plotly-calplot - pypi Package Compare versions

Comparing version 0.1.7 to 0.1.8

plotly_calplot/date_extractors.py

+from typing import Any, List, Tuple
+
+import numpy as np
+import pandas as pd
+
+
+def get_month_names(data: pd.DataFrame, x: str) -> List[str]:
+    return list(data[x].dt.month_name().unique())
+
+
+def get_date_coordinates(
+    data: pd.DataFrame, x: str
+) -> Tuple[Any, List[float], List[int]]:
+    month_days = []
+    for m in data[x].dt.month.unique():
+        month_days.append(data.loc[data[x].dt.month == m].max()[x].day)
+
+    month_positions = (np.cumsum(month_days) - 15) / 7
+    weekdays_in_year = [i.weekday() for i in data[x]]
+
+    # sometimes the last week of the current year conflicts with next year's january
+    # pandas will give those weeks the number 52 or 53, but this is bad news for this plot
+    # therefore we need a correction, for a more in-depth explanation check
+    # https://stackoverflow.com/questions/44372048/python-pandas-timestamp-week-returns-52-for-first-day-of-year
+
+    weeknumber_of_dates = (
+        data[x].apply(lambda x: get_weeknumber_of_date(x)).values.tolist()
+    )
+
+    return month_positions, weekdays_in_year, weeknumber_of_dates
+
+
+def get_weeknumber_of_date(d: pd.Timestamp) -> int:
+    """
+    Pandas week returns ISO week number, this function
+    returns gregorian week date
+    """
+    return int(d.strftime("%W"))

plotly_calplot/layout_formatter.py

+from typing import Any, List
+
+import pandas as pd
+from plotly import graph_objects as go
+
+
+def decide_layout(
+    dark_theme: bool,
+    title: str,
+    month_names: List[str],
+    month_positions: Any,
+) -> go.Layout:
+    if dark_theme:
+        layout = go.Layout(
+            title=title,
+            yaxis=dict(
+                showline=False,
+                showgrid=False,
+                zeroline=False,
+                tickmode="array",
+                ticktext=["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
+                tickvals=[0, 1, 2, 3, 4, 5, 6],
+                autorange="reversed",
+            ),
+            xaxis=dict(
+                showline=False,
+                showgrid=False,
+                zeroline=False,
+                tickmode="array",
+                ticktext=month_names,
+                tickvals=month_positions,
+            ),
+            font={"size": 10, "color": "#fff"},
+            paper_bgcolor=("#333"),
+            plot_bgcolor=("#333"),
+            margin=dict(t=20, b=20),
+            showlegend=False,
+        )
+    else:
+        layout = go.Layout(
+            title=title,
+            yaxis=dict(
+                showline=False,
+                showgrid=False,
+                zeroline=False,
+                tickmode="array",
+                ticktext=["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
+                tickvals=[0, 1, 2, 3, 4, 5, 6],
+                autorange="reversed",
+            ),
+            xaxis=dict(
+                showline=False,
+                showgrid=False,
+                zeroline=False,
+                tickmode="array",
+                ticktext=month_names,
+                tickvals=month_positions,
+            ),
+            font={"size": 10, "color": "#9e9e9e"},
+            plot_bgcolor=("#fff"),
+            margin=dict(t=20, b=20),
+            showlegend=False,
+        )
+
+    return layout
+
+
+def create_month_lines(
+    cplt: List[go.Figure],
+    month_lines_color: str,
+    month_lines_width: int,
+    data: pd.DataFrame,
+    weekdays_in_year: List[float],
+    weeknumber_of_dates: List[int],
+) -> go.Figure:
+    kwargs = dict(
+        mode="lines",
+        line=dict(color=month_lines_color, width=month_lines_width),
+        hoverinfo="skip",
+    )
+    for date, dow, wkn in zip(data, weekdays_in_year, weeknumber_of_dates):
+        if date.day == 1:
+            cplt += [go.Scatter(x=[wkn - 0.5, wkn - 0.5], y=[dow - 0.5, 6.5], **kwargs)]
+            if dow:
+                cplt += [
+                    go.Scatter(
+                        x=[wkn - 0.5, wkn + 0.5], y=[dow - 0.5, dow - 0.5], **kwargs
+                    ),
+                    go.Scatter(x=[wkn + 0.5, wkn + 0.5], y=[dow - 0.5, -0.5], **kwargs),
+                ]
+    return cplt
+
+
+def update_plot_with_current_layout(
+    fig: go.Figure,
+    cplt: go.Figure,
+    row: int,
+    layout: go.Layout,
+    width: Any,
+    total_height: Any,
+) -> go.Figure:
+    fig.update_layout(layout)
+    fig.update_xaxes(layout["xaxis"])
+    fig.update_yaxes(layout["yaxis"])
+    fig.update_layout(width=width, height=total_height)
+    fig.add_traces(cplt, rows=[(row + 1)] * len(cplt), cols=[1] * len(cplt))
+    return fig

plotly_calplot/raw_heatmap.py

+from typing import List
+
+import numpy as np
+import pandas as pd
+from plotly import graph_objects as go
+
+
+def create_heatmap_without_formatting(
+    data: pd.DataFrame,
+    x: str,
+    y: str,
+    weeknumber_of_dates: List[int],
+    weekdays_in_year: List[float],
+    gap: int,
+    year: int,
+    colorscale: str,
+    name: str,
+) -> List[go.Figure]:
+    raw_heatmap = [
+        go.Heatmap(
+            x=weeknumber_of_dates,
+            y=weekdays_in_year,
+            z=data[y],
+            xgap=gap,  # this
+            ygap=gap,  # and this is used to make the grid-like apperance
+            showscale=False,
+            colorscale=colorscale,  # user can setup their colorscale
+            hovertemplate="%{customdata[0]} <br>%{customdata[1]}=%{z} <br>Week=%{x}",
+            customdata=np.stack((data[x].astype(str), [name] * data.shape[0]), axis=-1),
+            name=str(year),
+        )
+    ]
+    return raw_heatmap

plotly_calplot/single_year_calplot.py

+from pandas.core.frame import DataFrame
+from plotly import graph_objects as go
+
+from plotly_calplot.date_extractors import get_date_coordinates, get_month_names
+from plotly_calplot.layout_formatter import (
+    create_month_lines,
+    decide_layout,
+    update_plot_with_current_layout,
+)
+from plotly_calplot.raw_heatmap import create_heatmap_without_formatting
+
+
+def year_calplot(
+    data: DataFrame,
+    x: str,
+    y: str,
+    fig: go.Figure,
+    row: int,
+    year: int,
+    name: str = "y",
+    dark_theme: bool = False,
+    month_lines_width: int = 1,
+    month_lines_color: str = "#9e9e9e",
+    gap: int = 1,
+    width: int = 800,
+    colorscale: str = "greens",
+    title: str = "",
+    month_lines: bool = True,
+    total_height: int = None,
+) -> go.Figure:
+    """
+    Each year is subplotted separately and added to the main plot
+    """
+
+    month_names = get_month_names(data, x)
+    month_positions, weekdays_in_year, weeknumber_of_dates = get_date_coordinates(
+        data, x
+    )
+
+    # the calendar is actually a heatmap :)
+    cplt = create_heatmap_without_formatting(
+        data, x, y, weeknumber_of_dates, weekdays_in_year, gap, year, colorscale, name
+    )
+
+    if month_lines:
+        cplt = create_month_lines(
+            cplt,
+            month_lines_color,
+            month_lines_width,
+            data[x],
+            weekdays_in_year,
+            weeknumber_of_dates,
+        )
+
+    layout = decide_layout(dark_theme, title, month_names, month_positions)
+    fig = update_plot_with_current_layout(fig, cplt, row, layout, width, total_height)
+
+    return fig

plotly_calplot/utils.py

+from datetime import date
+
+import pandas as pd
+from pandas.core.frame import DataFrame
+
+
+def fill_empty_with_zeros(
+    selected_year_data: DataFrame, x: str, dark_theme: bool, year: int
+) -> pd.DataFrame:
+    year_min_date = date(year=year, month=1, day=1)
+    year_max_date = date(year=year, month=12, day=31)
+    df = pd.DataFrame({x: pd.date_range(year_min_date, year_max_date)})
+    final_df = df.merge(selected_year_data, how="left")
+    if not dark_theme:
+        final_df = final_df.fillna(0)
+    return final_df

PKG-INFO

 Metadata-Version: 2.1
 Name: plotly-calplot
-Version: 0.1.7
+Version: 0.1.8
 Summary: Calendar Plot made with Plotly
@@ -9,9 +9,17 @@ Home-page: https://github.com/brunorosilva/plotly-calplot
 Author-email: [e-mail]
-Requires-Python: >=3.7.1,<4.0.0
+Requires-Python: >=3.8,<4.0.0
 Classifier: License :: OSI Approved :: MIT License
 Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.10
 Classifier: Programming Language :: Python :: 3.8
 Classifier: Programming Language :: Python :: 3.9
+Requires-Dist: flake8 (>=4.0.1,<5.0.0)
+Requires-Dist: isort (>=5.10.1,<6.0.0)
+Requires-Dist: mypy (>=0.942,<0.943)
+Requires-Dist: numpy (>=1.22.3,<2.0.0)
 Requires-Dist: pandas (>=1.0.5,<2.0.0)
 Requires-Dist: plotly (>=5.4.0,<6.0.0)
+Requires-Dist: pytest (>=7.1.1,<8.0.0)
+Requires-Dist: pytest-cov (>=3.0.0,<4.0.0)
+Requires-Dist: vulture (>=2.3,<3.0)
 Project-URL: Repository, https://github.com/brunorosilva/plotly-calplot
@@ -18,0 +26,0 @@ Description-Content-Type: text/markdown

plotly_calplot/__init__.py

@@ -1,1 +0,3 @@
-from .calplot import calplot
+from .calplot import calplot  # noqa: F401
+
+__version__ = "0.0.2"

plotly_calplot/calplot.py

@@ -1,3 +0,1 @@
-import numpy as np
-import pandas as pd
 from pandas.core.frame import DataFrame
@@ -7,166 +5,6 @@ from plotly import graph_objects as go
 
+from plotly_calplot.single_year_calplot import year_calplot
+from plotly_calplot.utils import fill_empty_with_zeros
 
-def get_weeknumber_of_date(d):
-    """
-    Pandas week returns some strange values, this function fixes'em
-    """
-    if d.month == 1 and d.week > 50:
-        return 0
-    elif d.month == 12 and d.week < 10:
-        return 53
-    else:
-        return d.week
 
-
-def year_calplot(
-    data: DataFrame,
-    x,
-    y,
-    name,
-    year,
-    fig,
-    row,
-    month_lines,
-    month_lines_width,
-    month_lines_color,
-    colorscale,
-    gap,
-    title,
-    dark_theme,
-    width,
-    total_height,
-):
-    """
-    Each year is subplotted separately and added to the main plot
-    """
-    month_names = list(data[x].dt.month_name().unique())
-    month_days = []
-    for month in data[x].dt.month.unique():
-        month_days.append(data.loc[data[x].dt.month == month].max()[x].day)
-
-    month_positions = (np.cumsum(month_days) - 15) / 7
-
-    weekdays_in_year = [i.weekday() for i in data[x]]
-
-    # sometimes the last week of the current year conflicts with next year's january
-    # pandas will give those weeks the number 52 or 53, but this is bad news for this plot
-    # therefore we need a correction, for a more in-depth explanation check
-    # https://stackoverflow.com/questions/44372048/python-pandas-timestamp-week-returns-52-for-first-day-of-year
-
-    weeknumber_of_dates = (
-        data[x].apply(lambda x: get_weeknumber_of_date(x)).values.tolist()
-    )
-
-    # the calendar is actually a heatmap :)
-    cplt = [
-        go.Heatmap(
-            x=weeknumber_of_dates,
-            y=weekdays_in_year,
-            z=data[y],
-            xgap=gap,  # this
-            ygap=gap,  # and this is used to make the grid-like apperance
-            showscale=False,
-            colorscale=colorscale,  # user can setup their colorscale
-            hovertemplate="%{customdata[0]} <br>%{customdata[1]}=%{z} <br>Week=%{x}",
-            customdata=np.stack((data[x].astype(str), [name] * data.shape[0]), axis=-1),
-            name=str(year),
-        )
-    ]
-
-    if month_lines:
-        kwargs = dict(
-            mode="lines",
-            line=dict(color=month_lines_color, width=month_lines_width),
-            hoverinfo="skip",
-        )
-        for date, dow, wkn in zip(data[x], weekdays_in_year, weeknumber_of_dates):
-            if date.day == 1:
-                cplt += [
-                    go.Scatter(x=[wkn - 0.5, wkn - 0.5], y=[dow - 0.5, 6.5], **kwargs)
-                ]
-                if dow:
-                    cplt += [
-                        go.Scatter(
-                            x=[wkn - 0.5, wkn + 0.5], y=[dow - 0.5, dow - 0.5], **kwargs
-                        ),
-                        go.Scatter(
-                            x=[wkn + 0.5, wkn + 0.5], y=[dow - 0.5, -0.5], **kwargs
-                        ),
-                    ]
-
-    #    if row == 0:
-    if dark_theme:
-        layout = go.Layout(
-            title=title,
-            yaxis=dict(
-                showline=False,
-                showgrid=False,
-                zeroline=False,
-                tickmode="array",
-                ticktext=["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
-                tickvals=[0, 1, 2, 3, 4, 5, 6],
-                autorange="reversed",
-            ),
-            xaxis=dict(
-                showline=False,
-                showgrid=False,
-                zeroline=False,
-                tickmode="array",
-                ticktext=month_names,
-                tickvals=month_positions,
-            ),
-            font={"size": 10, "color": "#fff"},
-            paper_bgcolor=("#333"),
-            plot_bgcolor=("#333"),
-            margin=dict(t=20, b=20),
-            showlegend=False,
-        )
-        fig.update_layout(layout)
-        fig.update_xaxes(layout["xaxis"])
-        fig.update_yaxes(layout["yaxis"])
-
-    else:
-        layout = go.Layout(
-            title=title,
-            yaxis=dict(
-                showline=False,
-                showgrid=False,
-                zeroline=False,
-                tickmode="array",
-                ticktext=["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
-                tickvals=[0, 1, 2, 3, 4, 5, 6],
-                autorange="reversed",
-            ),
-            xaxis=dict(
-                showline=False,
-                showgrid=False,
-                zeroline=False,
-                tickmode="array",
-                ticktext=month_names,
-                tickvals=month_positions,
-            ),
-            font={"size": 10, "color": "#9e9e9e"},
-            plot_bgcolor=("#fff"),
-            margin=dict(t=20, b=20),
-            showlegend=False,
-        )
-    fig.update_layout(layout)
-    fig.update_xaxes(layout["xaxis"])
-    fig.update_yaxes(layout["yaxis"])
-    fig.update_layout(width=width, height=total_height)
-    fig.add_traces(cplt, rows=[(row + 1)] * len(cplt), cols=[1] * len(cplt))
-
-    return fig
-
-
-def fill_empty_with_zeros(selected_year_data: DataFrame, x, dark_theme, year: int):
-    year_min_date = "01-01-" + str(year)
-    year_max_date = "31-12-" + str(year)
-    df = pd.DataFrame({x: pd.date_range(year_min_date, year_max_date)})
-    final_df = df.merge(selected_year_data, how="left")
-    if not dark_theme:
-        final_df = final_df.fillna(0)
-    return final_df
-
-
 def calplot(
@@ -188,3 +26,3 @@ data: DataFrame,
     space_between_plots: float = 0.08,
-):
+) -> go.Figure:
     """
@@ -198,3 +36,3 @@ Yearly Calendar Heatmap
         one value column for displaying in the plot
-    
+
     x : str
@@ -243,3 +81,3 @@ The name of the date like column in data
         according to the amount of years in data
-    
+
     space_between_plots: float = 0.08
@@ -246,0 +84,0 @@ controls the vertical space between the plots

pyproject.toml

 [tool.poetry]
 name = "plotly_calplot"
-version = "0.1.7"
+version = "0.1.8"
 description = "Calendar Plot made with Plotly"
@@ -11,5 +11,12 @@ authors = ["Bruno Rodrigues Silva <[e-mail]>"]
 [tool.poetry.dependencies]
-python = "^3.7.1"
+python = ">=3.8,<4.0.0"
 plotly = "^5.4.0"
 pandas = "^1.0.5"
+isort = "^5.10.1"
+flake8 = "^4.0.1"
+vulture = "^2.3"
+mypy = "^0.942"
+numpy = "^1.22.3"
+pytest = "^7.1.1"
+pytest-cov = "^3.0.0"
 
@@ -16,0 +23,0 @@ [tool.poetry.dev-dependencies]

setup.py

@@ -11,7 +11,15 @@ # -*- coding: utf-8 -*-
 install_requires = \
-['pandas>=1.0.5,<2.0.0', 'plotly>=5.4.0,<6.0.0']
+['flake8>=4.0.1,<5.0.0',
+ 'isort>=5.10.1,<6.0.0',
+ 'mypy>=0.942,<0.943',
+ 'numpy>=1.22.3,<2.0.0',
+ 'pandas>=1.0.5,<2.0.0',
+ 'plotly>=5.4.0,<6.0.0',
+ 'pytest-cov>=3.0.0,<4.0.0',
+ 'pytest>=7.1.1,<8.0.0',
+ 'vulture>=2.3,<3.0']
 
 setup_kwargs = {
     'name': 'plotly-calplot',
-    'version': '0.1.7',
+    'version': '0.1.8',
     'description': 'Calendar Plot made with Plotly',
@@ -27,3 +35,3 @@ 'long_description': '# Calendar Heatmap with Plotly\nMaking it easier to visualize and costumize time relevant or time series data with plotly interaction.\n\nThis plot is a very similar to the contribuitions available on Github and Gitlab profile pages and to [Calplot](https://github.com/tomkwok/calplot) - which is a pyplot implementation of the calendar heatmap, thus it is not interactive right off the bat.\n\nThe first mention I could find of this plot being made with plotly was in [this forum post](https://community.plotly.com/t/colored-calendar-heatmap-in-dash/10907/16) and it got my attention as something it should be easily available to anyone.\n\n# Installation\n``` bash\npip install plotly-calplot\n```\n\n# Examples\n\nIn [this Medium article](https://medium.com/@brunorosilva/5fc322125db7) I covered lot\'s of usage methods for this library.\n``` python\nfrom plotly_calplot import calplot\n\nfig = calplot(df, x="date", y="value")\nfig.show()\n# you can also adjust layout and your usual plotly stuff\n```\n\n<img src="https://github.com/brunorosilva/plotly-calplot/blob/main/assets/images/example.png?raw=true">\n',
     'install_requires': install_requires,
-    'python_requires': '>=3.7.1,<4.0.0',
+    'python_requires': '>=3.8,<4.0.0',
 }
@@ -30,0 +38,0 @@

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Improved metrics

Total package byte prevSize

16790

22.13%

Number of package files

11

83.33%

Lines of code

358

28.78%