Commit edffa4cd authored by Biersteker, Levi's avatar Biersteker, Levi
Browse files

added mnp_hokaggregation.py

added aggregation plot tool for preparing plots to be used during manual validation validation of mnp runs
parent a282c69a
......@@ -27,6 +27,66 @@ def rand_web_color_hex():
return "#{}".format(rgb)
def fix_bt(code_in, as_mnp=False):
"""
Parser for Beheertype codes to repair abbreviated codes and/or MNP extension
eg. 02.01 --> N02.01 if as_mnp==False
02.01 --> N01.01.00 if as_mnp==True
N05.01 --> N05.01 if as_mnp==False
N05.01 --> N05.01.00 if as_mnp==True
N04.02.01 --> N04.02.01 if as_mnp==False
N04.02.01 --> N04.02.01 if as_mnp==True
BT Code is as follows <N><AA>.<BB>.<CC>
N = letter, may be missing
A = two digits indicating top level beheertype "TOP"
B = two digits indicating sub level beheertype "SUB"
C = two digits indicating neergeschaald beheertype if > 00. Else "00" as used in MNP param notation "NEER"
N04.02.00 is equivalent to N04.02
:param code_in: beheertypecode in any format
:param as_mnp: return MNP style beheertypecode, default=False
:return: beheertypecode
"""
if isinstance(code_in, numbers.Number):
code_in = str(code_in)
# Identify parts of code
parts = code_in.split(".")
if len(parts) == 1:
raise Exception("Unexpected BT code: {}".format(code_in))
elif len(parts) == 2:
top, sub = parts
neer = None
elif len(parts) == 3:
top, sub, neer = parts
# Verify TOP and add "N" is required
if top.isdigit():
top = "N{0}".format(top.zfill(2))
else:
assert top[0].isupper(), "Unexpected BT Code format: {}".format(code_in)
# Verify SUB
assert sub.isdigit and len(sub) == 2, "Unexpected BT Code format: {}".format(
code_in
)
# Verify NEER and check if it is neergeschaald or not
keep_neer = False
if neer:
assert neer.isdigit and len(neer) == 2, "Unexpected BT Code format: {}".format(
code_in
)
if int(neer) > 0:
keep_neer = True
# Construct output
head = "{}.{}".format(top, sub)
if keep_neer:
return "{}.{}".format(head, neer)
elif as_mnp:
return "{}.00".format(head)
else:
return head
def pnt2square_id(east_north, size, of="full"):
......@@ -69,6 +129,21 @@ def pnt2square_id(east_north, size, of="full"):
raise
def draagkracht_ww(frequentie, trouw, th):
"""
Draagkracht formule volgens Wieger Wamelink. Zie MS Teams discussie 16-07-2020 MNP2020/Fase 7 Objectivering Draagk..
:param frequentie: gemiddelde frequentie van soort X binnen beheertype Y
:param trouw: gemiddelde trouw van soort X binnen beheertype Y
:param th: minumum draagkracht value
:return: draagkracht soort X - beheertype Y combinatie, gemaximaliseerd tot 1.
"""
dk = np.min([np.multiply(np.divide(np.max([frequentie, trouw]), 100), 5), 1])
if dk >= th:
return dk
else:
return np.nan
def gen_squares(x_ul, y_ul, nrow, ncol, size):
"""
Generate geodataframe with square polygons
......
import rasterio as rio
from rasterio.plot import show
import pandas as pd
import geopandas as gp
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
import os
def mnp_hokaggregation(hok_csv, hokken_polygon_path, background_raster_path, outputdir):
"""
make aggregationplots for use in manual validation of mnp runs. can both aggregate validation data and species
model gridcells per hok.
:param hok_csv: csv file with value per hok
:param hokken_polygon_path: path to hokken polygon file
:param background_raster_path: path to raster file to be used as background. raster should only contain on value
lower than or equal to 0, and 1's.
:param outputdir: directory to save plot
:return:
"""
# load hokken
hokken = gp.read_file(hokken_polygon_path)
if "ID" not in hokken.columns:
raise KeyError("No hok identifiercolumn called ID column in shapefile")
# load and prepare background raster
rast = rio.open(background_raster_path)
background = rast.read(1)
background[background < 0] = 0
cmp = colors.ListedColormap([[0.97, 0.97, 0.97, 1], [0.8, 0.8, 0.8, 1]])
# Load counts data and shapes
counts = pd.read_csv(hok_csv)
colnames = list(counts.columns)
colnames[colnames.index("UH_ID")] = "ID"
counts.columns = colnames
counthokken = pd.merge(hokken, counts, on="ID")
if "COUNT_KEY_VALUE" in colnames:
bounds = np.array([1, 10, 100, 1000, 10000, 40000])
explabel = "_"
valuecolumn = "COUNT_KEY_VALUE"
extend = "neither"
ylabel_text = "Number of gridcells in Hok"
elif "OPS_COUNT" in colnames:
bounds = np.array([1, 10, 50, 100, 500, 1000])
explabel = "_validation"
valuecolumn = "OPS_COUNT"
extend = "max"
ylabel_text = "number of observations in NDFF"
else:
raise Exception("no value column in columns")
# Make the figure and plot, you know the drill
plt.ioff()
fig, ax = plt.subplots(figsize=(15, 15))
plt.axis("off")
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size=0.25, pad=0.1)
show(background, transform=rast.transform, ax=ax, cmap=cmp)
counthokken.plot(
ax=ax,
alpha=0.8,
column=valuecolumn,
cmap="YlOrRd",
edgecolor="darkgray",
norm=colors.BoundaryNorm(boundaries=bounds, ncolors=256, extend=extend),
linewidth=0.8,
legend=True,
cax=cax,
)
# cax is a seperate axes dedicated tot the colorbar legend on the right
cax.set_ylabel(ylabel_text, size=20, fontweight="semibold")
cax.tick_params(labelsize=15)
# make outputdir if not exists and save figure
outputdir = outputdir
if not os.path.exists(outputdir):
os.makedirs(outputdir)
plt.savefig(
outputdir + "/" + os.path.basename(hok_csv)[:-4] + ".png", bbox_inches="tight", dpi=400
)
fig.clear()
plt.close(fig)
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