Readme and Jupyter notebook added

notebook/utils/__init__.py

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+import matplotlib as mpl
+
+mpl.rcParams["figure.dpi"] = 150
+
+from .generate_inputs import *
+from .plot_luminosity import *
+from .plot_population import *
+from .plot_temperature import *
+from .animate import *

notebook/utils/animate.py

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+import warnings
+
+from matplotlib import animation
+from matplotlib import colors as cls
+from matplotlib import pyplot as plt
+from matplotlib import colorbar as cb
+from matplotlib import cm as cm
+import matplotlib
+import numpy as np
+
+from IPython.display import HTML
+
+import lue.data_model as ldm
+import campo
+
+
+warnings.filterwarnings("ignore", category=matplotlib.MatplotlibDeprecationWarning)
+
+
+def animate(show_notebook=True):
+    # import data
+    dataset = ldm.open_dataset("daisy_world.lue")
+    field_dataframe = campo.dataframe.select(
+        dataset.climate, property_names=["temperature"]
+    )
+    daisy_dataframe = campo.dataframe.select(
+        dataset.daisies, property_names=["age", "mask", "breed"]
+    )
+    x_coords = daisy_dataframe["daisies"]["site"]["breed"]["coordinates"][:, 0].data
+    y_coords = daisy_dataframe["daisies"]["site"]["breed"]["coordinates"][:, 1].data
+
+    nr_timesteps = field_dataframe["climate"]["surface"]["temperature"][0].shape[0]
+
+    field_property = field_dataframe["climate"]["surface"]["temperature"]
+
+    # get metadata
+    cellsize = 10
+    minx = field_property[0].xcoord[0].data
+    miny = field_property[0].ycoord[0].data
+    maxx = field_property[0].xcoord[-1].data + cellsize
+    maxy = field_property[0].ycoord[-1].data + cellsize
+    extent = (minx, maxx, miny, maxy)
+
+    #########
+
+    # create the figure
+    f = plt.figure()
+    # [left, bottom, width, height]
+    ax1 = f.add_axes([0.8, 0.05, 0.1, 0.9])
+    ax2 = f.add_axes([0.05, 0.05, 0.7, 0.9])
+    plt.axis("off")
+
+    # making normalization scheme between -30 and 80 degrees Celcius
+    norm = cls.Normalize(vmin=-30, vmax=80)
+    cmap = cm.coolwarm
+    # and corresponding legend
+    leg = cb.ColorbarBase(ax1, cmap=cmap, norm=norm, orientation="vertical")
+    leg.set_label("Temperature (degrees Celcius)")
+
+    # no agent colour receives full transparency
+    # acmap = cm.get_cmap("binary").copy()  # DID NOT WORK FOR ME (MY MPL VERSION?)
+    acmap = cm.binary
+    acmap.set_under("green", alpha=0.0)
+
+    # use imshow to plot the field over time
+    # and scatter to plot the agents
+    def init():
+        # temperature field
+        field_values = field_dataframe["climate"]["surface"]["temperature"][0][0].data
+        im = ax2.imshow(
+            field_values, norm=norm, extent=extent, cmap=cmap, animated=True
+        )
+
+        # daisies, mask the alives
+        agent_values = daisy_dataframe["daisies"]["site"]["breed"]["values"][:, 0]
+        agent_active = daisy_dataframe["daisies"]["site"]["mask"]["values"][:, 0]
+        agent_values = np.where(agent_active == 1, agent_values, -99)
+        agents = ax2.scatter(
+            x_coords,
+            y_coords,
+            marker="o",
+            c=agent_values,
+            s=50,
+            cmap=acmap,
+            vmin=0,
+            vmax=1,
+            animated=True,
+        )
+
+        # time label
+        month = ax2.text(0.02, 0.975, "step = 0", transform=ax2.transAxes, fontsize=10)
+        return im, agents, month
+
+    def animate_ts(i):
+        ax2.clear()
+        ax2.axis("off")
+
+        # temperature field
+        field_values = field_dataframe["climate"]["surface"]["temperature"][0][i].data
+        im = ax2.imshow(
+            field_values, norm=norm, extent=extent, cmap=cmap, animated=True
+        )
+
+        # daisies, mask the alives
+        agent_values = daisy_dataframe["daisies"]["site"]["breed"]["values"][:, i]
+        agent_active = daisy_dataframe["daisies"]["site"]["mask"]["values"][:, i]
+        agent_values = np.where(agent_active == 1, agent_values, -99)
+
+        agents = ax2.scatter(
+            x_coords,
+            y_coords,
+            marker="o",
+            c=agent_values,
+            s=50,
+            cmap=acmap,
+            vmin=0,
+            vmax=1,
+            animated=True,
+        )
+
+        # time label
+        month = ax2.text(
+            0.02, 0.975, f"step = {i+1}", transform=ax2.transAxes, fontsize=10
+        )
+
+        return im, agents, month
+
+    im_ani = animation.FuncAnimation(
+        f, animate_ts, interval=75, blit=True, frames=nr_timesteps, init_func=init
+    )
+    # im_ani.save("im.mp4", dpi=100,  metadata={"artist":"Judith Verstegen"})
+
+    if show_notebook:
+        plt.close(im_ani._fig)
+        return HTML(im_ani.to_html5_video())
+    else:
+        plt.show()
+
+
+if __name__ == "__main__":
+    animate(False)

notebook/utils/generate_inputs.py

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+def generate_input_files(rows=30, cols=30):
+    cellsize = 10
+    x0 = 0
+    y0 = 0
+
+    x1 = x0 + cols * cellsize
+    y1 = y0 + rows * cellsize
+
+    with open("area_extent.csv", "w") as content:
+        content.write(f"{x0},{y0},{x1},{y1},{cols},{rows}")
+
+    agent_id = 0
+
+    with open("location_daisies.csv", "w") as content:
+        for r in range(rows, 0, -1):
+            r -= 1
+            for c in range(cols):
+                x = x0 + c * cellsize + cellsize / 2
+                y = y0 + r * cellsize + cellsize / 2
+                content.write(f"{x},{y}\n")
+                agent_id += 1
+
+
+if __name__ == "__main__":
+    generate_input_files(19, 19)

notebook/utils/plot_luminosity.py

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+import matplotlib.pyplot as plt
+
+import lue.data_model as ldm
+import campo
+
+
+def plot_luminosity():
+    dataset = ldm.open_dataset("daisy_world.lue")
+    prop = campo.dataframe.select(dataset.climate, property_names=["solar_luminosity"])
+
+    values = prop["climate"]["surface"]["solar_luminosity"][0].data[:, 0, 0]
+
+    plt.plot(values)
+    plt.ylabel("Solar luminosity (-)")
+    plt.xlabel("Time step")
+    plt.show()
+
+
+if __name__ == "__main__":
+    plot_luminosity()

notebook/utils/plot_population.py

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+import numpy
+
+import matplotlib.pyplot as plt
+
+import lue.data_model as ldm
+import campo
+
+
+def plot_population():
+    dataset = ldm.open_dataset("daisy_world.lue")
+
+    daisy_dataframe = campo.dataframe.select(
+        dataset.daisies, property_names=["mask", "breed"]
+    )
+
+    breed = daisy_dataframe["daisies"]["site"]["breed"]["values"]
+    mask = daisy_dataframe["daisies"]["site"]["mask"]["values"]
+
+    nr_timesteps = breed.shape[1]
+    nr_agents = breed.shape[0]
+
+    daisy_b = numpy.zeros(nr_timesteps)
+    daisy_w = numpy.zeros(nr_timesteps)
+
+    for ts in range(0, nr_timesteps):
+        breed_ts = breed[:, ts]
+        mask_ts = mask[:, ts]
+        breeds_alive = numpy.ma.masked_where(mask_ts != 1, breed_ts)
+        w = numpy.ma.masked_where(breeds_alive == 1, numpy.ones(breed.shape[0]))
+        b = numpy.ma.masked_where(breeds_alive == 0, numpy.ones(breed.shape[0]))
+        daisy_b[ts] = len(numpy.ma.compressed(b))
+        daisy_w[ts] = len(numpy.ma.compressed(w))
+
+    plt.plot(daisy_b, label="black daisies", color="k")
+    plt.plot(daisy_w, label="white daisies", color="lightgrey")
+    plt.ylim([0, nr_agents])
+    plt.ylabel("Population size (#)")
+    plt.xlabel("Time step")
+    plt.legend()
+    plt.show()
+
+
+if __name__ == "__main__":
+    plot_population()

notebook/utils/plot_temperature.py

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+import numpy
+import matplotlib.pyplot as plt
+
+import lue.data_model as ldm
+import campo
+
+
+def plot_temperature():
+    dataset = ldm.open_dataset("daisy_world.lue")
+    init_properties = campo.dataframe.select(
+        dataset.climate, property_names=["init_temp"]
+    )
+    properties = campo.dataframe.select(dataset.climate, property_names=["temperature"])
+
+    t_init = init_properties["climate"]["surface"]["init_temp"][0].data
+    t_init_mean = numpy.mean(t_init)
+
+    values = properties["climate"]["surface"]["temperature"][0].data
+
+    nr_timesteps = values.shape[0]
+
+    avg_temp = numpy.zeros(nr_timesteps + 1)
+
+    avg_temp[0] = t_init_mean
+
+    for ts in range(0, nr_timesteps):
+        avg_temp[ts + 1] = numpy.mean(values[ts])
+
+    plt.plot(avg_temp)
+    plt.ylabel("Temperature (degrees Celcius)")
+    plt.xlabel("Time step")
+    plt.show()
+
+
+if __name__ == "__main__":
+    plot_temperature()