Update heatmap of recorded phone sensors
Meng Li
parent
bc06477d89
commit
cefcb0635b
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@ -14,7 +14,8 @@ rule heatmap_sensors_per_minute_per_time_segment:
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participant_file = "data/external/participant_files/{pid}.yaml",
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time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
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params:
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pid = "{pid}"
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pid = "{pid}",
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time_segments_type = config["TIME_SEGMENTS"]["TYPE"]
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output:
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"reports/interim/{pid}/heatmap_sensors_per_minute_per_time_segment.html"
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script:
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@ -26,7 +26,7 @@ features = pd.read_csv(snakemake.input["all_sensor_features"])
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if time_segments_type == "FREQUENCY":
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features["local_segment_label"] = features["local_segment_label"].str.split("\d+", expand=True, n=1)[0]
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features["local_segment_label"] = features["local_segment_label"].str.replace(r"[0-9]{4}", "")
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if time_segments_type == "EVENT":
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features["local_segment_label"] = "event"
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@ -1,4 +1,3 @@
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from plotly_color_utils import sample_colorscale
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import pandas as pd
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import plotly.express as px
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import yaml
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@ -59,7 +58,7 @@ time_segments = pd.read_csv(snakemake.input["time_segments_file"])["label"].uniq
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phone_data_yield = pd.read_csv(snakemake.input["phone_data_yield"], parse_dates=["local_segment_start_datetime", "local_segment_end_datetime"]).sort_values(by=["pid", "local_segment_start_datetime"])
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if time_segments_type == "FREQUENCY":
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phone_data_yield["local_segment_label"] = phone_data_yield["local_segment_label"].str.split("\d+", expand=True, n=1)[0]
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phone_data_yield["local_segment_label"] = phone_data_yield["local_segment_label"].str.replace(r"[0-9]{4}", "")
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html_file = open(snakemake.output[0], "w", encoding="utf-8")
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if phone_data_yield.empty:
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@ -5,6 +5,11 @@ from importlib import util
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from pathlib import Path
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import yaml
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# import filter_data_by_segment from src/features/utils/utils.py
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spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "features" / "utils" / "utils.py"))
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mod = util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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filter_data_by_segment = getattr(mod, "filter_data_by_segment")
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def colors2colorscale(colors):
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colorscale = []
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@ -16,85 +21,83 @@ def colors2colorscale(colors):
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colorscale.append([1, colors[i]])
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return colorscale
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def getSensorsPerMinPerSegmentHeatmap(phone_data_yield, pid, time_segment, html_file):
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x_axis_labels = [pd.Timedelta(minutes=x) for x in phone_data_yield.columns]
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fig = go.Figure(data=go.Heatmap(z=phone_data_yield.values.tolist(),
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x=x_axis_labels,
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y=phone_data_yield.index,
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zmin=0, zmax=16,
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colorscale=colors2colorscale(colors),
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colorbar=dict(thickness=25, tickvals=[1/2 + x for x in range(16)],ticktext=[x for x in range(16)])))
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fig.update_layout(title="Number of sensors with any data per minute for " + time_segment + " segments. Pid: "+pid+". Label: " + label + "<br>y-axis shows the start (date and time) of a time segment.<br>x-axis shows the time since the start of the time segment.<br>z-axis (color) shows how many sensors logged at least one row of data per minute.")
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fig["layout"].update(margin=dict(t=160))
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html_file.write(fig.to_html(full_html=False, include_plotlyjs="cdn"))
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# import filter_data_by_segment from src/features/utils/utils.py
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spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "features" / "utils" / "utils.py"))
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mod = util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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filter_data_by_segment = getattr(mod, "filter_data_by_segment")
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def getDataForPlot(phone_data_yield_per_segment):
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# calculate the length (in minute) of per segment instance
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phone_data_yield_per_segment["length"] = phone_data_yield_per_segment["timestamps_segment"].str.split(",").apply(lambda x: int((int(x[1])-int(x[0])) / (1000 * 60)))
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# calculate the number of sensors logged at least one row of data per minute.
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phone_data_yield_per_segment = phone_data_yield_per_segment.groupby(["local_segment", "length", "local_date", "local_hour", "local_minute"])[["sensor", "local_date_time"]].max().reset_index()
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# extract local start datetime of the segment from "local_segment" column
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phone_data_yield_per_segment["local_segment_start_datetimes"] = pd.to_datetime(phone_data_yield_per_segment["local_segment"].apply(lambda x: x.split("#")[1].split(",")[0]))
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# calculate the number of minutes after local start datetime of the segment
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phone_data_yield_per_segment["minutes_after_segment_start"] = ((phone_data_yield_per_segment["local_date_time"] - phone_data_yield_per_segment["local_segment_start_datetimes"]) / pd.Timedelta(minutes=1)).astype("int")
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# impute missing rows with 0
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columns_for_full_index = phone_data_yield_per_segment[["local_segment_start_datetimes", "length"]].drop_duplicates(keep="first")
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columns_for_full_index = columns_for_full_index.apply(lambda row: [[row["local_segment_start_datetimes"], x] for x in range(row["length"] + 1)], axis=1)
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full_index = []
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for columns in columns_for_full_index:
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full_index = full_index + columns
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full_index = pd.MultiIndex.from_tuples(full_index, names=("local_segment_start_datetimes", "minutes_after_segment_start"))
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phone_data_yield_per_segment = phone_data_yield_per_segment.set_index(["local_segment_start_datetimes", "minutes_after_segment_start"]).reindex(full_index).reset_index().fillna(0)
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# transpose the dataframe per local start datetime of the segment and discard the useless index layer
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phone_data_yield_per_segment = phone_data_yield_per_segment.groupby("local_segment_start_datetimes")[["minutes_after_segment_start", "sensor"]].apply(lambda x: x.set_index("minutes_after_segment_start").transpose())
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phone_data_yield_per_segment.index = phone_data_yield_per_segment.index.get_level_values("local_segment_start_datetimes")
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return phone_data_yield_per_segment
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def getSensorsPerMinPerSegmentHeatmap(phone_data_yield, pid, label, time_segment, html_file):
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if phone_data_yield.empty:
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html_file.write("There is no sensor data of " + time_segment + " segments for " + pid + " (pid) and " + label + " (label).<br>")
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else:
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phone_data_yield.sort_index(inplace=True)
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x_axis_labels = [pd.Timedelta(minutes=x) for x in phone_data_yield.columns]
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fig = go.Figure(data=go.Heatmap(z=phone_data_yield.values.tolist(),
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x=x_axis_labels,
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y=phone_data_yield.index,
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zmin=0, zmax=16,
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colorscale=colors2colorscale(colors),
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colorbar=dict(thickness=25, tickvals=[1/2 + x for x in range(16)],ticktext=[x for x in range(16)])))
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fig.update_layout(title="Number of sensors with any data per minute for " + time_segment + " segments. Pid: "+pid+". Label: " + label + "<br>y-axis shows the start (date and time) of a time segment.<br>x-axis shows the time since the start of the time segment.<br>z-axis (color) shows how many sensors logged at least one row of data per minute.")
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fig["layout"].update(margin=dict(t=160))
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html_file.write(fig.to_html(full_html=False, include_plotlyjs="cdn"))
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return
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colors = ["red", "#3D0751", "#423176", "#414381", "#3F5688", "#42678B", "#42768C", "#45868B", "#4A968A", "#53A485", "#5FB57E", "#76C170", "#91CF63", "#B4DA55", "#D9E152", "#F8E755", "#DEE00F"]
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pid = snakemake.params["pid"]
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time_segments_labels = pd.read_csv(snakemake.input["time_segments_labels"], header=0)
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time_segments_type = snakemake.params["time_segments_type"]
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time_segments_labels = pd.read_csv(snakemake.input["time_segments_labels"])
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with open(snakemake.input["participant_file"], "r", encoding="utf-8") as f:
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participant_file = yaml.safe_load(f)
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label = participant_file["PHONE"]["LABEL"]
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phone_data_yield = pd.read_csv(snakemake.input["phone_data_yield"], parse_dates=["local_date_time"])
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if time_segments_type == "FREQUENCY":
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phone_data_yield["assigned_segments"] = phone_data_yield["assigned_segments"].str.replace(r"[0-9]{4}#", "#")
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time_segments_labels["label"] = time_segments_labels["label"].str.replace(r"[0-9]{4}", "")
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if time_segments_type == "PERIODIC":
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phone_data_yield["assigned_segments"] = phone_data_yield["assigned_segments"].str.replace(r"_RR\d+SS#", "#")
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time_segments_labels["label"] = time_segments_labels["label"].str.replace(r"_RR\d+SS", "")
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html_file = open(snakemake.output[0], "a", encoding="utf-8")
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if phone_data_yield.empty:
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html_file.write("There is no sensor data for " + pid + " (pid) and " + label + " (label).")
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else:
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for time_segment in time_segments_labels["label"]:
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data_for_plot = pd.DataFrame()
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for time_segment in set(time_segments_labels["label"]):
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phone_data_yield_per_segment = filter_data_by_segment(phone_data_yield, time_segment)
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if phone_data_yield_per_segment.empty:
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html_file.write("There is no sensor data of " + time_segment + " segments for " + pid + " (pid) and " + label + " (label).<br>")
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else:
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# calculate the length (in minute) of per segment instance
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phone_data_yield_per_segment["length"] = phone_data_yield_per_segment["timestamps_segment"].str.split(",").apply(lambda x: int((int(x[1])-int(x[0])) / (1000 * 60)))
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# calculate the number of sensors logged at least one row of data per minute.
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phone_data_yield_per_segment = phone_data_yield_per_segment.groupby(["local_segment", "length", "local_date", "local_hour", "local_minute"])[["sensor", "local_date_time"]].max().reset_index()
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# extract local start datetime of the segment from "local_segment" column
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phone_data_yield_per_segment["local_segment_start_datetimes"] = pd.to_datetime(phone_data_yield_per_segment["local_segment"].apply(lambda x: x.split("#")[1].split(",")[0]))
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# calculate the number of minutes after local start datetime of the segment
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phone_data_yield_per_segment["minutes_after_segment_start"] = ((phone_data_yield_per_segment["local_date_time"] - phone_data_yield_per_segment["local_segment_start_datetimes"]) / pd.Timedelta(minutes=1)).astype("int")
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# impute missing rows with 0
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columns_for_full_index = phone_data_yield_per_segment[["local_segment_start_datetimes", "length"]].drop_duplicates(keep="first")
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columns_for_full_index = columns_for_full_index.apply(lambda row: [[row["local_segment_start_datetimes"], x] for x in range(row["length"] + 1)], axis=1)
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full_index = []
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for columns in columns_for_full_index:
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full_index = full_index + columns
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full_index = pd.MultiIndex.from_tuples(full_index, names=("local_segment_start_datetimes", "minutes_after_segment_start"))
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phone_data_yield_per_segment = phone_data_yield_per_segment.set_index(["local_segment_start_datetimes", "minutes_after_segment_start"]).reindex(full_index).reset_index().fillna(0)
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# transpose the dataframe per local start datetime of the segment and discard the useless index layer
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phone_data_yield_per_segment = phone_data_yield_per_segment.groupby("local_segment_start_datetimes")[["minutes_after_segment_start", "sensor"]].apply(lambda x: x.set_index("minutes_after_segment_start").transpose())
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phone_data_yield_per_segment.index = phone_data_yield_per_segment.index.get_level_values("local_segment_start_datetimes")
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# get heatmap
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getSensorsPerMinPerSegmentHeatmap(phone_data_yield_per_segment, pid, time_segment, html_file)
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if not phone_data_yield_per_segment.empty:
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data_for_plot_per_segment = getDataForPlot(phone_data_yield_per_segment)
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if time_segments_type == "EVENT":
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data_for_plot = pd.concat([data_for_plot, data_for_plot_per_segment], axis=0)
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else:
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getSensorsPerMinPerSegmentHeatmap(data_for_plot_per_segment, pid, label, time_segment, html_file)
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if time_segments_type == "EVENT":
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getSensorsPerMinPerSegmentHeatmap(data_for_plot, pid, label, "event", html_file)
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html_file.close()
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@ -6,7 +6,7 @@ time_segments_type = snakemake.params["time_segments_type"]
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phone_data_yield = pd.read_csv(snakemake.input[0])
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if time_segments_type == "FREQUENCY":
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phone_data_yield["local_segment_label"] = phone_data_yield["local_segment_label"].str.split("\d+", expand=True, n=1)[0]
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phone_data_yield["local_segment_label"] = phone_data_yield["local_segment_label"].str.replace(r"[0-9]{4}", "")
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if time_segments_type == "EVENT":
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phone_data_yield["local_segment_label"] = "event"
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@ -1,835 +0,0 @@
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"""
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Source: https://github.com/plotly/plotly.py/pull/3136
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=====
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colors
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=====
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Functions that manipulate colors and arrays of colors.
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-----
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There are three basic types of color types: rgb, hex and tuple:
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rgb - An rgb color is a string of the form 'rgb(a,b,c)' where a, b and c are
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integers between 0 and 255 inclusive.
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hex - A hex color is a string of the form '#xxxxxx' where each x is a
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character that belongs to the set [0,1,2,3,4,5,6,7,8,9,a,b,c,d,e,f]. This is
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just the set of characters used in the hexadecimal numeric system.
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tuple - A tuple color is a 3-tuple of the form (a,b,c) where a, b and c are
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floats between 0 and 1 inclusive.
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-----
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Colormaps and Colorscales:
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A colormap or a colorscale is a correspondence between values - Pythonic
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objects such as strings and floats - to colors.
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There are typically two main types of colormaps that exist: numerical and
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categorical colormaps.
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Numerical:
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----------
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Numerical colormaps are used when the coloring column being used takes a
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spectrum of values or numbers.
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A classic example from the Plotly library:
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```
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rainbow_colorscale = [
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[0, 'rgb(150,0,90)'], [0.125, 'rgb(0,0,200)'],
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[0.25, 'rgb(0,25,255)'], [0.375, 'rgb(0,152,255)'],
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[0.5, 'rgb(44,255,150)'], [0.625, 'rgb(151,255,0)'],
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[0.75, 'rgb(255,234,0)'], [0.875, 'rgb(255,111,0)'],
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[1, 'rgb(255,0,0)']
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]
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```
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Notice that this colorscale is a list of lists with each inner list containing
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a number and a color. These left hand numbers in the nested lists go from 0 to
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1, and they are like pointers tell you when a number is mapped to a specific
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color.
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If you have a column of numbers `col_num` that you want to plot, and you know
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```
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min(col_num) = 0
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max(col_num) = 100
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```
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then if you pull out the number `12.5` in the list and want to figure out what
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color the corresponding chart element (bar, scatter plot, etc) is going to be,
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you'll figure out that proportionally 12.5 to 100 is the same as 0.125 to 1.
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So, the point will be mapped to 'rgb(0,0,200)'.
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All other colors between the pinned values in a colorscale are linearly
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interpolated.
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Categorical:
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------------
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Alternatively, a categorical colormap is used to assign a specific value in a
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color column to a specific color everytime it appears in the dataset.
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A column of strings in a panadas.dataframe that is chosen to serve as the
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color index would naturally use a categorical colormap. However, you can
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choose to use a categorical colormap with a column of numbers.
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Be careful! If you have a lot of unique numbers in your color column you will
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end up with a colormap that is massive and may slow down graphing performance.
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"""
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from __future__ import absolute_import
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import decimal
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from numbers import Number
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import six
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from _plotly_utils import exceptions
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# Built-in qualitative color sequences and sequential,
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# diverging and cyclical color scales.
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DEFAULT_PLOTLY_COLORS = [
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"rgb(31, 119, 180)",
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"rgb(255, 127, 14)",
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"rgb(44, 160, 44)",
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"rgb(214, 39, 40)",
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"rgb(148, 103, 189)",
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"rgb(140, 86, 75)",
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"rgb(227, 119, 194)",
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"rgb(127, 127, 127)",
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"rgb(188, 189, 34)",
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"rgb(23, 190, 207)",
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]
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PLOTLY_SCALES = {
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"Greys": [[0, "rgb(0,0,0)"], [1, "rgb(255,255,255)"]],
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"YlGnBu": [
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[0, "rgb(8,29,88)"],
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[0.125, "rgb(37,52,148)"],
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[0.25, "rgb(34,94,168)"],
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[0.375, "rgb(29,145,192)"],
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[0.5, "rgb(65,182,196)"],
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[0.625, "rgb(127,205,187)"],
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[0.75, "rgb(199,233,180)"],
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[0.875, "rgb(237,248,217)"],
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[1, "rgb(255,255,217)"],
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],
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"Greens": [
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[0, "rgb(0,68,27)"],
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[0.125, "rgb(0,109,44)"],
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[0.25, "rgb(35,139,69)"],
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[0.375, "rgb(65,171,93)"],
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[0.5, "rgb(116,196,118)"],
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[0.625, "rgb(161,217,155)"],
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[0.75, "rgb(199,233,192)"],
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[0.875, "rgb(229,245,224)"],
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[1, "rgb(247,252,245)"],
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],
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"YlOrRd": [
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[0, "rgb(128,0,38)"],
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[0.125, "rgb(189,0,38)"],
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[0.25, "rgb(227,26,28)"],
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[0.375, "rgb(252,78,42)"],
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[0.5, "rgb(253,141,60)"],
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[0.625, "rgb(254,178,76)"],
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[0.75, "rgb(254,217,118)"],
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[0.875, "rgb(255,237,160)"],
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[1, "rgb(255,255,204)"],
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],
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"Bluered": [[0, "rgb(0,0,255)"], [1, "rgb(255,0,0)"]],
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# modified RdBu based on
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# www.sandia.gov/~kmorel/documents/ColorMaps/ColorMapsExpanded.pdf
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"RdBu": [
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||||
[0, "rgb(5,10,172)"],
|
||||
[0.35, "rgb(106,137,247)"],
|
||||
[0.5, "rgb(190,190,190)"],
|
||||
[0.6, "rgb(220,170,132)"],
|
||||
[0.7, "rgb(230,145,90)"],
|
||||
[1, "rgb(178,10,28)"],
|
||||
],
|
||||
# Scale for non-negative numeric values
|
||||
"Reds": [
|
||||
[0, "rgb(220,220,220)"],
|
||||
[0.2, "rgb(245,195,157)"],
|
||||
[0.4, "rgb(245,160,105)"],
|
||||
[1, "rgb(178,10,28)"],
|
||||
],
|
||||
# Scale for non-positive numeric values
|
||||
"Blues": [
|
||||
[0, "rgb(5,10,172)"],
|
||||
[0.35, "rgb(40,60,190)"],
|
||||
[0.5, "rgb(70,100,245)"],
|
||||
[0.6, "rgb(90,120,245)"],
|
||||
[0.7, "rgb(106,137,247)"],
|
||||
[1, "rgb(220,220,220)"],
|
||||
],
|
||||
"Picnic": [
|
||||
[0, "rgb(0,0,255)"],
|
||||
[0.1, "rgb(51,153,255)"],
|
||||
[0.2, "rgb(102,204,255)"],
|
||||
[0.3, "rgb(153,204,255)"],
|
||||
[0.4, "rgb(204,204,255)"],
|
||||
[0.5, "rgb(255,255,255)"],
|
||||
[0.6, "rgb(255,204,255)"],
|
||||
[0.7, "rgb(255,153,255)"],
|
||||
[0.8, "rgb(255,102,204)"],
|
||||
[0.9, "rgb(255,102,102)"],
|
||||
[1, "rgb(255,0,0)"],
|
||||
],
|
||||
"Rainbow": [
|
||||
[0, "rgb(150,0,90)"],
|
||||
[0.125, "rgb(0,0,200)"],
|
||||
[0.25, "rgb(0,25,255)"],
|
||||
[0.375, "rgb(0,152,255)"],
|
||||
[0.5, "rgb(44,255,150)"],
|
||||
[0.625, "rgb(151,255,0)"],
|
||||
[0.75, "rgb(255,234,0)"],
|
||||
[0.875, "rgb(255,111,0)"],
|
||||
[1, "rgb(255,0,0)"],
|
||||
],
|
||||
"Portland": [
|
||||
[0, "rgb(12,51,131)"],
|
||||
[0.25, "rgb(10,136,186)"],
|
||||
[0.5, "rgb(242,211,56)"],
|
||||
[0.75, "rgb(242,143,56)"],
|
||||
[1, "rgb(217,30,30)"],
|
||||
],
|
||||
"Jet": [
|
||||
[0, "rgb(0,0,131)"],
|
||||
[0.125, "rgb(0,60,170)"],
|
||||
[0.375, "rgb(5,255,255)"],
|
||||
[0.625, "rgb(255,255,0)"],
|
||||
[0.875, "rgb(250,0,0)"],
|
||||
[1, "rgb(128,0,0)"],
|
||||
],
|
||||
"Hot": [
|
||||
[0, "rgb(0,0,0)"],
|
||||
[0.3, "rgb(230,0,0)"],
|
||||
[0.6, "rgb(255,210,0)"],
|
||||
[1, "rgb(255,255,255)"],
|
||||
],
|
||||
"Blackbody": [
|
||||
[0, "rgb(0,0,0)"],
|
||||
[0.2, "rgb(230,0,0)"],
|
||||
[0.4, "rgb(230,210,0)"],
|
||||
[0.7, "rgb(255,255,255)"],
|
||||
[1, "rgb(160,200,255)"],
|
||||
],
|
||||
"Earth": [
|
||||
[0, "rgb(0,0,130)"],
|
||||
[0.1, "rgb(0,180,180)"],
|
||||
[0.2, "rgb(40,210,40)"],
|
||||
[0.4, "rgb(230,230,50)"],
|
||||
[0.6, "rgb(120,70,20)"],
|
||||
[1, "rgb(255,255,255)"],
|
||||
],
|
||||
"Electric": [
|
||||
[0, "rgb(0,0,0)"],
|
||||
[0.15, "rgb(30,0,100)"],
|
||||
[0.4, "rgb(120,0,100)"],
|
||||
[0.6, "rgb(160,90,0)"],
|
||||
[0.8, "rgb(230,200,0)"],
|
||||
[1, "rgb(255,250,220)"],
|
||||
],
|
||||
"Viridis": [
|
||||
[0, "#440154"],
|
||||
[0.06274509803921569, "#48186a"],
|
||||
[0.12549019607843137, "#472d7b"],
|
||||
[0.18823529411764706, "#424086"],
|
||||
[0.25098039215686274, "#3b528b"],
|
||||
[0.3137254901960784, "#33638d"],
|
||||
[0.3764705882352941, "#2c728e"],
|
||||
[0.4392156862745098, "#26828e"],
|
||||
[0.5019607843137255, "#21918c"],
|
||||
[0.5647058823529412, "#1fa088"],
|
||||
[0.6274509803921569, "#28ae80"],
|
||||
[0.6901960784313725, "#3fbc73"],
|
||||
[0.7529411764705882, "#5ec962"],
|
||||
[0.8156862745098039, "#84d44b"],
|
||||
[0.8784313725490196, "#addc30"],
|
||||
[0.9411764705882353, "#d8e219"],
|
||||
[1, "#fde725"],
|
||||
],
|
||||
"Cividis": [
|
||||
[0.000000, "rgb(0,32,76)"],
|
||||
[0.058824, "rgb(0,42,102)"],
|
||||
[0.117647, "rgb(0,52,110)"],
|
||||
[0.176471, "rgb(39,63,108)"],
|
||||
[0.235294, "rgb(60,74,107)"],
|
||||
[0.294118, "rgb(76,85,107)"],
|
||||
[0.352941, "rgb(91,95,109)"],
|
||||
[0.411765, "rgb(104,106,112)"],
|
||||
[0.470588, "rgb(117,117,117)"],
|
||||
[0.529412, "rgb(131,129,120)"],
|
||||
[0.588235, "rgb(146,140,120)"],
|
||||
[0.647059, "rgb(161,152,118)"],
|
||||
[0.705882, "rgb(176,165,114)"],
|
||||
[0.764706, "rgb(192,177,109)"],
|
||||
[0.823529, "rgb(209,191,102)"],
|
||||
[0.882353, "rgb(225,204,92)"],
|
||||
[0.941176, "rgb(243,219,79)"],
|
||||
[1.000000, "rgb(255,233,69)"],
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
def color_parser(colors, function):
|
||||
"""
|
||||
Takes color(s) and a function and applies the function on the color(s)
|
||||
In particular, this function identifies whether the given color object
|
||||
is an iterable or not and applies the given color-parsing function to
|
||||
the color or iterable of colors. If given an iterable, it will only be
|
||||
able to work with it if all items in the iterable are of the same type
|
||||
- rgb string, hex string or tuple
|
||||
"""
|
||||
if isinstance(colors, str):
|
||||
return function(colors)
|
||||
|
||||
if isinstance(colors, tuple) and isinstance(colors[0], Number):
|
||||
return function(colors)
|
||||
|
||||
if hasattr(colors, "__iter__"):
|
||||
if isinstance(colors, tuple):
|
||||
new_color_tuple = tuple(function(item) for item in colors)
|
||||
return new_color_tuple
|
||||
|
||||
else:
|
||||
new_color_list = [function(item) for item in colors]
|
||||
return new_color_list
|
||||
|
||||
|
||||
def validate_colors(colors, colortype="tuple"):
|
||||
"""
|
||||
Validates color(s) and returns a list of color(s) of a specified type
|
||||
"""
|
||||
from numbers import Number
|
||||
|
||||
if colors is None:
|
||||
colors = DEFAULT_PLOTLY_COLORS
|
||||
|
||||
if isinstance(colors, str):
|
||||
if colors in PLOTLY_SCALES:
|
||||
colors_list = colorscale_to_colors(PLOTLY_SCALES[colors])
|
||||
# TODO: fix _gantt.py/_scatter.py so that they can accept the
|
||||
# actual colorscale and not just a list of the first and last
|
||||
# color in the plotly colorscale. In resolving this issue we
|
||||
# will be removing the immediate line below
|
||||
colors = [colors_list[0]] + [colors_list[-1]]
|
||||
elif "rgb" in colors or "#" in colors:
|
||||
colors = [colors]
|
||||
else:
|
||||
raise exceptions.PlotlyError(
|
||||
"If your colors variable is a string, it must be a "
|
||||
"Plotly scale, an rgb color or a hex color."
|
||||
)
|
||||
|
||||
elif isinstance(colors, tuple):
|
||||
if isinstance(colors[0], Number):
|
||||
colors = [colors]
|
||||
else:
|
||||
colors = list(colors)
|
||||
|
||||
# convert color elements in list to tuple color
|
||||
for j, each_color in enumerate(colors):
|
||||
if "rgb" in each_color:
|
||||
each_color = color_parser(each_color, unlabel_rgb)
|
||||
for value in each_color:
|
||||
if value > 255.0:
|
||||
raise exceptions.PlotlyError(
|
||||
"Whoops! The elements in your rgb colors "
|
||||
"tuples cannot exceed 255.0."
|
||||
)
|
||||
each_color = color_parser(each_color, unconvert_from_RGB_255)
|
||||
colors[j] = each_color
|
||||
|
||||
if "#" in each_color:
|
||||
each_color = color_parser(each_color, hex_to_rgb)
|
||||
each_color = color_parser(each_color, unconvert_from_RGB_255)
|
||||
|
||||
colors[j] = each_color
|
||||
|
||||
if isinstance(each_color, tuple):
|
||||
for value in each_color:
|
||||
if value > 1.0:
|
||||
raise exceptions.PlotlyError(
|
||||
"Whoops! The elements in your colors tuples "
|
||||
"cannot exceed 1.0."
|
||||
)
|
||||
colors[j] = each_color
|
||||
|
||||
if colortype == "rgb" and not isinstance(colors, six.string_types):
|
||||
for j, each_color in enumerate(colors):
|
||||
rgb_color = color_parser(each_color, convert_to_RGB_255)
|
||||
colors[j] = color_parser(rgb_color, label_rgb)
|
||||
|
||||
return colors
|
||||
|
||||
|
||||
def validate_colors_dict(colors, colortype="tuple"):
|
||||
"""
|
||||
Validates dictionary of color(s)
|
||||
"""
|
||||
# validate each color element in the dictionary
|
||||
for key in colors:
|
||||
if "rgb" in colors[key]:
|
||||
colors[key] = color_parser(colors[key], unlabel_rgb)
|
||||
for value in colors[key]:
|
||||
if value > 255.0:
|
||||
raise exceptions.PlotlyError(
|
||||
"Whoops! The elements in your rgb colors "
|
||||
"tuples cannot exceed 255.0."
|
||||
)
|
||||
colors[key] = color_parser(colors[key], unconvert_from_RGB_255)
|
||||
|
||||
if "#" in colors[key]:
|
||||
colors[key] = color_parser(colors[key], hex_to_rgb)
|
||||
colors[key] = color_parser(colors[key], unconvert_from_RGB_255)
|
||||
|
||||
if isinstance(colors[key], tuple):
|
||||
for value in colors[key]:
|
||||
if value > 1.0:
|
||||
raise exceptions.PlotlyError(
|
||||
"Whoops! The elements in your colors tuples "
|
||||
"cannot exceed 1.0."
|
||||
)
|
||||
|
||||
if colortype == "rgb":
|
||||
for key in colors:
|
||||
colors[key] = color_parser(colors[key], convert_to_RGB_255)
|
||||
colors[key] = color_parser(colors[key], label_rgb)
|
||||
|
||||
return colors
|
||||
|
||||
|
||||
def convert_colors_to_same_type(
|
||||
colors,
|
||||
colortype="rgb",
|
||||
scale=None,
|
||||
return_default_colors=False,
|
||||
num_of_defualt_colors=2,
|
||||
):
|
||||
"""
|
||||
Converts color(s) to the specified color type
|
||||
Takes a single color or an iterable of colors, as well as a list of scale
|
||||
values, and outputs a 2-pair of the list of color(s) converted all to an
|
||||
rgb or tuple color type, aswell as the scale as the second element. If
|
||||
colors is a Plotly Scale name, then 'scale' will be forced to the scale
|
||||
from the respective colorscale and the colors in that colorscale will also
|
||||
be coverted to the selected colortype. If colors is None, then there is an
|
||||
option to return portion of the DEFAULT_PLOTLY_COLORS
|
||||
:param (str|tuple|list) colors: either a plotly scale name, an rgb or hex
|
||||
color, a color tuple or a list/tuple of colors
|
||||
:param (list) scale: see docs for validate_scale_values()
|
||||
:rtype (tuple) (colors_list, scale) if scale is None in the function call,
|
||||
then scale will remain None in the returned tuple
|
||||
"""
|
||||
colors_list = []
|
||||
|
||||
if colors is None and return_default_colors is True:
|
||||
colors_list = DEFAULT_PLOTLY_COLORS[0:num_of_defualt_colors]
|
||||
|
||||
if isinstance(colors, str):
|
||||
if colors in PLOTLY_SCALES:
|
||||
colors_list = colorscale_to_colors(PLOTLY_SCALES[colors])
|
||||
if scale is None:
|
||||
scale = colorscale_to_scale(PLOTLY_SCALES[colors])
|
||||
|
||||
elif "rgb" in colors or "#" in colors:
|
||||
colors_list = [colors]
|
||||
|
||||
elif isinstance(colors, tuple):
|
||||
if isinstance(colors[0], Number):
|
||||
colors_list = [colors]
|
||||
else:
|
||||
colors_list = list(colors)
|
||||
|
||||
elif isinstance(colors, list):
|
||||
colors_list = colors
|
||||
|
||||
# validate scale
|
||||
if scale is not None:
|
||||
validate_scale_values(scale)
|
||||
|
||||
if len(colors_list) != len(scale):
|
||||
raise exceptions.PlotlyError(
|
||||
"Make sure that the length of your scale matches the length "
|
||||
"of your list of colors which is {}.".format(len(colors_list))
|
||||
)
|
||||
|
||||
# convert all colors to rgb
|
||||
for j, each_color in enumerate(colors_list):
|
||||
if "#" in each_color:
|
||||
each_color = color_parser(each_color, hex_to_rgb)
|
||||
each_color = color_parser(each_color, label_rgb)
|
||||
colors_list[j] = each_color
|
||||
|
||||
elif isinstance(each_color, tuple):
|
||||
each_color = color_parser(each_color, convert_to_RGB_255)
|
||||
each_color = color_parser(each_color, label_rgb)
|
||||
colors_list[j] = each_color
|
||||
|
||||
if colortype == "rgb":
|
||||
return (colors_list, scale)
|
||||
elif colortype == "tuple":
|
||||
for j, each_color in enumerate(colors_list):
|
||||
each_color = color_parser(each_color, unlabel_rgb)
|
||||
each_color = color_parser(each_color, unconvert_from_RGB_255)
|
||||
colors_list[j] = each_color
|
||||
return (colors_list, scale)
|
||||
else:
|
||||
raise exceptions.PlotlyError(
|
||||
"You must select either rgb or tuple for your colortype variable."
|
||||
)
|
||||
|
||||
|
||||
def convert_dict_colors_to_same_type(colors_dict, colortype="rgb"):
|
||||
"""
|
||||
Converts a colors in a dictionary of colors to the specified color type
|
||||
:param (dict) colors_dict: a dictionary whose values are single colors
|
||||
"""
|
||||
for key in colors_dict:
|
||||
if "#" in colors_dict[key]:
|
||||
colors_dict[key] = color_parser(colors_dict[key], hex_to_rgb)
|
||||
colors_dict[key] = color_parser(colors_dict[key], label_rgb)
|
||||
|
||||
elif isinstance(colors_dict[key], tuple):
|
||||
colors_dict[key] = color_parser(colors_dict[key], convert_to_RGB_255)
|
||||
colors_dict[key] = color_parser(colors_dict[key], label_rgb)
|
||||
|
||||
if colortype == "rgb":
|
||||
return colors_dict
|
||||
elif colortype == "tuple":
|
||||
for key in colors_dict:
|
||||
colors_dict[key] = color_parser(colors_dict[key], unlabel_rgb)
|
||||
colors_dict[key] = color_parser(colors_dict[key], unconvert_from_RGB_255)
|
||||
return colors_dict
|
||||
else:
|
||||
raise exceptions.PlotlyError(
|
||||
"You must select either rgb or tuple for your colortype variable."
|
||||
)
|
||||
|
||||
|
||||
def validate_scale_values(scale):
|
||||
"""
|
||||
Validates scale values from a colorscale
|
||||
:param (list) scale: a strictly increasing list of floats that begins
|
||||
with 0 and ends with 1. Its usage derives from a colorscale which is
|
||||
a list of two-lists (a list with two elements) of the form
|
||||
[value, color] which are used to determine how interpolation weighting
|
||||
works between the colors in the colorscale. Therefore scale is just
|
||||
the extraction of these values from the two-lists in order
|
||||
"""
|
||||
if len(scale) < 2:
|
||||
raise exceptions.PlotlyError(
|
||||
"You must input a list of scale values that has at least two values."
|
||||
)
|
||||
|
||||
if (scale[0] != 0) or (scale[-1] != 1):
|
||||
raise exceptions.PlotlyError(
|
||||
"The first and last number in your scale must be 0.0 and 1.0 "
|
||||
"respectively."
|
||||
)
|
||||
|
||||
if not all(x < y for x, y in zip(scale, scale[1:])):
|
||||
raise exceptions.PlotlyError(
|
||||
"'scale' must be a list that contains a strictly increasing "
|
||||
"sequence of numbers."
|
||||
)
|
||||
|
||||
|
||||
def validate_colorscale(colorscale):
|
||||
"""Validate the structure, scale values and colors of colorscale."""
|
||||
if not isinstance(colorscale, list):
|
||||
# TODO Write tests for these exceptions
|
||||
raise exceptions.PlotlyError("A valid colorscale must be a list.")
|
||||
if not all(isinstance(innerlist, list) for innerlist in colorscale):
|
||||
raise exceptions.PlotlyError("A valid colorscale must be a list of lists.")
|
||||
colorscale_colors = colorscale_to_colors(colorscale)
|
||||
scale_values = colorscale_to_scale(colorscale)
|
||||
|
||||
validate_scale_values(scale_values)
|
||||
validate_colors(colorscale_colors)
|
||||
|
||||
|
||||
def make_colorscale(colors, scale=None):
|
||||
"""
|
||||
Makes a colorscale from a list of colors and a scale
|
||||
Takes a list of colors and scales and constructs a colorscale based
|
||||
on the colors in sequential order. If 'scale' is left empty, a linear-
|
||||
interpolated colorscale will be generated. If 'scale' is a specificed
|
||||
list, it must be the same legnth as colors and must contain all floats
|
||||
For documentation regarding to the form of the output, see
|
||||
https://plot.ly/python/reference/#mesh3d-colorscale
|
||||
:param (list) colors: a list of single colors
|
||||
"""
|
||||
colorscale = []
|
||||
|
||||
# validate minimum colors length of 2
|
||||
if len(colors) < 2:
|
||||
raise exceptions.PlotlyError(
|
||||
"You must input a list of colors that has at least two colors."
|
||||
)
|
||||
|
||||
if scale is None:
|
||||
scale_incr = 1.0 / (len(colors) - 1)
|
||||
return [[i * scale_incr, color] for i, color in enumerate(colors)]
|
||||
|
||||
else:
|
||||
if len(colors) != len(scale):
|
||||
raise exceptions.PlotlyError(
|
||||
"The length of colors and scale must be the same."
|
||||
)
|
||||
|
||||
validate_scale_values(scale)
|
||||
|
||||
colorscale = [list(tup) for tup in zip(scale, colors)]
|
||||
return colorscale
|
||||
|
||||
|
||||
def find_intermediate_color(lowcolor, highcolor, intermed, colortype="tuple"):
|
||||
"""
|
||||
Returns the color at a given distance between two colors
|
||||
This function takes two color tuples, where each element is between 0
|
||||
and 1, along with a value 0 < intermed < 1 and returns a color that is
|
||||
intermed-percent from lowcolor to highcolor. If colortype is set to 'rgb',
|
||||
the function will automatically convert the rgb type to a tuple, find the
|
||||
intermediate color and return it as an rgb color.
|
||||
"""
|
||||
if colortype == "rgb":
|
||||
# convert to tuple color, eg. (1, 0.45, 0.7)
|
||||
lowcolor = unlabel_rgb(lowcolor)
|
||||
highcolor = unlabel_rgb(highcolor)
|
||||
|
||||
diff_0 = float(highcolor[0] - lowcolor[0])
|
||||
diff_1 = float(highcolor[1] - lowcolor[1])
|
||||
diff_2 = float(highcolor[2] - lowcolor[2])
|
||||
|
||||
inter_med_tuple = (
|
||||
lowcolor[0] + intermed * diff_0,
|
||||
lowcolor[1] + intermed * diff_1,
|
||||
lowcolor[2] + intermed * diff_2,
|
||||
)
|
||||
|
||||
if colortype == "rgb":
|
||||
# back to an rgb string, e.g. rgb(30, 20, 10)
|
||||
inter_med_rgb = label_rgb(inter_med_tuple)
|
||||
return inter_med_rgb
|
||||
|
||||
return inter_med_tuple
|
||||
|
||||
|
||||
def unconvert_from_RGB_255(colors):
|
||||
"""
|
||||
Return a tuple where each element gets divided by 255
|
||||
Takes a (list of) color tuple(s) where each element is between 0 and
|
||||
255. Returns the same tuples where each tuple element is normalized to
|
||||
a value between 0 and 1
|
||||
"""
|
||||
return (colors[0] / (255.0), colors[1] / (255.0), colors[2] / (255.0))
|
||||
|
||||
|
||||
def convert_to_RGB_255(colors):
|
||||
"""
|
||||
Multiplies each element of a triplet by 255
|
||||
Each coordinate of the color tuple is rounded to the nearest float and
|
||||
then is turned into an integer. If a number is of the form x.5, then
|
||||
if x is odd, the number rounds up to (x+1). Otherwise, it rounds down
|
||||
to just x. This is the way rounding works in Python 3 and in current
|
||||
statistical analysis to avoid rounding bias
|
||||
:param (list) rgb_components: grabs the three R, G and B values to be
|
||||
returned as computed in the function
|
||||
"""
|
||||
rgb_components = []
|
||||
|
||||
for component in colors:
|
||||
rounded_num = decimal.Decimal(str(component * 255.0)).quantize(
|
||||
decimal.Decimal("1"), rounding=decimal.ROUND_HALF_EVEN
|
||||
)
|
||||
# convert rounded number to an integer from 'Decimal' form
|
||||
rounded_num = int(rounded_num)
|
||||
rgb_components.append(rounded_num)
|
||||
|
||||
return (rgb_components[0], rgb_components[1], rgb_components[2])
|
||||
|
||||
|
||||
def n_colors(lowcolor, highcolor, n_colors, colortype="tuple"):
|
||||
"""
|
||||
Splits a low and high color into a list of n_colors colors in it
|
||||
Accepts two color tuples and returns a list of n_colors colors
|
||||
which form the intermediate colors between lowcolor and highcolor
|
||||
from linearly interpolating through RGB space. If colortype is 'rgb'
|
||||
the function will return a list of colors in the same form.
|
||||
"""
|
||||
if colortype == "rgb":
|
||||
# convert to tuple
|
||||
lowcolor = unlabel_rgb(lowcolor)
|
||||
highcolor = unlabel_rgb(highcolor)
|
||||
|
||||
diff_0 = float(highcolor[0] - lowcolor[0])
|
||||
incr_0 = diff_0 / (n_colors - 1)
|
||||
diff_1 = float(highcolor[1] - lowcolor[1])
|
||||
incr_1 = diff_1 / (n_colors - 1)
|
||||
diff_2 = float(highcolor[2] - lowcolor[2])
|
||||
incr_2 = diff_2 / (n_colors - 1)
|
||||
list_of_colors = []
|
||||
|
||||
for index in range(n_colors):
|
||||
new_tuple = (
|
||||
lowcolor[0] + (index * incr_0),
|
||||
lowcolor[1] + (index * incr_1),
|
||||
lowcolor[2] + (index * incr_2),
|
||||
)
|
||||
list_of_colors.append(new_tuple)
|
||||
|
||||
if colortype == "rgb":
|
||||
# back to an rgb string
|
||||
list_of_colors = color_parser(list_of_colors, label_rgb)
|
||||
|
||||
return list_of_colors
|
||||
|
||||
|
||||
def label_rgb(colors):
|
||||
"""
|
||||
Takes tuple (a, b, c) and returns an rgb color 'rgb(a, b, c)'
|
||||
"""
|
||||
return "rgb(%s, %s, %s)" % (colors[0], colors[1], colors[2])
|
||||
|
||||
|
||||
def unlabel_rgb(colors):
|
||||
"""
|
||||
Takes rgb color(s) 'rgb(a, b, c)' and returns tuple(s) (a, b, c)
|
||||
This function takes either an 'rgb(a, b, c)' color or a list of
|
||||
such colors and returns the color tuples in tuple(s) (a, b, c)
|
||||
"""
|
||||
str_vals = ""
|
||||
for index in range(len(colors)):
|
||||
try:
|
||||
float(colors[index])
|
||||
str_vals = str_vals + colors[index]
|
||||
except ValueError:
|
||||
if colors[index] == "," or colors[index] == ".":
|
||||
str_vals = str_vals + colors[index]
|
||||
|
||||
str_vals = str_vals + ","
|
||||
numbers = []
|
||||
str_num = ""
|
||||
for char in str_vals:
|
||||
if char != ",":
|
||||
str_num = str_num + char
|
||||
else:
|
||||
numbers.append(float(str_num))
|
||||
str_num = ""
|
||||
return (numbers[0], numbers[1], numbers[2])
|
||||
|
||||
|
||||
def hex_to_rgb(value):
|
||||
"""
|
||||
Calculates rgb values from a hex color code.
|
||||
:param (string) value: Hex color string
|
||||
:rtype (tuple) (r_value, g_value, b_value): tuple of rgb values
|
||||
"""
|
||||
value = value.lstrip("#")
|
||||
hex_total_length = len(value)
|
||||
rgb_section_length = hex_total_length // 3
|
||||
return tuple(
|
||||
int(value[i : i + rgb_section_length], 16)
|
||||
for i in range(0, hex_total_length, rgb_section_length)
|
||||
)
|
||||
|
||||
|
||||
def colorscale_to_colors(colorscale):
|
||||
"""
|
||||
Extracts the colors from colorscale as a list
|
||||
"""
|
||||
color_list = []
|
||||
for item in colorscale:
|
||||
color_list.append(item[1])
|
||||
return color_list
|
||||
|
||||
|
||||
def colorscale_to_scale(colorscale):
|
||||
"""
|
||||
Extracts the interpolation scale values from colorscale as a list
|
||||
"""
|
||||
scale_list = []
|
||||
for item in colorscale:
|
||||
scale_list.append(item[0])
|
||||
return scale_list
|
||||
|
||||
|
||||
def convert_colorscale_to_rgb(colorscale):
|
||||
"""
|
||||
Converts the colors in a colorscale to rgb colors
|
||||
A colorscale is an array of arrays, each with a numeric value as the
|
||||
first item and a color as the second. This function specifically is
|
||||
converting a colorscale with tuple colors (each coordinate between 0
|
||||
and 1) into a colorscale with the colors transformed into rgb colors
|
||||
"""
|
||||
for color in colorscale:
|
||||
color[1] = convert_to_RGB_255(color[1])
|
||||
|
||||
for color in colorscale:
|
||||
color[1] = label_rgb(color[1])
|
||||
return colorscale
|
||||
|
||||
|
||||
def named_colorscales():
|
||||
"""
|
||||
Returns lowercased names of built-in continuous colorscales.
|
||||
"""
|
||||
from _plotly_utils.basevalidators import ColorscaleValidator
|
||||
|
||||
return [c for c in ColorscaleValidator("", "").named_colorscales]
|
||||
|
||||
|
||||
def get_colorscale(name):
|
||||
"""
|
||||
Returns the colorscale for a given name. See `named_colorscales` for the
|
||||
built-in colorscales.
|
||||
"""
|
||||
from _plotly_utils.basevalidators import ColorscaleValidator
|
||||
|
||||
if not isinstance(name, str):
|
||||
raise exceptions.PlotlyError("Name argument have to be a string.")
|
||||
|
||||
name = name.lower()
|
||||
if name[-2:] == "_r":
|
||||
should_reverse = True
|
||||
name = name[:-2]
|
||||
else:
|
||||
should_reverse = False
|
||||
|
||||
if name in ColorscaleValidator("", "").named_colorscales:
|
||||
colorscale = ColorscaleValidator("", "").named_colorscales[name]
|
||||
else:
|
||||
raise exceptions.PlotlyError(f"Colorscale {name} is not a built-in scale.")
|
||||
|
||||
if should_reverse:
|
||||
colorscale = colorscale[::-1]
|
||||
return make_colorscale(colorscale)
|
||||
|
||||
|
||||
def sample_colorscale(colorscale, samplepoints, low=0.0, high=1.0, colortype="rgb"):
|
||||
"""
|
||||
Samples a colorscale at specific points.
|
||||
Interpolates between colors in a colorscale to find the specific colors
|
||||
corresponding to the specified sample values. The colorscale can be specified
|
||||
as a list of `[scale, color]` pairs, as a list of colors, or as a named
|
||||
plotly colorscale. The samplepoints can be specefied as an iterable of specific
|
||||
points in the range [0.0, 1.0], or as an integer number of points which will
|
||||
be spaced equally between the low value (default 0.0) and the high value
|
||||
(default 1.0). The output is a list of colors, formatted according to the
|
||||
specified colortype.
|
||||
"""
|
||||
from bisect import bisect_left
|
||||
|
||||
try:
|
||||
validate_colorscale(colorscale)
|
||||
except exceptions.PlotlyError:
|
||||
if isinstance(colorscale, str):
|
||||
colorscale = get_colorscale(colorscale)
|
||||
else:
|
||||
colorscale = make_colorscale(colorscale)
|
||||
|
||||
scale = colorscale_to_scale(colorscale)
|
||||
validate_scale_values(scale)
|
||||
colors = colorscale_to_colors(colorscale)
|
||||
colors = validate_colors(colors, colortype="tuple")
|
||||
|
||||
if isinstance(samplepoints, int):
|
||||
samplepoints = [
|
||||
low + idx / (samplepoints - 1) * (high - low) for idx in range(samplepoints)
|
||||
]
|
||||
elif isinstance(samplepoints, float):
|
||||
samplepoints = [samplepoints]
|
||||
|
||||
sampled_colors = []
|
||||
for point in samplepoints:
|
||||
high = bisect_left(scale, point)
|
||||
low = high - 1
|
||||
interpolant = (point - scale[low]) / (scale[high] - scale[low])
|
||||
sampled_color = find_intermediate_color(colors[low], colors[high], interpolant)
|
||||
sampled_colors.append(sampled_color)
|
||||
return validate_colors(sampled_colors, colortype=colortype)
|
||||
Loading…
Reference in New Issue