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9953d1a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | import io
import pandas as pd
import geopandas
import mercury as mr
import matplotlib.pyplot as plt
import contextily as cx
import numpy as np
def adjust_coordinates(row):
row.lat += np.random.normal(0, 2e-2)
row.lon += np.random.normal(0, 2e-2)
return row
def load_data():
df_receiver = pd.read_csv("../receiver.csv", encoding="latin-1", delimiter=";")
df_fishes = pd.read_excel("../20230310_Receiver_Polen.xlsx")
df_stoer_ids = pd.read_excel("../20230310_Receiver_Polen.xlsx",
sheet_name = "eingesetzte Akustik-Tags")
df_fishes_vemco = pd.read_excel("../Daten Vemco Receiver.xlsx", decimal=',').copy()
#df_stoer_ids_vemco = pd.read_excel("Daten Vemco Receiver.xlsx",)
#sheet_name = "eingesetzte Akustik-Tags")
df_fishes_vemco = df_fishes_vemco.drop(columns=["Longitude", "Latitude"])
df_receiver_vemco = pd.read_csv("../Sturgeons.txt",
delimiter=",").copy()
df_fishes_vemco_m = pd.merge(left=df_fishes_vemco, right=df_receiver_vemco, how="left", on="Receiver", suffixes=("", "_y"))
receiver = df_receiver.copy()
receiver_vemco = df_receiver_vemco.copy()
receiver_formatted = receiver_vemco.rename(columns={
"Receiver" : "receiver_sn",
"Latitude": "lat",
"Longitude": "lon",
"Station.name" : "station_name"})[["receiver_sn",
"lat", "lon",
"station_name"]]
all_receivers = pd.concat([receiver, receiver_formatted])
fishes = df_fishes.copy()
fishes = fishes.loc[fishes[" ID"].isin(df_stoer_ids["ID"])]
fishes = fishes.rename(columns={" Receiver":"receiver_sn"})
fishes = pd.merge(fishes, all_receivers[["lon", "lat", "receiver_sn"]],
on="receiver_sn", how="left")
df_fishes_vemco_form = df_fishes_vemco_m.drop(columns="ID")
df_fishes_vemco_form = df_fishes_vemco_form.rename(columns={"Date.and.Time.nocor": "Date and Time (UTC)",
"Longitude" : "lon",
"Latitude": "lat",
"Transmitter": " ID",
"Receiver" : "receiver_sn"
})
all_fishes = pd.concat([fishes, df_fishes_vemco_form])
all_fishes = all_fishes.drop_duplicates([" ID", "receiver_sn"]).reset_index().drop(columns="index")
all_fishes.index = list(all_fishes.index)
all_fishes = all_fishes.apply(adjust_coordinates, axis=1)
gdf_fishes = geopandas.GeoDataFrame(
all_fishes, geometry=geopandas.points_from_xy(
all_fishes.lon, all_fishes.lat), crs = 4326)
receiver_vemco = df_receiver_vemco.copy()
df_wm = gdf_fishes.to_crs(epsg=3857)
gdf_receiver = geopandas.GeoDataFrame(
all_receivers,
geometry=geopandas.points_from_xy(all_receivers.lon, all_receivers.lat),
crs = 4326)
gdf_receiver['coords'] = gdf_receiver.to_crs(epsg=3857)['geometry'].apply(
lambda x: x.representative_point().coords[:])
gdf_receiver['coords'] = [coords[0] for coords in gdf_receiver['coords']]
return gdf_receiver, df_wm
def dontcall():
gdf_receiver = pd.merge(gdf_receiver,
df_wm.groupby("receiver_sn")["ID"].count().reset_index().rename(columns={" ID": "fish_count"}),
on="receiver_sn", how="left")
#gdf_receiver = gdf_receiver.rename(columns={" ID_x": "fish_count"})
gdf_receiver.fillna(0, inplace=True)
gdf_receiver.drop_duplicates("receiver_sn")
ax = df_wm.plot(figsize=(20, 20), alpha=1, edgecolor='k')
gdf_receiver.drop_duplicates("receiver_sn")[:7].to_crs(epsg=3857).plot(ax=ax, marker="^",
markersize=300, color="r",
alpha=.45,
label="Receiver Station")
gdf_receiver.drop_duplicates("receiver_sn")[7:].to_crs(epsg=3857).plot(ax=ax, marker="^",
markersize=100, color="g",
alpha=.45,
label="Receiver Station Polen")
cx.add_basemap(ax)
for idx, row in gdf_receiver.drop_duplicates("receiver_sn").iterrows():
plt.annotate(text=int(row["fish_count"]), xy=row['coords'],
horizontalalignment=str(np.random.choice(['left'])), size=20)
ax = gdf_receiver[:7].to_crs(epsg=3857).plot( marker="^", markersize=300, color="r", alpha=.6, label="Receiver Station", figsize=(15, 20))
ax = gdf_receiver[7:].to_crs(epsg=3857).plot( ax=ax, marker="^", markersize=150, color="b", alpha=.6, label="Receiver Station Vemco", figsize=(15, 20))
df_wm.loc[df_wm[" ID"].isin(only_one.index)].plot(ax=ax, cmap="tab20b", markersize=100, figsize=(15, 20), alpha=.7, edgecolor='k', categorical=True, column=" ID", legend=True, legend_kwds={'bbox_to_anchor':(1, 1.05),'fontsize':16,'frameon':True}, label="an einem Receiver")
df_wm.loc[df_wm[" ID"].isin(two_to_four.index)].plot(cmap="tab20c", markersize=100, marker="s", ax=ax, figsize=(15, 20), alpha=.8, edgecolor='k', categorical=True, column=" ID", legend=False, legend_kwds={'bbox_to_anchor':(1, 1.05),'fontsize':16,'frameon':True}, label="an zwei bis vier Receivern")
df_wm.loc[df_wm[" ID"].isin(more_than_four.index)].plot( ax=ax, markersize=120, marker="d", figsize=(15, 20), alpha=.7, edgecolor='k', categorical=True, column=" ID", legend=False, legend_kwds={'bbox_to_anchor':(1, 1.05),'fontsize':16,'frameon':True}, label="an mehr als vier Receivern")
cx.add_basemap(ax)
ax.legend(loc="upper right")
for idx, row in gdf_receiver.iterrows():
plt.annotate(text=str(" "), xy=row['coords'],
horizontalalignment='left', size=20)
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