8,
+	)
+	_add_table(
+		doc,
+		table_data,
+		row_colors=row_colors,
+		column_widths_cm=col_widths_cm,
+		font_size_pt=float(font_pt),
+		autofit=False,
+	)
+
+
 def _add_table(
 	doc: Document,
 	table_data: list[list[str]],
@ -813,7 +881,7 @@ def create_docx_report(

 	doc.add_page_break()

-	# Farm-wide maps + charts + risk table, anchored at the n8n-supplied centroid.
+	# Risk table, then farm-wide maps and charts anchored at the n8n-supplied centroid.
 	centroid_row = pd.Series({
 		"lat": centroid_lat,
 		"lng": centroid_lng,
@ -825,6 +893,13 @@ def create_docx_report(
 	has_cg = bool(((type_values == "0") & mask_within).any())
 	has_ic = bool(((type_values != "0") & mask_within).any())

+	risk_table_data, risk_row_colors = build_risk_table_data(turbine_df, lang)
+	if risk_table_data and len(risk_table_data) > 1:
+		_add_title(doc, s.turbine_risk_assessment, size_pt=14)
+		_add_paragraph(doc, s.turbine_risk_assessment_desc, size_pt=10)
+		_add_table(doc, risk_table_data, row_colors=risk_row_colors)
+		doc.add_page_break()
+
 	if has_cg:
 		_add_title(doc, s.cloud_to_ground_lightnings, size_pt=14)
 		if start_date and end_date:
@ -849,6 +924,18 @@ def create_docx_report(
 		)
 		if cg_chart is not None:
 			_add_image_from_bytes(doc, cg_chart.to_image(format="png", width=1200, height=900, scale=2, engine="kaleido"), content_width)
+		_add_detailed_strike_list_section(
+			doc,
+			s.detailed_cg_events,
+			s.detailed_cg_events_desc,
+			s.detailed_cg_events_empty,
+			centroid_lat,
+			centroid_lng,
+			lightning_df,
+			"cg",
+			content_width,
+			lang,
+		)
 		doc.add_page_break()

 	if has_ic:
@ -875,13 +962,18 @@ def create_docx_report(
 		)
 		if ic_chart is not None:
 			_add_image_from_bytes(doc, ic_chart.to_image(format="png", width=1200, height=900, scale=2, engine="kaleido"), content_width)
-			doc.add_page_break()
-
-	risk_table_data, risk_row_colors = build_risk_table_data(turbine_df, lang)
-	if risk_table_data and len(risk_table_data) > 1:
-		_add_title(doc, s.turbine_risk_assessment, size_pt=14)
-		_add_paragraph(doc, s.turbine_risk_assessment_desc, size_pt=10)
-		_add_table(doc, risk_table_data, row_colors=risk_row_colors)
+		_add_detailed_strike_list_section(
+			doc,
+			s.detailed_ic_events,
+			s.detailed_ic_events_desc,
+			s.detailed_ic_events_empty,
+			centroid_lat,
+			centroid_lng,
+			lightning_df,
+			"ic",
+			content_width,
+			lang,
+		)
 		doc.add_page_break()

 	# Daily breakdown
@ -1081,30 +1173,6 @@ def create_docx_report(
 		)
 		doc.add_page_break()

-	# Farm-wide lightning event table, anchored at the n8n-supplied centroid.
-	_add_title(doc, s.detailed_lightning_data, size_pt=14)
-	table_data, row_colors = build_lightning_event_table_data(centroid_lat, centroid_lng, lightning_df, lang)
-	if table_data and table_data[0]:
-		table_data[0] = [str(h).replace(" ", "\u00A0", 1) if " " in str(h) else str(h) for h in table_data[0]]
-	available_width_cm = float(content_width) * 2.54
-	min_col_widths_cm = [1, 3.2, 1.6, 1.6, 1.9, 1.4, 2.0, 1.8]
-	font_pt, col_widths_cm = _fit_one_line_table_layout(
-		table_data=table_data,
-		available_width_cm=available_width_cm,
-		min_col_widths_cm=min_col_widths_cm,
-		max_font_pt=15,
-		min_font_pt=8,
-	)
-	_add_table(
-		doc,
-		table_data,
-		row_colors=row_colors,
-		column_widths_cm=col_widths_cm,
-		font_size_pt=float(font_pt),
-		autofit=False,
-	)
-	doc.add_page_break()
-
 	# Appendix
 	_add_title(doc, s.appendix, size_pt=16)
 	_add_title(doc, s.risk_calc_method, size_pt=14)
--- a/src/reporting/docx_sections.py
+++ b/src/reporting/docx_sections.py
@ -1,6 +1,6 @@
 from __future__ import annotations

-from typing import Any, Callable
+from typing import Any, Callable, Literal

 import numpy as np
 import pandas as pd
@ -280,17 +280,45 @@ def build_lightning_event_table_data(
 	centroid_lng: float,
 	lightning_df: pd.DataFrame,
 	lang: ReportLanguage | None = None,
+	strike_type: Literal["cg", "ic", "all"] = "all",
 ) -> tuple[list[list[str]], list[str]]:
 	s = get_strings(lang or get_report_language())
+	include_type_column = strike_type == "all"
+	include_height_column = strike_type != "cg"
+
+	if strike_type == "cg":
+		source_df = lightning_df[lightning_df["p_type"].astype(str) == "0"].copy()
+	elif strike_type == "ic":
+		source_df = lightning_df[lightning_df["p_type"].astype(str) != "0"].copy()
+	else:
+		source_df = lightning_df.copy()
+
+	header = [
+		s.col_no,
+		s.col_time_local,
+		s.col_lat,
+		s.col_lng,
+		s.col_current_amps,
+	]
+	if include_height_column:
+		header.append(s.col_height_m)
+	if include_type_column:
+		header.append(s.col_lightning_type)
+	header.append(s.col_proximity_km)
+
+	if source_df.empty:
+		return [header], ["lightgrey"]
+
 	pre = precompute_distances_and_rings(
-		centroid_lat, centroid_lng, lightning_df, config.distance_rings
+		centroid_lat, centroid_lng, source_df, config.distance_rings
 	)
 	rows: list[list[str]] = []
 	row_colors: list[str] = []
 	outermost_km = max(config.distance_rings) / 1000.0
 	rings_km = [r / 1000.0 for r in config.distance_rings]
+	proximity_index = len(header) - 1

-	for i, rec in enumerate(lightning_df.itertuples(index=False)):
+	for i, rec in enumerate(source_df.itertuples(index=False)):
 		proximity = float(pre["dists_km"][i])
 		if proximity > outermost_km:
 			continue
@ -310,48 +338,43 @@ def build_lightning_event_table_data(
 		except Exception:
 			local_time = str(getattr(rec, "local_time", ""))[:19]

-		lightning_type = s.lightning_type_cg if str(rec.p_type) == "0" else s.lightning_type_ic
-		height_val = getattr(rec, "ic_height", "")
-		if height_val == "":
-			height_val = getattr(rec, "height", "")
-
-		rows.append(
-			[
+		row = [
 			"",
 			local_time,
 			f"{rec.lat:.5f}",
 			f"{rec.lng:.5f}",
 			str(rec.current),
-				str(height_val),
-				lightning_type,
-				f"{proximity:.2f}",
 		]
-		)
+		if include_height_column:
+			height_val = getattr(rec, "ic_height", "")
+			if height_val == "" or pd.isna(height_val):
+				height_val = getattr(rec, "inCloudHeight", "")
+			row.append("" if height_val == "" or pd.isna(height_val) else str(height_val))
+		if include_type_column:
+			lightning_type = s.lightning_type_cg if str(rec.p_type) == "0" else s.lightning_type_ic
+			row.append(lightning_type)
+		row.append(f"{proximity:.2f}")
+		rows.append(row)
 		row_colors.append(color)

+	if rows:
+		if include_type_column:
+			type_index = proximity_index - 1
 			sorted_data = sorted(
 				zip(rows, row_colors),
-		key=lambda x: (0 if x[0][6] == s.lightning_type_cg else 1, float(x[0][7])),
+				key=lambda x: (0 if x[0][type_index] == s.lightning_type_cg else 1, float(x[0][proximity_index])),
+			)
+		else:
+			sorted_data = sorted(
+				zip(rows, row_colors),
+				key=lambda x: float(x[0][proximity_index]),
 			)
-	if sorted_data:
 		rows, row_colors = zip(*sorted_data)
 		rows = list(rows)
 		row_colors = list(row_colors)
 		for idx, row in enumerate(rows):
 			row[0] = str(idx + 1)
-	else:
-		rows, row_colors = [], []

-	header = [
-		s.col_no,
-		s.col_time_local,
-		s.col_lat,
-		s.col_lng,
-		s.col_current_amps,
-		s.col_height_m,
-		s.col_lightning_type,
-		s.col_proximity_km,
-	]
 	return [header] + rows, ["lightgrey"] + row_colors


--- a/src/reporting/strings.py
+++ b/src/reporting/strings.py
@ -98,6 +98,12 @@ class ReportStrings:
    storm_cells: str
    storm_cells_daily_viz: str
    detailed_lightning_data: str
+    detailed_cg_events: str
+    detailed_ic_events: str
+    detailed_cg_events_desc: str
+    detailed_ic_events_desc: str
+    detailed_cg_events_empty: str
+    detailed_ic_events_empty: str
    appendix: str
    risk_calc_method: str
    how_risk_determined: str
@ -308,6 +314,19 @@ _STRINGS_EN = ReportStrings(
    storm_cells="Storm Cells",
    storm_cells_daily_viz="Daily storm cells visualization.",
    detailed_lightning_data="Detailed Lightning Event Data",
+    detailed_cg_events="Detailed Cloud-to-Ground Lightning List",
+    detailed_ic_events="Detailed Intercloud Lightning List",
+    detailed_cg_events_desc=(
+        "Current (amps): peak current of the cloud-to-ground strike.\n"
+        "Proximity (km): air distance from the strike to the wind farm centroid."
+    ),
+    detailed_ic_events_desc=(
+        "Current (amps): peak current of the in-cloud strike.\n"
+        "Height (m): in-cloud flash height.\n"
+        "Proximity (km): air distance from the strike to the wind farm centroid."
+    ),
+    detailed_cg_events_empty="No cloud-to-ground lightning events were recorded within the analysis area during this period.",
+    detailed_ic_events_empty="No intercloud lightning events were recorded within the analysis area during this period.",
    appendix="Appendix",
    risk_calc_method="1. Risk Calculation Method",
    how_risk_determined="How Risk Scores Are Determined:",
@ -328,14 +347,14 @@ _STRINGS_EN = ReportStrings(
    max_risk_score="Maximum Risk Score: ~2.500 (close distance, high current)",
    typical_range="Typical Range: 0.010 - 1.000 for most lightning events",
    risk_categories="Risk Score Categories (Fixed Color Intervals):",
-    risk_cat_very_low="Very Low Risk (<0.1): Blue - Distant lightning with low current",
-    risk_cat_low="Low Risk (0.1-0.2): Teal - Moderate distance lightning",
-    risk_cat_med_low="Med-Low Risk (0.2-0.4): Green - Closer lightning",
-    risk_cat_medium="Medium Risk (0.4-0.6): Yellow - Moderate risk lightning",
-    risk_cat_med_high="Med-High Risk (0.6-0.8): Orange - High risk lightning",
-    risk_cat_high="High Risk (0.8-1.0): Dark Orange - Very high risk lightning",
-    risk_cat_very_high="Very High Risk (1.0-1.2): Red - Extreme risk lightning",
-    risk_cat_critical="Critical Risk (>1.2): Dark Red - Critical risk lightning",
+    risk_cat_very_low="Very Low Risk (<0.1): Blue",
+    risk_cat_low="Low Risk (0.1-0.2): Teal",
+    risk_cat_med_low="Med-Low Risk (0.2-0.4): Green",
+    risk_cat_medium="Medium Risk (0.4-0.6): Yellow",
+    risk_cat_med_high="Med-High Risk (0.6-0.8): Orange",
+    risk_cat_high="High Risk (0.8-1.0): Dark Orange",
+    risk_cat_very_high="Very High Risk (1.0-1.2): Red",
+    risk_cat_critical="Critical Risk (>1.2): Dark Red",
    risk_chart="3. Risk Score Calculation Chart",
    chart_reference="Chart Reference Guide:",
    risk_chart_desc_1="The following chart shows how distance and current magnitude affect risk scores.",
@ -527,6 +546,19 @@ _STRINGS_TR = ReportStrings(
    storm_cells="Fırtına Hücreleri",
    storm_cells_daily_viz="Fırtına hücrelerinin dağılımı",
    detailed_lightning_data="Ayrıntılı Yıldırım Olay Verileri",
+    detailed_cg_events="Ayrıntılı Yıldırım Listesi",
+    detailed_ic_events="Ayrıntılı Şimşek Listesi",
+    detailed_cg_events_desc=(
+        "Akım (amper): yıldırımın akım büyüklüğü.\n"
+        "Yakınlık (km): yıldırımın rüzgar çiftliğinin merkez noktasına (centroid) olan kuş uçuşu mesafesi."
+    ),
+    detailed_ic_events_desc=(
+        "Akım (amper): şimşeğin akım büyüklüğü.\n"
+        "Yükseklik (m): şimşeğin oluştuğu yükseklik.\n"
+        "Yakınlık (km): şimşeğin rüzgar çiftliğinin merkez noktasına olan kuş uçuşu mesafesi."
+    ),
+    detailed_cg_events_empty="Bu dönemde analiz alanında yıldırım olayı kaydedilmemiştir.",
+    detailed_ic_events_empty="Bu dönemde analiz alanında şimşek olayı kaydedilmemiştir.",
    appendix="Ek",
    risk_calc_method="1. Risk Hesaplama Yöntemi",
    how_risk_determined="Risk Skorları Nasıl Belirlenir:",
@ -547,14 +579,14 @@ _STRINGS_TR = ReportStrings(
    max_risk_score="Maksimum Risk Skoru: ~2.500 (yakın mesafe, yüksek akım)",
    typical_range="Tipik Aralık: çoğu yıldırım olayı için 0.010 - 1.000",
    risk_categories="Risk Skoru Kategorileri (Sabit Renk Aralıkları):",
-    risk_cat_very_low="Çok Düşük Risk (<0.1): Mavi - Uzak, düşük akımlı yıldırım",
-    risk_cat_low="Düşük Risk (0.1-0.2): Turkuaz - Orta mesafeli yıldırım",
-    risk_cat_med_low="Orta-Düşük Risk (0.2-0.4): Yeşil - Daha yakın yıldırım",
-    risk_cat_medium="Orta Risk (0.4-0.6): Sarı - Orta riskli yıldırım",
-    risk_cat_med_high="Orta-Yüksek Risk (0.6-0.8): Turuncu - Yüksek riskli yıldırım",
-    risk_cat_high="Yüksek Risk (0.8-1.0): Koyu Turuncu - Çok yüksek riskli yıldırım",
-    risk_cat_very_high="Çok Yüksek Risk (1.0-1.2): Kırmızı - Aşırı riskli yıldırım",
-    risk_cat_critical="Kritik Risk (>1.2): Koyu Kırmızı - Kritik riskli yıldırım",
+    risk_cat_very_low="Çok düşük risk (<0.1): Mavi",
+    risk_cat_low="Düşük Risk (0.1-0.2): Turkuaz",
+    risk_cat_med_low="Orta-Düşük Risk (0.2-0.4): Yeşil",
+    risk_cat_medium="Orta Risk (0.4-0.6): Sarı",
+    risk_cat_med_high="Orta-Yüksek Risk (0.6-0.8): Turuncu",
+    risk_cat_high="Yüksek Risk (0.8-1.0): Koyu Turuncu",
+    risk_cat_very_high="Çok Yüksek Risk (1.0-1.2): Kırmızı",
+    risk_cat_critical="Kritik Risk (>1.2): Koyu Kırmızı",
    risk_chart="3. Risk Skoru Hesaplama Grafiği",
    chart_reference="Grafik Referans Kılavuzu:",
    risk_chart_desc_1="Aşağıdaki grafik, mesafe ve akım büyüklüğünün risk skorlarını nasıl etkilediğini gösterir.",
--- a/src/visualization/maps.py
+++ b/src/visualization/maps.py
@ -11,6 +11,8 @@ COORDINATE_PLANE_RING_LINE_WIDTH = 4
 COORDINATE_PLANE_LIGHTNING_SIZE_MIN = 10
 COORDINATE_PLANE_LIGHTNING_SIZE_MAX = 24
 COORDINATE_PLANE_LIGHTNING_CURRENT_SCALE = 800
+COORDINATE_PLANE_TURBINE_NAME_FONT_SIZE = 9
+COORDINATE_PLANE_TURBINE_TEXT_POSITION = 'middle center'

 def plot_turbine_map(turbine_row: pd.Series, lightning_df: pd.DataFrame, turbine_df: pd.DataFrame) -> go.Figure:
    turbine_lat = turbine_row['lat']
@ -371,8 +373,8 @@ def plot_coordinate_plane(turbine_row: pd.Series, lightning_df: pd.DataFrame, tu
            line=dict(color='black', width=1)
        ),
        text=turbine_df['name'].tolist(),
-        textfont=dict(size=18, color='black'),  # turbine name label font size
-        textposition='middle center',
+        textfont=dict(size=COORDINATE_PLANE_TURBINE_NAME_FONT_SIZE, color='black'),
+        textposition=COORDINATE_PLANE_TURBINE_TEXT_POSITION,
        name='Wind Turbines',
        showlegend=True
    ))
@ -394,7 +396,7 @@ def plot_coordinate_plane(turbine_row: pd.Series, lightning_df: pd.DataFrame, tu
                    line=dict(color='black', width=1)
                ),
                text=['S'] * len(storm_df),
-                textfont=dict(size=18, color='white'),  # storm "S" label font size
+                textfont=dict(size=18, color='white'),
                textposition='middle center',
                name='Storm Cells',
                showlegend=True
@ -521,8 +523,8 @@ def plot_intercloud_coordinate_plane(turbine_row: pd.Series, lightning_df: pd.Da
            line=dict(color='black', width=1)
        ),
        text=turbine_df['name'].tolist(),
-        textfont=dict(size=24, color='black'),
-        textposition='middle center',
+        textfont=dict(size=COORDINATE_PLANE_TURBINE_NAME_FONT_SIZE, color='black'),
+        textposition=COORDINATE_PLANE_TURBINE_TEXT_POSITION,
        name=s.map_wind_turbines,
        showlegend=True
    ))
@ -694,8 +696,8 @@ def plot_cloud_to_ground_coordinate_plane(turbine_row: pd.Series, lightning_df:
            line=dict(color='black', width=1)
        ),
        text=turbine_df['name'].tolist(),
-        textfont=dict(size=18, color='black'),  # turbine name label font size
-        textposition='middle center',
+        textfont=dict(size=COORDINATE_PLANE_TURBINE_NAME_FONT_SIZE, color='black'),
+        textposition=COORDINATE_PLANE_TURBINE_TEXT_POSITION,
        name=s.map_wind_turbines,
        showlegend=True
    ))
@ -717,7 +719,7 @@ def plot_cloud_to_ground_coordinate_plane(turbine_row: pd.Series, lightning_df:
                    line=dict(color='black', width=1)
                ),
                text=['S'] * len(storm_df),
-                textfont=dict(size=18, color='white'),  # storm "S" label font size
+                textfont=dict(size=18, color='white'),
                textposition='middle center',
                name=s.map_storm_cells,
                showlegend=True
--- a/src/visualization/storm_cells.py
+++ b/src/visualization/storm_cells.py
@ -13,6 +13,10 @@ from src.config import config
 from src.reporting.strings import ReportLanguage, get_report_language, get_strings
 from src.utils import parse_period_string_to_datetime
 from src.visualization.basemap import add_satellite_basemap
+from src.visualization.maps import (
+    COORDINATE_PLANE_TURBINE_NAME_FONT_SIZE,
+    COORDINATE_PLANE_TURBINE_TEXT_POSITION,
+)

 def format_datetime_for_display(datetime_str: str) -> str:
    """
@ -177,8 +181,8 @@ def create_storm_cells_coordinate_plane(storm_data: List[Dict], turbine_df: pd.D
                line=dict(color='black', width=1)
            ),
            text=turbine_df['name'].tolist(),
-            textfont=dict(size=12, color='black'),
-            textposition='middle center',
+            textfont=dict(size=COORDINATE_PLANE_TURBINE_NAME_FONT_SIZE, color='black'),
+            textposition=COORDINATE_PLANE_TURBINE_TEXT_POSITION,
            name=s.map_wind_turbines,
            showlegend=True,
            hovertemplate=(