Mine kommentarer og eksempler til Geodetic levelling

environment-dev-geod-lev.yml

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+name: fire-dev-geod-lev
+channels:
+  - conda-forge
+dependencies:
+  - black
+  - click
+  - fiona
+  - gama
+  - graphviz
+  - matplotlib
+  - numpy
+  - openpyxl
+  - oracledb
+  - pandas
+  - pyarrow
+  - pip
+  - pre-commit
+  - pylint
+  - pyproj
+  - pytest-cov
+  - pytest-freezegun
+  - pytest-mock
+  - pytest
+  - python=
+  - rich
+  - scipy
+  - shapely
+  - sphinx-click
+  - sphinx
+  - sphinx_rtd_theme
+  - sphinxcontrib-programoutput
+  - sqlalchemy
+  - xmltodict
+  - astropy
+  - jplephem
+  - cartopy

src/fire/api/geodetic_levelling/__init__.py

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+"""
+geodetic_levelling
+"""
+
+import fire.api.geodetic_levelling.contour_plot
+import fire.api.geodetic_levelling.geodetic_correction_levelling_obs
+import fire.api.geodetic_levelling.histogram
+import fire.api.geodetic_levelling.metric_to_gpu_transformation
+import fire.api.geodetic_levelling.tidal_transformation
+import fire.api.geodetic_levelling.time_propagation
+import fire.api.geodetic_levelling.geophysical_parameters
+
+__version__ = "1.0.0"

src/fire/api/geodetic_levelling/contour_plot.py

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+"""This module contains functions for the generation of contour plots showing
+3rd precision levelling recalculated vs. DVR90
+"""
+
+from pathlib import Path
+
+from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
+import cartopy
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mticker
+import matplotlib.tri as tri
+import numpy as np
+import pandas as pd
+
+
+def extract_heights_from_fire_project(
+    fire_project: str,
+    excel_inputfolder: Path,
+) -> pd.DataFrame:
+    """Extract heights from a FIRE project.
+
+    Extracts adjusted heights and database heights from a FIRE project and returns the
+    extracted heights and corresponding height differences in a DataFrame.
+
+    Note that only the heights of DVR90 defining points are extracted from the FIRE project.
+
+    Args:
+    fire_project: str, name of FIRE project with adjusted heights and database heights,
+    e.g. "3prs"
+    excel_inputfolder: Path, folder containing FIRE project/excel file
+
+    Returns:
+    pd.DataFrame, a DataFrame with extracted heights and height differences
+
+    Raises:
+    ? Hvis input mapper eller filer ikke findes
+
+    Input file:
+    FIRE project/excel file with adjusted heights and database heights, e.g. "3prs.xlsx"
+    """
+    excel_inputfile = excel_inputfolder / f"{fire_project}.xlsx"
+
+    punktoversigt_df = pd.read_excel(excel_inputfile, sheet_name="Punktoversigt")
+    kontrolberegning_df = pd.read_excel(excel_inputfile, sheet_name="Kontrolberegning")
+
+    # DataFrame with DVR90 defining points in sheet "Punktoversigt"
+    # (i.e. all G.I. or G.M. points with a database height calculated 2000-02-11 13:30:00)
+    punktoversigt_df = punktoversigt_df[
+        (punktoversigt_df["Hvornår"] == "2000-02-11 13:30:00")
+        & (
+            punktoversigt_df["Punkt"].str.startswith("G.I.")
+            | punktoversigt_df["Punkt"].str.startswith("G.M.")
+        )
+    ]
+
+    # DataFrame with point id, database height and latitude/longitude of DVR90 defining points
+    punktoversigt_df = punktoversigt_df[["Punkt", "Kote", "Nord", "Øst"]]
+
+    # DataFrame with point id and adjusted heights of all points in sheet "Kontrolberegning"
+    kontrolberegning_df = kontrolberegning_df[["Punkt", "Ny kote"]]
+
+    kontrolberegning_df = kontrolberegning_df.set_index("Punkt")
+
+    # DataFrame with point id, database height, adjusted height and latitude/longitude of
+    # DVR90 defining points
+    points_df = punktoversigt_df.join(kontrolberegning_df, on="Punkt", how="inner")
+
+    # Add column with height differences (adjusted height - database height) to DataFrame
+    points_df["Diff"] = points_df["Ny kote"] - points_df["Kote"]
+
+    return points_df
+
+
+def contour_plot_recalc_vs_dvr90(
+    fire_project: str,
+    excel_inputfolder: Path,
+    outputfolder: Path,
+) -> None:
+    """Generate contour plot showing 3rd precision levelling recalculated vs. DVR90.
+
+    Generates contour plot showing 3rd precision levelling recalculated vs. DVR90
+    (recalculated height - DVR90 height).
+
+    Args:
+    fire_project: str, name of fire project with recalculated/adjusted heights and
+    DVR90/database heights, e.g. "3prs"
+    excel_inputfolder: Path, folder containing FIRE project/excel file
+    outputfolder: Path, folder for output contour plot
+
+    Returns:
+    None
+
+    Raises:
+    ?
+
+    Input file:
+    FIRE project/excel file with adjusted heights and database heights, e.g. "3prs.xlsx"
+
+    Output file:
+    Png file with contour plot
+    """
+    # Make sure that the output folder exists
+    outputfolder.mkdir(parents=True, exist_ok=True)
+
+    # DataFrame with height differences of DVR90 defining points
+    # (adjusted heights - database heights)
+    points_df = extract_heights_from_fire_project(fire_project, excel_inputfolder)
+
+    # Generation of contour plot
+    fig = plt.figure(figsize=(7, 4), dpi=500)
+    crs = ccrs.PlateCarree()
+    ax = plt.axes(projection=crs)
+
+    ax.set_title("3rd precision levelling recalculated vs. DVR90")
+    ax.add_feature(cartopy.feature.OCEAN, zorder=0)
+    ax.add_feature(cartopy.feature.LAND, zorder=0, edgecolor="black")
+    ax.add_feature(cartopy.feature.COASTLINE)
+
+    # Levels for contour plot
+    min_level = np.floor(points_df["Diff"].min() * 1000)
+    max_level = np.ceil(points_df["Diff"].max() * 1000)
+    levels = np.arange(min_level, max_level + 1)
+
+    triangulation = tri.Triangulation(points_df["Øst"], points_df["Nord"])
+    triangles = triangulation.triangles
+
+    # Mask off unwanted triangles across the Great Belt
+    east_tri = points_df.Øst.values[triangles] - np.roll(
+        points_df.Øst.values[triangles], 1, axis=1
+    )
+    north_tri = points_df.Nord.values[triangles] - np.roll(
+        points_df.Nord.values[triangles], 1, axis=1
+    )
+    maxi = np.max(np.sqrt(east_tri**2 + north_tri**2), axis=1)
+
+    triangulation.set_mask(maxi > 0.80)
+
+    ax.tricontour(
+        triangulation,
+        points_df["Diff"] * 1000,
+        levels,
+        linewidths=0.5,
+        colors="k",
+        transform=crs,
+    )
+
+    contour_regions = ax.tricontourf(
+        triangulation,
+        points_df["Diff"] * 1000,
+        levels,
+        transform=crs,
+    )
+
+    ax.plot(points_df["Øst"], points_df["Nord"], "ko", ms=2, transform=crs)
+
+    fig.colorbar(contour_regions, ax=ax, label=r"$\Delta$H [mm] (recalculated - DVR90)")
+
+    gl = ax.gridlines(crs=crs, draw_labels=True)
+    gl.top_labels = False
+    gl.right_labels = False
+    gl.xlocator = mticker.FixedLocator([8, 9, 10, 11, 12, 13, 14])
+    gl.ylocator = mticker.FixedLocator([55, 56, 57, 58, 59])
+    gl.xformatter = LONGITUDE_FORMATTER
+    gl.yformatter = LATITUDE_FORMATTER
+
+    ax.set_xlim([7.9, 13.1])
+    ax.set_ylim([54.3, 58.1])
+
+    plt.savefig(outputfolder / f"{fire_project}.png")
+    plt.close()